bird population responses to conservation …
TRANSCRIPT
The Pennsylvania State University
The Graduate School
School of Forest Resources
BIRD POPULATION RESPONSES TO
CONSERVATION GRASSLANDS IN PENNSYLVANIA
A Dissertation in
Ecology by
Andrew Mark Wilson
2009 Andrew Mark Wilson
Submitted in Partial Fulfillment of the Requirements
for the Degree of
Doctor of Philosophy
May 2009
The dissertation of Andrew Wilson was reviewed and approved* by the following:
Margaret Brittingham Professor of Wildlife Resources Dissertation Advisor Chair of Committee
Duane Diefenbach Adjunct Associate Professor of Wildlife Ecology
Murali Haran Assistant Professor of Statistics
Walter Tzilkowski Associate Professor of Wildlife Science
David Eissenstat Professor of Woody Plant Physiology Chair, Intercollege Graduate Degree in Ecology
*Signatures are on file in the Graduate School
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ABSTRACT
Grassland obligate birds have been in precipitous decline across North America
for several decades. One of the principal mechanisms for stemming these declines is
farmland conservation programs, among which the Conservation Reserve Program has
been particularly successful in delivering millions of acres of new grasslands. Evidence
of the efficacy of such programs for grassland bird conservation in the eastern United
States is lacking. In 2000 the Conservation Reserve Enhancement Program (CREP) was
established in Pennsylvania with the provision of grassland wildlife habitat one of its key
aims. I show that within Pennsylvania, grassland obligates species showed sustained
declines from the 1960s onwards. I use several bird survey data sources to assess the
population-scale effects of CREP on a range of species within the state.
A bird monitoring program was established in 2001 to assess farmland and
grassland bird population trends in southern Pennsylvania. Trends for the period 2001 to
2005 provide scant evidence of population-scale changes in bird numbers; most grassland
obligates continued to decline, but declines for some species were slower in areas with
high rates of CREP enrollment. Eastern meadowlark was the only songbird for which I
could demonstrate a strong response to higher rates of CREP enrollment, results for
grasshopper sparrow and bobolink suggest that these species may have benefited, but the
results are more equivocal. My analysis was hampered by the short time-series – fields
were still being planted when the monitoring was curtailed. Further, the study coincided
with the emergence of West Nile virus (WNV) in Pennsylvania, which further
complicated the analysis. I show that WNV caused significant decreases in seven
common bird species, notably corvids and cavity nesting songbirds.
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A survey of winter raptor numbers initiated in 2001 provided an ideal opportunity
to test the hypothesis that CREP also provides habitat for wintering birds of prey. I show
that at broad spatial scales there were increases in raptor numbers in areas where there
was the most CREP, the results being particularly pronounced for northern harrier.
Although these relationships are merely correlational, they do suggest that the new
habitat provided by CREP has been sufficient to cause a significant shift in the wintering
populations of this species.
I use point count data from the 2nd Pennsylvania Breeding Bird Atlas to estimate
grassland songbird populations and assess the relative importance of farmland for these
species. I show that although densities are often low, farmland supports the majority of
the populations of most grassland songbirds in Pennsylvania. I suggest that management,
enrollment and re-enrollment of CREP fields could be better targeted. Large CREP fields
in south central Pennsylvania should be managed specifically for eastern meadowlarks
and grasshopper sparrows, while in the west of the state; further research could allow a
targeting of CREP toward populations of the threatened Henslow’s sparrow. Small fields,
which may be less suitable for grassland obligates, should provide habitat for species
characteristic of early successional habitats, including the declining Field Sparrow.
Population estimates show that CREP fields support only a small proportion of the
population of most grassland songbirds. This highlights the importance of conservation
efforts in known areas of high population density, such as reclaimed surface mines.
Better targeting and careful management of CREP fields could potentially increase their
value for grassland birds, but more research is needed if this is to be realized.
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TABLE OF CONTENTS
LIST OF FIGURES ............................................................................................................ x
LIST OF TABLES........................................................................................................... xiii
ACKNOWLEDGEMENTS............................................................................................. xiv
Chapter 1: Introduction and motivation – Population trends of grassland birds in Pennsylvania ........................................................................................................................1
Historical perspective...............................................................................................1 CRP literature...........................................................................................................6 Methods....................................................................................................................9 Definition of species guilds .....................................................................................9 Population trend estimation ...................................................................................11
Results.........................................................................................................................12 Species guild assignment .......................................................................................12 Population trends 1970 to 2007 .............................................................................13
Discussion...................................................................................................................15
Thesis aims .................................................................................................................16
Chapter 2: Landscape scale population trends of common birds in the Lower Susquehanna River basin Conservation Reserve Enhancement Program region ..............25 Introduction.................................................................................................................25 Methods ......................................................................................................................26
Bird and habitat surveys.........................................................................................26 Spatial data analysis and enumeration of CREP enrollment .................................27 Redundancy Analysis to derive species guilds ......................................................30
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Analysis of population trends ................................................................................31
Results.........................................................................................................................33 Land cover and land use change ............................................................................33 CREP enrollment within the study areas ...............................................................34 Bird community .....................................................................................................35 Categorizing bird species into habitat guilds .........................................................35 Bird population trends 2001-2005 .........................................................................36 Effect of CREP enrollment on bird population trends...........................................36
Discussion...................................................................................................................38 Issues of study scale and timing.............................................................................38 Bird population responses......................................................................................40 Extrinsic factors: West Nile Virus .........................................................................42 Unknown responses of scarce species ...................................................................44
Conclusions.................................................................................................................45
Chapter 3: Bayesian spatial models reveal confounded effects of the Conservation Reserve Enhancement Program and West Nile Virus on farmland bird populations........64
Introduction.................................................................................................................64
Methods ......................................................................................................................65
Model data .............................................................................................................65 Bayesian spatial model ..........................................................................................67
Results.........................................................................................................................70 Model evaluation ...................................................................................................70 CREP effects..........................................................................................................71 West Nile virus effects...........................................................................................71
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Discussion...................................................................................................................72
Modeling approach ................................................................................................72 CREP effects..........................................................................................................73 West Nile virus effects...........................................................................................75
Chapter 4: Association of wintering raptors with Conservation Reserve Enhancement Program grasslands in Pennsylvania..................................................................................90 Introduction.................................................................................................................90 Methods ......................................................................................................................92 Bird surveys ...........................................................................................................92 Data sources ...........................................................................................................93 Trends from log-linear Poisson regression ............................................................94 Bayesian spatial model ..........................................................................................95 Results.........................................................................................................................98 Regional trends from log-linear Poisson Regression.............................................98 Bayesian model evaluation ....................................................................................98 CREP effect and other environmental covariates ..................................................99 Discussion.................................................................................................................100 Advantages of the Bayesian modeling approach .................................................100 Effects of CREP on winter raptor numbers .........................................................101 Possible effects of West Nile virus ......................................................................102 Conclusions..........................................................................................................103 Chapter 5: Population scale effects of the Conservation Reserve Enhancement Program on farmland songbirds in Pennsylvania – population size context ..................................115
Introduction...............................................................................................................115
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Methods ....................................................................................................................116
Data sources – 2nd Pennsylvania Breeding Bird Atlas Point Counts..................116 Population estimates ............................................................................................117 Relative importance of habitats for grasslands birds ...........................................119 Population estimates for CREP lands in the LSR CREP region..........................120 Breeding Bird Survey trends 1995 to 2007..........................................................122
Results.......................................................................................................................122 Population estimates ............................................................................................123 Relative importance of habitats for grasslands birds ...........................................124 CREP lands population estimates for the LSR CREP region ..............................125 Evidence of CREP effect in BBS trends..............................................................125 Background population size changes...................................................................126
Discussion.................................................................................................................127 Targeting CREP towards key species ..................................................................127 Why a lack of evidence for population change? ..................................................130 How important is grassland bird conservation in Pennsylvania? ........................132
Chapter 6: Conservation and research recommendations ...............................................143 CREP as part of a broader conservation plan ......................................................144 Recommendations for future research .................................................................146
REFERENCES ................................................................................................................149
Appendix A: Refereed journal articles pertaining to bird population and ecology studies with respect Conservation Reserve Programs fields...............................................165
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Appendix B: Survey routes for Pennsylvania Game Commission bird monitoring surveys (2001-2005) with mean percentage land use within 250m of survey stops for 2001-2004 as estimated by field surveyors. .....................................................................168
Appendix C: Bird population trend and summary data from the Pennsylvania Game
Commission bird monitoring survey for 60 common bird species.........................172 Appendix D: Example WINBUGS code for Bayesian spatial models used in chapters 3
and 4........................................................................................................................232 Appendix E: Example SURVIV code for estimating bird densities using Removal with
Distance method .....................................................................................................234
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LIST OF FIGURES
Figure 1.1: Conservation Reserve Enhancement Program areas in Pennsylvania .......20
Figure 1.2: Percentage of arable land enrolled in the Conservation Reserve Enhancement Program by program year and program area ......................21
Figure 1.3: Location of studies in 64 refereed journal articles pertaining to bird population and ecology studies with respect Conservation Reserve Programs fields ..........................................................................................22
Figure 1.4: Redundancy Analysis bi-plots of species scores with land cover gradients and species guilds from USGS Breeding Bird Survey counts of 110 common birds in Pennsylvania between 1998 and 2007...........................23
Figure 1.5: Population indices and 95% confidence intervals of three common bird species guilds, and for the eight most numerous species in CREP fields in Pennsylvania for 1970 to 2007 ..................................................................24
Figure 2.1: The 20 counties of the Lower Susquehanna River basin CREP and the 84 landscapes around bird survey routes........................................................54
Figure 2.2: Landsat satellite imagery derived land cover types within the 84 study landscapes in the Lower Susquehanna River basin CREP, by county, circa year 2000 ...................................................................................................55
Figure 2.3: Percentage of farmland enrolled in CREP by 1st June 2004 for each of the 84 landscapes Lower Susquehanna River basin CREP.............................56
Figure 2.4: Percentage of farmland enrolled in CREP by 1st June 2004 for each of the 84 landscapes of southern Pennsylvania, plotted against % of farmland previously in grassland and % of the total landscape in farmed ...............57
Figure 2.5: Estimated percentage of six main land uses within 250 m point count radii in the Lower Susquehanna River basin CREP during 2001 to 2004. Linear regression lines show estimated annual changes.......................................58
Figure 2.6: Cumulative CREP enrollment, as a % of farmland, by 31st May of each year, summed across 84 landscapes in the Lower Susquehanna River basin CREP................................................................................................59
Figure 2.7: Enrollment in the Lower Susquehanna River basin CREP, as a % of farmland, by 31st May 2004, by county and practice ................................60
Figure 2.8: RDA bi-plot showing relationships between birds and main habitats gradients for 2001 count data ....................................................................61
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Figure 2.9: Annual population changes between 2001 and 2005 for the PGC grassland, “Wentworth 8”, Field nesters species guilds compared with the other common species, in landscapes of southern Pennsylvania with four levels of CREP enrollment by 1st June 2004 .............................................62
Figure 2.10: Mosquito Culex. samples tested positively for West Nile virus in the Lower Susquehanna river CREP region in 2000 to 2004..........................63
Figure 3.1: Locations of the 84 survey routes with Thiessen polygons showing the neighborhood structure used in the Conditional Autoregressive model and the 20 counties of the Lower Susquehanna River basin CREP region......83
Figure 3.2: WNV-positive dead birds and human population size for 67 counties of Pennsylvania in 2003.................................................................................84
Figure 3.3: Mean mosquito and bird carcass WNV-positive samples per county in Pennsylvania (n=67) and the Lower Susquehanna River basin (n=20) in years 2000 to 2005. Bird samples are corrected for human population size for each county ..........................................................................................85
Figure 3.4: Examples plots of actual counts versus predicted counts from Bayesian spatial models ............................................................................................86
Figure 3.5: Estimate of the effect of % forest cover on bird counts for 30 for which forest was a significant covariate in Bayesian spatial models. A large negative parameter implies avoidance, positive suggests selection. Error bars are 95% credibility intervals ..............................................................87
Figure 3.6: Bird population trends for seven species significantly impacted by West Nile virus in the Lower Susquehanna River basin CREP region in 2001 to 2005 ...........................................................................................................88
Figure 3.7: Estimated percentage change in populations of 17 common bird species between 2001 and 2005 that can be attributed to CREP, against the lower 95% credible limit of those estimates........................................................89
Figure 4.1: Wintering Red-tailed Hawk population trends (and 95% CI) for Pennsylvania and CREP regions for 2001-2008 .......................................90
Figure 4.2: Correction factors for varying survey effort (hours per county) for four raptor species. Correction factors derived from Bayesian models ............91
Figure 4.3: Wintering raptor population trends (and 95% credible intervals) in Pennsylvania for 2001 to 2008 showing the changes in estimated trend as a result of including variable effort effects in the model ..........................92
Figure 4.4: Estimated wintering raptor population trends (and 95% credible intervals) in Pennsylvania for 2001 to 2008 for scenarios where there was no CREP, and where enrollment was higher then at present ....................................93
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Figure 4.5: Winter and breeding season population trends (and 95% CI) for American kestrels in the Lower Susquehanna River basin CREP region..................94
Figure 4.6: Trend in northern harriers on Christmas Bird Count (CBC) circles in the Atlantic Flyway ........................................................................................95
Figure 5.1: Estimates of outer distance bands – the limits of audible detection, against proportion of birds detected within 75 m, for 12 grassland songbirds in Pennsylvania............................................................................................137
Figure 5.2: Crude songbird population density estimates and estimated distribution of population among 12 habitat types..........................................................138
Figure 5.3: Population indices for 1995 to 2007 for 11 common songbirds associated with farmland in Pennsylvania ................................................................140
Figure 5.4: Population indices for grassland songbirds in Pennsylvania and the Lower Susquehanna River basin for 2001 to 2005.............................................142
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LIST OF TABLES
Table 1.1: Population trends of common bird species for three guilds associated with farmland in Pennsylvania for 1970 to 2007 ..............................................18
Table 2.1: CREP enrollment categories as devised for analysis of bird population changes in the Lower Susquehanna River basin CREP between 2001 and 2005 ...........................................................................................................47
Table 2.2: Land use and land use change within 250 m of point count locations ......48
Table 2.3: Occupancy and counts of the 60 commonest bird species on PGC bird monitoring surveys in 2001 .......................................................................49
Table 2.4: Species showing significant population changes in the Lower Susquehanna River basin CREP between 2001 and 2005.........................52
Table 2.5: Bird species showing a significant apparent effect of the rate of CREP enrollment on their population trends in the Lower Susquehanna River basin CREP between 2001 and 2005.........................................................53
Table 3.1: Parameter estimates for Bayesian spatial models of counts of 60 common bird species in the Lower Susquehanna River basin in 2001 to 2005 .......78
Table 3.2: Estimated bird population changes attributable to CREP in the Lower Susquehanna River Basin during 2001 to 2005 ........................................82
Table 4.1: Pennsylvania Winter Raptor Survey coverage and effort 2001-2008 .....105
Table 4.2: Land cover types and CREP enrollment by CREP region.......................106
Table 4.3: Parameter estimates from Bayesian spatial models of winter raptor counts in Pennsylvania between 2001 and 2008.................................................107
Table 4.4: Goodness of fit of Bayesian models and changes in the Deviance Information Criterion (DIC) for models that do not include effort effects and spatial effects ....................................................................................108
Table 5.1: Population estimates (thousands of males) for key grassland songbirds in Pennsylvania and the Lowers Susquehanna River basin.........................134
Table 5.2: Estimates of bird population sizes in CREP fields in the Lower Susquehanna River basin of Pennsylvania and estimates of absolute population changes in the region from USGS Breeding Bird Survey derived trends coupled with 2nd PPBA derived population size estimates135
Table 5.3: Population trends of common bird species for three guilds associated with farmland in Pennsylvania for 1995 to 2007 ............................................136
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ACKNOWLEDGEMENTS
First and foremost, I would like to thank my advisor, Margaret Brittingham - her generosity with time, advice and providing opportunities not only enabled me to study at Penn State but helped make the process enjoyable. I am also indebted to my doctoral committee, Duane Diefenbach, Murali Haran, Wally Tzilkowski and Heather Karsten for their encouragement and advice. Dave Mortensen and Dave Eissenstat provided a great deal of encouragement and support in their roles as chairs of the Intercollege Graduate Degree Program (IGDP) in Ecology, I feel honored to have been part of the program during their stewardship. I would also like to thank the staff the of the International Student Services, Graduate School, School of Forest Resources and IGDP in Ecology, in particular Mary Hudson, for their tireless efforts to ensure a smooth transition through graduate school. Special thanks also to Peter Hudson, without whom I’m sure I would never have had the opportunity to study at Penn State.
I am also grateful to Pennsylvania Game Commission staff for their help and encouragement, in particular Scott Klinger and Mike Pruss. Barry Isaacs at the Pennsylvania USDA was a valued mentor, providing many opportunities to meet biologists and other staff at USDA and other government agencies. I thank Dan Brauning, Mike Lanzone, Bob Mulvihill, Trish Miller and the rest of the 2nd Pennsylvania Breeding Bird Atlas (PBBA) team for their roles in organizing and managing the 2nd PBBA Point Counts, which kept me gainfully employed during summer months and provided additional data for my thesis research. George Farnsworth (Xavier University) and Wayne Thogmartin (USGS) kindly supplied statistical advice and code, which saved an enormous amount of time and effort.
My research was only possible due to the many hours of bird surveys conducted by a great many fieldworkers, both for the PGC bird monitoring program and Winter Raptor Surveys. I would particularly like to thank Greg Grove for organizing the Winter Raptor Surveys and so willingly sharing the data. Liz Dlugosz, Franz Lichtner and Jake Mohlman assisted with PGC bird monitoring data inputting.
My peers and colleagues in the Brittingham Lab and wider graduate student community of the School of Forest Resources and IGDP in Ecology provided professional advice, academic stimulus and a surfeit of social opportunities. During my time at Penn State I met a many great people, among who was my wife Sue. I thank Sue for her understanding, love and inspiration, and for giving my career renewed purpose. Finally, I thank my family for their help and encouragement. In particular I’d like to thank my mother and late father, to whom I owe everything. I dedicate my thesis to my father, and in doing so, extend my gratitude to the many people who have helped me achieve things that he was extremely proud of.
Financial support was provided by the Pennsylvania Game Commission, IGDP in Ecology and School of Forest Resources.
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Chapter 1
Introduction and motivation –
Population trends of grassland birds in Pennsylvania
Historical perspective
Grassland obligate bird populations have been in steady decline across North
America for the past four decades or more (Vickery 2001, Sauer et al. 2008). The
declines are of such magnitude that they have been predicted to become a “prominent
wildlife conservation crisis of the 21st Century” (Brennan and Kuvlesky 2005). Declines
have been particularly steep in the eastern United States, which due to bird distributional
shifts following the large-scale destruction of native tall-grass prairies of the Midwest,
supports significant populations of some grassland bird species (Norment 2002, Sauer et
al. 2008). However, the declines in species often associated with grasslands are by no
means restricted to native grassland habitats; in certain parts of their breeding range these
species are largely dependent on agricultural grasslands and croplands. Further, these
declines in farmland birds are not unique to North America; population decreases of
similar timing and magnitude in a range of farmland bird species in Europe have received
a great deal of attention from conservation biologists and policy makers alike (Donald et
al. 2001, Gregory et al. 2005, Mattison and Norris 2005).
Changes in bird populations associated with farmland and grassland habitats are,
of course, not new - the fortunes of these birds have long been inextricably linked with
agriculture. Prior to European settlement, the birds that we now associate with these
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habitats in North America would have been abundant in the vast swathe of native
grassland that stretched from Texas north into Canada, and from the Rockies east to the
Mississippi. Some species quickly adapted to the new openings created by the agriculture
of early settlers in the forested east of North America (Askins 1999). These were boom
times for some species of open country, although at a continental scale this may have
been very short-lived as by the mid 19th Century the vast tall-grass prairies were rapidly
going under the plow. Within little more than a century, 99% of the tall-grass prairie had
disappeared (Noss et al. 1995).
In Pennsylvania, farmland birds would likely have been rare or absent prior to
European settlement; in the words of Todd (1940) “There could have been few if any
birds such as Bob-white, Prairie Horned Lark, Bobolink, Meadowlark, Vesper Sparrow,
Savannah Sparrow and Grasshopper Sparrow”. Although there were some natural
grasslands in the state, estimated at around 230 square miles (596 square km) in extent
(Latham and Thorne 2007), and natural disturbance events could have maintained a
patchwork of early successional habitats; there can be no doubt that grassland obligate
birds were restricted in range until the forests were cleared. By the time the first
comprehensive accounts of Pennsylvania’s avifauna were published in the mid to late
19th Century, many of these species were described as common, if not abundant (Stone
1894, Todd 1940, McWilliams and Brauning 2000). Other species colonized rather later;
for example the Henslow’s sparrow Ammodramus henslowii was not confirmed to nest in
the state until 1913 (Todd 1940) while the Savannah sparrow Passerculus sandwichensis
appears to have been a scarce bird until the early decades of the 20th Century
(McWilliams and Brauning 2000). Thus, the presence of these species in appreciable
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numbers in Pennsylvania is largely attributable to the clearing of forests for agriculture
and coal-mining, and it is therefore not surprising that their numbers have continued to be
driven by changes in our anthropogenically altered landscapes.
Farmland extent peaked at almost 20 million acres (8,093,713 ha), around 70% of
Pennsylvania, in the late 19th Century (PA DEP 2008). During the first half of the 20th
Century many farmed areas in PA were abandoned as farmers headed west to more fertile
lands, leaving huge areas of farmland to regenerate as forest. Today 25% of the state is
open habitat, the majority of which is farmland (Myers et al. 2000). During recent
decades, development has been the main cause of farmland loss, particularly in the
southeast of the state. Between 1969 and 1992, for example, 27% of farmland was lost in
the Lehigh Valley while around Philadelphia losses were 37% (Goodrich et al. 2002).
Losses of pasture have been particularly dramatic with less than 50% of the 1978 acreage
now remaining (PFBC and PGC 2005).
Although there has been a steady decline in the area of grassland and farmland in
Pennsylvania since the peak in late 19th Century, the rate of decline of birds associated
with these habitats cannot be explained fully by loss of habitat. The main driver behind
the more recent avian population losses is changes in the management and ownership of
farmland, leading to fewer, larger farms that are more intensively managed (Bolgiano
2000). The drive to improve agricultural efficiency has led to increased use of herbicides
and pesticides, which has removed important seed and invertebrate food sources (Beecher
et al. 2002). Increased use of fertilizer has resulted in a much more rapid growth of hay
crops, which allows them to be cut frequently and early, thereby excluding grassland
nesting birds that no longer have a suitable time-window in which to reproduce (Fawley
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and Best 1991). In 1950, the mean first cutting date for timothy Phleum pratense and
clover Trifolium spp. hay was July 5; by 1990 the mean first cutting was a full month
earlier (Klinger 2008), a date which coincides with the peak of first nesting attempts for
many grassland bird species. In addition, there has been a change in the hay crops planted
- during the past 25 years the acreage of alfalfa has increased by 45% while other hays
have declined by 17% (Klinger 2008).
Some of these changes are not new. Todd remarked in 1940, “This custom of
early mowing is good economy for the farmer and secures better quality of hay, but it is
disastrous for the Bobolink”. However, it was during the 1940s to 1980s that the rate of
“intensification” of farming practices reached its peak (Perkins and Jamison 2008,
Newton 2004). During those decades, even those species that had previously shown great
adaptability in colonizing arable farmland, such as vesper sparrow Pooecetes gramineus
and red-winged blackbird Agelaius phoeniceus, went into rapid decline. This period also
coincided with a discontinuity in Federal set-aside programs. During 1956 to 1973
between 500,000 and 600,000 acres (200,000-240,000 ha) of farmland in Pennsylvania
were idled through the Soil Bank and latterly the Feed Grain programs (Klinger 2008).
These large areas of set-aside are likely to have provided important refuges for farmland
and grasslands birds at that time but when these federal programs were discontinued,
farmland was put back into agricultural production.
Although declines of grassland birds in North America are often attributed to
habitat loss and modification on the breeding grounds, it should be noted that most
grasslands birds are migratory, with some species, notably bobolink Dolichonyx
oryzivorus and dickcissel Spiza Americana, wintering in central and South America. Both
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bobolink (Lopez-Lanus et al. 2007) and dickcissel (Avery et al. 2001) are considered
agricultural pests in parts of their wintering ranges and are controlled as such, introducing
additional population pressures outside of the breeding season. Other grassland obligates,
such as grasshopper and Henlow’s sparrows, winter mainly in the southern USA and may
also be affected by habitat loss and modification outside of the breeding season, but the
extent to which these are population limiting is poorly understood (Plentovich et al. 1998,
Reynolds and Krausman 1998, Holimon et al. 2008). Population limitations on grassland
birds on migratory routes and wintering grounds deserves further study, but there is now
sufficient evidence that most of these species are to a large extent limited by factors on
the breeding grounds (Vickery and Herkert 2001), highlighting the importance of
conservation efforts within those areas.
Due to concerns about the wide-ranging environmental impacts of agricultural
intensification in the United States, the Conservation Reserve Program (CRP) was
introduced in the 1985 Farm Bill, with key aims of curtailing excess agricultural
production and reducing soil erosion (Isaacs and Howell 1988). The CRP is a voluntary
private agricultural lands retirement program in which farmers convert erodible arable
land to perennial cover, typically grass-legume mixes, for contract periods of 10-15
years, in return for a rental income, primarily from Federal funds. The CRP resulted in
the creation of millions of acres of grasslands across agricultural areas of the United
States. Numerous studies have shown that the new habitat created by CRP has benefited
grassland bird species (e.g. Johnson and Igle 1995, Ryan et al. 1998, Swanson et al.
1999), but most grassland obligates have continued to decline since the introduction of
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CRP (Norment 2001), suggesting it has not been sufficient to compensate for continuing
population losses across the farmland and grassland landscapes.
Due to unfavorable local economic conditions, CRP enrollment was low in the
northeast United States (Wiebe and Gollehon 2006). To allow for better tailoring to local
economic conditions and conservation requirements, a subsidiary program, the
Conservation Reserve Enhancement Program (CREP) was authorized in the 1996 Farm
Bill.
CRP literature
Farm Bill programs, and the Conservation Reserve Program (CRP) in particular,
have been cited as being important mechanisms for delivering wildlife conservation on
agricultural lands (Gray and Teels 2006). The effects of CRP enrollment on bird
populations have received considerable attention by ornithologists. By September 2008 at
least 64 refereed journal articles pertaining directly to studies of the populations and
ecology of birds in CRP fields had been published (Appendix A), in addition to several
review articles and scores of unpublished reports. Several studies showed that grassland
obligate bird counts or densities are higher in areas with higher CRP enrollment (Herkert
1998, Haroldson et al. 2006, Guidice and Haroldson 2007, Herkert 2007a, Nielson et al.
2008). Rather few studies have demonstrated that CRP enrollment has been sufficient to
change population trends at large scales (Ryan et al. 1998) although Veech (2006)
showed that increasing trends were more frequent in landscapes with more CRP.
However, Murphy (2003) did not find evidence that this translated into a positive
relationships at the state scale. Further, at the population (continental) scale, most
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grassland obligate species have continued to decline since CRP was introduced in 1985
(Sauer et al. 2008), although declines may now be less steep, which suggests that the
conclusions regarding the impact of CRP grassland on bird populations are very scale
dependent. However detecting positive impacts of CRP at the bird population scale is
fundamental to the perception of overall benefits of the program; and perhaps more
importantly, focuses attention on the importance of the program among a suite of
potential mechanisms for reversing long-term downward trends in grassland bird
populations.
The majority of published research has focused on game species (ducks and
galliformes) or passerines during the breeding season. Only two of the 64 published
papers focused solely on bird populations and ecology during the winter (Best et al. 1998,
Littlefield and Johnson 2005), one on post-breeding dispersal (Guzy and Ribic 2007) and
two on year-round relationships (McCoy et al. 2001, Boisvert et al. 2005).
Most of the evidence for population-scale responses to CRP is from the Great
Plains and Midwest. Substantial CRP acreage has been established for more than two
decades in those regions, where relatively large numbers of grassland birds are found in
both agricultural and remnant native grasslands. It does not follow that similar
population-scale effects would be found in areas with more modest CRP enrollment rates,
or areas where grassland bird densities are low or populations fragmented. There have
been single published studies in neighboring Maryland (Gill et al. 2006) and Ohio
(Swanson et al. 1999) but otherwise there is scant information pertaining to avian use of
CRP/CREP in Pennsylvania and surrounding states. Regionally specific evaluation of
conservation programs could be important because more targeted management could
8
yield greater benefits (Whittingham et al. 2007). Very little is known about the meta-
population dynamics of grassland bird in fragmented grassland landscapes, such as those
found in Pennsylvania. It is therefore important that the Pennsylvania Conservation
Reserve Enhancement Program is monitored and assessed for its efficacy at benefiting
grassland birds at the population scale.
A study of the “Effects of local and landscape features on avian land use and
productivity on Conservation Reserve Enhancement Program fields” in south-central
Pennsylvania was completed as a doctoral thesis by Kevin Wentworth (2006). This
contributed a considerable amount to our knowledge of the response of birds to CREP in
Pennsylvania. Key findings were that 31 species used CREP fields during the breeding
season, of which 19 were confirmed to nest. The red-winged blackbird was easily the
most numerous species, accounted for around half of all bird observations and nests
located. Other numerous species included shrub and edge birds such as common
yellowthroat Geothlypis trichas, field sparrow Spizella pusilla, song sparrow Melospiza
melodia and indigo bunting Passerina cyanea – all of which were found in higher
densities than in most studies further west. Grassland obligates were scarce, the most
numerous being grasshopper sparrow Ammodramus savannarum which had a mean
density of 0.1 singing males per hectare. Paired comparisons with nearby active hay
fields showed that CREP fields had much higher bird species richness and abundance
than the agricultural grasslands that are typical of the area. Wentworth’s (2006) study
suggested that although some CREP fields in Pennsylvania supported grassland obligate
birds, the small average field size and the fragmented nature of grasslands in a mosaic of
mixed farmland and forests may explain why densities were lower than those reported in
9
other studies. Also, it is important to note that most CREP fields in Wentworth’s study
were either rolled-over from previous CRP contracts or were only between one and three
years old at the time of the bird surveys in 2002 to 2004.
To gain a more recent perspective of farmland and grassland bird population
changes in Pennsylvania, I used United States Geological Survey (USGS) Breeding Bird
Survey (BBS) data to estimate recent population trends. Trends of individual species and
guilds of species (Szaro 1986) were examined such that changes in population trajectories
that could be attributed to the creation of conservation grasslands in the state since 2001
are set in the context of longer-term trends. I used Poisson regression models to estimate
trend indices and their accompanying 95% confidence intervals, which are not available
at the state-scale from other sources.
Methods
Definition of species guilds
I used Redundancy Analysis (RDA) in Program CANOCO (ter Braak and
Šmilaur 2002) to relate BBS counts to land cover data, with the aim of defining guilds of
bird species that are characteristic of farmland in Pennsylvania. Land cover data for circa
year 2000 were derived from Landsat 7 ETM+ satellite imagery as part of a cooperative
project between the U.S. Geological Survey (USGS) and the U.S. Environmental
Protection Agency (USEPA). To ensure temporal compatibility between bird counts and
land cover data, I used the mean BBS counts for each route for the years 1998 to 2002.
The ArcGIS buffer tool (ESRI 2004) was used to extract land cover information within
10
400 m buffers of each BBS routes. I chose 400 m because this is the radius within which
birds are counted on from BBS stops. Digitized BBS routes locations were downloaded
from the USGS Patuxent Wildlife Research Center website. I grouped land cover types
into eight categories: water, developed, hay, arable, coniferous/mixed forest, deciduous
forest, wetland, and transitional/other.
RDA derives linear relationships between species counts and environmental
gradients (ter Braak and Šmilaur 2002) – in this case land cover. These relationships can
be displayed in a bi-plot to illustrate the association between the species and the
environmental gradients. I assigned each species to a land cover gradient by calculating
the shortest Euclidean distance between each species location and 50 evenly spaced
points along each habitat gradient, the shortest of these 400 distances for each species (50
points on each of 8 gradients) was then used to define its species guild.
Species closest to the origin of the bi-plot are those which show the least
specialization (or, are found in most habitats) and are considered habitat generalists. I
combined generalists with species associated with the “developed” land cover gradient
into a “Generalists/developed” bird species guild. I considered species associated with the
“arable” and “hay” gradients to be farmland species which were further divided into two
groups: those that nest within grass and arable fields – the “Field nesters” species guild,
and other species – the “Farmland generalists” guild. Assignment to these two guilds
were based on prior knowledge of the nesting habits of these species in Pennsylvania and
informed by species account in avifaunal publications (Brauning 1992, McWilliams and
Brauning 2000). All other species were associated primarily with forested, transitional,
and wetlands and are not therefore considered in this analysis.
11
Population trend estimation
Bird population trends for 1970 to 2007 were estimated using program TRIM
(TRends and Indices for Monitoring data). TRIM statistical software is designed to
analyze time-series of counts with missing observations, using Poisson regression
(Pannekoek and van Strien 2001). I restricted analysis to 110 species for which there was
sufficient BBS data to calculate statewide population indices. The linear population trend
for the period 1970 to 2007 was estimated from the modeled slope parameter. The
direction and significance of the trend is classified according to the overall slope and 95%
confidence interval (= slope +/- 1.96 times the standard error of the slope):
• Strong increase - increase significantly more than 5% per year (5% would mean a
doubling in abundance within 15 years). Criterion: lower limit of confidence
interval > 1.05.
• Moderate increase - significant increase, but not significantly more than 5% per
year. Criterion: 1.00 < lower limit of confidence interval < 1.05.
• Stable - no significant increase or decline, and it is certain that trends are less than
5% per year. Criterion: confidence interval encloses 1.00 but lower limit > 0.95
and upper limit < 1.05.
• Uncertain - no significant increase or decline, but not certain if trends are less
than 5% per year. Criterion: confidence interval encloses 1.00 but lower limit <
0.95 or upper limit > 1.05.
• Moderate decline - significant decline, but not significantly more than 5% per
year. Criterion: 0.95 < upper limit of confidence interval < 1.00.
• Steep decline - decline significantly more than 5% per year (5% would mean a
halving in abundance within 15 years). Criterion: upper limit of confidence
interval < 0.95.
12
The average trend across a species guild can be considered to be an indicator
index for a group of birds that share broadly similar ecological requirements (Gregory et
al. 2005). The guild indices were the geometric means of the index values for each
species. Note that I did not weight this mean by population size; therefore, the relative
population change of a scarce species has the same effect on the index as that for a
common species. The variance for each index ( I ) is calculated as follows:
2
2
var( )var( ) t
t t
III
T I� �� �≈ � �� �
� � � ��
where there are T indices to be averaged and the index for each species is denoted It..
Standard errors (SE) and 95% confidence intervals (1.96 * SE) around each index value
could then be calculated (Gregory et al. 2005).
Results
Species guild assignment
The bi-plot resulting from the redundancy analysis showed the relationships
between each of the 110 common bird species and the land cover types (Figure 1.4).
Species located close to the land cover gradients are most associated with that habitat.
Those toward the left of the plot are most associated with the farmland land cover
gradients (hay and arable) and those on the right most associated with forested land cover
types. The species associated with farmland (hay & arable) were then divided into those
13
that typically nest within fields: 10 species (“Field nesters”), and those associated with
non-cropped habitats - 17 species (“Farmland generalists”). A further 19 species that are
common within the farmed landscape but also found in other habitats were grouped into a
“Generalists/developed” guild. The remaining 64 were not associated with farmland.
The “Farmland generalists” guild includes species that predominantly hunt or feed
in open areas e.g. red-tailed hawk, American kestrel, eastern bluebird, eastern kingbird
and barn swallow; species associated with woodlots, shrubby areas and scattered trees in
open country, e.g. yellow warbler and orchard oriole; and species that are associated with
human dwellings, e.g. common grackle and the non-native rock pigeon, European
starling and house sparrow. While many of these farmland generalists are by no means
restricted to farmland, BBS counts suggest that in Pennsylvania they are found in highest
densities in farmed landscapes. Most of these species are the characteristic birds of
farmed areas in Pennsylvania and are often much more numerous than the “Field
nesters”, which could be considered to be farmland/grassland obligates. Further, many of
the species in the “Generalists/developed” guild are also numerous in farmland
landscapes, but these species often reach their highest densities in suburban and forest
edge settings. These 46 species, in three species guilds, include most of the common
species in farmland areas of Pennsylvania.
Population trends 1970 to 2007
Killdeer and bobolink were the only species in the “Field nesters” guild whose
populations increased during the period 1970 to 2007 (Table 1.1). Savannah sparrow
populations were stable but populations of the other species declined significantly. Ring-
14
necked pheasant was the most rapidly declining species, decreasing by an average of
7.4% per year, representing a loss of 94% of the population between 1970 and 2007. It
should be noted though that this species is not native to North America and population
trends are clouded by changes in patterns of releasing captive-bred birds for hunting
(Diefenbach et al. 2000). Among the other field nesters, estimated population losses
between 1970 and 2007 were 77% for horned lark, 55% for field sparrow, 81% for vesper
sparrow, 63% for grasshopper sparrow, 56% for red-winged blackbird and 74% for
eastern meadowlark.
Among the “Farmland generalists”, only four species declined significantly:
purple martin (70%), European starling (13%), common grackle (47%) and house
sparrow (40%). Ten of the farmland generalists increased significantly, but it should be
noted that among these are four species whose ranges have expanded northwards through
Pennsylvania in recent decades: red-bellied woodpecker, fish crow, northern mockingbird
and orchard oriole (McWilliams and Brauning 2000).
The trend for the “Field nesters” species guild is strongly divergent from that of
the other two guilds (Figure 1.5). Species in the “Farmland generalist” guild fared much
better on average, increasing steadily through the 1970s, 1980s and 1990s, followed by a
small decrease during the current decade. Species in the “Generalists/developed” guild
showed a similar pattern, with a steady increase in numbers until 2001. The overall trend
for the eight species found to be most numerous in CREP fields in a study in southern
Pennsylvania (Wentworth 2006) – henceforth called the “Wentworth 8” was negative but
the decrease was much shallower than that of the “Field nesters” guild, with which it
overlaps by five species. The three additional species in the “Wentworth 8”: common
15
yellowthroat, song sparrow and indigo bunting are shrub/edge species that have not
shown the large declines typical of the field nesters. Indeed the common yellowthroat and
indigo bunting were found to be more associated with forest in the RDA species guild
assignment, trends for these two species are shown in addition to the 46 farmland and
generalist species for completeness (Table 1.1)
Discussion
The contrasting fortunes of field nesting species and habitat generalists within
Pennsylvania have resulted in a significant change in the assemblage of farmland birds.
While some species have increased, perhaps due to the increase in non-farmed habitats
associated with developments, the overwhelming pattern of decrease for species that nest
within agricultural fields is compelling evidence that losses of farmland and changes in
agricultural practices are the principle drivers of these declines. The decline slowed
between the mid 1980s and mid 1990s but has since continued at the same rate noted
during the 1970s and early 1980s, with little evidence of a slowing or reversal in that
trend in recent years. The period of rapid decline during the mid 1970s through mid
1980s coincides with the period when Federal set-aside programs were discontinued
(Klinger 2008). It should be noted that some of the field nesting species are also
associated with tilled land in Pennsylvania, notably horned larks and vesper sparrows,
which may benefited from the large acreage of idled fields in the 1950s through 1970s. It
is also interesting to note that two field nesters which didn’t show a strong decline:
bobolink and Savannah sparrow, are more widespread in northern Pennsylvania than in
16
the south (Brauning 1992), suggesting that changes in agricultural practices may vary
regionally within Pennsylvania.
The sharp drop in the indices for the “Farmland generalists” and
“Generalists/developed” guilds between 2002 and 2003 can be attributed to increased
mortality of certain species due to West Nile virus (WNV) which arrived in Pennsylvania
in 2000 (PWNVSP 2008). WNV caused rapid but short-term population declines of
vulnerable species across North America, including American crow Corvus
brachyrhynchos, blue jay Cyanocitta cristata, tufted titmouse Baeolophus bicolor, house
wren Troglodytes aedon and eastern bluebird (LaDeau et al. 2007). I believe that the
conflation of the effects of West Nile virus with those of changes in habitat through
CREP severely hampers interpretation of the coarse-scale population trends presented
here.
In chapters two and three I examine bird population trends at a smaller, landscape
scale within the Lower Susquehanna River Basin CREP region. In the light of the results
from those studies I discuss the potential limitations for detecting statewide population-
scale changes in grassland birds in Pennsylvania in chapter five.
Thesis aims
The objectives of my research were to build on the findings of Kevin Wentworth
to determine avian responses to CREP in Pennsylvania. Specifically, I examined new and
existing bird monitoring data to determine whether there was evidence for large-scale
population level responses to the provision of new grassland habitat. These analyses
17
examined responses over several spatial and temporal scales: (1) breeding bird population
statewide using existing United States Geological Survey (USGS) Breeding Bird Survey
(BBS) data for 1970 to 2007; (2) breeding bird populations at the landscape scale using
data from a bird monitoring program established in LSR by the Pennsylvania Game
Commission during 2001-2005; (3) random location point count data from the 2nd
Pennsylvania Breeding Bird Atlas during 2004 to 2008; and (4) winter raptor numbers at
the county scale from the Pennsylvania Winter Raptor Survey 2001-2008.
18
Table 1.1: Population trends of common bird species for three guilds associated with farmland in Pennsylvania for 1970 to 2007. Data from the USGS Breeding Bird Survey. Trends estimated using log-linear Poisson Regression.
annual population
change %
Guild and species name mean SE trend and significance
% change
1970 to
2007
Field nesters
ring-necked pheasant Phasianus colchicus -7.36 0.27 Steep decline (p<0.01) -93.6
killdeer Charadrius vociferus 0.64 0.16 Moderate increase (p<0.01) 25.8
horned lark Eremophila alpestris -3.97 0.45 Moderate decline (p<0.01) -76.7
field sparrow Spizella pusilla -2.21 0.12 Moderate decline (p<0.01) -55.3
vesper sparrow Pooecetes gramineus -4.59 0.34 Moderate decline (p<0.01) -81.6
Savannah sparrow Passerculus sandwichensis -0.02 0.20 Stable -0.7
grasshopper sparrow Ammodramus savannarum -2.75 0.28 Moderate decline (p<0.01) -63.4
bobolink Dolichonyx oryzivorus 0.80 0.20 Moderate increase (p<0.01) 33.2
red-winged blackbird Agelaius phoeniceus -2.26 0.11 Moderate decline (p<0.01) -56.1
eastern meadowlark Sturnella magna -3.68 0.15 Moderate decline (p<0.01) -74.1
Farmland generalists
red-tailed hawk Buteo jamaicensis 4.88 0.41 Moderate increase (p<0.01) 455.8
American kestrel Falco sparverius 0.69 0.31 Moderate increase (p<0.05) 28.1
turkey vulture Cathartes aura 3.90 0.37 Moderate increase (p<0.01) 296.4
rock pigeon Columba livia -0.17 0.19 Stable -5.9
red-bellied woodpecker Melanerpes carolinus 8.96 0.42 Strong increase (p<0.01) 2095.9
eastern kingbird Tyrannus tyrannus 0.01 0.20 Stable 0.4
white-eyed vireo Vireo griseus 3.16 0.61 Moderate increase (p<0.01) 206.5
fish crow Corvus ossifragus 2.89 0.60 Moderate increase (p<0.01) 178.9
purple martin Progne subis -3.26 0.42 Moderate decline (p<0.01) -69.7
barn swallow Hirundo rustica -0.09 0.14 Stable -3.2
eastern bluebird Sialia sialis 3.94 0.26 Moderate increase (p<0.01) 302.0
northern mockingbird Mimus polyglottos 1.14 0.18 Moderate increase (p<0.01) 50.4
European starling Sturnus vulgaris -0.38 0.13 Moderate decline (p<0.01) -12.8
yellow warbler Dendroica petechia 2.28 0.14 Moderate increase (p<0.01) 125.1
common grackle Quiscalus quiscula -1.77 0.12 Moderate decline (p<0.01) -47.4
orchard oriole Icterus spurius 3.78 0.43 Moderate increase (p<0.01) 280.3
house sparrow Passer domesticus -1.39 0.10 Moderate decline (p<0.01) -39.6
Generalists/developed
Canada goose Branta canadensis 3.15 1.06 Moderate increase (p<0.01) 205.4
wood duck Aix sponsa 4.62 0.73 Moderate increase (p<0.01) 408.3
mourning dove Zenaida macroura 2.40 0.12 Moderate increase (p<0.01) 134.9
19
chimney swift Chaetura pelagica 0.44 0.15 Moderate increase (p<0.01) 17.1
belted kingfisher Ceryle alcyon -0.39 0.27 Stable -13.1
downy woodpecker Picoides pubescens 1.30 0.18 Moderate increase (p<0.01) 59.2
northern flicker Colaptes auratus -2.09 0.12 Moderate decline (p<0.01) -53.3
blue jay Cyanocitta cristata 0.96 0.12 Moderate increase (p<0.01) 41.1
American crow Corvus brachyrhynchos 1.13 0.09 Moderate increase (p<0.01) 49.9
Carolina chickadee Poecile carolinensis 6.10 0.77 Moderate increase (p<0.01) 742.9
tufted titmouse Baeolophus bicolor 3.44 0.15 Moderate increase (p<0.01) 237.9
Carolina wren Thryothorus ludovicianus 6.40 0.36 Strong increase (p<0.01) 833.0
house wren Troglodytes aedon -0.05 0.11 Stable -1.8
American robin Turdus migratorius 0.11 0.08 Stable 4.0
gray catbird Dumetella carolinensis 1.22 0.11 Moderate increase (p<0.01) 54.7
song sparrow Melospiza melodia 0.40 0.09 Moderate increase (p<0.01) 15.5
northern cardinal Cardinalis cardinalis 1.77 0.10 Moderate increase (p<0.01) 88.1
brown-headed cowbird Molothrus ater -1.03 0.18 Moderate decline (p<0.01) -31.1
house finch Carpodacus mexicanus 10.31 0.61 Strong increase (p<0.01) 3320.0
Additional common species in CREP
common yellowthroat Geothlypis trichas 1.22 0.11 Moderate increase (p<0.01) 6.09
indigo bunting Passerina cyanea 1.16 0.14 Moderate increase (p<0.01) 51.5
# eight most numerous species in CREP fields in LSR (Wentworth 2006).
20
Figure 1.1: Conservation Reserve Enhancement Program areas in Pennsylvania
21
Figure 1.2: Percentage of arable land enrolled in the Conservation Reserve Enhancement Program by program year and program area (LSR= Lower Susquehanna River Basin, USR=Upper Susquehanna River Basin, Ohio=Ohio River Basin)
0
0.5
1
1.5
2
2.5
3
3.5
4
2000 2001 2002 2003 2004 2005 2006 2007
% o
f ara
ble
land
LSRUSROhio
Source of CREP acreage data: Conservation Reserve Program – Monthly Contract Report, September 2008 (USDA, Farm Service Agency). Area of arable land estimated from Landsat Enhanced Thematic Mapper (ETM) derived land cover data for circa 2000.
22
Figure 1.3: Location of studies in 64 refereed journal articles pertaining to bird population and ecology studies with respect Conservation Reserve Programs fields (see Appendix A for full list of articles)*
* Murphy (2003) is excluded – this large-scale analysis included data from 40 states east of the Rockies.
23
Figure 1.4: Redundancy Analysis bi-plots of species scores with land cover gradients (a) and species guilds (b) from USGS Breeding Bird Survey counts of 110 common birds in Pennsylvania between 1998 and 2002. Only the first and second axis are displayed for clarity.
24
Figure 1.5: Population indices and 95% confidence intervals for three common bird species guilds and for the eight most numerous species in CREP fields (Wentworth 2006) in Pennsylvania for 1970 to 2007. Shaded area represents the CREP enrollment phase.
25
Chapter 2
Landscape scale population trends of common birds in the Lower Susquehanna
River basin Conservation Reserve Enhancement Program region
Introduction
In 2001 the Pennsylvania Game Commission (PGC) established a bird survey
program to monitor the effects of the Conservation Reserve Enhancement Program
(CREP) on grassland and other farmland birds in the Lower Susquehanna River (LSR)
basin CREP. Previous research in the LSR CREP region (Wentworth 2006) demonstrated
that CREP fields support primarily farmland generalist species, such as the red-winged
blackbird Agelaius phoeniceus or shrub and edge species such as the common
yellowthroat Geothlypis trichas, field sparrow Spizella pusilla, song sparrow Melospiza
melodia and indigo bunting Passerina cyanea, with lower numbers of grassland
specialists such as grasshopper sparrows Ammodramus savannarum and eastern
meadowlarks Sturnella magna. Bird species diversity, abundance and nesting success
was higher in CREP fields than in paired hayfields of a similar size. I hypothesize that
given the relatively small populations of some of these species in southern Pennsylvania,
the extensive new grasslands provided by CREP are sufficient to elicit a population level
response, either by slowing or by reversing declines in areas where there is the most
26
CREP grassland. However, small bird population sizes may result in difficulty detected
statistically significant trends due to low statistical power, hence a lack of evidence for an
effect does not imply that a population response did not occur.
Due to budget constraints, the PGC bird monitoring program was curtailed in
2005. To test my hypothesis I examined population trends of common birds within the 20
county study area, for the period 2001 to 2005. Due to the rather short time series, this
analysis should be considered an investigation of initial population level responses.
Although CREP was expanded to 23 counties in the Upper Susquehanna River basin in
2003 and 16 counties in the Ohio River basin in 2004, there are, as yet, no specific
programs to monitor the effects of CREP on bird populations in those areas.
Methods
Bird and habitat surveys
The PGC bird monitoring protocol was based on the United States Geological
Survey (USGS) Breeding Bird Survey (BBS) (Sauer and Droege 1990) with slight
modifications. Survey route start locations were selected randomly within areas
dominated by farmland, by the PGC, according to Landsat 7 ETM+ land cover data: they
were not selected to coincide with CREP agreements, very few of which were in place at
that time (Figure 2.1). Survey routes were established along public roads - mainly quiet
township roads; major highways, where traffic noise could reduce bird detectability, were
avoided. Point counts of 3-minute duration were conducted at up to 50 stops along each
survey route - the mean number of counts per survey route was 33.4 (range 20-50). The
27
counts were approximately 800 m (0.5 miles) apart and all birds seen or heard within a
250 m radius of each survey point were counted. Counts were conducted between sunrise
and mid-morning and unsuitable weather (wind, fog and rain) was avoided. A team of 12
highly-skilled bird surveyors, employed by the PGC, carried out bird surveys on 90
routes, twice per season, once in May and once in June. The bird surveyors were never
informed about the CREP enrollment in the areas in which they conducted surveys. In
2004 and 2005, due to budget constraints, only the June surveys were conducted. There
was a decrease in survey participation during the five years: 90 were routes were
surveyed in 2001, 77 in 2002, 75 in 2003, 72 in 2004 and 68 in 2005. This reduction,
combined with the reduction in counts from two per year to one, resulted in a 63%
reduction in the number of bird counts conducted between 2001 and 2005. A list of
survey routes is provided in Appendix B.
Bird surveyors also conducted habitat surveys by estimating the percentage of the
point count circle (radius of 250 m) in each of 17 land use categories (Appendix B).
Habitat surveys were carried out on a separate date to the bird visit, usually later in the
summer – the median date for habitat surveys was 6th July, with extremes of 24th May
and 24th August (inter-quartile range was 26th June to 21st July). Habitat surveys were
taken in each of the years 2001 to 2004.
Spatial data analysis and enumeration of CREP enrollment
My analysis was concerned with “landscape-scale” population changes – my
definition of landscape is the area within 500 m of each survey route. Although the bird
survey protocol stipulated that only birds within 250 m of point count locations were to
28
be counted, subsequent discussion with the fieldworkers suggested that in most cases all
birds were counted. The 90 surveys routes were digitized in ArcGIS geographic
information system software (ESRI 2004).The ArcView GIS “Buffer tool” was used to
define the landscape surrounding each survey routes as all areas within 500 m of that
route. Some adjacent survey routes were almost contiguous, and hence the landscapes
overlapped and could not, therefore, be considered spatially independent (see end of
Appendix B). In these cases, data from the two contiguous landscapes were combined.
Additionally, one route was surveyed in only one year and was not included in the
analysis, reducing the sample size to 84 landscapes. The landscapes averaged 921
hectares (range 516-1,962). Land cover extent in each landscape was derived from
Landsat 7 ETM+ satellite imagery circa year 2000. The estimated area of farmland was
the sum of the grassland and arable land cover types.
Digitized maps of CREP agreements were supplied by the Pennsylvania Natural
Resources Conservation Service. Although there are 14 CREP conservation practices
(CP) in the LSR CREP, I restricted my analysis to those that are grassland: CP1 –
introduced grasses and legumes (cool-season grasses), CP2 – native grasses (warm-
season grasses), CP10 - grass already established, and CP21 – grassed filter strips.
CREP was considered to be available as a habitat for grassland birds after one full
growing season, which was a liberal assessment of grassland bird habitat created by
CREP, because some CREP fields may not be suitable for some grassland bird species
until after more than one growing season. To assess the extent of CREP enrollment with
which to compare bird populations I used annual growing seasons of 1 June to 31 May
(Wentworth et al. in prep.), hence, by the 2005 bird surveys, only CREP fields sown
29
before 31 May 2004 were considered to be available for birds. The 31st May cut-off date
was chosen because I considered that fields sown before that date have sufficient time
during the following months to grow adequate cover for nesting grassland birds in the
subsequent calendar year.
The area of CREP, by CP and year of enrollment, was calculated for each of the
84 landscapes and then compared to the estimated area of farmland (from Landsat 7
ETM+ land cover data) to produce an estimate of the percentage of farmland enrolled in
CREP in each landscape and year (growing season). The landscapes were then
categorized into four levels of CREP enrollment ( c ) by ranking them according to the
mean of % of farmland enrolled at 31st May of each year (c), weighted by the number of
years since enrollment:
2000 2001 2002 2003 2004( 5) ( 4) ( 3) ( 2) ( 1)15
c c c c cc
× + × + × + × + ×=
Hence 2% of farmland enrolled over 4 years has the same measure as 4% enrolled
over two years. These weighted averages were then classifying based on split-points
where there was a visual step change in enrollment (Figure 2.3) while trying to maintain
reasonable sample sizes in each category, resulting in the following categories:
none/negligible CREP (mean of 0 to 0.04%), low CREP (0.07 to 0.84%), medium CREP
(0.97 to 1.57%) and high CREP (1.83 to 7.25%) landscapes (Table 2.1).
There are potentially several methods with which to enumerate CREP enrollment
rates, including enrollment as a percentage of the total landscape and as a percentage of
grassland. The percentage of farmland enrolled in CREP within the 84 landscapes was
not correlated to the % of existing grassland (R2=0.035), or the % of farmland overall in
each landscape (R2=0.061) (Figure 2.4). I concluded that using other potential measures
30
of CREP extent, such as % increase in grassland as a result of enrollment, or % of total
landscape enrolled, would not produce different results from the “% of farmland
enrolled” metric. A potentially useful metric for measuring CREP enrollment rates would
be the increase in potential grassland bird habitat as a result of the program. It is
important to note that most of the existing agricultural grassland in the area does not
provide good habitat for grassland nesting birds because it is mown, often more than
once, during the bird nesting season. It is therefore not possible to assess potential
grassland bird habitat from existing land cover data.
Redundancy Analysis to derive species guilds
Because of the small sample sizes for many species, analysis at the species guild
level could be more informative for evaluating the overall patterns of population trends
with respect to CREP enrollment. Species guilds (Szaro 1986) have been used
extensively to evaluate the responses of suites of species to environmental perturbations
(e.g., Telleria and Santos 1995, Gregory 2005). The results for a grassland species guild
could be compared with those for other species to evaluate whether population changes
can be attributable to CREP, or whether other extrinsic factors might have influenced bird
population levels.
Point level bird and habitat data (n=2,970) from the first survey year (2001) was
used to derive species guilds. Redundancy Analysis (RDA) was conducted using Program
Canoco (ter Braak and Šmilaur 2002) to derive linear relationships between bird counts
and land use. The land use data relate only to habitat in the immediate vicinity of the
survey locations and include more detailed land use categorization than is available for
31
land cover data. These relationships were then plotted to show which species are most
closely related to which environmental gradients. In this study, the environmental
gradients were the percentage cover of eight major land use types, as estimated by the
bird surveyors: developed (residential and commercial), arable, pasture, hay, fallow,
herbaceous, shrub and woodland. The Euclidean distance between the location of each
species and 50 evenly spaced locations along each of the eight habitat gradients in 3-
dimensional space was calculated, the guild assignment was based on the shortest of
these 400 distances for each species. This novel approach of classifying guilds insures
that my definitions are based on the best spatially explicit habitat data available (chapter
1).
Analysis of population trends
Bird population trends for the years 2001 to 2005 were estimated using log-linear
models with Poisson errors, conducted in Program TRIM (TRends and Indices for
Monitoring data). See Chapter 1 for more details of this method. The possible effects of
CREP on population changes at the landscape (survey route) scale was evaluated by
including the percentage of farmland enrolled in CREP as a covariate with four
categories: high CREP, medium CREP, low CREP and none/negligible (Table 2.1).
Wald-tests (Pannekoek and van Strien 2001) were used to test for the significance of
CREP enrollment on population trends, henceforth called the CREP effect. Population
trends for the study area and each of the four categories of CREP enrollment were
estimated using the modeled slope parameter (linear trend) for the period 2001 to 2005. If
there were significant difference between trends in the four CREP categories and the
32
differences showed a pattern whereby populations fared better with higher enrollment,
this result was considered a positive CREP effect.
Analysis was restricted to those species for which there were sufficient sample
sizes for TRIM to calculate population indices for each of the four CREP categories. This
suite of 60 bird species included all species that were located on at least 20% of survey
routes, or averaged more than one bird per route in the 2001 baseline year. In this
analysis I combined the counts for two chickadee species: black-capped and Carolina.
These species are ecological equivalents. The study area includes the southern edge of the
black-capped chickadee’s and northern edge of the Carolina chickadee’s ranges, but the
two species’ ranges overlap and there is a zone in which hybridization occurs (Reudink et
al. 2007) making specific identification unreliable.
Data points were weighted by the number of survey stops for each route, thereby
ensuring that the contribution of counts from each route was in proportion to the area
surveyed. Counts were conducted in at least four of the five years on 67 of the 84 surveys
routes. Trends were estimated in two ways, 1) from both May and June counts for all 84
routes, and 2) only from June counts for the 67 routes with at least four years of data. The
latter ensures greater comparability between years, but the lower sample sizes may
reduce the power to detect significant population changes. It should be noted that counts
of many species were lower on the June surveys than on May surveys (see Appendix C).
Population trends and the effects of CREP enrollment rates for species guilds
were estimated by averaging (geometric mean) the indices across guilds as in Chapter 1. I
also calculated CREP effects for the guild of 10 “Field nesters” and for the eight species
found to be most numerous in CREP fields in the study area (“Wentworth 8”) as defined
33
in Chapter 1. The grassland guild defined in this study (henceforth “PGC grassland
species”) can be considered to include those bird species possibly affected by CREP
enrollment rates; the “Wentworth 8” are those likely affected by CREP enrollment rates
(because it has been established that they use CREP extensively in the LSR); while the
“field nesters” can be considered to include the target grassland nesting species that it is
hoped will benefit from CREP.
Results
Land cover and land use change
Land cover within the 84 landscapes varied but there was little difference in land
cover of the study areas among counties (Figure 2.2). A high percentage of the
landscapes was farmland (mean 74.5%, range 45-93%), a smaller proportion forested
(mean 18%, range 2-43%) and only a small area urban/developed (mean 1.14%, range
0.02-5.1%), and other habitats (mean=5.2%, range 0.7-11.8%).
The land cover percentages above, derived from satellite imaged sources, differ
from the estimates of land use within the 250 m radii of each point count location, taken
in habitat surveys by the bird surveyors (Table 2.2). The mean estimated percentage of
farmland within these radii in 2001 was 54.2%; while 20.7% was estimated to be
developed. The estimate of forest cover was, however, virtually identical to that derived
from the Landsat 7 ETM+ land cover map, at 18%. The much higher proportion of land
use estimated to be in development when compared to land cover data for the wider
34
landscapes is attributable to the roadside survey protocol – most of the residential and
commercial property in the study area is in linear developments along roads.
The habitat survey data provided evidence of land use change during the four
years 2001 to 2004. There was an increase in the area of fallow land and herbaceous
vegetation of 1% of land cover per year (Figure 2.5), which equates to an 87.7% increase
in the area of these habitats between 2001 and 2004. Observers were not given
information about CREP field locations but based on the vegetation characteristics; it is
likely that they were included in these categories, which could account for most of that
observed increase. There was also an increase in the estimated area of developed land of
6.8% between 2001 and 2004 (Figure 2.5). The estimated decreases in the area of row
crops (14%) and hay (8.8%) over the same period suggested that active farmland was lost
to residential development and as well as CREP enrollment.
CREP enrollment within the study areas
By 31st May 2004 an estimated 2.4% of farmland within the LSR was enrolled in
CREP (Figure 2.6), a percentage that grew steadily each year. Of that, 85% was cool-
season grassland (CP1), 10% was warm-season grassland (CP2), 3.5% existing grassland
(CP10) and 1.7% filter-strips (CP21). Note that existing grassland CRP contracts were
enrolled as CP10 in some counties and CP1 in others. Hence, a portion of the CP1
grassland was existing grassland cover, although mowing regimes changed when the
contracts were rolled over into CREP, hence this grassland would have become more
suitable for breeding birds.
35
CREP enrollment varied widely between landscapes and counties. Enrollment
rates within the study landscapes ranged from none in the three landscapes in Chester
County to an average of 7.5% in the two landscapes in Montour County (Figure 2.7) by
31st May 2004. Generally, enrollment was highest in central Pennsylvania and lowest in
the southeast of the state.
Bird community
A total of 169 bird species was observed across all routes during the five survey
years, a total that includes some migrants and late departing winter visitors. Of the
breeding species, many were too scarce to allow a statistical analysis of population
trends. My analysis is restricted to the 60 most common and widespread species of
farmed landscapes in southern Pennsylvania (Table 2.3). Note that this list includes
several species that are associated with forests and woodlots, which comprised about
18% of the land cover within the study landscapes. Eight species were observed on 100%
of survey routes in 2001. The five most numerous species were European starling,
common grackle, American robin, red-winged blackbird and house sparrow.
Categorizing bird species into habitat guilds
Redundancy Analysis (RDA) proved to be a successful method of assigning the
60 bird species into guilds. The RDA bi-plot (Figure 2.8) showed that habitat gradients
followed a logical pattern, moving counter-clockwise from the most disturbed -
developed, through arable, pasture, hay, fallow, herbaceous and scrub to the least
disturbed - forest. The first axis explained 76.2% of the species-habitat relations, the
36
second a further 14.2% and the third a further 3%. Only the first two axes were plotted
for clarity. Species guilds for each of the 60 bird species are shown in Table 2.3. I
consider those species which could potentially most benefit from CREP to be those
associated with the hay, fallow and herbaceous gradients (Figure 2.8). This guild,
henceforth called the PGC grassland species guild, was composed of 15 species. This
guild includes grassland obligates, facultative grassland species (e.g. American kestrel,
eastern kingbird and eastern bluebird) and species of old fields and early successional
habitats (brown thrasher, field sparrow and indigo bunting). It is therefore a broader guild
than the “Field nester” guild derived in chapter 1 and includes several species that were
not found in large numbers in CREP fields in Wentworth’s study.
Bird population trends 2001-2005
Of the 60 bird species, there were significant population increases for 14 between
2001 and 2005, while 21 declined significantly (Table 2.4), the remaining 25 species
were either stable or trends were uncertain. Three of the 15 species in the PGC grassland
guild increased significantly: horned lark, brown thrasher, eastern meadowlark; while six
showed significant declines: American kestrel, ring-necked pheasant, eastern bluebird,
field sparrow, grasshopper sparrow and indigo bunting. Detailed results for each species
are in Appendix C.
Effect of CREP enrollment on bird population trends
Population trends of 10 of the 60 species showed a positive correlation with the
rate of CREP enrollment while trends of one species (brown-headed cowbird Molothrus
37
ater) showed a significant negative correlation. Three showed significant difference
between enrollment categories but with no clear trend: Savannah sparrow, northern
cardinal Cardinalis cardinalis and house sparrow (Table 2.5). Five of the 15 species in
the grassland species guild showed a positive CREP effect: American kestrel, eastern
kingbird, grasshopper sparrow, song sparrow and eastern meadowlark. Positive CREP
effects were shown for six of the 45 other species; this is 13.3% of those species
compared with 33.3% of grassland species.
The estimate population change between 2001 and 2005 for the PGC grassland
guild in areas with no or little CREP was -23.3% (95% CI: -37 to -9) while in areas of
high CREP enrollment the mean change was +59.6% (95% CI: +34 to +85). The mean
population changes in the low and medium CREP categories were between the two
extremes (Figure 2.9) but were not consistent with my hypothesis that population trends
would be increasingly more favorable with higher CREP enrollment rates. Population
changes for the other 45 species showed a similar pattern, suggesting that some of the
differences in population trends between the CREP categories were due to extrinsic
factors. However the net difference in population trends in no CREP and high CREP
areas equates to a 108% net increase in the PGC grassland guild in high CREP areas over
5 years compared with a 26% net increase in other species.
Similar patterns were evident when comparing the “Wentworth 8” with the other
52 species in the study (Figure 2.9) – although the net differences between no CREP and
high CREP were less divergent: at 64% for the “Wentworth 8” and 41% for other species.
There were no differences in the net effects for the “Field nesters” guild and the other 50
species (38%).
38
Discussion
Issues of study scale and timing
The specially designed PGC bird monitoring surveys generating a substantial
amount of information about bird populations and population changes in the Lower
Susquehanna River Basin between 2001 and 2005. Because the density of survey routes
was much higher than it is for the ongoing USGS BBS, and because the survey routes
were targeted at farmland, the number of detections of farmland and grasslands birds was
much higher than those on BBS routes in the same region. Fewer than 20 BBS counts are
conducted in the region each year. To this end I conclude that the PGC bird monitoring
surveys were worthwhile because it is unlikely that existing surveys would have provided
sufficient data to analyze trends with respect to CREP enrollment. However, the
reduction in the number of PGC bird monitoring surveys conducted each year reduced
the overall sample sizes and made direct comparison of trends between years more
difficult, thereby evoking the need to use a trend analysis method that interpolated
missing values. The log-linear regression analysis conducted in program TRIM proved to
be particularly useful due to problem of missed counts on some survey routes, but it
should be noted that the lack of data for 2005 for a considerable number of routes (21 of
84) gives some cause for concern regarding the geographical representation of data for
that year.
The curtailment of the PGC bird monitoring surveys in 2005 inhibits any
investigation of the medium-term impacts of CREP on bird populations. That my results
pertain to bird population responses during the phase of CREP grassland establishment is
39
an important caveat that must be placed before drawing any conclusions. It is not
currently known how much the vegetation in CREP fields changes post-establishment,
much less how birds might respond to those changes.
Responses to new grassland planting can be rapid. Wentworth (2006) found
grassland obligate birds nesting in CREP fields within one or two years of planting.
Experimental CRP/CREP plantings covering 92 contiguous hectares in Maryland
attracted high densities of grasshopper sparrows within one month of planting (Gill et al.
2006), even though the species was not considered to be common in the surrounding area.
Although grasshopper sparrow numbers stabilized (at high densities) two years after
grassland establishment, Gill et al. (2006) noted singing birds in surrounding arable
farmland, perhaps suggesting that the CRP/CREP fields were acting as a population
source for the surrounding fields. The rapid response of grasshopper sparrows to the new
grasslands in Maryland suggested colonization of CREP fields in the Lower Susquehanna
River basin could be rapid, but it is important to note that the size of the contiguous
conservation grassland in that study is exceptional, and perhaps unparalleled in
Pennsylvania. The rapidity of response is likely to vary between species: those species
preferring more established grasslands such as eastern meadowlark (Volkert 1992,
Warren and Anderson 1995) and bobolink (Bollinger 1995) are much less likely to
colonize newly seeded CREP fields in the first year of establishment, in fact colonization
could potentially lag by several years.
I note that the monitoring protocol might not be adequate to detect population
changes or effects of CREP enrollment for certain species. Notable among these are game
birds, both wild turkey Meleagris gallopavo and ring-necked pheasant have been found
40
to nest in CREP fields in southern PA (Wentworth 2006) but these species are most vocal
during late winter and early spring and less detectable during late spring when the bird
surveys were carried out. Less vocal species would be easily missed by the roadside
surveys, especially in fields of standing-vegetation, such as CREP, it is therefore possible
that I was unable to detect significant effects of CREP enrollment on populations of these
two species.
Bird population responses
Although many studies have shown that grassland birds use CRP, often at higher
species diversity and abundances than in agricultural grasslands (Best et al. 1998; Ryan et
al. 1998; Weber et al. 2002), few studies have been able to demonstrate a positive effect
at the population scale (Murphy et al. 2003). Herkert (1998) confirms that grasshopper
sparrow numbers in the mid-continental USA had fared better in areas with most CRP,
and replicated a similar finding for the Henslow’s sparrow Ammodramus henslowii
(Herkert 2007a), in both cases suggesting that CRP had been sufficient to reverse
downward population trends. Recent studies showed that ring-necked pheasant numbers
were higher in landscapes with more CRP in the northern Great Plains (Nielson et al.
2008) and Minnesota (Guidice and Haroldson 2007) but in both cases the positive effects
of CRP were negated by general declines across the populations. I believe the study
presented here is the first to examine the effects of conservation programs on bird
population responses away from the Midwest and Great Plains, and is the first to focus on
CREP rather than CRP.
41
There was evidence that population trends of four grassland species were
positively associated with CREP enrollment; two of these species were grassland
obligates: grasshopper sparrow and eastern meadowlark, and three were facultative
grassland species: American kestrel, eastern kingbird and song sparrow. The grasshopper
sparrow was the most numerous grassland obligate in surveys of CREP fields by
Wentworth during 2002-2004; the eastern meadowlark, although somewhat less
common, was among the six most numerous species on transect counts (Wentworth
2006). American kestrel and eastern kingbird are not field nesting species but both hunt
over open land, especially grasslands and abandoned fields (McWilliams and Brauning
2000). It is therefore quite feasible that CREP has provided sufficient additional foraging
habitat to benefit these species. The song sparrow is a very common bird found in a
variety of early successional habitat in the study area, and was found to be the second
most numerous species (after red-winged blackbird) in Wentworth’s 2006 study.
Of the other 10 species in the PGC grassland guild, there were suggestions that
both brown thrasher (Fig C.37.3) and field sparrow (Fig C.45.3) trends were positively
associated with CREP and that vesper sparrow trends may have been negatively
associated with CREP enrollment (Fig C.46.3), but none of these significant. The field
sparrow was found to be one of the most numerous nesting species in CREP fields by
Wentworth, but there is no evidence that brown thashers use this habitat in Pennsylvania.
The vesper sparrow, along with the horned lark, is a species often associated with arable
and fallow fields in Pennsylvania (McWilliams and Brauning 2000). It should be
considered possible that such species will not benefit from CREP because the vegetation
cover tends to be too tall and dense. Indeed, if existing fallow and marginally viable
42
(perhaps patchily vegetated) arable fields are enrolled in CREP, species that require a
bare-ground component could be detrimentally affected. It is interesting to note that
Murphy (2003) detected a significant negative correlation between vesper sparrow trends
and the rate of CREP enrollment at the state scale across the eastern USA.
The downward trend in bird grassland populations in landscapes with no CREP
continues the trajectory highlighted in chapter 1. There is also evidence of continued land
use change in the region from the habitat survey data. The increase in development over
the fours years 2001 to 2004 is in-line with long-term trends demonstrated by Goodrich et
al. (2002), suggesting that the corresponding decrease in farmland area and potential
fragmentation of farmland habitats has contributed to the observed reductions in
grassland and farmland bird populations. Conservation programs have been suggested to
reduce development sprawl by up to one half (Johnson and Maxwell 2001) thereby
providing a mechanism, in addition to direct habitat creation, through which these
programs could benefit farmland and grassland bird populations.
Extrinsic factors: West Nile virus
Despite the evidence for positive effects of CREP enrollment on the populations
of several species at the landscape scale, there is no evidence that these were sufficient to
overcome continuing population losses elsewhere – most grassland declined in the Lower
Susquehanna River basin between 2001 and 2005. Further, I have reason to suspect that
there are extrinsic factors that may have contributed to the apparent CREP effects
because some very unlikely species (rock pigeon, American crow, house wren, northern
cardinal, house sparrow) were among those showing a positive effect of CREP
43
enrollment. It should also be noted that annual population changes are estimated from
linear trends, but some of the population trends (Appendix C) were clearly not linear.
Some of the population changes may be largely driven by short-term effects of abiotic
factors, such as cold winters, which cause increased mortality of resident species, or cool
wet springs and summers which can depress nesting success.
A strong candidate for an extrinsic factor that may have influenced my results is
West Nile virus (WNV) which was first detected in Pennsylvania in 2000 (PWNVSP
2008). West Nile virus has been shown to have an effect on bird population levels of
certain species at regional and national scales (LaDeau et al. 2007). American crow and
house wren populations declined concurrently with the emergence of WNV while those
of gray catbird and northern cardinal did not declines, but regional declines could have
been masked (LaDeau et al. 2007). Rock pigeon and house sparrow were not included in
that study. I suggest that the apparent CREP effects for American crow, house wren and
possibly other species may be due to the confounding of areas with none or low CREP
enrollment (southeast Pennsylvania) with area of highest WNV prevalence. The
population trends for certain species show clear and sometimes marked downward trends
in 2003 or 2004 – following the years when WNV prevalence was highest in
Pennsylvania (PWNVSP 2008). Examples include blue jay (A3.20.2) and American crow
(A3.21.2), both of which were particularly badly affected by WNV in LaDeau et al.’s
(2007) study. Conversely, the positive CREP effects for grassland species such as
grasshopper sparrow, eastern meadowlark and song sparrow are unlikely to be due to
WNV, because the population trends for those species do not show a downward spike in
the population trajectory in either 2003 or 2004. It is an unfortunate coincidence that
44
areas with the lowest rates of CREP enrollment tended to be those in areas where West
Nile virus prevalence appears to have been highest (Figure 3.10), and vice-versa.
Although the estimates of West Nile virus prevalence for the four CREP enrollment
categories are crude, the consistent pattern between years suggests this may be an
important confounding factor in my analysis.
Unknown responses of scarce species
This study does not address the potential benefits of CREP for locally rare and
scarce bird species. Wentworth (2006) detected three Henslow’s sparrows, two
dickcissels Spiza americana and two blue grosbeaks Guiraca caerulea in surveys of 116
CREP fields between 2002 and 2004, and also found a single dickcissel nest. These
species were also detected on the PGC bird monitoring surveys, but because numbers
were small (<10 individuals per year) it was not possible to include them in my analysis.
However, given that these species are so scarce in the Lower Susquehanna River Basin
CREP region, colonization of CREP fields in modest numbers could contribute
significantly to the long-term viability of their populations in an area that is at the edge of
their ranges. The dickcissel was a common bird in Pennsylvania in the 19th Century but
was extirpated by the 1920s, returning to nest in the state in small numbers since 1983
(McWilliams and Brauning 2000). Most recent nesting records are thought to relate to
invasions as a result of drought in the Midwest, however since 2004 a relatively stable
population has become established centered in Cumberland County in south-central
Pennsylvania; a count of 39 singing males around Newville in July 2006 representing an
exceptional number for Pennsylvania. Although these birds are found in a variety of
45
habitats, the population was initially centered on CREP fields (Andrew Markel pers.
comm.).
Population-scale responses to CRP enrollment have been documented for
Henslow’s sparrow population in the Midwest (Herkert 2007). Most of the Henslow’s
sparrows in Pennsylvania are in the west of the state, in the Ohio basin CREP region
(Brauning 1992); it is absent from most of the Lower Susquehanna River CREP region,
with the exception of the westernmost county (Somerset), where it is locally common on
reclaimed surface mines (Mattice et al. 2005). By the end of 2007, only 7% of CREP
grassland in Pennsylvania was in the Ohio basin (USDA 2008), hence, there is a disjoint
between new habitat and source populations for one of the species that could potentially
benefit from CREP in Pennsylvania. In neighboring Ohio Henslow’s sparrows were
found to be the third most abundant species in CRP fields, although they were not found
in the majority of fields (Swanson et al. 1999).
Conclusions
This study has provided evidence of population level responses of grassland birds
to the initial creation of grassland fields through CREP in southern Pennsylvania.
Localized stabilization and increases of populations of some species against a backdrop
of continuing declines elsewhere provides evidence of benefits of CREP for grassland
bird species. However, longer term monitoring will be needed to see whether these
responses elicit a reversal of the long-term decrease in population levels of these species
at a larger scale.
46
The combined effects of the complications associated with the emergence of West
Nile virus and the sparseness of data for some species may have reduced power to detect
significant effects of CREP enrollment on bird populations. It should also be noted that
many of the CREP fields in the study area had been sown for only one or two years by
the end of our 5-year study. Together with the relatively short time-series, it is perhaps
surprising that significant CREP effects were identified. Lack of evidence for a positive
effect of CREP on populations of other species does not preclude the possibility that
those species also benefited, while we should be mindful that there is the potential for
CREP to have had a negative affect on populations of others. Further investigation of the
confounded effects of West Nile virus and CREP enrollment rates in these data can be
found in Chapter 3.
47
Table 2.1: CREP enrollment categories as devised for analysis of bird population changes in the Lower Susquehanna River basin CREP between 2001 and 2005.
CREP enrollment category
Number of landscapes
Split-points (ranges) for landscapes in
each category*
Mean % enrolled by 31 May 2004
None (and negligible) 21 0 - 0.04 0.08 Low 31 0.07 - 0.84 1.40 Medium 19 0.97 - 1.57 3.58 High 13 1.83 – 7.25 7.43
* weighted mean of % of farmland enrolled at 31 May 2000, 2001, 2002, 2003 and 2004 (see page 29)
48
Table 2.2: Land use as estimated by bird surveyors within 250 m of point count locations (n=3,003) in the Lower Susquehanna River CREP Program area for 2001 to 2004. Change between years was calculated from a sub-sample with comparable data in each year.
land use estimated % land use within 250 m
radius of point counts
% change 2001-2002 (n=2,034)
% change 2002-2003 (n=1,584)
% change 2003-2004 (n=1,602)
2001 2002 2003 2004 mean se mean se mean se developed 20.68 22.16 22.64 23.61 0.93 0.20 -0.20 0.20 0.90 0.29arable 29.53 29.14 25.78 26.40 -0.04 0.31 -3.78 0.41 0.45 0.51pasture 7.69 7.63 6.78 7.06 0.40 0.18 -0.01 0.15 0.34 0.23hay 15.69 14.83 16.53 16.00 -1.10 0.29 1.11 0.36 -1.92 0.41orchard 0.42 0.50 0.51 0.57 0.20 0.05 -0.04 0.04 0.09 0.05fallow 1.34 1.97 3.31 2.36 0.51 0.16 1.31 0.21 -0.25 0.26herb 2.30 2.09 2.78 2.67 -0.27 0.15 0.59 0.19 0.79 0.23shrub 1.54 1.44 1.41 2.04 0.07 0.07 0.23 0.09 0.69 0.19forest 18.02 17.45 17.67 16.96 -0.53 0.18 0.52 0.20 -0.97 0.34water 0.79 0.92 0.65 0.84 0.00 0.06 0.08 0.09 0.00 0.09surface mine 1.16 1.23 1.22 0.93 -0.01 0.05 -0.03 0.06 -0.30 0.09other 0.54 0.41 0.51 0.37 -0.16 0.09 -0.04 0.06 -0.05 0.10
49
Table 2.3: Occupancy and counts of the 60 commonest bird species on PGC bird monitoring surveys (PGC) in 2001. The species guild as defined using PGC bird monitoring data is noted.
% occupancy 2001 counts in 2001
Species PCG guild routes stops
per route per stop
Canada goose Branta canadensis generalist 27.4 0.9 4.7 0.134 mallard Anas platyrhynchos generalist 38.1 1.9 1.7 0.047 ring-necked pheasant Phasianus colchicus grassland 61.9 6.9 2.4 0.069 red-tailed hawk Buteo jamaicensis generalist 31.0 1.2 0.5 0.013 American kestrel Falco sparverius grassland 42.9 2.3 1.1 0.032 turkey vulture Cathartes aura generalist 25.0 1.9 1.0 0.028 killdeer Charadrius vociferus pasture 96.4 13.4 6.4 0.182 rock pigeon Columba livia pasture 88.1 9.3 18.8 0.534 mourning dove Zenaida macroura developed 100.0 39.3 25.3 0.717 yellow-billed cuckoo Coccyzus americanus shrub 29.8 2.5 0.8 0.024 chimney swift Chaetura pelagica developed 56.0 4.2 2.8 0.080 red-bellied woodpecker Melanerpes carolinus shrub 85.7 9.8 3.8 0.108 downy woodpecker Picoides pubescens forest 65.5 4.4 1.9 0.053 northern flicker Colaptes auratus generalist 66.7 13.2 2.4 0.069 willow flycatcher Empidonax traillii pasture 42.9 3.6 1.6 0.046 eastern phoebe Sayornis phoebe scrub 83.3 6.1 2.2 0.062 great crested flycatcher Myiarchus crinitus forest 60.7 4.6 1.8 0.052 eastern kingbird Tyrannus tyrannus grassland 65.5 4.2 1.9 0.054 red-eyed vireo Vireo olivaceus forest 58.3 10.5 5.5 0.155 blue jay Cyanocitta cristata forest 94.0 11.6 5.4 0.153 American crow Corvus brachyrhynchos generalist 100.0 40.1 26.0 0.740 fish crow Corvus ossifragus generalist 32.1 2.1 1.1 0.030 horned lark Eremophila alpestris grassland 53.6 4.3 2.7 0.077 tree swallow Tachycineta bicolor generalist 76.2 4.9 3.3 0.094 barn swallow Hirundo rustica pasture 98.8 24.8 24.0 0.682
50
% occupancy 2001 counts in 2001
Species PCG guild routes stops
per route per stop
chickadee sp. Poecile carolinensis/atricapillus forest 56.0 3.5 1.8 0.052 tufted titmouse Baeolophus bicolor forest 73.8 13.6 6.5 0.184 white-breasted nuthatch Sitta carolinensis forest 54.8 4.2 1.8 0.051 Carolina wren Thryothorus ludovicianus forest 61.9 5.7 2.2 0.063 house wren Troglodytes aedon forest 70.2 19.0 8.1 0.231 blue-gray gnatcatcher Polioptila caerulea forest 28.6 1.2 0.5 0.014 eastern bluebird Sialia sialis grassland 91.7 10.7 5.2 0.148 wood thrush Hylocichla mustelina forest 92.9 13.5 6.7 0.189 American robin Turdus migratorius developed 100.0 68.1 61.2 1.737 gray catbird Dumetella carolinensis shrub 100.0 29.8 15.2 0.432 northern mockingbird Mimus polyglottos developed 95.2 23.4 9.7 0.275 brown thrasher Toxostoma rufum grassland 46.4 3.2 1.3 0.037 European starling Sturnus vulgaris pasture 100.0 41.5 89.9 2.552 yellow warbler Dendroica petechia pasture 71.6 8.9 4.0 0.115 ovenbird Seiurus aurocapilla forest 47.6 2.7 1.3 0.037 common yellowthroat Geothlypis trichas pasture 94.0 19.9 8.0 0.227 scarlet tanager Piranga olivacea forest 47.6 4.0 1.7 0.047 eastern towhee Pipilo erythrophthalmus forest 64.3 8.6 3.5 0.100 chipping sparrow Spizella passerina developed 100.0 38.3 20.0 0.567 field sparrow Spizella pusilla grassland 83.3 15.3 6.4 0.183 vesper sparrow Pooecetes gramineus grassland 60.7 8.3 3.7 0.105 Savannah sparrow Passerculus sandwichensis grassland 35.7 5.3 3.2 0.090 grasshopper sparrow Ammodramus savannarum grassland 79.8 8.2 3.8 0.109 song sparrow Melospiza melodia grassland 96.4 45.6 22.5 0.638 northern cardinal Cardinalis cardinalis forest 100.0 34.2 14.8 0.419 indigo bunting Passerina cyanea grassland 92.9 34.9 16.2 0.461 bobolink Dolichonyx oryzivorus grassland 14.3 1.7 1.2 0.034 red-winged blackbird Agelaius phoeniceus grassland 100.0 39.2 42.4 1.205
51
% occupancy 2001 counts in 2001
Species PCG guild routes stops
per route per stop
eastern meadowlark Sturnella magna grassland 52.4 10.4 7.2 0.204 common grackle Quiscalus quiscula developed 98.8 48.3 86.8 2.465 brown-headed cowbird Molothrus ater shrub 85.7 8.2 4.2 0.119 orchard oriole Icterus spurius pasture 28.6 1.9 0.8 0.024 Baltimore oriole Icterus galbula shrub 75.0 6.3 2.7 0.076 house finch Carpodacus mexicanus developed 88.1 15.8 10.6 0.301 American goldfinch Carduelis tristis generalist 88.1 15.1 9.1 0.259 house sparrow Passer domesticus developed 97.6 28.1 36.3 1.031
52
Table 2.4: Significant bird population changes in the Lower Susquehanna River basin CREP between 2001 and 2005. Mean annual change calculated using log-linear Poisson regression from PGC bird monitoring data.
Increasing population trends
Decreasing population trends
Species Mean annual
change
Species Mean annual
change
turkey vulture +10.8 ** ring-necked pheasant -11.0 *
red-tailed hawk +14.4 * American kestrel -14.8 **
yellow-billed cuckoo +14.3 * killdeer -5.5 **
red-bellied woodpecker +6.2 ** eastern phoebe -8.3 **
northern flicker +14.3 * blue jay -8.1 **
horned lark +6.9 * American crow -11.8 **
tree swallow +10.8 * fish crow -13.3 **
Carolina Wren +5.7 * chickadee sp. -8.1 **
American robin +2.7 * tufted titmouse -12.8 **
brown thrasher +15.7 * white-breasted nuthatch -14.5 *
yellow warbler +8.0 ** eastern bluebird -11.3 **
scarlet tanager +10.0 * northern mockingbird -3.7 **
Baltimore oriole +10.9 ** European starling -4.3 *
cedar waxwing -10.8 **
ovenbird -9.4 **
common yellowthroat -5.1 *
field sparrow -6.7 **
grasshopper sparrow -9.6 **
indigo bunting -5.6 **
eastern meadowlark -6.4 **
house finch -8.9 *
American goldfinch -9.6 **
* significant at p<0.05 ** significant at p<0.01
53
Table 2.5: Significant apparent effects of the rate of CREP enrollment (“CREP effect”) on bird population trends in the Lower Susquehanna River basin CREP between 2001 and 2005. P values are the results of Wald-tests of the significance of the CREP covariate on population trends.
Species Habitat guild CREP effect P American kestrel grassland positive 0.0499 rock pigeon pasture positive 0.0497 eastern kingbird grassland positive 0.0121 American crow generalist positive 0.0005 house wren forest positive 0.0037 gray catbird shrub positive 0.0008 Savannah sparrow grassland undetermined 0.0238 grasshopper sparrow grassland positive 0.0285 song sparrow grassland positive 0.0032 northern cardinal forest undetermined 0.0183 eastern meadowlark grassland positive 0.0341 brown-headed cowbird shrub negative 0.0436 American goldfinch generalist positive <0.0001 house sparrow developed undetermined 0.0461
54
Figure 2.1: The 20 counties of the Lower Susquehanna River basin CREP (white) and the 84 landscapes around bird survey routes (shaded black).
55
Figure 2.2: Landsat satellite imagery derived Landsat 7 ETM+ land cover types within the 84 study landscapes in the Lower Susquehanna River basin CREP, by county, circa year 2000.
56
Figure 2.3: Percentage of farmland enrolled in CREP by 1st June 2004 for each of the 84 landscapes in the Lower Susquehanna River basin CREP. CREP enrollment is mean of % of farmland at 31st May of the years 2000 to 2004, weighted by number of years to May 2005. Categories of enrollment (n=sample size) based on split-points where there was visual step change (black lines) in the weighted mean. None (weighted mean of 0 to 0.04%), low CREP (0.07 to 0.84%), medium CREP (0.97 to 1.57%) and high CREP (1.83 to 7.25%)
57
Figure 2.4: Percentage of farmland enrolled in CREP by 1st June 2004 for each of the 84 landscapes of southern Pennsylvania, plotted against % of farmland previously in grassland (left) and % of the total landscape farmed (right).
y = 0.067x - 0.166R2 = 0.035
0
2
4
6
8
10
12
10 20 30 40 50
% of farmland in grass
% fa
rmla
nd in
CR
EP
(by
06-0
1-04
)
y = -0.057x + 6.278R2 = 0.061
0
2
4
6
8
10
12
40 50 60 70 80 90 100
% of landscape in farmland%
farm
land
in C
RE
P (b
y 06
-01-
04)
58
Figure 2.5: Estimated percentage of six main land uses within 250 m point count radii in the Lower Susquehanna River basin CREP during 2001 to 2004. Linear regression lines show estimated annual changes
59
0
0.5
1
1.5
2
2.5
2000 2001 2002 2003 2004
by 31st May of year
% o
f far
mla
nd e
nro
lled
CP21CP10CP2CP1
Figure 2.6: Cumulative CREP enrollment, as a % of farmland, by 31st May of each year, summed across 84 landscapes in the Lower Susquehanna River basin CREP. CP21=filter strips, CP10=rollover CRP (cool-season) grassland, CP2=warm-season grassland, CP1=cool-season grassland.
60
Figure 2.7: Enrollment in the Lower Susquehanna River basin CREP, as a % of farmland, by 31st May 2004, by county and practice. Note that these data relate to the CREP within the 84 study landscapes only, not the whole program.CP21=filter strips, CP10=rollover CRP (cool-season) grassland, CP2=warm-season grassland, CP1=cool-season grassland. Note that rollover from CRP was enrolled as CP1 in some counties and CP10 in others.
0
1
2
3
4
5
6
7
8
Ada
ms
Bed
ford
Ber
ks
Che
ster
Col
umbi
a
Cum
berla
nd
Dau
phin
Fran
klin
Fulto
n
Juni
ata
Lanc
aste
r
Leba
non
Mon
tour
Nor
thum
berla
nd
Per
ry
Sch
uylk
ill
Sny
der
Som
erse
t
Uni
on
Yor
k
% o
f far
mla
nd e
nrol
led
CP21CP10CP2CP1
61
Figure 2.8: RDA bi-plot of relationships between birds (each dot is one species) and main habitats gradients (lines) for 2001 count data. The proximity of a species to a habitat gradient shows the strength of affinity for, or specialization in that habitat. Generalist species cluster toward the center of the plot, specialists are found toward the edges. The 15 species in the PGC grassland species guild are highlighted with large circles, numbers relate to the species (listed below).
1. ring-necked pheasant 2. American kestrel 3. horned lark 4. eastern kingbird 5. eastern bluebird 6. brown thrasher 7. field sparrow 8. vesper sparrow 9. Savannah sparrow 10. grasshopper sparrow 11. song sparrow 12. indigo bunting 13. bobolink 14. red-winged blackbird 15. eastern meadowlark
62
Figure 2.9: Annual population changes between 2001 and 2005 for the “PGC grassland”, “Wentworth 8” and “Field nesters” species guilds compared with the other common species, in landscapes of southern Pennsylvania with four levels of CREP enrollment by 1st June 2004 (see Figure 2.3).
Note: “PGC grassland” guild includes species most closely associated with grassland in the
region. “Wentworth 8” includes species identified as most numerous in CREP fields in southern PA in a study by Kevin Wentworth in 2002-2004. “Field nesters” are grassland obligates.
63
Figure 2.10: Mosquito Culex. samples tested positively for West Nile virus in the Lower Susquehanna river CREP region in 2000 to 2004. Mosquito sample results are by county (PWNVSP 2008). The figures for the four CREP enrollment rates are the annual mean number of positive tests for the counties in which each survey route is located. County data (n=20) are weighted by number of survey routes (n=84) hence the means include pseudo-replicates. CREP enrollment is mean of % of farmland at 31st May of the years 2000 to 2004, weighted by number of years to May 2005: no CREP (weighted mean of 0 to 0.04%), low CREP (0.07 to 0.84%), medium CREP (0.97 to 1.57%) and high CREP (1.83 to 7.25%)
64
Chapter 3
Bayesian spatial models reveal confounded effects of the Conservation Reserve
Enhancement Program and West Nile virus on farmland bird populations
Introduction
Analysis of bird population trend data for the Lower Susquehanna River (LSR)
basin CREP (Conservation Reserve Enhancement Program) for 2001 to 2005 revealed
likely impacts of West Nile virus (WNV) on bird population levels (chapter 2).
Unfortunately, WNV prevalence appeared to be confounded with CREP enrollment rates,
which introduced the possibility that apparent effects of CREP on bird population trends
were spurious. In this chapter I use another analytical approach, Bayesian Spatial Models,
to parse out the effects of CREP and WNV.
Bayesian spatial models are increasingly used by avian ecologists, particularly
those whose focus is large or population-scale processes (e.g. Link and Sauer 2002, Sauer
and Link 2002, Thogmartin et al. 2004, Link and Sauer 2007, Nielson et al. 2008). These
models have several methodological advantages over traditional models (Sauer et al.
2005, Kristan and Scott 2006). Important among these is an ability to construct spatially
explicit models, thereby accounting for otherwise unexplained spatial dependence, which
could inflate Type 1 errors, leading to spurious results and inappropriate inferences
(Lennon 2000, Diniz-Filho et al. 2003, Sauer et al. 2005).
65
I hypothesized that WNV had a population scale impact on bird populations in the
Lower Susquehanna River basin between 2001 and 2005, the result of which would be
that the frequentist statistical techniques adopted in chapter 2 would not be sufficient to
provide conclusive proof of effects of CREP enrollment on bird populations, because of
the difficulties of incorporating more than one covariate in these models. Using models
that account for temporal and spatial variation in both CREP enrollment and WNV
prevalence, I hope to increase confidence that the effects of CREP on population trends
shown in chapter 2 are genuine, or alternatively, that the results that I suggested were
spurious, can indeed be attributed to an effect of WNV.
Methods
Model data
Bird and land cover data are the same as in chapter 2. Bird counts were from a
bird monitoring survey organized by the Pennsylvania Game Commission (PGC) to
evaluate the effects of CREP enrollment in the Lower Susquehanna River basin in
southern Pennsylvania (see chapter 2). Counts were available for 84 survey routes for up
to five of the years 2001 to 2005. Although counts were conducted in May and June in
2001 to 2003, I use only the June count data, which were available for all five years. Not
all routes were surveyed every year, therefore there are 80 missing data points from the
potential sample of 420 (84 x 5 years).
Landsat 7 ETM+ land cover data are used to account for differences in land use
between survey route locations. Because survey locations were chosen within areas
66
dominated by farmland, land use in the immediate vicinity (500 m) of the survey routes
were relatively homogenous (chapter 2). However, there is considerable evidence from
the literature that landscape scale metrics are useful predictors of grassland bird
populations, for example % forest cover at the scale of 800ha landscapes was an
important predictor of BBS counts of grassland bird in the Midwest (Thogmartin et al.
2006, Murray et al. 2008). The mean size of landscapes in my study is 921ha. More
generally, the percentage of forest and developed land are known to be reliable
determinants of bird community composition in mixed landscapes (Titeux et al. 2004)
including Pennsylvania (Bishop and Myers 2005). I include these two metrics, plus the %
of hay, as determined by land cover data derived from Landsat 7 ETM+ satellite imagery
circa year 2000, as described in chapter 2.
CREP enrollment rates for the landscapes around each survey route are those
derived in chapter 2: the weighted mean percentage of farmland enrolled by 31st May of
the years 2000 to 2004.
Estimates of WNV prevalence are from the Pennsylvania West Nile virus
Surveillance Program (PWNVSP 2008). Since 2000, mosquitoes have been routinely
sampled in each county in Pennsylvania. The number of WNV-positive samples is
thought to be a good indicator of the intensity of virus transmission (CDC 2003). Data are
also available on the number of dead birds reported by the public (PWNVSP 2008) but
these are highly correlated with human population density (Figure 3.2). Correcting these
data for human population density and comparing with mosquito samples for the Lower
Susquehanna River basin region shows that bird mortality may have peaked in 2002; one
year earlier than the peak in WNV-positive mosquito samples (Figure 3.3). However,
67
because bird mortality is not estimated using standardized procedures and reporting rates
could be highly influenced by other factors, such as human population density (Ward et
al. 2006) and publicity, I considered the mosquito samples to be a more reliable proxy of
WNV prevalence.
Bayesian spatial model
I modeled bird counts with respect to land cover, CREP enrollment and WNV
prevalence as a Poisson random variable in a Bayesian spatial model using Markov-chain
Monte Carlo (MCMC) methods (Banerjee et al. 2004). Following similar examples (e.g.,
Link and Sauer 2007, Nielson et al. 2008) I modeled the expected count (�) of each
species in each landscape (i) and year (j) as follows:
where � is the intercept, j1 is the first year (2001), �i is the linear trend, �ik are effects of p
environmental covariates xijk, �j are random year effects, �ij are random routes specific
effects, �ij are survey effort effects and �ij are Poisson errors. There were five
environmental covariates: the percentage of each landscape in forested, developed and
hay land cover types (circa 2000), the % of farmland enrolled in CREP in each year and
the number of WNV-positive mosquito samples in each year. Hence the two covariates
that are of interest (CREP & WNV) vary by year while the three land cover metrics were
assumed constant through time. Both CREP and WNV values relate to the previous year
– CREP extent includes that enrolled up to 31st May in the previous year and WNV
samples are also for year j-1. Peak bird mortality from WNV is late summer and fall
( )11
ln p
ij i ik ijk j ij ij ijk
j j xλ µ γ β α ω ψ ε=
� = + − + + + + + � �
68
(PWNVSP 2008); it is therefore assumed that this results in reduced populations of adult
birds the following spring.
Although it is known that there was some change in land use during the study
period (chapter 2), I do not consider this of sufficient magnitude to merit inclusion in the
model. Land cover percentages were standardized (mean was subtracted and then divided
by standard deviation) to improve model convergence (Gilks and Roberts 1996). Because
survey routes were of different lengths, I included a survey effort effect (�ij) which was
the number of survey stops per survey route and year.
The random route-specific effect (�ij) was used to control for spatial
autocorrelation in counts between adjoining routes. A Gaussian conditional
autoregressive (CAR) prior distribution was used to incorporate spatial dependency in the
model (Banerjee et al. 2004, Thogmartin et al. 2004). The CAR models assume that
counts on each survey route are spatially dependent on counts of neighboring routes. The
neighborhood structure was calculated using program GeoDaTM (Anselin 2003) to
produce a map of Thiessen polygons around each survey route (Figure 3.1), adjacent
polygons were then labeled and counted to provide the matrices used in the CAR
component of the Spatial model.
I fitted the model using WinBUGS (Speigelhalter et al. 2004). The code for
estimating parameters of this model is in Appendix D.Vague prior distributions were
used to begin the MCMC sampling. Parameters for fixed effects (environmental variables
and time trend) were assigned flat normal distributions with mean of 0.0 and variance of
100 (precision = 1/variance = 0.01). I determined an appropriate “burn-in” by inspection
of trace plots from the MCMC process and fitted models using chains of a further 30,000
69
iterations. Convergence was further checked by dividing the standard deviation for each
parameter estimate by its MCMC error – ideally the MCMC error should be less than 5%
of sample standard deviation (Spiegelhalter et al. 2004). The predictive capabilities of the
models were assessed by comparing predicted counts (�) with actual counts using
ordinary least-squares linear regression. The R2 value is therefore a measure of
divergence of predicted counts from actual counts.
I estimated the overall effect of CREP on bird populations by comparing
predicted counts with and without the estimated CREP coefficient. I set all covariates to
their mean – for the land cover parameters this was zero so I could exclude them from the
predicted models. Hence there were two predictions, both included mean WNV
prevalence values, a “no CREP” prediction had CREP set to zero enrollment and an
“actual CREP” prediction had the mean CREP values across the region for each year.
Our estimate of the % population change attributable to CREP was the therefore the
difference in estimated counts for 2005 between the “actual CREP” and “no CREP”
predictions for an “average” landscape. These calculations were included within the
Bayesian model; hence 95% credibility intervals were calculated.
Model goodness-of-fit was measured by the posterior predictive p-value (Gelman
et al. 1996). A p-value close to 0.0 or 1.0 indicates the data do not agree with the
proposed model, hence a value near 0.5 indicates an adequate fit. The significance of
each parameter was determined by 95% credibility intervals, which are the Bayesian
analogy of confidence intervals (Banerjee et al. 2004).
70
Results
Model evaluation
Visual inspection of MCMC trace plots suggested that all 60 models converged
within 10,000 iterations. The MCMC error after a further 30,000 iterations was less than
5% of the sample standard deviations (sd) for nearly all parameters, the few exceptions
being MCMC errors for estimates of intercept, some of which were 7 or 8% of the
sample sd. Goodness of fit (posterior predictive p-values) were adequate for the majority
of species, 52 of the 60 were closer to 0.5 than to 0 or 1.
Predicted counts were typically very close to actual counts, for 57 of 60 species
R2 exceeded the 0.827 reported by Nielson et al. (2008) for Ring-necked Pheasant counts
on BBS routes in the northern Great Plains (Table 3.1). While this does not infer that the
models would accurately predict counts in new locations or years, it does suggest that the
covariates included in the model explained a considerable portion of the inter site and
inter year variation in bird counts. The models consistently overestimated low (and zero)
counts and underestimated the higher counts but plots showed that the relationship
between actual and predicted counts were remarkably close to unity for most species
(Figure 3.4).
Land cover covariates were included in the model in an attempt to correct for the
potential nuisance effects of differing landscape compositions. Although the land cover
covariates are not of particular interest in their own right, it is encouraging to note that
the parameter estimates make ecological sense. Forest cover proved to be a particularly
strong predictor, being significantly positively associated with 14 forest species and
significantly negatively associated with 16 open country species (Table 3.1). The strength
71
of the relationships shows a pattern from the forest avoiding species such as bobolink,
savannah sparrow, and horned lark across a gradient to forest obligates such as scarlet
tanager, red-eyed vireo and ovenbird (Figure 3.5). Rather few models showed significant
effects of developed and hay land cover on species counts, perhaps because forest extent
explained so much of the variance in bird counts (Table 3.1).
CREP effects
Significant effects of CREP were noted for five species (Table 3.1). Of these, two
are grassland obligates (bobolink and eastern meadowlark) and one is a facultative
grassland species (American kestrel). There were also positive CREP effects for northern
flicker and negative for northern cardinal.
Estimated overall effects of CREP for 17 common bird species associated with
grassland and CREP fields in Pennsylvania (chapters 1 and 2) show that populations of
most species were estimated to be higher than they would have been without CREP
(Table 3.2). However, because of wide credible intervals, most of the effects were not
statistically significant. The evidence for a population scale effect of CREP on eastern
meadowlark is strong, the significant responses for American kestrel and bobolink, while
substantial, are based on rather small sample sizes (Table 3.2), and hence should be
treated with caution.
West Nile virus effects
The models detected significant negative effects of WNV on counts of six species
and positive association between of WNV and American robin counts (Table 3.1).
72
Estimated population trends for these species show that American crow, blue jay, tufted
titmouse and eastern bluebird all showed significant declines following the peak WNV
years of 2002 and 2003, chickadees decreased after 2002 and great-crested flycatcher
decreased after 2003 (Figure 3.6).
Discussion
Modeling approach
Previous attempts at evaluating evidence for population level responses to CREP
enrollment in southern Pennsylvania were complicated by a spatial and temporal
confounding of CREP enrollment and WNV prevalence (chapter 2). Unfortunately,
analyzing data sets with missing counts, such as was the case with the PGC bird
monitoring data, is not straightforward. Some techniques that might be adopted would
exclude samples with missing data (van Strien et al. 2000), which would not have been
possible here because so many samples had missing data from one or more years.
Analyzing trends using approaches that interpolate missing data is a useful way forward,
but the approach used in chapter 2 lacks flexibility in that only categorical covariates can
be incorporated into the models. As well as averaging out useful variation in covariate
data, categorical techniques require sufficient samples in each level, which is restrictive.
The Bayesian techniques used in this chapter allow the inclusion of more covariates,
includes all samples, and obviates the need to impose artificial classification on
continuous variables. Another distinct advantage of Bayesian techniques is the ability to
incorporate spatial dependency in models. There are now numerous examples of
Bayesian Spatial models having been successfully applied to analysis of large-scale bird
73
survey data. I believe the analysis presented in this chapter is a further example of the
superiority of this approach over Frequentist methods.
CREP effects
The Bayesian spatial models detected significant landscape-scale effects of CREP
enrollment on five species. Of these five, those for northern flicker and northern cardinal
are likely spurious – chance effects as a result of multiple (n=60) tests. CREP has likely
benefited three species to such an extent that I was able to detect bird population level
responses in five years: American kestrel, bobolink and eastern meadowlark. Evidence of
population responses for several other species is equivocal based on the results of this
study.
Attempts at detecting population-scale effects of perturbation on birds are fraught
with problems. Firstly, counting wild birds is a very inexact science with many potential
sources of error (Bibby et al. 2000). Survey techniques that do not incorporate effects of
declining detection rates with distance from the observer (distance sampling) are
particularly liable to low statistical power to detect significant population trends
(Diefenbach et al. 2003). Secondly, without knowledge of meta-population (Scheiman et
al. 2007) and source-sink (Pulliam 1998) dynamics, it is difficult to be certain that effects
are not being under or over estimated. Thirdly, and perhaps of prime importance in my
study, is that population responses are likely to show non-linearity, both in relation to the
size of the perturbation (e.g. amount of CREP) and medium or long-term temporal
effects. The short time series of this study therefore precludes forming conclusions
74
regarding the medium or long-term population effects of the large-scale planting of
grasslands in Pennsylvania.
Those caveats not withstanding, it could be considered surprising that landscape-
scale population effects were detected for at least three species. The ability to detect
significant effects depends upon the size of the effect and the spread of the data. Hence,
for species that are very common in the LSR region, the addition of c.30,000 ha of
grasslands in the region might only have the potential to increase the population by a
relatively small amount – CREP grassland are only 0.99% of the land cover in the Lower
Susquehanna River basin. In contrast, scarce species, whose populations are limited by
habitat availability, might be expected to show a numerically large response, but the
ability to detect that response is limited by small samples sizes. This lack of statistical
power to detect significant population trends is particularly crucial for short time-series
(Thogmartin et al. 2007). Using empirical evidence from my study I suggest that it is
unlikely that CREP effects of less than 20% would be statistically significant (Figure
3.7). This is a large change – similar in magnitude to the 5% per annum change that
Thogmartin et al. (2007) suggest is required to detect significant trends in short-term
studies. In a study of 10 species in four separate High Plain states 20% of total
populations were attributed to CRP for only 6 of 35 cases (McLachlan et al. 2007).
Further, CRP enrollment has been established for more than 20 years in those states, and
the percentage of the landscapes in conservation cover was higher than that in the LSR
CREP region.
I conclude that detecting population level responses due to CREP within five
years of the first CREP fields being sown was ambitious. It is therefore somewhat
75
surprising that population-scale responses were shown for some species. A lack of
evidence for population-scale responses for other species does not preclude CREP having
been of considerable benefit for those species. Looking forward, if those species continue
to decline in the wider countryside at rates similar to those shown in the last 30+ years,
the at present rather modest contributions to total population size attributable to CREP
could become much more important.
West Nile virus effects
This study was not designed to detect effects of WNV on bird populations;
indeed interest in the potential effects of the virus on bird counts was stirred by a desire
to exclude spurious effects when evaluating the effects of CREP enrollment. However,
the bird count data, coupled with mosquito sample data, provide compelling evidence of
effects of WNV on bird populations in southern Pennsylvania. That this analysis is
spatially explicit – taking into account spatial as well as temporal heterogeneity in WNV
prevalence provides an added dimension, thereby strengthening the case that effects of
WNV on bird populations are not merely temporally correlative. It is particularly striking
that the species for which I detected WNV effects were similar to those detected by
LaDeau et al. (2007), even though the methods, data sources and scales of that study were
very different.
One important caveat is that I assume the WNV positive mosquito samples are a
good proxy for mortality of birds, but Figure 3.3 indicates that this may not be quite true.
Bird mortality in Pennsylvania peaked one year earlier than the peak of positive mosquito
samples; hence there could be some temporal mismatch as a result of increased immunity
76
to birds exposed to the virus over time. However, for several species, bird populations
showed steep declines in both 2003 and 2004, suggesting that the high WNV in both
2002 and 2003 resulted in sufficient bird mortality to reduce bird populations.
For five species, negative effects of WNV were detected in both my study and
LaDeau et al. (2007). I detected negative effects for an additional species (great crested
flycatcher) which was not included in LaDeau’s study, and failed to detect an effect for
one species – house wren Troglodytes aedon. One other difference was that I detected a
positive effect of WNV on American Robin, while LaDeau et al. reported a negative
effect. However, visual inspection of the Breeding Bird Survey trends in LaDeau’s paper
show that there was no large single year decline for this species, as was evident for the
others, indeed there appears to have been an upward spike in the population in 2002.
Could it be that American Robin actually benefited from WNV? Given that American
crows and blue jays are major nest predators (Kilpatrick et al. 2007), it is quite feasible
that reductions in their population levels could elicit a positive population response in
heavily predated species.
As with the ability to detect CREP effects, it is likely that other species were
affected by WNV but because of small population changes or small sample sizes, these
effects were not statistically significant. Among species whose populations decreased in
2002-2004 are American kestrel, eastern phoebe and white-breasted nuthatch. In a study
of nesting American kestrels in southeast Pennsylvania nest occupancy in 2004 was less
than half that of 1992-1999 and 95% of adult birds exhibited serum antibodies to WNV
(Medica et al. 2007). It is interesting to note that the above three species are cavity
nesters, as are four of the six species for which significant effects were detected. I
77
postulate that cavity nesting birds could be particularly susceptible to WNV because
certain mosquitos, notably the eastern treehole mosquito Ochlerotatus triseriatus, a
known WNV vector, breed in tree cavities (Barker et al. 2003).
The population level responses of some bird species to WNV are striking and
large – I estimated declines in numbers of American crows, bluejays, tufted titmice and
eastern bluebirds were 31%, 22%, 48% and 51% respectively between 2002 and 2004.
National BBS data suggested WNV effects can be short-lived, and indeed the PGC bird
monitoring counts show something of a recovery in numbers for all these species in 2005.
However, because short-term effects can be so large, I urge all avian ecologists carrying
out population studies of birds in North America, and other areas affected by WNV, to
consider the potential for confounding effects of WNV, and indeed other avian diseases.
78
Table 3.1: Parameter estimates for Bayesian spatial models of counts of 60 common bird species in the Lower Susquehanna River
basin in 2001 to 2005. Land cover covariates (ß2, ß3 and ß4) are from Landsat 7 ETM data circa 2000.
species �
(intercept)
ß0
(trend)
ß2
(developed)
ß3
(hay)
ß4
(forest)
ß5
(CREP)
ß6
(WNV)
goodness
of fit � R2
Canada goose Branta Canadensis -2.484 0.412 -0.160 -0.213 -0.686 0.080 -0.002 0.25 0.78
mallard Anas platyrhynchos -2.151 -0.022 0.151 -0.097 -0.836 * 0.028 0.009 0.46 0.98
ring-necked pheasant Phasianus colchicus -0.725 -0.105 -0.261 -0.084 -0.063 -0.006 0.003 0.71 0.67
red-tailed hawk Buteo jamaicensis -1.897 0.223 0.003 0.030 -0.136 -0.014 -0.009 0.72 0.84
American kestrel Falco sparverius -2.182 -0.300 0.120 -0.042 -0.303 0.121 * -0.005 0.57 0.87
killdeer Charadrius vociferous 0.452 -0.049 0.076 0.038 -0.397 * -0.012 -0.003 0.82 0.93
rock pigeon Columba livia 0.732 0.006 0.098 0.174 -0.407 * 0.010 0.001 0.66 0.97
turkey vulture Cathartes aura -5.155 0.393 -0.160 -0.138 -0.392 * 0.128 0.018 0.47 0.88
mourning dove Zenaida macroura 2.329 0.042 -0.031 0.016 -0.079 0.023 0.001 0.53 0.93
yellow-billed cuckoo Coccyzus americanus -2.686 0.388* -0.179 -0.131 0.458 * -0.093 0.009 0.53 0.95
chimney swift Chaetura pelagica -0.953 0.032 0.169 0.093 -0.207 -0.030 -0.006 0.56 0.99
red-bellied woodpecker Melanerpes carolinus 0.142 0.107 -0.205 * -0.087 0.095 0.023 -0.001 0.68 0.90
downy woodpecker Picoides pubescens -0.279 -0.004 -0.142 -0.142 0.157 -0.076 0.007 0.64 0.89
northern flicker Colaptes auratus -0.386 -0.100 -0.219 * -0.033 -0.111 0.068 * 0.005 0.64 0.90
willow flycatcher Empidonax traillii -1.159 0.049 0.073 0.013 -0.521 * -0.017 0.003 0.71 0.92
79
species �
(intercept)
ß0
(trend)
ß2
(developed)
ß3
(hay)
ß4
(forest)
ß5
(CREP)
ß6
(WNV)
goodness
of fit � R2
eastern phoebe Sayornis phoebe 0.072 -0.166 -0.168 * 0.077 0.221 * 0.034 0.003 0.74 0.93
great crested flycatcher Myiarchus crinitus -1.021 0.033 -0.196 -0.091 0.409 * -0.066 -0.011 * 0.42 0.92
eastern kingbird Tyrannus tyrannus -0.388 -0.035 -0.067 0.072 -0.082 -0.019 -0.005 0.73 0.90
red-eyed vireo Vireo olivaceus -0.577 0.028 -0.133 -0.124 0.625 * -0.010 -0.006 0.63 0.98
blue jay Cyanocitta cristata 0.794 -0.150* -0.038 -0.015 0.102 -0.005 -0.007 * 0.69 0.87
American crow Corvus brachyrhynchos 2.656 -0.162* 0.078 0.018 0.203 * 0.008 -0.010 * 0.69 0.84
fish crow Corvus ossifragus -3.153 -0.328 0.136 0.080 0.031 0.016 0.010 0.31 0.81
horned lark Eremophila alpestris -0.666 0.141 0.171 -0.015 -1.108 * 0.027 0.004 0.48 0.95
tree swallow Tachycineta bicolor 0.550 0.053 0.102 -0.120 -0.089 0.017 -0.008 0.64 0.98
barn swallow Hirundo rustica 1.723 0.019 -0.025 -0.075 -0.263 * 0.019 0.001 0.73 0.95
chickadee sp. Poecile carolinensis/atricapillus -1.217 -0.010 -0.019 -0.103 * 0.468 -0.020 -0.010 * 0.57 0.99
tufted titmouse Baeolophus bicolor 1.365 -0.205 -0.1623 -0.052 0.463 * -0.004 -0.014 * 0.74 0.85
white-breasted nuthatch Sitta carolinensis -0.811 -0.248* -0.264 * -0.162 0.245 * 0.065 -0.001 0.67 0.88
Carolina wren Thryothorus ludovicianus -0.355 0.055 -0.076 -0.157 0.297 * -0.006 -0.002 0.66 0.90
house wren Troglodytes aedon 1.041 -0.042 -0.090 -0.010 0.071 0.035 -0.003 0.89 0.88
blue-gray gnatcatcher Polioptila caerulea -2.850 -0.002 -0.226 0.056 0.417 -0.069 0.009 0.57 0.97
eastern bluebird Sialia sialis 0.434 -0.185 -0.113 0.110 0.061 0.017 -0.008 * 0.70 0.87
wood thrush Hylocichla mustelina 0.881 -0.048 -0.098 -0.134 0.318 * 0.009 -0.002 0.70 0.95
80
species �
(intercept)
ß0
(trend)
ß2
(developed)
ß3
(hay)
ß4
(forest)
ß5
(CREP)
ß6
(WNV)
goodness
of fit � R2
American robin Turdus migratorius 3.295 0.031 0.014 0.015 -0.136 * -0.007 0.003 * 0.50 0.87
gray catbird Dumetella carolinensis 1.746 -0.033 -0.071 -0.005 0.126 0.022 0.002 0.65 0.83
northern mockingbird Mimus polyglottos 1.363 -0.022 -0.040 -0.020 -0.159 * 0.002 0.001 0.73 0.93
brown thrasher Toxostoma rufum -1.335 0.083 -0.163 0.119 -0.023 0.068 0.002 0.78 0.85
European starling Sturnus vulgaris 2.853 -0.045 0.096 * 0.029 -0.299 * 0.027 0.003 0.53 0.98
yellow warbler Dendroica petechia 1.099 0.373* -0.101 -0.070 -0.345 * -0.012 -0.004 0.71 0.96
ovenbird Seiurus aurocapilla -2.178 -0.070 -0.160 -0.188 0.955 * -0.067 0.000 0.26 0.92
common yellowthroat Geothlypis trichas -0.279 -0.004 -0.142 -0.142 0.157 -0.076 0.007 0.64 0.90
scarlet tanager Piranga olivacea -1.771 0.163 -0.036 -0.177 0.576 * -0.016 -0.004 0.62 0.94
eastern towhee Pipilo erythrophthalmus -0.296 -0.108 -0.127 0.056 0.422 * 0.001 -0.002 0.80 0.91
chipping sparrow Spizella passerina 1.904 0.016 0.005 -0.030 0.086 0.006 0.002 0.61 0.91
field sparrow Spizella pusilla 1.047 -0.110* -0.162 0.000 0.106 0.010 -0.002 0.85 0.83
vesper sparrow Pooecetes gramineus -1.488 -0.080 0.310 0.133 -0.207 -0.014 0.004 0.41 0.87
Savannah sparrow Passerculus sandwichensis -1.952 -0.043 0.392 * 0.100 -1.111 * 0.038 0.006 0.47 0.98
grasshopper sparrow Ammodramus savannarum -1.209 -0.123 -0.025 -0.045 -0.255 0.021 0.002 0.65 0.98
northern cardinal Cardinalis cardinalis 1.860 0.035 -0.012 0.011 0.218 * -0.034 * -0.002 0.75 0.96
song sparrow Melospiza melodia 1.813 -0.001 0.049 0.047 -0.169 * -0.001 0.002 0.86 0.86
indigo bunting Passerina cyanea 1.245 -0.037 -0.005 -0.059 0.140 0.019 0.003 0.78 0.99
81
species �
(intercept)
ß0
(trend)
ß2
(developed)
ß3
(hay)
ß4
(forest)
ß5
(CREP)
ß6
(WNV)
goodness
of fit � R2
bobolink Dolichonyx oryzivorus -4.640 -0.304 -0.157 -0.046 -1.328 * 0.338 * 0.017 0.47 0.99
red-winged blackbird Agelaius phoeniceus 2.668 0.062 -0.026 -0.074 -0.153 0.008 0.001 0.74 0.99
eastern meadowlark Sturnella magna -0.343 -0.029 -0.028 -0.054 -0.192 0.096 * -0.007 0.68 0.99
common grackle Quiscalus quiscula 3.276 -0.029 0.063 0.044 -0.346 * -0.002 0.001 0.59 1.00
brown-headed cowbird Molothrus ater -0.663 -0.041 -0.001 -0.037 0.172 -0.051 -0.005 0.66 0.98
orchard oriole Icterus spurius -3.464 -0.012 -0.008 0.071 -0.038 0.033 0.001 0.66 0.88
Baltimore oriole Icterus galbula -0.405 0.174 -0.085 -0.018 0.220 -0.028 -0.004 0.69 0.95
house finch Carpodacus mexicanus 1.217 -0.093 0.122 -0.135 -0.116 -0.010 -0.003 0.87 0.99
American goldfinch Carduelis tristis 0.548 -0.147* 0.048 -0.100 -0.012 -0.014 0.001 0.79 0.99
house sparrow Passer domesticus 1.938 0.026 0.089 0.029 -0.388 * -0.003 0.000 0.69 1.00
* significant - 95% credible intervals do not overlap zero � posterior predictive p-value (Gelman et al. 1996)
82
Table 3.2: Estimated bird population changes attributable to CREP in the Lower Susquehanna River Basin during 2001 to 2005
species total birds
counted%
change (95% credible intervals)
ring-necked pheasant 945 -1.9 (-25.4 - 29.2) American kestrel 513 60.4 (10.8 - 141.2) killdeer 2,605 -2.5 (-13.9 - 9.5) horned lark 1,277 5.6 (-18.1 - 29.6) eastern kingbird 1,094 -3.6 (-17.9 - 12.3) eastern bluebird 2,098 4.4 (-12.2 - 25.9) brown thrasher 800 23.4 (-2.2 - 57.0) common yellowthroat 3,582 2.6 (-10.3 - 16.1) field sparrow 2,990 4 (-16.2 - 21.1) vesper sparrow 1,662 -0.2 (-6.6 - 6.9) Savannah sparrow 1,190 8.3 (-13.8 - 33.3) grasshopper sparrow 2,115 3.5 (-9.9 - 18.6) song sparrow 10,728 -3.8 (-22.1 - 17.2) indigo bunting 7,377 5.1 (-7.5 - 16.3) bobolink 422 143.7 (38.1 - 426.7) red-winged blackbird 19,274 1.9 (-5.9 - 10.8) eastern meadowlark 2,293 31.5 (10.1 - 57.0)
83
Figure 3.1: Locations of the 84 survey routes (black) and Thiessen polygons (white) of the neighborhood structure used in the Conditional Autoregressive model and (inset) the 20 counties of the Lower Susquehanna River basin CREP region.
84
Figure 3.2: WNV-positive dead birds and human population size for 67 counties of Pennsylvania in 2003
85
Figure 3.3: Mean mosquito (left) and bird carcass (right) WNV-positive samples per county in Pennsylvania (gray) (n=67) and the Lower Susquehanna River basin (black) (n=20) in years 2000 to 2005. Bird samples are corrected for human population size.
86
Figure 3.4: Example plots of actual counts versus predicted counts from Bayesian spatial models. Chosen plots are for a species showing a close to unity relationship– eastern meadowlark and a species showing an average fit – mourning dove. The line represents a unity relationship, not the best fit.
87
Figure 3.5: Estimate of the effect of % forest cover on bird counts for 30 species for which forest cover was a significant covariate in Bayesian spatial models. A large negative parameter implies avoidance, positive suggests selection. Error bars are 95% credibility intervals.
Species codes: BOBO = bobolink, SAVS = Savannah sparrow, HOLA = horned lark, MALL= mallard, YWAR=yellow warbler, WIFL=willow flycatcher, ROPI=rock pigeon, KILL=killdeer, TUVU=turkey vulture, HOSP=house sparrow, COGR=common grackle, EUST=European starling, BARS=barn swallow, SOSP=song sparrow, NOMO=northern mockingbird, AMRO=American robin, AMCR=American crow, NOCA=northern cardinal, EAPH=eastern phoebe, WBNU=white-breasted nuthatch, CARW=Carolina wren, TUTI=tufted titmouse, WOTH=wood thrush, GCFL=great crested flycatcher, RSTO=rufous-sided towhee, YBCU=yellow-billed cuckoo, CHIC=chickadee sp., SCTA=scarlet tanager, REVI=red-eyed vireo, OVEN=ovenbird
88
Figure 3.6: Bird population trends for seven species significantly impacted by West Nile virus in the Lower Susquehanna River basin CREP region in 2001 to 2005. Gray bars are estimates of WNV prevalence (WNV-positive mosquito samples per year) – scale on second y-axis.
89
Figure 3.7: Estimated percentage change in populations of 17 common bird species between 2001 and 2005 that can be attributed to CREP, against the lower 95% credible limit of those estimates. Species right of the y-axis showed a significant CREP effect. Fitted line is a 2nd order polynomial, showing a y-axis intercept of 20 – an approximation for the magnitude of change required to detect significant CREP effects.
90
Chapter 4
Association of wintering raptors with Conservation Reserve Enhancement Program
grasslands in Pennsylvania
Introduction
Many North American raptor species are associated with open country, both
during the breeding season and in the winter months. In some parts of their range, raptors
have adapted well to the new opportunities created by the post European settlement
expansion of agricultural, indeed some species, such as the American kestrel Falco
sparverius and red-tailed hawk Buteo jamaicensis are now characteristic farmland
species. Despite this close association with farmland, the effects of farmland conservation
programs on raptor populations have received rather little attention. In a sample of 64
peer-reviewed studies of the effects of CRP on birds (Appendix A), very few include
raptors and only one (Littlefield 2005) was solely concerned with them. This is lack of
research on the potential benefits of conservation grasslands for birds of prey is
somewhat surprising - CRP grasslands should provide ideal foraging habitat for a variety
of raptor species and, including nesting northern harrier Circus cyaneus (Kantrud and
Higgins 1992, Luttschwager and Higgins 1992).
One significant hindrance to studying the effects of CRP on raptors is that
population densities are generally low, hence obtaining sufficient sample sizes is
logistically challenging. In Chapters 2 and 3 I provide evidence for a positive effect of
CREP enrollment on American kestrel numbers in Pennsylvania between 2001 and 2005,
91
but acknowledge that the sample was small. There was no evidence of any effect of
CREP enrollment on red-tailed hawk populations, but again, sample sizes were very
small. However, there has been circumstantial evidence that CREP fields in Pennsylvania
attract significant numbers of foraging raptors (Anne Bodling pers. comm.).
In this chapter I use data from a citizen science study – the Pennsylvania Winter
Raptor Survey (WRS), to test the hypothesis that CREP enrollment has influenced the
numbers and distribution of wintering raptors in Pennsylvania. Because winter raptor
numbers are thought to be influenced by winter weather conditions, in particular snowfall
(Grove in press), I include snowfall data into models to parse out weather effects from
CREP enrollment effects. Data are from midwinter surveys in the years 2001 to 2008.
These monitoring data are for a longer time period than the PGC bird monitoring surveys
(chapters 2 and 3) and cover the whole state of Pennsylvania, rather than the 20 county
Lower Susquehanna River basin CREP region, therefore, this is currently the only
evaluation of the effects of CREP enrollment on bird population in the Upper
Susquehanna River Basin and Ohio River Basin CREP regions, which commenced in
2003 and 2004 respectively (see Figure 1.1 and 1.2, chapter 1).
This analysis is restricted to the four commonest raptors on Pennsylvania
farmland during the winter months: red-tailed hawk, rough-legged hawk, northern harrier
and American kestrel. Two of these species, red-tailed hawk and American kestrel are
fairly common and widespread breeding birds in Pennsylvania. Both species nest in tress
but forage mainly in open country, especially during the winter months (McWilliams and
Brauning 2000). Populations of residents may be augmented by migrants from breeding
populations further north, while significant numbers move further south during prolonged
92
periods of cold winter weather (Grove in press). The northern harrier also nests in
Pennsylvania, but it is a scarce species with a population likely to be in the low hundreds
of pairs, at the most. Breeding harriers are principally found in the north and west of the
state, often on reclaimed surface mine grasslands. They are more numerous in southern
Pennsylvania during the winter. It is assumed that the wintering birds include migrants
from further north (Grove in press). The fourth species, the rough-legged hawk, does not
nest in Pennsylvania; the closest breeding grounds are in the tundra regions of Canada.
Periodically, on cycles of five to seven years, much larger numbers than is typical are
noted in Pennsylvania. During the 2001-2008 study period one such influx occurred in
2004, with higher than average number also in the preceding and subsequent winters
(Grove in press).
Methods
Bird Surveys
The Pennsylvania Winter Raptor Survey was established in 2001 by a group of
amateur ornithologists associated with the Pennsylvania Society for Ornithology. The
aims of the survey were to assess mid-winter distributions of raptors and vultures in
Pennsylvania and detect long-term trends (Grove in press). Roadside counts were
conducted by volunteer fieldworkers once per winter, between mid January and mid
February. Routes were chosen by the volunteer and were a minimum of 10 miles in
length but not more than 100 miles. Busy roads were avoided. Counts were carried out
between mid morning and mid afternoon. Foggy, windy, rainy and snowy days were
avoided. Raptors, vultures and owls were counted from a moving vehicle and from a
93
series of stops. It was recommended that surveys were carried out by teams of drivers and
spotters. The survey route, duration and timing were standardized between years.
Survey coverage increased during the first three years of the survey but was
constant from 2004 to 2008. The total number of survey hours increased from 253 in
2001 to approximately 500 each year from 2004 to 2008 (Table 4.1). Surveys were
conducted in all 67 counties of Pennsylvania, although in no more than 63 counties in any
one year (Table 4.1). In some counties there are several non-overlapping routes, the mean
number of routes per county being 2.0. Although surveys routes were not randomly
selected, they cover a considerable proportion of the available habitat in many counties –
during 2004 to 2008 the WRS survey routes covered approximately 9,000 miles – double
the density of coverage of the USGS Breeding Bird Survey (mean of 91 routes per year,
each 50 miles).
Counters coordinated with each other to ensure that routes did not overlap and
routes were restricted to within one county. Raptor counts were therefore aggregated for
each county, which is the sampling unit used in this analysis. The 67 counties of
Pennsylvania average 1,780 square-km. The large size of the sampling units is justified
for wintering raptors because they are not territorial and appear to be very mobile during
and between winters.
Data Sources
I estimated the extent of each major land cover type in each county in circa year
2000 from Landsat 7 ETM data in ArcGIS (ESRI 2004). Grassland and arable land cover
types were combined to provide an estimate of farmland extent (Table 4.2). I estimated
94
CREP enrollment rates as a percentage of the total farmland for each county and program
year. CREP enrollment data were obtained from the USDA monthly contract reports
(USDA 2008) by summing the number of acres enrolled in the following grassland
conservation practices: CP01 – introduced grasses and legumes (cool season grasses),
CP02 – native grasses (warm season grasses), CP10 (vegetative cover – grass already
established) and CP21 – filter strips (grasses). Most CREP grasslands are sown during the
spring, hence the previous year’s CREP enrollment was assumed to be available to
raptors as foraging habitat by the time of the surveys in the following January to
February.
Cumulative January snowfall totals for each year in 10 climatic regions of
Pennsylvania were obtained from PASC (2008). These totals were then matched to the (4
to 11) counties from which each climatic region is composed. Although there is some
spatial and temporal mismatch between snowfall data and bird survey data, I believe that
the available snowfall data are a good proxy measure for the severity of the winter
leading up to the bird counts.
Trends from log-linear Poisson regression
Population trends for the four commonest raptor species: northern harrier, red-
tailed hawk, rough-legged hawk and American kestrel were estimated for the years 2001
to 2008 using program TRIM (TRends and Indices for Monitoring data). See chapters 1
and 2 for further details. Because CREP enrollment differed in both scale and timing
between the three CREP regions (Figure 1.2, chapter 1), I calculated separate trends for
each region by including region as a covariate. Although this analysis is very coarse, and
95
does not correct for substantial differences in land use, climate and topography among
those regions, it is a useful exploratory step to evaluate whether raptor populations trends
differed between the LSR, where CREP enrollment was earliest and most substantial, the
two subsequent programs (USR and Ohio), and the eight counties of the Delaware
Valley, where there is no CREP.
Bayesian spatial model
Winter raptor population trends were modeled with respect to rates of CREP
enrollment at the county scale. The WRS counts were modeled as Poisson random
variables using Bayesian models, which allow incorporation of spatial structure and
“nuisance” variables such as environmental factors (Thogmartin et al. 2006). This
modeling method is increasingly viewed as being very useful for analyzing the results are
large-scale bird population surveys such as the USGS BBS (Link et al. 2002, Link and
Sauer 2002, Sauer and Link 2002, Thogmartin et al. 2006) and Christmas Bird Count
(CBC) (Link et al. 2006, Link and Sauer 2007).
The model structure was the same as that used in chapter 3. I modeled the
expected count (�) of each species in each county (i) and year (j) as follows:
where � is the intercept, j1 is the first year (2001), �i is the linear trend, �ik are
effects of p environmental covariates xijk, �j are random year effects, �ij are random
county specific effects, �ij are survey effort effects and �ij are Poisson errors. Covariates
included CREP enrollments rates, % of county in urban land use, % in farmland and
( )11
ln p
ij i ik ijk j ij ij ijk
j j xλ µ γ β α ω ψ ε=
� = + − + + + + + � �
96
grassland, and the cumulative January snowfall for the climactic region in which the
county was located. CREP and snowfall covariates were year dependent, land cover
covariates were constant through time. The latter three environmental covariates were
standardized (mean was subtracted and then divided by standard deviation) to improve
model convergence (Gilks and Roberts 1996).
After Link and Sauer (2007) I included a survey effort effect (�ij) to correct for
variation in effort between counties and years. The effect of the number of hours spent
counting raptors () on the number of raptors counted is modeled as:
(1/ )( ) ( / ) Bf ξ ξ ξ=
As an inverse power function, a B=1 would indicate a linear relationship between
effort and counts, B>1 would suggest diminishing returns, and B<1 would suggest
increasing returns with extra effort. Link and Sauer (2007) included a second parameter
to allow the effect of effort on CBC counts of Carolina Wren Thryothorus ludovicianus to
reach an asymptote but found that there was no evidence that the more simple
formulation shown above was not adequate. I hypothesized that it was likely that there
would be diminishing returns of increased effort primarily because increased effort was
usually attributed to additional survey routes within counties, and that the best areas for
raptors would be surveyed first, with additional survey routes added in areas expected to
produce lower raptor counts.
The random route specific effect is used to control for any spatial correlation in
counts between adjoining counties. I used a Gaussian conditional autoregressive (CAR)
to incorporate spatial relationships between neighboring counties (Thogmartin et al.
2004; 2006).
97
I fitted the model using Markov-chain Monte Carlo (MCMC) methods in
WinBUGS (Speigelhalter et al. 2004). I used vague prior distributions (Link et al. 2002)
to begin the MCMC sampling. Parameters for fixed effects (environmental variables and
time trend) were assigned flat normal distributions with mean of 0.0 and variance of 100
(precision = 1/variance = 0.01). An appropriate burn-in period was determined by visual
inspection of trace plots from the MCMC process and models were fitted using chains of
a subsequent 100,000 iterations.
The full model for each species was compared to models without the spatial effect
(�ij) and without the effort effect (�ij). The need to incorporate these effects was my main
justification for using Bayesian models rather than more simple frequentist general linear
models. The most parsimonious among the “full”, “non-spatial” and “no effort-effect”
models were selected using the Deviance Information Criterion (DIC), which is the
Bayesian equivalent of Akaike’s Information Criterion (AIC) (Burnham and Anderson
2002). A lower DIC infers a better model. Predicted birds/hour values for the average
county were calculated within the MCMC step of the model by back-transforming the
model with annual mean values of survey effort, snowfall and CREP enrollment across
the 67 counties. Land cover metrics were set to the average which, because they were
standardized to zero, removed these parameters from the predictive model. I measured
model goodness-of-fit with the posterior predictive p-value (Gelman et al. 1996). A p-
value close to 0.0 or 1.0 indicates the data do not agree with the proposed model; a value
near 0.5 indicates an adequate fit.
The significance of each parameter was determined by 95% credible intervals.
The predicted raptor population trend for a county with a high level of CREP enrollment
98
was compared with predictions for a county with no enrollment by back-transforming the
model. Average snowfall and survey effort data for each year were used together with
CREP values for the county with the approximate 90th percentile of enrollment - the 8th
ranked value for each year. Note that these CREP enrollment rates are only slightly
higher than the median (10th/11th ranked) among the 20 counties of the LSR.
Results
Regional trends from log-linear Poisson Regression
During 2001 to 2008 red-tailed hawk counts increased significantly in the LSR
CREP area, did not change significantly in the USR CREP area and decreased
significantly in the Ohio Basin (Figure 4.1). Northern harrier counts increased by an
average of 20% per year in Pennsylvania during the period. This increase was attributable
to significant increase in the LSR CREP area, counts in the USR and Ohio increased but
not significantly, while counts in the Delaware Valley where there is no CREP decreased
significantly. Counts of rough-legged hawks and American kestrels also declined
significantly in the Delaware Valley, but there was no strong evidence that linear trends
differed among the three CREP areas.
Bayesian model evaluation
Models converged well: a 20,000 iteration burn-in was required for the model for
Red-tailed Hawk counts; models for the other three species converged by 10,000
iterations. The MCMC errors of the seven model parameters were generally less than 5%
99
of the standard deviation of the parameter estimates (Spiegelhalter et al. 2004), the only
exceptions being errors for the trend for three of the four species (Table 4.3), which were
6-8%.
For all four species, the full model, which included both spatial and effort effects,
provided a better fit (lower DIC) than models which did not include spatial or effort
effects (Table 4.4). The effect of observer effort was significant for all four species
(Table 4.3). The correction factor applied to counts due to the observer effect resulted in
a downward adjustment of estimates for counties and years where effort was less than the
average (7.2 hours), while for counties and years where effort was greater than the
average, the counts were adjusted upwards. For most counties and years the correction
factors fell between 0.7 and 1.2 for all four species (Figure 4.2). Inclusion of the effort
effect did little to change the estimates of birds per hour for northern harrier and rough-
legged hawk but resulted in reducing estimates for the earlier years of the time series and
increasing count estimates later in the time series for red-tailed hawk and American
kestrel (Figure 4.3). This correction suggests that the increase in survey effort through the
time series was directed into areas where raptors were less likely to be encountered –
which supports my hypothesis, that the best areas for wintering raptors are more likely to
be chosen first by surveyors.
CREP effect and other environmental covariates
There was not strong support in the model for January snowfall totals to have a
significant effect on raptor counts (Table 4.3). However, counts of all four species were
positively associated with higher rates of CREP enrollment (Table 4.3). The effect of
100
CREP was strongest for northern harrier: the parameter estimate of 0.305 translating into
a 35% increase in Northern Harrier counts for each additional 1% of farmland enrolled.
The result of this effect over the eight year period was that the predicted counts for a
county with a high rate CREP enrollment farmland increased nine times faster than those
for a county with no CREP (Figure 4.4).
Discussion
Advantages of the Bayesian modeling approach
Citizen Science bird surveys are cost-effective ways of conducting long-term and
large-scale bird monitoring (Carlson and Schmiegelow 2002), which would be
prohibitively expensive to gather using professional fieldworkers (Battersby and
Greenwood 2004). Data from such surveys have proved to be highly valuable in
highlighting bird population changes and hence draw attention to environmental change
(e.g. Robbins et al. 1999, Chamberlain 2000, Coppedge et al. 2001). However, by their
nature, citizen science surveys often use less rigorous survey methods than would be the
ideal because they often have aims of providing enjoyment to volunteers and because
simple techniques are required to maximize participation. Because of this lack of rigor,
analyzing data from citizen science surveys often requires care, to account for potential
nuisance effects, such as observer effects and temporal and spatial variations in survey
effort. The Bayesian models used in my analysis are relatively straightforward to
implement but allow a very flexible approach to incorporating nuisance effects that are
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difficult to implement otherwise. As expected, the inclusion of spatial structure improved
model fit, which in itself is a good justification for this approach. The inclusion of a non-
linear effort effect also improved model fit and suggested that trends would have been
biased downwards if the effect of increased effort had not been included. It is important
to note that in my models the effect of increased effort was non-linear due to spatial
heterogeneity in observer effort through time – an important consideration in surveys of
this nature.
Effects of CREP on winter raptor numbers
Spatio-temporal patterns in WRS counts support my hypothesis that CREP has
benefited all four raptor species Pennsylvania. While the results for three species are
marginal, my models suggest that the large increase in northern harrier numbers in
Pennsylvania could be almost entirely attributable to CREP. However, it is not possible
to say whether the mechanism behind the increase is due to range shifts of individual
birds or changes in mortality and/or recruitment.
Breeding populations in Pennsylvania and elsewhere could have increased, but
there is no evidence of that from BBS data (Sauer et al. 2008). Christmas Bird Counts of
wintering northern harrier, red-tailed hawk and American kestrels in central USA have
been shown to fluctuate with the climatic influences of the El Niño–Southern Oscillation
(Kim et al. 2008) but there was no evidence of an overall trend, which suggests that
raptors may show weather induced winter range shifts that can be large-scale but short-
term. However, there is no evidence that wintering northern harrier numbers in the
Atlantic Flyway changed appreciably either north or south of Pennsylvania during the
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period 2001 to 2008 (Figure 4.6). However, the Atlantic coast states from Delaware to
Florida support relatively high wintering numbers of this species (Root 1988), therefore a
northward range shift in a small proportion of that population could be sufficient to result
in a large increase in wintering numbers in Pennsylvania.
Regardless of whether the upward trend in northern harrier numbers in
Pennsylvania is due to increasing population size or geographical shifts in winter ranges,
the strength of association with CREP enrollment, both temporally and spatially, provide
compelling evidence that CREP provides an important new winter habitat resource for
this species in the state. Conservation Reserve Program (CRP) fields were also found to
be particularly important foraging sites for this species in Texas (Littlefield and Johnson
2005). Northern Harriers feed predominantly on small mammals and are often
encountered in idle and abandoned fields with vegetative cover (MacWhirter et al. 1996).
Further, they roost communally and placement of roosts is related to density of prey in
surrounding areas (Natureserve 2008). Communal roosts have been observed in CREP
fields in Pennsylvania (Anne Bodling pers. comm.) indicating that these fields provide
secure roost sites, access to abundant prey, or both. Rough-legged hawks are found in
highest concentrations where prey, and particular vole densities are highest (Bechard et
al. 2002). Red-tailed hawks (Preston and Beane 1993) and American kestrels (Smallwood
et al. 2002) also frequent open grassy fields and prey mainly on mammals during the
winter months, again, CREP fields should provide ideal foraging habitat for these
species.
Possible effects of West Nile virus
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Although I did not find an association between breeding season counts of
American kestrels and West Nile virus (WNV) in the Lower Susquehanna River basin,
the large decline in numbers between 2002 and 2004 conform to the pattern shown by
other species affected by WNV (chapter 3). Further, Medica et al. (2007) suggested that
WNV may have been responsible for increased mortality of American kestrels in
Pennsylvania. I did not attempt to incorporate potential WNV effects into my models
because of uncertainties with regards the provenance of raptors wintering in
Pennsylvania. It is interested to note that trends in American kestrel counts from the
WRS and PGC bird monitoring survey in the LSR CREP region are closely correlated
(Figure 4.5), suggesting that the kestrels counted in winter are largely resident birds that
breed in Pennsylvania. WRS counts show a recovery in numbers since the 2002 to 2004
decline but note that numbers have not recovered in the Delaware Valley, suggesting
spatial heterogeneity in factors that could be driving population trends. It is also worth
noting that red-tailed hawk number decreased sharply in 2003 but subsequently regained
the upward trajectory in counts – it is possible that this species was also affected by
WNV but that the effect on population size was small compared to that of other species
(chapter 3).
Conclusions
My study provides evidence that, at a very coarse scale, wintering raptor
population trends show a positive correlation with CREP enrollment, supporting
anecdotal evidence that CREP fields in Pennsylvania have provided an important new
foraging resource for these species. It does not follow that this will be beneficial at the
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population-scale because it is not known to what extent winter habitat and over-winter
mortality are limiting in these species. However, the current upward trajectory in
wintering populations of these species in Pennsylvania is encouraging, especially against
a backdrop of long-term shallow population declines in both northern harrier and
American kestrel populations at the national scale (Sauer et al. 2008).
The winter raptor surveys have provided a very useful insight into raptor
population trends in Pennsylvania. That I was able to use those data to investigate
impacts of CREP enrollment on winter bird populations – which are largely overlooked
(chapter 1) demonstrates the value of long-term citizen science bird monitoring programs.
Although the Christmas Bird Count provides some data on trends of these species, the
targeted effort of WRS is a much more efficient survey method – on average 4.4 Red-
tailed hawks and 0.99 American Kestrels were counted per hour of effort on WRS,
compared with 0.61 and 0.17 respectively on CBC (in Pennsylvania) during the same
time period (from Audubon’s CBC site: http://audubon2.org). Numbers of breeding
raptors reported on BBS routes in Pennsylvania each year are very low, ranging from 82
to 166 red-tailed hawks, and 43 to 83 American kestrels per year over the period 2001-
2007; the much larger numbers reported on WRS routes suggest that WRS is much more
effective at monitoring these species during the winter than BBS is during the breeding
season. Also, WRS is timed to coincide with peak numbers of wintering raptors, which
CBC potentially misses because it is earlier in the winter. I suggest that similar winter
raptor surveys in other states could add a great deal to our knowledge of the population
dynamics of these species and urge that the efforts of volunteer ornithologists are
encouraged and supported wherever possible by state agencies.
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Table 4.1: Pennsylvania Winter Raptor Survey coverage and effort and total counts of four species for 2001 to 2008
survey coverage total birds counted Year counties hours Northern
Harrier Red-tailed
Hawk Rough-
legged Hawk American
Kestrel 2001 45 253 24 1,141 44 343 2002 56 314 30 1,399 21 392 2003 61 392 28 1,182 99 357 2004 62 514 94 2,052 341 265 2005 63 494 70 2,610 200 433 2006 61 478 80 2,184 93 488 2007 61 505 107 2,218 87 511 2008 62 504 133 2,390 88 510
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Table 4.2: Land cover types and Conservation Reserve Enhancement Program grassland (CREP) enrollment by CREP region. Land cover data from Landsat 7 ETM data, circa 2000. CREP enrollment rates are the means % of farmland in CREP by the end of 2006 for counties within each region.
% cover by region
% CREP enrollment by county
CREP region counties
urban (c.2000)
farmland (c.2000)
CREP (2006) mean se
Lower Susquehanna River basin 20 4.2 45.7 2.48 3.46 0.95 Upper Susquehanna River basin 23 2.4 22.6 0.67 0.65 0.16 Ohio River basin 16 7.5 33.1 0.31 0.33 0.08 Delaware Valley (no CREP) 8 23.1 26.7 0 0 0 Pennsylvania total 67 6.8 32.9 0.94 1.34 0.33
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Table 4.3: Parameter estimates from Bayesian spatial models of winter raptor counts in Pennsylvania between 2001 and 2008. Credible intervals are 95% (2.5% and 97.5%) and 90% (5% and 95%).
mean s.d. MCMC
error 2.5% 5% median 95% 97.5%
northern harrier � (intercept) -2.669 0.293 0.013 -3.265 -3.141 -2.675 -2.171 -2.056 (effort) 1.966 0.947 0.015 1.205 1.264 1.761 3.293 3.899ß (trend) 0.046 0.068 0.003 -0.100 -0.070 0.049 0.149 0.173ß (snowcover) -0.219 0.181 0.005 -0.602 -0.541 -0.203 0.053 0.095ß (urban) -0.095 0.261 0.006 -0.634 -0.537 -0.085 0.317 0.393ß (farmland) 0.050 0.190 0.005 -0.334 -0.267 0.053 0.354 0.412ß (CREP) 0.305 0.059 0.002 0.193 0.210 0.304 0.403 0.423
red-tailed hawk � (intercept) 1.749 0.139 0.008 1.431 1.508 1.754 1.968 2.015 (effort) 1.356 0.088 0.003 1.206 1.227 1.348 1.512 1.550ß (trend) 0.015 0.033 0.002 -0.049 -0.039 0.014 0.071 0.090ß (snowcover) 0.030 0.040 0.001 -0.048 -0.035 0.030 0.095 0.107ß (urban) 0.033 0.123 0.006 -0.207 -0.169 0.034 0.234 0.274ß (farmland) 0.289 0.095 0.005 0.099 0.131 0.291 0.445 0.477ß (CREP) 0.042 0.020 0.001 0.002 0.008 0.041 0.076 0.082
rough-legged hawk � (intercept) -2.105 0.729 0.046 -3.731 -3.454 -2.042 -0.987 -0.741 (effort) 2.003 0.824 0.016 1.240 1.298 1.802 3.337 3.981ß (trend) 0.035 0.174 0.011 -0.321 -0.247 0.023 0.345 0.403ß (snowcover) 0.193 0.108 0.003 -0.019 0.016 0.193 0.373 0.408ß (urban) -0.055 0.254 0.007 -0.563 -0.478 -0.052 0.358 0.442ß (farmland) 0.005 0.197 0.006 -0.379 -0.316 0.004 0.329 0.399ß (CREP) 0.179 0.055 0.002 0.075 0.091 0.178 0.270 0.288
American kestrel � (intercept) 0.068 0.193 0.010 -0.337 -0.268 0.071 0.381 0.435 (effort) 1.419 0.127 0.002 1.211 1.238 1.404 1.647 1.706ß (trend) -0.044 0.046 0.002 -0.141 -0.121 -0.044 0.032 0.048ß (snowcover) -0.110 0.062 0.001 -0.231 -0.211 -0.110 -0.008 0.013ß (urban) -0.084 0.193 0.007 -0.467 -0.402 -0.083 0.233 0.294ß (farmland) 0.358 0.141 0.006 0.073 0.123 0.360 0.583 0.624ß (CREP) 0.059 0.024 0.001 0.013 0.020 0.059 0.098 0.106
108
Table 4.4: Goodness of fit of Bayesian models (Table 4.3) and changes in the Deviance Information Criterion (DIC) for models of winter raptor numbers. The full models included effort effects, to account for changes in survey effort between years and counties, and spatial effects to account for spatial autocorrelation.
DIC full model � DIC
goodness
of fit � DIC no effort
effect no spatial
effect northern harrier 0.425 1025 20.0 38.2 red-tailed hawk 0.745 3107 17.9 37.0 rough-legged hawk 0.422 1170 9.4 58.5 American kestrel 0.464 1996 57.9 141.5
� posterior predictive p-value (Gelman et al. 1996)
109
Figure 4.1: Wintering hawk population trends (and 95% CI) for Pennsylvania (all PA) and CREP regions for 2001-2008. Percentage annual change is the linear trend from log-linear models with Poisson errors. LSR=Lower Susquehanna River basin CREP region, USR=Upper Susquehanna River basin, Ohio=Ohio River basin. There is no CREP in eight counties of the Delaware Valley.
110
Figure 4.2: Correction factors for varying survey effort (hours per county) for four raptor species. Correction factors were derived from Bayesian models. Black vertical line represents median survey effort, gray lines represent 25th and 75th percentiles.
111
Figure 4.3: Wintering raptor population trends (and 95% credible intervals) in Pennsylvania for 2001 to 2008 showing the changes in estimated trend as a result of including variable effort effects in the model
112
Figure 4.4: Estimated wintering raptor population trends (and 95% credible intervals) in Pennsylvania for 2001 to 2008 for scenarios where there was no CREP, and where enrollment was higher then at present (5% of farmland enrolled by 2007)
113
Figure 4.5: Winter and breeding season population trends (and 95% CI) for American
kestrels in the Lower Susquehanna River basin CREP region. Population indices calculated using log-linear Poisson regression. Breeding season data from Pennsylvania Game Commission (PGC) bird monitoring surveys.
114
Figure 4.6: Trend in northern harriers on Christmas Bird Count (CBC) circles in the Atlantic Flyway. NE USA includes Connecticut, Maine, Massachusetts, New Jersey, New York, New Hampshire, Rhode Island and Vermont. SE USA includes Delaware, Georgia, Florida, Maryland, North Carolina, South Carolina and Virginia. Data from Audubon’s CBC site: http://audubon2.org/cbchist/graph.html
115
Chapter 5
Population scale effects of the Conservation Reserve Enhancement Program on
farmland songbirds in Pennsylvania – population size context
Introduction
Stemming or reversing long-term declines in grassland bird populations in
Pennsylvania and elsewhere is an ambitious aim. If it is to be achieved, it is imperative
that populations in both agricultural and non-agricultural habitats are considered.
Maintaining, expanding and managing native grasslands in the Prairie and Great Plains
states is vital in those regions. In Pennsylvania, where native grasslands are rare, non-
agricultural grasslands could also play a role in the conservation of grassland obligates
because reclaimed surface mines support high densities of some species (Yahner and
Rohrbaugh 1996, Mattice et al. 2005). Further, within farmland, the role of land
retirement programs such as CREP must be assessed within the matrix of production
agriculture fields, which support substantial populations of grassland birds in some areas.
While conservation measures will require a multi-pronged approach, it is important to
consider whether conserving high density populations within certain areas, such as
reclaimed surface mines and CREP fields is sufficient, or whether increased attention
must be focused on hayfields, pastures and other agricultural fields.
In this concluding chapter, I aim to estimate the population sizes of grassland
songbirds in CREP fields in Pennsylvania and compare those to regional and statewide
population estimates and put them in the context of estimated changes in population size.
116
I will also estimate the relative importance of agricultural grassland, rowcrops and non-
agricultural grasslands for these species; important information that has previously been
lacking. I believe this information is vital if we are to appraise where future conservation
efforts should be directed, and whether efforts to conserve grasslands birds outside of
conservation areas need to be redoubled. I acknowledge that these population size
estimates are crude and provisional, pending the availability of more complete data and
the development of more sophisticated population estimate models. None-the-less, I hope
that these estimates are sufficient to suggest that tailoring and targeting of CREP
enrollment and management to species or groups of species within specific parts of
Pennsylvania be considered.
Methods
Data sources – 2nd Pennsylvania Breeding Bird Atlas Point Counts
To produce bird population estimates, I used provisional data from 20,923 point
counts conducted across Pennsylvania during 2004 to 2007 as part of the 2nd
Pennsylvania Breeding Bird Atlas (PBBA) project. The 2nd PPBA will provide a second
generation bird atlas, updating the previous atlas for which fieldwork was conducted
during the 1980s (Brauning 1993). In addition to recording presence/absence and
breeding status in the state’s 4,928 atlas blocks, the 2nd PBBA includes an additional
component to estimate bird densities. To do this, a point count methodology devised to
assess detection probabilities using the “Removal Method” (Farnsworth 2002) was
adopted, with some modifications. A team of up to 12 professional fieldworkers counted
117
all birds seen or heard in five consecutive 75-second time intervals, inside and outside of
a 75 m radius. Counts were conducted in suitable weather, for a period of 5 hours from
30 minutes before sunrise, during late May to early July. GPS coordinates were taken at
each location and a quick 16 point habitat and weather assessment made. The project was
completed in 2008, but data for the final year are not yet available. Note that data used in
my analysis are provisional, subject to validation and checking.
Population estimates
The probability that a bird is detected during a bird survey is highly dependent on
a variety of factors, among which observer, species behavior, time of day, time of season,
weather and habitat are particularly important (Bibby et al. 2000). To maximize detection
and minimize potential error, bird surveys should be designed to reduce bias and increase
precision. However carefully surveys are designed, it is not possible to completely
eliminate errors, hence, calibration of counts is often required, especially if the aim of the
study is to estimate population densities. The bird survey protocol used for the 2nd PBBA
Point Counts allows correction for varying detection probabilities over both time and
distance to be incorporated. These corrections will be species specific and, for those
species with sufficient sample sizes, could also vary with observers and habitat.
To calculate provisional population estimates of grassland bird species in
Pennsylvania, I use an analytical method developed by G. Farnsworth (Xavier College of
Arts and Science) and D. Diefenbach (Penn State) and others (Farnsworth et al. 2006).
This method has not been used in published studies and may require further refinement.
A correction to counts is made according to the estimated probability of detection during
118
the timed count period (as in Farnsworth 2002) but an additional component is included –
allowing estimation of changes in detectability as a function of distance from the
observer.
The detection function for each time interval models the probability of failing to
detect a bird (Q) based on the probability that it does not sing during the time interval (q)
plus the probability that it does sing, but is not detected (as an increasing function of
distance from the observer (r):
where q and a are parameters to be estimated. The maximum distance to which a bird can
be detected (rm) is estimated by setting Q(r) equal to 1. The model was formulated by G.
Farnsworth in SURVIV – a stand alone program which was designed to estimate survival
rates from user specified cell probability functions (White 1983). An example of the
SURVIV code can be found in Appendix E. In addition to statewide population
estimates, I estimated population sizes for the Lower Susquehanna River basin CREP
based on a subset of 6,271 point counts from that region.
Note that these population estimates assume that the bird point count locations are
representative of habitats across the landscape, which is unlikely to be the case with
roadside surveys (Betts et al. 2007). To assess the validity of this assumption I calculated
the Landsat 7 ETM land cover (circa 2005) within 150 m of all point count locations, and
compared it to actual land cover statistics for the state. Calculations were conducted in
ArcGIS (ESRI 2004). I chose a 150 m radius because a preliminary analysis showed that
this is representative of rm – the distance within which most birds are detected, for a range
( )
2
r
rQ q
a� �= +� �� �
119
of grassland songbirds. As expected, the point count samples included a substantial over-
representation of developed land (21.5% versus 9.6% statewide), small over-
representation of farmland (27.5% vs. 23.4%) and an under-representation of forest
(47.8% versus 62.9%). Of the farmland, extent of row crops in the point count sample
was very close to that statewide (9.18% vs. 9.12%) but pasture and grass was over
represented by (18.3% vs. 14.2%). Unfortunately, land cover data do not differentiate
between pasture and ungrazed grasslands, and hence it is impossible to know to what
extent grassland bird habitats are over represented. For the purposes of my study I note
that the resulting population sizes of grassland birds may be over-estimated due to the
apparent over representation of grassland.
I produced population estimates for 12 songbird species previously identified as
being associated with grasslands in Pennsylvania – nine are grassland obligates and three
are facultative grassland species found in CREP fields in Pennsylvania (Wentworth
2006). I included Henslow’s sparrow, for which there was insufficient data to include in
the analyses in chapters 2 and 3. According to “Partners In Flight” (PIF) population
estimates (Blancher et al. 2004), which are based on USGS BBS data, this is the
grassland obligate songbird for which Pennsylvania is the most important in terms of its
share of the global population (Table 5.1). For most other grassland obligates,
Pennsylvania is estimated to support less than 1% of the global population; slightly more
for the three facultative grassland species in this study.
Relative importance of habitats for grasslands birds
120
I estimated the densities of each species in broad habitat types using 2nd PBBA
point count data. The observers classified each location by the dominant habitat type. I
estimated the density in each of the 12 habitat types using only singing birds within 75 m.
Hence the mean density of singing males per ha for each habitat type was:
12( 75 ) /10,000
n
i
i
x
n π=
× ×
�
where xi are the counts of birds within 75 m of each point (i) and n is the number of
points in each habitat category. This direct population estimate method is likely to
underestimate abundance of certain species because it does not correct for undetected
birds. Estimates of the percentage of Henslow’s and grasshopper sparrow that went
undetected during 5-minute point counts on reclaimed surface mines in Pennsylvania
were 56% and 88% respectively (Diefenbach et al. 2007). However, the relative
population estimates are sufficient to gauge what proportion of the grassland bird
population is found on reclaimed surface mines, agricultural grassland and row crops. I
assume that detection probabilities do not differ among different types of open habitat,
but acknowledge that this may not be the case. The estimated densities were extrapolated
to statewide population size estimates using the proportion of those habitats within the
sample, although as previously noted, this sample is somewhat biased.
Population estimates for CREP lands in the LSR CREP region
I provide two estimates of the populations of these songbirds within CREP fields
in the LSR. The first is based on densities estimated in CREP fields by Wentworth
(2006), extrapolated to the 32,400 ha of grassland CREP in the region by the end of 2006
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(USDA 2008). I also express this figure as a percentage of the total LSR population as
previously estimated from 2nd PBBA data.
A second estimate of the population of songbirds in the LSR and within CREP
fields was calculated from a sample of 6,271 2nd PBBA point counts. These data were
divided into 3 strata: 1) those identified by field observers as being primarily within
reclaimed surface mines, 2) those containing some CREP grassland with a 150 m radius
of the point count location, and 3) the remainder. To identify point counts in the second
stratum I used ArcGIS to calculate the amount of grassland CREP within each 150 m
point count radii using digitized CREP boundaries supplied by the Pennsylvania Natural
Resources Conservation Service. Only CREP fields enrolled by 31st May in the year
before the relevant point count was conducted were included. The sample sizes were 21
for stratum 1, 187 for stratum 2 and 6,063 for stratum 3.
I calculated population estimates for each stratum by multiplying the mean
density of singing birds, by area surveyed and then by the extent of farmland and
grassland in the region (11,782km2 from Landsat 7 ETM+ land cover data). The area (ha)
surveyed within each stratum was calculated as follows:
where ni is the number of point counts in stratum i, pi is the mean percentage of the radius
in farmland and rm is the distance within which the species is detected (estimated
previously – see Methods: Population estimates).
Because the 187 point count radii which included some CREP also included
farmland and grassland that wasn’t CREP, I subtracting the proportion of the population
likely to be outside of CREP in proportion to the extent of that habitat within this stratum
2( /100) ( ( ) /10,000)i i mn p rπ× × ×
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(CREP estimated to be 46.2% of farmland and grassland). I assumed that densities in
these non CREP areas were the same as in stratum 3. I then calculated the overall
population size for all 6,271 point using the same method but without stratification, and
estimated the percentage of that estimate that was in stratum 2 (radii including some
CREP).
Note that I did not use this method to calculate populations of common
yellowthroats, song sparrows, indigo buntings because stratification based on farmland
and grassland extent does not make sense for these more generalist species.
Breeding Bird Survey trends 1995 to 2007
To estimate statewide population trends before and during the roll-out of CREP in
Pennsylvania, I calculated population indices from BBS counts using Program TRIM (see
chapter 1 and 2). I used BBS data for 1995 to 2007, thereby providing an index spanning
a 13 year period, with six years prior to the first large-scale planting of CREP fields (in
2001), and six year after the initial roll-out. I tested for differences in the linear
population trends for pre and post CREP roll-out using Wald-Tests (Pannekoek and van
Strien 2001).
I used the previously derived population estimates to provide approximations of
the mean annual absolute population changes during the periods 1995-2001 and 2001-
2007 by back casting the 2nd PPBA based population size estimates using BBS index
values. Note that the population size estimates are for 2004 to 2007, so I first assumed
that the population size was the mean for those four years.
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Results
Population estimates
The population estimates produced using the removal model combined with
distance sampling were very similar to those produced by straightforward extrapolation
(direct estimates) based only on birds within 75 m (Table 5.1). I believe that the estimates
may therefore be conservative, but this could be partly negated by the potential over-
representation of grassland bird habitats in the 2nd PBBA point count sample. The
removal model with distance method may need refining but it is a promising
methodological development, providing very plausible estimates and confidence
intervals. It is interesting to note that the estimated outer distance band (rm) has a very
tight curvilinear relationship with the proportion of birds that are detected inside of the
first (75 m) distance band (Figure 5.2), suggesting that the method of including birds
outside of 75 m performs well in this regard. My results also demonstrate why utilizing
the counts of birds outside of 75 m is useful – it increased sample sizes by between two
and six-fold for the 12 species included. The larger sample sizes gives more potential for
including other covariates, such as observer, in the final models, and could be particularly
useful in increasing precision for species that are scarce.
The population estimates from 2nd PBBA data are generally larger than PIF
estimates (Table 5.1) – appreciably so for horned lark (2.8x higher), field sparrow (4.3x),
Savannah sparrow (5x) and Henslow’s sparrow (4.5x). Population estimates for the
Lower Susquehanna River basin CREP region – the initial focus for CREP enrollment,
show that it is a particularly important area for grasshopper sparrows, supporting 48% of
the statewide population. At the other extreme, the LSR is less important for the
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bobolink, supporting 13.7% of statewide population. The LSR also supports 53.8% of the
state’s horned lark population and 41% of vesper sparrows – but there is little evidence
that either of these species use CREP fields extensively.
Relative importance of habitats for grasslands birds
The relative importance of the three main potential habitats for grassland
obligates: reclaimed surface mines, hay fields, and row crops demonstrate substantial
differences among species (Figure 5.2). Reclaimed surface mines support the highest
densities of field sparrows, vesper sparrows, grasshopper sparrow, eastern meadowlarks
and in particular, Henslow’s sparrows. Hay fields support the highest densities of
Savannah sparrows and bobolinks but row crops support the highest densities of horned
larks. Red-winged blackbird densities are highest in emergent wetlands, but are almost as
high in hay fields. However, because hay fields and row crops are much more extensive
in Pennsylvania, these habitats support the bulk of the populations of all species except
one – Henslow’s sparrow populations appear to be split roughly 50:50 between surface
mines and hay fields.
This analysis demonstrates the importance of agricultural areas dominated by row
crops for horned larks, vesper sparrows and to a lesser extent Savannah sparrows in
Pennsylvania. In contrast, grasshopper sparrows, bobolinks and eastern meadowlark
populations are highly concentrated in areas with the most hayfields.
Note that this analysis is crude because very few point count radii are 100%
within one habitat type. I suggest that the populations attributed to row crops and hay
fields are slightly underestimated because the small numbers of grassland birds detected
125
in radii dominated by other habitats, such as residential, were mostly in the portions of
the radii that were in farmland (pers. obs.).
CREP lands population estimates for the LSR CREP region
My estimates of CREP grassland bird populations in the LSR from 2nd PBBA data
suggest that CREP fields support c.10,000 singing male (�pair) of grassland obligate
songbirds together with c.16,500 male red-winged blackbirds (Table 5.2). For most
species, the CREP population is a relatively small percentage of the regional population
but for bobolink it is estimated to be approximately 22% of the LSR population.
Although these population estimates are admittedly crude, it is interesting to note that
they are in close agreement with estimates based on extrapolation of Wentworth’s (2006)
density estimates from a sample of 116 fields in 2002-2004 (Table 5.2). Considering that
the two estimates were derived using very different field survey and analytical
techniques, it is encouraging that there is broad agreement. The one exception is eastern
meadowlark, which Wentworth found to be scarce in CREP fields but 2nd PPBA data
suggests that CREP supports approximately 8% of the LSR population – the second
highest for any of these species.
Evidence of CREP effect in BBS trends
All eleven common field nesting songbirds showed population declines during
1995 to 2001, with statistically significant declines for seven species (Table 5.3). The
downward trend continuing during 2001 to 2007 for all but three species: horned lark,
indigo bunting and red-winged blackbird although the change in trend for these three
126
species was not statistically significant (Table 5.3). Visual inspection of 1995 to 2007
population trends for the field nesters (Figure 5.3) demonstrates why Wald-Tests did not
detect significant changes in trends for the periods pre and post 2001 – the trends in
numbers of field sparrows, vesper sparrows, Savannah sparrows and eastern
meadowlarks in particular show a remarkably consistent downward trajectory. Of these
11 species, the population trajectories of only two: horned lark and grasshopper sparrow,
show a turning point around 2001, but as previously stated, neither is statistically
significant.
Background population size changes
Back casting the 2nd PBBA population estimates for the LSR using BBS trends
suggests that absolute changes in population sizes have been substantial (Table 5.2). The
loss of field sparrows for the 12 years 1995 to 2007, for example, is approximately
35,000 singing males – representing a halving of population size. Grasshopper sparrows
declined even more steeply, with an estimated loss of 65,000 singing males – more than
half of the population, between 1995 and 2001. A subsequent shallow increase in
grasshopper sparrow numbers equates to approximately 2,400 males, slightly less than
the estimated population now found in CREP fields. For most species, the changes
between either 1995-2001 or 2001-2007 were much larger than the estimated populations
within CREP lands. One notable exception is the bobolink, which has a relatively small
population in the LSR, a significant proportion of which I have shown to be associated
with CREP fields. It is interesting to note that the BBS trend for this species was positive
overall between 1995 and 2001 but in shallow decline between 2001 and 2007. However,
127
more than any other species in this analysis, the statewide BBS trend may not reflect
trends in the LSR, which has only 13.7% of the statewide population (Table 5.1).
Generally, BBS trends and PGC bird monitoring data trends (chapter 2) for the LSR for
2001 to 2005 are similar, although there was a divergence in estimated trends for horned
lark (Figure 5.4).
Discussion
Targeting CREP towards key species
Grassland obligate bird species have varying ecological requirements, which are
reflected in the differences in their distribution within the state. Future re-enrollment of
CREP, and modifications of management, could be targeted toward the species that they
are most likely to benefit (Burger 2006). This targeting could be both geographically and
management orientated. Although CREP fields support a wide range of bird species, I
suggest that they have the potential to most benefit five grassland obligate songbirds:
field sparrow, grasshopper sparrow, Henslow’s sparrow, bobolink, and eastern
meadowlark. Other species, such as horned lark and vesper sparrow show little
association with CREP fields in Pennsylvania. A further suite of early successional and
scrub species are also found in CREP fields, often in appreciable numbers, but because
they have not declined as much as the aforementioned grassland obligates, and because
CREP contributes a relatively small amount to the total available habitat for this species,
targeted enrollment or management would be less likely to result in population scale
responses. However, it is important to note that CREP is not merely a grassland bird
128
conservation program, and fields and buffers may be enrolled primarily to reduce erosion
and protect water supply. In those cases, if it is unlikely that grassland obligates will use
CREP fields because of small field size or proximity to treelines, managing for early
successional species could be worthwhile.
The Lower Susquehanna River basin region, in which CREP was initially
introduced, is particularly important for two common species that may benefit from
CREP – grasshopper sparrow and eastern meadowlark. My study provides evidence that
CREP supports substantial populations of both these species in the region. It is interesting
to note that statewide BBS trend data show a change in the trajectory of grasshopper
sparrow populations around 2001, with a previous substantial decline marginally
reversed. However, PGC bird monitoring data showed a continued downward trend in the
LSR during 2001 to 2005, and I have not been able to demonstrate a statistically
significant correlation between CREP enrollment and population trends at the landscape
scale (Chapter 3). The estimated contribution of CREP lands to the population size of
eastern meadowlarks in the region is substantial, and yet, there is no evidence from BBS
data of an upturn in the long-term downward trend in numbers.
The Bobolink has become a scarce and localized bird in the LSR region
(Brauning 1993), with 2nd PPBA data suggesting that it holds only a small proportion of
the statewide population. However, there is strong evidence that CREP enrollment has
the potential to contribute significantly to populations in that region. The Bobolink is
much commoner in the Upper Susquehanna River Basin CREP region but there is
currently a paucity of evidence of how CREP is used by grassland birds in that region, in
which there was more CREP grassland than the LSR by the end of 2007 (USDA 2008).
129
CREP enrollment in the USR may also potentially benefit the Savannah sparrow more
than in the LSR, because it too has a primarily northerly distribution.
The Ohio basin has relatively little CREP grassland compared to the two
Susquehanna CREP regions, but conversely, has some of the most important
concentrations of grassland obligate birds in Pennsylvania. CREP fields in the Ohio
Basin could potentially be of importance to the Henslow’s sparrow, which is now largely
confined to the western third of the state. The Henslow’s sparrow is awarded the highest
priority for grassland bird conservation in eastern and Midwestern North America by PIF
(Blancher et al. 2004) and should be a priority target species for conservation grasslands
in Pennsylvania. Evidence elsewhere suggests that this species has benefited from CRP
(Herkert 1997, 2007a, 2007b) but we currently have little evidence of widespread use of
CREP fields by this species in Pennsylvania.
Intensive surveys of reclaimed surface mines in western Pennsylvania found the
population of Henslow’s sparrows to much be higher than previously thought, with an
estimated population of 11,050 males on reclaimed surface mines in a nine county study
area, which encompassed the core of this species’ range in The Commonwealth
(Diefenbach et al. 2007). The populations on agricultural grasslands are thought to be
modest, but 2nd PBBA population estimates presented here suggest that half of the
statewide population is found in hay fields. The population of this species in
Pennsylvania could therefore be approximately an order of magnitude larger than the PIF
estimates, and yet, it is a species that has certainly declined in the state – very few are
counted on BBS routes each year. CREP fields could potentially add an important new
resource for this species, but there is a need for targeted surveys of CREP fields in the
130
Ohio Basin, and the small parts of the other two CREP regions in which this species is
found, such that the use of CREP by this species can be assessed. Management of CREP
fields in those areas could be adapted to better suit this species, which prefers large fields
with short-sparse vegetation and appreciable litter cover (Bollinger 1995).
Why a lack of evidence for population change?
During the 12 year period 1995 to 2007 there was little evidence of statewide
changes in population trends of songbird species most likely to be affected by CREP
enrollment. Of the grassland obligates, only three species were found to be fairly
numerous in 116 CREP fields surveyed during 2002 to 2004 (Wentworth 2006) – field
sparrow and grasshopper sparrow and particularly the red-winged blackbird, which
constituted around 50% of the bird community. There was no evidence of any change in
the downward population trajectory of the field sparrow but there was a slowing of the
downward trend in grasshopper sparrow numbers and a slight reversal in the downward
trend in red-winged blackbird numbers around 2001. The change in the trend of
grasshopper sparrows was not statistically significant but was numerically large. I
consider grasshopper sparrow and red-winged blackbird to be the only species for which
BBS trend data suggest of a potential effect of CREP enrollment on bird population
trends at the state scale. The non-significant upward swing in horned lark numbers during
the period is unlikely to be attributable to CREP enrollment because this species was
scarce in CREP fields by Wentworth (2006) and is generally associated with much more
sparsely vegetated fields in Pennsylvania.
131
Vesper sparrow, which is associated with sparse cover (Mitchell et al. 2000)
showed a more rapid (and significant) downward population trend during 2001 to 2007
than during the previous six years. It is possible that CREP enrollment had a negative
effect on populations of this species because they are often associated with recently
abandoned fields and bare ground (pers. obs) – such as might be found in marginal and
erodible fields that are likely to be enrolled in CREP. It should be noted that there is no
direct or even anecdotal evidence of negative effects of CREP enrollment on this or other
species, but the possibility of negative effects must be considered.
Several generalist species nested in CREP fields in Pennsylvania, notably the
song sparrow, which was the second most numerous species during surveys of CREP
fields in 2002 to 2004 (Wentworth 2006). There is no evidence from BBS data of a
population-scale effect of CREP enrollment on numbers of this or any other generalist
species. This result is to be expected - while densities found in CREP fields may be
appreciable, the numbers found in CREP fields from only a very small proportion of the
statewide populations, and hence are not likely to be sufficient to affect large-scale
population trends.
The lack of strong evidence for statewide changes in breeding bird population
trajectories since 2001 does not preclude a positive effect of CREP enrollment on bird
populations. As pointed out in chapter 3, statistical power to detect significant trends over
short time-periods is likely to have severely hampered my analysis. Also, agricultural
landscapes, which support the bulk of the populations of common grassland birds are
complex and dynamic landscapes (Giudice and Haroldson 2007) and hence separating the
potential effects of conservation grasslands from other changes in the landscape is
132
problematic. Because of continued reductions in the quantity and quality of grassland
bird nesting habitat across the farmland landscape, it is possible that the benefits of
conservation grasslands are not adequate to compensate for ongoing declines elsewhere
(Rodgers 1999, Giudice and Haroldson 2007). Additionally, conservation grasslands
could potentially act as population sinks rather than sources if reproductive success is
low, although evidence suggests that this is generally not the case (McCoy et al. 1999,
Wentworth 2006). However, CREP fields in Pennsylvania are often small in size and
close to tree lines, woodlots and forest edges (Wentworth et al. in prep.). Such fields have
the potential to become population sinks if bird nests are particularly vulnerable to high
densities of opportunistic nest predators (O’Leary and Nyberg 2000, Renfrew et al.
2005). Future targeted enrollment or re-enrollment of large CREP fields within open
farmland could reduce the likelihood that CREP fields become population sinks,
strengthening the case for a switch from program driven to objective driven planning
(Burger 2006) now that the initial establishment phase is almost complete. This shift in
planning would require greater liaison between wildlife biologists and land managers
(Weber et al. 2005), paying particular attention to species and region specific limitations.
How important is grassland bird conservation in Pennsylvania?
The decline in field nesting birds in Pennsylvania is of conservation concern as is
the case across North America. Population estimates derived from PIF data (Blancher et
al. 2004) suggest that Pennsylvania supports rather modest populations of these species,
with the exception of Red-winged Blackbird for which I estimated a statewide population
well in excess of 1 million males. Although Pennsylvania supports relatively small
133
proportions of the continental populations of these species, it is imperative that The
Commonwealth plays its part in their conservation. Grassland bird species in particular
are likely to face continued threats throughout their range but especially in the
agriculturally intensive Midwest, which could result in peripheral states such as
Pennsylvania playing an increasingly important role. During recent years, for example,
the rapid expansion of corn production in the Midwest for biofuel production has resulted
in concern being expressed for grassland bird populations there (Bies 2006), whereas
changes in cropping patterns in the east have been rather modest (Matuszeski 2007).
134
Table 5.1: Population estimates (thousands of males) for key grassland songbirds in Pennsylvania and the Lowers Susquehanna River (LSR) basin. Estimates are based on point counts conducted for the 2nd Pennsylvania Breeding Bird Atlas (PBBA). Comparisons are made with estimates by Partners in Flight (PIF) (Blancher et al. 2004).
2nd PBBA estimate
PIF removal with distance method
Pennsylvania LSR
species
population
estimate
% of
global
direct
est.
method males 95% LCL 95% UCL males 95% LCL 95% UCL
% in
LSR
horned lark 15 0.1 38 42 35 48 22 18 27 53.8
common yellowthroat 850 2.6 1,420 1,310 1,340 1,410 200 190 220 15.5
field sparrow 50 0.4 217 214 199 229 47 40 54 22.1
vesper sparrow 20 0.2 23 23 19 28 9.6 6.8 12.4 41.0
Savannah sparrow 30 0.1 154 152 140 163 23 19 28 15.3
grasshopper sparrow 90 0.1 115 115 106 124 55 49 61 48.0
Henslow's sparrow 1.9 2.4 8.9 8.6 2.5 14.8 1.9 1.0 2.9 22.5
song sparrow 1,700 3.1 3,420 3,440 3,390 3,490 827 804 850 24.1
indigo bunting 940 3.3 1,380 1,670 1,640 1,710 355 338 372 21.2
bobolink 70 0.4 96 95 85 104 13.0 9.9 16.1 13.7
red-winged blackbird 1,500 0.7 1,165 1,140 1,110 1,170 312 296 328 27.3
eastern meadowlark 46 0.5 98 99 89 109 37 31 43 37.3
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Table 5.2: Estimates of bird population sizes in CREP fields in the Lower Susquehanna River basin of Pennsylvania and estimates of absolute population changes in the region from USGS Breeding Bird Survey derived trends coupled with 2nd PPBA derived population size estimates.
estimates extrapolated from
Wentworth 2006
estimates from
2nd PBBA data
estimated changes
from BBS
males per
km2
males in
CREP
% in
CREP
male
popn.
males in
CREP
% in
CREP
1995-
2001
2001-
2007
horned lark 0.0 0 0.0 15,900 40 0.3 -6,000 4,800
common yellowthroat 7.8 2,900 1.4 n.a. n.a. n.a. -7,200 -1,200
field sparrow 11.4 3,900 7.0 39,900 2,700 6.8 -19,200 -15,600
vesper sparrow 1.1 400 3.4 6,600 240 3.7 -600 -1,800
Savannah sparrow 1.9 700 2.4 19,500 0 0 -7,800 600
grasshopper sparrow 11.3 3,800 5.9 43,100 2,500 5.8 -65,400 2,400
Henslow's sparrow 0.33 100 4.9 1,200 0 0 n.a. n.a.
song sparrow 37.5 13,900 1.7 n.a. n.a. n.a. -60,600 -178,200
indigo bunting 14.7 5,400 1.5 n.a. n.a. n.a. -31,200 -46,200
bobolink 10.3 3,500 23.0 8,700 1,800 22.3 600 -1,800
red-winged blackbird 70.1 23,800 6.5 226,000 16,500 7.3 -32,400 -21,000
eastern meadowlark 0.9 300 0.7 24,200 2,000 8.2 -12,600 -15,600
n.a. not applicable
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Table 5.3: Population trends of common songbird species for three guilds associated with farmland in Pennsylvania for 1995 to 2007 and Wald-Tests for differences in linear population trends for six years pre and post initiation of CREP enrollment (2001). Data from the USGS Breeding Bird Survey.
Guild and species
mean %
change
1995-2001
mean %
change
2001-2007
Wald-
Test P
horned lark Eremophila alpestris -2.34 8.73 ** 2.77 0.096
field sparrow Spizella pusilla -3.84 * -2.88 * 0.46 0.497
vesper sparrow Pooecetes gramineus -3.22 -6.99 ** 0.57 0.450
Savannah sparrow Passerculus sandwichensis -4.68 * -5.65 ** 0.14 0.710
grasshopper sparrow Ammodramus savannarum -11.68 ** -3.90 3.68 0.055
bobolink Dolichonyx oryzivorus -2.73 * -2.61 0.00 0.957
red-winged blackbird Agelaius phoeniceus -1.55 * 0.07 2.29 0.130
eastern meadowlark Sturnella magna -3.35 * -5.62 ** 1.44 0.229
song sparrow Melospiza melodia -0.99 -0.80 0.06 0.813
common yellowthroat -0.82 -3.0 * 5.46 0.020
indigo bunting -1.29 * 0.39 2.85 0.091
Statistically significant changes (P<0.05): ** strong change (>5% per year) * moderate change (<5% per year). See methods for more details.
137
Figure 5.1: Estimates of outer distance bands – the limits of audible detection, against proportion of birds detected within 75 m, for 12 grassland songbirds in Pennsylvania. Error bars are 95% confidence intervals.
Species codes: FISP=field sparrow, EAME=eastern meadowlark, VESP=vesper sparrow, BOBO=bobolink, RWBL=red-winged blackbird, SAVS=Savannah Sparrow, INBU=indigo bunting, HOLA=horned lark, COYE=common yellowthroat, SOSP=song sparrow, GRSP=grasshopper sparrow, EABL=eastern bluebird, BRTH=brown thrasher, HESP=Henslow’s sparrow.
138
Figure 5.2: Crude songbird population density estimates and estimated distribution of population among 12 habitat types Pennsylvania in 2004 to 2007. Left y-axis is the estimated percentage of the total population (gray bars), right y-axis is estimated density (black diamonds - males per km2).
139
Figure 5.2: contd.
140
Figure 5.3: Population indices for 1995 to 2007 for 11 common songbirds associated with farmland in Pennsylvania. Linear trends for six-year periods pre and post initial CREP enrollment are presented. Indices are relative to a value of 1 in 2001. Annual indices are shown with a square symbol, linear trends with a triangular symbol. Data are from the USGS Breeding Bird Survey.
141
Figure 5.3 contd.
142
Figure 5.4: Population indices (2001=1) for grassland songbirds in Pennsylvania (USGS BBS trends – gray line) and the Lower Susquehanna River basin (PGC bird monitoring trends – black line) for 2001 to 2005.
143
Chapter 6
Conservation and research recommendations
Conservation of declining grassland birds and farmland wildlife in general has
received a great deal of attention in the last twenty years or so from scientists,
conservationists, and policy makers. In order to prevent further declines in farmland
wildlife, billions of dollars have been spent preserving remnant grasslands and planting
of conservation grasslands through federal programs. Loss of farmland to development
has spurred a concerted effort to permanently protect farmland from development and
non-agricultural use, protecting and preserving millions of acres of farmland across the
USA. However, despite these considerable efforts, farmland bird populations have
continued to decline both in Pennsylvania and elsewhere. Stemming and reversing these
declines requires that farmland conservation efforts are ramped up. This will require an
increased commitment to conservation through future farm bills, maintenance and
enhancement of partnerships between government and non-government organizations,
and a further commitment to research, such that management is adaptive and based on the
best evidence for ways of combining agricultural and wildlife needs.
In chapters 2, 3 and 4, I provide evidence of population scale effects of the
Conservation Reserve Enhancement Program on some grassland and farmland bird
species in Pennsylvania. It should be noted that these results are correlative and in the
absence of important information about meta-population dynamics, I can not say with
144
certainty that I have demonstrated cause and effect relationships. I reiterate my
conclusions in chapter 3 that five years of monitoring data may be insufficient to
demonstrate population scale effects for breeding songbirds – it is interesting to note that
the results for the four raptor species, based on a longer time series, were more
conclusive.
CREP could potentially play an important role in conserving grassland bird
populations in Pennsylvania, but I as pointed out in chapter 5, the potential of the
program could be hitherto limited by a lack of targeted management and enrollement. In
this concluding chapter I assess CREP’s role within a broad spectrum of conservation
measures for grassland birds and highlight future research priorities, such that the
program’s potential can be maximized.
CREP as part of a broader conservation plan
As there is very little natural grassland in Pennsylvania, conservation efforts for
grassland obligate bird species have primarily focused on preserving and improving
farmland habitats. It should be noted though that a considerable population of grassland
obligate birds is found in reclaimed surface mines, and that conservation of primary
surface mine grasslands, hand-in-hand with farmland conservation, should be a priority.
While native grassland preservation is a primary conservation focus in some
states, preservation of farmland in the face of development is a priority in Pennsylvania,
which leads the nation in the number of farms and acres preserved. During the last twenty
years almost 400,000 acres on over 3,500 farms have been protected through the
145
Pennsylvania Agricultural Conservation Easement Purchase Program, many of these in
the southeast of the state, where development pressure is greatest (PDA 2008). Such
measures are commendable for a broad range of conservation and socio-economic
reasons; however, declining farmland birds will only benefit if those preserved acres are
managed sympathetically for the species concerned.
Managing farmland for wildlife typically involves one of two approaches 1) land
retirement and planting of conservation cover, and 2) adjustment of crop management,
for example by restricting mowing during the bird nesting season. The primary means of
delivering these measures has been through farm bill programs such as CRP and CREP,
but most grassland bird species have continued to decline since the introduction of CRP
(Norment 2002), suggesting that it has not been sufficient to compensate for continuing
population losses across the farmed landscape on the breeding or wintering grounds.
In addition to CRP and CREP, there are other farm bill programs that aim to
increase the wildlife value of farmland. These include the Environmental Quality
Incentives Program (EQIP) and Wildlife Habitat Incentive Program (WHIP). EQIP aims
to support environmentally friendly pest management and conservation projects on
working agricultural lands. While the primary aims of the program are to reduce soil
erosion and improve water quality, there are also financial incentives to manage hay and
small grain crops with the aims of providing bird nesting habitat. More than 53,000 acres
of working farmland were enrolled in EQIP in Pennsylvania in 2007 alone (NRCS 2008),
and while the proportion of these acres that are managed for birds may be small, the
potential for this program to improve the value of farmland for wildlife is considerable
146
due to its scale. Conversely, although WHIP is focused solely on developing and
improving wildlife habitat, it is a much smaller program, and while local benefits for
wildlife might be considerable, it is unlikely that without major expansion, it will be
sufficient to contribute significantly to farmland bird population at the state scale. There
has been an emphasis shift away from land retirement programs to working lands
programs such as EQIP during the current decade (Claassen 2007), but as yet there has
been no assessment of their impacts on bird populations.
Recommendations for future research
The responses of grassland and other birds to conservation grasslands in
Pennsylvania appear to be quite different to that in many other studies, most of which
which have been conducted in Midwest or Prairie states (Chapter 1). CREP grasslands in
Pennsylvania support higher numbers of early successional and edge species, such as
song sparrow, indigo bunting and common yellowthroats, and relatively low densities of
grassland birds. This is likely due to differences in landscape composition (more forests),
smaller field sizes, smaller source populations of grassland birds and possibly due to
different vegetation structure (Wentworth et al. in prep.).
CREP management should be adaptive based on our best knowledge of the
circumstances under which it successfully attracts grassland obligate birds in
Pennsylvania. Because we cannot assume that information from studies in other regions
is applicable in Pennsylvania, I urge that more studies are needed. I suggest the following
research priorities:
147
1) revisit a large sample of the 116 CREP fields surveyed by Wentworth between
2002 and 2004 to see whether bird populations and vegetation characteristics
have changed during the intervening years.
2) continue/restart the PGC bird monitoring program to assess population scale
changes – I reiterate a key finding that five years of monitoring data was not
sufficient to identify population scale effects.
3) conduct a desktop study of the digitized CREP field data to evaluate the
configuration, placing in the landscape and size of CREP fields, and thereby
deduce how many CREP fields could potentially provide habitat for grassland
birds.
4) conduct more field surveys of birds in warm-season CREP fields to see
whether the conclusions of Wentworth et al. (in prep.) that these fields do not
provide suitable habitat for these species in Pennsylvania, hold true.
5) conduct more field surveys of CREP fields in the parts of western
Pennsylvania in which Henslow’s sparrows are found to evaluate whether
CREP has provided new habitat for this species of high conservation
importance.
Note that some of these suggestions could be amalgamated into a smaller number
of studies to increase cost-effectiveness. The aims should be to inform management of
existing contracts, and direct special efforts in targeting retention of those contracts once
they begin to expire in 2011. CREP fields in Pennsylvania could play an increasingly
important role in conserving grassland birds if populations on working agricultural lands
148
continue to decline. However, the low average density of grassland obligates in
Pennsylvania CREP fields at present suggests that either field management or
configuration are often not ideal for these species. The aims of state and federal agencies
should be to maximize the wildlife potential of this program within the constraints of its
other aims of reducing soil erosion and improving water quality. I believe that spending
relatively small amount of money on research and subsequently on experimental
management changes, could lead to substantial net gains in grassland bird populations,
providing much better net returns on public expenditure.
149
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United States Department of Agriculture (USDA). 2008. Conservation Reserve Program – Monthly Contract Report, September 2008. USDA, Farm Service Agency. van Strien, A., J. Pannekoek, W. Hagemeijer, and T. Verstrael. 2000. A loglinear Poisson regression method to analyse bird monitoring data. In: Anselin, A. (ed.) Bird Numbers 1995, Proceedings of the International Conference and 13th Meeting of the European Bird Census Council, Pärnu, Estonia. Bird Census News 13:33-39. Veech, J. A. 2006. A comparison of landscapes occupied by increasing and decreasing populations of grassland birds. Conservation Biology 20:1422-1432. Vickery, P. 2001. The recent advances in grassland bird research: where do we go from here? Auk 118:1-7. Volkert, W. K. 1992. Responses of grassland birds to a large-scale prairie planting project. Passenger Pigeon 54:190-196. Walk, J. W., K. Wentworth, E. L. Kershner, E. K. Bollinger, and R. E. Warner. 2004. Renesting decisions and annual fecundity of female Dickcissels (Spiza americana) in Illinois. Auk 121:1250-1261. Ward, M. R., D. E. Stallknecht, J. Willis, M. J. Conroy, and W. R. Davidson. 2006. Wild bird mortality and west nile virus surveillance: Biases associated with detection, reporting, and carcass persistence. Journal of Wildlife Diseases 42:92-106. Warner, R. E., P. C. Mankin, L. M. David, S. L. Etter. 1999. Declining survival of ring-necked pheasant chicks in Illinois during the late 1900s. Journal of Wildlife Management 63:705-710. Warren, K. A., and J. T. Anderson. 2005. Grassland songbird nest-site selection in response to mowing in West Virginia. Wildlife Society Bulletin 33:285-292. Weber, W. L., J. L. Roseberry, and A. Woolf. 2002. Influence of the Conservation Reserve Program on landscape structure and potential upland wildlife habitat. Wildlife Society Bulletin 30:888-898. Wentworth, K. W. 2006. Effects of local and landscape features on avian use and productivity on Pennsylvania Conservation Reserve Enhancement Program fields. PhD thesis. Pennsylvania State University, University Park, Pennsylvania. Wentworth, K., M. Brittingham, and A. Wilson (in prep). Conservation reserve enhancement program fields: Benefits for grassland, scrub and edge species. (accepted: Journal of Soil and Water Conservation)
164
White, G. C. 1983. Numerical estimation of survival rates from band recovery and biotelemetry data. Journal of Wildlife Management. 47:716-728. Whittingham, M. J., J. R. Krebs, R. D. Swetnam, J. A. Vickery, J. D. Wilson, and R. P. Freckleton. 2007. Should conservation strategies consider spatial generality? Farmland birds show regional not national patterns of habitat association. Ecology Letters 10:25-35. Wiebe, K., and N. Gollehon. (eds). 2006. Agricultural Resources and Environmental Indicators, 2006 Edition. Economic Information Bulletin No. (EIB-16). United States Department of Agriculture, Washington, DC. Wilson, A., and M. Brittingham. 2007. Impacts of the Conservation Reserve Enhancement Program on the Regional Trends in Bird and Eastern Cottontail Populations. Final Job Report 01004B. Pennsylvania Game Commission, Harrisburg, Pennsylvania. Winter, M. and J. Faaborg. 1999. Patterns of area sensitivity in grassland-nesting birds. Conservation Biology 13:1424-1436. Yahner, R. H., and R. W. Rohrbaugh, Jr. 1996. Birds on reclaimed surface mines. Northeast Wildlife 53:11-18.
165
Refereed journal articles pertaining to bird population and ecology studies with respect to Conservation Reserve Programs fields. Articles were located using the ISI Web of Knowledge database up to and including 16th October 2008, using a combination of key words: CRP, “Conservation Reserve Program”, CREP, “Conservation Enhancement Reserve Program”, avian, bird, duck, waterbird, pheasant, bobwhite, harrier, hawk, songbird, passerine, sparrow, dickcissel, bobolink, meadowlark. Note that review papers are not included in this list. Only papers with specific information pertaining to bird populations on conservation grasslands were included.
author(s) journal volume: pages year publ. study area (states) species study years season
Berthelsen & Smith J. Soil & Water Conservation 50: 672-675 1995 TX passerines 1988-1989 breeding Berthelsen J. Soil & Water Conservation 44: 504-507 1989 TX ring-necked pheasant 1988 breeding Best et al. Am. Midland Naturalist 139: 311-324 1998 IN, IA, KS, MI, MO, NE all 1992-1995 winter Best et al. Wildlife Society Bulletin 25: 864-877 1997 IN, IA, KS, MI, MO, NE all 1991-1995 breeding Boisvert et al. Western N. American Naturalist 65: 36-44 2005 CO sharp-tailed grouse 1999-2000 year round Chapman et al. Agriculture, Ecosystems & Env. 104:577-585 2004 OK all 1998-1999 breeding Clawson & Rotella J. Field Ornithology 69: 180-181 1998 MT passerines 1993-1994 breeding Coppedge et al. Ecological Applications 11: 47-59 2001 OK all 1965-1995 breeding Coppedge et al. Biological Conservation 115: 431-441 2004 MT all 1965-2015 breeding Cunningham Professional Geographer 57: 51-65 2005 MN passerines 1998-1999 breeding Davison & Bollinger The Auk 117: 147-153 2000 IL all 1997 breeding Delisle & Savidge J. Wildlife Management 61: 318-325 1997 NE passerines 1991-1995 breeding Eggebo et al. Wildlife Society Bulletin 31: 779-785 2003 SD ring-necked pheasant 1998-2000 breeding Gill et al Wildlife Society Bulletin 34: 944-956 2006 MD grasshopper sparrow 1999-2005 breeding Giudice & Haroldson J. Field Ornithology 78: 140-151 2007 MN ring-necked pheasant 1994-1997 breeding Granfors et al. J. Field Ornithology 67: 222-235 1996 KS eastern meadowlark 1991 breeding Greenfield et al. Am. Midland Naturalist 149: 344-353 2003 MS northern bobwhite 1995-1996 breeding Greenfield et al. Wildlife Society Bulletin 30: 527-538 2002 MS northern bobwhite 1995-1996 breeding Guidice & Haroldson J. Field Ornithology 78: 140-151 2007 MI ring-neck pheasant 1974-1997 breeding Guzy & Ribic Wilson J. Ornithology 119: 198-204 2007 WI eastern meadowlark 2002-2004 post-
breeding Haroldson et al. J. Wildlife Management 70: 1276-1284 2006 MN ring-necked pheasant,
gray partridge and meadowlarks
1990-1998 breeding
Appendix A
166
author(s) journal volume: pages year publ. study area (states) species study years season
Henningsen & Best J. Wildlife Management 69: 198-210 2005 IA all 2001-2002 breeding Herkert Wildlife Society Bulletin 26: 227-231 1998 IL, IN, IA, KS, MI, MN,
MT, NE, ND, OH, OK, SD, WI
grasshopper Sparrow 1979-1994 breeding
Herkert J. Field Ornithology 68: 235-244 1997 IL Henslow’s sparrow 1975-1995 breeding Herkert J. Wildlife Management 71: 1229-1233 2007 IL Henslow’s sparrow 1976-2004 breeding Herkert J. Wildlife Management 71: 2749-2751 2007 IL, IN, IA, KS, KY, MD,
MI, MN, MO, NY, NC, OH, OK, PA, VA, WV, WI
Henslow’s sparrow 1987-2005 breeding
Hickerman et al. Southwestern Naturalist 51: 524-530 2006 KS passerines 2001 breeding Horn & Koford Wildlife Society Bulletin 28: 653-659 2000 ND passerines 1997-1998 breeding Howard et al. Condor 103: 530-536 2001 CO all 1998 breeding Hughes et al. J. Wildlife Management 63: 523-529 1999 KS dickcissel 1994-1995 breeding Hughes et al. J. Wildlife Management 64: 1004-1008 1999 KS mourning Dove 1994-1995 breeding Johnson & Igle Wilson Bulletin 107: 709-718 1995 ND all 1967-1990 breeding Johnson & Schwartz Conservation Biology 7: 934-937 1993 MN, MT, ND, SD all 1990-1991 breeding Kantrud J. Soil & Water Conservation 48: 238-242 1993 MN, ND ducks 1989-1991 breeding King & Savidge Wildlife Society Bulletin 23: 377-385 1995 NE all 1989-1990 breeding Klute et al. Am. Midland Naturalist 137: 206-212 1997 KS all 1998 breeding Leddy et al. Wilson Bulletin 111: 100-104 1999 MI passerines 1995 breeding Littlefield & Johnson Southwestern Naturalist 50: 448-452 2005 TX northern harrier 1989-1995 winter Lupis et al. Wildlife Society Bulletin 34: 957-962 2006 UT Gunnison sage-grouse 2001-2002 breeding Luttschwager et al. Wildlife Society Bulletin 34: 957-962 1994 SD ducks 1989-1990 breeding McCoy et al. Am. Midland Naturalist 145: 1-17 2001 MO passerines 1993-1995 breeding
& winter McCoy et al. J. Wildlife Management 63: 530-538 1999 MO passerines 1993-1995 breeding Merrill et al. J. Wildlife Management 63: 189-198 1999 MI greater prairie-chicken 1986-1996 breeding Millenbah et al. Wilson Bulletin 108: 760-770 1996 MI passerines 1992 breeding Murphy Auk 120: 20-34 2003 40 states east of the Rocky
Mountains passerines 1980-1998 breeding
Murray & Best J. Wildlife Management 67: 611-621 2003 IA passerines 1999-2000 breeding Murray et al. Biomass & Bioenergy 25: 167-175 2002 IA passerines 1999-2000 breeding
167
author(s) journal volume: pages year publ. study area (states) species study years season
Nielson et al. Auk 125: 434-444 2008 ID, KS, MN, MO, ND, NE, OR, SD, UT
ring-necked pheasant 1987-2005 breeding
Oneal et al. J. Wildlife Management 72: 654-664 2008 IL ducks 2004-2005 breeding Patterson & Best Am. Midland Naturalist 135: 153-167 1996 IA all 1991-1993 breeding Reynolds et al. J. Wildlife Management 65: 765-780 2001 ND, SD, MT ducks 1992-1995 breeding Reynolds et al. Wildlife Society Bulletin 34: 963-974 2006 ND, SD ducks 2005 breeding Riley Wildlife Society Bulletin 23: 386-390 1995 IA ring-necked pheasant 1986-1998 breeding Robel et al. Southwestern Naturalist 48: 460-464 2003 KS dickcissel 1994 breeding Robel et al. J. Range Management 51: 132-138 1998 KS passerines 1992-1995 breeding Rodgers Wildlife Society Bulletin 27: 654-665 1999 KS ring-necked pheasant 1966-1995 breeding Sladek et al. Southern J. Applied Forestry 32: 111-119 2008 MS passerines 2002-2004 breeding Smith & Lomolino Oecologia 138: 592-602 2004 OK all 1997-1999 breeding Sovada et al. J. Wildlife Management 64:820-831 2000 MN, ND, SD ducks 1993-1995 breeding Swanson et al. J. Soil & Water Conservation 54: 390-394 1999 OH all 1993 breeding Veech Conservation Biology 20: 1422-1432 2006 IA, IL, IN, KS, MI, MN,
MO, MT, ND, NE, NM, OH, OK, SD, TX, UT, WI, WY
all 1982-2002 breeding
Walk et al. Auk 121: 1250-1261 2004 IL dickcissel 1999-2000 breeding Warner et al. J. Wildlife Management 63: 705-710 1999 IL ring-necked pheasant 1982-1996 breeding White J. Wildlife Management 69: 1528-1537 2005 GA northern bobwhite 1997-2000 breeding
168
Survey routes for Pennsylvania Game Commission bird monitoring surveys (2001-2005) with mean percentage land use within 250m of survey stops for 2001-2004 as estimated by field surveyors.
land use
county route name
number of stops
resi
dent
ial
com
mer
cial
corn
soy
grai
n
mis
c. c
rop
past
ure
hay
orch
ard
fallo
w
herb
acio
us
shru
b
deci
duou
s
coni
fero
us
mix
ed
wat
er
stri
pmin
e
othe
r
Adams ADA1 47 20.9 5.2 15.4 9.9 4.0 4.7 19.1 6.3 0.0 3.4 4.0 1.0 1.7 0.1 0.0 0.2 3.1 0.9 ADA2 34 16.2 2.6 14.8 5.0 1.6 2.9 8.4 15.0 1.5 4.3 9.4 1.7 11.5 1.7 0.4 0.8 1.6 0.6 ADA3 32 24.6 2.8 6.9 4.9 1.5 2.2 5.2 15.4 3.6 1.8 11.2 3.2 10.9 0.0 0.0 0.5 5.0 0.3 Bedford BED1 50 16.2 0.2 8.1 1.8 0.7 1.1 20.3 18.8 0.0 0.0 0.7 5.5 18.5 0.0 2.9 0.2 4.9 0.2 BED2 44 12.6 0.2 27.5 2.3 3.4 1.9 14.0 19.7 0.1 0.1 0.5 2.3 12.3 0.1 0.0 0.0 2.7 0.4 BED3 50 17.2 0.0 22.4 2.1 1.2 4.3 12.1 14.6 0.2 0.2 1.0 2.8 17.0 0.0 2.8 0.1 1.8 0.2 BED4 50 22.0 0.4 13.3 0.7 2.3 1.6 5.8 17.9 2.7 0.3 1.8 4.0 25.5 0.0 0.0 0.3 0.7 0.9 Berks BER1 24 15.6 4.5 28.3 5.7 5.9 0.8 9.3 15.0 0.3 0.8 1.1 0.0 9.3 0.0 0.0 1.0 0.5 2.1 BER2 28 19.3 2.1 15.9 5.2 3.8 1.4 8.8 18.2 0.0 1.2 1.3 0.9 17.4 0.2 0.6 1.4 1.8 0.7 BER3 28 15.1 1.6 33.6 6.3 6.4 5.0 8.0 13.6 0.0 0.6 0.4 0.1 7.0 0.0 0.1 0.4 0.6 0.9 BER4 22 30.5 2.3 8.1 1.5 2.2 0.5 15.5 13.9 0.0 0.8 4.2 4.4 13.7 0.0 0.5 1.0 0.9 0.0 BER5 28 25.7 3.3 13.9 2.6 4.9 1.1 7.8 12.0 0.7 2.6 2.5 2.0 15.9 0.1 1.4 1.3 1.8 0.6 BER6 27 12.5 2.4 29.6 5.5 5.6 1.3 7.1 16.4 0.0 1.3 0.7 0.0 12.8 0.5 1.1 0.7 2.1 0.4 Chester CHE1 22 23.0 3.1 12.4 7.2 1.5 0.3 10.6 8.7 0.0 6.3 0.3 0.0 21.1 0.0 0.0 2.1 0.9 2.5 CHE2 22 28.5 0.0 12.3 2.7 0.0 1.2 15.2 12.1 3.7 0.1 1.9 2.8 13.3 0.0 0.0 2.6 3.2 0.4 CHE3 23 19.6 0.7 12.4 2.6 0.0 0.0 4.6 6.7 1.5 5.0 2.3 2.2 31.9 0.4 0.0 3.3 2.2 4.6 Columbia COL1 27 24.9 0.0 23.9 9.6 4.5 0.7 2.4 7.6 0.9 7.2 3.4 0.5 11.3 0.0 2.1 0.9 0.0 0.0 COL2 22 28.4 0.0 11.5 4.5 1.5 0.3 1.3 2.9 1.9 2.7 4.1 0.5 31.3 0.0 7.9 1.1 0.0 0.1
COL3 21 37.7 0.0 17.5 6.9 3.0 0.1 0.7 7.3 1.1 3.4 1.1 0.4 19.1 0.0 0.9 0.7 0.1 0.0 COL4 29 32.9 0.3 14.0 5.6 2.5 0.4 3.1 10.1 0.2 6.6 1.1 0.3 12.2 0.2 9.8 0.2 0.1 0.5 Cumberland CUM1 39 26.2 0.9 11.5 1.3 5.4 5.1 11.8 22.3 0.0 0.3 1.6 0.0 12.8 0.0 0.0 0.5 0.0 0.3 CUM2 40 28.1 0.2 26.1 4.1 4.8 6.3 11.7 9.5 1.3 0.0 0.7 0.0 6.3 0.0 0.0 0.0 0.0 0.8 CUM3 31 29.7 3.0 11.5 4.8 4.1 1.6 4.9 26.7 0.0 0.5 2.1 0.0 8.0 0.0 0.0 1.0 0.0 2.1
Appendix B
169
land use
county route name
number of stops
resi
dent
ial
com
mer
cial
corn
soy
grai
n
mis
c. c
rop
past
ure
hay
orch
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fallo
w
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acio
us
shru
b
deci
duou
s
coni
fero
us
mix
ed
wat
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stri
pmin
e
othe
r
CUM4 48 29.6 3.8 23.0 4.9 3.8 4.3 11.7 4.4 2.2 0.0 0.9 0.0 8.6 0.0 0.0 0.0 0.0 2.8 CUM5 40 29.2 0.8 23.8 12.4 9.7 2.0 4.2 9.6 0.0 0.0 0.4 0.0 5.7 0.0 0.0 0.0 0.0 2.0 Dauphin DAU1 40 11.2 0.0 27.3 9.6 2.6 0.3 9.1 10.2 0.2 5.7 1.4 0.4 19.5 1.9 0.0 0.6 0.1 0.1 DAU2 32 14.5 0.8 26.2 11.7 1.3 0.6 5.6 15.7 0.1 2.5 1.7 0.6 17.6 0.0 0.3 0.6 0.1 0.0 DAU3 31 12.3 0.2 26.3 3.9 0.0 0.3 9.2 12.3 0.0 0.0 2.9 0.0 31.6 0.0 1.0 0.2 0.0 0.0 Franklin FRA1 44 17.2 0.0 13.3 1.0 4.5 0.0 5.8 28.1 0.0 2.7 2.6 2.1 18.8 0.0 0.0 0.4 2.1 1.3 FRA2 30 9.7 0.1 32.8 3.3 5.5 0.1 3.7 31.9 0.0 3.2 0.5 0.1 6.9 0.0 0.1 0.4 1.4 0.2 FRA3 36 21.7 0.4 11.4 2.8 5.7 0.1 5.8 25.3 0.0 0.9 3.5 1.3 18.7 0.3 0.6 0.0 1.3 0.1 FRA4 36 27.0 1.1 21.5 5.6 3.0 0.0 5.6 18.4 0.2 0.6 1.1 1.5 12.2 0.3 0.1 0.0 1.5 0.3 Franklin FRA5 31 21.7 0.0 11.8 1.2 5.2 0.0 6.4 28.9 0.2 0.8 1.0 0.9 17.5 0.4 1.9 0.4 1.6 0.2 FRA6 42 19.5 0.2 10.6 1.5 6.6 1.0 8.5 33.4 1.0 1.2 2.9 0.5 9.7 0.0 0.1 1.8 1.4 0.0 FRA7 39 19.8 1.7 38.3 3.3 3.5 0.7 5.2 17.5 0.9 1.2 0.6 0.5 5.3 0.1 0.1 0.1 1.2 0.0 FRA8 42 15.9 0.4 6.7 1.1 5.0 0.1 5.0 41.3 0.1 0.9 0.9 1.9 17.4 0.4 0.2 0.1 2.1 0.4 FRA9 34 17.5 0.6 6.9 0.3 1.5 0.1 5.7 28.7 0.4 2.8 1.8 5.6 23.4 0.1 0.5 2.1 2.0 0.1 Fulton FUL1 25 8.4 1.1 4.0 0.4 3.0 0.0 2.5 25.0 0.0 1.6 2.7 1.9 42.5 0.1 5.7 0.2 0.6 0.2 FUL2 28 12.9 0.4 1.3 0.0 1.8 0.3 9.4 29.8 0.0 0.2 0.7 1.2 30.9 1.1 8.8 0.2 0.7 0.3 FUL3 30 12.9 0.1 11.9 0.0 3.5 2.9 16.4 28.2 0.0 0.0 0.9 2.9 15.9 0.4 2.2 0.0 1.7 0.1 FUL4 27 6.5 0.8 6.6 0.0 1.0 0.0 7.9 30.7 0.2 1.7 4.4 3.3 29.6 0.6 5.6 0.3 0.7 0.3 Juniata JUN1 38 16.0 0.1 19.2 2.1 2.3 0.0 5.8 16.5 1.6 1.0 2.6 1.1 10.2 0.0 19.5 0.6 0.6 0.9 JUN2 43 19.7 0.0 18.9 3.0 5.9 0.1 3.2 11.5 0.0 4.3 6.9 1.2 3.5 0.1 19.5 0.1 0.8 1.4 JUN3 37 8.0 2.4 24.3 3.6 4.7 0.3 11.5 26.2 0.0 2.2 4.6 0.9 7.1 0.6 2.5 0.2 0.9 0.0 JUN4 44 13.2 0.0 14.1 3.4 3.5 0.7 7.6 25.1 0.0 1.8 2.2 1.2 18.5 0.6 2.0 1.1 2.6 2.6 JUN5 24 14.3 0.2 6.8 2.2 5.2 0.0 13.4 13.1 0.0 0.6 3.6 4.4 5.8 0.0 29.0 1.3 0.0 0.1 JUN6 42 22.1 0.0 13.2 4.0 6.0 0.0 7.1 20.6 0.0 5.1 2.4 0.3 14.1 1.2 0.4 0.0 0.7 2.9 Lancaster LAN1 43 30.5 0.0 28.5 3.1 3.9 2.9 9.0 14.4 0.0 0.0 0.0 0.0 4.7 0.0 0.0 2.6 0.0 0.4 LAN2 38 30.6 0.0 23.1 1.8 0.8 0.0 19.5 11.1 0.0 1.3 0.2 0.2 10.0 0.0 0.0 0.3 0.2 1.0
Appendix B
contd.
170
land use
county route name
number of stops
resi
dent
ial
com
mer
cial
corn
soy
grai
n
mis
c. c
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past
ure
hay
orch
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fallo
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othe
r
LAN3 35 21.1 0.0 22.5 2.4 2.8 0.5 12.4 23.0 0.0 0.0 1.3 2.1 11.0 0.0 0.0 0.3 0.6 0.0 LAN4 50 32.1 0.0 24.3 2.6 2.0 0.4 8.6 16.5 0.4 0.2 2.7 0.0 6.7 0.0 0.0 2.9 0.5 0.1 LAN5 34 36.5 1.5 22.7 5.0 0.2 0.0 4.9 5.7 0.9 0.2 0.0 0.0 19.3 0.0 0.0 1.5 1.6 0.0 LAN6 28 24.9 0.0 28.7 2.1 8.2 0.0 5.1 8.0 0.4 1.2 1.8 0.0 11.6 0.0 0.0 5.2 0.0 2.9 LAN7 36 42.7 0.0 21.8 4.1 5.0 0.2 2.2 5.4 0.0 0.0 1.1 0.0 17.2 0.0 0.0 0.2 0.2 0.0 Lebanon LEB1 27 22.5 1.8 36.8 7.4 4.3 0.1 2.7 9.1 0.0 1.8 0.8 0.0 10.4 0.0 0.0 1.4 0.8 0.2 LEB2 28 26.6 0.0 35.9 6.4 8.0 0.0 7.1 13.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.1 0.0 LEB3 28 17.9 0.7 35.6 6.1 1.1 0.5 8.3 5.4 0.2 6.8 2.9 0.0 10.3 0.9 2.0 1.2 0.2 0.0 LEB4 30 14.8 0.3 24.3 3.4 0.8 0.3 8.2 8.1 0.0 7.8 3.4 0.7 26.3 0.3 0.0 0.1 0.0 1.2 Montour MON1 22 18.3 0.6 30.8 10.8 8.0 0.2 3.4 13.5 0.0 6.5 1.3 0.1 4.6 0.8 0.0 0.0 0.7 0.5 MON2 25 16.4 0.0 13.3 10.6 4.1 0.4 2.7 13.6 2.4 15.2 3.7 1.7 12.8 0.0 0.5 0.4 0.9 1.6 Northumberland NOR1 21 7.4 0.6 18.7 7.8 0.8 0.0 12.0 5.5 0.0 1.0 9.3 0.0 36.4 0.0 0.0 0.0 0.5 0.0 NOR2 22 9.3 3.2 28.3 16.4 0.5 0.5 4.0 5.0 0.3 2.3 3.3 0.0 22.7 0.7 0.3 0.0 0.1 3.0 NOR3 26 10.8 0.0 39.6 14.0 0.4 1.5 6.5 6.2 1.5 2.1 1.3 0.0 15.4 0.0 0.0 0.0 0.0 0.6 NOR4 23 11.9 0.0 23.1 10.9 0.5 0.9 5.5 4.6 0.8 0.9 0.1 0.2 32.9 0.0 7.6 0.1 0.0 0.0 NOR5 20 25.7 0.0 23.1 8.0 2.6 0.0 2.4 6.4 3.9 6.1 1.2 1.1 17.6 0.1 0.5 0.1 1.3 0.3 NOR6 21 21.3 0.0 27.4 9.0 7.2 0.6 4.1 9.3 1.0 6.3 2.1 0.9 8.3 0.0 0.0 2.0 0.5 0.0 NOR7 24 24.0 0.4 21.5 2.5 0.0 2.4 7.2 9.1 0.3 2.2 6.4 1.5 20.0 0.0 1.1 1.0 0.3 0.2 Perry PER1 39 15.2 0.0 20.3 3.6 6.3 5.8 12.1 22.2 0.3 0.3 3.2 1.3 8.1 0.0 0.0 0.4 0.4 0.8 PER2 41 19.4 1.1 24.8 9.4 9.0 1.7 4.9 8.1 1.0 0.0 5.0 0.4 12.1 0.0 0.0 0.0 0.0 3.1 Schuykill SCH1 24 12.4 0.5 20.2 3.2 4.1 7.2 4.2 4.1 1.3 4.2 1.8 1.5 29.1 0.4 3.3 1.6 0.1 0.9 SCH2 29 16.1 0.2 10.1 3.3 6.5 4.3 0.6 6.8 1.1 6.6 7.3 3.2 22.9 2.8 6.5 0.9 0.9 0.0 SCH3 46 22.8 3.2 9.2 0.1 3.0 3.3 6.8 8.9 0.2 3.6 5.4 1.7 11.0 4.1 11.1 1.8 1.2 2.5 SCH4 0 19.3 1.6 14.8 5.4 0.8 1.8 6.1 6.1 0.5 2.9 9.5 2.5 10.3 0.6 12.9 4.9 0.0 0.0 Somerset SOM1 50 27.1 1.2 21.0 5.1 2.8 0.0 3.3 17.4 0.2 2.3 3.1 4.7 4.2 0.6 4.1 0.0 1.7 1.2 SOM2 50 21.7 0.0 23.6 4.8 2.0 0.3 7.0 14.4 0.9 2.4 1.9 2.8 12.4 0.0 5.7 0.0 0.2 0.0
Appendix B
contd.
171
land use
county route name
number of stops
resi
dent
ial
com
mer
cial
corn
soy
grai
n
mis
c. c
rop
past
ure
hay
orch
ard
fallo
w
herb
acio
us
shru
b
deci
duou
s
coni
fero
us
mix
ed
wat
er
stri
pmin
e
othe
r
SOM3 36 30.0 0.0 16.2 5.1 3.8 0.3 4.5 16.6 0.5 5.8 2.4 3.3 7.3 0.0 1.3 0.5 0.4 2.3 Snynder SNY1 35 26.2 0.2 15.4 1.3 4.9 1.8 7.4 11.0 0.3 3.9 0.5 0.8 8.9 0.7 16.2 0.0 0.4 0.0 SNY2 48 17.7 0.0 16.0 2.3 2.5 1.0 9.3 16.8 0.1 7.5 0.5 1.2 19.9 1.4 1.9 0.4 1.5 0.0 SNY3 40 9.6 0.4 15.0 0.5 4.9 3.1 7.5 24.3 0.3 0.2 3.9 5.0 16.7 0.2 0.7 4.8 1.7 1.1 SNY4 30 12.4 0.2 10.0 0.7 2.8 1.9 14.4 21.3 0.1 0.7 5.1 5.1 16.8 0.1 3.9 0.2 2.9 1.3 SNY5 37 8.5 0.2 18.8 2.6 7.3 4.6 7.4 20.7 0.0 0.8 0.3 4.6 16.4 1.0 0.6 3.6 1.7 0.9 Union UNI1 23 22.4 0.0 27.1 11.6 3.4 0.0 4.6 8.2 1.7 3.2 0.9 0.7 13.8 0.0 1.5 0.1 0.8 0.1 UNI2 39 24.9 0.0 26.5 8.2 1.1 0.1 6.2 13.6 0.6 4.1 0.6 0.7 9.5 0.4 2.0 0.2 0.5 0.7 UNI3 32 26.5 0.0 28.0 6.4 2.8 0.0 7.6 13.8 0.0 1.8 0.9 0.2 8.7 0.0 1.6 0.4 0.2 1.3 York YOR1 47 24.8 1.7 16.3 4.5 1.5 1.5 4.0 6.4 0.0 1.6 4.7 2.7 26.1 0.0 0.0 1.0 1.6 1.6 YOR2 38 25.9 2.1 11.3 7.6 3.1 2.4 4.7 2.3 0.1 1.5 3.7 4.0 25.8 0.4 1.0 1.1 1.2 1.8 YOR3 38 30.1 2.7 8.5 6.9 2.3 1.1 5.6 10.1 0.3 1.1 4.3 2.7 19.1 0.2 0.6 0.5 2.6 1.3 YOR4 44 18.3 1.9 19.0 7.2 2.1 0.4 4.3 4.2 0.2 0.8 2.3 2.0 33.1 0.8 0.0 1.5 1.3 0.7 YOR5 31 14.0 4.2 17.0 4.9 2.1 4.3 7.2 2.3 0.0 1.0 4.3 10.3 25.2 0.0 0.1 0.8 1.9 0.4 YOR6 22 42.0 3.2 3.3 1.3 0.0 0.0 4.8 3.9 0.3 0.0 3.7 1.8 32.8 0.0 0.0 0.4 1.5 1.0 Data for the following pairs of pairs of routes were combined due to their near contiguous coverage: DAU1 and DAU2 JUN3 and JUN5 NOR1 and NOR2 SNY2 and SNY4 SNY3 and SNY5
Appendix B
contd.
172
Appendix C
Bird population trend and summary data from the Pennsylvania Game Commission bird monitoring survey for 60 common bird species
173
C.1 Canada Goose Branta canadensis
Fig. C.1.1 Birds per stop by year and month Overall trend uncertain
mean annual change +3.9% (se = 5.8) CREP effect not significant
Wald-test 5.85, p = 0.1191 Total birds counted 3,486 Birds per survey stop 0.175 Abundance (count) rank 25th Percentage of routes 41.9 Ubiquity rank 52nd
Fig. C.1.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.1.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.5
1
1.5
2
2.5
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.5
1
1.5
2
2.5
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-40
-20
0
20
40
60
80
none low medium high
CREP enrollment
annu
al %
cha
nge
-40
-20
0
20
40
60
80
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.05 0.1
0.15 0.2
0.25 0.3
2001 2002 2003 2004 2005 Year
May June
174
C.2 Mallard Anas platyrhynchos
Fig. C.2.1 Birds per stop by year and month Overall trend uncertain mean annual change +0.7% (se = 5.5) CREP effect not significant Wald-test 7.79, p = 0.0507 Total birds counted 1,084 Birds per survey stop 0.054 Abundance (count) rank 44th Percentage of routes 44 Ubiquity rank 50th
Fig. C.2.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.2.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.5
1
1.5
2
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0
0.5
1
1.5
2
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
-80 -60 -40 -20
0
20
40
60
80
none low medium high CREP enrollment
annu
al %
cha
nge
-80 -60 -40 -20
0
20 40 60 80
none low medium high CREP enrollment
annu
al %
cha
nge
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08
2001 2002 2003 2004 2005 Year
May June
175
C.3 Ring-necked Pheasant Phasianus colchicus
Fig. C.3.1 Birds per stop by year and month Overall trend Mod. decline (p<0.05) mean annual change -11.0% (se = 4.3) CREP effect not significant Wald-test 0.93, p = 0.8171 Total birds counted 1,527 Birds per survey stop 0.076 Abundance (count) rank 39th Percentage of routes 58.5 Ubiquity rank 40th
Fig. C.3.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.3.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
00.20.40.60.8
11.21.41.61.8
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
00.20.40.60.8
11.21.41.61.8
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
-60
-40
-20
0
20
40
none low medium high CREP enrollment
annu
al %
cha
nge
-60
-40
-20
0
20
40
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.02 0.04 0.06 0.08
0.1
0.12 0.14
2001 2002 2003 2004 2005 Year
May June
176
C.4 Red-tailed Hawk Buteo jamaicensis
Fig. C.4.1 Birds per stop by year and month Overall trend Mod. increase (p<0.05) mean annual change +14.4% (se = 5.9) CREP effect not significant Wald-test 5.50, p = 0.1389 Total birds counted 372 Birds per survey stop 0.019 Abundance (count) rank 61st Percentage of routes 41.3 Ubiquity rank 53rd
Fig. C.4.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only
Fig. C.4.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.5
1
1.5
2
2.5
3
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
0
0.5
1
1.5
2
2.5
3
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
-60 -40 -20
0
20
40
60
80
100 120
none low medium high CREP enrollment
annu
al %
cha
nge
-60 -40 -20
0
20 40 60 80
100 120
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.005 0.01
0.015 0.02
0.025 0.03
2001 2002 2003 2004 2005 Year
May June
177
C.5 American Kestrel Falco sparverius
Fig. C.5.1 Birds per stop by year and monthOverall trend Steep decline (p<0.01)
mean annual change -14.8% (se = 4.7)
CREP effect significantly positiveWald-test 7.82, p = 0.0499
Total birds counted 428
Birds per survey stop 0.02
Abundance (count) rank 59th
Percentage of routes 37.4
Ubiquity rank 55th
Fig. C.5.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits)
a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig C.5.3 Annual population change (95% confidence limits) by CREP enrollment rate(mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31)
a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-50
-30
-10
10
30
50
70
90
none low medium high
CREP enrollment
annu
al %
cha
nge
-50 -30 -10 10 30 50 70 90
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.01
0.02
0.03
0.04
2001 2002 2003 2004 2005Year
May June
178
C.6 Turkey Vulture Cathartes aura
Fig. C.6.1 Birds per stop by year and month Overall trend Mod. increase (p<0.01) mean annual change +10.8% (se = 3.4) CREP effect not significant Wald-test 7.62, p = 0.0545 Total birds counted 726 Birds per survey stop 0.036 Abundance (count) rank 54th Percentage of routes 29 Ubiquity rank 59th
Fig. C.6.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.6.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
1
2
3
4
5
6
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0 1 2 3 4 5 6
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
-300
-100
100
300
500
700
900
none low medium high CREP enrollment
annu
al %
cha
nge
-300
-100
100
300
500
700
900
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.01 0.02 0.03 0.04 0.05 0.06 0.07
2001 2002 2003 2004 2005 Year
May June
179
C.7 Killdeer Charadrius vociferus
Fig. C.7.1 Birds per stop by year and month Overall trend Mod. decline (p<0.01) mean annual change -5.5% (se = 2.6) CREP effect not significant Wald-test 7.21, p = 0.0654 Total birds counted 3,037 Birds per survey stop 0.152 Abundance (count) rank 28th Percentage of routes 77.4 Ubiquity rank 29th
Fig. C.7.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.7.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.5
1
1.5
2
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
0
0.5
1
1.5
2
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
-80 -60 -40 -20
0
20
40
60
80
none low medium high CREP enrollment
annu
al %
cha
nge
-80 -60 -40 -20
0
20 40 60 80
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.05
0.1
0.15
0.2
0.25
2001 2002 2003 2004 2005 Year
May June
180
C.8 Rock Pigeon Columba livia
Fig. C.8.1 Birds per stop by year and month Overall trend uncertain mean annual change -5.1% (se = 2.6) CREP effect significantly positive Wald-test 7.83, p = 0.0497 Total birds counted 9,862 Birds per survey stop 0.494 Abundance (count) rank 11th Percentage of routes 88.7 Ubiquity rank 20th
Fig. C.8.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) *b. fully comparable data (n=67, June surveys only)
Fig. C.8.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2 0.4 0.6 0.8
1
1.2 1.4
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0
0.2 0.4 0.6 0.8
1
1.2 1.4
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
-30 -20 -10
0
10 20 30 40 50 60 70
none low medium high CREP enrollment
annu
al %
cha
nge
-30 -20 -10
0
10 20 30 40 50 60 70
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.1 0.2 0.3 0.4 0.5 0.6
2001 2002 2003 2004 2005 Year
May June
181
C.9 Mourning Dove Zenaida macroura
Fig. C.9.1 Birds per stop by year and month Overall trend uncertain mean annual change +7.1% (se = 1.3) CREP effect not significant Wald-test 3.39, p = 0.3354 Total birds counted 14,913 Birds per survey stop 0.747 Abundance (count) rank 6th Percentage of routes 100 Ubiquity rank =1st
Fig. C.9.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.9.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
-10
-5
0
5
10
15
20
none low medium high CREP enrollment
annu
al %
cha
nge
-10
-5
0
5
10
15
20
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.2 0.4 0.6 0.8
1
1.2
2001 2002 2003 2004 2005 Year
May June
182
C.10 Yellow-billed Cuckoo Coccyzus americanus
Fig. C.10.1 Birds per stop by year and month Overall trend Mod. increase (p<0.05) mean annual change +14.3% (se = 6.4) CREP effect not significant Wald-test 4.94, p = 0.1764 Total birds counted 450 Birds per survey stop 0.023 Abundance (count) rank 58th Percentage of routes 29.9 Ubiquity rank 58th
Fig. C.10.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) a. all data (n=84 routes, 2 surveys per year) b. fully comparable data (n=67, June surveys only)
Fig. C.10.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31)
a. all data (n=84 routes, May & June surveys) a. all data (n=84 routes, 2 surveys per year) b. fully comparable data (n=67, June surveys only)
0
0.5
1
1.5
2
2.5
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.5
1
1.5
2
2.5
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-150
-100
-50
0
50
100
150
200
none low medium high
CREP enrollment
annu
al %
cha
nge
-150 -100 -50
0
50
100
150
200
none low medium high
CREP enrollment
annu
al %
cha
nge
00.005
0.01 0.015
0.02 0.025
0.03 0.035
0.04 0.045
0.05
2001 2002 2003 2004 2005 Year
May June
183
C.11 Chimney Swift Chaetura pelagica
Fig. C.11.1 Birds per stop by year and month Overall trend uncertain
mean annual change +3.3% (se = 3.8) CREP effect not significant
Wald-test 1.18, p = 0.7588 Total birds counted 2,037 Birds per survey stop 0.102 Abundance (count) rank 36th Percentage of routes 57.5 Ubiquity rank 42nd
Fig. C.11.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.11.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.5
1
1.5
2
2.5
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.5
1
1.5
2
2.5
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-30
-20
-10
0
10
20
30
40
50
60
none low medium high
CREP enrollment
annu
al %
cha
nge
-30
-20
-10
0
10
20
30
40
50
60
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.05
0.1
0.15
0.2
0.25
2001 2002 2003 2004 2005 Year
May June
184
C.12 Red-bellied Woodpecker Melanerpes carolinus
Fig. C.12.1 Birds per stop by year and month Overall trend Mod. increase (p<0.01) mean annual change +6.2% (se = 2.2) CREP effect not significant Wald-test 6.62, p = 0.0852 Total birds counted 2,494 Birds per survey stop 0.125 Abundance (count) rank 30th Percentage of routes 89.2 Ubiquity rank 18th
Fig C.12.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig.C.12.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6 1.8
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6 1.8
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
-20 -10
0
10 20 30 40 50
none low medium high CREP enrollment
annu
al %
cha
nge
-20 -10
0
10 20 30 40 50
none low medium high CREP enrollment
annu
al %
cha
nge
00.02 0.04 0.06 0.08 0.1
0.12 0.14 0.16 0.18
2001 2002 2003 2004 2005 Year
May June
185
C.13 Downy Woodpecker Picoides pubescens
Fig. C.13.1 Birds per stop by year and month Overall trend stable
mean annual change +2.6% (se = 3.8) CREP effect not significant
Wald-test 6.71, p = 0.0819 Total birds counted 867 Birds per survey stop 0.043 Abundance (count) rank 50th Percentage of routes 60.2 Ubiquity rank 39th
Fig. C.13.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.13.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-40
-20
0
20
40
60
none low medium high
CREP enrollment
annu
al %
cha
nge
-40
-20
0
20
40
60
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.01 0.02 0.03 0.04 0.05 0.06
2001 2002 2003 2004 2005 Year
May June
186
C.14 Northern Flicker Colaptes auratus
Fig. C.14.1 Birds per stop by year and month Overall trend stable mean annual change -3.1% (se = 3.4) CREP effect not significant Wald-test 2.35, p = 0.5027 Total birds counted 584 Birds per survey stop 0.029 Abundance (count) rank 38th Percentage of routes 77.7 Ubiquity rank 27th
Fig. C.14.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.14.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
-40 -30 -20 -10
0
10
20
30
40
50
60
none low medium high CREP enrollment
annu
al %
cha
nge
-40 -30 -20 -10
0
10 20 30 40 50 60
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.005 0.01
0.015 0.02
0.025 0.03
0.035
2001 2002 2003 2004 2005 Year
May June
187
C.15 Willow Flycatcher Empidonax traillii
Fig. C.15.1 Birds per stop by year and month Overall trend Uncertain mean annual change +2.7% (se = 4.0) CREP effect not significant Wald-test 2.4, p = 0.4932 Total birds counted 752 Birds per survey stop 0.038 Abundance (count) rank 53rd Percentage of routes 38.3 Ubiquity rank 54th
Fig. C.15.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.15.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
-60 -40 -20
0
20
40
60
80
100
none low medium high CREP enrollment
annu
al %
cha
nge
-80 -60 -40 -20
0 20
40
60
80
100
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.01 0.02 0.03 0.04 0.05 0.06
2001 2002 2003 2004 2005 Year
May June
188
C.16 Eastern Phoebe Sayornis phoebe
Fig. C.16.1 Birds per stop by year and month Overall trend Mod. decline (p<0.01)
mean annual change -8.3% (se = 3.2) CREP effect not significant
Wald-test 6.7, p = 0.0781 Total birds counted 1,044 Birds per survey stop 0.052 Abundance (count) rank 45th Percentage of routes 67.8 Ubiquity rank 36th
Fig. C.16.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.16.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-60
-40
-20
0
20
40
60
none low medium high
CREP enrollment
annu
al %
cha
nge
-40 -30 -20 -10
0
10
20
30
40
50
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.01 0.02 0.03 0.04 0.05 0.06 0.07
2001 2002 2003 2004 2005 Year
May June
189
C.17 Great Crested Fycatcher Myiarchus crinitus
Fig. C.17.1 Birds per stop by year and month Overall trend uncertain
mean annual change -5.3% (se = 3.3) CREP effect not significant
Wald-test 3.2, p = 0.3623 Total birds counted 1,134 Birds per survey stop 0.057 Abundance (count) rank 43rd Percentage of routes 60.8 Ubiquity rank 38th
Fig. C.17.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.17.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-40
-30
-20
-10
0
10
20
30
40
none low medium high
CREP enrollment
annu
al %
cha
nge
-40 -30 -20 -10
0
10
20
30
40
none low medium high
CREP enrollment
annu
al %
cha
nge
00.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
2001 2002 2003 2004 2005 Year
May June
190
C.18 Eastern Kingbird Tyrannus tyrannus
Fig. C.18.1 Birds per stop by year and month Overall trend uncertain
mean annual change -1.1% (se = 3.2) CREP effect significantly positive
Wald-test 10.94, p = 0.0121 Total birds counted 1,229 Birds per survey stop 0.062 Abundance (count) rank 42nd Percentage of routes 68.3 Ubiquity rank 35th
Fig. C.18.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.18.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-40
-20
0
20
40
60
80
100
none low medium high
CREP enrollment
annu
al %
cha
nge
-40
-20
0
20
40
60
80
100
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08
2001 2002 2003 2004 2005 Year
May June
191
C.19 Red-eyed Vireo Vireo olivaceus
Fig. C.19.1 Birds per stop by year and month Overall trend uncertain mean annual change +4.0% (se = 2.8) CREP effect not significant Wald-test 5.63, p = 0.1311 Total birds counted 2,862 Birds per survey stop 0.143 Abundance (count) rank 26th Percentage of routes 62.6 Ubiquity rank 37th
Fig. C.19.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.19.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
-40 -30 -20 -10
0
10 20 30 40 50
none low medium high CREP enrollment
annu
al %
cha
nge
-40 -30 -20 -10
0
10 20 30 40 50
none low medium high CREP enrollment
annu
al %
cha
nge
0 0.02 0.04 0.06 0.08 0.1
0.12 0.14 0.16 0.18 0.2
2001 2002 2003 2004 2005 Year
May June
192
C.20 Blue Jay Cyanocitta cristata
Fig. C.20.1 Birds per stop by year and month Overall trend Mod. decrease (p<0.01)
mean annual change -8.1% (se = 2.1) CREP effect not significant
Wald-test 3.97, p = 0.2649 Total birds counted 3,760 Birds per survey stop 0.188 Abundance (count) rank 23rd Percentage of routes 95.5 Ubiquity rank 14th
Fig. C.20.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, 2 surveys per year) b. fully comparable data (n=67, June surveys only)
Fig. C.20.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-20
-10
0
10
20
30
40
none low medium high
CREP enrollment
annu
al %
cha
nge
-20
-10
0
10
20
30
40
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.05 0.1
0.15 0.2
0.25 0.3
2001 2002 2003 2004 2005 Year
May June
193
C.21 American Crow Corvus brachyrhychos
Fig. C.21.1 Birds per stop by year and month Overall trend Steep decline (p<0.01)
mean annual change -11.8% (se = 1.6) CREP effect significantly positive
Wald-test 17.65, p = 0.0005 Total birds counted 14,215 Birds per survey stop 0.71 Abundance (count) rank 7th
Percentage of routes 98.1 Ubiquity rank 9th
Fig. C.21.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig C.21.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-30
-20
-10
0
10
20
30
none low medium high
CREP enrollment
annu
al %
cha
nge
-30
-20
-10
0
10
20
30
none low medium highCREP enrollment
annu
al %
cha
nge
00.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
1
2001 2002 2003 2004 2005 Year
May June
194
C.22 Fish Crow Corvus ossifragus
Fig. C.22.1 Birds per stop by year and month Overall trend Mod. decline (p<0.01)
mean annual change -13.3% (se = 4.4) CREP effect not significant
Wald-test 2.73, p = 0.436 Total birds counted 934 Birds per survey stop 0.047 Abundance (count) rank 47th Percentage of routes 43.4 Ubiquity rank 51st
Fig. C.22.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig C.22.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.5
1
1.5
2
2.5
3
3.5
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.5
1
1.5
2
2.5
3
3.5
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-60
-40
-20
0
20
40
60
80
100
120
none low medium high
CREP enrollment
annu
al %
cha
nge
-60
-40
-20
0
20
40
60
80
100
120
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08
2001 2002 2003 2004 2005 Year
May June
195
C.23 Horned Lark Eremophila alpestris
Fig. C.23.1 Birds per stop by year and month Overall trend Mod. Increase (p<0.05)
mean annual change +6.9% (se = 3.5) CREP effect not significant
Wald-test 1.62, p = 0.6557 Total birds counted 1,739 Birds per survey stop 0.087 Abundance (count) rank 37th Percentage of routes 55.5 Ubiquity rank 43rd
Fig. C.23.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.23.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
00.20.40.60.8
11.21.41.61.8
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
00.20.40.60.8
11.21.41.61.8
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-30
-20
-10
0
10
20
30
none low medium high
CREP enrollment
annu
al %
cha
nge
-30
-20
-10
0
10
20
30
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.02 0.04 0.06 0.08
0.1 0.12 0.14
2001 2002 2003 2004 2005 Year
May June
196
C.24 Tree Swallow Tachycineta bicolor
Fig. C.24.1 Birds per stop by year and month Overall trend Mod. increase (p<0.01) mean annual change +10.8% (se = 3.4) CREP effect not significant Wald-test 7.62, p = 0.0545 Total birds counted 2,048 Birds per survey stop 0.103 Abundance (count) rank 35th Percentage of routes 77.6 Ubiquity rank 28th
Fig. C.24.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.24.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.5
1
1.5
2
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
0
0.5
1
1.5
2
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
-40 -30 -20 -10
0
10
20
30
40
50
none low medium high CREP enrollment
annu
al %
cha
nge
-40 -30 -20 -10
0
10 20 30 40 50
none low medium high CREP enrollment
annu
al %
cha
nge
00.02 0.04 0.06 0.08 0.1
0.12 0.14 0.16 0.18
2001 2002 2003 2004 2005 Year
May June
197
C.25 Barn Swallow Hirundo rustica
Fig. C.25.1 Birds per stop by year and month Overall trend Uncertain
mean annual change +3.4% (se = 1.8) CREP effect not significant
Wald-test 4.83, p = 0.1848 Total birds counted 13,190 Birds per survey stop 0.66 Abundance (count) rank 8th Percentage of routes 97.9 Ubiquity rank 10th
Fig. C.25.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, 2 surveys per year) b. fully comparable data (n=67, June surveys only)
Fig. C.25.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-10
0
10
20
30
none low medium high
CREP enrollment
annu
al %
cha
nge
-10
0
10
20
30
none low medium high
CREP enrollment
annu
al %
cha
nge
00.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
2001 2002 2003 2004 2005 Year
May June
198
C.26 Black-capped / Carolina ChickadeePoecile atricapilla / carolinensis
Fig. C.26.1 Birds per stop by year and month Overall trend Uncertain
mean annual change +3.4% (se = 4.2) CREP effect not significant
Wald-test 3.97, p = 0.2649 Total birds counted Black-capped 549
Carolina 671 Birds per survey stop both 0.61 Abundance (count) rank Black-capped 57
Carolina 55
Percentage of routes both 56.2 Ubiquity rank Black-capped 56
Carolina 61
Fig. C.26.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.26.3 a. all data (n=84 routes, May & June surveys) (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.5
1
1.5
2
2.5
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.5
1
1.5
2
2.5
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-40
-30
-20
-10
0
10
20
30
40
none low medium high
CREP enrollment
annu
al %
cha
nge
-40
-30
-20
-10
0
10
20
30
40
none low medium high
CREP enrollment
annu
al %
cha
nge
00.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
0.1
2001 2002 2003 2004 2005 Year
May June
199
C.27 Tufted Titmouse Baeolophus bicolor
Fig. C.27.1 Birds per stop by year and month Overall trend steep decline (p<0.01)
mean annual change -12.8% (se = 2.5) CREP effect not significant
Wald-test 2.2, p = 0.5163 Total birds counted 4,021 Birds per survey stop 0.201 Abundance (count) rank 21st Percentage of routes 81.2 Ubiquity rank 25th
Fig. C.27.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.27.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.5
1
1.5
2
2.5
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.5
1
1.5
2
2.5
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-30
-20
-10
0
10
20
30
40
50
none low medium high
CREP enrollment
annu
al %
cha
nge
-30
-20
-10
0
10
20
30
40
50
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.05 0.1
0.15 0.2
0.25 0.3
0.35
2001 2002 2003 2004 2005 Year
May June
200
C.28 White-breasted Nuthatch Sitta carolinensis
Fig. C.28.1 Birds per stop by year and month Overall trend steep decline (p<0.05) mean annual change -14.5% (se = 4.1) CREP effect not significant Wald-test 4.6, p = 0.2033 Total birds counted 584 Birds per survey stop 0.029 Abundance (count) rank 56th Percentage of routes 47.2 Ubiquity rank 45th
Fig. C.28.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.28.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2 0.4 0.6 0.8
1
1.2 1.4
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0
0.2 0.4 0.6 0.8
1
1.2 1.4
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
-60 -40 -20
0
20 40 60 80
none low medium high CREP enrollment
annu
al %
cha
nge
-60 -40 -20
0
20 40 60 80
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.01 0.02 0.03 0.04 0.05 0.06
2001 2002 2003 2004 2005 Year
May June
201
C.29 Carolina Wren Thryothorus ludovicianus
Fig. C.29.1 Birds per stop by year and month Overall trend Mod. Increase (p<0.05)
mean annual change +5.7% (se = 2.9) CREP effect not significant
Wald-test 7.50, p = 0.0729 Total birds counted 1,474 Birds per survey stop 0.074 Abundance (count) rank 41st Percentage of routes 69.1 Ubiquity rank 34th
Fig. C.29.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.29.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.5
1
1.5
2
2.5
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.5
1
1.5
2
2.5
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-30
-20
-10
0
10
20
30
40
none low medium high
CREP enrollment
annu
al %
cha
nge
-30 -20 -10
0
10
20
30
40
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.02 0.04 0.06 0.08
0.1 0.12
2001 2002 2003 2004 2005 Year
May June
202
C.30 House Wren Troglodytes aedon
Fig. C.30.1 Birds per stop by year and month Overall trend stable
mean annual change -0.5% (se = 1.8) CREP effect significantly positive
Wald-test 13.27, p = 0.0037 Total birds counted 5,274 Birds per survey stop 0.264 Abundance (count) rank 17th Percentage of routes 89.6 Ubiquity rank 17th
Fig. C.30.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.30.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
00.20.40.60.8
11.21.41.61.8
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
00.20.40.60.8
11.21.41.61.8
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-20 -10
0
10 20 30 40 50
none low medium high CREP enrollment
annu
al %
cha
nge
-20
-10
0
10
20
30
40
50
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.05 0.1
0.15 0.2
0.25 0.3
0.35
2001 2002 2003 2004 2005 Year
May June
203
C.31 Blue-gray Gnatcatcher Polioptila caerulea
Fig. C.31.1 Birds per stop by year and month Overall trend Uncertain
mean annual change +4.1% (se = 5.4) CREP effect not significant
Wald-test 0.31, p = 0.9509 Total birds counted 415 Birds per survey stop 0.02 Abundance (count) rank 60th Percentage of routes 36.5 Ubiquity rank 57th
Fig. C.31.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, 2 surveys per year) b. fully comparable data (n=67, June surveys only)
Fig. C.31.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.5
1
1.5
2
2.5
3
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.5
1
1.5
2
2.5
3
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-60
-40
-20
0
20
40
60
80
none low medium high
CREP enrollment
annu
al %
cha
nge
-60
-40
-20
0
20
40
60
80
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.005 0.01
0.015 0.02
0.025 0.03
0.035
2001 2002 2003 2004 2005 Year
May June
204
C.32 Eastern Bluebird Sialia sialis
Fig. C.32.1 Birds per stop by year and month Overall trend steep decline (p<0.01)
mean annual change -11.3% (se = 2.5) CREP effect significant
Wald-test 8.6, p = 0.0351 Total birds counted 2,425 Birds per survey stop 0.121 Abundance (count) rank 31st Percentage of routes 84.7 Ubiquity rank 24th
Fig. C.32.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.32.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-30
-20
-10
0
10
20
30
40
none low medium high
CREP enrollment
annu
al %
cha
nge
-30 -20 -10
0
10
20
30
40
none low medium high
CREP enrollment
annu
al %
cha
nge
00.02 0.04 0.06 0.08
0.1 0.12 0.14 0.16 0.18
2001 2002 2003 2004 2005 Year
May June
205
C.33 Wood Thrush Hylocichla mustelina
Fig. C.33.1 Birds per stop by year and month Overall trend uncertain mean annual change -2.6% (se = 1.9) CREP effect not significant Wald-test 5.22, p = 0.1563 Total birds counted 3,788 Birds per survey stop 0.190 Abundance (count) rank 22nd Percentage of routes 93.4 Ubiquity rank 15th
Fig. C.33.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.33.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2 0.4 0.6 0.8
1
1.2 1.4
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0
0.2 0.4 0.6 0.8
1
1.2 1.4
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
-20
-10
0
10
20
30
40
none low medium high CREP enrollment
annu
al %
cha
nge
-20
-10
0
10
20
30
40
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.05
0.1
0.15
0.2
0.25
2001 2002 2003 2004 2005 Year
May June
206
C.34 American Robin Turdus migratorius
Fig. C.34.1 Birds per stop by year and month Overall trend Mod. increase (p<0.05)
mean annual change +2.7% (se = 1.1) CREP effect not significant
Wald-test 4.58, p = 0.2056 Total birds counted 38,699 Birds per survey stop 1.94 Abundance (count) rank 3rd Percentage of routes 100 Ubiquity rank =1st
Fig. C.34.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.34.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-20
-15
-10
-5
0
5
10
15
20
none low medium high
CREP enrollment
annu
al %
cha
nge
-20
-15
-10
-5 0
5
10
15
20
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.5 1
1.5 2
2.5
2001 2002 2003 2004 2005 Year
May June
207
C.35 Gray Catbird Dumetella carolinensis
Fig. C.35.1 Birds per stop by year and month Overall trend stable (p<0.05)
mean annual change -1.9% (se = 1.5) CREP effect significantly positive
Wald-test 16.66, p = 0.0008 Total birds counted 8,016 Birds per survey stop 0.401 Abundance (count) rank 13th Percentage of routes 99.2 Ubiquity rank 7th
Fig. C.35.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.35.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-20
-10
0
10
20
30
40
none low medium high
CREP enrollment
annu
al %
cha
nge
-20 -10
0
10
20
30
40
none low medium high
CREP enrollment
annu
al %
cha
nge
00.05
0.1 0.15
0.2 0.25
0.3 0.35
0.4 0.45
0.5
2001 2002 2003 2004 2005 Year
May June
208
C.36 Northern Mockingbird Mimus polyglottos
Fig. C.36.1 Birds per stop by year and month Overall trend Mod. Decline (p<0.01) mean annual change -3.7% (se = 1.5) CREP effect not significant Wald-test 4.96, p = 0.1750 Total birds counted 9,358 Birds per survey stop 0.468 Abundance (count) rank 15th Percentage of routes 95.9 Ubiquity rank 13th
Fig. C.36.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.36.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
-20
-15
-10
-5
0
5
10
15
20
none low medium high
CREP enrollment
annu
al %
cha
nge
-20 -15 -10 -5 0
5
10
15
20
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.05 0.1
0.15 0.2
0.25 0.3
0.35 0.4
2001 2002 2003 2004 2005 Year
May June
209
C.37.1 Brown Thrasher Toxostoma rufum
Fig. C.37.1 Birds per stop by year and month Overall trend strong increase (p<0.01)
mean annual change +15.7% (se = 4.1) CREP effect not significant
Wald-test 4.76, p = 0.19 Total birds counted 920 Birds per survey stop 0.046 Abundance (count) rank 48th Percentage of routes 54.6 Ubiquity rank 44th
Fig. C.37.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.37.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.5
1
1.5
2
2.5
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0
0.5
1
1.5
2
2.5
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-60 -40 -20
0 20 40 60 80
100 120 140
none low medium high CREP enrollment
annu
al %
cha
nge
-60 -40 -20
0 20
40
60
80
100 120 140
none low medium high CREP enrollment
annu
al %
cha
nge
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07
2001 2002 2003 2004 2005 Year
May June
210
C.38 European Starling Sturnus vulgaris
Fig. C.38.1 Birds per stop by year and month Overall trend Mod. decline (p<0.05)
mean annual change -4.3% (se = 1.9) CREP effect not significant
Wald-test 5.08, p = 0.1661 Total birds counted 49,857 Birds per survey stop 2.496 Abundance (count) rank 1st Percentage of routes 100 Ubiquity rank =1st
Fig. C.38.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.38.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-20-15-10-505
1015202530
none low medium high
CREP enrollment
annu
al %
cha
nge
-20-15-10-5 05
1015202530
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.5 1
1.5 2
2.5 3
3.5
2001 2002 2003 2004 2005 Year
May June
211
C.39 Yellow Warbler Dendroica petechia
Fig. C.39.1 Birds per stop by year and month Overall trend Mod. increase (p<0.01) mean annual change +8.0% (se = 2.7) CREP effect not significant Wald-test 7.00, p = 0.0718 Total birds counted 2,820 Birds per survey stop 0.141 19976.000 Abundance (count) rank 27th Percentage of routes 78 Ubiquity rank 26th
Fig. C.39.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.39.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
00.20.40.60.8
11.21.41.61.8
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0 0.2 0.4 0.6 0.8
1 1.2 1.4 1.6 1.8
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
-40 -30 -20 -10
010 20 30 40 50 60
none low medium high CREP enrollment
annu
al %
cha
nge
-40 -30 -20 -10
0102030405060
none low medium highCREP enrollment
annu
al %
cha
nge
0
0.05 0.1
0.15 0.2
0.25
2001 2002 2003 2004 2005 Year
May June
212
C.40 Ovenbird Seiurus aurocapillus
Fig. C.40.1 Birds per stop by year and month Overall trend Mod. decline (p<0.01) mean annual change -9.4% (se = 3.6) CREP effect not significant Wald-test 5.49, p = 0.1392 Total birds counted 822 Birds per survey stop 0.041 Abundance (count) rank 51st Percentage of routes 46.2 Ubiquity rank 49th
Fig. C.40.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.40.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6 1.8
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6 1.8
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
-40 -30 -20 -10
0
10 20 30 40 50
none low medium high CREP enrollment
annu
al %
cha
nge
-40 -30 -20 -10
0
10 20 30 40 50
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.01 0.02 0.03 0.04 0.05 0.06
2001 2002 2003 2004 2005 Year
May June
213
C.41 Common Yellowthroat Geothlypis trichas
Fig. C.41.1 Birds per stop by year and month Overall trend Mod. decline (p<0.05)
mean annual change -5.1% (se = 1.8) CREP effect not significant
Wald-test 5.07, p = 0.1667 Total birds counted 4,449 Birds per survey stop 0.223 Abundance (count) rank 18th Percentage of routes 92.1 Ubiquity rank 16th
Fig. C.41.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.41.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-25
-20
-15
-10
-5
0
5
10
15
20
none low medium high
CREP enrollment
annu
al %
cha
nge
-25 -20 -15 -10 -5 0
5
10
15
20
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.05 0.1
0.15 0.2
0.25 0.3
2001 2002 2003 2004 2005 Year
May June
214
C.42 Scarlet Tanager Piranga olivacea
Fig. C.42.1 Birds per stop by year and month Overall trend Mod. increase (p<0.05) mean annual change +10.0% (se = 4.3) CREP effect not significant Wald-test 1.48, p = 0.6876 Total birds counted 879 Birds per survey stop 0.044 Abundance (count) rank 49th Percentage of routes 46.7 Ubiquity rank 47th
Fig. C.42.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.42.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6 1.8
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6 1.8
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
-40
-20
0
20
40
60
none low medium high CREP enrollment
annu
al %
cha
nge
-40
-20
0
20
40
60
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.01 0.02 0.03 0.04 0.05 0.06 0.07
2001 2002 2003 2004 2005 Year
May June
215
C.44 Chipping Sparrow Spizella passerina
Fig. C.44.1 Birds per stop by year and month Overall trend stable (p<0.05)
mean annual change -1.3% (se =1.4) CREP effect not significant
Wald-test 7.71, p = 0.0524 Total birds counted 10,940 Birds per survey stop 0.548 Abundance (count) rank 10th Percentage of routes 99.8 Ubiquity rank =4th
Fig. C.44.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.44.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2 0.4 0.6 0.8
1
1.2 1.4
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
-20 -15 -10 -5 0
5
10 15 20
none low medium high CREP enrollment
annu
al %
cha
nge
-20 -15 -10 -5 0
5
10
15
20
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.1 0.2 0.3 0.4 0.5 0.6 0.7
2001 2002 2003 2004 2005 Year
May June
216
C.45 Field Sparrow Spizella pusilla
Fig. C.45.1 Birds per stop by year and month Overall trend Mod. decline (p<0.01)
mean annual change -6.7% (se = 1.9) CREP effect not significant
Wald-test 6.93, p = 0.0743 Total birds counted 3,586 Birds per survey stop 0.180 Abundance (count) rank 24th Percentage of routes 86 Ubiquity rank 23rd
Fig. C.45.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.45.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-40
-30
-20
-10
0
10
20
30
40
50
none low medium high
CREP enrollment
annu
al %
cha
nge
-40 -30 -20 -10
0
10
20
30
40
50
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.05
0.1
0.15
0.2
0.25
2001 2002 2003 2004 2005 Year
May June
217
C.46 Vesper Sparrow Pooecetes gramineus
Fig. C.46.1 Birds per stop by year and month Overall trend uncertain mean annual change -4.4% (se = 3.0) CREP effect not significant Wald-test 2.68, p = 0.4443 Total birds counted 2,154 Birds per survey stop 0.108 Abundance (count) rank 34th Percentage of routes 57.9 Ubiquity rank 41st
Fig. C.46.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.46.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
-40 -30 -20 -10
0
10
20
30
40
none low medium high CREP enrollment
annu
al %
cha
nge
-40 -30 -20 -10
0
10 20 30 40
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.02 0.04 0.06 0.08 0.1
0.12 0.14 0.16
2001 2002 2003 2004 2005 Year
May June
218
C.47 Savannah Sparrow Passerculus sandwichensis
Fig. C.47.1 Birds per stop by year and month Overall trend uncertain mean annual change -2.6% (se = 3.4) CREP effect significant Wald-test 9.46, p = 0.0238 Total birds counted 1,514 Birds per survey stop 0.076 Abundance (count) rank 40th Percentage of routes 46.7 Ubiquity rank 48th
Fig. C.47.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.47.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
-40 -30 -20 -10
0
10
20
30
40
none low medium high CREP enrollment
annu
al %
cha
nge
-40 -30 -20 -10
0
10 20 30 40
none low medium high CREP enrollment
annu
al %
cha
nge
00.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
0.1
2001 2002 2003 2004 2005 Year
May June
219
C.48 Grasshopper Sparrow Ammodramus savannarum
Fig. C.48.1 Birds per stop by year and month Overall trend Mod. decline (p<0.05)
mean annual change -9.6% (se = 2.5) CREP effect significantly positive
Wald-test 9.06, p = 0.0285 Total birds counted 2,215 Birds per survey stop 0.111 Abundance (count) rank 33rd Percentage of routes 71.8 Ubiquity rank 33rd
Fig. C.48.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.48.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-50
-30
-10
10
30
50
70
none low medium high
CREP enrollment
annu
al %
cha
nge
-50
-30
-10
10
30
50
70
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.02 0.04 0.06 0.08
0.1 0.12 0.14 0.16
2001 2002 2003 2004 2005 Year
May June
220
C.49 Song Sparrow Melospiza melodia
Fig. C.49.1 Birds per stop by year and month Overall trend stable mean annual change -1.1% (se = 1.2) CREP effect significantly positive Wald-test 12.5, p = 0.0059 Total birds counted 12,583 Birds per survey stop 0.630 Abundance (count) rank 9th Percentage of routes 97.6 Ubiquity rank 12th
Fig. C.49.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.49.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2 0.4 0.6 0.8
1
1.2
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
-15 -10
-5 0
5
10 15 20 25
none low medium high CREP enrollment
annu
al %
cha
nge
-15 -10
-5 0
5
10 15 20 25
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
2001 2002 2003 2004 2005 Year
May June
221
C.50 Northern Cardinal Cardinalis cardinalis
Fig. C.50.1 Birds per stop by year and month Overall trend stable mean annual change +0.8% (se = 1.0) CREP effect significant Wald-test 10.03, p = 0.0183 Total birds counted 9,358 Birds per survey stop 0.468 Abundance (count) rank 12th Percentage of routes 100 Ubiquity rank =1st
Fig. C.50.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.50.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
-20 -15 -10
-5 0
5
10
15
20
none low medium high CREP enrollment
annu
al %
cha
nge
-20 -15 -10
-5
0
5
10 15 20
none low medium high CREP enrollment
annu
al %
cha
nge
0 0.1 0.2 0.3 0.4 0.5 0.6
2001 2002 2003 2004 2005 Year
May June
222
C.51 Indigo Bunting Passerina cyanea
Fig. C.51.1 Birds per stop by year and month Overall trend Mod. decline (p<0.01)
mean annual change -5.6% (se = 1.7) CREP effect not significant
Wald-test 5.45, p = 0.1418 Total birds counted 5,274 Birds per survey stop 0.264 Abundance (count) rank 14th Percentage of routes 89.6 Ubiquity rank 22nd
Fig. C.51.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.51.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-30
-20
-10
0
10
20
none low medium high CREP enrollment
annu
al %
cha
nge
-30
-20
-10
0
10
20
none low medium high CREP enrollment
annu
al %
cha
nge
0
0.2 0.4 0.6 0.8
1
1.2 1.4
2001 2002 2003 2004 2005 Year
May June
223
C.52 Bobolink Dolichonyx oryzivorus
Fig. C.52.1 Birds per stop by year and month Overall trend Uncertain
mean annual change -0.6% (se = 9.1) CREP effect not significant
Wald-test 7.53, p = 0.0569 Total birds counted 808 Birds per survey stop 0.040 Abundance (count) rank 52nd Percentage of routes 23.3 Ubiquity rank 60th
Fig. C.52.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.52.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
00.20.40.60.8
11.21.41.61.8
2
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
00.20.40.60.8
11.21.41.61.8
2
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-150
-100
-50
0
50
100
150
200
250
none low medium high
CREP enrollment
annu
al %
cha
nge
-150 -100 -50
0
50
100
150
200
250
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08
2001 2002 2003 2004 2005 Year
May June
224
C.53 Red-winged Blackbird Agelaius phoeniceus
Fig. C.53.1 Birds per stop by year and month Overall trend stable mean annual change +0.3% (se = 1.3) CREP effect not significant Wald-test 2.60, p = 0.4576 Total birds counted 28,058 Birds per survey stop 1.405 Abundance (count) rank 4th Percentage of routes 99.8 Ubiquity rank =4th
Fig. C.53.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.53.3 Annual population change (95% confidence limits) by CREP enrollment rate (rate is the mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2 0.4 0.6 0.8
1
1.2 1.4
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005year
popu
latio
n in
dex
(200
1=1)
-20 -15 -10
-5 0 5
10 15 20
none low medium high CREP enrollment
annu
al %
cha
nge
-20 -15 -10
-5 0
5
10
15
20
none low medium high CREP enrollment
annu
al %
cha
nge
00.2 0.4 0.6 0.8
11.2 1.4 1.6 1.8
2001 2002 2003 2004 2005 Year
May June
225
C.54 Eastern Meadowlark Sturnella magna
Fig. C.54.1 Birds per stop by year and month Overall trend Mod. decline (p<0.01)
mean annual change -6.4% (se = 2.1) CREP effect significantly positive
Wald-test 8.66, p = 0.0341 Total birds counted 4,210 Birds per survey stop 0.226 Abundance (count) rank 20th Percentage of routes 73.4 Ubiquity rank 32nd
Fig. C.54.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.54.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-40
-30
-20
-10
0
10
20
30
40
50
none low medium high
CREP enrollment
annu
al %
cha
nge
-40
-30
-20
-10
0
10
20
30
40
50
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.05 0.1
0.15 0.2
0.25 0.3
2001 2002 2003 2004 2005 Year
May June
226
C.55 Common Grackle Quiscalus quiscula
Fig. C.55.1 Birds per stop by year and month Overall trend stable
mean annual change -1.4% (se = 1.4) CREP effect not significant
Wald-test 3.02, p = 0.3879 Total birds counted 48,596 Birds per survey stop 2.433 Abundance (count) rank 2nd Percentage of routes 99 Ubiquity rank 8th
Fig. C.55.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.55.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-10
-5
0
5
10
15
20
25
none low medium high
CREP enrollment
annu
al %
cha
nge
-10
-5 0
5
10
15
20
25
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.5 1
1.5 2
2.5 3
3.5
2001 2002 2003 2004 2005 Year
May June
227
C.56 Brown-headed Cowbird Molothrus ater
Fig. C.56.1 Birds per stop by year and month Overall trend Uncertain
mean annual change +1.8% (se = 3.0) CREP effect significantly negative
Wald-test 8.12, p = 0.0436 Total birds counted 2,496 Birds per survey stop 0.12 Abundance (count) rank 29th Percentage of routes 77.3 Ubiquity rank 30th
Fig. C.56.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, 2 surveys per year) b. fully comparable data (n=67, June surveys only)
Fig. C.56.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6
2001 2002 2003 2004 2005 year
popu
latio
n in
dex
(200
1=1)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-50
-40
-30
-20
-10
0
10
20
30
40
none low medium high
CREP enrollment
annu
al %
cha
nge
-50
-40
-30
-20
-10
0
10
20
30
40
none low medium high
CREP enrollment
annu
al %
cha
nge
00.02 0.04 0.06 0.08
0.1 0.12 0.14 0.16 0.18
2001 2002 2003 2004 2005 Year
May June
228
C.57 Baltimore Oriole Icterus galbula
Fig. C.57.1 Birds per stop by year and month Overall trend Mod. increase (p<0.01)
mean annual change +10.9% (se = 3.9) CREP effect not significant
Wald-test 1.64, p = 0.6502 Total birds counted 2,309 Birds per survey stop 0.12 Abundance (count) rank 32nd Percentage of routes 74.7 Ubiquity rank 31st
Fig. C.57.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.57.2 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
00.20.40.60.8
11.21.41.61.8
2
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
00.20.40.60.8
11.21.41.61.8
2
2001 2002 2003 2004 2005
year
popu
latio
n in
dex
(200
1=1)
-40
-30
-20
-10
0
10
20
30
40
50
none low medium high
CREP enrollment
annu
al %
cha
nge
-30
-20
-10
0
10
20
30
40
50
none low medium high
CREP enrollment
annu
al %
cha
nge
0
0.05
0.1
0.15
0.2
0.25
2001 2002 2003 2004 2005 Year
May June
229
C.58 House Finch Carpodacus mexicanus
Fig. C.58.1 Birds per stop by year and month Overall trend Steep decline (p<0.05)
mean annual change -8.9% (se = 2.0) CREP effect not significant
Wald-test 1.76, p = 0.6232 Total birds counted 5,450 Birds per survey stop 0.273 Abundance (count) rank 16th Percentage of routes 89.2 Ubiquity rank 19th
Fig. C.58.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.58.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
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C.59 American Goldfinch Carduelis tristis
Fig. C.59.1 Birds per stop by year and month Overall trend Steep decline (p<0.01)
mean annual change -9.6% (se = 1.6) CREP effect significantly postive
Wald-test 30.99, p < 0.0001 Total birds counted 4,437 Birds per survey stop 0.22 Abundance (count) rank 19th Percentage of routes 88.6 Ubiquity rank 21st
Fig. C.59.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig C.59.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
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C.60 House Sparrow Passer domesticus
Fig. C.60.1 Birds per stop by year and month Overall trend stable
mean annual change -0.6% (se = 1.4) CREP effect significant
Wald-test 8.0, p = 0.0461 Total birds counted 18,552 Birds per survey stop 0.929 Abundance (count) rank 5th Percentage of routes 97.8 Ubiquity rank 11th
Fig. C.60.2 Population trend between 2001 and 2005 (dotted line = 95% confidence limits) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
Fig. C.60.3 Annual population change (95% confidence limits) by CREP enrollment rate (mean % of farmland in CREP 2001-2005: none=<0.07, low=0.1-1.27, medium=1.42-2.97, high>3.31) a. all data (n=84 routes, May & June surveys) b. fully comparable data (n=67, June surveys only)
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Appendix D
Example WINBUGS code for Bayesian spatial models used in chapters 3 and 4
{ #priors# av ~ dflat() b0 ~ dnorm(0,1.0E-6) b1 ~ dnorm(0, 1.0E-6) b2 ~ dnorm(0, 1.0E-6) b3 ~ dnorm(0, 1.0E-6) b4 ~ dnorm(0, 1.0E-6) b5 ~ dnorm(0, 1.0E-6) b6 ~ dnorm(0, 1.0E-6) eta ~ dnorm(0, 1.0E-6) for (i in 1:N) { noise[i] ~ dnorm(0.0, taunoise) log(lambda[i])<- av + yeareffect[year[i]] + obs[observer[i]] + eta*firstyear[i] + b0*(year[i]-
fixedyear) + rte[route[i]] + b1*stops[i] + b2*urban[i] + b3*hay[i] + b4*forest[i] + b5*CRP[i] + b6*WNV[i] + noise[i]
z[i]~ dpois(lambda[i]) zfcount[i] ~ dpois(lambda[i]) err[i] <- pow(z[i]-lambda[i],2)/lambda[i] ferr[i] <- pow(zfcount[i]-lambda[i],2)/lambda[i] eps[i] <- z[i]-lambda[i] resid[i] <- taunoise*(z[i]-lambda[i]) aresid[i] <- abs(z[i]-lambda[i]) logpred[i]<-log(lambda[i]) - (b5*CRP[i]) pred[i]<-exp(logpred[i]) log2pred[i]<-log(lambda[i]) + (b5*CRP[i]) pred2[i]<-exp(log2pred[i]) } gof1 <- sum(err[1:N]) fgof <- sum(ferr[1:N]) diffgof <- gof1-fgof gof<- step(diffgof) taunoise ~ dgamma(0.5,0.001) sdnoise <- 1/sqrt(taunoise) #### route effects ###### rte[1:nroutes] ~ car.normal(adj[], weights[], num[], taurte) taurte ~ dgamma(0.5, 0.001) sdrte <- 1/sqrt(taurte) #### observer effects ###### for(j in 1:nobservers){ obs[j] ~ dnorm(0.0,tauobs) } tauobs ~ dgamma(0.5, 0.0001) sdobs <- 1/sqrt(tauobs) #### year effects ####
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for(y in 1:nyears) { yeareffect[y] ~ dnorm(0.0, tauyear) yearplustrend[y] <- yeareffect[y] + b0*(y-fixedyear) } tauyear ~ dgamma(0.001, 0.001) sdyears <- 1/sqrt(tauyear) ##### predicted values divided by mean number of stops per routes (34.33571) to calculate mean counts per stop #### predicted values for NO CREP ########## c[1]<- mean(pred[1:84])/34.33571 c[2]<- mean(pred[85:168])/34.33571 c[3]<- mean(pred[169:252])/34.33571 c[4]<- mean(pred[253:336])/34.33571 c[5]<- mean(pred[337:420])/34.33571 #### predicted values for Actual ########## d[1]<- mean(lambda[1:84])/34.33571 d[2]<- mean(lambda[85:168])/34.33571 d[3]<- mean(lambda[169:252])/34.33571 d[4]<- mean(lambda[253:336])/34.33571 d[5]<- mean(lambda[337:420])/34.33571 #### predicted values for double CREP ########## e[1]<- mean(pred2[1:84])/34.33571 e[2]<- mean(pred2[85:168])/34.33571 e[3]<- mean(pred2[169:252])/34.33571 e[4]<- mean(pred2[253:336])/34.33571 e[5]<- mean(pred2[337:420])/34.33571 change1<-((d[5]/c[5])-1)*100 change2<-((e[5]/c[5])-1)*100 } } }
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Appendix E
Example SURVIV code for estimating bird densities using Removal with Distance method (code written by G. Farnsworth, Xavier University)
PROC TITLE 'Density GRSP'; PROC MODEL NPAR=2; INLINE rc=75.; INLINE q1=S(1); INLINE rm1=500.*S(2)+75.; INLINE aa1=rm1*rm1/(1.-q1); INLINE Qc1=q1+rc*rc/aa1; COHORT=1305; 95: aa1*(Qc1-q1-Qc1**2./2.+q1**2./2.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 27: aa1*(Qc1**2./2.-q1**2./2.-Qc1**3./3.+q1**3./3.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 22: aa1*(Qc1**3./3.-q1**3./3.-Qc1**4./4.+q1**4./4.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 11: aa1*(Qc1**4./4.-q1**4./4.-Qc1**5./5.+q1**5./5.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 12: aa1*(Qc1**5./5.-q1**5./5.-Qc1**6./6.+q1**6./6.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 571: aa1*(1.-Qc1-1./2.+Qc1**2./2.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 227: aa1*(1./2.-Qc1**2./2.-1./3.+Qc1**3./3.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 158: aa1*(1./3.-Qc1**3./3.-1./4.+Qc1**4./4.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 90: aa1*(1./4.-Qc1**4./4.-1./5.+Qc1**5./5.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 92: aa1*(1./5.-Qc1**5./5.-1./6.+Qc1**6./6.) /(aa1*(1.-q1-1./6.+q1**6./6.)); LABELS; S(1)=q1; S(2)=(rm-75)/500; PROC ESTIMATE NSIG=6 MAXFN=32000 NAME=M_0; INITIAL; s(1)=0.3; PROC TEST; PROC STOP; PROC TITLE 'Density GRSP';
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PROC MODEL NPAR=2; INLINE rc=75.; INLINE q1=S(1); INLINE qd=S(2)*.00001; INLINE rm2=1305./(20924.*qd*3.1416*(1.-(1.-q1**6.)/(6.-6.*q1))); INLINE aa1=rm2/(1.-q1); INLINE Qc1=q1+rc*rc/aa1; COHORT=1305; 95: aa1*(Qc1-q1-Qc1**2./2.+q1**2./2.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 27: aa1*(Qc1**2./2.-q1**2./2.-Qc1**3./3.+q1**3./3.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 22: aa1*(Qc1**3./3.-q1**3./3.-Qc1**4./4.+q1**4./4.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 11: aa1*(Qc1**4./4.-q1**4./4.-Qc1**5./5.+q1**5./5.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 12: aa1*(Qc1**5./5.-q1**5./5.-Qc1**6./6.+q1**6./6.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 571: aa1*(1.-Qc1-1./2.+Qc1**2./2.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 227: aa1*(1./2.-Qc1**2./2.-1./3.+Qc1**3./3.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 158: aa1*(1./3.-Qc1**3./3.-1./4.+Qc1**4./4.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 90: aa1*(1./4.-Qc1**4./4.-1./5.+Qc1**5./5.) /(aa1*(1.-q1-1./6.+q1**6./6.)); 92: aa1*(1./5.-Qc1**5./5.-1./6.+Qc1**6./6.) /(aa1*(1.-q1-1./6.+q1**6./6.)); LABELS; S(1)=q1; S(2)=Density; PROC ESTIMATE NSIG=6 MAXFN=75000 NAME=M_0; INITIAL; S(1)=0.3; PROC TEST; PROC STOP;
Andrew Mark Wilson
Education Ph.D., Ecology, May 2009. The Pennsylvania State University (PSU), University Park. BSc (honours), Applied Statistics, June 1992. Sheffield Hallam University/University of York.
Employment History 2nd Pennsylvania Breeding Bird Atlas - Field ornithologist, May-July, 2004-2008. British Trust for Ornithology - Scientific Officer/Training Officer, August 1994–May 2004. Naturetrek - International Wildlife Tour Guide, 2001-present (part-time). University of York - Research Assistant, York Health Economics Consortium, Aug 1992-Aug 1994.
Awards Environment and Natural Resources Institute (PSU) – Outstanding graduate student Award, 2008. Roger M. Latham Award, School of Forest Resources (PSU), 2007. J. Brian Horton Memorial Award, Intercollege Graduate Degree Program in Ecology (PSU), 2006. United State Department of Agriculture - Outstanding volunteer assistant, 2006.
Selected Publications Vickery, J.A., Robinson, L.J., Atkinson, P.W., Marshall, J.P., West, T.M., Gillings, S., Wilson, A.M.,
Kirby, W. & Norris, K. (in press). The management of arable crop stubbles for farmland birds in English lowland farmland: their distribution and use by birds at national and regional scales. Journal of Applied Ecology.
Gillings, S., Wilson, A.M., Conway, G.J., Vickery, J.A. & Fuller, R.J. 2008. Distribution and abundance of birds and their habitats within the lowland farmland of Britain in winter. Bird Study. 55: 8-22.
Wilson, A.M., Vickery, J. & Pendlebury, C. 2007. Agri-environment schemes as a tool for reversing declining populations of grassland waders: Mixed benefits from Environmentally Sensitive Areas in England. Biological Conservation. 136: 128-135.
Wilson, A.M., Fuller, R.J., Day, C. & Smith, G. 2005. Nightingales Luscinia megarhynchos in scrub habitats in the southern fens of East Anglia, England: associations with soil type and vegetation structure. Ibis 147: 498-511.
Wilson, A.M., Vickery, J.A., Brown, A. Langston, R.H.W., Smallshire, D., Wotton, S. & Vanhinsbergh, D. 2005. Changes in the numbers of breeding waders on lowland wet grasslands in England and Wales between 1982 and 2002. Bird Study 52: 55-69.
Wilson, A.M. & Vickery, J.A. 2005. Decline in Yellow Wagtail Motacilla flava breeding on lowland wet grassland in England and Wales between 1982 and 2002. Bird Study 52: 88-92.
Wilson, A.M., Ausden, M. & Milsom, T.P. 2004. Changes in breeding wader populations on lowland wet grasslands in England and Wales: causes and potential solutions. Ibis 146: 32-40.
Sheldon, R., Bolton, M., Gillings, S. & Wilson, A. 2004. Conservation management of Lapwing Vanellus vanellus on lowland arable farmland in the UK. Ibis (Suppl.2): 41-49.
Wilson, A.M., Henderson, A.C.B. & Fuller, RJ. 2002. Status of the Nightingale Luscinia megarhynchos in Britain at the end of the 20th Century with particular reference to climate change. Bird Study 49: 193-204.
Henderson, I.G., Wilson, A.M., Steele, D. & Vickery, J.A. 2002. Population estimates, trends and habitat associations of breeding Lapwing Vanellus vanellus , Curlew Numenius arquata and Snipe Gallinago gallinago in Northern Ireland in 1999. Bird Study 49: 17-25.
Wilson, A.M., Vickery, J.A. & Browne, S.J. 2000. Numbers and distribution of Northern Lapwings Vanellus vanellus breeding in England and Wales in 1998. Bird Study 48: 2-17.
Chamberlain, D.E., Wilson, A.M., Browne, S.J. & Vickery, J.A. 1999. Effects of habitat type and management on the abundance of Skylarks in the breeding season. Journal of Applied Ecology 36: 856-870.