relationship between bird abundances and landscape characteristics: the influence of scale

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Environmental Monitoring and Assessment (2005) 105: 209–228 DOI: 10.1007/s10661-005-3694-x c Springer 2005 RELATIONSHIP BETWEEN BIRD ABUNDANCES AND LANDSCAPE CHARACTERISTICS: THE INFLUENCE OF SCALE SARAH P. BRENNAN and GARY D. SCHNELL Department of Zoology and Sam Noble Oklahoma Museum of Natural History, University of Oklahoma, Norman, Oklahoma, USA ( author for correspondence, e-mail: [email protected]) (Received 26 May 2004; accepted 14 July 2004) Abstract. Scale is important to consider when investigating effects of the environment on a species. Breeding Bird Survey (BBS) data and landscape metrics derived from aerial photographs were eval- uated to determine how relationships of bird abundances with landscape variables changed over a continuous range of 16 spatial scales. We analyzed the average number of birds per stop (1985–1994) for five songbird species (family Cardinalidae) for each of 50 stops on 198 BBS transects throughout six states in the Central Plains, USA. Land along each transect was categorized into six cover types, and landscape metrics of fractal dimension (a measure of shape complexity of habitat patches), edge density, patch density, and percent area were calculated, with principal components used to construct composite environmental variables. Associations of bird abundances and landscape variables changed in accordance with small scale changes. Abundances of three species were correlated with edge den- sity and one with component I, which subsumes initial variables of patch density for urban, closed forest, open forest, and open country. Fractal dimension and component II (summarizing amount of closed forest versus open country) were associated with the most species. Correlation patterns of fractal dimension with northern cardinal (Cardinalis cardinalis) and painted bunting (Passerina ciris) abundances were similar, with highest correlations at intermediate to small scales, suggesting indi- rectly that these species thrive in areas where local habitat conditions are most important. Multiscale analysis can provide insight into the spatial scale(s) at which species respond, a topic of intrinsic sci- entific interest with applied implications for researchers establishing protocols to assess and monitor avian populations. Keywords: blue grosbeak, Cardinalidae, dickcissel, fractal dimension, indigo bunting, landscape, multiple scales, northern cardinal, painted bunting, scale continuum 1. Introduction The scale at which the environment is studied in relation to ecological processes has an influence on our perception of patterns and of the distributions of species (Naveh and Leiberman, 1984; Wiens et al., 1987; Flather and Sauer, 1996; Bolger et al., 1997). In addition, species may respond to the surrounding environment at different scales (B¨ ohning-Gaese, 1997; MacFaden and Capen, 2002). Understanding the effects of scale can be particularly important for those who set policies and manage lands for conservation purposes (Saab, 1999; Meyer et al., 2002). Factors at a local scale such as vegetational structure often are most important for birds (Wiens et al., 1987; Pribil and Picman, 1997), while at a broad scale

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Environmental Monitoring and Assessment (2005) 105: 209–228DOI: 10.1007/s10661-005-3694-x c© Springer 2005

RELATIONSHIP BETWEEN BIRD ABUNDANCES AND LANDSCAPECHARACTERISTICS: THE INFLUENCE OF SCALE

SARAH P. BRENNAN and GARY D. SCHNELL∗Department of Zoology and Sam Noble Oklahoma Museum of Natural History,

University of Oklahoma, Norman, Oklahoma, USA(∗author for correspondence, e-mail: [email protected])

(Received 26 May 2004; accepted 14 July 2004)

Abstract. Scale is important to consider when investigating effects of the environment on a species.Breeding Bird Survey (BBS) data and landscape metrics derived from aerial photographs were eval-uated to determine how relationships of bird abundances with landscape variables changed over acontinuous range of 16 spatial scales. We analyzed the average number of birds per stop (1985–1994)for five songbird species (family Cardinalidae) for each of 50 stops on 198 BBS transects throughoutsix states in the Central Plains, USA. Land along each transect was categorized into six cover types,and landscape metrics of fractal dimension (a measure of shape complexity of habitat patches), edgedensity, patch density, and percent area were calculated, with principal components used to constructcomposite environmental variables. Associations of bird abundances and landscape variables changedin accordance with small scale changes. Abundances of three species were correlated with edge den-sity and one with component I, which subsumes initial variables of patch density for urban, closedforest, open forest, and open country. Fractal dimension and component II (summarizing amount ofclosed forest versus open country) were associated with the most species. Correlation patterns offractal dimension with northern cardinal (Cardinalis cardinalis) and painted bunting (Passerina ciris)abundances were similar, with highest correlations at intermediate to small scales, suggesting indi-rectly that these species thrive in areas where local habitat conditions are most important. Multiscaleanalysis can provide insight into the spatial scale(s) at which species respond, a topic of intrinsic sci-entific interest with applied implications for researchers establishing protocols to assess and monitoravian populations.

Keywords: blue grosbeak, Cardinalidae, dickcissel, fractal dimension, indigo bunting, landscape,multiple scales, northern cardinal, painted bunting, scale continuum

1. Introduction

The scale at which the environment is studied in relation to ecological processes hasan influence on our perception of patterns and of the distributions of species (Navehand Leiberman, 1984; Wiens et al., 1987; Flather and Sauer, 1996; Bolger et al.,1997). In addition, species may respond to the surrounding environment at differentscales (Bohning-Gaese, 1997; MacFaden and Capen, 2002). Understanding theeffects of scale can be particularly important for those who set policies and managelands for conservation purposes (Saab, 1999; Meyer et al., 2002).

Factors at a local scale such as vegetational structure often are most importantfor birds (Wiens et al., 1987; Pribil and Picman, 1997), while at a broad scale

210 S. P. BRENNAN AND G. D. SCHNELL

measures like heterogeneity, amount of edge, climate, and community interac-tions influence bird communities (McGarigal and McComb, 1995; O’Connor et al.,1996). Studying relationships at multiple levels allows one to evaluate the effect ofscale-dependent patterns on avian species (Wiens, 1989; Kotliar and Wiens, 1990).Numerous studies have looked at how the relationship between avian populationsand habitat changes with scale (e.g. Bohning-Gaese, 1997; Pribil and Picman, 1997;Drolet et al., 1999; Saab, 1999; MacFaden and Capen, 2002; Tarvin and Garvin,2002; Westphal et al., 2003) but few have incorporated more than three scale levels(Wiens et al., 1986; Hecnar and M’Closkey, 1997; Meyer et al., 2002; Brennan,2004).

In this study, we have investigated the influence of scale on the relationshipbetween avian abundance and landscape characteristics based on abundance datafor five bird species of the family Cardinalidae from Breeding Bird Surveys (BBS)(Robbins et al., 1986) and landscape variables over a continuum of 16 spatial scalesfrom the local to regional level. Our purposes were (1) to evaluate the changes in therelationship between abundance and landscape characteristics at multiple scales; (2)to determine the scale(s) at which certain variables have the strongest relationship toabundance; (3) to observe which landscape variables have the strongest associationwith bird abundance; and (4) to compare findings of this investigation with thosefrom a similar analysis of flycatchers (Tyrannidae; Brennan, 2004).

2. Methods

2.1. BIRD DATA

We used bird data collected on BBS, which are annual censuses that provide in-formation on relative abundances of bird species (Robbins et al., 1986). Surveytransects are 39.43 km (24.5 mile) long and visited once a year in June. Observersrecord all birds seen and heard for 50, 3-min point counts conducted at 0.80-km(0.5-mile) intervals along the transect (see Figure 1). Our analyses incorporateddata collected from 1985 to 1994 on 198 transects located throughout Kansas,Oklahoma, Texas, Missouri, Arkansas, and Louisiana (see Figure 2) for the fol-lowing species: (1) northern cardinal (Cardinalis cardinalis); (2) blue grosbeak(Passerina cerulea); (3) indigo bunting (P. cyanea); (4) painted bunting (P. ciris);and (5) dickcissel (Spiza americana). Data for these species should provide reliableindices of abundances given that these birds are readily observable if present, arenot secretive, and are easily identified.

The 198 transects used were surveyed a minimum of five times in the 10-year period of 1985–1994. The average number of birds per stop per year foreach transect provided an abundance index for each species at each of the50 stops for each of the 198 transects. Since two species (indigo and paintedbuntings) occurred on less than 80% of the transects, we used a kriging method

BIRD ABUNDANCES, LANDSCAPE VARIABLES, AND SCALE 211

Figure 1. Example of land cover types adjacent to a typical Breeding Bird Survey transect. Opencircles indicate the 50 stops where bird counts were made.

(van Horssen et al., 1999) to determine the approximate edge of the species’ rangeand, thus, which transects to include in the analysis. Kriging interpolates data valuesmeasured at irregularly spaced sample points to provide estimated values through-out an area. As a result, we used 125 of the 198 transects for the indigo bunting(Figure 2c) and 134 for the painted bunting (Figure 2d).

2.2. LANDSCAPE DATA

We obtained digital aerial photographs of the landscape along and adjacent toeach of the 198 BBS transects from the National Aerial Photography Program,US Department of Agriculture. These photos were taken from December throughMarch in 1989, 1990, and 1991 near the midpoint of years for which bird censuseswere evaluated.

For each transect, we classified cover types of land within approximately 1.20km on either side and extending 0.40 km (0.25 miles) beyond each end of the tran-sect. Thus, the area evaluated for each transect was 40.23 km long and 2.40 kmwide. We used a habitat classification defined in Certain (2000) that categorizedthe landscapes along these 198 transects into six cover type: (1) urban; (2) closedforest (less than one canopy width between trees); (3) open forest (≥1 and <20canopy widths between trees); (4) open country (≥20 canopy widths between trees);(5) water; and (6) bare ground (e.g. see Figure 1). We digitized the transects us-ing ArcInfo 8.x (Environmental Systems Research Institute, Redlands, California),geo-rectified them to provide accurate perimeter and area measurements, and im-ported these transects into ArcView 3.2 (Environmental Systems Research Institute,Redlands, California) for analysis of the cover types.

Sixteen scales for each transect were evaluated. A local scale of 0.80 km (segmentlength 1 = 1 point count and one-half of distance to each of two adjacent stops)

212 S. P. BRENNAN AND G. D. SCHNELL

Figure 2. Average number of five cardinalid species per stop on 198 Breeding Bird Survey transects(1985–1994). Bold lines in panels c and d indicate approximate edge of range and extent of transectsused in analyses. ‘Natural-breaks’ method (ArcView) used to identify appropriate breaks betweenclasses (represented by differentially shaded symbols) using Jenk’s optimization, which minimizessum of variances within classes.

BIRD ABUNDANCES, LANDSCAPE VARIABLES, AND SCALE 213

was the smallest employed. We then increased the segment length by 0.80-kmincrements until the segment length equaled 9.66 km (=12 point counts). We alsoevaluated segment lengths of 13.68 km (=17 point counts), 17.70 km (=22 pointcounts), 20.12 km (=25 point counts), and 40.23 km (=50 point counts).

Based on formulations in FRAGSTATS (McGarigal and Marks, 1995), we calcu-lated the following landscape metrics: area-weighted mean patch fractal dimension(referred to hereafter as fractal dimension), edge density, patch density, and percentclass area. Fractal dimension, edge density, and patch density were ascertained foreach segment. In addition, we determined patch density and percent class area foreach cover type within each segment.

Fractal dimension reflects shape complexity across a range of spatial scales.Values tend to approach 1 when shapes have simple perimeters, such as circles orsquares, and values close to 2 indicate shapes with convoluted, complex perimeters.Edge density (m/ha) is the amount of edge of a particular cover type or for all covertypes in a segment. Patch density (number of patches per 100 ha) can be used as anindication of spatial heterogeneity within a landscape. Higher values are obtainedwhen there is a high degree of spatial heterogeneity. Percent class area (100 × squaremeters of particular cover type per square meter of segment area) is a measure oflandscape composition indicating the percentage of the landscape that is comprisedof a particular cover type.

2.3. STATISTICAL ANALYSIS

We preformed a principal component analysis (NTSYS-pc; Rohlf, 2003) based onthe entire route for the 198 transects using the 10 landscape variables. These werepatch density and percent class area calculated for the first five cover types: urban,closed forest, open forest, open country, and water (Table I). The sixth cover type,bare ground, was present in only a few transects and, therefore, if included couldhave unduly affected the composite variables. We standardized variables (mean0, standard deviation 1) and created a Pearson product–moment correlation matrixamong variables from which the first two principal components axes were extracted.We limited our examination to the first two axes because eigenvalues began to leveloff at relatively low values beyond the second component (Brennan, 2004). Thefirst two components had eigenvalues of 3.32 and 2.00, respectively, thus explaining33.2 and 20.0% of the total variance of the 10 standardized variables (Table I).

For projections of transects onto principal components we used the matrix op-eration

P = FtO,

where O is the standardized data matrix having 10 (rows) variables and 198 tran-sects (columns), and Ft the transposed matrix of component loadings of the 10variables on the two components. We divided projections by 100.5, making them

214 S. P. BRENNAN AND G. D. SCHNELL

TABLE ILoadings (correlations) of 10 landscape variables onfirst two principal components based on 198 BreedingBird Survey transects (segment length 50)

Principal componenta

Variable I II

Patch densityUrban 0.77 −0.18

Closed forest 0.87 0.00

Open forest 0.90 −0.04

Open country 0.87 0.27

Water 0.60 −0.05

Percent of total areaUrban 0.18 −0.05

Closed forest 0.02 0.92

Open forest −0.11 0.23

Open country 0.01 −0.98

Water 0.03 0.20

Eigenvalue 3.32 2.00

Percent explained 33.2 20.0

aRelatively high loadings (> |0.75|) in bold.

numerically consistent with average taxonomic distances among transects (Rohlf,2003). Projections were calculated for each of the 16 spatial scales. For all cal-culations, we used the F-matrix from the analysis of all stops combined, but theO-matrix was changed, based on the landscape measures for the particular spatialscale being analyzed.

We also calculated product–moment correlations of abundances of each birdspecies with each of the five landscape variables at each of the 16 scales. Transectswere partitioned into 1–50 segments, a segment referring to a section that containeda determined number of point counts. For example, when investigating a scale of20.12 km, which incorporated 25 point counts, each transect was comprised oftwo segments. For a spatial scale of 17.70 km (22 point counts), a single transectalso contained two segments; six point counts remained at the end of the transectand were not used. In all cases for transects ending with less than the designatednumber of point counts for a segment for a particular scale, we omitted the leftoverpoint counts from the analysis. For each segment of each transect we calculated theaverage abundance for each species, as well as each landscape metric.

We employed resampling (Simon, 1997; Blank et al., 2001) to create an appro-priate distribution for statistical evaluation because adjacent segments within a tran-sect were not likely to be statistically independent. Shuffling the bird-abundancedata first by entire transects and then by segments within the transect took into

BIRD ABUNDANCES, LANDSCAPE VARIABLES, AND SCALE 215

account the spatial autocorrelation within transects (Brennan, 2004). First, we shuf-fled the order of transects, resulting in bird-abundance data for each transect beingpaired randomly with landscape data for one of the transects. Second, we shuffledabundance data according to segments within each transect, with the outcome thatbird-abundance values for segments were paired randomly with segment landscapevalues. Each shuffle was done without replacement. Then, for the twice-shuffleddata, with bird-abundance data by segment randomly associated with landscapedata, we calculated the product–moment correlation based on the paired values foreach segment for all transects. We repeated this overall procedure 10,000 timesto create an appropriate distribution of correlations against which to evaluate thestatistical significance of the correlation value obtained from the original data.

3. Results

3.1. LANDSCAPE VARIABLES

Based on whole transects (50 stops), we obtained high values for fractal dimensionin the eastern and central regions of the study area, with lower values in the west(Figure 3a), indicating that cover types generally had less complex edges in thewest. Edge density (Figure 3b) was low in the west, highest in central and north-central areas, and moderate to low in the east. Total patch density (not figured)tended to be relatively low in the east and west, with higher values in transitionalcentral areas. This landscape variable did not exhibit correlations above 0.15 withabundances of any of the bird species (Table II) and, thus, is not given furtherattention.

Principal component I projections based on the full lengths of transects tendedto be higher in the central region and lower values in the western and to someextent in the eastern part of the study area (Figure 4a). The variables having thehighest loadings were patch densities of urban, closed forest, open forest, and opencountry (Table I). This component reflected the fact that transects in the centralregion tended to have more patches of these four cover types than do transects inthe east and particularly the west. Component I projections were similar to thoseof total patch density.

Landscape component II exhibited high values in the east and east-central portionof the study area and low values in the northwest (Figure 4b). It had high positiveloadings on percent area of closed forest and a high negative loading on percentarea of open country (Table I). More closed forest and less open country were foundin the east-central part of the study region.

3.2. TRENDS OVER MULTIPLE SCALES

Significant relationships between bird abundance and landscape variables over spa-tial scales can be subsumed under three general trends, which involved fractal

216 S. P. BRENNAN AND G. D. SCHNELL

Figure 3. Distribution of the landscape variables fractal dimension (dimensionless) and edge den-sity (m/ha) based on 198 Breeding Bird Survey transects. Natural-breaks method used to identifyappropriate classes, with classes being represented by differentially shaded symbols.

dimension, edge density, and principal component II. In addition abundances ofone species were correlated significantly with principal component I. All correla-tion values of bird abundances for each species with all landscape variables at eachspatial scale are given in Table II.

BIRD ABUNDANCES, LANDSCAPE VARIABLES, AND SCALE 217

TABLE IICorrelations (∗p < 0.05;∗∗p < 0.01) of landscape variables with average numbers offive cardinalids per stop at each of the 16 spatial scales

Segment Fractal Total patchlength dimension Edge density density Component I Component II

Northern cardinal1 0.2674∗∗ 0.0058 −0.0041 −0.0129 0.3747∗∗

2 0.3491∗∗ 0.2469∗∗ −0.0129 0.0152 0.4017∗∗

3 0.3718∗∗ 0.2666∗∗ 0.0354 0.0177 0.4316∗∗

4 0.4031∗∗ 0.2686∗∗ 0.0430 0.0188 0.4418∗∗

5 0.4027∗∗ 0.2715∗∗ 0.0499 0.0280 0.4428∗∗

6 0.4174∗∗ 0.2804∗∗ 0.0501 0.0259 0.4568∗∗

7 0.4153∗∗ 0.2814∗∗ 0.0564 0.0326 0.4582∗∗

8 0.4143∗∗ 0.2848∗∗ 0.0605 0.0344 0.4639∗∗

9 0.4219∗∗ 0.2859∗∗ 0.0577 0.0354 0.4720∗∗

10 0.4057∗∗ 0.2822∗∗ 0.0619 0.0346 0.4671∗∗

11 0.4159∗∗ 0.2873∗∗ 0.0476 0.0251 0.4804∗∗

12 0.4209∗∗ 0.2905∗∗ 0.0579 0.0269 0.4824∗∗

17 0.4100∗∗ 0.3109∗∗ 0.0630 0.0337 0.5118∗∗

22 0.3995∗∗ 0.2969∗∗ 0.0637 0.0314 0.5016∗∗

25 0.3707∗∗ 0.2901∗∗ 0.0541 0.0212 0.5035∗∗

50 0.3002∗∗ 0.2972∗∗ 0.0611 0.0321 0.5361∗∗

Blue grosbeak1 0.1635∗∗ −0.0077 −0.0237 −0.0248 0.2111∗∗

2 0.2316∗∗ 0.1307∗∗ −0.0062 −0.0222 0.2600∗∗

3 0.2660∗∗ 0.1539∗∗ −0.0161 −0.0230 0.2911∗∗

4 0.2852∗∗ 0.1595∗∗ −0.0166 −0.0283 0.3159∗∗

5 0.3066∗∗ 0.1683∗∗ −0.0187 −0.0303 0.3282∗∗

6 0.3179∗∗ 0.1711∗∗ −0.0177 −0.0310 0.3382∗∗

7 0.3245∗∗ 0.1701∗∗ −0.0178 −0.0355 0.3450∗∗

8 0.3288∗∗ 0.1737∗∗ −0.0160 −0.0323 0.3565∗∗

9 0.3538∗∗ 0.1858∗∗ −0.0170 −0.0332 0.3743∗∗

10 0.3510∗∗ 0.1859∗∗ −0.0184 −0.0330 0.3638∗∗

11 0.3431∗∗ 0.1778∗∗ −0.0190 −0.0378 0.3797∗∗

12 0.3494∗∗ 0.1793∗∗ −0.0211 −0.0401 0.3744∗∗

17 0.3714∗∗ 0.2009∗∗ −0.0170 −0.0401 0.4015∗∗

22 0.3763∗∗ 0.2016∗∗ −0.0114 −0.0369 0.4154∗∗

25 0.3651∗∗ 0.1952∗∗ −0.0201 −0.0396 0.4068∗∗

50 0.3608∗∗ 0.1919∗∗ −0.0298 −0.0499 0.4765∗∗

Indigo bunting1 0.1158∗∗ −0.0118 −0.0889 −0.1045∗ 0.3589∗∗

2 0.1690∗∗ 0.0581 −0.0869 −0.1097∗ 0.4034∗∗

(Continued on next page.)

218 S. P. BRENNAN AND G. D. SCHNELL

TABLE II(Continued )

Segment Fractal Total patchlength dimension Edge density density Component I Component II

3 0.1826∗∗ 0.0527 −0.0896 −0.1114 0.4191∗∗

4 0.2012∗∗ 0.0520 −0.0936 −0.1128 0.4310∗∗

5 0.2232∗∗ 0.0547 −0.0865 −0.1118 0.4337∗∗

6 0.2303∗∗ 0.0522 −0.0935 −0.1205 0.4442∗∗

7 0.2309∗∗ 0.0542 −0.0914 −0.1216 0.4476∗∗

8 0.2448∗∗ 0.0509 −0.0921 −0.1196 0.4574∗∗

9 0.2613∗∗ 0.0671 −0.0850 −0.1192 0.4576∗∗

10 0.2684∗∗ 0.0527 −0.0923 −0.1236 0.4552∗∗

11 0.2508∗∗ 0.0492 −0.0950 −0.1250 0.4552∗∗

12 0.2616∗∗ 0.0517 −0.1006 −0.1348 0.4678∗∗

17 0.2726∗∗ 0.0418 −0.1099 −0.1344 0.4984∗∗

22 0.2638∗∗ 0.0679 −0.1065 −0.1341 0.4697∗∗

25 0.2686∗∗ 0.0466 −0.1125 −0.1516 0.4728∗∗

50 0.2907∗∗ 0.0474 −0.1361 −0.1571 0.4616∗∗

Painted bunting1 0.1223∗∗ 0.0186 0.0960 0.0566 0.1651∗∗

2 0.2045∗∗ 0.2380∗∗ −0.0114 −0.0183 0.1713∗∗

3 0.1888∗∗ 0.2117∗∗ 0.1354 0.2117 0.1945∗∗

4 0.2077∗∗ 0.2246∗∗ 0.1223 0.0784 0.1919∗∗

5 0.1988∗∗ 0.2260∗∗ 0.1198 0.0701 0.1975∗∗

6 0.1996∗∗ 0.2232∗∗ 0.1195 0.0738 0.2042∗∗

7 0.1919∗∗ 0.2331∗∗ 0.1279 0.0725 0.2076∗∗

8 0.1962∗∗ 0.2323∗∗ 0.1315 0.0816 0.2047∗∗

9 0.1991∗∗ 0.2269∗∗ 0.1307 0.0837 0.2140∗∗

10 0.1909∗∗ 0.2323∗∗ 0.1268 0.0754 0.2014∗∗

11 0.2049∗∗ 0.2389∗∗ 0.1170 0.0727 0.2086∗∗

12 0.1903∗ 0.2336∗∗ 0.1277 0.0742 0.2118∗∗

17 0.1769∗ 0.2424∗∗ 0.1125 0.0666 0.2121∗∗

22 0.1434 0.2442∗∗ 0.1204 0.0586 0.2040∗∗

25 0.1473∗ 0.2429∗∗ 0.1241 0.0663 0.2034∗∗

50 0.1179 0.3190∗∗ 0.1130 0.1099 0.1691∗∗

Dickcissel1 −0.0521 0.0065 0.1105 0.0971∗ −0.3244∗∗

2 −0.0797 −0.0139 −0.0095 0.0675 −0.3574∗∗

3 −0.0901∗ −0.0155 0.1124 0.1086∗ −0.3890∗∗

4 −0.0884 −0.0123 0.1101 0.1120∗ −0.3981∗∗

5 −0.0672 0.0006 0.1090 0.1106 −0.3980∗∗

(Continued on next page)

BIRD ABUNDANCES, LANDSCAPE VARIABLES, AND SCALE 219

TABLE II(Continued)

Segment Fractal Total patchlength dimension Edge density density Component I Component II

6 −0.0768 0.0039 0.1218 0.1244∗ −0.4110∗∗

7 −0.0631 0.0060 0.1176 0.1189∗ −0.4066∗∗

8 −0.0530 0.0127 0.1185 0.1258∗ −0.4223∗∗

9 −0.0435 0.0176 0.0896 0.1280∗ −0.4372∗∗

10 −0.0353 0.0189 0.1123 0.1238∗ −0.4150∗∗

11 −0.0399 0.0238 0.1323 0.1437∗ −0.4448∗∗

12 −0.0323 0.0215 0.1306 0.1502∗ −0.4355∗∗

17 0.0121 0.0539 0.1479 0.1659∗ −0.4645∗∗

22 0.0071 0.0440 0.1340 0.1308∗ −0.4552∗∗

25 0.0650 0.0614 0.1380 0.1612∗ −0.4291∗∗

50 0.1293 0.0785 0.1372 0.1668∗ −0.4668∗∗

Northern cardinals and painted buntings had a similar pattern of correlations withfractal dimension, as did blue grosbeaks and indigo buntings (Figure 5). Correlationsfor both northern cardinals and painted buntings increased to 3.22 km (segmentlength 4), after which values remained fairly constant until approximately 9.66 km(segment length 12), where correlations decreased slightly. Blue grosbeaks andindigo buntings had similar patterns in that correlations increased to 13.68 km(segment length 17) and then were fairly level. These four species tended to havehigher abundances in areas with irregularly shaped patches, which generally arefound in the eastern and central regions of the study area when considering entiretransects (Figure 3a). Northern cardinal correlations were higher than those for theother species.

The correlation patterns of edge density with numbers of northern cardinals,blue grosbeaks, and painted buntings were very similar (Figure 6). Correlations forall three species increased until 4.83 km (segment length 6), after which valuestended to level off. For the painted bunting the correlation increased between 20.12and 40.23 km (segment lengths 25 and 50). These three species tended to be moreabundant in areas with greater amounts of edge, which at the 50-segment-lengthlevel (i.e. entire transect) generally were found in the central and north-centralregion, and to a lesser extent in the eastern region of the study area (Figure 3b).

The correlations of principal component I and abundances of dickcissels wererelatively weak but in general increased from 0.80 to 13.68 km (segment lengths 1–17), after which values remained fairly constant (Figure 7). Dickcissels were moreabundant in the central region of the study area where there was more patches ofurban, closed forest, open forest, and open country. Associations were statisticallysignificant at 14 of 16 spatial scales analyzed.

220 S. P. BRENNAN AND G. D. SCHNELL

Figure 4. Projections of 198 Breeding Bird Survey transects onto first two principal components basedon 10 landscape variables measured for total length of transect (i.e. segment length 50). Natural-breaksmethod used to identify appropriate classes, with classes being represented by differentially shadedsymbols.

BIRD ABUNDANCES, LANDSCAPE VARIABLES, AND SCALE 221

Figure 5. Correlations over range of segment lengths of fractal dimension with average number ofbirds per stop for northern cardinals, blue grosbeaks, indigo buntings, and painted buntings. Opensymbols indicate nonsignificant values (p > 0.05), gray symbols significant values (p < 0.05), andblack symbols highly significant values (p < 0.01).

Figure 6. Correlations over range of segment lengths of edge density with average number of birdsper stop for northern cardinals, blue grosbeaks, and painted buntings. Open symbols indicate non-significant values (p > 0.05) and black symbols highly significant values (p < 0.01).

222 S. P. BRENNAN AND G. D. SCHNELL

Figure 7. Correlations over range of segment lengths of landscape principal component I and averagenumber of dickcissels per stop. Open symbols indicate nonsignificant values (p > 0.05) and graysymbols significant values (p < 0.05).

Correlation patterns of component II projections with abundances of northerncardinals and indigo buntings were very similar through 13.68 km (segment length17; Figure 8). For the northern cardinal, the correlations leveled off and then roseslightly at the largest scale (40.23 km, segment length 50), while for the blue gros-beak they dropped slightly and then remained relatively constant. The correlationsincreased until 13.68 km (segment length 17), after which the curve remainedfairly level. The correlations of blue grosbeak abundance and principal componentII showed a general increase from 0.80 to 40.23 km (segment lengths 1–50). Paintedbuntings also had a positive association with component II but correlations were lowand relatively similar at 4.83 km (segment length 6) and beyond. For these species,abundances tended to be higher in areas where there was more closed forest relativeto open country.

The dickcissel had negative instead of positive correlations with component II(Figure 8). The negative correlations gradually increased until 9.66 km (segmentlength 12) and were most pronounced at 40.23 km (segment length 50). Dickcisselstended to be more abundant in northwestern region of the study area, which hadmore open country and less closed forest.

3.3. SCALE AND CLOSEST ASSOCIATIONS

For the 13 sets of statistically significant correlations of bird abundances with land-scape measures, seven reached a maximum correlation at a low to intermediatescale (ca. 3.22–17.70 km, segment lengths 4–22) and then remained relatively con-stant. Four patterns involved increases in correlations from the smallest scale to thelargest. In contrast, two graphs – those for northern cardinals and painted buntingswith fractal dimension (Figure 5) – were notable in that the highest correlationswere obtained for intermediate scales, with a marked drop in correlations with bothsmaller and larger scales.

BIRD ABUNDANCES, LANDSCAPE VARIABLES, AND SCALE 223

Figure 8. Correlations over range of segment lengths of values of landscape principal component IIand average number of birds per stop for northern cardinals, blue grosbeaks, indigo buntings, paintedbuntings, and dickcissels. Open symbols indicate nonsignificant values (p > 0.05) and black symbolshighly significant values (p < 0.01).

Edge density was significantly correlated with northern cardinals, blue gros-beaks, and painted buntings, with the highest correlation occurring at intermediateand larger scales. Correlations for edge density and abundance of northern cardinalwere highest at 13.68 km (segment length 17), blue grosbeak at 17.70 km (segmentlength 22), and painted bunting at 40.23 km (segment length 50; Figure 6).

Component I was significantly correlated to dickcissels at eight spatial scaleswith the highest correlation at 40.23 km (segment length 50; Figure 7). The great-est correlations with component II also occurred at 40.23 km (segment length 50)

224 S. P. BRENNAN AND G. D. SCHNELL

for northern cardinals, blue grosbeaks, and dickcissels, while the highest correla-tion occurred at an intermediate scale for indigo and painted buntings (13.68 km,segment length 17; Figure 8).

3.4. INFLUENTIAL LANDSCAPE VARIABLES

Of the five landscape variables, we investigated (fractal dimension, edge density,total patch density, component I, and component II) only two – fractal dimensionand principal component II – were significantly associated with abundances of atleast four species at a majority of the spatial scales. Abundances of all except thepainted bunting significantly correlated with fractal dimension at all spatial scales(Figure 5) and abundances of four of the five cardinalids were strongly associatedwith component II (Figure 8).

4. Discussion

4.1. TRENDS OVER MULTIPLE SCALES

Throughout the ranges of scales studied, associations of bird abundances and land-scape variables changed only gradually with small changes in scale. Our findingsfor the five species and five landscape variables showed no abrupt changes in thestrength of associations with small changes in scale, and this held over the entirerange of scales. While studies investigating two or three scales (e.g. Saab, 1999;MacFaden and Capen, 2002) have shown general trends in relationships betweenspecies and landscape variables, the gradual nature of changes in the strength ofassociations, such as those found in our study might have not have been appreciatedhad only two or three spatial scales been analyzed.

4.2. INFLUENTIAL LANDSCAPE VARIABLES

The four species having positive correlations with fractal dimension – northerncardinal, blue grosbeak, indigo bunting, and painted bunting – appeared to preferareas with some woody vegetation that had irregular edges and thus comparativelyhigh fractal dimensions. The northern cardinal typically is found in areas withshrubs and or small trees, including forest edges and forest interior (Dow, 1969;Halkin and Linville, 1999); the blue grosbeak is characteristic of forest edges,hedgerows, stream edges, and multistage pine forests (Ingold, 1993); the indigobunting often inhabits brushy and weedy areas along edges, including riparianhabitats and clearings in open deciduous forests (Payne, 1992); and the paintedbunting usually is found in partially open areas containing scattered brush andtrees, riparian thickets and weedy and shrubby areas (Lowther et al., 1999). Incontrast, areas mostly comprised of agriculture fields typically have low shape

BIRD ABUNDANCES, LANDSCAPE VARIABLES, AND SCALE 225

complexity, and these four cardinalids usually do not occur there or they are in verylow densities.

As noted in Section 3, two species, the northern cardinal and painted bunting,showed a pattern of correlations with fractal dimension where the highest valuesoccurred at intermediate scales. For the northern cardinal, correlations increasedreadily to a scale of 3.22 km (segment length 4) and then remained at a relativelyhigh level until 17.70 km (segment length 22), after which they declined. Likewisewith the painted bunting the correlations peaked at 3.22 km (segment length 4);they remained relatively constant until 9.66 km (segment length 12), after whichthey declined. Thus, these two species typically were at their highest densities inareas with irregularly shaped patches and at their lowest in regularly shaped plotsbased on measurements made at relatively small and intermediate scales. It is clearthat these species thrive in areas where relatively local habitat conditions are mostinfluential. As quantified by Kopachena and Crist (2000), the largest wooded areasin northeastern Texas supporting painted buntings often were irregular clumps oftrees or long, narrow strips of land like those found along intermediate streams;they noted that the patches of woodland associated with this species were of unevenage and had a high ratio of edge to area. According to Parmalee (1959), the paintedbunting in Oklahoma was most common in open areas dissected by small stands orstrips of land. The situations described by these authors would result in relativelyhigh fractal dimension when measured at intermediate to small scales in our study.

Three species, the northern cardinal, blue grosbeak, and painted bunting, werepositively correlated with edge density, a result not surprising given that they areknown to commonly inhabit areas with edge (Ingold, 1993; Halkin and Linville,1999; Lowther et al., 1999). Although indigo and painted buntings share consider-able overlap in their ranges throughout our study region, along with similar habitatdescriptors (Payne, 1992; Lowther et al., 1999), indigo buntings did not show a sig-nificant relationship to edge density. One reason may be because the song perchesoccupied by indigo buntings are more often found near linear edges of woodlands,while painted buntings usually are associated with areas of irregularly clumpedtrees or trees along intermittent streams (Kopachena and Crist, 2000). Both inter-mittent streams and irregularly shaped areas tend to have a relatively high amountof edge.

Component II, representing the percent of closed forest versus open country,was significantly correlated with abundances of all five species (Figure 8). Theseassociations highlight important habitat requirements of these birds. The northerncardinal had a relatively strong positive association with principal component II andtends to be found at the edge of forests, as well as the interior (Halkin and Linville,1999). Birds such as the blue grosbeak, indigo bunting, and painted bunting, whichfrequent partly open and shrubby habitats (Payne, 1992; Ingold, 1993; Lowther etal., 1999), also had a positive relationship, albeit somewhat weaker in the case ofthe painted bunting abundance. Low correlations with painted buntings may be aresult of the species occurring in wooded areas in an otherwise mostly open region

226 S. P. BRENNAN AND G. D. SCHNELL

(Kopachena and Crist, 2000). Areas with these characteristics to some extent aresubsumed within the limited number of broad cover types we used to define thelandscape, resulting in low correlations for this species.

The fifth species – the dickcissel – showed a negative association to landscapeprincipal component II. This bird is an obligate grassland species (Vickery et al.,1999) and commonly found in a variety of open grassland habitats including hay-fields, lightly grazed pastures, restored grasslands, and fallow areas of agricul-tural landscapes (Temple, 2002). In fact, a study by Zimmerman (1993) foundthat males occupied fallow fields and unmowed hayfields earlier in the spring thenwhen they claimed territories in prairie grassland areas, reinforcing the notion thatagriculture fields provide highly suitable habitats for breeding dickcissels. Overall,principal component II subsumed some of the most important habitat factors influ-encing the range limits, distributions, and abundances of the majority of the birdsexamined.

4.3. COMPARISON BETWEEN TAXONOMIC GROUPS

Brennan (2004) conducted similar analyses for eight species of tyrannid flycatchersfor the same study area; there were notable similarities in results. In both investi-gations, fractal dimension and principal component II were the landscape variablesmost closely correlated with abundance data for the species evaluated, indicatingthat these factors encapsulated aspects of the landscape important to species in bothgroups.

The landscape variable edge density was significantly correlated to three speciesof cardinalids as well as three species of tyrannids. This suggests that amount ofedge was an important habitat characteristic for certain species in both groups ofbreeding birds.

Both studies indicate that a multiscale approach allows investigators to determinehow changes in spatial scale affect the relationship between bird abundances andlandscape variables. Investigations such as that by Fuhlendorf et al. (2002) suggestthat multiscale studies can be important when attempting to best manage and moni-tor populations and habitats. Our current study indicates that spatial aspects of habi-tat configurations have a notable influence on bird abundances. While abundances ofsome species are similarly associated with particular landscape characteristics thereare notable interspecific differences, even among relatively closely related species.Documenting such similarities and differences can provide new insight concerninga complex of factors that affect avian abundances across species’ ranges.

Acknowledgements

Support for the first author was provided through a George Miksch SuttonScholarship in Ornithology, and a M. Blanche Adams and M. Frances Adams

BIRD ABUNDANCES, LANDSCAPE VARIABLES, AND SCALE 227

Memorial Scholarship, as well as by the University of Oklahoma Graduate StudentSenate. We thank Keith L. Pardieck for providing the BBS raw data, David L. Cer-tain for delimited orthophotographs, Todd D. Fagin and May Yuan for ArcView andArcInfo guidance and advice, Daniel J. Hough for computer assistance, KanwaljitAulakh and Simone D. Norman for entering bird data, Joseph P. Roberts for help-ing to proof bird data, and Victoria L. Adams for assistance with data organization.May Yuan, William J. Matthews, and Peter D. Vickery provided helpful commentson the manuscript.

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