using occupancy models to predict grassland bird...

USING OCCUPANCY MODELS TO PREDICT GRASSLAND BIRD

DISTRIBUTIONS IN SOUTHEASTERN ALBERTA

A Thesis

Submitted to the Faculty of Graduate Studies and Research

In Partial Fulfillment of the Requirements

for the Degree of

Masters of Science

In

Biology

University of Regina

by

Nathan David Clements

Regina, Saskatchewan

October, 2014

Copyright 2014: N.D. Clements

UNIVERSITY OF REGINA

FACULTY OF GRADUATE STUDIES AND RESEARCH

SUPERVISORY AND EXAMINING COMMITTEE

Nathan David Clements, candidate for the degree of Master of Science in Biology, has presented a thesis titled, Using Occupancy Models to Predict Grassland Bird Distributions in Southeastern Alberta, in an oral examination held on August 29, 2014. The following committee members have found the thesis acceptable in form and content, and that the candidate demonstrated satisfactory knowledge of the subject material. External Examiner: Dr. Erin Bayne, University of Alberta

Co-Supervisor: Dr. Mark Brigham, Department of Biology

Co-Supervisor: Dr. Stephen Davis, Adjunct

Committee Member: Dr. Christopher Somers, Department of Biology

Chair of Defense: Dr. Katya Herman, Faculty of Kineiology & Health Studies

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ABSTRACT

Widespread population declines of grassland birds have stimulated a variety of

conservation plans, many of which promote a landscape approach to conservation.

Identifying needs for survival and reproduction, and prioritizing key habitat requirements

that influence where species occur on the landscape is an essential first step to guiding

long term management efforts. I used single-species, single-season occupancy models to

(1) identify landscape factors influencing the distribution of grassland bird species, (2)

generate predictions of species distributions in southeastern Alberta, and (3) evaluate the

predictive performance of each species model with two sources of evaluation data. In

2012, I conducted three repeat surveys at each of 870 point count locations to generate

occupancy models, and evaluated the predictive performance of each model using a

partitioned data set from 2012 and an independent data set of 1398 point counts collected

in 2011. Records of detection/non-detection were collected for: Baird's Sparrow

(Ammodramus bairdii), Brown-headed Cowbird (Molothrus ater), Chestnut-collared

Longspur (Calcarius ornatus), Clay-colored Sparrow (Spizella pallida), Horned Lark

(Eremophila alpestris), Long-billed Curlew (Numenius americanus), Marbled Godwit

(Limosa fedoa), McCown's Longspur (Rhynchophanes mccownii), Savannah Sparrow

(Passerculus sandwichensis), Sprague's Pipit (Anthus spragueii), Upland Sandpiper

(Bartramia longicauda), Vesper Sparrow (Pooecetes gramineus), Western Meadowlark

(Sturnella neglecta), and Willet (Tringa semipalmata). Models that best explained

detectability typically included both date and time of day of the survey. The amount of

grassland on the landscape was a broadly useful measure in predicting occupancy.

Variables describing landscape topography and the geographic location (i.e., latitude and

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longitude) also aided in understanding where species occur and provided a more

complete explanation of resource selection. I show that the models generated for four

species were accurate predictors of occupancy. The remaining models require further

investigation to develop accurate predictions of occupancy, but are presented for

exploratory purposes and can be used to guide and refine future predictive modelling

efforts. I suggest that, depending on the application, these models and maps be used to

guide local conservation plans for birds, and can be used as a valuable reference for

prioritizing conservation activities in southeast Alberta. These models add to our

understanding of resource selection at the landscape level, and can assist in the process of

ensuring key habitats are identified and conserved.

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ACKNOWLEDGEMENTS

I would like to express my sincere appreciation to Dr. Stephen Davis whose

support and continual encouragement has significantly influenced my development as

both student of ecology, and more importantly, a student of life. I would also like to

thank my committee members, Drs. Mark Brigham and Christopher Somers for lending

their knowledge, time, and direction throughout this process. In addition, I would like to

thank the students in the Bird and Bat lab for the constant support, advice, and weekly

reminder that I was not alone in the pursuit of learning. Special thanks to Joe Poissant for

his help in improving my thesis. This has truly been a positive experience, and I cannot

thank them enough.

This project would not have been completed without the help of a great field

crew: S. James, J. Unruh, G. Foley, A. Stone, and especially D. Sawatzky. The early

morning commutes to transect, sunrises, click of the multi-coloured pens, and bird chatter

are the memories I will cherish most. I recognise that travel, intense point count surveys,

the nuances of data entry and field work in general can be demanding, and I would like to

thank my field crew for their commitment to this project. I thank the landowners in my

study area that provided access to over 1200 quarter sections of private land.

Both financial and in-kind support for this project was provided by: Canadian

Wildlife Federation, Alberta Conservation Association, University of Regina, and

Environment Canada - Canadian Wildlife Service. Without the aforementioned support,

the goals of this project would have been unattainable. The continuous support provided

by my employer, the Canadian Wildlife Federation, was extremely important from a

personal perspective; thank you.

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POST DEFENSE ACKNOWLEDGEMENT

I would like to thank my external examiner, Dr. Erin Bayne, for travelling to Regina for

my defense. His time and thoughtful insight were very helpful.

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DEDICATION

I dedicate this thesis to my loving wife, Crystal, daughter, Aven, and son, Fisher.

They were unwavering in their continual encouragement and support for my endeavours.

The trade-offs associated with balancing education, work, and family life are difficult,

and I truly cannot underscore their importance for my research. All decisions have

consequences, and the cost of self-improvement by education was at the expense of our

time, which I can only hope to improve upon in our future.

I would also like to thank my family and friends for their support. This self-

fulfilling goal often led to me declining invites, and in most cases, you understood my

reasoning. To Mom and Dad, thank you. Growing up, I watched your every move and

observed the sacrifices you both made for your family. I understand more now than ever

just how difficult those decisions must have been. To Garnet and Gail, I feel truly

blessed to have you in my life and appreciate all the support you have shown to me and

my family.

It would be remiss of me if three of my mentors in ecology were not mentioned,

Paul Pryor, Stan Clements, and Glen Clements never passed up an opportunity to share

their knowledge. Thank you for helping foster my passion for conservation.

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TABLE OF CONTENTS

ABSTRACT ............................................................................................................................ I

ACKNOWLEDGEMENTS ............................................................................................... III

POST DEFENSE ACKNOWLEDGEMENT ................................................................. IV

DEDICATION ....................................................................................................................... V

TABLE OF CONTENTS ................................................................................................... VI

LIST OF TABLES .............................................................................................................. IX

LIST OF FIGURES ........................................................................................................... XII

LIST OF APPENDICES ................................................................................................... XV

LIST OF ABBREVIATIONS AND TERMINOLOGY ............................................. XVI

1. GENERAL INTRODUCTION .................................................................................... 1

1.1 Background ............................................................................................................... 1

1.2 Species Occurrence Records ................................................................................... 4

1.3 Accounting for Imperfect Detectability ................................................................ 5

1.4 Environmental Variables ........................................................................................ 8

1.5 Evaluation of Predictive Performance .................................................................. 9

1.6 Study Species ........................................................................................................... 10

1.7 Study Objectives ..................................................................................................... 13

1.8 Literature Cited ...................................................................................................... 14

2. PREDICTING GRASSLAND SONGBIRD DISTRIBUTION PATTERNS

USING OCCUPANCY MODELS ................................................................................. 30

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2.1 Introduction............................................................................................................. 30

2.2 Methodology ............................................................................................................ 33

2.2.1 Study Area ........................................................................................................ 33

2.2.2 Sampling Design and Data Collection, 2011 ............................................... 34

2.2.3 Sampling Design and Data Collection, 2012 ............................................... 36

2.2.4 GIS Methodology............................................................................................. 37

2.2.4.1 Explanatory Variables ............................................................................. 38

2.2.5 Statistical Analysis........................................................................................... 41

2.2.5.1 Determining Variable Scale and Collinearity ...................................... 41

2.2.5.2 Model Selection ......................................................................................... 42

2.2.5.3 Model Evaluation ..................................................................................... 43

2.2.5.4 Uninformative Parameters and Predictions......................................... 44

2.3 Results ...................................................................................................................... 45

2.3.1 General Results ................................................................................................ 45

2.3.2 Detection Probability ...................................................................................... 46

2.3.3 Occurrence Probability .................................................................................. 47

2.3.4 Model Evaluation ............................................................................................ 48

2.4 Discussion ................................................................................................................ 50

2.4.1 Accounting for Imperfect Detection ............................................................. 51

2.4.2 Resource Selection ........................................................................................... 53

2.4.2.1 Response to Cover Type .......................................................................... 54

2.4.2.2 Responses to Topographic Landscape Features .................................. 57

2.4.2.3 Influence of Geographic Location ......................................................... 59

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2.4.2.4 Influence of Well Density ........................................................................ 60

2.5 Conclusions .............................................................................................................. 61

2.6 Literature Cited ...................................................................................................... 63

3. CONCLUSIONS AND MANAGEMENT IMPLICATIONS .............................. 101

3.1 Summary................................................................................................................ 101

3.2 Management Implications................................................................................... 102

3.3 Literature Cited .................................................................................................... 104

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LIST OF TABLES

Table 2.1. Description of each variable used to build the species occupancy models with

the detection/non-detection avian survey records collected in the Special Areas,

2012. ........................................................................................................................... 87

Table 2.2. Pearson’s correlation coefficients for selected variables used to explain

species occurrence in the Special Areas, Alberta. Values are for the 400 m

landscape scale and were used to examine collinearity between predictor

variables. Variable codes are found in Table 2.1.................................................... 89

Table 2.3. Ranked species frequency of occurrence (%) based on the 2012 data used to

build species occupancy models, with mean occurrence rates (Ψ) and detection

probabilities (ρ) from surveys conducted in the Special Areas, Alberta. ............... 90

Table 2.4. Top-ranking models explaining the influence of spatial and temporal variables

on species detection (ρ) and occurrence (Ψ) in the Special Areas, 2012. Models

include a detection component (date [JDATE], time [TIME], wind [WIND]) and

occurrence component (grassland [GRASS], woody cover [WOODY], vegetative

greenness [MSAVI], surface water [WATER], solar radiation [HLI], wetness

[CTI], latitude [YLAT], longitude [XLONG], and well density [WD]). Only

those models with a cumulative Akaike Information Criterion (AIC) weight (wi)

of 0.95 for each species with corresponding number of parameters (K), value of

the negative log-likelihood function (LogLik), and the change in AIC value from

the top model (∆AIC) are presented. ........................................................................ 91

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Table 2.5. Coefficient estimates (β) and unconditional standard errors (SE) for intercepts

and variables used to predict species occupancy in the Special Areas, 2012.

Variables used to explain the detection probability (ρ) and occurrence probability

(Ψ) are presented separately and zeros indicate that a covariate was considered but

not included in the final model. Species abbreviations are BAIS (Baird’s

Sparrow), BHCO (Brown-headed Cowbird), CCLO (Chestnut-collared

Longspur), CCSP (Clay-colored Sparrow), HOLA (Horned Lark), LBCU (Long

billed Curlew), MAGO (Marbled Godwit), MCLO (McCown’s Longspur), SAVS

(Savannah Sparrow), SPPI (Sprague’s Pipit), UPSA (Upland Sandpiper), VESP

(Vesper Sparrow), WEME (Western Meadowlark), and WILL (Willet). ............. 94

Table 2.6. Comparison of area under the curve (AUC) scores from the 2012 partitioned

testing data (n = 218). Testing data which were not adjusted to account for

imperfect detection are indicated by Unadjusted, and training data which accounts

for imperfect detection are indicated by Adjusted. Upper (Cl+) and lower (Cl-)

confidence limits are indicated. ................................................................................ 98

Table 2.7. Predictive performance of occupancy models used to estimate occurrence

probabilities of 14 grassland bird species within the Special Areas, Alberta.

Predictive performance was assessed using two sources of evaluation data, the

partitioned 2012 data (n=218) and the independent sample collected in 2011

(n=1498). Performance was measured based on area under the curve (AUC)

scores and Kappa qualitative scores. Frequency of occurrence, or observed

prevalence of species are indicated for each data set along with 95% confidence

limits (CI) for both measures. ................................................................................... 99

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Appendix A. Table A.1. Variable shrinkage estimates when model averaging was

incorporated to assist with model selection uncertainty in the Special Areas, 2012.

The relative variable importance values are on a scale from 0 to 1, giving an equal

weight in the global model used to predict species occupancy. Positive and

negative species-habitat relationships are indicated (+/-). Variables used to

explain the detection component, ρ, are (date [JDATE], time [TIME], wind

[WIND]) and variables used to explain the occurrence component, Ψ, are

(grassland [GRASS], woody cover [WOODY], vegetative greenness [MSAVI],

surface water [WATER], solar radiation [HLI], wetness [CTI], latitude [YLAT],

longitude [XLONG], and well density [WD]). ...................................................... 108

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LIST OF FIGURES

Figure 2.1. Geographic location of the Special Areas municipality (shaded grey) in

south-eastern Alberta, Canada. ................................................................................. 85

Figure 2.2. Point count survey locations conducted in the Special Areas, 2011-2012.

Black circles indicate 2012 survey locations (visited using a repeated survey

design) and grey circles indicate 2011 survey location (visited using a removal

sampling design). ....................................................................................................... 86

Figure 2.3. Species relationships between probability of detection (ρ) and date within the

Special Areas of Alberta, 2012. Each figure is based on the same standardised

scale of mean = 0 and variance = 1, where negative values represents survey

conducted earlier in the survey season and positive values represent surveys

conducted later in the survey season. The 95% confidence intervals are indicated

by the shaded grey area. ............................................................................................ 93

Figure 2.4. Species relationships between probability of occurrence (Ψ) and modified

Soil-adjusted Vegetation Index within a 400 m radius of the point count centre for

surveys conducted within the Special Areas of Alberta, 2012. Each figure is

based on the same standardised scale of mean = 0 and variance = 1, where

negative values indicate more bare ground and positive values indicate more

green vegetative biomass. The 95% confidence intervals are indicated by the

shaded grey area......................................................................................................... 95

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Figure 2.5. Species relationships between probability of occurrence (Ψ) and Compound

Topographic Index within a 400 m radius of the point count centre for surveys

conducted within the Special Areas of Alberta, 2012. Each figure is based on the

same standardised scale of mean = 0 and variance = 1, where negative values

indicate more mesic areas and positive values indicate more xeric areas on the

landscape. The 95% confidence intervals are indicated by the shaded grey area.96

Figure 2.6. Species relationships between probability of occurrence (Ψ) and Heat Load

Index within a 400 m radius of the point count centre for surveys conducted

within the Special Areas of Alberta, 2012. Each figure is based on the same

standardised scale of mean = 0 and variance = 1, where negative values represent

less solar radiation and positive values represent higher rates of solar radiation on

the landscape. The 95% confidence intervals are indicated by the shaded grey

area.............................................................................................................................. 97

Appendix B. Figure B.1. Baird's Sparrow (Ammodramus bairdii) expected probability of

occurrence for the Special Areas, Alberta.............................................................. 109

Appendix B. Figure B.2. Brown-headed Cowbird (Molothrus ater) expected probability

of occurrence for the Special Areas, Alberta. ........................................................ 110

Appendix B. Figure B.3. Chestnut-collard Longspur (Calcarius ornatus) expected

probability of occurrence for the Special Areas, Alberta. ..................................... 111

Appendix B. Figure B.4. Clay-colored Sparrow (Spizella pallida) expected probability of


Appendix B. Figure B.5. Horned Lark (Eremophila alpestris) expected probability of


xiv

Appendix B. Figure B.6. Long-billed Curlew (Numenius americanus) expected


Appendix B. Figure B.7. Marbled Godwit (Limosa fedoa) expected probability of


Appendix B. Figure B.8. McCown's Longspur (Rhynchophanes mccownii) expected


Appendix B. Figure B.9. Savannah Sparrow (Passerculus sandwichensis) expected


Appendix B. Figure B.10. Sprague's Pipit (Anthus spragueii) expected probability of


Appendix B. Figure B.11. Upland Sandpiper (Bartramia longicauda) expected


Appendix B. Figure B.12. Vesper Sparrow (Pooecetes gramineus) expected probability

of occurrence for the Special Areas, Alberta. ........................................................ 120

Appendix B. Figure B.13. Western Meadowlark (Sturnella neglecta) expected


Appendix B. Figure B.14. Willet (Tringa semipalmata) expected probability of


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LIST OF APPENDICES

APPENDIX A – Relative variable importance for grassland bird occupancy models

generated in the Special Areas, Alberta. ................................................................ 107

APPENDIX B – Probability of occurrence maps for bird species in the Special Areas,

Alberta. ..................................................................................................................... 109

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LIST OF ABBREVIATIONS AND TERMINOLOGY

BIAS – difference between the expected value of an estimator and true value of the

parameter.

DETECTION PROBABILITY– relates to the probability a target species is present at the

time of the survey and detected at a site.

OCCUPANCY – Parameter of interest in ecological research (also referred to as

occurrence). The proportion of sites occupied calculated from the two processes

occurring in a general sampling situation (occurrence and detection probability).

OCCURRENCE PROBABILITY – relates to the probability of sites used by a target

species during the sampling period.

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1. GENERAL INTRODUCTION

1.1 Background

All organisms require adequate quantities of usable resources to survive.

Understanding that animals select an area to live based on such resources as food, water,

and shelter have led conservation practitioners to develop a number of ways to identify

and prioritise areas that meet a given species’ basic requirements. Determining which

resources are selected at a higher propensity than others is of particular interest because it

may provide insight into species requirements for survival and reproduction (Manly et al.

2002; MacKenzie et al. 2006). By quantifying point count data with geographic and

environmental variables across the landscape, results have shown to be linked to

reproductive success and environmental suitability (Pearce and Ferrier 2001; Bock and

Jones 2004; Peterson 2006). Species Distribution Models (SDMs) typically combine

species occurrence or abundance data with geographical and environmental variables to

gain ecological insight about how species interact with the composition and spatial

arrangements of habitats, and thus to predict distributions across landscapes (Gusian and

Thuiller 2005; Elith and Leathwick 2009; Franklin 2010). SDMs are a commonly used

tool to describe and delineate the geographical distributions of flora and fauna (Guisan

and Thuiller 2005). These models are used in a wide variety of applications, including

assessing the impacts of land cover change, guiding and refining future research efforts,

projecting the impacts of climate change, supporting conservation prioritization and

reserve selection, and guiding species reintroduction (Elith and Leathwick 2009; Franklin

2009).

The emergence of qualitative mapping and distribution modelling has been

associated with advances in statistical techniques and geographic information technology

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(Elith et al. 2006; Franklin 2010). Studies of species-habitat relationships combined with

advances in statistical analysis, such as generalised linear models, provide opportunities

to explain species-habitat relationships (e.g., Austin 1985; Guisan and Zimmermann

2000). Concurrently, technological advances in physical geography have provided new

spatial data including digital models of surface elevation, climate patterns, and remote

sensing of the surface condition of the earth. Geographic Information Systems (GIS)

have provided conservation practitioners with a platform to efficiently manipulate and

store spatial data (e.g., Laurent et al. 2005; Shirley et al. 2013). SDMs have been used

extensively over the past decade and are considered an essential tool in the fields of

ecology and conservation biology (Guisan and Thuiller 2005; Elith et al. 2006; Elith and

Leathwick 2009; Franklin 2010).

Species distribution patterns can be portrayed in relatively simple means, such as

habitat suitability indices and species range maps, which are practical tools used to

identify where a species is likely to occur. However, both tools are often criticised due to

limitations of statistical rigour (Boyce et al. 2002). Alternatively, species distribution

patterns may be developed using ecological niche theory, which is based on the idea that

each species has a unique set of requirements that must be provided by the habitat(s) to

persist (Hutchison 1957). Conservation practitioners use SDMs to identify key habitat

variables that species require, which in turn, can be used to predict species occurrence or

abundance for a given area. Accurate predictions are essential for identifying suitable

habitat for species, conservation planning/management, and forecasting biological

consequences of global change (Guisan and Thuiller 2005; Collinge 2009; Franklin

2009).

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Although SDMs are considered a proficient tool for coalescing species-habitat

relationships, species responses to environmental gradients are often made with certain

assumptions. The primary assumptions of SDMs are that species are in equilibrium with

their environment, and that adequate sampling of all environmental gradients has been

conducted (Guisan and Thuiller 2005; Elith and Leathwick 2009). Many SDMs assume

that a species ecological niche is stable in both space and time, and in most cases,

researchers cannot directly sample the entire area of interest. Therefore, using

distribution models to predict species occurrence in an unbalanced ecosystem setting

(e.g., disease, invasions, or climate change), can be misleading when models do not

incorporate new combinations of environmental variables or responses to environmental

factors outside their original gradient (Elith and Leathwick 2009). Additional

complications with SDMs include: 1) environmental factors, or combinations thereof,

that may limit distributions or biotic interactions, 2) outcomes influenced by genetic

variability and evolutionary changes, and 3) dispersal pathways that are difficult to

predict (Midgley et al. 2006; Dormann 2007; De Marco et al. 2008). However, SDMs

are considered one of the best approaches for predicting species distributions when

assumptions are valid and results are rigorously assessed (Elith and Leathwick 2009;

Franklin 2010).

Generally, the following steps are taken to construct a SDM once the study area

has been defined (Guisan and Thuiller 2005; Elith and Leathwick 2009): (1) collect

sufficient records of known species occurrence and non-occurrence or abundance, (2)

extract values of environmental variables presumed to impact occurrence or abundance

for each site or area of interest, (3) use the environmental variables to fit an appropriate

4

modelling algorithm used to predict the likelihood of species occurrence or abundance,

(4) use the model to predict the likelihood of occurrence or abundance across an area of

interest, (5) evaluate the model for realism and uncertainty, and (6) evaluate the models

fitted response using independent or partitioned testing data to evaluate model predictions

to determine usefulness.

1.2 Species Occurrence Records

A common method of counting organisms is by surveying an area using point

counts (Hutto et al. 1986). Point count surveys involve surveyors standing at a particular

site and recording the occurrence or number of individuals within a given radius of the

observer for a set period of time (Johnson 1995). Point counts provide useful baseline or

“snapshot” information about local biodiversity and can lead to a better understanding of

ecosystem health (Gregory et al. 2005). However, counting organisms can be difficult, as

some organisms are mobile, some can be more cryptic or inhabit areas in low-density,

and others may avoid detection all together (Elphick 2008). Counting can be influenced

by many factors including the probability an observer detects an individual (MacKenzie

et al. 2002) and within-season dispersal after breeding failure (Betts et al. 2008).

Additionally, most counts have other associated errors such as portions of the population

being unavailable for detection, detection mistakes, sound transmission, or erroneous

counts (Elphick 2008; Lozier et al. 2009).

Primarily, there are two types of occurrence data used to develop SDMs;

presence-only and presence-absence, or more properly known as detection/non-detection

when there is a chance individuals are present but not detected (MacKenzie et al. 2006).

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Each method performs better with more detection records (Wisz et al. 2008), and

detection/non-detection records that are unbiased and error free (Brotons et al. 2004;

Graham et al. 2007). In cases where species are more cryptic or occur less frequently, the

focus should be on improving the quality of detection/non-detection data (MacKenzie et

al. 2006; Lobo 2008). Presence-only records describe known detections, but lack

information about non-detections. Examples of presence-only data are those collected by

radio telemetry or historical records such as those available from museum and herbarium

collections. Despite the constraints and criticisms, presence-only data are effective when

used to map species distributions, and continue to receive much attention (Pearce and

Boyce 2006). Although some have acknowledged that predictions would be more robust

if non-detection data were included, most suggest the lack of systematic survey data

should not devalue presence-only data (Phillips et al. 2009). The advantage of

detection/non-detection data is that it conveys more information about each surveyed

location, allowing the examination of bias and species prevalence given the survey effort

(Brotons et al. 2004; Phillips et al. 2009). Non-detection data are also sometimes viewed

as misleading because some species are not easily detected, which can be especially

problematic for mobile species. Consequently, statistical advances have led to modelling

techniques that account for imperfect detection (hereafter, occupancy models; MacKenzie

et al. 2002; 2006). There is a growing acceptance that accounting for imperfect detection

using count data is essential to maximizing the predictive performance of SDMs

(MacKenzie 2006; Lobo et al. 2008).

1.3 Accounting for Imperfect Detectability

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Virtually all detection/non-detection data can be affected by false absences

(species present but not detected) because they do not account for the possibility that a

species is present but may not elicit a cue for detection, or goes undetected by the

observer (MacKenzie et al. 2002; Wintle et al. 2004; Rocchini et al. 2011; Kery 2012).

When detection/non-detection data that do not account for imperfect detection are

analysed using logistic regression, a commonly used approach for modelling species

occurrence (Guisan and Zimmermann 2000), it is expected to yield biased results (Tyre et

al. 2003; Gu and Swihart 2004). Gu and Swihart (2004) determined that logistic

regression models were sensitive to low levels of imperfect detection. Logistic

regression models the relationship between habitat and species occurrence (a combination

of occurrence and detection probability), just not where a species might be (occurrence)

(MacKenzie et al. 2006). For example, MacKenzie (2006) compared modelling results of

similarly fitted resource selection probability functions and occupancy models. The

results showed that imperfectly detecting a species can lead to erroneous conclusions

about resource use. As such, failure to account for imperfect detections in count data can

lead to biased estimates of habitat relations, inappropriate management decisions, and

substantially change the predictive ability of a SDM (MacKenzie 2006; Kery and

Schmidt 2008; Marsh and Trenham 2008).

Methods used to account for detection errors include distance sampling (Buckland

2001), double-observer sampling (Nichols et al. 2000), removal sampling (Farnsworth et

al. 2002), and repeated count sampling (MacKenzie et al. 2002; 2006). These

methodologies all aim to minimise imperfect detection but have associated trade-offs

including cost, survey effort, and the type of detection biases addressed. Removal

7

sampling (specifically in the context of avian species), assumes that singing frequency

influences the probability of detection when birds are surveyed (Farnsworth et al. 2002).

Removal sampling is widely used because it is considered an efficient way to collect data

over large areas (Lele et al. 2012), but there are considerable arguments about bias

(Johnson 2008). The main assumption of removal sampling pertains to it being done on a

closed population. This means that immigration and emigration do not occur during the

sampling period, and that all species of interest are available for detection within the

count period (Johnson 2008). However, birds tend to be mobile and sing in irregular

bouts which may not satisfy the assumptions. Both distance and double observer

sampling address the probability that an individual is missed given it is present during the

survey. However, unlike removal sampling, neither approach effectively deals with the

probability that an individual elicits a cue during the counting period (Elphick 2008).

Repeated counts are often used to provide a robust evaluation of imperfect

detection, and are considered an optimal method to determine occupancy (MacKenzie

2005; MacKenzie et al. 2006; Bailey et al. 2007). The general assumption of repeated

surveys is that each count is conducted on a closed population (MacKenzie et al. 2006).

This means occurrence probability remains constant for the duration of the repeated

counts. Additional assumptions include: (1) the target species is identified correctly; (2)

occurrence and detection probabilities are similar across sites, or heterogeneity can be

accounted for using covariates; and (3) detection of species is independent for each site

visit (MacKenzie et al. 2006). Assuming true occupancy status does not change

throughout the sampling period, changes in detection and non-detection can be attributed

8

to detectability. Thus, a robust estimation of occurrence can be used to estimate species

occupancy, once detection biases have been accounted for.

There are three specific survey designs commonly employed to conduct repeated

counts; 1) “standard” design where each counting site is sampled on numerous occasions,

2) “double sampling” design where repeated surveys are conducted at a subset of

counting sites, and 3) “removal sampling” design where each counting site is sampled a

maximum amount of times, but once the target species is detected, no further surveys are

required (Mackenzie et al. 2006). A common criticism of repeated surveys is the

associated trade-off between feasibility and survey effort (MacKenzie et al. 2006; Lele et

al. 2012). Assessing the trade-offs between survey methodologies, such as increasing the

number of repeated site visits at the expense of decreasing the amount of sampling sites,

is an important consideration (MacKenzie and Royle 2005). On the other hand,

understanding the biology of a target species can provide further insight about the trade-

offs of an optimal design structure (MacKenzie et al. 2006; Bailey et al. 2007). A general

rule of thumb used is that rare species require more sampling sites and common species

require increased number of repeated surveys (MacKenzie et al. 2006). The removal

design is likely to be more efficient if detection probability is relatively constant;

however the standard method provides more flexibility when detection probability are

unknown, and multiple species are targeted for study (MacKenzie et al. 2006).

1.4 Environmental Variables

Early applications of SDMs concentrated on explaining the ecological drivers of

species distributions (Mac Nally 2000), whereas much of the current focus is to generate

9

predictions about distribution over space and time (Austin 2007; Elith and Leathwick

2009). Moreover, with the growing complexity of modelling algorithms, greater

availability of spatial data, and the demand for mapped conservation products, an

increasing number of authors have focused on predicting species distributions within a

given area (Elith and Leathwick 2009; Franklin 2009). Motivated by the availability of

spatial variables and the idea that a model will identify important variables to predict

distributions, some studies include many candidate predictors (Austin 2002; Franklin

2009). However, many investigators advise that variable selection be based on the

known or perceived drivers of ecological processes (Austin 2002; Cunningham and

Johnson 2006; Elith et al. 2006; Elith and Leathwick 2009; Lobo et al. 2008; Franklin

2009), as both the variable selection process and the resulting inferences will be

improved. While it appears logical to assess all variables that either indirectly (distal) or

directly (proximal) relate to a particular resource, Austin (2002) argued that the use of

indirect variables has a tendency to be more error prone over time and space compared

with directly correlated predictors. As such, indirect predictors such as elevation and

slope are often replaced with more functionally relevant proximal predictors such as

temperature, rainfall, water availability, soil moisture and solar radiation (Elith and

Leathwick 2009).

1.5 Evaluation of Predictive Performance

The application of a model to predict species distributions relies heavily on its

predictive accuracy. Model evaluations are necessary to test the predictive performance

of SDMs, and are essential to provide confidence in a distribution models predictive

10

ability (Fielding and Bell 1997; Guisan and Zimmermann 2000; Vaughan and Ormerod

2005; Barry and Elith 2006). In fact, ecological modelling may have little relevance if

the predictions are not assessed for accuracy (Verbyla and Litaitis 1989). Evaluations of

detection/non-detection data are limited to validation techniques such as partitioning,

resampling schemes, or tests with independent datasets (Araujo et al. 2005). However,

reliable independent validation data can be difficult and costly to obtain and most often

contain some level of inherent bias or uncertainty (Pearson et al. 2007). In cases where

independent data are unavailable, a dataset can be split into datasets used to build (or

train) and validate (test) models (Fielding and Bell 1997).

When prediction is the aim, model evaluation involves statistical analyses that

assess predictive performance (Liu et al. 2011). By comparing the models predictions

against reality, a confusion matrix can be built to determine false presence and false

absence errors (Fielding and Bell 1997), both of which assist in determining predictive

performance. Further evaluations of predictive performance are based on a small number

of statistical tests including area under the receiver operating characteristic curve, Kappa,

and correlation coefficients (Fielding and Bell 1997; Allouche et al. 2006; Liu et al.

2011). Once a species-habitat model has provided evidence of predictive ability, it can

be used to guide future management strategies, justify funding for research and

conservation activities, prioritise conservation activities and assess species status for

listing under endangered species legislation (Aldridge and Boyce 2007; COSEWIC

2011).

1.6 Study Species

11

Concern over the continent-wide population declines of grassland birds has

stimulated a variety of conservation plans, many of which either explicitly or implicitly

promote a landscape approach to conservation (Berlanga et al. 2010). Much of

conservation theory has been focused on species as the unit of interest, however a

landscape approach provides tools and concepts for managing lands to achieve social,

economic, and environmental objectives across a given area (Berlanga et al. 2010).

Current population trend data for grassland birds suggest that the causes of declines are

not locally isolated phenomena but involve the cumulative loss and degradation of

grassland habitat throughout the Americas (Vickery et al. 1999; Askins et al. 2007).

Approximately 85% of the world’s temperate grassland biome has disappeared since the

late 1800’s (Millennium Ecosystems Assessment 2005), and within Canada, only an

estimated 20% of native grassland remains (White et al. 2000). This substantial rate of

loss has reduced the capacity, or ability of natural areas to provide vital ecosystem

services (Millennium Ecosystem Assessment 2005). Therefore, efforts to either stabilise

or increase local grassland bird populations may only be possible once there is a clear

understanding of resource selection and species occupancy in a given area (Dunning et al

1995; Vickery et al. 1999; Brennan and Kuvlesky 2005).

Identifying influential drivers of habitat selection across the landscape has a

strong history in theoretical ecology (MacArthur 1960; Austin 2002) and is essential to

wildlife management (Leopold 1933; Franklin 2009). Knowledge of grassland bird

distributions on the landscape is important for conservation because birds tend to exhibit

different behaviour patterns, sometimes even at different scales, that likely influence both

occurrence and detection (Hutto 1985; Donovan et al. 2002; Chandler et al. 2009). There

12

is no one single scale that best measures ecological patterns (Levin 1992), thus the

appropriate scale is usually dictated by the objectives of the study, the ecosystem being

studied, and available data (Elith and Leathwick 2009). With birds, it is presumed that

habitat selection first occurs at broad geographic scales which are suggested to be

genetically linked, whereas selection at finer scales is likely a combination of learned

behaviour and the availability of food and shelter (Wiens 1972; Johnson 1980; Hutto

1985; Wiens 1989). Therefore, selection begins at the scale of the geographic range of

the species, then occurs at broad landscape scales for populations, followed by

establishing home ranges within regions containing a population, and then at a finer scale

to satisfy individual life history requirements (Johnson 1980). Consequently,

investigators often examine species responses to landscape scales based on the concept of

a selection orders to measure how species interact with spatial habitat arrangements on

the landscape (Addicott et al. 1987; Askins et al. 2007), and provide a context for species

requirements to survive and reproduce.

Much of the initial research on breeding bird responses to landscape cover

focused on species inhabiting forests (Robbins et al. 1989; Terborgh 1989; Finch 1991).

Recent studies of grassland birds suggest responses to landscape factors at scales from

200 to 1600 m (Ribic and Sample 2001; Bakker et al. 2002; Brontons et al. 2005;

Cunningham and Johnson 2006; Ribic et al. 2009). For example, Ribic and Sample

(2001) established that responses to landscape scales at both 200 m and 400 m radius

could be used to explain the distribution of Grasshopper Sparrow (Ammodramus

savannarum). Davis et al. (2013) found that landscapes within 400 m of grassland

parcels influenced abundance of grassland songbirds, but pointed out that the precise

13

scale is difficult to determine because of the strong correlations between landscape scales

(400 m – 1600 m radius) within the region.

Even with considerable research devoted to understanding habitat selection, it is a

poorly understood process (Jones 2001). No single measure can describe the many

factors that influence habitat selection, however Cunningham and Johnson (2011) suggest

that identifying a broadly influential measure such as the amount of a given land cover

type would aid analysis and reduce some of the confusion in interpreting overlapping

predictor variables. Calculating percent cover assumes that a targeted cover type can

accurately be defined as useful to a species and that this level of cover can be reliably

identified from spatial imagery or maps (Cunningham and Johnson 2011). For example,

Davis et al. (2013) found that abundance of two specialist species, Spragues’s Pipit

(Anthus spragueii) and Baird’s Sparrow (Ammodramus bairdii), were similarly

influenced by the amount of native grassland on the landscape between two sampling

seasons. However, generalist species such as Savannah Sparrow (Passerculus

sandwichensis) and Western Meadowlark (Sturnella neglecta) exhibited a mixed

response to the amount of habitat and landscape cover over the two seasons. Although

percent cover estimates from spatial data is not an exact representation of habitat in the

landscape, it has proven to be reliable and an easy means to conceptualise for

conservation managers relative to other measures (Cunningham and Johnson 2011).

1.7 Study Objectives

Distribution models provide conservation managers with GIS products (e.g., maps

and shape files) of where species typically occur in a given area, which in turn, provide a

14

context for species requirements to survive and reproduce (Manly et al. 2002; MacKenzie

et al. 2006), and reduce uncertainty in conservation planning (Elith and Leathwick 2009).

By modelling grassland bird distributions, my research will lead to a better understanding

of resource selection, and assist in the process of ensuring key habitats are identified and

conserved (Askins et al. 2007). My objectives are to: (1) determine landscape factors that

influence the distribution of grassland birds, (2) use occupancy modelling to generate

Species Distribution Models (SDMs) and predict probability of occurrence for grassland

birds in southeastern Alberta, and (3) evaluate the predictive performance of the SDM

models.

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30

2. PREDICTING GRASSLAND SONGBIRD DISTRIBUTION PATTERNS

USING OCCUPANCY MODELS

2.1 Introduction

Concern over population declines of grassland birds has stimulated a variety of

conservation plans, many of which explicitly promote a landscape approach to

conservation (Berlanga et al. 2010). Population trend data for grassland birds suggest

that the causes of declines are not locally isolated phenomena but involve the cumulative

loss and degradation of grassland habitat throughout the Americas (Vickery et al. 1999;

Askins et al. 2007). Approximately 85% of the world’s temperate grassland biome has

disappeared since the late 1800’s (Millennium Ecosystems Assessment 2005), and within

Canada, only 20% of native grassland remains (White et al. 2000). This substantial loss

of habitat has reduced the ability of native grasslands to provide vital ecosystem services

(Millennium Ecosystem Assessment 2005). As a result, many grassland-dependent taxa

are now of conservation concern, with grassland birds having declined more than any

other group in North America (Samson and Knopf 1994). However, predicting

occupancy patterns on the landscape can provide great value in assessing the impacts of

environmental change, and give the ability to stabilise or increase grassland bird

populations once a clear understanding of resource selection has been achieved (Dunning

et al. 1995; Vickery et al. 1999; Brennan and Kuvlesky 2005).

Determining which resources are selected at a higher propensity than others is of

particular interest because it may provide insight into species requirements for survival

and reproduction (Manly et al. 2002; MacKenzie et al. 2006). For birds, resource

selection begins at the scale of the geographic range of the species, then at broad

landscape scales for populations, followed by individual levels to establish home ranges,

31

and finally at a finer scale to satisfy individual life history requirements (Johnson 1980).

Much of the initial resource selection research was focused on local habitat attributes

such as vegetation structure (Wiens 1973; Fisher and Davis 2010), and investigations of

breeding bird responses to environmental conditions primarily targeted forest-dwelling

species (Robbins et al. 1989; Terborgh 1989; Finch 1991). Studies of nesting grassland

birds suggest that birds respond to landscape factors at scales from 200 to 1600 m

(Bakker et al. 2002; Brontons et al. 2005; Ribic et al. 2009). For example, Ribic and

Sample (2001) found that responses to landscape scales at both 200 and 400 m radius

could be used to explain the distribution of Grasshopper Sparrow (Ammodramus

savannarum). Davis et al. (2013) found that landscapes within 400 m of grassland

parcels influenced abundance of grassland songbirds, but pointed out that determining the

precise scale was difficult because of the strong correlations between scales (400 – 1600

m radius) within the region. No single measure can describe the many factors that

influence resource selection, however Cunningham and Johnson (2011) suggest that

identifying a broadly influential measure such as the amount of a given land cover type

has proven to be reliable and easy to conceptualise relative to other measures for

conservation managers.

Identifying that birds select habitats based on a set of geographic and

environmental characteristics is fundamental to understanding resource selection (Manly

et al. 2002; MacKenzie et al. 2006). Species distribution models typically combine count

data with geographical and environmental variables to understand how species interact

with the composition and spatial arrangements of habitats, which in turn, can be used to

predict species distributions across an area of interest (Gusian and Thuiller 2005; Elith

32

and Leathwick 2009; Franklin 2010). Using functionally relevant variables such as the

amount of a particular habitat and spatially explicit covariates describing topography are

essential to pinpointing the ecological drivers of resource selection and predicting

distributions across a larger area of interest (Austin 2002; Elith and Leathwick 2009;

Cunningham and Johnson 2011). Accurate detections of individuals are also an

important consideration for distribution modelling because birds are mobile, and tend to

exhibit different activity patterns which likely influence both occurrence and detection

probability across the landscape (Hutto 1985; Donovan et al. 2002; Chandler et al. 2009).

Virtually all count data can be affected by false absences (species present but not

detected; MacKenzie et al. 2006), and recent investigations have demonstrated that not

accounting for imperfect detection will yield biased results (Tyre et al. 2003; Gu and

Swihart 2004; Marsh and Trenham 2008). Occupancy models are a type of species

distribution model which enable researchers to account for imperfect detection

(MacKenzie et al. 2002; 2006). Models that account for detectability bias can lead to

better management decisions, assist in ensuring key habitats are identified and conserved

by improving our understanding of resource selection and the predictive ability of

distribution models. A reliable distribution model can be used to guide future

management strategies, justify funding for research and conservation activities, prioritise

conservation activities and assess species status with confidence (Aldridge and Boyce

2007; COSEWIC 2011).

My objectives were to determine landscape factors that influence the distribution

of grassland birds, use occupancy models to generate predictions of species distribution,

33

and evaluate the predictive performance of 14 grassland bird species models constructed

based on data from southeastern Alberta, Canada.

2.2 Methodology

2.2.1 Study Area

I conducted my study within the boundary of a municipality named the Special

Areas (www.specialareas.ab.ca), in southeastern Alberta (51°18’31”N and 110°56’19”

W; Figure 2.1). Bordered to the South by the Red Deer River, the Special Areas

encompass 2 million ha of land predominately used for livestock grazing, annual

cropping, and oil and gas extraction. Grazing is the focal agricultural activity, although

roughly 25% of the study area is under dryland and irrigational farming practices (mainly

wheat/fallow; AAFC 2012). The area is characterised by undulating plains with

hummocky uplands occurring towards the northern portions. Soil characteristics are

primarily Brown Chernozemic, although Dark Brown Chernozemic soils become more

prominent as the moisture gradient increases from south to north (Natural Regions

Committee 2006). Characterised by short hot summers and cold winters, the area

typically follows a continental summer-high weather pattern with maximum precipitation

occurring in June. Mean annual temperature for the warmest month (July) is

approximately 18° Celsius (Natural Regions Committee 2006). Drying winds, low

summer precipitation, high summer temperatures, and intense sunshine contribute to a

mean annual precipitation of approximately 333 mm (Natural Regions Committee 2006).

The Special Areas is located in the dry mixed-grass prairie and northern fescue

grassland natural subregions of Alberta, and extends into the transitional zone of the

34

central parkland (Natural Regions Committee 2006). Native vegetation consists

primarily of blue grama (Bouteloua gracilis), green needlegrass (Nassella viridula),

needle-and-thread grass (Hesperostipa comata), june grass (Bromus tectorum), northern

wheatgrass (Elymus macrourus), western wheatgrass (Pascopyrum smithii), northern

rough fescue (Festuca altaica), and plains rough fescue (Festuca hallii). Sedges (Carex

spp), sage (Artemsia spp.), and various other forbs commonly occur throughout the area.

Shrubs such as western snowberry (Symphoricarpos occidentalis), silverberry (Elaeagnus

commutata) and rose (Rosa spp.) occur sporadically in mesic sites. The area supports a

diverse community of grassland birds, including several species listed by the Species at

Risk Act in Canada, such as Sprague’s Pipit (Anthus spragueii), McCown's Longspur

(Rhynchophanes mccownii), Chestnut-collared Longspur (Calcarius ornatus),

Loggerhead Shrike (Lanius ludovicianus), Burrowing Owl (Athene cunicularia), Long-

billed Curlew (Numenius americanus), and Ferruginous Hawk (Buteo regalis).

2.2.2 Sampling Design and Data Collection, 2011

I used results from avian surveys conducted in 2011 as an independent source of

species occurrence records to evaluate the predictive power of the occupancy models

developed using point count data I collected in 2012.

In 2011, I used the national draft Sprague’s Pipit Resource Selection Function

model developed by the Canadian Wildlife Service (S. Davis unpubl. data) to randomly

stratify the sampling area for surveys. Although the model is specific to Sprague’s Pipit,

it is largely based on the amount of grassland in the study area and thus relevant to most

birds that require grassland habitat. I used a Geographic Information System (ArcMap

35

10.0; ESRI 2011) to designate each legal section of land (256 ha; McKercher and Wolfe

1986) in the Special Areas as either suitable or unsuitable habitat according to the model.

The ratio of suitable to unsuitable habitat was approximately 2:1; therefore, I sampled

twice as much suitable habitat as unsuitable habitat. I randomly selected legal sections

[n=100 suitable and n=50 unsuitable] to use as starting points for each transect. The

closest road or trail to the northeast corner of the legal section was used as the starting

point for each transect. I delineated a 16 km transect by randomly selecting a direction

from each starting point and maintained that direction until I encountered a road

intersection. I used a random number generator to select a number between 1 and 4 (1 =

North, 2 = East, 3=South, and 4=West) and assigned the direction to the next intersection.

Point count locations were plotted every 800 m along each transect to maintain a

reasonable distance between sampling locations (Cunningham and Johnson 2006). All

surveys were conducted either along gravelled grid roads and trails (e.g. those

constructed by the oil and gas industry) or at sites > 150 m from roads. On-road and off-

road transects were sampled at a 1:1 ratio to address roadside sampling bias (Ralph et al.

1995).

I conducted 1398 avian point counts (mid-May to early-July; Figure 2.2), using a

removal sampling design (Farnsworth et al. 2002). Each location was surveyed by a

trained observer for three equal intervals of 2.5 minutes, comprising an overall point

count duration of 7.5 minutes. The number and species of bird were recorded within both

a 100 m and 400 m radius of the observer (Hutto et al 1986). Point count locations were

located using a hand-held global positioning system unit. All point counts were

conducted between sunrise and 0930 CST on days with winds <20 km/hr and no

36

precipitation or unseasonal weather events. Since the principal objective of this study

was to determine occupancy, I converted abundance records to reflect species that were

detected or not detected within 400 m of the survey point.

2.2.3 Sampling Design and Data Collection, 2012

I used ArcMap 10.0 and classified satellite imagery from Agriculture and Agri-

foods Canada 2011 crop mapping inventory (AAFC 2012) to calculate the ratio of

grassland to cropland cover within the study area. Distinguishing native grassland from

planted grassland, and accurately identifying shrub cover using the satellite imagery was

impossible. Therefore, I calculated the amount of grassland habitat by combining

grassland, pasture/hayland, and shrubland cover types, which resulted in approximately

75% of the study area being considered grassland cover. The ratio of grassland to

cropland was approximately 3:1; therefore, I used this ratio to stratify my random

sampling effort.

I classified each quarter section (64 ha; McKercher and Wolfe 1986) in the

Special Areas as either Low (0-25%), Moderate (25.01-75%), or High (75.01-100%)

based on the percentage of grassland cover. I randomly selected 150 quarter sections

designated “high” and 50 quarter sections designated “moderate and low” to assign

starting locations for transect surveys. Point counts were conducted along a 24 km

transect following the methods used in 2011. However, during the 2012 breeding season

(mid-May to early-July), I employed a standard repeated survey design to account for

potential detection bias (Mackenzie et al. 2002; 2006). Each point count was surveyed by

trained observers for a 5 minute period, and the number and species of bird were recorded

37

within both a 100 m and 400 m radius of the observer (Figure 2.2). Surveyors recorded

date, time of day and wind speed at the start of each point count. Each of the 870 point

count locations was visited three times within a five day period by a different observer to

reduce surveyor bias (MacKenzie et al. 2006). This repeated sampling design provides

less biased estimates of occupancy given heterogeneous detection probabilities

(MacKenzie and Royle 2005; MacKenzie et al. 2006). Observers alternated starting

points from either end of the transects to ensure that sampling at each site occurred at

different times throughout the survey period (MacKenzie et al. 2006). I converted

abundance records to reflect species that were detected or not detected within 400 m of

the survey point.

2.2.4 GIS Methodology

All explanatory variables were constructed using ArcMap 10.0 (ESRI 2011). I

applied a 5 km buffer to the boundary of my study area to ensure that all landscape

information could be extracted for counts near the study area boundary. I used the

polygon to raster tool to generate a 30 x 30 m grid of the study area, encompassing

2,771,922 cells which I used to match the processing extent and resolution for each

spatially explicit explanatory variable (see below). I selected twelve explanatory

variables based on existing knowledge of grassland bird distributions and categorised

these into five model classes; detection, land cover, geographic location, topography, and

well density (Table 2.1). All digital data were projected by the Universal Transverse

Mercator (UTM Zone 12N) Coordinate system using the World Geodetic System WGS

1984.

38

2.2.4.1 Explanatory Variables

Most bird detections during point count surveys are based on auditory cues, and

consequently song rate activity can greatly influence detection probability (Alldredge et

al. 2007). Therefore, I included date, time of day, and wind speed as visit-specific

covariates to assess variation in species detection rates.

I calculated the amount of grassland, woody cover, surface water, and vegetative

greenness to help explain land cover influences on occurrence probability. Using the

AAFC land cover classification imagery, I independently derived the percentage of

grassland (grassland and hay/pastureland areas combined) and percentage of woody

cover (shrubland, coniferous, and deciduous tree areas combined) variables in raster

format. Surface water was also derived by combining the watercourses, wetlands, and

water-bodies vector data (Natural Resources Canada 2007). The surface water data were

in vector format, therefore I used the polygon to raster tool to represent surface water in a

single binary raster format. The new raster files for grassland, woody vegetation, and

surface water were reclassified into a binary raster datasets, where each 30 x 30 m raster

cell containing the target variable was set equal to “1” and everything else was set equal

to “0”. I built a modified Soil-adjusted Vegetation Index (MSAVI2), which quantified

plant biomass and provided an index to vegetative greenness on the landscape from

satellite imagery obtained from GeoBase.ca (Natural Resources Canada 2004b). Since

models which include several remote sensing predictors from the same area tend to be

highly correlated (Zimmerman et al. 2007), I chose the MSAVI2 over other indices like

Normalized Difference Vegetative Index for my analyses as it is better suited to regions

39

with a high degree of exposed soil (Qi et al. 1994). To derive the MSAVI2, I built two

raster files composed of red and near-infrared multispectral band data. Both

multispectral bands were constructed by mosaicking the 1:50,000 Landsat 7 ortho-image

map-sheets that intersected the study area, using the model builder tool. Using the red

band and near-infrared band indices, I calculated an index for vegetative greenness

ranging between -1 and +1 for each 30 x 30 m in raster calculator (Qi et al. 1994).

I used the geographic location of a point count (i.e., latitude and longitude) within

the modelling process to model spatial autocorrelation (Rangel et al. 2006) and identify

the geographic influence on species occurrence across the study area. Both the latitude

and longitude variables (i.e., geographic location) were constructed by using the 30 x 30

m study area raster file as a base layer. Each cell was converted from cell to centroid

point, and each point was assigned an arbitrary value representing cell geometry in

increasing values for latitude and longitude. Both latitude and longitude shape files were

then converted to a raster format using the point to raster tool.

I derived two topographic variables to assist in explaining the influence of local

habitat conditions on occurrence probability. Both the Compound Topographic Index

(CTI) and Heat Load Index (HLI) variables were constructed from a Digital Elevation

Model (DEM). The CTI is a function of both slope and upstream contribution to

determine wetness accumulation based on flow direction. This index reflects the state of

soil moisture across the landscape (Gessler et al. 1995). The HLI is a function of folding

the aspect so that the highest values are southwest and the lowest values are northeast

while also accounting for steepness of slope, and reflects the state of solar radiation

across the landscape (McCune and Keon 2002). I constructed the DEM by first

40

mosaicking the 1:50,000 National Topographic System map sheets tiles that intersected

the study area (Natural Resources Canada 2004a), using the model builder tool. I then

used the Fill DEM tool to smooth out imperfections in the DEM. To derive the CTI

gradient, I constructed raster files for flow direction, flow accumulation and contributing

areas based on the 30 x 30 m pixel resolution using the raster calculator tool. I used

Gessler et al.’s (1995) equation for calculating CTI in raster calculator to derive a new

raster reflecting an index of soil moisture for the study area. I used the HLI equation

provided by McCune and Keon (2002) in raster calculator to derive the Heat Load Index

based on the 30 x 30 m pixel resolution.

I used oil and gas well density as a proxy for the amount of oil and gas

disturbance on the landscape (Kalyn-Bogard 2011). I built the well density layer by first

determining the number of well sites, both active and inactive, occurring in the study area

(IHS Energy Group 2012). I then calculated how many well sites occurred within a circle

of 2.59 km2 (radius = 908 m which represented 1 square mile) for each cell in the 30 x 30

m study area raster file using the point density tool.

For each explanatory variable, I ran a moving window analysis at varying radii

(200 m, 400 m, 800 m, and 900 m) using a circular focal window with the focal statistics

tool. The varying radii represented the percentage of each variable in raster format

within the circular areas, which I then used to determine which scale was most relevant to

proceed with for analysis (Cunningham and Johnson 2006).

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2.2.5 Statistical Analysis

All statistical analyses were conducted in R statistical software program (R

Development Core Team 2013). Explanatory variables were standardised using a z-score

procedure, normalizing values to a mean of 0 and variance of 1, which optimised the

maximum likelihood estimators for use by the modelling algorithm (Fiske and Chandler

2011).

2.2.5.1 Determining Variable Scale and Collinearity

I calculated correlation coefficients for each covariate at the four scales (i.e.,

grass200 m, grass400 m, grass800 m, and grass900 m…woody200 m, woody400 m,

woody800 m, and woody900 m…) to examine correlation between spatial scales and

determine if multiple scales should be considered for investigation (Porter and Church

1987; Wiens 1989). Using the mean focal window estimates from the point count

locations conducted in 2012 (n=870), I determined that Pearson correlation coefficient at

all scales were highly correlated (r>.80), and therefore proceeded with analysis using data

from the 400 m scale (Fletcher and Koford 2002). While I acknowledge that bird

occurrence may be influenced by multiple landscape scales, the 400 m scale consistently

led to lower AIC values. Furthermore, the 400 m scale is consistent with the bird surveys

I conducted, closely resembles the area of a legal quarter section (the common unit of

land management and ownership; McKercher and Wolfe 1986), and has been identified

as a relevant scale in previous studies for the region (Davis et al. 2013). I then tested for

multicollinearity among each of the covariates at the 400 m extent. I found that CTI and

HLI were moderately correlated (r=-.72), but retained both to determine their relative

42

importance for each species (Table 2.2). All variables were inspected for non-linear

relationships.

2.2.5.2 Model Selection

I used occupancy models (unmarked package; Fiske and Chandler 2011) to

explain variation in grassland bird occupancy as a function of the five model classes (i.e.,

detection, land cover, geographic location, topography, and well density). Akaike’s

Information Criterion (AIC; Akaike 1974) was used to evaluate and rank all candidate

models (Burnham and Anderson 2002). Models with the lowest AIC score and highest

AIC weight (Wi) were considered to be the best to fit the data of those considered

(Burnham and Anderson 1998), and model averaging was used to deal with model

selection uncertainty for species with competing models.

I initially fit each species occupancy model for detection probability (i.e., date,

time of day, and wind), which was then kept constant to model the occurrence probability

component (Tyre et al. 2003; Mackenzie et al. 2006). I examined all subsets and ranked

performance independently for each model class against a null model. The top candidate

model set from the detection, land cover, topography, geographic location, and well

density model classes were combined to form the global model.

I identified all models within a cumulative weight of 95% and only included

models which contained the grassland variable (MuMIN package; Barton 2013). I based

all predictions on the model averaged parameter estimates, which were weighted with a

variable shrinkage factor depending on how important each variable was across

competing models.

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2.2.5.3 Model Evaluation

I evaluated each species model using two approaches: 1) using the independent

dataset collected in 2011, and 2) by randomly partitioning point count data from 2012. In

both cases, I created Receiver Operator Characteristic (ROC) plots, calculated Area

Under the Curve (AUC), and calculated Kappa statistics (PresenceAbsence package;

Freeman and Moisen 2008). The AUC score for each species model represents the

likelihood that the model correctly identifies a true positive detection for a species that is

higher than a false positive detection. According to Pearce and Ferrier (2000), models

are deemed “useful” for predicting occupancy if AUC values > 0.7, “good” if values >

0.8, and “excellent” if AUC values > 0.9. Unlike the ROC approach, the Kappa statistic

is a threshold-dependent performance measure (Pearce and Ferrier 2002). For Kappa, I

used the true skill statistic recommended by Allouche et al. (2006) to select a threshold

for model evaluation and comparison because it minimises the mean error rate for

positive and negative observations. According to Landis and Koch’s (1977) guidelines,

models with Kappa values k<0.2 are considered “poor”, 0.2<k>0.4 are considered “fair”,

0.4<k>0.75 are considered “good”, and k>0.75 are considered “excellent”. I deemed

species occupancy models “reliable” if AUC > 0.7 and Kappa > 0.2 under both

evaluation data sets, “marginal” in a models ability to predict if they were lacking in a

performance test, and “unreliable” if no test result was successful.

I used Huberty’s (1994) rule to determine the partitioning ratio of training to

testing data needed for evaluation purposes with the 2012 data set. I used their equation

and found that 25% was needed to evaluate models with the 2012 data and therefore

44

partitioned the count data into 75% for building the models (n = 652) and 25% for

evaluating (n = 218) the models. Based on the 25% ratio for testing data, I specified 25%

of the points from each transect using a random number generator.

Imperfect detection is a common problem associated with most evaluation data

sets. For example, the point count records I collected in 2011 do not explicitly account

for imperfect detection, and therefore are not considered the same currency as the data I

used to build my occupancy models. In the case of partitioned data, Rota et al. (2011)

recommended adjusting the testing data for imperfect detection as it yields better

performance results. Therefore, since the problem of imperfect detection was still present

with my 2012 testing data, I used equation 1 to adjust predictions of occupancy for

imperfect detection, where Ψ represents occurrence probability and ρ is detection

probability.

EQUATION 1

The adjustments were calculated by using the model averaged fixed effects from the

detectability model to predict detection probability for each visit to survey sites. I then

multiplied the predicted probability of occurrence by the predicted probability of

detection to arrive at a prediction of occupancy at each site, assuming heterogeneous

detection probability.

2.2.5.4 Uninformative Parameters and Predictions

Since the primary goal of my research was to predict occupancy, I identified and

then discarded variables that had no discernible effect on occupancy predictions for my

final models (e.g., Pagano and Arnold 2009). I sequentially eliminated the least

45

important covariate from each species global model identified using the model parameter

estimate and standard error. If eliminating a covariate led to a reduction in AIC and no

net loss of the models predictive performance (i.e., Kappa and AUC), I considered the

variable uninformative and discarded it from the model (Arnold 2010). This approach

was continued until no additional covariate could be eliminated without leading to an

increase in AIC or decrease in predictive performance. I used 95% confidence intervals

(CI) to evaluate global models and only discuss parameters whose slopes do not include

zero. I used the parameter estimates from each species final model to predict species

occurrence for each 30 x 30 m pixel across the study area (raster package; Hijmans and

van Etten 2013).

2.3 Results

2.3.1 General Results

I conducted 870 point counts in 2012, and analysed detection/non-detection

records for 14 grassland bird species detected at 40 or more locations (Table 2.3). I made

an exception for McCown’s Longspur, which was modelled given its status as a priority

species. The most frequently occurring species was Western Meadowlark, with the

highest probability of occurrence (Ψ=.99) and probability of detection (ρ =.91), followed

by Vesper Sparrow, Savannah Sparrow, Brown-headed Cowbird, Clay-colored Sparrow,

Horned Lark, Sprague’s Pipit, Baird’s Sparrow, Marbled Godwit, Willet, Chestnut-

collared Longspur, Long billed Curlew, Upland Sandpiper, and McCown’s Longspur

(Table 2.3).

I used the top model to estimate occupancy for Chestnut-collared Longspur,

Upland Sandpiper, and Western Meadowlark, as the model selection process indicated no

46

competitive models within a cumulative AIC weight (wi) <0.95. I used the model

averaging results for the remaining species to estimate occupancy because of model

selection uncertainty (Table 2.4). Variable shrinkage factors on coefficient weights used

to model occupancy are provided in Appendix A. Table A.1. The detection models that

best explained the data typically included both date and time of day (Table 2.5). In

addition to the amount of grassland in the landscape, occurrence probability was best

explained by topographic and geographic location variables. In particular, grassland

songbird occurrence was often best explained by a combination of the modified soil-

adjusted vegetative index, compound topographic index, and heat load index, whereas

shorebird occurrence was most often associated with surface water and the compound

topographic index.

2.3.2 Detection Probability

Date influenced detection rates positively for Baird’s Sparrow, Chestnut-collared

Longspur, Horned Lark, Sprague’s Pipit, and negatively for Western Meadowlark and

Willet (Figure 2.3). Surveyors were more likely to detect Western Meadowlark and

Willet earlier in the season whereas Baird’s Sparrow, Chestnut-collared Longspur,

Horned Lark, and Sprague’s Pipit were more readily detected as the breeding season

progressed. Baird’s Sparrow, Clay-colored Sparrow, Long-billed Curlew, Savannah

Sparrow, Vesper Sparrow, and Western Meadowlark were most likely to be detected near

sunrise (Table 2.5). Conversely, detection of Horned Lark, Marbled Godwit, Sprague’s

Pipit, and Upland Sandpiper increased later in the morning. Increasing wind speeds

47

negatively influenced detection rates for Clay-colored Sparrow, Marbled Godwit,

Savannah Sparrow, Sprague’s Pipit, and Vesper Sparrow (Table 2.5).

2.3.3 Occurrence Probability

Brown-headed Cowbird, Horned Lark, Long-billed Curlew, Marbled Godwit, and

Vesper Sparrow probability of occurrence was negatively associated with the amount of

grassland habitat on the landscape (Table 2.5). This suggests that the occurrence of these

species was positively associated with the amount of cropland in the landscape since both

were inversely correlated (r=-0.87). In contrast, Baird’s Sparrow, Chestnut-collared

Longspur, and Sprague’s Pipit exhibited a strong positive association with increasing

grassland cover in the landscape (Table 2.5). Long-billed Curlew was the only species

that was negatively influenced by increasing proportion of woody vegetation on the

landscape (Table 2.5). Brown-headed Cowbird and Clay-colored Sparrow occurrence

was positively correlated with the modified soil-adjusted vegetation index, selecting areas

with increased green vegetation (Figure 2.4). Long-billed Curlew, McCown’s Longspur,

and Vesper Sparrow occurrence was negatively influenced by the presence of surface

water on the landscape (Table 2.5).

Occurrence of Baird’s Sparrow, Long-billed Curlew, Marbled Godwit, Sprague’s

Pipit, Upland Sandpiper, and Western Meadowlark was negatively correlated with the

compound topographic index, indicating they selected more xeric areas (Figure 2.5). In

contrast, Clay-colored and Vesper sparrows selected areas that were more mesic. The

occurrence of Baird’s Sparrow, Brown-headed Cowbird, Chestnut-collared Longspur,

Horned Lark, and Savannah Sparrow were negatively correlated with the heat load index,

48

indicating they were more likely to occur in areas that were exposed to less solar

radiation (Figure 2.6).

Latitude and longitude influenced occurrence probability for all species except

Brown-headed Cowbird and Upland Sandpiper (Table 2.5; Appendix B. Figure B.1, B.3-

B.10, B.12-B.14). Horned Lark, McCown’s Longspur, and Sprague’s Pipit occurrence

was most common towards the eastern portion of the study area (Appendix B. Figure B.5,

B.8, B.10) whereas Savannah Sparrow was most common towards the western portion

(Appendix B. Figure B.9). Baird’s Sparrow, Chestnut-collared Longspur, Horned Lark,

Long-billed Curlew, Marbled Godwit, McCown’s Longspur, Sprague’s Pipit, and

Western Meadowlark occurrence decreased towards the northern portion of the study

area whereas Clay-colored and Vesper sparrow occupancy increased northwards

(Appendix B. Figure B.1, B.3-8, B.10, B.12, B.13). Well density did not influence

occurrence of any of the grassland songbird species, but the occurrence of Long-billed

Curlew and Marbled Godwit were positively correlated with well density and Willet

occurrence was negatively correlated (Table 2.5).

2.3.4 Model Evaluation

I found that predictive performance typically increased once the 2012 testing data

were adjusted to account for imperfect detection (Table 2.6). I therefore evaluated

species models for predictive power using the adjusted testing data from 2012, and

determined that 8 of the 14 models were considered reliable based on receiver operating

characteristic curves and the AUC scores (Table 2.7). The model performances for

Western Meadowlark and Chestnut-collared Longspur were considered excellent

49

(AUC>0.90). Predictive performance of the McCown’s Longspur and Baird’s Sparrow

models were considered good (0.80<AUC>0.90), and the Sprague’s Pipit, Vesper

Sparrow, Horned Lark, and Savannah Sparrow models were considered useful

(0.70<AUC>0.80). Models for the remaining species did not adequately predict

occupancy (AUC<0.70). I found 11 of 14 species models were suitable predictors of

occupancy (k>0.20) when evaluated with the Kappa statistic. Baird’s Sparrow, Chestnut-

collared Longspur, Western Meadowlark, Sprague’s Pipit, and Vesper Sparrow were

considered good (0.40<k>0.75). Horned Lark, Willet, Savannah Sparrow, Clay-colored

Sparrow, Brown-headed Cowbird, and Marbled Godwit models were considered fair

(0.20<k>0.40). McCown’s Longspur, Long-billed Curlew and Upland sandpiper models

were considered poor and not useful predictors of occupancy (k<0.20).

I found that 6 of the 14 species models, when evaluated with the 2011 data that

was not corrected for detection bias, had suitable predictive performance based on

receiver operating characteristic curves and the AUC scores (Table 2.7). McCown’s and

Chestnut-collared longspurs models were considered good (0.80<AUC>0.90). Sprague’s

Pipit, Western Meadowlark, Baird’s Sparrow, and Clay-colored Sparrow models were

considered useful (0.70<AUC>0.80) but models for the remaining species did not

adequately predict occupancy. I found that 7 of 14 species models were suitable

predictors of occupancy when evaluated for Kappa. Chestnut-collared Longspur,

Sprague’s Pipit, Baird’s Sparrow, Clay-colored Sparrow, Western Meadowlark, Horned

Lark, Savannah Sparrow models were all considered fair (0.20<k>0.40) but the models

for the remaining species did not adequately predict species occupancy (k<0.20).

50

Overall, occupancy models tended to have the greatest predictive performance

when detection probability was moderate to high (ρ >0.5). Based on the combined

evaluation results of Kappa and AUC statistics, Baird’s Sparrow, Chestnut-collared

Longspur, Sprague’s Pipit, and Western Meadowlark models adequately predicted

occupancy across the study area (Table 2.7). However, Clay-colored Sparrow, Horned

Lark, Savannah Sparrow, and Vesper Sparrow models were marginal in their ability to

adequately predict occupancy, and the Brown-headed Cowbird, Long-billed Curlew,

Marbled Godwit, McCown’s Longspur, Upland Sandpiper, and Willet models were

unreliable.

2.4 Discussion

The usefulness of a species distribution model relies heavily on its predictive

performance (Fielding and Bell 1997; Guisan and Zimmermann 2000; Barry and Elith

2006). Tests for both AUC and Kappa are often used to measure the predictive

performance of a species distribution model, though AUC has received more attention

(Fielding and Bell 1997; Liu et al 2011). I evaluated the predictive performance of each

species occupancy model using two sets of testing data and tests for AUC and Kappa, as

both statistical measures are useful in comparing a model’s predictive performance, but

have known drawbacks. My results indicate that models for four species - Baird’s

Sparrow, Chestnut-collared Longspur, Sprague’s Pipit, and Western Meadowlark - were

“reliable” predictors of occupancy, and thus useful as conservation tools. Although the

McCown’s Longspur model performed within reliable limits when evaluated for AUC,

the model was deemed unsuccessful at predicting occupancy when evaluated using the

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Kappa statistic; possibly because the statistic is strongly influenced by species prevalence

(Fielding and Bell 1997). This underscores the need to test for both discrimination

capacity and reliability (Austin 2007; Lobo et al. 2008). Unfortunately, the ten remaining

distribution models remain unreliable and require further research to develop accurate

predictions of occupancy.

2.4.1 Accounting for Imperfect Detection

I attempted to reduce the effects of detection variability by restricting counts to

certain calendar periods, time of day, and weather conditions. I also minimised the

effects of observer bias, roadside sampling bias, and within-season breeding dispersal.

Although I acknowledge that not all factors known to affect detectability were accounted

for (e.g., habitat features, predation, and social interaction), I further reduced detection

variability statistically, by adjusting point count data for imperfect detection (i.e., date,

time of day, wind). Thus, robust estimates of detection probability were used to build the

occupancy models, with many aspects of detection bias accounted for.

My results show that the detectability of seven species was influenced by survey

date. For temperate North American grasslands, Ralph et al. (1995) suggested that late

May to the first week in July was the period where most species are vocal and therefore

detected, but acknowledged that peak breeding activity often overlaps with this period.

Variability in singing rates have been hypothesised to be correlated with the breeding

stage of individuals (Diehl 1981; Wilson and Bart 1985), and males typically sing more

frequently during the mate selection stage or while establishing and defending a breeding

territory. As the reproductive season progresses towards incubation, and brood rearing,

52

males are less likely to defend breeding territories. For instance, I found that the

detectability of Western Meadowlark and Willet decreased throughout the survey season,

likely following the progression from egg-laying to brood rearing or lack of territory and

mate defence due to reproductive failure. Whereas, I found Baird’s Sparrow, Chestnut-

collared Longspur, and Sprague’s Pipit detectability increased as the survey season

progressed, indicative of later arriving migratory species that are in the mate selection

stages, or establishing and defending territories. I also established that the detectability

of Horned Lark increased as the survey season progressed, suggesting this early

migratory species may be re-nesting after nest failure or initiating multiple clutches and

still defending breeding territories.

My results indicate that the time of day a survey was conducted influenced the

detectability of nine species. Many species display temporal variation in singing

behaviour during the day that influences detectability (Robbins 1981; Johnson 1995;

Diefenbach et al. 2007; Elphick 2008). For example, most songbirds concentrate singing

activity during a three to five hour period after sunrise (Ralph et al. 1995, Staicer et al.

1996). My findings are consistent with this as the detectability of Baird’s Sparrow, Clay-

colored Sparrow, Long-billed Curlew, Savannah Sparrow, and Western Meadowlark was

greater earlier in the morning, a time when songbirds sing most intensely and frequently

(Staicer et al. 1996). Horned Lark, Marbled Godwit, Sprague’s Pipit, and Upland

Sandpiper peak vocalization periods occurred later in the morning, perhaps due to

increased temperature or enhanced visual display.

Wind speed can also influence detectability (Anderson and Ohmart 1977; Johnson

2008). I found that wind speed influenced the detection of six species despite restricting

53

my surveys to days and times when wind speed was <20 km/h. The degree to which

wind affects acoustic surveys depends on a combination of factors including the species,

habitat, and an observers hearing ability (Johnson 2008). Standardised count

methodology recommends that surveys should be discontinued if wind speeds reach 20

km/hr (Ralph et al. 1995), although the slightest of breezes can influence the ability of an

observer to detect singing males (Johnson 2008). My results suggest that the detectability

of Sprague’s Pipit, a species that primarily sings while conducting aerial displays

(Robbins and Dale 1999), was negatively influenced by wind. On the other hand, I found

a positive relationship between wind speed and the detectability of McCown’s Longspur,

a species which also sings while conducting aerial displays. Although McCown’s

Longspur displays are much shorter and closer to the ground (~10 m; With 2010), the

positive relationship to wind I found for this species is likely influenced by an observer

effect. Another conceivable explanation is that the small number of McCown’s Longspur

occurrences (detected at 3% of the point count locations in 2012) was correlated simply

by chance with higher amounts of wind. I also found that wind negatively influenced

detectability of Clay-colored, Savannah, and Vesper sparrows and highlights the

importance of ensuring that surveys are conducted during relatively calm conditions.

2.4.2 Resource Selection

My results show that integrating count data with landscape level features is an

effective way to understand resource selection by grassland birds. Occupancy models

have the implicit assumption that a species distribution can be accurately characterised,

and therefore predicted from spatial data (MacKenzie et al. 2006; Elith and Leathwick

54

2009). This relationship allows for distributions to be mapped without having to collect

fine-scale habitat data, which can be costly to acquire and often increases complexity and

uncertainty in the analysis (Cunningham and Johnson 2006). A substantial number of

metrics have proven to be useful in describing avian resource selection at the landscape

scale (Fahrig 2003). Identifying the amount of a targeted land cover type provides clear

recommendation for conservation management (Cunningham and Johnson 2011). I used

the percentage of grassland in a landscape as a broadly influential metric to explain

resource selection. Some grassland birds are known to be area sensitive (Johnson and Igl

2001; Ribic et al. 2009), and have also been shown to respond to metrics representing

vegetative structure and composition of plant communities across the landscape (Davis

2004). Habitat diversity or variation in the vegetation structure across the landscape is

related to moisture, aspect, topography, and disturbance patterns (Ribic et al. 2009).

Therefore, I further attempted to explain within-cover type variability by modelling

additional factors with known ecological relationships at the landscape scale. Explaining

the complex factors that influence where a bird occurs on the landscape is difficult;

however the occupancy estimates I generated provide a more complete explanation of

resource selection relative to cover type alone, and can aid in future conservation

management.

2.4.2.1 Response to Cover Type

The models I evaluated show that grassland bird occurrence varied with the

amount of grassland cover within 400 m of the survey location. The amount of grassland

cover had a positive influence for nine species indicating that these species were more

55

common in native and tame grassland landscapes. The selection of grassland habitats for

breeding by most of these species has been well documented (Owen and Myers 1973;

Johnson et al. 2004; Davis et al. 2013). Many species of birds found in grassland habitats

are uniquely adapted to the grassland biome and depend on it for various stages of their

life histories. However, some grassland birds now primarily depend on

anthropogenically altered habitats within the ecosystem to survive and reproduce. I

found that Brown-headed Cowbird, Horned Lark, Long-billed Curlew, and Vesper

Sparrow were negatively correlated with grassland cover, suggesting these species select

a variety of habitats to breed in, including cropland (Davis and Duncan 1999; Johnson et

al. 2004; Coppedge et al. 2006; Davis et al. 2013).

I found that occurrence patterns for six species were influenced by the modified

soil-adjusted vegetation index. Identifying where species more commonly occur in

response to changing plant communities across the landscape can be valuable for

providing a more complete explanation of resource selection, relative to cover type alone

(Cunningham and Johnson 2011). The modified soil-adjusted vegetation index I

constructed is correlated with the photosynthetic activity of vegetation, total plant cover,

bare ground, and plant biomass on the landscape, which was represented as the amount of

green vegetation within 400 m. Relationships between occupancy and amount of green

vegetation are not particularly well documented, but the modified soil-adjusted

vegetation index identifies similar vegetative characteristics to the known breeding

habitat requirements of grassland birds. For instance, Baird’s Sparrow, Chestnut-collard

Longspur, and Sprague’s Pipit are associated with grassland cover with short to moderate

vegetation (i.e., 20 - 100 cm) and low to moderate litter accumulation (Johnson et al.

56

2004), areas that the modified soil-adjusted vegetation index characterises across the

landscape as having lower amounts of green vegetation. A positive response to green

vegetation was identified for Clay-colored Sparrow, a species which tends to occur in

areas with high cover diversity, and moderate to high amounts of herbaceous biomass

(Davis and Duncan 1999).

I found little evidence that the amount of woody cover influenced grassland bird

resource selection. The negative response I found for Long-billed Curlew is similar to

that from other studies (Dugger and Dugger 2002). Long-billed Curlew prefers open

nesting areas with short vegetative cover, and also tend to use cropland dominated

landscapes for foraging, roosting, and nesting (Prescott 1997; Dechant et al. 2003). For

birds in grassland habitats, authors have often reported that the amount of woody cover

can be an important predictor of species occurrence (McMaster and Davis 2001: Bakker

et al. 2002; Fletcher and Koford 2002). For example, Prescott et al. (1995) and Davis and

Duncan (1999) determined Clay-colored Sparrows select habitats in areas with increased

woody cover. Conversely, McMaster and Davis (2001) and Davis et al. (1999) reported

that Chestnut-collared Longspur, McCown’s Longspur, and Sprague’s Pipit occur more

frequently in areas with low visual obstruction and little or no woody vegetation, perhaps

as a predator avoidance strategy. For instance, nest predation of McCown’s Longspur in

Colorado was six times higher when shrub cover was present within 1 m of the nest

(With 1994). Therefore, my findings, or lack thereof in some cases, suggest that woody

cover within 400 m may not be as strong of a predictor of species occurrence at the

landscape level, and examining smaller scale responses may better explain resource

selection. Another conceivable explanation for the weak response to woody cover that I

57

found is that the spatial data used to generate the values for the woody cover variable

were not accurate.

My models show that the amount of surface water within 400 m influenced

occurrence of four species. I hypothesised that the amount of surface water on the

landscape would positively influence water-dependent species. I found negative

responses to surface water for Long-billed Curlew and Vesper Sparrow, species that

usually occur in drier sites (Prescott et al. 1995; Gratto-Trevor 2000). My results show

Willet was positively influenced by the amount of surface water on the landscape,

possibly reflecting their occurrence at foraging sites. I also found McCown’s Longspur

occurrence was positively influenced by surface water, even though this species has been

said to prefer drier, sandier sites (Wershler et al. 1991). This longspur may be selecting

marginal breeding areas in my study area because this is the edge of its range. Another

plausible explanation is that the small number of McCown’s Longspur detections

(detected at 3 % of the point count locations in 2012) was correlated simply by chance

with higher amounts of surface water (i.e., location of the Red Deer River).

2.4.2.2 Responses to Topographic Landscape Features

I found evidence that eight of fourteen species responded to the compound

topographic index I used to assess variation in levels of soil moisture across the study

area. The compound topographic index takes into account topographic features including

slope, flow accumulation, flow direction, and contributing area, to form a representation

of the amount of soil moisture across the landscape. Soil moisture is known to influence

structural changes in plant communities and avian food sources (Wiens 1974; Cody 1985;

58

Wiens 1989). I found that six species selected breeding areas that were low lying and

thus able to retain more moisture. My results further show that two species, Clay-colored

and Vesper sparrows, selected areas that are less likely to accumulate moisture.

Determining why species respond to the compound topographic index at the landscape

level is difficult. My findings are not consistent between species, and broad scale

relationships between grassland birds and levels of soil moisture may also reflect

occurrence responses to topographic landscape features, structural changes in plant

communities, and annual precipitation events. Therefore, grassland bird responses to soil

moisture at the landscape level deserve further research, which will help to more

accurately describe species response patterns and lead to a better understanding of these

relationships.

Six of the species for whom I had enough data, occurred more frequently in

locations exposed to lower amounts of solar radiation. The heat load index I constructed

uses slope, aspect, and latitude to provide a measure of the variation in solar radiation

across the landscape. Most often, solar radiation is measured using proxy variables such

as slope or aspect, which often have indirect effects on the distribution of a species

(Austin 2002; Elith and Leathwick 2009). I found Baird’s Sparrow, Brown-headed

Cowbird, Chestnut-collared Longspur, Horned Lark, Savannah Sparrow, and Willet to

select sites in areas that received less solar radiation, perhaps due to the resulting

structural changes in plant communities produced by variation in the amount of solar

radiation. There are few published examples of bird distributions in relation to solar

radiation. This can be interpreted to mean that solar radiation is not a primary driver of

resource selection but it could also mean that it simply has not been considered to date.

59

Yet, high exposure to solar radiation has been shown to influence habitat diversity on the

landscape (McCune and Keon 2002; Natural Regions Committee 2006) and has been

hypothesised to reduce cold stresses associated with breeding birds (With and Webb

1993). Local variation in solar radiation across the landscape has also been suggested to

provide critical climate refugia (Austin and Niel 2010), perhaps influencing where

species occur. Therefore, the relationships I found between species occurrence and solar

radiation deserve further attention.

2.4.2.3 Influence of Geographic Location

I found that latitude and longitude were useful in predicting the geographic

location of where a species was likely to occur. Considering the large study area (20,000

km2), transition from prairie to parkland, and various anthropogenic activities occuring, I

predicted species occurrence would vary North-to-South and East-to-West. In addition to

broad-scale landscape changes in the study area, occurrence responses to the latitude and

longitude variables may also reflect gradients in precipitation, population distributions,

and other landscape characteristics (Niemuth et al. 2005; Elith and Leathwick 2009).

Higher occurrence was more common in the drier and less woody southern portion of the

study area for eight species, as compared to the more mesic and wooded areas to the

north. With the exception of Horned Lark, the distributions of these eight species are

near to their known northern limits in Alberta (Semenchuk 2007). In particular,

McCown’s Longspur was restricted to the south-eastern corner of the study area, likely

delineating the northwest edge of this species North American range. I also found that

Vesper and Clay-colored sparrow occurrence increased towards the northern portion of

60

the study area. This is consistent with their known distributions that extend into more

mesic and wooded areas (Semenchuk 2007). My models show that Horned Lark,

McCown’s Longspur, and Sprague’s Pipit occurrence increase towards the eastern

boundary of the study area, whereas Savannah Sparrow increased towards the western

boundary. Both Horned Lark and Savannah Sparrow occur further north and west than

my study area; therefore I consider my findings for these species to be indicative of a

regional selection within their overall range, perhaps due to the land cover matrix within

the study area. For example, Savannah Sparrows occur more commonly in areas without

extensive cultivation (McMaster and Davis 2001), whereas Horned Larks tend to occupy

cultivated areas (Owen and Myers 1973). The responses to longitude I found for

McCown’s Longspur and Sprague’s Pipit are consistent with these birds, likely reaching

the western extent of their ranges within the study area (Semenchuk 2007).

2.4.2.4 Influence of Well Density

Anthropogenic disturbances are likely to influence where a bird may occur on the

landscape (Askins et al. 2007), and our ability to characterise grassland bird responses to

these disturbances is limited. Oil and gas wells have a cumulative disturbance on the

landscape which includes the construction of roads and well pads, noise, vehicular traffic,

invasive plants, and soil compaction that may reduce both habitat quality and quantity

(Askins et al. 2007; Leu et al. 2008). I predicted that species would exhibit a strong

negative response to oil and gas well densities, especially in the southwest portion of my

study area where the degree of disturbance is high. However, the relationship between

species occurrence and well density was weak, and in the case of some shorebirds,

61

opposite to what I expected. Both Long-billed Curlew and Marbled Godwit were more

common in areas with higher well densities. Willet occurred more in areas with lower

well densities. Although I found that some species do respond negatively with increasing

well densities, the responses were not as strong as expected. Other studies in the mixed

northern grasslands (Dale et al. 2009; Hamilton et al. 2011; Kalyn Bogard and Davis

2014) have reported correlations between avian abundance and well density to vary

between species. For example, both Hamilton et al. (2011) and Kalyn Bogard and Davis

(2014) suggest that the abundance of the generalist Savannah Sparrow tends to increase

in areas with higher gas well densities. Sprague’s Pipit response varied between studies

as Hamilton et al. (2011) found a weak negative effect of well density on abundance

while Kayln Bogard and Davis (2014) found that abundance was not influenced by well

proximity or density. It is possible that some of the species I investigated respond to well

density in a specific manner, however I suggest that other factors, or combinations

thereof, need to be explored. For instance, refining the oil and gas well data by

separating active from inactive well sites may lead to improved estimates of occurrence

and identify specific ecological response patterns due to noise and increased human

activity on the landscape.

2.5 Conclusions

Understanding and accounting for potentially complex species-habitat

relationships is challenging. However, my results demonstrate that occupancy modelling

holds promise because it accounts for imperfect detection, while providing quantitative

insights into the magnitude and significance of species-habitat relationships. I found that

62

variation in seasonal breeding behaviour exists, and adjusting count data for imperfect

detection led to improved estimates of occupancy. Among the geographic and

environmental variables I used to predict occupancy, the composition of grassland cover

was a strong predictor of occurrence across species. Identifying species-specific

responses to grassland composition is useful for understanding where species occur on

the landscape, which provides a tool that conservation practitioners can use for

protecting, managing, and restoring habitats. By incorporating additional location-

specific landscape level factors associated with vegetation composition, topography, and

geographic location, my results provide a more complete explanation of resource

selection by the species I studied. My study also demonstrates that occupancy modelling

approaches hold promise for making justifiable inferences about species distribution. At

the same time, some of my predictions of species occupancy were weak and require

further investigation to develop more accurate predictions.

Overall, occupancy models provide intuitive, spatially referenced predictions that

conservation practitioners can incorporate into future conservation planning. The

modelling equations can be used to create maps of species distributions on the landscape,

which can be used to guide future management strategies, justify funding for research,

and prioritise conservation activities. Lastly, the modelling algorithm is extremely

flexible and can be extended to include occupancy based surveys and estimates of other

taxa. For example, if detection/non-detection data were readily available for other avian

species-at-risk such as Ferruginous Hawk (Buteo regalis) or Loggerhead Shrikes (Lanius

ludovicianus), conservation practitioners could apply occupancy modelling to minimise

63

the effects of imperfect detection and better understand these species distributions within

their range.


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85

Figure 2.1. Geographic location of the Special Areas municipality (shaded grey) in

south-eastern Alberta, Canada.

86

Figure 2.2. Point count survey locations conducted in the Special Areas, 2011-2012.

Black circles indicate 2012 survey locations (visited using a repeated survey design) and

grey circles indicate 2011 survey location (visited using a removal sampling design).

87

Table 2.1. Description of each variable used to build the species occupancy models with

the detection/non-detection avian survey records collected in the Special Areas, 2012.

CLASS VARIABLE VARIABLE

CODE

VARIABLE

DESCRIPTION SOURCE REFERENCE

Detection

Date JDATE Ordinal date of

survey during the

survey period

Visit-

specific

Repeated

surveys, 2012

Time TIME Time of day survey

was conducted

Visit-

specific

Repeated

surveys, 2012

Wind WIND Wind (km/h) during

point count visit

Visit-

specific

Repeated

surveys, 2012

Land Cover

Percentage of

grassland

(400m radius)

GRASS Grassland and

Pastureland/Hayland

categories combined

Agriculture

and Agri-

foods

Canada

AAFC 2012

Percentage of

woody cover (400m radius)

WOODY Shrubland,

Coniferous, and Deciduous

categories combined

Agriculture

and Agri-foods

Canada

AAFC 2012

Percentage of

surface water

(400m radius)

WATER Watercourses,

Wetlands, and

Waterbodies

categories combined

GeoBase.ca Natural

Resources

Canada 2007

Percentage of

soil-adjusted

vegetative

index (400m

radius)

MSAVI Calculated from Red

band and Near-

infrared band data

GeoBase.ca Natural

Resources

Canada

2004b

Topography

Compound

topographic

index (400m

radius)

CTI Index of soil

moisture (DEM)

GeoBase.ca

(1:50,000

map sheets)

Natural

Resources

Canada 2004a

Heat load

index (400m

radius)

HLI Index of solar

radiation (DEM)

GeoBase.ca

(1:50,000

map sheets)

Natural

Resources

Canada 2004a

Geographic

location

Latitude YLAT North and South

geographic

distribution

30 x 30 m

study area

grid

Longitude XLONG West and East

geographic

distribution

30 x 30 m

study area

grid

88

Density

Well density

in km/2 (400m

radius)

WD Oil and Gas well

density

IHS Well

Data

(Canada):

online database

IHS Energy

Group 2012

89

Table 2.2. Pearson’s correlation coefficients for selected variables used to explain

species occurrence in the Special Areas, Alberta. Values are for the 400 m landscape

scale and were used to examine collinearity between predictor variables. Variable codes

are found in Table 2.1.

WD GRASS WOODY CTI HLI MSAVI WATER XLONG YLAT

WD 1.00

GRASS 0.10 1.00

WOODY 0.00 -0.14 1.00

CTI -0.04 0.14 -0.11 1.00

HLI 0.00 -0.03 0.22 -0.72 1.00

MSAVI -0.11 -0.11 0.18 -0.18 0.18 1.00

WATER -0.03 0.01 -0.04 0.14 -0.08 0.02 1.00

XLONG -0.02 -0.30 0.04 -0.27 0.20 0.05 -0.12 1.00

YLAT -0.11 -0.24 0.18 -0.04 0.12 0.48 0.04 0.08 1.00

90

Table 2.3. Ranked species frequency of occurrence (%) based on the 2012 data used to

build species occupancy models, with mean occurrence rates (Ψ) and detection

probabilities (ρ) from surveys conducted in the Special Areas, Alberta.

SPECIES

FREQUENCY

(% of sites

detected)

OCCURRENCE

PROBABILITY

(Ψ)

DETECTION

PROBABILITY

(ρ)

Western Meadowlark (Sturnella neglecta) 0.96 0.99 0.91

Vesper Sparrow (Pooecetes gramineus) 0.82 0.93 0.70

Savannah Sparrow (Passerculus sandwichensis) 0.79 0.84 0.76

Brown-headed Cowbird (Molothrus ater) 0.70 0.83 0.70

Clay-colored Sparrow (Spizella pallida) 0.69 0.76 0.74

Horned Lark (Eremophila alpestris) 0.62 0.75 0.59

Sprague's Pipit (Anthus spragueii) 0.48 0.45 0.67

Baird's Sparrow (Ammodramus bairdii) 0.39 0.33 0.61

Marbled Godwit (Limosa fedoa) 0.34 0.64 0.26

Willet (Tringa semipalmata) 0.27 0.55 0.20

Chestnut-collared Longspur (Calcarius ornatus) 0.21 0.07 0.59

Long-billed Curlew (Numenius americanus) 0.19 0.27 0.24

Upland Sandpiper (Bartramia longicauda) 0.11 0.18 0.67

McCown's Longspur (Rhynchophanes mccownii) 0.03 0.0002 0.19

91

Table 2.4. Top-ranking models explaining the influence of spatial and temporal variables

on species detection (ρ) and occurrence (Ψ) in the Special Areas, 2012. Models include a

detection component (date [JDATE], time [TIME], wind [WIND]) and occurrence

component (grassland [GRASS], woody cover [WOODY], vegetative greenness

[MSAVI], surface water [WATER], solar radiation [HLI], wetness [CTI], latitude

[YLAT], longitude [XLONG], and well density [WD]). Only those models with a

cumulative Akaike Information Criterion (AIC) weight (wi) of 0.95 for each species with

corresponding number of parameters (K), value of the negative log-likelihood function

(LogLik), and the change in AIC value from the top model (∆AIC) are presented.

SPECIES MODEL K LogLik ∆AIC wi

Baird's

Sparrow ρ(JDATE-TIME)Ψ(GRASS-HLI-MSAVI-YLAT) 8 -744.75 0.00 0.27

ρ(JDATE-TIME)Ψ(GRASS+CTI-HLI-MSAVI-YLAT) 9 -743.77 0.04 0.27

ρ(JDATE-TIME)Ψ(GRASS+CTI-HLI-YLAT) 8 -745.24 0.98 0.17

ρ(JDATE-TIME)Ψ(GRASS+CTI-MSAVI-YLAT) 8 -745.28 1.07 0.16

ρ(JDATE-TIME)Ψ(GRASS-HLI-YLAT) 7 -746.49 1.47 0.13

Brown-

headed

Cowbird

ρ(-JDATE)Ψ(-GRASS-HLI+MSAVI) 6 -1259.60 0.00 0.81

ρ(-JDATE)Ψ(-GRASS+MSAVI) 5 -1262.05 2.92 0.19

Chestnut-

collared

Longspur

ρ(JDATE)Ψ(GRASS-MSAVI-HLI-YLAT) 7 -445.78 0.00 1.00

Clay-

colored

Sparrow

ρ(-TIME-WIND)Ψ(GRASS-CTI+MSAVI+YLAT) 8 -1073.84 0.00 0.77

ρ(-TIME-WIND)Ψ(GRASS-CTI+YLAT) 7 -1076.60 3.53 0.13

ρ(-TIME)Ψ(GRASS-CTI+MSAVI+YLAT) 7 -1076.92 4.17 0.10

Horned

Lark ρ(JDATE+TIME)Ψ(-GRASS-HLI-MSAVI+XLONG-YLAT) 9 -1127.31 0.00 0.58

ρ(TIME)Ψ(-GRASS-HLI-MSAVI+XLONG-YLAT) 8 -1128.65 0.68 0.42

Long-billed Curlew

ρ(-TIME)Ψ(-GRASS+CTI-WATER+WD-WOODY-YLAT) 9 -481.91 0.00 0.35

ρ(-TIME)Ψ(-GRASS+CTI-WATER-WOODY-YLAT) 8 -483.53 1.23 0.19

ρ(-TIME)Ψ(-GRASS+CTI-WATER+WD-YLAT) 8 -484.20 2.58 0.10

ρ(-TIME)Ψ(-GRASS+CTI+WD-WOODY-YLAT) 8 -484.27 2.71 0.09

ρ(-TIME)Ψ(-GRASS+CTI-WATER-YLAT) 7 -485.59 3.36 0.07

ρ(.)Ψ(-GRASS+CTI-WATER+WD-WOODY-YLAT) 8 -484.77 3.71 0.05

ρ(-TIME)Ψ(-GRASS+CTI-WOODY-YLAT) 7 -485.94 4.06 0.05

92

ρ(.)Ψ(-GRASS+CTI-WATER-WOODY-YLAT) 7 -486.33 4.84 0.03

ρ(-TIME)Ψ(-GRASS+CTI+WD-YLAT) 7 -486.59 5.35 0.02

ρ(-TIME)Ψ(-GRASS-WATER+WD-WOODY-YLAT) 8 -485.74 5.65 0.02

ρ(-TIME)Ψ(-GRASS+CTI-YLAT) 6 -487.99 6.16 0.02

ρ(.)Ψ(-GRASS+CTI+WD-WOODY-YLAT) 7 -487.05 6.27 0.02

Marbled

Godwit ρ(TIME-WIND)Ψ(-GRASS+CTI-MSAVI+WD-YLAT) 9 -777.18 0.00 0.44

ρ(TIME)Ψ(-GRASS+CTI-MSAVI+WD-YLAT) 8 -779.12 1.89 0.17

ρ(WIND)Ψ(-GRASS+CTI-MSAVI+WD-YLAT) 8 -779.38 2.40 0.13

ρ(.)Ψ(-GRASS+CTI-MSAVI+WD-YLAT) 7 -780.53 2.71 0.11

ρ(TIME-WIND)Ψ(-GRASS+CTI+WD-YLAT) 8 -779.87 3.39 0.08

ρ(TIME)Ψ(-GRASS+CTI+WD-YLAT) 7 -781.74 5.13 0.03

ρ(-WIND)Ψ(-GRASS+CTI+WD-YLAT) 7 -782.11 5.87 0.02

McCown's

Longspur ρ(WIND)Ψ(GRASS+XLONG-YLAT) 5 -67.92 0.00 0.46

ρ(WIND)Ψ(GRASS-WATER+XLONG-YLAT) 7 -67.47 1.11 0.26

ρ(.)Ψ(GRASS+XLONG-YLAT) 5 -69.84 1.84 0.18

ρ(.)Ψ(GRASS-WATER+XLONG-YLAT) 6 -69.41 2.99 0.10

Savannah

Sparrow ρ(-TIME-WIND)Ψ(GRASS-HLI-XLONG) 7 -1105.64 0.00 0.52

ρ(-TIME)Ψ(GRASS-HLI-XLONG) 6 -1107.33 1.38 0.26

ρ(-TIME-WIND)Ψ(GRASS-HLI) 6 -1107.48 1.68 0.22

Sprague's Pipit

ρ(JDATE+TIME-WIND)Ψ(GRASS+CTI+XLONG-YLAT) 9 -862.64 0.00 0.38

ρ(JDATE+TIME)Ψ(GRASS+CTI+XLONG-YLAT) 8 -864.02 0.75 0.26

ρ(JDATE+TIME-WIND)Ψ(GRASS+CTI-YLAT) 8 -864.61 1.94 0.14

ρ(JDATE+TIME)Ψ(GRASS+CTI-YLAT) 7 -865.97 2.65 0.10

ρ(JDATE+TIME-WIND)Ψ(GRASS-YLAT) 7 -866.55 3.81 0.06

ρ(JDATE+TIME-WIND)Ψ(GRASS+XLONG-YLAT) 8 -865.61 3.94 0.05

Upland

Sandpiper ρ(TIME)Ψ(GRASS+CTI) 5 -315.86 0.00 1.00

Vesper

Sparrow ρ(-TIME-WIND)Ψ(-GRASS-CTI-WATER+YLAT) 8 -1183.51 0.00 0.56

ρ(-TIME)Ψ(-GRASS-CTI-WATER+YLAT) 7 -1184.75 0.47 0.44

Western

Meadowlark ρ(-JDATE-TIME)Ψ(GRASS+CTI-YLAT) 7 -652.94 0.00 1.00

Willet ρ(-JDATE)Ψ(GRASS+CTI-HLI+WATER-WD) 8 -651.93 0.00 0.21

ρ(-JDATE)Ψ(GRASS+CTI-HLI-WD) 7 -653.20 0.55 0.16

ρ(-JDATE)Ψ(GRASS-HLI+WATER-WD) 7 -653.27 0.68 0.15

ρ(-JDATE)Ψ(GRASS+CTI-HLI+WATER) 7 -653.75 1.64 0.09

ρ(-JDATE)Ψ(GRASS+CTI-WD) 6 -654.80 1.75 0.09

ρ(-JDATE)Ψ(GRASS+CTI+WATER-WD) 7 -653.80 1.75 0.06

ρ(-JDATE)Ψ(GRASS-HLI+WATER) 6 -655.14 2.43 0.06

ρ(-JDATE)Ψ(GRASS-HLI-WD) 6 -655.18 2.52 0.06

ρ(-JDATE)Ψ(GRASS+CTI-HLI) 6 -655.23 2.61 0.06

ρ(-JDATE)Ψ(GRASS+CTI+WATER) 6 -655.48 3.10 0.04

93

Baird’s Sparrow Chestnut-collared Longspur

Sprague’s Pipit Western Meadowlark

Figure 2.3. Species relationships between probability of detection (ρ) and date within the

Special Areas of Alberta, 2012. Each figure is based on the same standardised scale of

mean = 0 and variance = 1, where negative values represents survey conducted earlier in

the survey season and positive values represent surveys conducted later in the survey

season. The 95% confidence intervals are indicated by the shaded grey area.

94

Table 2.5. Coefficient estimates (β) and unconditional standard errors (SE) for intercepts and variables used to predict species

occupancy in the Special Areas, 2012. Variables used to explain the detection probability (ρ) and occurrence probability (Ψ)

are presented separately and zeros indicate that a covariate was considered but not included in the final model. Species

abbreviations are BAIS (Baird’s Sparrow), BHCO (Brown-headed Cowbird), CCLO (Chestnut-collared Longspur), CCSP

(Clay-colored Sparrow), HOLA (Horned Lark), LBCU (Long billed Curlew), MAGO (Marbled Godwit), MCLO (McCown’s

Longspur), SAVS (Savannah Sparrow), SPPI (Sprague’s Pipit), UPSA (Upland Sandpiper), VESP (Vesper Sparrow), WEME

(Western Meadowlark), and WILL (Willet).

DETECTION

PROBABILITY (ρ) BAIS BHCO CCLO CCSP HOLA LBCU MAGO MCLO SAVS SPPI UPSA VESP WEME WILL

INTERCEPT (β) 0.429(0.09) -0.063(0.07) 0.349(0.12) 1.044(0.07) 0.367(0.07) -1.163(0.81) -1.058(0.11) -1.452(0.52) 1.157(0.06) 0.723(0.08) -1.392(0.25) 0.831(0.06) 2.350(0.09) -1.389(0.15)

JULIAN DATE 0.440(0.09) -0.300(0.06) 0.821(0.12) 0 0.109(0.07) 0 0 0 0 0.354(0.08) 0 0 -0.420(0.08) -0.283(0.09)

TIME -0.310(0.08) 0 0 -0.198(0.07) 0.310(0.06) -0.218(0.09) 0.139(0.07) 0 -0.208(0.06) 0.292(0.08) 0.304(0.14) -0.331(0.06) -0.350(0.08) 0

WIND 0 0 0 -0.157(0.06) 0 0 -0.137(0.08) 0.443(0.23) -0.113(0.06) -0.128(0.08) 0 -0.086(0.05) 0

OCCURRENCE

PROBABILITY (Ψ) BAIS BHCO CCLO CCSP HOLA LBCU MAGO MCLO SAVS SPPI UPSA VESP WEME WILL

INTERCEPT (β) -0.717(0.13) 1.56(0.20) -2.633(0.27) 1.133(0.12) 1.088(0.14) -1.018(0.24) 0.587(0.29) -8.310(2.19) 1.694(0.13) -0.196(0.11) -1.499(0.26) 2.644(0.34) 4.397(0.44) 0.194(0.27)

GRASSLAND 1.424(0.16) -0.428(0.21) 1.643(0.28) 0.388(0.11) -1.124(0.17) -0.679(0.20) -0.047(0.16) 0.449(0.61) 0.839(0.11) 1.507(0.15) 0.201(0.17 -1.367(0.42) 0.591(0.21) 0.122(0.16)

SHRUBLAND 0 0 0 0 0 -0.731(0.43) 0 0 0 0 0 0 0 0

VEGETATIVE GREENNESS -0.247(0.14) 0.379(0.17) -0.531(0.23) 0.275(0.12) -0.371(0.14) 0 -0.418(0.19) 0 0 0 0 0 0 0

SURFACE WATER 0 0 0 0 0 -0.344(0.17) 0 -0.613(0.72) 0 0 0 -0.353(0.12) 0 0.298(0.19)

SURFACE WATER2

0 0 0 0 0 0 0 0 0 0 0 0 0 0

WETNESS 0.294(0.18) 0 0 -0.502(0.11) 0 0.4756(0.17) 0.937(0.19) 0 0 0.239(0.11) 0.766(0.17) -0.494(0.14) 0.424(0.25) 0.522(0.28)

HEAT LOAD -0.393(0.18) -0.261(0.11) -0.846(0.21) 0 -475(0.11) 0 0 0 -0.454(0.10) 0 0 0 0 -0.528(0.27)

LONGITUDE 0 0 0 0 0.507(0.13) 0 0 2.112(1.04) -0.220(0.12) 0.221(0.12) 0 0 0 0

LATITUDE -0.880(0.14) 0 -1.809(0.26) 1.080(0.15) -0.677(0.14) -1.333(0.26) -0.622(0.22) -4.187(1.55) 0 -0.457(0.11) 0 0.779(0.17) -1.444(0.33) 0

WELL DENSITY 0 0 0 0 0 0.698(0.39) 1.126(0.46) 0 0 0 0 0 0 -0.396(0.24)

95

Chestnut-collared Longspur Clay-colored Sparrow

Horned Lark Marbled Godwit

Figure 2.4. Species relationships between probability of occurrence (Ψ) and modified

Soil-adjusted Vegetation Index within a 400 m radius of the point count centre for

surveys conducted within the Special Areas of Alberta, 2012. Each figure is based on the

same standardised scale of mean = 0 and variance = 1, where negative values indicate

more bare ground and positive values indicate more green vegetative biomass. The 95%

confidence intervals are indicated by the shaded grey area.

96

Clay-colored Sparrow Long-billed Curlew Marbled Godwit

Sprague’s Pipit Upland Sandpiper

Figure 2.5. Species relationships between probability of occurrence (Ψ) and Compound Topographic Index within a 400 m radius of the

point count centre for surveys conducted within the Special Areas of Alberta, 2012. Each figure is based on the same standardised scale of

mean = 0 and variance = 1, where negative values indicate more mesic areas and positive values indicate more xeric areas on the

landscape. The 95% confidence intervals are indicated by the shaded grey area.

97

Baird’s Sparrow Chestnut-collared Longspur

Horned Lark Savannah Sparrow

Figure 2.6. Species relationships between probability of occurrence (Ψ) and Heat Load Index

within a 400 m radius of the point count centre for surveys conducted within the Special Areas of

Alberta, 2012. Each figure is based on the same standardised scale of mean = 0 and variance =

1, where negative values represent less solar radiation and positive values represent higher rates

of solar radiation on the landscape. The 95% confidence intervals are indicated by the shaded

grey area.

98

Table 2.6. Comparison of area under the curve (AUC) scores from the 2012 partitioned testing

data (n = 218). Testing data which were not adjusted to account for imperfect detection are

indicated by Unadjusted, and training data which accounts for imperfect detection are indicated

by Adjusted. Upper (Cl+) and lower (Cl-) confidence limits are indicated.

SPECIES

TESTING

DATA

(2012)

AUC CI (+) CI (-)

Baird's Sparrow Unadjusted 0.84 0.90 0.79

Adjusted 0.85 0.90 0.80

Brown-headed Cowbird Unadjusted 0.61 0.70 0.52

Adjusted 0.61 0.70 0.53

Chestnut-collared Longspur Unadjusted 0.88 0.93 0.83

Adjusted 0.90 0.95 0.86

Clay-colored Sparrow Unadjusted 0.68 0.76 0.60

Adjusted 0.68 0.76 0.61

Horned Lark Unadjusted 0.75 0.81 0.68

Adjusted 0.75 0.81 0.68

Long-billed Curlew Unadjusted 0.66 0.75 0.58

Adjusted 0.67 0.75 0.58

Marbled Godwit Unadjusted 0.68 0.75 0.60

Adjusted 0.67 0.74 0.60

McCown's Longspur Unadjusted 0.88 0.96 0.80

Adjusted 0.88 0.97 0.80

Savannah Sparrow Unadjusted 0.74 0.82 0.66

Adjusted 0.74 0.82 0.66

Sprague's Pipit Unadjusted 0.78 0.84 0.72

Adjusted 0.79 0.85 0.73

Upland Sandpiper Unadjusted 0.58 0.70 0.47

Adjusted 0.58 0.70 0.46

Vesper Sparrow Unadjusted 0.78 0.86 0.71

Adjusted 0.78 0.86 0.71

Western Meadowlark Unadjusted 0.98 1.00 0.96

Adjusted 0.98 1.00 0.96

Willet Unadjusted 0.66 0.75 0.58

Adjusted 0.69 0.77 0.61

99

Table 2.7. Predictive performance of occupancy models used to estimate occurrence

probabilities of 14 grassland bird species within the Special Areas, Alberta. Predictive

performance was assessed using two sources of evaluation data, the partitioned 2012 data

(n=218) and the independent sample collected in 2011 (n=1498). Performance was measured

based on area under the curve (AUC) scores and Kappa qualitative scores. Frequency of

occurrence, or observed prevalence of species are indicated for each data set along with 95%

confidence limits (CI) for both measures.

SPECIES TESTING

DATA

OBSERVED

PREVALENCE

AUC

value

95%

(Cl)

AUC

Ranking

Kappa

value

95%

(Cl)

Kappa

Ranking

Baird's Sparrow 2012 0.41 0.85 0.05 Good 0.56 0.11 Good

2011 0.38 0.76 0.03 Useful 0.34 0.04 Fair

Brown-headed

Cowbird

2012 0.72 0.61 0.09 Poor 0.23 0.14 Fair

2011 0.55 0.60 0.03 Poor 0.16 0.05 Poor

Chestnut-collared

Longspur

2012 0.20 0.90 0.05 Excellent 0.55 0.12 Good

2011 0.17 0.84 0.03 Good 0.45 0.05 Fair

Clay-colored Sparrow 2012 0.71 0.68 0.08 Poor 0.26 0.14 Fair

2011 0.56 0.71 0.03 Useful 0.31 0.05 Fair

Horned Lark 2012 0.58 0.75 0.06 Useful 0.39 0.12 Fair

2011 0.53 0.68 0.03 Poor 0.25 0.05 Fair

Long-billed Curlew 2012 0.18 0.67 0.08 Poor 0.15 0.08 Poor

2011 0.12 0.64 0.04 Poor 0.09 0.03 Poor

Marbled Godwit 2012 0.31 0.67 0.07 Poor 0.22 0.10 Fair

2011 0.19 0.57 0.04 Poor 0.07 0.03 Poor

McCown's Longspur 2012 0.03 0.88 0.08 Good 0.17 0.15 Poor

2011 0.01 0.88 0.07 Good 0.08 0.04 Poor

Savannah Sparrow 2012 0.81 0.74 0.08 Useful 0.27 0.12 Fair

2011 0.64 0.65 0.03 Poor 0.23 0.05 Fair

Sprague's Pipit 2012 0.49 0.79 0.06 Useful 0.46 0.12 Good

2011 0.37 0.77 0.02 Useful 0.38 0.05 Fair

Upland Sandpiper 2012 0.12 0.58 0.12 Poor 0.09 0.08 Poor

2011 0.07 0.59 0.06 Poor 0.02 0.01 Poor

100

Vesper Sparrow 2012 0.82 0.78 0.07 Useful 0.42 0.13 Good

2011 0.79 0.64 0.04 Poor 0.19 0.06 Poor

Western Meadowlark 2012 0.96 0.98 0.02 Excellent 0.47 0.21 Good

2011 0.90 0.77 0.05 Useful 0.27 0.06 Fair

Willet 2012 0.25 0.69 0.08 Poor 0.30 0.14 Fair

2011 0.19 0.56 0.04 Poor 0.06 0.03 Poor

101

3. CONCLUSIONS AND MANAGEMENT IMPLICATIONS

3.1 Summary

The objectives of my research were to: (1) determine factors that influence the

distribution of grassland birds on the landscape, (2) use occupancy modelling to generate Species

Distribution Models (SDMs) and predict the probability of occurrence for grassland birds within

the Special Areas of southeastern Alberta, and (3) evaluate the predictive performance of the

occupancy models.

In chapter 2, I show that using count data, in conjunction with occupancy models and the

geographical and environmental variables across the landscape, is useful for understanding how

species interact with the composition of cover types. My results show that occurrence data are

influenced by detectability, and a repeated survey design is useful in that it allows for statistically

adjusting occurrence data for imperfect detection. I found identifying species occurrence

patterns to the amount of grassland cover type was a useful measure for explaining occurrence,

and corresponded strongly with previous studies (Owen and Myers 1973; Johnson et al. 2004;

Davis et al. 2013). The additional landscape features I used to describe variation in species

occurrence further helped to explain resource selection. Both topography and the geographic

location on the landscape were important for explaining where species occur. Bird species

exhibited mixed responses to variation in vegetative biomass across the landscape, as explained

by the modified soil-adjusted vegetation index. The surface water, woody cover, and well

density variables also influenced occurrence, but again I found the results varied among species.

Clearly, further research is needed to fully understand the complex features that influence where

species occur on the landscape, but these models provide an objective, quantitative method for

evaluating landscapes for conservation.

102

Predictive distribution models provide a graphical means to interpret patterns of resource

selection across an area of interest. The spatially explicit maps produced by my analysis are

considered an index of a species probability of occurrence (MacKenzie et al. 2006) based on the

coarse-grained landscape variables identified in each model. Both the maps and models can be

used in a wide variety of applications, including assessing the impacts of land cover change,

guiding and refining future research efforts, supporting conservation prioritization and reserve

selection, and guiding species reintroduction (Elith and Leathwick 2009; Franklin 2010).

Regardless of the application, evaluation is a necessary step to provide confidence in the

predictive ability of a distribution model (Fielding and Bell 1997; Elith and Leathwick 2009).

By comparing model predictions with two evaluation datasets, I demonstrated that my models

hold promise in making justifiable inferences about species distribution. I showed that

distribution models for four species provide evidence for use as a conservation tool. The

remaining species models, which had poor predictive performance, require further investigation

to determine whether, or the extent to which they can be improved. Overall, my models

provided quantitative insights into the magnitude and importance of species-habitat relationships

at the landscape level, and a level of confidence in how useful the distribution models are to

predict species occurrence.

3.2 Management Implications

Given that conservation resources will always be limited, strategic approaches to

developing management actions for ecological communities at the landscape level are necessary

(Berlanga et al. 2010). The models and Geographic Information System products (e.g., maps

103

and shape files) that I generated will allow conservation practitioners to pinpoint areas on the

landscape that have the highest likelihood for successful conservation management actions.

Additionally, occupancy models are applicable in multi-species management actions, as the

single-species, single-season models I produced can be refined into multi-species occupancy

models (Royle and Dorazio 2008). For example, if Baird’s Sparrow, Chestnut-collared

Longspur, and Sprague’s Pipit are collectively targeted as species of conservation concern, a

multi-species model can be used to address explicit questions about what, where, and how to

manage an area for these grassland bird species

Distribution models provide a foundation for policymakers, conservation planners and

land managers to make informed decisions about conservation strategies. However, like any

predictive modelling approach, the models I present here are imperfect for a variety of reasons.

Given the range of detection and occurrence probabilities I observed, employing the standard

survey design with three repeated visits proved to be flexible for the primary species

investigated. However, the low number of McCown’s Longspur detections suggests that more

sampling sites are needed to improve occupancy estimates. The precision of my occupancy

estimates for Long-billed Curlew, Willet, and Marbled Godwit were also low, and increasing the

number of repeated visits to each site would improve precision. For example, with no

consideration of cost, MacKenzie and Royle (2005) suggest that increasing the amount of

repeated surveys to seven would reach optimal precision. Predictive models should incorporate

annual variation in occurrence and demographic patterns (MacKenzie et al. 2006) to increase

their rigour for conservation management and planning efforts. Incorporating interactions

between variables within a multi-scale analysis may explain the data better than a single spatial

104

scale I used (i.e., 400 m radius), but there is no guarantee this will provide more insight into

explaining occupancy patterns. Additionally, my models are limited by the availability of

accurate spatial data within the study area. More accurate environmental variables should result

in better resource selection models. Clearly improvements can be made to each species model,

yet in the absence of more accurate spatial data, the predictive distribution models I generated

provide a strong basis for making informed management decisions. These models and maps

should be used to guide regional conservation planning for birds, and can be used as a valuable

reference to guide and refine future research efforts in other regions.


BERLANGA, H. K., J. A. RICH, T. D. ARIZMENDI, M. C. BEARDMORE, C. J. BLANCHER, P. J.

BUTCHER, G. S. COUTURIER, A. R. DAYER, A. A. DEMAREST, D. W. EASTON, W. E.

GUSTAFSON, M. INIGO-ELIAS, E. KREBS, E. A. PANJABI, A. O. RODRIGUEZ

CONTRERAS, V. ROSENBERG, K. V. RUTH, J. M. SANTANA CASTELLÓN, E. VIDAL, R.

MA, AND T. WILL. 2010. Saving Our Shared Birds: Partners in Flight Tri-National

Vision for Landbird Conservation. Cornell Lab of Ornithology: Ithaca, NY. Available at:

www.savingoursharedbirds.org. Accessed online October 5, 2012.


Songbird abundance in native and planted grassland varies with type and amount of

grassland in the surrounding landscape. Journal of Wildlife Management 77(5): 908-919.


105

ELITH, J., AND J. R. LEATHWICK. 2009. Species distribution models: ecological explanation and

prediction across space and time. Annual Review of Ecology, Evolution, and Systematics

40: 677-697.

FIELDING, A. H., AND J. F. BELL. 1997. A review of methods for the assessment of prediction

errors in conservation presence/absence models. Environmental Conservation 24: 38–49.

FRANKLIN, J. 2010. Moving beyond static species distribution models in support of conservation

biogeography. School of Geographical Sciences and Urban Planning and School of Life

Sciences, Arizona State University, Tempe AZ USA.

JOHNSON, D. H., L. D. IGL, AND J. A. DECHANT (Series Coordinators). 2004. Effects of

management practices on grassland birds. Northern Prairie Wildlife Research Center,

Jamestown, ND. Jamestown, ND: Northern Prairie Wildlife Research Center Online.

http://www.npwrc.usgs.gov/resource/literatr/grasbird/index.htm (Version 12AUG2004).

Accessed online December 12, 2012.

MACKENZIE, D. I., AND J. A. ROYLE. 2005. Designing efficient occupancy studies: General

advice and tips on allocation of survey effort. Journal of Applied Ecology 42: 1105-1114.

106

MACKENZIE, D. I., J. D. NICHOLS, J. A. ROYLE, K. P. POLLOCK, L. L. BAILEY, AND J. E.

HINES. 2006. Occupancy estimation and modeling: inferring patterns and dynamics of

species occurrence. Academic Press, San Diego, California, USA.

OWENS, R. A., AND M. T. MYRES. 1973. Effects of agriculture upon populations of native

passerine birds of an Alberta fescue grassland. Canadian Journal of Zoology 51:697-713.

ROYLE, J. A., AND R. M. DORAZIO. 2008. Hierarchical modeling and inference in ecology: the

analysis of data from populations, metapopulations and communities. Academic Press,

New York, New York, USA.

107

APPENDICIES

APPENDIX A – Relative variable importance for grassland bird occupancy models generated in

the Special Areas, Alberta.

108

Appendix A. Table A.1. Variable shrinkage estimates when model averaging was incorporated to assist with model selection

uncertainty in the Special Areas, 2012. The relative variable importance values are on a scale from 0 to 1, giving an equal

weight in the global model used to predict species occupancy. Positive and negative species-habitat relationships are indicated

(+/-). Variables used to explain the detection component, ρ, are (date [JDATE], time [TIME], wind [WIND]) and variables

used to explain the occurrence component, Ψ, are (grassland [GRASS], woody cover [WOODY], vegetative greenness

[MSAVI], surface water [WATER], solar radiation [HLI], wetness [CTI], latitude [YLAT], longitude [XLONG], and well

density [WD]).

SPECIES ρ(JDATE) ρ(TIME) ρ(WIND)

Ψ(GRASS) Ψ(WOODY) Ψ(WATER) Ψ(MSAVI) Ψ(YLAT) Ψ(XLONG) Ψ(HLI) Ψ(CTI) Ψ(WD)

Baird's Sparrow 1.00 (-)1.00 1.00 (-)0.70 (-)1.00 (-)0.84 0.60

Brown-headed Cowbird (-)1.00 (-)1.00 1.00 (-)0.81

Chestnut-collared Longspur* 1.00 1.00 (-)1.00 (-)1.00 (-)1.00

Clay-colored Sparrow (-)1.00 (-)0.90 1.00 0.87 1.00 (-)1.00

Horned Lark 0.58 1.00 (-)1.00 (-)1.00 (-)1.00 1.00 (-)1.00

Long-billed Curlew (-)0.90 (-)1.00 (-)0.80 (-)0.81 (-)1.00 0.98 0.65

Marbled Godwit 0.73 (-)0.68 (-)1.00 (-)0.86 (-)1.00 1.00

McCown's Longspur 0.72 1.00 0.36 (-)1.00 1.00

Savannah Sparrow (-)1.00 (-)0.74 1.00 (-)0.78 (-)1.00

Sprague's Pipit 1.00 1.00 (-)0.64 1.00 (-)1.00 0.70 0.89

Upland Sandpiper* 1.00 1.00 1.00

Vesper Sparrow (-)0.56 (-)1.00 (-)1.00 1.00 (-)1.00

Western Meadowlark* (-)1.00 (-)1.00 1.00 (-)1.00 1.00

Willet (-)1.00 1.00 0.64 (-)0.78 0.73 (-)0.75

* indicates species with single top model

109

APPENDIX B – Probability of occurrence maps for bird species in the Special Areas,

Alberta.

Appendix B. Figure B.1. Baird's Sparrow (Ammodramus bairdii) expected probability of

occurrence for the Special Areas, Alberta.

110

Appendix B. Figure B.2. Brown-headed Cowbird (Molothrus ater) expected probability

of occurrence for the Special Areas, Alberta.

111

Appendix B. Figure B.3. Chestnut-collard Longspur (Calcarius ornatus) expected

probability of occurrence for the Special Areas, Alberta.

112

Appendix B. Figure B.4. Clay-colored Sparrow (Spizella pallida) expected probability of


113

Appendix B. Figure B.5. Horned Lark (Eremophila alpestris) expected probability of


114

Appendix B. Figure B.6. Long-billed Curlew (Numenius americanus) expected


115

Appendix B. Figure B.7. Marbled Godwit (Limosa fedoa) expected probability of


116

Appendix B. Figure B.8. McCown's Longspur (Rhynchophanes mccownii) expected


117

Appendix B. Figure B.9. Savannah Sparrow (Passerculus sandwichensis) expected


118

Appendix B. Figure B.10. Sprague's Pipit (Anthus spragueii) expected probability of


119

Appendix B. Figure B.11. Upland Sandpiper (Bartramia longicauda) expected


120

Appendix B. Figure B.12. Vesper Sparrow (Pooecetes gramineus) expected probability

of occurrence for the Special Areas, Alberta.

121

Appendix B. Figure B.13. Western Meadowlark (Sturnella neglecta) expected


122

Appendix B. Figure B.14. Willet (Tringa semipalmata) expected probability of


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