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2015-06-18

Influence of Habitat Disturbances on Endemic

Grassland Bird Distributions in Loamy Ecological

Range Sites at Canadian Forces Base Suffield,

Alberta

McWilliams, Benjamin

McWilliams, B. (2015). Influence of Habitat Disturbances on Endemic Grassland Bird Distributions

in Loamy Ecological Range Sites at Canadian Forces Base Suffield, Alberta (Unpublished master's

thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/26516

http://hdl.handle.net/11023/2306

master thesis

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UNIVERSITY OF CALGARY

Influence of Habitat Disturbances on Endemic Grassland Bird Distributions in Loamy

Ecological Range Sites at Canadian Forces Base Suffield, Alberta

by

Benjamin Earl McWilliams

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTER OF SCIENCE

GRADUATE PROGRAM IN GEOGRAPHY

CALGARY, ALBERTA

JUNE, 2015

© Benjamin Earl McWilliams 2015

ii

ABSTRACT

Many grassland birds are at risk and habitat disturbance may have an important

influence on the persistence of these species. Military bases provide an opportunity to

examine the influence of habitat disturbance on grassland birds, whose distributions are

influenced by vegetation structure. Spatial autocovariate generalized linear models were

developed for four primary endemic grassland bird species from point count data

collected in loamy ecological range sites during spring 2013 and 2014. These models

indicated habitat disturbances influenced bird distribution, but the response differed

among species. The strongest response was to fire; relative abundance of two species

increased with greater fire impact while the other two decreased. A Wilcoxon Signed-

Rank test of matched burned and unburned areas showed fire reduced remotely sensed

grassland vegetation (p < 0.001). These results indicate disturbed and undisturbed areas

provide a range of habitats suitable to the endemic grassland bird species studied.

iii

ACKNOWLEDGEMENTS

I would like to first thank my supervisor, Dr. Darren Bender, for guidance, fruitful

discussions, and the time and effort he put into facilitating the successful conclusion of

this study. Also, the members of my thesis examination committee, Dr. Stephania

Bertazzon, Dr. Greg McDermid, and Dr. Paul Galpern, for feedback that improved the

final product of this thesis. I specifically thank Stefania Bertazzon for assistance in

application of spatial statistical methods.

Many CFB Suffield staff provided assistance throughout this study. Drew Taylor

joined me in early morning point counts. Brent Smith taught me a much more detailed

appreciation of grassland vegetation dynamics than I would have acquired otherwise.

Marty Gartry provided access and context to several of the databases, as well as hundreds

of hours in their development. Performing bird surveys in the busy landscape of CFB

Suffield would not have been possible without support and coordination provided by

Mike Locke, Brian Talty, John Deruyter and many at Range Control. Many others in the

Range Sustainability Section also indirectly contributed to my research, often through

informal discussions of land use activities occurring at CFB Suffield.

Prior to the inception of this thesis, Brenda Dale provided invaluable training in

conducting point counts, as well as encouragement to pursue grassland bird research.

Finally, I thank my wife and children for tolerating the time I needed to dedicate

to this study and for frequently helping me take my mind off school.

iv

DISCLAIMER

Opinions expressed or implied in this publication are those of the author,

and do not represent the views of the Department of National Defence, the

Canadian Forces, or any agency of the Government of Canada.

v

TABLE OF CONTENTS

ABSTRACT ........................................................................................................................ ii

ACKNOWLEDGEMENTS ............................................................................................... iii

DISCLAIMER ................................................................................................................... iv

TABLE OF CONTENTS .................................................................................................... v

LIST OF TABLES ........................................................................................................... viii

LIST OF FIGURES ............................................................................................................ x

LIST OF SYMBOLS AND ABBREVIATIONS .............................................................. xi

CHAPTER 1: INTRODUCTION ............................................................................ 1

1.1 Study Context .......................................................................................................... 1

1.2 Influence of Topography on Grassland Vegetation .................................................. 4

1.3 Grassland Disturbance Ecology ................................................................................ 5

1.3.1 Fire Ecology of Grassland Birds and Vegetation ............................................. 5

1.3.2 Off-road Vehicle Disturbance .......................................................................... 8

1.3.3 Road Disturbance ............................................................................................. 9

1.4 Remote Sensing Vegetation Indices ....................................................................... 10

1.5 Research Objectives ................................................................................................ 11

CHAPTER 2: METHODS .................................................................................... 14

2.1 Study Area .............................................................................................................. 14

2.2 Study Species .......................................................................................................... 17

2.2.1 McCown’s longspur ................................................................................. 18

2.2.2 Chestnut-collared longspur ...................................................................... 19

vi

2.2.3 Sprague’s pipit ......................................................................................... 20

2.2.4 Baird’s sparrow ........................................................................................ 21

2.3 Identifying Bird Distribution .................................................................................. 23

2.4 Environmental Variables ........................................................................................ 28

2.4.1 Topographic Variables ................................................................................... 29

2.4.2 Disturbance Variables ..................................................................................... 30

2.5 Expectations of Bird Responses to Topographic and Disturbance Variables......... 37

2.6 Statistical Analysis .................................................................................................. 40

2.5.1 Spatial Exploratory Data Analysis ................................................................. 40

2.5.2 Multicollinearity Analysis .............................................................................. 40

2.5.3 Accounting for Spatial Autocorrelation ......................................................... 41

2.5.4 Disturbance Modelling Analysis .................................................................... 43

2.5.5 Bivariate Regression of Relative Abundance and Composite Fire Index ...... 46

2.5.6 Influence of Burn Status and Topographic Position on Vegetation ............... 46

CHAPTER 3: RESULTS ..................................................................................... 49

3.1 Spatial Exploratory Data Analysis .......................................................................... 49

3.2 Correlation Matrix .................................................................................................. 49

3.3 Disturbance Modelling Analysis............................................................................. 52

3.4 Bivariate Response of Endemic Bird Study Species to Fire ................................... 56

3.5 Influence of Burn Status and Topographic Position on Vegetation ....................... 58

CHAPTER 4: DISCUSSION ............................................................................... 61

4.1 Disturbance Modelling............................................................................................ 62

vii

4.1.1 Disturbance: Fire, Trafficking, Pipelines ....................................................... 62

4.1.2 Topography: Slope, Solar Radiation, Topographic Position .......................... 69

4.1.3 Limitation of the model results ....................................................................... 72

4.2 Model Evaluation .................................................................................................... 73

4.3 Influence of Burn Status and Topographic Position on Vegetation ....................... 77

4.4 Implications of Habitat Heterogeneity for Management of Grassland Birds ......... 79

5.0 CONCLUSIONS ........................................................................................... 82

5.1 Conclusion .............................................................................................................. 82

5.2 Future Research ...................................................................................................... 83

REFERENCES ................................................................................................... 86

APPENDIX A. PHOTOGRAPHIC EXAMPLES OF HABITAT

DISTURBANCES AT CFB SUFFIELD. .................................................... 105

viii

LIST OF TABLES

Table 1. Endemic mixed-grass prairie birds common at CFB Suffield and their status

(SARA = Species at Risk Act, COSEWIC = Committee on the Status of

Endangered Wildlife in Canada). .............................................................................. 18

Table 2. Summary of environmental variables used in modelling, their expected

relationship with vegetation structure, rationale for inclusion in modelling, and

expected response of study species (MCLO = McCown’s longspur, CCLO =

chestnut-collared longspur, SPPI = Sprague’s pipit, BASP = Baird’s sparrow). ..... 39

Table 3. First stage topography and observation variable group models.......................... 44

Table 4. Correlation matrix of dependent and independent variables (MCLO =

McCown’s longspur, CCLO = chestnut-collared longspur, SPPI = Sprague’s

pipit, BASP = Baird’s sparrow, FICO = composite fire index, TRCO =

composite trail index, PLSL = pipeline sum length, RD_DIST = distance to road,

RE = relative elevation, SOLRAD = solar radiation, OBS = Observer, TIME =

time of point count survey, CLOUD = cloud cover, WIND = wind speed, SATVI

= soil adjusted total vegetation index). ..................................................................... 50

Table 5. Top ranked second-stage autocovariate generalized linear models.

Disturbance variables are in bold. ............................................................................. 53

Table 6. Environmental variable parameter estimates of top-ranked models as

identified by AICc and agreement with expectations of coefficient (E(β sign))

sign (+/-). ................................................................................................................... 54

ix

Table 7. Model evaluation statistics averaged across random, spatial, and temporal k-

folds. .......................................................................................................................... 56

x

LIST OF FIGURES

Figure 1. Study species arranged according to relative vegetation preferences (adapted

from Knopf 1996). .................................................................................................... 13

Figure 2. Spatial distribution of sampled point counts, loamy ecological range site,

and land use management areas. ............................................................................... 15

Figure 3. Spatial extents and frequency of fire at CFB Suffield between 1994 and

2013. .......................................................................................................................... 16

Figure 4. Area burned each year at CFB Suffield between 1972 and 2013. ..................... 16

Figure 5. Spatial extent of off-road trails in 2006. ............................................................ 33

Figure 6. Spatial extent of pipeline network in 2014. ....................................................... 35

Figure 7. Observed study species relative abundance by the composite fire index

(MCLO = McCown’s longspur, CCLO = chestnut-collared longspur, SPPI =

Sprague’s pipit, BASP = Baird’s sparrow). .............................................................. 58

Figure 8. Notched boxplot comparison of SATVI values between burned and

unburned areas stratified by topographic position. ................................................... 59

Figure 9. SATVI image from the approximate center of the Manoeuvre Training Area

of CFB Suffield calculated from the 12 July 2014 Landsat 8 image, overlaid with

fire extents from 2011-2013. An image stretch of 2.5 standard deviations was

applied to aid visual interpretation. ........................................................................... 60

xi

LIST OF SYMBOLS AND ABBREVIATIONS

L

ρ

AC

AICc

BASP

CCLO

CLOUD

CFB

COSEWIC

CWG

DEM

EPG

ERS

FICO

GIS

GLM

GLMM

MCLO

soil adjustment constant used in the soil adjusted total vegetation index

coefficient of autocovariate term

autocovariate term

Akaike’s Information Criterion adjusted for small sample sizes

Baird’s sparrow

chestnut-collared longspur

cloud cover

Canadian Forces Base

Committee on the Status of Endangered Wildlife in Canada

crested wheat grass

digital elevation model

Experimental Proving Ground

ecological range site

fire composite index

Geographic Information System

Generalized Linear Modelling

Generalized Linear Mixed Models

McCown’s longspur

xii

MTA

NDVI

NWA

OBS

OLI

PC

PLSL

RD_DIST

RE

RSVIs

SAC

SARA

SATVI

SLOPE

SOLRAD

SPPI

SWIR

TIME

TRCO

TSLF

WIND

Manoeuvre Training Area

Normalized Difference Vegetation Index

National Wildlife Area

observer

operational land imager

point count

pipeline sum length

distance to road

relative elevation

remote sensing vegetation indices

spatial autocorrelation

Species at Risk Act

Soil Adjusted Total Vegetation Index

percent slope

solar radiation

Sprague’s pipit

shortwave infrared

time of point count survey

trail composite index

time since last fire

wind speed

1

CHAPTER 1: INTRODUCTION

1.1 Study Context

Many of the primary endemic North American grassland birds (Knopf 1996) are

at risk of extinction. In Canada, several species have been formally identified as

threatened or endangered under the federal Species at Risk Act (2002), and most

grassland birds are protected under the Migratory Birds Convention Act (1994). The

decline of grassland bird populations has been described as an unfolding conservation

crisis (Brennan and Kuvlesky 2005). Grassland birds appear to have been primarily

placed at risk due to the extensive conversion of native grasslands to agricultural use

during settlement of North America (Samson et al. 2004, Askins et al. 2007). While

retention of remaining grasslands is obviously important, how these grasslands should be

managed to support bird populations is less certain. North American grasslands

developed under the influence of habitat disturbances including herbivory, fire, and

drought (Samson et al. 2004, Askins et al. 2007). However, many of these disturbances

have changed, such as replacement of native grazers with domestic livestock (Wallace

and Dyer 1995, Hartnett et al. 1997) and suppression of fire (Umbanhowar 1996). As

well, new forms of disturbance have emerged as a result of petroleum and natural gas

extraction, extensive networks of transportation infrastructure, and other land uses such

as military training. Understanding how contemporary habitat disturbance regimes

influence remaining grassland bird populations is important for land managers concerned

with avian conservation, or simply compliance with legislation protecting avian species.

2

Landscape mosaics, caused by spatial heterogeneity in factors which modify

habitat, have an important influence on the distribution of many wildlife species as

habitat ranging from early successional to climax conditions is provided (Urban et al.

1987, Pickett and Cadenasso 1995). Warren et al. (2007) noted that biodiversity was

higher on military training areas than national parks in the USA, and that high

biodiversity on military training areas in several European countries had also been

documented. They argued that both habitat disturbance caused by military training and

the undisturbed buffer areas required for safety around military training were the cause

(Warren et al. 2007). Examples of military training related habitat disturbance include

fire and off-road manoeuvers by military vehicles. Habitat disturbance associated with

training vehicle manoeuvers has been suggested to be analogous to historical disturbances

caused by Plains Bison (Bison bison; Leis et al. 2005, Limb et al. 2010). Warren et al.

(2007) proposed a heterogeneous disturbance hypothesis, where multiple disturbances

occurring across space and time at a landscape scale will maximize biodiversity, and

provides an alternative to the intermediate disturbance hypothesis of Connell (1978).

Historically, grassland birds evolved under a regime of disturbance (Askins et al. 2007),

so it is reasonable to expect that endemic species may display responses consistent with

the heterogeneous disturbance hypothesis.

One of the most important aspects of grassland habitat disturbance is modification

of grassland vegetation. Vegetation structure is acknowledged as one of the strongest

determinants of grassland bird habitat use (Weins 1969, Fisher and Davis 2010), and

different species are associated with different amounts of vegetation structure (Knopf

1996). These species specific preferences, in combination with the ability to survey for

3

multiple species simultaneously, make grassland birds an efficient group of organisms to

explore the importance of habitat disturbance. Fisher and Davis (2010) reviewed 57

studies examining relationships between vegetation characteristics and grassland bird

abundance, density, occurrence, and territory and nest site selection. Their review found 9

variables important for the prediction of habitat use by grassland birds, including,

coverage of bare ground, grass, dead vegetation, forbs, and litter; indices of vegetation

density, vegetation volume; and, litter depth, and vegetation height (Fisher and Davis

2010). However, Fisher and Davis (2010) limited their review to fine-scale vegetation

attributes directly measured in the field. Duro et al. (2007) and Franklin (2009) provided

some direction on coarser-scale environmental variables useful to monitoring biodiversity

or modelling species distributions, although some of the variables they identified may be

best suited to predictive models. If inference is the goal, limiting variables to those with a

sound ecological basis is critical (Burnham and Anderson 2002, Li and Wu 2004).

Among the variables identified for coarser-scale study were those that can be grouped as

topographic, disturbance, and remote sensing vegetation indices (RSVIs; Duro et al.

2007, Franklin 2009). Topography and disturbance are ecologically linked to vegetation

while RSVIs are a more direct description of vegetation variance. Variables from these

three groups can be related to grassland vegetation structure and therefore could be

important components of grassland bird distribution models.

The remainder of Chapter 1 will review the ecological linkages between both

topography and disturbance and grassland vegetation structure to provide the foundation

for the use of variables described in Chapter 2. It will also provide background on the use

of RSVIs in grasslands, explaining how vegetation indices relate to components of

4

grassland vegetation. Finally, it will provide the overall goal and specific objectives of

this study.

1.2 Influence of Topography on Grassland Vegetation

In the absence of disturbance, topography is one of the main determinants of local

scale differences in vegetation structure in grasslands. Topographic variables indirectly

describe abiotic factors important for vegetation growth, including soil moisture (Sulebak

et al. 2000, Sørensen et al. 2006, Liu et al. 2012), and amount of solar radiation (Temps

and Coulson 1977), which is importantly linked to expected temperature differences

(Bennie et al 2008). For example, in dry-mixed grass prairie, vegetation structure is

reduced on hilltops due to moisture restrictions which can restrict growth, and favor

short-grass species which contribute less to structure, such as blue grama (Bouteloua

gracilis; Coupland 1950, Barnes et al. 1983). Phillips et al. (2012) found topographic

position could influence mixed-grass prairie canopy structure, where hill summits had

reduced grass canopy height, total standing crop, and remotely sensed vegetation index

values compared to toeslope areas. Similarly, Milchunas et al. (1989) found primary

production was lower, while bare ground was higher, on ridgetops compared to swales in

short-grass prairie. The amount of solar radiation received at a given point is influenced

by the orientation, or aspect, of a slope to the sun (Temps and Coulson 1977, Fu and Rich

2002). Models of solar radiation, or some measure of aspect, have been related to field

measurements (Walton et al. 2005, Gong et al. 2008, Han et al. 2011) and remote sensing

indices (Dong et al. 2009, Sabetraftar et al. 2011, Liu et al. 2012, Zhan et al. 2012) of

vegetation, or both (Xie et al. 2009). While rarely a major focus, topography has been

5

investigated in relation to the distribution of grassland birds in some studies (Weins 1969,

Weins et al. 2008, Dieni and Jones 2003, Vallecillo et al. 2009, With 2010), and has an

important influence on grassland vegetation.

1.3 Grassland Disturbance Ecology

While topography may provide an expectation of grassland vegetation structure,

realized structure is further influenced by natural and anthropogenic disturbances. Many

forms of disturbance result in changes to vegetation structure and correspond to changes

in bird occupancy or abundance. The response of grassland birds to petroleum and natural

gas infrastructure and development (Linnen 2008, Dale et al. 2009, Hamilton et al. 2011,

Rodgers 2013, Kalyn-Bogard and Davis 2014, Ludlow et al. 2015) and cattle grazing

(Dale 1983, Fondell and Ball 2004, Fritcher et al. 2004, Bleho 2009, Derner et al. 2009,

Henderson and Davis 2014) have been studied in the mixed-grass prairie with varying

responses between bird species observed. However, other types of disturbances have been

studied to a lesser extent, particularly in the dry-mixed-grass prairie. Appendix A

contains photographic examples of several important grassland habitat disturbances and

illustrates some their influence on vegetation structure.

1.3.1 Fire Ecology of Grassland Birds and Vegetation

There have been a number of studies examining the influence of fire on grassland

birds in the northern prairies. However, most studies were conducted either in tall-grass

(Zimmerman 1992, Herkert 1994, Zimmerman 1997, Fuhlendorf et al. 2006, Powell

2006, Coppedge et al. 2008), or in moist-mixed-grass and fescue prairies (Huber and

6

Steuter 1984, Pylypec 1991, Johnson 1997, Madden et al. 1999, Danley et al. 2004,

Ludwick and Murphy 2006, Grant et al. 2010). In contrast, a single peer-reviewed study

was found exploring the influence of fire on bird populations in northern dry-mixed-grass

prairie (Richardson et. al. 2014). Although, the wildlife inventory of the Canadian Forces

Base (CFB) Suffield National Wildlife Area (NWA) also provided information regarding

several species responses to fire in dry-mixed-grass prairie (Dale et al. 1999). Fire is an

important habitat disturbance in tall-grass and moist-mixed-grass prairies, where it can

reduce the cover of grass and shrubs (Higgins et al. 1989, Madden et al. 1999), and can

have associated positive relationships with several grassland bird species (Huber and

Steuter 1984, Herkert 1994, Johnson 1997, Madden et al. 1999, Danley et al. 2004).

However, in the more limited vegetation structure of dry-mixed-grass prairie, where

average grass heights are lower (Weaver 1958, Seastedt 1995), it is possible that negative

relationships could be found (Madden et al. 1999, Winter 1999).

The influence of fire on vegetation structure is a temporal process, and both the

time since last fire and frequency of fire are important for its description. Grassland fires

reduce living and dead plant material and expose bare soil (Grant et al. 2010, Shay et al.

2001, Vermeire et al. 2014, see also photographic examples from the study area in

Appendix A: Fig. A1 & A2). Without further disturbance, grassland structure will recover

in following years, but there will be a lagged effect. Pylypec and Romo (2003) found that

after spring burning in Festuca and Stipa-Agropyron dominated communities the total

current years living and standing dead biomass took 8 years to plateau, while litter took

11 years. Similarly, for mixed-grass sites in Montana and southern Saskatchewan,

Wakimoto et al. (2004) found that organic and litter cover were mostly restored on

7

burned sites >10 years old. The timeline of recovery to pre-burn conditions in frequently

burned areas is much less certain. In dry-mixed-grass prairie, single fire events might

only reduce mid-grasses for one or two years (Erichsen-Arychuk et al. 2002). In contrast,

frequent fire in can shift dominant grass species from mid-grasses, such as needle and

thread (Hesperostipa comata), to short-grasses, such as blue grama (Weerstra 2005,

Smith and McDermind 2014), in loamy range sites. Reestablishment of dominance by

mid-grasses may take decades (Smith and McDermid 2014). This shift in species

composition from mid-grasses to short-grasses can influence vegetation structure.

Coupland (1950) found the short-grass species blue grama to have leaf heights ranging

from 4 to 10 cm and culms 13 to 30 cm tall, while needle and thread leaves were

generally 10 to 12 cm, and culms were 25 to 30 cm.

Recently after a fire, when vegetation is typically significantly reduced,

abundance of many species of grassland birds may also be reduced (Grant et al. 2010,

Powell 2006). Several studies in mixed-grass prairie have found that bird abundance can

stabilize or return to pre-burn counts after a short-term period of 2 to 3 years post fire

(Pylypec 1991, Grant et al. 2010, Roberts et al. 2012). Although, in dry-mixed-grass

prairie it may take longer; Richardson et al. (2014) found most species had returned to

pre-burn relative abundance in four to five years. However, only a few studies have

examined longer-term fire effects on grassland birds (Johnson 1997, Madden et al. 1999).

Understanding ecological processes involved in the distribution of a species is

important. Vallecillo et al. (2009) caution that species distributions may respond more

strongly to ecological processes, such as fire disturbances, than to habitat suitability

derived from land cover, and that failing to incorporate this information into species

8

distribution models may detract from their predictive capability. In Australia, Reside et

al. (2012) found a range of fire responses from savanna-restricted birds, and commented

that understanding individual species fire preferences is important for conservation

planning.

1.3.2 Off-road Vehicle Disturbance

The influence of habitat structure modifications by off-road vehicle manoeuvers

(hereafter trafficking) on birds is not well studied. However, trafficking can influence

vegetation structure, where plant and plant litter cover are reduced exposing bare soil

(Hirst et al. 2003, DND 2011; see also photographic examples in Appendix A: Fig. A3 &

A4). These impacts can vary based on soil moisture during trafficking (Halvorson et al.

2003, Althoff and Thien 2005), the number of vehicle passes (Prosser et al. 2000,

Caldwell et al. 2006), and by type of vehicle (Hirst et al. 2003). Recovery from

trafficking disturbance is not well understood and may be delayed due to soil compaction

and/or death of plants that have been trafficked (Hirst et al. 2003, DND 2011).

Severinghaus and Severinghaus (1982) observed tracked-vehicle trafficking could modify

habitat and was associated with changes to the bird community species composition,

although none of their four study areas were in mixed-grass prairie. However, low density

trafficking may have minimal impact. Hubbard et al. (2006) concluded that grasshopper

sparrow (Ammodramus savannarum) and eastern meadowlark (Sturnella magna) nest site

selection was unaffected by low levels of trafficking disturbance conducted prior to the

breeding season. Off-road trails have been examined to some extent with respect to

civilian land uses. While off-road trails may have less impact on grassland birds habitat

9

use than roads (Sutter et al. 2000), avoidance of trails has also been found for some

species (Dale et al. 2009). Use of off-road trails can also contribute to the spread of

invasive species (Gelbard and Belnap 2003), potentially resulting in changes to

vegetation structure which may influence bird habitat use (Dale et al. 2009). Although the

influence of trafficking on birds has had limited study, the influence of trafficking on

vegetation suggests it could play an important role in the distribution of grassland birds.

1.3.3 Road Disturbance

Roads represent disturbances in the form of both habitat modification and visual

and auditory disturbances associated with their use. Sutter et al. (2000) observed that

several grassland bird species were less abundant along roads than trails, and noted that in

a 100 m radius point count a bisecting road can reduce suitable habitat by 20-30%. Non-

native vegetation is often associated with roads, and in some cases is used for quick

stabilization of road beds (Trombulak and Frissell 2000). The movement and sounds

associated with a road traffic can also have an important effect. High traffic volumes

were associated with reduced overall bird density out to a distance 500 m, and some

individual species at distances greater than 1000 m, in Dutch agricultural grasslands

(Reijnen et al. 1995). Similarly, near Boston, USA, Forman et al. (2002) found that

grassland bird occurrence and breeding was decreased to further distances near roads

with greater traffic volumes, and at a volume greater than 30,000 vehicles per day this

distance was over 1000 m. Roads with much lower traffic volumes, 700-10 vehicles per

day, in the sagebrush steppe of Wyoming, have been associated with declines within 100

m of the road (Ingelfinger and Anderson 2004). Ingelfinger and Anderson (2004)

10

suggested traffic volume alone may not have explained their findings and the changes to

habitat likely had an influence. While visual and auditory disturbances associated with

roads appear to influence grassland birds, the modification of vegetation structure

associated with roads likely also has an influence, and may be more important at lower

traffic volumes.

1.4 Remote Sensing Vegetation Indices

Remote sensing products have frequently been correlated with avian abundance or

richness (Oindo et al. 2000, Hurlbert and Haskell 2003, Gottschalk et al. 2005, Mcfarland

et al. 2012, Wood et al. 2013, Sheeren et al. 2014). Although the Normalized Difference

Vegetation Index (NDVI; Rouse et al. 1973) was frequently used, other RSVIs or

reflectance products have also been successfully correlated with avian distribution

metrics (Gottschalk et al. 2005, Coops et al. 2009, Shirley et al. 2013, Wood et al. 2013).

At Grassland National Park, Saskatchewan, the NDVI and several other RSVIs were

found to predict large amounts of variation in bird density (Guo et al. 2005a). However,

Guo et al. (2005b) concluded that the NDVI was not suitable for grassland biomass

estimation, because it is ineffective at describing dead vegetation, an important

component of grassland land cover; they found other RSVIs had better performance.

Therefore, while commonly used to examine bird distribution, the NDVI is not

necessarily ideal for grassland applications.

Reflectance information from the shortwave infrared (SWIR) region of the

electromagnetic spectrum is ideal for describing senescent vegetation (Gelder et al.

2009), but RSVIs of senescent vegetation are often derived from hyperspectral imagery

11

which allows precise selection of narrow bandwidths (Nagler et al. 2003, Daughtry et al.

2004, Guerschman et al. 2009, Serbin et al. 2009, Ren and Zhou 2012). However, the soil

adjusted total vegetation index (SATVI) is a Landsat compatible vegetation index, which

does include SWIR reflectance (Marsett et al. 2006). The SATVI formula includes

information from two shortwave infrared bands, such as bands 6 and 7 of Landsat 8,

while in comparison the NDVI is limited to band ratioing of the near infrared and red

bands (Marsett et al. 2006). As a result, the SATVI describes both photosynthetically

active and senescent vegetation variation (Marsett et al. 2006). Senescent vegetation as

described by Marsett et al. (2006) would include both standing dead and litter as defined

by Fisher and Davis (2010), and therefore the SATVI should describe vegetation

variation important to grassland birds. While the SATVI appears to have important utility

for grassland applications, it has seen limited or no use in the bird habitat modelling

literature. The relationship between RSVIs, particularly one that can describe variation in

senescent vegetation, and disturbance variables can provide a link between the body of

knowledge of grassland bird responses to vegetation structure and the literature

describing the impacts of grassland habitat disturbances.

1.5 Research Objectives

The primary goal of this study was to evaluate the importance of habitat

disturbances on the distribution of breeding endemic grassland bird species during the

breeding season. Specific objectives were to; 1) construct alternate species distribution

models to assess the relative importance and expected impacts of different disturbances;

2) evaluate the response of study species to fire; and, 3) describe the relationship between

12

vegetation, as quantified by the SATVI, and both fire and topographic position to provide

a link between both disturbance and topography and vegetation.

This study was limited to data collected during 2013 and 2014 within loamy

ecological ranges sites at CFB Suffield. If the four common endemic species found on

loamy range sites at CFB Suffield were arranged on a continuum of vegetation structure

found at CFB Suffield, according to their known habitat preferences (Fig. 1), McCowns’

longspur (Rhyncophanes mccownii, hereafter MCLO) would be found in the shortest

sparsest vegetation (With 2010); chestnut-collared longspur (Calcarius ornatus, hereafter

CCLO) at more intermediate amounts (Hill and Gould 1997); while Sprague’s pipit

(Anthus spraguueii, hereafter SPPI), and Baird’s sparrow (Ammodramus bairdii,

hereafter BASP) would prefer areas with greater amounts of structure (Robbins and Dale

1999, Green et al. 2002). These vegetation preferences provide the basis for expectations

regarding each species response to disturbance. Disturbed areas, where vegetation

structure was decreased, were expected to attract MCLO and CCLO while repelling SPPI

and BASP. With respect to the influence of fire and topographic position on vegetation,

burned areas were expected to be associated with lower SATVI values. Similarly,

hillcrests were expected to have lower SATVI values than depression areas. The

methodology used to evaluate the accuracy of these expectations is described in Chapter

2.

13

Figure 1. Study species arranged according to relative vegetation preferences (adapted

from Knopf 1996).

14

CHAPTER 2: METHODS

2.1 Study Area

The CFB Suffield (lat 50.27o N, long -111.18o E) was selected as the study area.

This Army base is located in southeastern Alberta, Canada, and is comprised of 2690 km2

of native dry-mixed-grass prairie and some areas of remnant cultivation. The Base is also

central to one of the largest relatively contiguous patches of native prairie remaining in

Alberta. Due to its vast extent, location, and predominantly native vegetation it has high

conservation value. CFB Suffield is zoned for a variety of land uses including military

training, petroleum development, defence research, cattle grazing, and wildlife

conservation. These diverse land uses make it an ideal location to evaluate the role of

several disturbances in prairie ecology. The Manoeuvre Training Area (MTA; Fig. 2) of

CFB Suffield has been used as a training area for armoured vehicles since 1972 (BATUS

et al. n.d.), with three to seven battle group-level exercises performed over approximately

28 days. Each battle group-level exercise typically involves 415 various vehicles ranging

from British Challenger II tanks to smaller 4x4 trucks and jeeps. Due to firing of live

ammunition during military training exercises, CFB Suffield provides a landscape

subjected to frequent fires which vary spatially (Fig. 3) and temporally, both between

years (Fig. 4) and across the growing season, although most fires occur in August and

September (Smith and McDermid 2014). The greatest number of fires that have occurred

in a single location over the entire known 42 year fire history of CFB Suffield was 19.

This fire frequency greatly exceeds the estimated fire return interval of 6 to 25 years for

the northern mixed-grass prairie (Higgins 1984, Bragg 1995).

15

Figure 2. Spatial distribution of sampled point counts, loamy ecological range site, and

land use management areas.

16

Figure 3. Spatial extents and frequency of fire at CFB Suffield between 1994 and 2013.

Figure 4. Area burned each year at CFB Suffield between 1972 and 2013.

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17

The average elevation across the Base is 719 m, ranging from 574 to 830 m.

Canadian climate normal data (1971-2000) for the Suffield, Alberta, Environment

Canada weather station, provided an average annual precipitation of 318 mm with a peak

of 58 mm rainfall in June and mean monthly temperature ranging from 19.1o C in July to

-11.2o C in January. The high evaporation to precipitation ratio in the dry-mixed-grass

prairie subregion during the summer months further reduces the effectiveness of the

relatively low precipitation (NRC 2006). CFB Suffield has been mapped according to the

specifications of the Alberta Grassland Vegetation Inventory (ASRD 2010). This

inventory generated a comprehensive classified polygon layer of ecological units

(ecological range sites [ERS]) based upon soil texture, land use, topographic, and

hydrological characteristics. Dominant native vegetation in the dry-mixed-grass prairie of

CFB Suffield include needle and thread and blue grama grasses, particularly on the

rolling topography of the loamy ERS (Coupland 1950, Coupland 1961, Adams et al.

2013), which is the most prevalent range site, occurring across approximately 30% of

CFB Suffield.

2.2 Study Species

Four primary endemic grassland bird species (Knopf 1996) were selected for

analysis. These were among the most frequently observed species, but were also sensitive

species that were either currently listed under the Species at Risk Act (2002; Environment

Canada 2014) or had previously been listed by the Committee on the Status of

Endangered Wildlife in Canada (COSEWIC 2014; Table 1). For each species, literature

documenting habitat associations including nest site selection and the influence of various

18

disturbances were reviewed. Where possible, an emphasis was placed on study findings

from dry-mixed-grass prairie, or where comparisons between the selected study species

were made.

Table 1. Endemic mixed-grass prairie birds common at CFB Suffield and their status

(SARA = Species at Risk Act, COSEWIC = Committee on the Status of Endangered

Wildlife in Canada).

Species SARA listing COSEWIC status Point count

occurrence

McCown’s longspur

special concern special concern 0.18

Chestnut-collared

longspur

threatened threatened 0.71

Sprague’s pipit

threatened threatened 0.50

Baird’s sparrow

no status special concern

(previously threatened

1989)

0.43

2.2.1 McCown’s longspur

MCLO breeding habitat is typically sparse vegetation, usually a matrix of short-

grass species, bare soil, and limited mid-grass species (Baldwin and Creighton 1972,

With 2010, Henderson and Davis 2014). Average vegetation height within MCLO

territories was 5.2 cm at Pawnee, Colorado (Baldwin and Creighton 1972). Within the

CFB Suffield NWA, MCLO abundance had negative relationships with both previous

years’ precipitation and the remote sensing based soil adjusted vegetation index, for both

of which higher values suggest greater vegetation structure (Weins 2006). Nest sites of

19

MCLO were described as relatively exposed and associated with sparse vegetation

(DuBois 1935, With and Webb 1993).

Grazing appears to increase MCLO abundance (Bleho 2009), particularly heavily

or overgrazed pastures (Kantrud and Kologiski 1983, Dale et al. 1999, With 2010). In the

CFB Suffield NWA, Dale et al. (1999) found that MCLO were almost entirely restricted

to areas affected by fire. Similarly, Richardson (2012) observed that MCLO were most

abundant in burned pastures that were grazed for two years of their study, but in the other

two years, MCLO were most abundant in grazed pastures that were not burned. Some

indication of preference for south-facing slopes early in the breeding season was

observed in Saskatchewan (With 2010). Overall, MCLO can be described as a species

that prefers short-grass vegetation and responds positively to disturbances that reduce

vegetation structure.

2.2.2 Chestnut-collared longspur

CCLO inhabit the same general habitat as MCLO but prefer taller and denser

vegetation (Baldwin and Creighton 1972, With 2010). Hill and Gould (1997) describes

typical CCLO breeding habitat as recently mowed or grazed, arid, short to mid-grass

native prairie, with vegetation less than 20-30 cm and minimal litter. Henderson and

Davis (2014) found that CCLO abundance decreased as vegetation volume increased.

Overall, descriptions of CCLO habitat preferences indicates high and extremely low

amounts of vegetation may be unattractive to the species. Generally, CCLO appear to

select nest sites with greater vegetation structure than what is available (Dieni and Jones

2003, Lusk and Koper 2013) although not to the same extent as SPPI and BASP (Dieni

20

and Jones 2003, Davis 2005). Dubois (1935) observed that CCLO nested in areas with

greater vegetation cover than MCLO. With (2010) noted that it is rare to find both CCLO

and MCLO nesting in the same pasture unless it is heterogeneous.

Grazing has been found to increase abundance of CCLO (Dale 1983, Fritcher et

al. 2004, Bleho 2009). Although Davis et al. (1999) found a lack of response to grazing;

they suggested vegetation structure across all their grazing intensity categories might

have been acceptable to CCLO. Response of CCLO to roads has not been firmly

established; Sutter et al. (2000) observed fewer CCLO near roads, while Sliwinski and

Koper (2012) found roads did not influence abundance. At CFB Suffield, Hamilton

(2010) found CCLO territories had reduced amounts of crested wheat grass (Agropyron

cristatum; hereafter CWG) compared to comparison plots. Similarly, Davis and Duncan

(1999) found CCLO occurred more frequently in native than tame pasture. However, in

Montana, Lloyd and Martin (2005) found that CWG monocultures were used about the

same as native grassland. Fire appears to be positively associated with CCLO in both dry-

mixed-grass (Richardson et al. 2014) and moist-mixed-grass areas (Huber and Steuter

1984, Madden et al. 1999). Overall, CCLO can be described as a species that prefers

reduced mixed-grass vegetation and responds positively to disturbances that reduce

vegetation structure.

2.2.3 Sprague’s pipit

Robbins and Dale (1999) describe typical SPPI breeding habitat as well drained,

open native grassland with intermediate height, thickness, and litter depth. Vegetation

reduction by grazing in dry-mixed-grass (Karasiuk et al. 1977, Robbins and Dale 1999)

21

and moist-mixed-grass (Dale 1983) prairies has been observed to reduce pipit abundance.

Similarly, abundance of SPPI was found to increase with vegetation volume (Henderson

and Davis 2014). However, if vegetation structure is too great, suitability for SPPI

decreases as well (Dale 1983, Sutter and Brigham 1998, Madden et al. 2000). SPPI nest

sites are generally located in relatively tall and dense vegetation with little bare soil

(Sutter 1997, Dieni and Jones 2003). Davis (2005) found SPPI nests were also in

relatively tall and dense vegetation, but also found that bare soil at nest sites was similar

to CCLO nests, and greater than BASP nest locations.

The influence of roads on SPPI is not clear. Sliwinski and Koper (2012) found

roads had no effect on SPPI relative abundance, while Sutter et al. (2000) observed fewer

SPPI along roadside point counts than trails. As well, territories of SPPI may be less

likely to include off-road vehicle trails (Hamilton 2010). Dale et al. (1999) observed fire

appeared to decrease SPPI abundance in the dry-mixed-grass prairie of the CFB Suffield

NWA. In contrast, in moist-mixed-grass prairie, SPPI were positively related to fire and

absent from areas that had not been previously burned (Madden et al. 1999). Non-native

vegetation is generally avoided by SPPI (Sutter 1996, Davis and Duncan 1999, Fisher

2010, Hamilton 2010). Overall, SPPI can be described as a species that prefers moderate

mixed-grass vegetation, and responds negatively to disturbances that reduce vegetation

structure in dry-mixed-grass prairie.

2.2.4 Baird’s sparrow

Green et al. (2002) describe typical BASP breeding habitat as mixed-grass or

fescue prairie with residual vegetation from previous years’ growth and scattered low

22

shrubs. Dechant et al. (2002) indicate that general habitat includes a range of vegetation

height between 20 to 100 cm, but areas <20 cm have been used. Dale (1983) found fewer

BASP in grazed areas of moist-mixed-grass prairie. Similarly, Henderson and Davis

(2014) found BASP abundance increased with vegetation volume. However, if vegetation

structure is too great, suitability for BASP may decrease (Madden et al. 2000). Bare soil

was minimal within areas with high numbers of breeding BASP (Sutter et al. 1995, Davis

2005). Nest sites are selected in areas with greater vegetation and litter amounts than

surroundings (Green et al. 2002, Dieni and Jones 2003). Davis (2005) found BASP

selected nest sites with taller and thicker vegetation than SPPI and CCLO.

While a native prairie endemic, the species has been documented in several

studies to accept tame pasture, especially those dominated by CWG (Sutter et al. 1995,

Davis and Duncan 1999), and even cropland seeded to perennial crops such as alfalfa, or

alfalfa, sweet clover, and non-native grasses mixes (Davis et al. 1999, but see Dale et al.

1997). Mahon (1995) suggested that vegetation structure is more important to BASP

habitat selection than species composition. Two studies found fewer BASP were

observed near roads (Sutter et al. 2000, Sliwinski and Koper 2012). Similar to SPPI,

BASP were observed to be less abundant in areas with recent fire impacts in the CFB

Suffield NWA (Dale et al. 1999). As well, similar to SPPI, BASP were positively related

to fire and absent from areas that had not been burned in moist-mixed-grass prairie

(Madden et al. 1999, Winter 1999). Overall, BASP can be described as a species that

prefers moderate mixed-grass vegetation, and responds negatively to disturbances that

reduce vegetation structure in dry-mixed-grass prairie.

23

As all four study species respond to vegetation structure, albeit in different ways,

a common suite of variables, which relate to vegetation structure, should be relevant to

models of their distribution. As well, study species vegetation structure preferences

indicate these species should display a range of responses to disturbances that alter

grassland structure.

2.3 Identifying Bird Distribution

Bird distribution was measured using point count (PC) sampling. Selection of

sampling methods typically represents a compromise between time, cost, precision and

accuracy, and other analytical considerations (Ralph et al. 1995). Because objectives of

this study did not require comparison of density between species and the benefits of

adjusting counts for differential detection by distance were uncertain, unadjusted counts

were used. In this study, bird PC data was considered an index of relative abundance

(Hutto et al. 1986) and was, therefore, used as a relative indication of bird distribution.

The index of relative abundance was assumed to be adequate for modelling purposes

because research objectives were focused on examining the relative importance of

disturbance effects, not quantifying the magnitude of the effects.

Bird point count data were collected using five minute point counts modified from

Hutto et al. (1986), with two fixed radii (100 m and 250 m). The method used was

analogous to the North American Breeding Bird Survey (Robbins et al. 1986), with some

minor exceptions including observation cut-off distance, distance between PCs, and

observation duration. The fixed distance of 100 m was selected because open prairie

habitat increases the distance to which more confident detection can be assumed. The

24

second fixed distance of 250 m was the maximum distance at which birds observations

were recorded to assist in improving independence of counts between PC stations (Hutto

et al. 1986, Dale et al. 2009, Hamilton et al. 2011). PCs were no closer than 600 m,

leaving at least a 100 m distance between the edges of any two PC areas. Auditory and

visual detections were recorded, as well as behaviour notations, particularly

contemporary song observations. Approximate bird locations were recorded on

datasheets and distance to a bird observation was estimated to be within either the zero to

100 m or 100 to 250 m distance categories. A laser range finder was frequently used on

birds near 100 and 250 m distances to maintain distance estimation calibration throughout

surveying. Birds flying over the PC area were recorded but were not counted unless their

behavior indicated use of the area. A total of 674 PCs were performed, with 279 between

June 1 and July 4, 2013, and 395 between May 26 and June 27, 2014. These date ranges

correspond with the breeding phenology of study species (Davis 2003), when these birds

are actively defending territories thorough song and related behavioral displays. Point

counts were only visited once in a survey year, but 271 PCs were visited in both 2013 and

2014. A PC protocol was followed that established conditions under which PC data were

collected. Point counts were conducted between a half-hour before sunrise (~05:00 am)

until approximately 3 hours after sunrise (~08:30 am). To maintain consistency in

detection conditions, counts were not performed during inclement weather, including

winds ≥ 20 km/h, precipitation beyond very light rain, or fog. The same two observers

collected point count data in both years. At the beginning of each survey year, both

observers spent several days together practicing bird identification, distance estimation,

PC data recording and survey protocol. The University of Calgary Life and

25

Environmental Sciences Animal Care Committee approved the PC protocol methodology

(Protocol AC15-0049).

After data collection, datasheets were interpreted in the office to determine the

detected number of assumed breeding pairs at each PC location. This breeding pair

assumption used a count of territorial males, as indicated by observed behaviour, to

indicate pairs, but also allowed females to contribute to pair count if no males for the

species were observed at the PC. Observed and identified lone females were rare and

occurred at <1% of PCs. Comparison of data sheets between adjacent PCs aided in

conservative estimates. Individuals which could have been double counted at two PC

locations were assigned to the PC they were estimated to be closest to.

Determining PC locations was contingent on several considerations. To efficiently

collect PC data, routes of up to twenty PC locations were established (Fig. 2) from

randomly generated route seed locations generated in a Geographic Information System

(GIS; ArcGIS Desktop 10.2; ESRI 2013), using Hawth’s Tools (Beyer 2004). Objectivity

of route development was further enhanced by using PC placement rules. For the

purposes of this study, point count sampling was restricted to loamy ERS polygons to

restrict the potentially confounding influence of soil texture on disturbance and bird

relative abundance relationships. Vegetation communities and their responses to

disturbance can differ between soils of different texture (Coupland 1950, Smith and

McDermid 2014). For clarity, the loamy ERS in the study area did not include areas that

were previously cultivated or seeded to tame pasture. Restriction of PCs to the loamy

ERS served to cluster PC locations. The spatial and temporal patterns of military training

also encouraged spatial clustering of sampling points because accessibility to large

26

portions of the military training area are restricted during training periods, and change on

a daily basis. As a result, many point count locations were in relatively close proximity to

other PCs (i.e., ≥ 600 m apart minimum), which was expected to have exacerbated the

effect of spatial autocorrelation (SAC), and influenced the statistical analysis approach

taken.

Roads have been documented to influence habitat use of some grassland bird

species (Sutter et al. 2000, Sliwinski and Koper 2012). Understanding the influence of

habitat modifications caused by road construction and maintenance, or visual and

auditory disturbances presented by on-road traffic, were not specifically objectives of this

study. Therefore point count locations were placed no closer than 300 m to a road. This

created a buffer of at least 50 m between roads and the 250 m radius edge of PC

locations. Roads on the ranges at CFB Suffield have a speed limit of 70 km/h or less,

generally have developed ditches and are surfaced with gravel. These road ditches are

less than 50 m wide, which helped exclude the influence of road ditch vegetation on PCs.

However, some influence of an edge effect was still expected. Tracked off-road vehicles,

such as tanks and armoured personnel carriers, are prohibited from travelling on roads.

This results in significant off-road traffic running parallel to roads resulting in a zone of

increased trafficking disturbances (Appendix A: Fig. A3), which tends to decrease with

distance from the road (Appendix A: Fig. A4).

While PCs are one of the most frequently used method to count birds (Rosenstock

et al. 2002, Ralph et al. 1995) considerable critique and discussion of these methods has

taken place (Barker and Sauer 1995, Rosenstock et al. 2002, Diefenbach et al. 2003,

Norvell et al. 2003, Howell et al. 2004, Johnson 2008, Efford and Dawson 2009, Simons

27

et al. 2009). One of the main issues frequently raised is failure to detect birds that are

present in the count area. Distance correction of point count data is an example of a

frequently suggested method to control one of the potential biases associated with

imperfect detection of birds during counts (Rosenstock et al. 2002, Norvell et al. 2003,

Farnsworth et al. 2005, Buckland et al. 2008). However, Johnson (2008) suggests that

regardless of corrections made for detection by distance, the availability of a species to

even be counted is an issue (i.e., present but hidden and silent during PC period), which

suggests that even adjusted counts only represent an index of abundance rather than an

absolute measure of abundance. As well, in a simulation study, Efford and Dawson

(2009) concluded methods to adjust counts can result in even greater variability in bias

than unadjusted counts, which may make inference from unadjusted counts more robust

in some cases. Further, the assumptions required to perform distance corrections may not

always be adequately satisfied (Johnson 2008, Efford and Dawson 2009), see Henderson

and Davis (2014) for a grassland example. While some studies have concluded that there

were important differences between adjusted and unadjusted counts (Norvell et al. 2003,

Simons et al. 2009), a study conducted in mixed-grass prairie concluded there were no

important differences (Rotella et al. 1999). Therefore, considerable uncertainty regarding

the best approach to quantify bird abundance from point count data still exists.

Although the PC protocol should have reduced variance in counts due to

observation conditions such variance cannot entirely be eliminated. The observation

variables of observer, time of survey, wind speed and cloud cover were recorded.

Individual observer bias can influence bird counts due to differences in hearing acuity

(Cyr 1981, Ramsey and Scott 1981), vision, and familiarity with study area bird calls and

28

markings (Kepler and Scott 1981, Simons et al. 2009). The same two observers collected

PC data in 2013 and 2014. Wind speed can interfere with aural detection of birds by

observers (Robbins 1981, Simons et al. 2009). As well, the energetic costs of territorial

displays involving flight are influenced by wind speed, and may influence SPPI flight

displays (Robbins 1998). Therefore, wind speed could influence the likelihood of a SPPI

being observed in the 5 minute PC duration, and might also apply to MCLO. Wind speed

was measured at the start of each point count using hand held anemometers, using Extech

Instruments, mini thermo-anemometer, model number 45118. What time a PC was

conducted at may also influence count as some species may be more likely to vocalize or

perform other territorial displays later in the day, such as MCLO (With 2010) , while

others may be less likely to, such as BASP (Mahon 1995). As well, cloud cover may

influence bird counts, but response or lack of response may vary by species (Robbins

1981). These four variables were included in the model selection process in order to

allow for statistical control for some of the variance inherent in the PC surveys methods

used.

2.4 Environmental Variables

The variables selected for modelling bird relative abundance were sampled

through the GIS around each PC location. Buffers of 250 m from PC coordinates were

created to match the maximum radius of PCs, and served as the sampling unit. Mean

values from rasters and the sum of lengths of linear features were calculated within these

circular polygons to provide summary values for each variable at a PC.

29

2.4.1 Topographic Variables

Several topographic variables (see below) were derived from a 30 m Digital

Elevation Model (DEM). The 30 m DEM had been re-sampled from a 10 m DEM, with a

1 m vertical resolution, generated from stereo aerial photography in support of the GVI

classification performed for Alberta Sustainable Resource Development (ASRD 2010). In

an attempt to formulate parsimonious biologically relevant models, topographic variables

were limited to relative elevation, solar radiation, and slope, which were assumed to

describe the most important topographic variations in dry-mixed-grass prairie.

Relative elevation provides a continuous measure of topographic position, where

higher values generally represent hilltops, or at least areas more elevated than their

surroundings. Relative elevation was calculated using a moving window (De Reu et al.

2013), so calculated values are influenced by the window size selected. A relative

elevation window of 200 m was identified in previous work to have the strongest

correlations with vegetation indices in relatively undisturbed areas at CFB Suffield

(McWilliams 2013) and, therefore, presumably vegetation structure. High average

relative elevation values contain a greater area of hillcrest or upper slope areas, while

lower values indicate PCs with more depression areas and toe slopes. Midrange values

could indicate either widespread flat areas, or an even mixture of crests and depressions.

A solar radiation model was generated using slope, aspect, and obstruction from

surrounding topographic features (Fu and Rich 2002). The model output describes

cumulative direct and diffuse irradiance each pixel of the DEM was expected to receive

under clear skies (Fu and Rich 2002). Radiation from across the growing season was

30

used, from 1 April to 30 Sept, because this period should be the most important to plant

growth.

Percent slope was also calculated from the DEM, using the nearest 8 pixels. Slope

alone does not necessarily have a strong direct link to vegetation structure, although it

does provide an indication of how non-flat a PC area is. High values of average slope

generally indicate rolling or hilly topography, while low values indicate flat areas. While

there has not been an emphasis on slope in the grassland bird habitat literature, anecdotal

observations throughout the study area indicate that the grassland primary endemic bird

species are not as common or absent from areas with more extreme slopes, such as

steeply sloped coulees. Slope describes an important component of topographic variation,

which relative elevation and solar radiation do not capture.

2.4.2 Disturbance Variables

Disturbance variables were selected to describe anthropogenic impacts on the

landscape. A composite fire index was developed to provide a continuous variable for

modelling using both time since last fire and fire frequency information. The composite

fire index was generated by summing an index of fire frequency and an index of number

of years since the last fire. There was a high correlation between the two types of

information, as areas with frequent fire also often had recent fire. Therefore, both

variables could not be used for modelling due to collinearity. Fire spatial extents from

1972 to 2013 had been digitized from the Landsat archive by CFB Suffield staff (CFB

Suffield 2013a), and these data were used to calculate time since last fire (TSLF) in years

and the fire frequency. Time since last fire was calculated according to equation (1).

31

However, all fires beyond 10 years were set equal to 10 years as the influence of fire on

grassland birds was assumed to be minimal beyond 10 years (Pylypec 1991, Pylypec and

Romo 2003, Wakimoto et al. 2004, Grant et al. 2010, Roberts et al. 2012, Richardson et

al. 2014).

TSLF = current year – year of last fire (1)

Fire frequency was calculated as the number of fires that occurred at a location in the last

20 years. While fires older than 20 years have a potential influence, dry-mixed-grass

prairie can at least partially recover from the effects of frequent fires over this time period

(Smith and McDermid 2014). It was assumed that the 20 year time frame would capture

the most important variation. Both indices were scaled between 0 and 100 using possible

minimum and maximum values to bound the scaling formula. To achieve a more equal

relative weighting of the two types of fire information, a maximum value of 10 possible

fires over 20 years was used for fire frequency. This was based upon the assumption that

generally a fire will not occur at the same location for at least two years in a row, due to a

lack of fuel. For both TSLF and fire frequency, the year of bird survey was used as the

reference year for calculations. Values of the composite fire index greater than 100

generally indicate areas which have been both relatively recently burned and exposed to

frequent fire. Field observations confirmed that no fires occurred at any PC prior to bird

surveys in either 2013 or 2014.

Another composite index variable was generated for off-road trails digitized from

2.5 m high resolution imagery. Digitized trail shapefiles were available from the CFB

32

Suffield trails database (2013b) for imagery from 2006 (Fig. 5) and 2013, and an average

of the sum lengths of these two years was calculated to provide the composite trail

variable. Detailed trafficking data were unavailable, but military training exercises occur

in the same general location for several years before they are reconfigured. Therefore, the

composite trail index was considered the best available option to describe trafficking due

to the limited data available. Exposed bare soil makes identification and digitization of

off-road trails easier, and provides a link with grassland vegetation structure, where areas

of bare soil in native prairie often have reduced vegetation structure. However, in more

heavily impacted areas where perennial grass plants may killed, colonization of bare soil

areas by seral vegetation species in following years may potentially confound this

relationship. Seral weedy species, such as kochia (Kochia scoparia) or russian thistle

(Salsola kali), are typically taller than climax perennial grass species. In some cases,

sweet clover (Melilotus spp.) also colonizes areas impacted by trafficking, which has

spread from some areas of CFB Suffield that were seeded to tame pasture in the past. In

moist years at CFB Suffield, such as 2010, many sweet clover plants had grown taller

than 180 cm. Therefore, relative to the average height of vegetation across the landscape,

a recently trafficked trail will generally represent lower height, but in following years, if

colonized by seral, weedy species, it can represent greater height, although detection of

trails covered with these seral species by remote sensing may be difficult. Additionally,

not all off-road vehicle traffic will create enough bare-soil to be detected during the

digitization exercise. In dry soil conditions a single vehicle pass may only crush

vegetation, while in wet conditions or many vehicle passes the impacts are much greater

and bare soil may be created (Halvorson et al. 2003, Althoff and Thien 2005). The

33

composite trail variable can therefore only be considered an index of off-road vehicle

traffic, as it did not capture all off-road traffic. As well, military trafficking cannot be

separated from industrial off-road traffic in the trail index. Military vehicles often use

industrial access trails during administrative movements and during range preparation

and clean up. The composite trail index variable was used to describe trafficking

disturbances, with the limited data available, and was assumed to have potential to

describe important variation in bird distribution.

Figure 5. Spatial extent of off-road trails in 2006.

34

Although PC placement avoided the immediate area near roads, a variable to

account for possible road related effects extending beyond 50 m was generated. This

variable also relates to trafficking impacts, which are often high close to roads, but

typically decrease with distance from roads (Appendix A: Fig. A4). Distance to the

nearest road was calculated for centroid points derived from the 30 m raster lattice of the

DEM, and then values from these points were averaged within the 250 m radius sampling

area. Natural logarithm and square root transformations of this distance variable were

explored, but they did not improve correlations with bird relative abundance. Distance to

road provides trafficking information supplementary to the composite trail variable, but

could also include non-vegetation road influences extending beyond 50 m.

A sum of pipeline lengths within the 250 m radius of each PC was calculated from

pipeline shapefiles from both 2013 and 2014 (Fig. 6), which were available in the CFB

Suffield pipeline database (CFB Suffield 2014). The sum of pipeline lengths for a

window of fixed area can be considered an index for petroleum and natural gas impacts

because nearly all wells are tied into a pipeline network. Although correlated with the

trail index to some extent, pipeline length provided a way to distinguish petroleum and

natural gas related disturbance from military training disturbance, where they overlap. As

well, the use of pipelines accounts for industrial disturbance at PCs where no wells would

be in the PC area, but pipelines ran through the radius, which occurred in some PCs.

35

Figure 6. Spatial extent of pipeline network in 2014.

While the use of pipeline sum length as an index of industrial disturbance

provides some information, it does not encompass the full range of petroleum and natural

gas impacts. The initial disturbance associated with drilling wells and installing pipelines

results in decreased vegetation, which generally recovers overtime. Potential ongoing

maintenance and servicing could result in additional periodic vegetation disturbance

throughout the life span of the infrastructure. Well sites and access trails are sometimes

mowed to reduce fire risk. As well, differences between petroleum and natural gas

development, operations, and infrastructure may be important in magnitude of bird

36

response (Linnen 2008). Unique to CFB Suffield, petroleum and natural gas wells and

associated infrastructure are placed below the soil surface in caissons to allow the passage

of military training vehicles throughout much of the CFB Suffield range and training

area, but not in all areas. Incorporating all this information, in addition to the other

variables included in modelling would be difficult without building overly complicated

models and running the risk of model overfitting. Also, a detailed examination of all

petroleum and natural gas impacts was beyond the scope of this study. It was assumed

that pipeline length would serve as an index of industrial development relevant to the

scale of the 250 m PC area.

One important aspect of petroleum and natural gas disturbance was the historic

use of non-native species to revegetate disturbed areas. CWG, and other introduced

forage species, were often used to revegetate disturbed grasslands in North America since

the 1950s (Richards et al. 1998). Prior to its designation, CWG was used to revegetate

natural gas pipeline right of ways in the Suffield NWA during the late 1970s and early

1980s (Henderson 2007) and across the rest of CFB Suffield as well (Karen Guenther

pers. comm.). While use of CWG for revegetation has been prohibited in areas of native

prairie in Alberta since 1993 (Alberta Environment 2003), CWG has invaded native

prairie areas adjacent to previous plantings at CFB Suffield (Henderson 2007, Rowland

2008, see also a photographic example from the study area in Appendix A: Fig. A5) and

further spread along transportation corridors such as roads and off-road trails, which often

run parallel to pipelines. From a vegetation structure perspective, areas of CWG are

substantially different from native prairie (Sutter and Brigham 1998, Christian and

Wilson 1999, Henderson and Naeth 2005). In a study of the influence of natural gas

37

development on grassland birds at CFB Suffield, Hamilton (2010) found CWG was

approximately 50% taller than native grasses on average, and these areas also had

increased litter. As well, some more recent petroleum development revegetation efforts at

CFB Suffield have used cultivars of native grass species selected for robust growth or, in

some cases, cover crops, which have also contributed to increased vegetation height

along pipeline right of ways (Karen Guenther pers. comm.). While vegetation height is

reduced immediately after a pipeline is constructed, many older pipelines at CFB Suffield

represent areas of increased vegetation height (see Appendix A: Fig. A6 for an example),

and they likely will for an extended period of time (Henderson 2007, Rowland 2008).

2.5 Expectations of Bird Responses to Topographic and Disturbance Variables

The descriptions of grassland bird species habitat associations and responses to a

range of disturbances provided a basis for expectations of each species response to

topographic and disturbance variables in this study (Table 2). However, in some cases,

these expectations were refined to reflect the dry-mixed-grass study area, and may not be

entirely consistent with studies describing these species responses in moist-mixed-grass

prairies.

Topographic variables were expected to influence study species relative

abundance. High relative elevation and solar radiation are associated with reduced

vegetation and, therefore, positive relationships with MCLO and CCLO were expected

for these variables. As SPPI and BASP prefer relatively taller vegetation, a negative

relationship was expected between their relative abundance and both relative elevation

38

and solar radiation. Slope was expected to have a negative relationship with all four study

species as these species are characteristic of upland mixed-grass habitat at CFB Suffield.

Expectations of study species response to disturbance variables followed the same

general pattern as topographic variables. Due to their preference for extremely low

vegetation structure (With 2010), MCLO were expected to respond positively to fire and

trafficking because vegetation structure is reduced by these disturbances. A negative

response from MCLO to pipeline length was expected because of the taller vegetation

that is often associated with pipeline disturbances. CCLO do not prefer vegetation as

short as MCLO (Hill and Gould 1997), but were expected to have the same direction of

response as MCLO for all variables; however, the strength of these responses was

expected to be weaker. Both SPPI (Robbins and Dale 1999) and BASP (Green et al.

2002) have relatively similar preferences for vegetation structure, which approximates

undisturbed native prairie on loamy range sites at CFB Suffield. Both species were

expected to respond negatively to disturbances that decrease vegetation. However, these

species were expected to respond differently to pipeline length, as avoidance of non-

native vegetation for SPPI (Sutter 1996, Davis and Duncan 1999, Fisher 2010, Hamilton

2010) and indifference to non-native vegetation by BASP (Sutter et al. 1995, Davis and

Duncan 1999, Davis et al. 1999) have been documented. Generally, areas strongly

influenced by variables that have been associated with reduced vegetation structure were

expected to attract MCLO and CCLO while repelling SPPI and BASP, in the dry-mixed-

grass context of CFB Suffield.

39

Table 2. Summary of environmental variables used in modelling, their expected

relationship with vegetation structure, rationale for inclusion in modelling, and expected

response of study species (MCLO = McCown’s longspur, CCLO = chestnut-collared

longspur, SPPI = Sprague’s pipit, BASP = Baird’s sparrow).

Environmental

variable

Vegetation

structure at

high values

Rationale

MC

LO

CC

LO

SP

PI

BA

SP

Topogra

phy

Relative

Elevation

Reduced Provides an indication of

average topographic

position.

+1 + - -

Solar

Radiation

Model

Reduced Influences

evapotranspiration and

therefore vegetation.

+ + - -

Percent Slope

Increased

variability

Provides an indication of

how flat the point count

area is.

- - - -

Dis

turb

ance

Composite fire

Index

Reduced Recent or frequent fires

result in decreased

vegetation structure.

+ + - -

Composite

Trail Index

Reduced Provides an indication of

trafficking disturbance.

+ + - -

Pipeline Sum

Length

Increased Provides an indication of

petroleum development

impacts.

- - - N

Distance to

Road

Increased Trafficking near roads may

extend into point counts.

- - + +

1Symbols indicate positive, +; negative, -; or, no expected response, N.

40

2.6 Statistical Analysis

2.5.1 Spatial Exploratory Data Analysis

Statistical analyses were conducted using R 3.1.2 (R Development Core Team

2014). Exploratory data analysis was performed for univariate relative abundance data for

each species. A global Moran’s I test was used to provide an indication of whether SAC

was present in the dependent variable prior to modelling (Bertazzon et al. 2014). Getis-

Ord general G statistic was used to assess whether unique areas of clustering could be

delineated and considered for development of spatially discrete models. While these

statistics were developed for use with linear data, they can be used as an indicator for

spatial processes in non-normal Poisson distributed datasets, although definitive inference

cannot be made (Bertazzon et al. 2014; and see below).

2.5.2 Multicollinearity Analysis

Variables were assessed for multicollinearity using a correlation matrix of

response and predictor variables. Predictor variable correlations were examined to

determine if they were excessively correlated (Pearson's r > |0.7|). No models were

constructed where two predictor variables were excessively correlated to prevent inflation

of standard errors of other estimated effects (Agresti 2002), which would interfere with

the ability to draw inference regarding the importance of environmental variables. If two

predictor variables were excessively correlated they were either combined into composite

variables, or the variable with the weaker correlation with the response variable was

removed, or, if similarly correlated with the response variable, the variable with the least

direct theoretical link was excluded from the analysis. Due to the non-linear nature of

41

Poisson distributed data, a second multicollinearity assessment was performed for each

selected model, where the correlation of beta coefficients were examined. Again, the

Pearson's r > |0.7| criteria was used to determine if variables within the model were

excessively correlated, to prevent inflation of standard errors of other estimated effects

(Agresti 2002). If an excessive correlation between predictor variables was detected the

model would have been excluded from further consideration.

2.5.3 Accounting for Spatial Autocorrelation

Comparison of bird distribution models was required to meet study objectives;

therefore, an information theoretic approach was adopted using Akaike’s Information

Criterion adjusted for small sample sizes (AICc) to support model selection (Burnham

and Anderson 2002). Relative abundance data were analyzed through Generalized Linear

Modelling (GLM) assuming a Poisson distribution and specifying a log link (Eq. 2).

ln[E(Y)] = β0 + β1x1i + β2x2i... + βpxpi + ε (2)

SAC between sample points, which can lead to a form of pseudoreplication, can

be addressed in more than one way when modelling data collected in spatially associated

groups. One commonly used analysis method for bird point count data is Generalized

Linear Mixed Models (GLMMs), where point counts can be nested within survey routes,

pastures, or other spatial units as a random effects term (White 2009, Hamilton et al.

2011, Rodgers 2013, Kalyn-Bogard and Davis 2014, Richardson et al. 2014). An

alternative is the inclusion of an autocovariate term (AC) in a GLM describing the spatial

42

structure of the residuals of the global model of the non-spatial version of the GLM

(Dormann et al. 2007). The distance to which SAC extends can be indicated by

generating a semi-variogram from non-spatial models residuals (Dormann et al. 2007).

This distance can be used to guide creation of the AC to describe the residual SAC not

accounted for by independent variables in the non-spatial global model (Bivand 2014).

The software Geoda 1.4.6 (Anselin 2005) was used to generate spatial weights matrices

required in the creation of the ACs. Equation 3 symbolizes an AC with its associated

coefficient ρ and constitutes an autocovariate model, which can, when properly specified,

produce a model with residuals free from SAC (Dormann et al. 2007). Assessment of

whether SAC was accounted for in the autocovariate models was performed by a global

Moran's I tests for residual SAC. Griffith (2010) demonstrated that Moran’s I is relevant

for Poisson regression, but notes that function-based test statistics have not yet been

developed for Poisson distributions. Therefore, the global Moran’s I test for linear models

(Bivand 2014) was used as an index of SAC present in the residuals to guide model

development, although strong inference cannot be drawn as to whether SAC was fully

eliminated (Bertazzon et al. 2014). While both GLMMs and GLMs with a spatial AC

both account for SAC, the GLM with a spatial AC is a spatially explicit approach as a

neighborhood size is used to associate PCs. The GLM with a spatial AC was selected as

the preferred method because it allows model evaluation to be performed using k-fold

cross validation. The GLMM approach implicitly accounts for SAC by treating PC and

routes as random model effect terms. Unfortunately, the structure of nested random

effects terms in a GLMM are not replicated in k-fold cross validation when random or

spatial k-folds are used, which prevents cross validation between these folds in GLMMs.

43

As well, statistical inference using GLMMs can be difficult even for statisticians (Bolker

et al. 2008).

y = Xβ + ρAC + ε (3)

2.5.4 Disturbance Modelling Analysis

The first study objective placed emphasis on constructing alternate models

containing disturbance terms, so a two-stage modelling approach was adopted. In the first

stage, model selection was performed separately for both topographic and observation

variable groups or components (Table 3). Variables from the top-ranked model and

models within <2 AICc units were combined, without repetition, and deemed a model

component. This at least partially addressed model selection uncertainty, as models

within <2 AICc units are similar with respect to how well they explain the dataset

(Burnham and Anderson 2002). Therefore, potentially important predictor variables were

retained for the second stage of modelling. First however, the log-likelihood of models

within <2 AICc units were compared to the top-ranked model, and models where the log-

likelihood was essentially the same but contained 1 additional variable were not

considered, as these variables are uninformative parameters (Burnham and Anderson

2002, Arnold 2010). As well, variables from models with an AICc higher than the null

model were not considered, even if they were within 2 AICc units (Burnham and

Anderson 2002). Subsequently, variables from both topographic and observation

components were then combined and fixed and made up the fixed component of the

44

second modelling stage. The ACs were an exception as residual SAC can change

substantially between different global models.

Table 3. First stage topography and observation variable group models.

Topographic Component Observation Component

RE1 + SOLRAD + SLOPE +AC1 OBS + TIME + CLOUD + WIND + AC2

RE + SOLRAD +AC1 OBS + TIME + CLOUD + AC2

RE + SLOPE +AC1 OBS + TIME + WIND + AC2

SOLRAD + SLOPE +AC1 OBS + CLOUD + WIND + AC2

RE +AC1 TIME + CLOUD + WIND + AC2

SOLRAD +AC1 OBS + TIME + AC2

SLOPE +AC1 OBS + CLOUD + AC2

OBS + WIND + AC2

TIME + CLOUD + AC2

TIME + WIND + AC2

CLOUD + WIND + AC2

OBS + AC2

TIME + AC2

CLOUD + AC2

WIND + AC2 1RE: relative elevation; SOLRAD: solar radiation; SLOPE: percent slope; OBS: observer;

TIME: time of survey; CLOUD: percent cloud cover; WIND: wind speed; and AC(x):

autocovariate term.

In the second stage of modelling, model selection was performed using individual

disturbance variables and fixed components from the first stage. This allowed selection to

focus on disturbance variables while allowing the fixed topographic and observation

components to detrend presumably important covariation in a bird species relative

abundance. A total of 16 models including possible variations of the global model (Eq.

4), were run through the model selection process in the second modelling stage for each

species.

45

FICO + TRCO + PLSL + RD_DIST + Fixed Component + AC (4)

Overdispersion and goodness of fit were assessed for the global model for each species

(Eq. 4). Model selection through AICc will not select models in the subset with poor

goodness of fit if the global model adequately fits the data (Burnham and Anderson

2002). The final model selected in the second-stage was assessed for SAC using a

residual global Moran’s I test. Compatibility with AICc methods was improved by

identifying model term beta coefficients where the 85% confidence intervals overlap

zero, which indicated uninformative parameters (Arnold 2010). RSVIs were deliberately

excluded from this analysis, as they were expected to reflect patterns of disturbance and

confound model selection focused on disturbance variables. To report standardized Beta

coefficients, all continuous independent variables were centered by subtracting their

mean and then dividing by their standard deviations.

Model evaluation using k-fold cross-validation was performed by partitioning the

dataset into training and testing datasets (Guisan and Zimmermann 2000). Similar to

Weins et al. (2008), 3-way cross-validation with random, spatial, and temporal k-fold was

used. Five random folds were generated, while three spatial folds were created to reflect

the land use zoning areas (i.e., MTA, experimental proving ground [EPG], and cattle

pastures; Fig. 2). Due to two years of available data, a two-fold cross-validation was

performed to investigate some limited temporal aspects of models’ performance.

Because species prevalence can influence values of threshold-dependent model

evaluation measures, threshold-independent methods are commonly used (Franklin

46

2009). Potts and Elith (2006) used threshold-independent methods, including Pearson’s

correlation coefficient Spearman’s rank correlation, and model calibration intercept and

slope, to compare predicted and observed count data. These four diagnostic values were

adopted to provide indications of how well models developed with training folds data

predicted bird relative abundance in the testing fold. Model calibration is simple linear

regression of predicted and observed values, and provides an indication of bias (intercept)

and spread (slope; Pearce and Ferrier 2000, Johnson et al. 2006). In a perfectly calibrated

model the intercept should be 0 and the slope should be 1 (Pearce and Ferrier 2000,

Johnson et al. 2006). Cohen (1992) provided effect size criteria for Pearson and

Spearman’s rank correlations for both of which, 0.1 is associated with a small effect size,

0.3 a medium, and 0.5 a large effect size.

2.5.5 Bivariate Regression of Relative Abundance and Composite Fire Index

To take a closer look at only the effect of fire, a post hoc bivariate regression of

bird relative abundance against the composite fire index was performed for each species.

Scatter plots were also generated to provide a visual assessment of the relationship for

each species. This provided a supporting analysis to complement the disturbance

modelling analysis.

2.5.6 Influence of Burn Status and Topographic Position on Vegetation

In order to provide a link between the influence of disturbance and topography on

vegetation, comparison of SATVI values for burned and unburned hill crest and

depression areas was performed. The composite fire index and topographic position were

47

selected for this investigation because they were variables expected to have the largest

influence on vegetation from their respective variable groups. As well, the clear

relationships between these two variables and vegetation allows for the most straight

forward interpretation.

The SATVI raster was derived from Landsat 8 operational land imager (OLI)

imagery acquired on 2 July 2013 and 12 July 2014. These images were the closest

acquisition dates to the breeding bird survey timeframe where the study area was cloud

free and only differ by 10 Julian days between years. Dark object subtraction 1 (Chavez

1996) was used to perform atmospheric radiometric corrections for both images, using

the Semi-automatic classification plugin version 3.1.3 (Congedo et al. 2013) in Qgis 2.6

(QGIS Development Team 2014). Landsat OLI digital number values were then

converted to top of atmosphere reflectance in accordance with methods from the United

States Geological Survey website (USGS 2013). The SATVI raster was calculated using

the formula in Marsett et al. (2006) and parametrized by assigning the soil adjustment

constant L = 0.5, the value Huete (1988) suggests for intermediate vegetation density.

Huete (1988) found that L = 0.5 reduced soil noise throughout a range of vegetation

densities, which certainly occur in a landscape with regular vegetation disturbance, such

as the CFB Suffield study area.

Random sampling points were generated along the edges of fires from the year

before the image year using Hawth’s Tools (Beyer 2004) in ArcGIS (ESRI 2013).

Relatively few fires had occurred by early July in the current year for both Landsat

images. Generation of sampling points was restricted to loamy ERS polygons and

excluded from areas that had burned in the previous 10 years. The previously described

48

relative elevation raster was used to guide selection of either the nearest hillcrest, or

depression, to random sampling points. The nearest matching hill crest, or depression, in

the burned area from the previous year was then selected to complete a matched pair of

burned and unburned points of one topographic position. Selection alternated between

these topographic positions to obtain balanced sample sizes. Sampling locations closer

than 3 pixels, or approximately 90 m, to the edge of the previous year’s fire were avoided

to prevent accidental placement of both points in burned or unburned areas due to errors

associated with image georectification or fire extent digitization. The points were then

queried for SATVI values. Up until the SATVI values had been obtained, the SATVI

layer was masked from view to mitigate selection bias of sampling locations across a

hilltop or within a depression. After the values were obtained, the SATVI layer was

checked to ensure that sampled points did not fall on areas with obvious trafficking

impacts or other soil disturbances, which could have severely confounded the effect of

fire. Confounded sampling points were discarded. The SATVI layer was also used to

double check that fire extent digitization error did not result in both points occurring in a

burned or unburned area.

Statistical analysis included a Wilcoxon signed rank test (Hollander and Wolfe

2013) for paired burned and unburned sampling points. However, sampling points were

not paired by adjacent hilltops and depressions. Therefore, a Mann-Whitney U test

(Hollander and Wolfe 2013) was also performed to determine if topographic position had

a significant influence on SATVI values. These statistical tests were chosen to describe

the relationship between vegetation, as described by remote sensing, and both disturbance

and topography.

49

CHAPTER 3: RESULTS

3.1 Spatial Exploratory Data Analysis

Spatial exploratory data analysis indicated that SAC was present in the relative

abundance data for all four species as indicated by large global Moran’s I statistics (p <

0.001). This exploratory analysis result supported the decision to use the AC in the GLM

modelling approach. Evaluation of maps of Getis-Ord general G statistic at each PC

location, for each species, indicated that there was some clustering of high and low

values. However, these appeared to be generally related to either fire impacts, or for

MCLO, one specific topographic feature. Therefore, it appeared that explanatory

environmental variables should adequately address these clusters, and that there were not

areas which suggested spatially discrete models would provide additional benefit.

3.2 Correlation Matrix

The correlation matrix revealed that many of the predictor variables were

correlated, even if only to a small degree (Table 4). Although vegetation indices were not

being used in disturbance modelling, a correlation greater than |0.7| was found between

the SATVI and the composite fire index indicating a high level of shared information.

However, no variables to be used in model development were correlated above |0.7|.

Further assessment of multicollinerity through examination of the correlation of model

Beta coefficients revealed that no models contained correlated predictor variables above

|0.7|.

50

Table 4. Correlation matrix of dependent and independent variables (MCLO =

McCown’s longspur, CCLO = chestnut-collared longspur, SPPI = Sprague’s pipit, BASP

= Baird’s sparrow, FICO = composite fire index, TRCO = composite trail index, PLSL =

pipeline sum length, RD_DIST = distance to road, RE = relative elevation, SOLRAD =

solar radiation, OBS = Observer, TIME = time of point count survey, CLOUD = cloud

cover, WIND = wind speed, SATVI = soil adjusted total vegetation index).

Variable MCLO CCLO SPPI BASP FICO TRCO PLSL

MCLO 1 0.12* -0.30** -0.28** 0.59** 0.05 -0.17**

CCLO 0.12* 1 -0.09* 0.10* 0.31** 0.20** -0.02

SPPI -0.30** -0.09* 1 0.28** -0.39** -0.09* 0.06

BASP -0.28** 0.10* 0.28** 1 -0.30** -0.05 0.03

FICO 0.59** 0.31** -0.39** -0.30** 1 0.31** -0.20**

TRCO 0.05 0.20** -0.09* -0.05 0.31** 1 0.20**

PLSL -0.17** -0.02 0.06 0.03 -0.20** 0.20** 1

RD_DIST 0.09* -0.19** -0.01 -0.07 0.03 -0.06 -0.17**

RE 0.14** 0.05 -0.01 -0.03 -0.03 -0.08* -0.05

SOLRAD 0.10* 0.19** -0.01 0.13** 0.08* 0.03 -0.03

SLOPE 0.08* -0.25** -0.03 -0.20** -0.14** -0.09* -0.01

OBS -0.11* -0.40** -0.08* -0.16** -0.03 -0.03 -0.04

TIME 0.03 -0.13** -0.02 -0.31** 0.06 0.02 0.03

CLOUD -0.04 0.07 0.06 0.19** -0.05 0.01 0.02

WIND 0.02 0.10* -0.03 0.03 0.07 0.05 0.04

SATVI -0.57** -0.21** 0.36** 0.33** -0.73** -0.26** 0.15**

51

Table 4. Continued.

Variable RD_DIST RE SOLRAD SLOPE OBS TIME CLOUD

MCLO 0.09*

0.14** 0.10* 0.08* -0.11* 0.03 -0.04

CCLO -0.19** 0.05 0.19** -0.25** -0.40** -0.13** 0.07

SPPI -0.01 -0.01 -0.01 -0.03 -0.08* -0.02 0.06

BASP -0.07 -0.03 0.13** -0.20** -0.16** -0.31** 0.19**

FICO 0.03 -0.03 0.08* -0.14** -0.03 0.06 -0.05

TRCO -0.06 -0.08* 0.03 -0.09* -0.03 0.02 0.01

PLSL -0.17** -0.05 -0.03 -0.01 -0.04 0.03 0.02

RD_DIST 1 0.00 -0.17** 0.21** -0.04 -0.02 0.02

RE 0.00 1 0.02 0.04 -0.02 0.02 -0.03

SOLRAD -0.17** 0.02 1 -0.25** 0.05 0.05 0.13**

SLOPE 0.21** 0.04 -0.25** 1 -0.02 0.06 -0.08*

OBS -0.04 -0.02 0.05 -0.02 1 0.09* -0.15**

TIME 0.03 0.02 0.05 0.06 0.09* 1 0.03

CLOUD -0.02 -0.03 0.13** -0.08* -0.15** 0.03 1

WIND -0.03 0.01 0.03 0.00 -0.18** 0.20** 0.27**

SATVI -0.05 -0.09* 0.00 -0.02 -0.01 0.04 0.12*

Table 4. Continued.

Variable WIND SATVI

MCLO 0.02 -0.57**

CCLO 0.10* -0.21**

SPPI -0.03 0.36**

BASP 0.03 0.33**

FICO 0.07 -0.73**

TRCO 0.05 -0.26**

PLSL 0.04 0.15**

RD_DIST -0.03 -0.05

RE 0.01 -0.09*

SOLRAD 0.03 0.00

SLOPE 0.00 -0.02

OBS -0.18** -0.01

TIME 0.20** 0.04

CLOUD 0.27** 0.12*

WIND 1 0.05

SATVI 0.05 1

*Sig. 0.05; **Sig. 0.001

52

3.3 Disturbance Modelling Analysis

Interpretation of the AICc results during model selection was clear and

unambiguous. There were no other models within 2 AICc units of the top model for

CCLO. The top-ranked models for SPPI and BASP were a subset of all models within 2

AICc units of the top-ranked model. Models within 2 AICc units of the top-ranked model

for MCLO were either the top-ranked model with additional uninformative parameters

(85% confidence intervals which included 0, and their presence had minimal influence on

the models negative log-likelihood), or, the top-ranked model contained one additional

variable than the remaining model. This additional variable had low relative importance,

and model averaging would have had minimal impact on inference draw from this model.

Therefore, only the top-ranked model for each species were reported (Table 5).

Disturbance variables were important predictors of bird relative abundance. The

composite fire index was included in top-ranked models for all four species, and for SPPI

and BASP it was the only disturbance variable (Table 5). As expected, MCLO and CCLO

responded positively to fire, while SPPI and BASP responded negatively (Table 6).

Additional disturbance variables were important predictors of the two longspur species.

Both the trail composite index and distance to road were present in the CCLO top-

ranking model (Table 5). As expected, CCLO relative abundance increased as the trail

index increased, and was also higher closer to roads (Table 6). The top-ranked model for

MCLO included both pipeline length and distance to road (Table 5). As expected, MCLO

relative abundance decreased as the sum of pipeline lengths increased, but was lower

closer to roads, contrary to the expectation (Table 6).

53

Topographic variables were also found to be important predictors for three species

relative abundance. Slope was an important predictor for MCLO, CCLO, and BASP

(Table 5): as expected, both CCLO and BASP had higher relative abundance in flatter

areas, while MCLO relative abundance was higher in sloped areas, contrary to the

expectation (Table 6). Solar radiation influenced MCLO and CCLO, and, as expected,

both species had higher relative abundance in areas exposed to more direct sunlight

(Table 6). Relative elevation was an important predictor only for MCLO, which, as

expected, had higher relative abundance in areas with higher average topographic

position (Table 6). No topographic variables were important predictors of SPPI relative

abundance.

Table 5. Top ranked second-stage autocovariate generalized linear models. Disturbance

variables are in bold.

Species Model Structure1

MCLO FICO + PLSL + RD_DIST + RE + SOLRAD + SLOPE + OBS + TIME +

CLOUD + AC1

CCLO FICO + TRCO + RD_DIST + SOLRAD + SLOPE + OBS + TIME + AC2

SPPI FICO + OBS + AC3

BASP FICO + SLOPE + OBS +TIME + CLOUD + AC4 1FICO: fire composite index; TRCO: trail composite index; PLSL: sum length of

pipelines; RD_DIST: distance to nearest road; RE: relative elevation; SOLRAD: solar

radiation; SLOPE: percent slope; OBS: observer; TIME: time of survey; CLOUD:

percent cloud cover; WIND: wind speed; and, AC(x): autocovariate term specific to each

model.

54

Parameter estimates of top-ranked models indicate that relationships between

environmental variables and bird relative abundance were generally in agreement with

expected, with the exception of distance from roads and slope for MCLO (Table 6).

Standardized beta coefficients indicate that fire generally had the strongest relationship

with bird relative abundance for all four species relative to other environmental variables

(Table 6), although this difference was minimal for CCLO.

Table 6. Environmental variable parameter estimates of top-ranked models as identified

by AICc and agreement with expectations of coefficient (E(β sign)) sign (+/-).

Species Variable1 β SE E(β sign) Agreement?

McCown’s longspur FICO 0.831 0.042 + Yes

PLSL -0.175 0.066 - Yes

RD_DIST 0.097 0.049 - No

RE 0.198 0.046 + Yes

SOLRAD 0.150 0.048 + Yes

SLOPE 0.330 0.048 - No

chestnut-collared longspur FICO 0.151 0.024 + Yes

TRCO 0.055 0.023 + Yes

RD_DIST -0.117 0.029 - Yes

SOLRAD 0.087 0.028 + Yes

SLOPE -0.093 0.028 - Yes

Sprague’s pipit FICO -0.388 0.042 - Yes

Baird’s sparrow FICO -0.354 0.043 - Yes

SLOPE -0.217 0.036 - Yes 1FICO: fire composite index; TRCO: trail composite index; PLSL: sum length of

pipelines; RD_DIST: distance to nearest road; RE: relative elevation; SOLRAD: solar

radiation; and SLOPE: percent slope.

Model evaluation using k-fold cross-validation indicated that overall species

distribution models predictive performance was moderate to poor. The correlations for

55

random k-folds suggest these models have some limited utility for prediction because

they generally exhibited moderate to strong correlations between predicted and observed

relative abundance, although all correlations were at or below 0.6 (Table 7). However,

model calibration intercept values for random folds indicate that these models had some

bias and model calibration slopes were lower than 0.5 indicating considerable spread.

According to Pearson and Spearman rank correlations, predictive performance was

generally highest between temporal folds, somewhat lower for random folds, and lowest

for spatial folds (Table 7). However, two deviations from this general trend were present,

where MCLO random folds had better prediction than temporal folds, and BASP spatial

folds had better prediction than random folds, as indicated by correlations (Table 7).

Model calibration slope indicated that for all species temporal k-folds performed better

than spatial k-folds. All model evaluation statistics indicated that decreased model

prediction between spatial folds appeared to be least for CCLO and BASP. These

evaluation results indicate that generally model predictions of bird relative abundance

were best between the two study years, and poorest between different land use

management areas (i.e., MTA, EPG, and grazing pastures).

56

Table 7. Model evaluation statistics averaged across random, spatial, and temporal k-

folds.

Folds

Evaluation

Measure

McCown’s

longspur

chestnut-

collared

longspur

Sprague’s

pipit

Baird’s

sparrow

Random

Pearson's r

0.601 0.585 0.404 0.462

Spearman's Rank

Correlation 0.571 0.603 0.411 0.513

Model Calibration:

Intercept 0.349 1.429 1.243 1.017

Model Calibration:

Slope 0.476 0.426 0.247 0.329

Spatial

Pearson's r

0.203 0.518 0.346 0.502

Spearman's Rank

Correlation 0.319 0.513 0.356 0.543

Model Calibration:

Intercept 0.332 1.583 1.610 0.987

Model Calibration:

Slope 0.140 0.326 0.142 0.281

Temporal

Pearson's r

0.569 0.652 0.523 0.584

Spearman's Rank

Correlation 0.566 0.669 0.510 0.618

Model Calibration:

Intercept 0.323 1.275 1.125 0.801

Model Calibration:

Slope 0.653 0.472 0.289 0.399

3.4 Bivariate Response of Endemic Bird Study Species to Fire

The composite fire index alone appeared to have an important influence on the

distribution of each of the four study species. Bivariate Poisson regressions of relative

abundance and the composite fire index for all species were significant (p < 0.001) and

indicated that a relatively large amount of variance is described for MCLO, while

decreasing amounts of variance are described for SPPI, BASP, and CCLO by fire, as

57

indicated by Cragg and Uhler’s pseudo R2’s of 0.52, 0.15, 0.12, and 0.09 respectively.

Generally, higher counts for MCLO were observed at higher values of the fire index,

while the opposite was true for SPPI and BASP (Fig. 7). The relationship between CCLO

relative abundance and fire was less clear, although it appeared that at higher fire index

values high relative abundance counts were more frequent than low counts (Fig. 7). These

results demonstrated the differences in direction and relative strength of the response of

the four study species to fire, but also provided an indication of the influence of fire

frequency impacts. All PC locations where the composite fire index was greater than 100

must have a history of repeated fire. Points above 100 on the fire index generally indicate

the greatest positive association with the fire index for MCLO and CCLO, and the

greatest negative association for SPPI and BASP (Fig. 7).

58

Figure 7. Observed study species relative abundance by the composite fire index (MCLO

= McCown’s longspur, CCLO = chestnut-collared longspur, SPPI = Sprague’s pipit,

BASP = Baird’s sparrow).

3.5 Influence of Burn Status and Topographic Position on Vegetation

A Wilcoxon Signed-Rank test indicated a difference between 107 paired

unburned (median = -0.0412) and burned (median = -0.0596) locations for values of the

SATVI (Z = 8.98, p < 0.001, r = 0.61). This confirmed that fire decreased observed

SATVI values, and therefore reduced vegetation. Similarly, A Mann-Whitney U test

indicated a difference between unpaired crest (median = -0.0535) and depression areas

(median = -0.0465) for values of the SATVI (Z = -3.89, p < 0.001, r = 0.27). Therefore,

topographic position also had an important influence on vegetation as measured by the

SATVI. The values for the SATVI were highest in unburned depression areas, second

59

highest on unburned crests, third highest in burned depressions, and lowest on burned

crests (Fig. 8). These results demonstrate how both disturbance and topography

contributed to vegetation heterogeneity.

The statistical relationship (described above) between the SATVI and fire is

visually apparent by inspection of the SATVI calculated for the 2014 Landsat 8 image

overlaid with the outlines of burned areas from the previous three years (Fig. 9). Areas

burned in 2013 appear darkest due to lower SATVI values, while areas burned in 2012

and 2011 generally appear lighter, and the generally lightest toned areas are those that

have not burned for at least several years. Some areas of low SATVI values in the image

are also apparent which correspond to roads, off-road trails and trafficking, and open

water, which are also areas with reduced or absent vegetation. This image illustrates how

fire influenced vegetation at a landscape scale, and contributed to coarse-scale vegetation

heterogeneity, or a habitat mosaic (Fig. 9).

Figure 8. Notched boxplot comparison of SATVI values between burned and unburned

areas stratified by topographic position.

60

Figure 9. SATVI image from the approximate center of the Manoeuvre Training Area of

CFB Suffield calculated from the 12 July 2014 Landsat 8 image, overlaid with fire

extents from 2011-2013. An image stretch of 2.5 standard deviations was applied to aid

visual interpretation.

61

CHAPTER 4: DISCUSSION

The results of this study demonstrate that both habitat disturbances and

topography influenced the distribution of endemic grassland birds during the breeding

season. Models estimating the relative abundance of the different study species generally

support the expectations in Table 2 that describe the expected response of each species to

individual disturbance and topography variables, with a few exceptions. These responses

to disturbance were different between species; some species responded positively to some

disturbances while others responded negatively. This range of responses is consistent

with the heterogeneous disturbance hypothesis, which posits that biodiversity will be

maximized if a range of disturbances occur across time and space at a landscape scale

(Warren et al. 2007). Fire was a particularly important predictor of relative abundance for

all four species, and areas with the highest fire index values appeared to have the greatest

influence on study species relative abundance. Both fire and topographic position were

shown to be related to vegetation, where the SATVI was lower in burned areas and hill

crests and higher in unburned areas and depressions. These differences in the SATVI

associated with fire and topographic position demonstrate that vegetation is being

influenced by both disturbance and topography. As vegetation is known to be an

important predictor of bird distributions (Weins 1969, Fisher and Davis 2010),

interpretation of the results will be framed in the context of study species responses to

vegetation structure, but other potential factors will also be considered in the discussion.

However, it is important to note that PCs were conducted in the loamy ERS only and

during two years of relatively normal precipitation. Extension of these results to years

with very high or low precipitation or non-loamy ERS may be invalid, particularly to

62

ERS with coarse textured soils where vegetation can respond differently to disturbances

such as fire (Smith and McDermid 2014).

4.1 Disturbance Modelling

4.1.1 Disturbance: Fire, Trafficking, Pipelines

While all disturbance variables were important for at least one of the four species,

the strongest disturbance related inference could be drawn from fire. The composite fire

index is a more precise descriptor of vegetation disturbance than either trails, pipelines, or

road distance, because of the highly consistent effect on vegetation in burned areas;

vegetation is always reduced to some extent by fire. Interpretation of the relationships of

study species relative abundance with off-road trails, distance from road and pipeline sum

length variables was more difficult. All three variables have the potential to be associated

with either reduced vegetation, due to recent disturbance, or taller vegetation due to seral

and invasive species colonizing bare ground caused by previous disturbances. As well,

fire impacts typically occur at a coarser scale. A single fire will generally influence

vegetation across an entire PC’s area, unless the PC happens to be near the fires edge;

trafficking and pipelines generally only influence vegetation in a portion of the 250 m

radius. Therefore, interpretation of the influence of fire on relative abundance were likely

the most straightforward because of the consistency and scale of fire impacts.

Fire was the most important predictor of study species relative abundance of the

seven disturbance and topographic variables used in modelling. The response of the four

grassland endemics to fire reflects Knopf’s (1996) indication of these species use of

habitat structure created by grazing pressure. At CFB Suffield, in loamy ERS, high fire

63

impacts result in the short vegetation structure on the left side of Figure 1, while the

vegetation structure on the right is associated with low or no fire impacts (and see

Appendix A: Fig. A1). This study’s findings are in agreement with Dale et al. (1999) who

observed lower occurrence of SPPI and BASP and higher occurrence of CCLO and

MCLO in areas of high fire disturbance in the CFB Suffield National Wildlife Area.

The response of the two longspur species to fire was generally consistent with the

limited information regarding these species responses to fire. No peer-reviewed studies

were found which directly quantified a relationship between MCLO and fire, although

Richardson (2012) found more MCLO in burned, grazed, or burned and grazed pastures

than unburned and ungrazed pastures. Fire suppression has been speculated to have

reduced MCLO breeding habitat (With 2010). This speculation appears to be confirmed

by this study’s findings of greater relative abundance of MCLO in areas with the highest

fire impacts. Similar to MCLO, CCLO have had minimal study in relation to fire. This

study’s findings are in agreement with available literature: in dry-mixed-grass prairie,

CCLO was found to have a positive relationship with burned areas (Richardson et al.

2014). Similarly, although limited numbers were observed, CCLO appeared to respond

positively to fire in two studies in the moist-mixed grass prairie (Huber and Steuter 1984,

Madden et al. 1999). While historically common in the moist-mixed grass prairie, CCLO

are now uncommon or absent, which has been partially attributed to a lack of adequate

fire (Madden et al. 1999, Ludwick and Murphy 2006, Grant et al. 2010). Both longspur

species demonstrated a positive response to fire consistent with their vegetation

preferences.

64

The observed responses of SPPI and BASP to fire in this study are inconsistent

with some previous studies. This study’s findings of a negative relationship between the

relative abundance of SPPI and BASP agree with those of Richardson et al. (2014),

whose research was also in dry-mixed-grass prairie. However, disagreement exists with

studies conducted within moist mixed-grass prairie (Johnson 1997, Madden et al. 1999,

Winter 1999, Danley et al. 2004). The response of these species to fire may depend on

how regional moisture differences influences vegetation structure. A general gradient of

increasing moisture exists from west to east across the northern mixed-grass prairie,

where eastern portions are referred to as the moist-mixed-grass prairie (Madden et al.

1999). Similar to the observations for SPPI in the moist-mixed grass prairie, anecdotal

observations of SPPI’s response to fire at CFB Wainwright suggest SPPI generally only

occur in areas which have been burned or mechanically cleared of vegetation, but after

grass litter has accumulated for a year or two (Shane Mascarin pers. comm.). CFB

Wainwright is located approximately 300 km north of CFB Suffield in the Aspen

Parkland ecoregion of Alberta, and similar to the moist-mixed-grass prairie, experiences

greater precipitation than CFB Suffield, which supports extensive cover of shrubs and

trembling aspen (Populus tremuloides; NRC 2006). The contrast between Richardson et

al. (2014) and this study’s findings with research from moist-mixed-grass areas and

anecdotal observations from CFB Wainwright appear to confirm what Madden et al.

(1999) and Winter (1999) both suggested: the response to fire is not geographically

consistent. Differences in vegetation structure associated with moisture differences

between dry- and moist-mixed-grass regions is a compelling explanation for observed

disparities in the responses of SPPI and BASP to fire.

65

Fire frequency appeared to importantly influence relative abundance of all four

study species. While the composite fire index blends information from the time since last

fire and fire frequency, the strongest associations between study species relative

abundance and the composite fire index was for values of the fire index over 100, and

values greater than 100 must have a component of frequent fire (Fig. 7). This suggests

that the findings of Smith and McDermid (2014) and Weerstra (2005), which indicate the

short-grass blue grama replaces the mid-grass needle and thread after frequent fire, have

important implications for grassland bird habitat suitability. This study’s findings of a

range of responses to fire indicates a mosaic of fire impacted and non-impacted habitats

can benefit endemic grassland bird species.

Trafficking appeared to have a less important influence on grassland bird relative

abundance than fire. Only the two longspur species responded to the indices of

trafficking, and these responses were relatively weak. While both MCLO and CCLO

responded to distance from road, only CCLO responded to the composite trail index. The

findings of this study are somewhat consistent with the results of Hubbard et al. (2006)

who found low density of trafficking, which had occurred prior to the breeding season, to

have no impact on two species of ground nesting grassland birds at Fort Riley, Kansas.

The apparent lack of response by both MCLO and SPPI and BASP to the composite trail

index may relate to the fact that trafficking typically influences localized areas within a

PC radius (Appendix A: Fig. A3 & A4), relative to fire, which generally results in a more

homogenous effect (Appendix A: Fig. A1 & A2) at the scale of a 250 m radius.

Presumably, the areas within a PC undisturbed by trafficking, with higher vegetation

structure, may have been enough to repel MCLO, preventing the expected positive

66

response, while providing areas suitable to SPPI and BASP. Trafficking disturbance may

be easier to avoid for SPPI and BASP to avoid compared to fire impacts at the scale of

the PC radius. As CCLO are an intermediate between these 3 species with respect to

vegetation preferences, localized reductions may have provided enough of an attraction to

increase relative abundance. The lack of response by SPPI and BASP to the composite

trail index contrasts with previous findings in southeastern Alberta for petroleum and

natural gas trails (Linnen 2008, Dale et al. 2009, Ludlow et al. 2015). However, as many

of the trails in the composite trail index were military caused, a difference between

petroleum and natural gas trails and military trails was possible. As suggested by both

Dale et al. (2009) and Linnen (2008), visual and auditory disturbances by regular,

although in some cases infrequent, use of petroleum and natural gas trails, and well sites,

may play a role. While some military-created trails will often be reused because they

follow tactically or logistically advantageous routes relative to local topography, many

military created trails will not be reused because vehicle operators have freedom to

manoeuver, which introduces a stochastic element to terrain usage. Therefore, the single

observed effect of the composite trail index on CCLO relative abundance may have been

related to habitat disturbance caused by off-road vehicles rather than the visual and

auditory disturbances associated with their operation.

The weak response of birds to distance from roads was not surprising as PCs

locations were placed at 300 m or greater from roads. As well, the generally increased

off-road military traffic near roads at CFB Suffield (Appendix A: Fig. A4) may account

for differences between this study’s results and other studies in mixed-grass prairie, such

as Sutter et al. (2000) and Sliwinski and Koper (2012). This study documented a

67

relatively weak but positive response by CCLO to roads, which contrasts with results of

Sutter et al. (2000) who found CCLO were less abundant near roads. Similarly, this study

did not find road distance to be an important predictor of relative abundance for BASP or

SPPI, while previous studies have found negative responses for BASP (Sutter et al. 2000,

Sliwinski and Koper 2012), and SPPI (Sutter et al. 2000, but see Sliwinski and Koper

2012). Response of MCLO to roads had not been documented in the surveyed literature.

The contrasting negative response of MCLO with the positive response of CCLO to

distance from road, could be related to vegetation structure. Further study which

explicitly measures vegetation structure would likely provide important detail into these

relationships. While proximity to roads may have an important negative impact for some

grassland species (Reijnen et al. 1995, Sutter et al. 2000, Forman et al. 2002, Ingelfinger

and Anderson 2004, Sliwinski and Koper 2012), this study’s survey design and the

elevated off-road vehicle use near roads in the study area makes comparison with other

studies findings difficult and may have contributed to differences in study findings.

Petroleum and natural gas disturbance, as described by the sum length of

pipelines, only appeared to be important to MCLO distribution. The negative association

between MCLO relative abundance and pipeline sum length reflects the findings of

Kalyn-Bogard and Davis (2014) who found reduced abundance of MCLO closer to

natural gas wells, although they found abundance was higher in areas with greater well

density. This negative relationship is consistent with the preferences of MCLO for low

vegetation (With 2010) and descriptions of increased vegetation height (Hamilton 2010)

and above ground biomass (Christian and Wilson 1999, Henderson and Naeth 2005)

associated with CWG. However, Kalyn-Bogard and Davis (2014) suggested something

68

other than vegetation played a role in the avoidance of natural gas well by MCLO in their

study, as vegetation differences were minimal between well sites and the surrounding

areas. This study’s findings of a lack of response by CCLO to industrial infrastructure

contrast with previous studies which found higher abundances further away from natural

gas wells (Rodgers 2013, Kalyn-Bogard and Davis 2014) and petroleum wells and access

roads (Linnen 2008). Similar to this study’s results, some authors had found a lack of

response to natural gas development for SPPI (Kalyn-Bogard and Davis 2014) and BASP

(Rodgers 2013, Kalyn-Bogard and Davis 2014). However, in other studies, SPPI (Linnen

2008, Dale et al. 2009, Rodgers 2013) and BASP (Linnen 2008, Dale et al. 2009) were

found to respond negatively to petroleum and/or natural gas developments. Some of the

inconsistency between this study and previous studies examining petroleum and natural

gas impacts could have been related to the use of pipeline sum length as an index, as

opposed to surrounding well density and/or distance to wells, which was used by most

authors (Linnen 2008, Dale et al. 2009, Hamilton 2010, Hamilton et al. 2011, Rodgers

2013, Kalyn-Bogard and Davis 2014). As well, the placement of many wellheads and

associated infrastructure below ground at CFB Suffield could also be a factor, because

this infrastructure modifies the physical structure of the grassland habitat. Overall,

pipeline related habitat disturbance appeared to be the least important predictor of

endemic grassland bird relative abundance as only a single species was affected.

Trafficking appeared to be more important than pipeline sum length as trafficking

influenced two species, and fire appeared to be the most important habitat disturbance as

all four endemic grassland birds showed relatively strong responses to the composite fire

index.

69

4.1.2 Topography: Slope, Solar Radiation, Topographic Position

The influence of topography on bird distribution was of secondary interest in this

study and was rarely included in other studies of grassland bird distribution. However,

topography has previously been found to be important to study species distributions

(Weins et al. 2008, With 2010) and has an important influence on grassland vegetation

structure. Therefore, the response of study species to topography is discussed to

supplement the previous interpretations of the influence of disturbance variables, because

not all variation in study species distribution can be attributed to disturbance.

Of the three topographic variables under examination, slope appeared to be most

associated with the relative abundance of study species. However, the response between

species was not consistent: CCLO and BASP responded negatively, SPPI showed no

response, and MCLO responded positively. This studies finding of apparent preference

by CCLO for flatter areas was consistent with descriptions of nest sites in flat areas

(Fairfield 1968), although Dieni and Jones (2003) found no difference in slope between

CCLO nest sites and random locations. Weins et al. (2008) models for SPPI and BASP

occurrence at CFB Suffield also indicated a preference for flatter areas, which was

consistent with this study’s findings for BASP, but not for SPPI. However, Dieni and

Jones (2003) did not find a difference in slope between SPPI and BASP nest sites

compared to random locations, which agrees with this study’s findings for SPPI, but not

BASP. Although MCLO were expected to prefer flatter areas this expectation was not

supported by the data. The finding of a relatively strong positive relationship between

slope and MCLO relative abundance could be related to the findings of positive

70

associations with the two other topographic variables for this species. A positive slope

association should be induced as high values for relative elevation and solar radiation are

not possible in flat areas. The inclusion of slope in top models for three of the four study

species indicates slope had an important influence on grassland bird distribution, even if

slope alone does not directly describe important vegetation variance.

Solar radiation was an important predictor of relative abundance for two out of

four study species. The relative abundance of both longspur species was positively

associated with solar radiation, which may be related to reduced vegetation on south-

facing slopes exposed to more solar radiation (Gong et al. 2008, Dong et al. 2009,

Sabetraftar et al. 2011, Han et al. 2011), and both longspurs preference for reduced

vegetation (Hill and Gould 1997, With 2010). The higher temperatures in areas exposed

to more direct solar radiation increases evapotranspiration resulting in reduced soil

moisture (Sulebak et al. 2000, Bennie et al. 2008), and soil moisture is the principle

limiting factor for plant growth in the dry-mixed-grass prairie (Clarke et al. 1947).

However, these positive relationships might also be related to more direct thermal

considerations and nesting phenology. With and Webb (1993) suggested both longspurs

may benefit from increased temperatures resulting from exposure to solar radiation due to

their earlier nesting dates when spring temperatures may be cooler. At Matador

Saskatchewan, MCLO were observed to establish territories on barren generally south

facing hillsides early in the year, and territories established in July were in flatter areas

(With 2010). If nest sites are considered, the lack of an important response by both SPPI

and BASP might be partially related to their selection for greater vegetation structure

(Dieni and Jones 2003, Davis 2005), which can moderate temperature by blocking

71

incoming solar radiation (With and Webb 1993). Solar radiation was important to the

distribution of both longspur species, but not SPPI and BASP, and may be indirectly

related through vegetation structure or more direct thermal influences.

Generally, topographic position appeared to have little impact on the grassland

endemic bird species in this study, and it was only found to be an important predictor of

MCLO relative abundance. The positive response of MCLO to relative elevation

indicates this species prefers areas of high topographic position, such as dry hillcrests or

ridges, which have reduced vegetation and more bare ground than adjacent habitats,

generally (Coupland 1950, Barnes et al. 1983, Milchunas et al. 1989, Phillips et al. 2012).

The lack of response by other species may be related to the presence of adequate

vegetation structure at other topographic positions within PCs, as hill crests generally

made up a small proportion of the 250 m radius PC area. The compound topographic

index, which describes topographic information similar to relative elevation (Weins

2006), has been used in model selection processes to develop predictive occurrence

models for MCLO (Weins 2006), SPPI (Weins 2006, Weins et al. 2008), and BASP

(Weins et al. 2008) at CFB Suffield. Top-ranked models for these three species did not

include the compound topographic index (Weins 2006, Weins et al. 2008), which is

consistent with this study’s results for SPPI and BASP, but contrasts with this study’s

top-ranked model for MCLO. However, their use of several RSVIs may have influenced

this. Indices such as tasselled cap wetness (Crist and Cicone 1986) would likely contain

soil moisture variance caused by topographic position, and was included in Weins (2006)

top-ranked model for MCLO. The modelling results of this study matched anecdotal

observations made for MCLO during data collection, where MCLO were repeatedly

72

observed to be restricted to dry hill tops in areas with minimal disturbance, but high

MCLO relative abundance was observed in areas with high fire impact, even in level

areas. Although relative elevation was not important for CCLO, SPPI or BASP, it was

important for MCLO, likely because this species was attracted to the reduced vegetation

on dry hilltops, which is generally in short supply elsewhere in the landscape.

Overall, relative elevation appeared to be the least important predictor of endemic

grassland bird relative abundance as only MCLO showed an important response. Solar

radiation appeared to be more important as both longspur species shown positive

responses, while slope appeared to be the most important topographic variable to endemic

grassland bird relative abundance.

4.1.3 Limitation of the model results

An important limitation on the inferences drawn from this study relate to

reproductive value of disturbed habitats. In a review of 109 studies, Bock and Jones

(2004) found that in most cases higher counts of birds were related to higher reproductive

success, but areas exposed to anthropogenic disturbance were more likely to have a

negative relationship between abundance and reproductive success than relatively natural

areas. For example, Lloyd and Martin (2005) found that CCLO appeared to use both

native and non-native habitats without preference, but reproductive success was lower in

the non-native habitat. One important reproductive consideration in a landscape prone to

fire is loss of nests or young birds unable to fly. However, many nests may fledge prior to

burns, some may survive burning events, and some nests may be initiated after a burn

(Kruse and Piehl 1986). After a fire is extinguished the risks directly related to fire

73

disturbance on reproduction are minimal, although, at CFB Suffield some areas tend to

burn frequently due to live ammunition targets being located in these areas. As well, this

study’s findings of CCLO attraction to areas trafficked could result in reproductive losses

due to increased visual and auditory disturbances (Frid and Dill 2002) or simply

increased risk of nests being accidentally driven over. With et al. (2008) suggest that

remaining grasslands may not adequately support some populations of grassland birds,

and these areas may be functioning as population sinks due to land use related

disturbances. Therefore, even though this study showed disturbed areas are more

attractive to some endemic breeding grassland bird species these areas may not actually

benefit the species population if their reproductive value is low.

4.2 Model Evaluation

Model cross-validation demonstrated that species distribution models explained

variance in bird relative abundance, but these models were not ideal for prediction. Cohen

(1992) suggested correlations, both Pearson’s and Spearman’s rank, of 0.1 represent a

small effect size, 0.3 a medium, and 0.5 a large effect size. If these criteria are applied to

the model evaluation results, the correlations from random k-folds (Table 7) suggests that

generally model performance was moderate to strong. However, these correlations were

near or less than 0.6 indicating substantially less than perfect agreement between

observed and predicted relative abundance. Model calibration slopes for spatial k-folds

were lower than 0.5, which indicates that substantial noise was present in the data. This

suggests that variance unaccounted for by predictor variables was present. This study’s

distribution models were not optimized to predict the distribution of bird species—they

74

were developed to draw inferences relating to the relative importance of disturbance

variables. Therefore, these models are not appropriate for accurate prediction of study

species distribution and should not be used to infer areas of non-occupancy.

Model performance was generally poorest between the three land use areas and

generally best between the two study years. The generally poor prediction between land

use areas (i.e., spatial folds) indicated variations between these geographic areas exists,

which was not unexpected. Fire and off-road traffic is concentrated in the MTA, domestic

grazing is restricted to cattle pastures, and grassland vegetation in the EPG is generally

the least disturbed. Differences between the four study species provided additional

information. Modelling results indicate that CCLO and BASP models performed better

than MCLO and SPPI between spatial folds, and, although these species models included

fire, they were less dependent upon fire to predict relative abundance. In contrast, in

MCLO and SPPI models, fire was either the most important environmental variable (i.e.,

MCLO) or the only environmental variable (i.e., SPPI) influencing prediction of their

relative abundance. Models for MCLO and SPPI might be considered to have failed

spatial cross-validation because correlations between observed and predicted relative

abundance had low to medium effect sizes (Cohen 1992) and relatively level model

calibration slopes (Table 7). These differences in predictive performance suggest that use

of models that include a heavily weighted fire variable, which were developed in areas

prone to fire, may be unsuitable in areas where fire infrequently occurs. Most of the dry-

mixed-grass prairie outside CFB Suffield is very infrequently burned. Therefore, models

developed in areas with substantially different land use will not necessarily be

75

interchangeable, particularly if they emphasize variables that describe land use related

disturbance.

Prediction of bird relative abundance was generally most consistent between

years, indicating that differences in the model predictions between 2013 and 2014 were

of less importance than differences between the three land use areas. Precipitation is an

important driver of grassland vegetation structure between years due to its large influence

on primary production (Clarke et al. 1947, Sims et al. 1978, Milchunas et al. 1989,

Vermeire et al. 2014), and abundance of MCLO, SPPI and BASP have been linked to

precipitation in some studies (Krause 1968, George et al. 1992, Weins 2006, Weins et al.

2008). If precipitation varied widely between study years predictive performance between

years could be expected to decrease, as models in this study did not include precipitation

related variables. However, Precipitation was relatively similar between 2012, 2013 and

2014, and these years were closer to average precipitation than amounts observed for

very dry or wet years (Environment Canada 2015). Due to the similarity between years,

precipitation was assumed to have low importance and was not included in modelling,

which appears to be supported by the temporal k-folds performance.

Although precipitation was assumed relatively consistent and, therefore,

considered to be of low importance, vegetation modification by grazing likely was more

variable in time and space and, thus, more important to bird abundance. Grazing modifies

vegetation structure (Derner et al. 2009, Henderson and Davis 2014), has been

documented to be important to study species distributions (Dale 1983, Fritcher et al.

2004, Bleho 2009, Henderson and Davis 2014), and very likely contributed to variance in

bird relative abundance in this study. However, reliably assigning a grazing impact to a

76

point count area is problematic, even where stocking rates are known, because grazing

impacts can vary significantly across larger pastures (Alder and Hall 2005, Launchbaugh

and Howery 2005). The cattle pastures at CFB Suffield are large and variability in

grazing impact is readily apparent through examination of RSVIs. As well, grazing by

wild ungulates, including elk (Cervus canadensis) and pronghorn antelope (Antilocapra

americana), occurs across CFB Suffield. An aerial survey of CFB Suffield estimated

5951 elk and 2921 pronghorn as of February 2014 (Petry 2014). Unfortunately, data

describing native ungulate habitat use was unavailable. Due to a lack of suitable methods

to describe grazing impacts, no grazing variables were included in this study’s bird

distribution models. These grazing impacts also likely differed spatially between land use

areas. Cattle grazing likely had an important impact, but was restricted to the cattle

pastures (Fig. 2). In contrast, native ungulate grazing may have had an impact in all land

use areas. Therefore, grazing likely contributed variance to bird relative abundance not

explained by selected disturbance models, which would result in reduced predictive

performance due to noisier data, particularly at PCs in cattle pastures.

Although this study did not evaluate the influence of grazing disturbance, an

interaction between grazing and fire has been documented in other studies. In these other

studies, burned areas were preferentially grazed which prolonged and contributed to

reduced vegetation structure (Zimmerman 1997, Erichsen-Arychuk et al. 2002, but see

Richardson et al. 2014) and can contribute to habitat heterogeneity (Knapp et al. 1999,

Fuhlendorf et al. 2006, McGranahan et al. 2012). With the wild and domestic grazing

populations at CFB Suffield, this interaction could be an important ecosystem process

operating at a landscape level. Exclusion of grazing from burned areas would be unlikely

77

to change direction of the response between bird relative abundance and fire, although the

magnitude of response might decrease to some extent.

The objective of modelling was to draw inference regarding the importance of

disturbance variables, not maximizing predictive performance. Although RSVIs are

known to be strong predictors of bird distribution (Hurlbert and Haskell 2003, Guo et al.

2005a, Weins et. al. 2008, Shirley et al. 2013), RSVIs were deliberately excluded from

disturbance modelling because they would have explained variance caused by the

disturbance variables used, confounding attempts to interpret the importance of

disturbance variables. The exclusion of RSVIs limits the utility of the models developed

in this study for purposes of accurately predicting bird distributions. Inclusion of RSVIs

would have captured vegetation variation caused by other types of disturbance, such as

cattle and wildlife grazing, trafficking which did not result in identifiable trails, and some

other aspects of military disturbances which were not adequately described by the

datasets used in this study. If predictive models were the goal, inclusion of RSVIs during

model selection would likely have led to better prediction.

4.3 Influence of Burn Status and Topographic Position on Vegetation

The relationship between vegetation and both disturbance and topography is

important as grassland birds respond importantly to vegetation (Weins 1969, Fisher and

Davis 2010). The reduced values of the SATVI in burned areas and on hillcrests indicated

reduced grassland vegetation structure. The results for burn status agrees with

comparisons of burned and unburned grassland areas through field measurements (Grant

et al. 2010, Shay et al. 2001, Vermeire et al. 2014) and remote sensing (Patterson and

78

Yool 1998, Smith et al. 2007, Dubinin et al. 2010). Similarly, this study’s findings of

lower SATVI values on hill tops and higher values in depressions is consistent with

findings of vegetation field measurements (Milchunas et al. 1989, Phillips et al. 2012),

and RSVIs (Phillips et al. 2012) in North American prairies. The clear relationship

between both fire and topographic position and vegetation, as measured by the SATVI,

provided a link between the bird habitat preference and disturbance and topography

bodies of literature.

Effective description of vegetation variation over large areas is valuable in

modelling the distribution of wildlife species (Franklin 2009), and presumably more so

for those with very specific vegetation preferences. As the SATVI is sensitive to both fire

and topographic position, it likely would be an important descriptor of variation in

grassland bird distribution. The relatively strong correlations between relative abundance

of all four study species with the SATVI, in directions which agree with descriptions of

their vegetation preferences (Table 4), also suggests its utility. As well, the high

correlation of the SATVI with the composite fire index (Table 4) suggests this index

successfully captures much of the variation caused by fire impacts, which may make

detailed fire history data redundant for grassland species distribution models containing

the SATVI. The correlation greater than r = |0.7| would generally preclude the use of both

variables anyway. This would also presumably apply to other disturbance variables, such

as trafficking, which may be better described by grassland competent RSVIs than by

digitization methods which may fail to capture some impacts during dry soil conditions.

The SATVI and other RSVIs, which can adequately capture variation in standing dead

79

grass and litter, should be targeted for further investigation and use in predictive

modelling of grassland bird distributions.

4.4 Implications of Habitat Heterogeneity for Management of Grassland Birds

The range of positive and negative responses by endemic grassland birds to

habitat disturbances found in this study generally provides support for the heterogeneous

disturbance hypothesis (Warren et al. 2007). The differences in grassland bird responses

to fire and trafficking, as well as the spatial and temporal distribution of these

disturbances, indicate that a range of habitat is being provided for a number of different

species of conservation concern, similar to what Warren et al. (2007) noted on U.S. and

European military training areas. There are areas of CFB Suffield, especially around the

periphery, that provide habitat for SPPI and BASP which are adverse to vegetation

disturbances, while some of the more central areas of CFB Suffield provide attractive

habitat for MCLO and CCLO, which prefer reduced vegetation and may benefit from

frequent vegetation disturbance (Fig. 3, Fig. 5). These disturbed habitats might also

benefit other grassland Species at Risk with similar habitat preferences (potential

examples include burrowing owl [Athene cunicularia]; Environment Canada 2012a, long-

billed curlew [Numenius americanus]; Environment Canada 2013, and Ord’s kangaroo

rat [Dipodomys ordii]; Environment Canada 2012b).

In order to ensure habitat is maintained for all four grassland bird species in this

study some disturbance may be required. However, setting a specific target for

proportions of habitat in different stages or states of disturbance will be problematic.

Disturbance modelling results indicate a trade-off between relative abundance of endemic

80

grassland species occurred in areas exposed to fire disturbance, particularly at high fire

frequencies. However, there are no existing mechanisms to prioritize habitat management

efforts between different species of similar conservation concern. For example, SPPI and

CCLO are both currently listed as threatened under the SARA (2002) but were found to

have contrasting associations with fire. Typical recommendations regarding fire

management for grassland birds are to ensure a mosaic of fire impacted habitats are

available (Herkert 1994, Johnson 1997, Madden et al. 1999, Fuhlendorf et al. 2006,

Powell 2008, Grant et al. 2010, Roberts et al. 2012, Richardson et al. 2014). The results

of this study support this recommendation: a range of fire impacted habitat, including

areas unburned or not burned in a long time, will provide a mixture of habitat with

suitable areas for all four grassland endemic species common on loamy range sites.

While native prairie has been identified as important for all four study species

(Hill and Gould 1997, Robbins and Dale 1999, Green et. al. 2002, With 2010),

interpretations of the importance of native prairie habitat to these species should

explicitly incorporate the heterogeneity of disturbance and habitat quality. For example,

habitat quality for a species preferring either very high or low amounts of vegetation

structure will be dynamic in areas subject to frequent fire because suitability in these

areas will continuously shift. Areas currently considered suitable for a species may be

unsuitable in the next breeding season if burned and the species prefers well developed

vegetation structure. Similarly, recently burned grasslands may become unsuitable over

time for species that prefer reduced vegetation, if fire is suppressed and vegetation

structure increases (potentially as quickly as two growing seasons; see Richardson 2012).

This study’s findings of fire as an important variable for modelling the distribution of

81

grassland birds supports Vallecillo et al.’s (2009) conclusion that predictive power of

distribution models may be poor if fire is not taken into account for highly fire sensitive

species.

Although several disturbances were found to have positive relationships with

grassland bird relative abundance, a simple conclusion that all disturbance is beneficial to

grassland birds is not supported. The negative response of MCLO to pipelines provides

an example of a disturbance that was not positive for any of the four study species. As

well, many forms of disturbance, including grazing, fire, and soil disturbance, may

promote the invasion of native grasslands by non-native plant species (Hobbs and

Huenneke 1992). Invasion of native prairie by CWG has occurred in the CFB Suffield

NWA (Henderson 2007, Rowland 2008) where disturbance is minimal. In the much more

intensively disturbed MTA, invasion of non-native species has the potential to occur at an

accelerated rate. Off-road vehicle trails have been correlated with a number of invasive

species in rangelands in Wyoming, USA (Manier et al. 2014), and many of these species

also occur at CFB Suffield. Although military vehicles arriving at CFB Suffield are

cleaned prior to entering the training area, the high amounts of off-road vehicle use due to

military training and access trails for petroleum and natural gas development and

operations suggest vehicle seed dispersal may be an important factor within CFB

Suffield. Therefore, while trafficking may provide a short-term benefit to CCLO as

vegetation structure is reduced, negative long-term impacts related to the spread of CWG

and other invasive species are possible. The full ramifications of a disturbance need to be

considered when evaluating the importance of habitat disturbances and approaches to

their management.

82

CHAPTER 5: CONCLUSIONS

5.1 Conclusion

Endemic grassland bird distribution were influenced by both habitat disturbances

and topography. Habitat disturbance appeared to have the most important influence: fire

influenced all four species, trafficking influenced only the two longspur species, and,

pipelines influenced only MCLO. While fire was positively associated with MCLO and

CCLO relative abundance and negatively with SPPI and BASP, the most extreme

responses of all four species were observed in areas where fire frequently occurred.

Topography also influenced bird distribution: slope was an important predictor of relative

abundance and was positively associated with MCLO, but negatively with CCLO and

BASP; solar radiation was positively associated with both longspur species; and relative

elevation was positively associated with MCLO relative abundance. Generally, results

obtained from top models of each species distribution were consistent with the species

expected response to both disturbance and topographic variables. Both disturbance and

topography were shown to influence vegetation, as quantified by the SATVI. Both fire

and hillcrests were associated with less vegetation, while unburned and depression areas

were associated with more vegetation. These findings provide a link between the

literature describing both habitat disturbances and bird habitat preferences. The responses

of the grassland birds to disturbance in this study suggests habitat heterogeneity is

important to this group of endemic species. Landscapes with ongoing disturbance appear

to be important for providing habitat suitable to a range of endemic grassland bird

species. Some contemporary disturbances may mimic aspects of the historical disturbance

83

regime under which these species evolved and support the persistence of these species in

the remaining portions of North American grasslands.

5.2 Future Research

The results of this study have contributed to the understanding of endemic

grassland bird species responses to habitat disturbances, particularly the impacts of fire.

However, several areas for future research were also identified. These areas for future

research include further exploration of trafficking impacts on grassland bird distribution,

assessment of the reproductive value of disturbed habitats, and evaluation of the utility of

the SATVI in comparison to other RSVIs for predicting grassland bird distributions.

While trafficking was found to be important for two species in this study,

adjustments to study design could provide important additional information. Future

examination of trafficking influences could benefit from measures of vegetation structure.

These might include field-based measurements of vegetation structure or those obtained

by very-high resolution remote sensing or photogrammetry. Measures of vegetation

structure would provide additional information that would support more detailed

interpretation of bird response to trafficking. As well, global positioning system (GPS)

tracking locations of off-road vehicles would provide a much more precise measure of the

spatial use of off-road vehicles as opposed to a trail index. Provided that GPS tracking

data were available, it might be possible to explore temporally-lagged relationships

between trafficking and bird relative abundance. If these two changes were incorporated

into future research, a deeper understanding of the influence of trafficking of bird

distribution could be obtained.

84

A study following nest success throughout the breeding season would provide

information as to areas that may be functioning as ecological sources or sinks (Pulliam

1988, Aldridge and Boyce 2007). The common approach to nest monitoring in

grasslands, where multiple visits to a nest are required to determine nesting success

(Dubois 1935, Dieni and Jones 2003, Davis 2005), is less viable in a landscape that is

frequently inaccessible, such as lands with heavily restricted access due to military

training. However, temperature data loggers can be used to monitor and measure some

aspects of nest success when nests are inaccessible (Weidinger 2006) and have been used

successfully with ground nesting birds (Hartman and Oring 2006, Schneider and

McWilliams 2007). Remote wildlife cameras are another potential option to measure

nest success. Although, wildlife cameras would be more prone to theft and destruction,

and their cost might be more limiting to sample sizes. Measuring reproductive success in

burned and unburned areas, as well as areas which are frequently trafficked, would

provide a higher level of certainty regarding disturbance related impacts on grassland

birds.

Related to the development of predictive models, further exploration of the utility

of the SATVI to describe grassland bird abundance would be useful. While Guo et al.

(2005a) performed an assessment of several RSVIs, the indices they used did not include

information from the SWIR bands of Landsat imagery, with the exception of tasselled-

cap transformation, which combines information from many Landsat bands (Crist and

Cicone 1986). A comparison of the predictive power of grassland bird distribution

models using the SATVI (Marsett et al. 2006), NDVI (Rouse et al. 1973), Tasselled-cap

transformation (Crist and Cicone 1986), and other RSVIs would support development of

85

potentially more useful predictive models in grassland ecoregions. Previous work

(McWilliams 2013) had also indicated that information contained in the 4th component of

a principal component analysis of a July 2010 Landsat image of CFB Suffield contained

information which related very closely to fire, heavily trafficked ground, and other areas

where grass litter was known to be reduced. Another promising remote sensing

alternative are canopy height models, such as those derived from LiDAR data (Ficetola et

al. 2014) or unmanned aerial vehicle photogrammetry (Lisein et al. 2013). Information

from a canopy height model may provide a better metric of vegetation structure than field

measurements, even if precision to the nearest decimeter was possible, as entire PC areas

could be described, instead of the small number of field-measured points which can

practically be collected. Ideally, concurrent collection of vegetation field measurements

to establish relationships with remotely sensed measures of vegetation would provide

valuable confirmation. If reliable relationships could link remotely sensed vegetation with

field measures of vegetation, such as those identified by Fisher and Davis (2010),

extension of the substantial information regarding bird habitat preferences for habitat

structure might be extended to coarser scales with high fidelity. While remote sensing has

been used extensively for bird research since the 1970s (Gottschalk et al. 2005),

continued developments in remote sensing leave substantial room for further

improvement.

Further research into these three areas would provide greater certainty regarding

the impacts of these habitat disturbances on grassland bird populations, as well as

potentially providing more useful remote sensing approaches for predictive grassland bird

distribution modelling.

86

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APPENDIX A. PHOTOGRAPHIC EXAMPLES OF HABITAT DISTURBANCES

AT CFB SUFFIELD.

Figure A1. Contrast in vegetation structure between an area burned in the previous year

(left) and an area with no recorded history of fire (right; photo credit: Department of

National Defence).

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Figure A2. Example of the relatively coarse spatial scale of fire habitat disturbance

(photo credit: Department of National Defence).

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Figure A3. Example of fine-scale vegetation structure heterogeneity created by

trafficking (photo credit: Department of National Defence). This photograph shows an

area which would be considered a moderate to high level of trafficking impact.

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Figure A4. Example of increased off-road vehicle impacts adjacent to roads (photo credit:

Department of National Defence).

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Figure A5. Example of the spatial extent and spread of crested wheat grass from a

pipeline disturbance. Crested wheat grass is in the dark green areas of the photo. (photo

credit: Department of National Defence).

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Figure A6. Example of disturbed vegetation associated with pipeline disturbance (photo

credit: Department of National Defence).