Habitat type % live fo rb cover - Sevilleta LTER

Consequences of woody plant encroachment for mammalian predators
Herman H. Shugart
Paolo D’Odorico
R. Michael Erwin
Henry M. Wilbur
Stephen A. Macko
Virginia Ann Seamster
Santa Fe, New Mexico
Bachelor of Science in Biology, University of Virginia, 2005
A Dissertation presented to the Graduate Faculty
of the University of Virginia in Candidacy for the Degree of
Doctor of Philosophy
Department of Environmental Sciences
University of Virginia
December, 2010

Abstract
Woody plant encroachment is a widespread process of land cover change from
grass- to woody plant-dominated habitats that has been observed in arid and semiarid
ecosystems around the world. Woody plant encroachment affects ecosystem
characteristics, including plant resource availability and microclimate, and it has the
potential to have bottom-up effects on a wide range of animals through changes in habitat
structure and the abundance and species richness of prey. Little is known about the
impact that woody plant encroachment may have on the ecology of mammalian
predators. This dissertation begins by assessing the consequences of woody plant
encroachment for mammalian predator ecology globally and then focuses on the ecology
of a widely distributed, North American predator, the coyote (Canis latrans).
A dataset containing information on the global distribution of mammals was used
to generate a list of carnivores found in woody plant-encroached areas. Noninvasive
genetic sampling and carbon isotope techniques were used to assess the individual-
specific feeding ecology of coyotes at the Sevilleta National Wildlife Refuge (NWR) and
Long Term Ecological Research (LTER) site in central New Mexico, USA. The specific
objective was to determine whether woody plant encroachment, which has occurred over
the past century at the Sevilleta NWR, has led to a shift in the base of the coyote food
chain from native C4 grasses to encroaching C3 woody plants. Habitat characteristics
associated with woody plant encroachment were assessed at multiple spatial scales to
determine whether spatial scale has an impact on the observed relationship between
coyote feeding ecology and woody plant encroachment.
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At least 97 mammalian carnivores are found in woody plant-encroached areas.
Spatial scale affects the strength of the relationship between coyote feeding ecology and
woody plant encroachment. A significant shift in the base of the coyote food chain from
C4 grasses to C3 plants was observed only when habitat characteristics were evaluated at
a small spatial scale. Further research regarding the effects of woody plant encroachment
on mammalian predator ecology, especially on predator fitness and the ecology of
specialized predators, is needed.

Table of contents
Abstract…………………………………………………………………………………..i
Table of contents………………………………………………………………………..iii
List of figures………………………………………………………………………….....v
List of tables…………………………………………………………………………….vii
List of appendices……………………………………………………………………...viii
Acknowledgements……………………………………………………………………...ix
Overview of chapters…………………………………………………………………….1
Chapter 1: Causes and consequences of woody plant encroachment………………...4
Definition, importance, and extent of woody plant encroachment………………..4
Causes of woody plant encroachment……………………………………………..8
Abiotic consequences of woody plant encroachment……………………………11
Biotic consequences of woody plant encroachment……………………………..13
Conclusion……………………………………………………………………….21
References………………………………………………………………………..23
Chapter 2: Dissertation methods………………………………………………………34
Study site…………………………………………………………………………34
Field data collection……………………………………………………………...37
Laboratory work………………………………………………………………….48
GIS analysis……………………………………………………………………...60
References………………………………………………………………………..63
Chapter 3: Genetic results……………………………………………………………...67
Results and discussion…………………………………………………………...67
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Conclusion……………………………………………………………………….87
References……………………………………………………………………….88
Chapter 4: Bottom-up effects of seasonal and long-term habitat change on predator
feeding ecology………………………………………………………………………….90
Introduction………………………………………………………………………91
Methods…………………………………………………………………………..96
Results…………………………………………………………………………..102
Discussion………………………………………………………………………107
Conclusion……………………………………………………………………...116
References………………………………………………………………………117
Chapter 5: The impact of spatial scale on the relationship between coyote feeding
ecology and local habitat characteristics…………………………………………….130
Introduction……………………………………………………………………..131
Methods…………………………………………………………………………135
Results…………………………………………………………………………..141
Discussion………………………………………………………………………146
Conclusion……………………………………………………………………...152
References………………………………………………………………………154
Conclusion……………………………………………………………………………..165

List of figures
Chapter 1
Chapter 2
Chapter 3
Figure 1. Map of woody plant-encroached sites…………………………………..6
Figure 2. Numbers of carnivores in woody plant-encroached areas…………….19
Figure 1. Study site map…………………………………………………………35
Figure 2. Scat transect map………………………………………………………37
Figure 3. Seasonal variation in rainfall at the study site…………………………41
Figure 4. Vegetation plot map…………………………………………………...44
Figure 1. Species ID results……………………………………………………...67
Figure 2. Scat samples per individual……………………………………………68
Figure 3. Gimlet family group map……………………………………………...74
Figure 4. ML-Relate family group map………………………………………….74
Figure 5. Structure analysis results………………………………………………76
Figure 6. BAPS analysis results………………………………………………….76
Figure 7. Genetic groups generated by Structure………………………………...77
Figure 8. Genetic groups generated by BAPS (K = 10)…………………………79
Figure 9. Genetic groups generated by BAPS (K = 2)…………………………..80
Figure 10. Map of individuals per scat transect………………………………….81
Figure 11. Inter-habitat differences in number of individuals …………………..81
Figure 12. Seasonal variation in number of individuals…………………………82
Figure 13. Inter-habitat variation in home range size……………………………83
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Chapter 4
Chapter 5
vi
Figure 14. Map of family groups relative to habitat use…………………………86
Figure 1. Map of study site………………………………………………………96
Figure 2. Inter-habitat and seasonal differences in live cover and coyote diet…105
Figure 1. Maps of land cover at the study site………………………………….144
Figure 2. Results of t-tests at multiple spatial scales…………………………...145

List of tables
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Table 1. Studies of woody plant encroachment…………………………………...7
Table 2. Summary of carnivores in woody plant-encroached areas……………..20
Table 1. Species ID primers……………………………………………………...49
Table 2. Size ranges for mtDNA amplified from different carnivore species…...49
Table 3. Microsatellite primer sequences………………………………………..51
Table 4. Masses for stable isotope analysis……………………………………...59
Table 5. Fractionation correction factors………………………………………...60
Table 1. Microsatellite success and error rates…………………………………..69
Table 2. Hardy-Weinberg test results……………………………………………70
Table 3. Linkage equilibrium test results………………………………………...71
Table 4. Observed and expected heterozygosity..………………………………..72
Table 5. Data for chi square analysis of pairs of related individuals…………….84
Table 6. Data for chi square analysis of trios of related individuals……………..85
Table 1. Two-way ANOVA of percent live cover……………………………...103
Table 2. Two-way ANOVA of percent live grass cover……………………….104
Table 3. Repeated measures ANOVA of coyote diet…………………………..106
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List of appendices
Chapter 1
Chapter 4
Chapter 5
Appendix 1. Woody plant encroachment references…………………………….32
Appendix 1. Intra-individual variation in diet………………………………….126
Appendix 2. δ 13 C values for vegetation samples and scat components………...127
Appendix 3. Inter-habitat and seasonal differences in forb cover……………...128
Appendix 4. Composition and diet of the small mammal community…………129
Appendix 1. Converting Landsat 7 ETM+ pixel values to reflectance values…162
Appendix 2. Discriminant function analysis of vegetation variables…………..163
Appendix 3. Discriminant function analysis of reflectance values from a Landsat
7 ETM+ image………………………………………………………………….164
Isotope Appendices
Appendix 1. Seasonal variation in coyote diet………………………………….168
Appendix 2. Assessing variation in vegetation end-member values……….......169
Appendix 3. Carbon and nitrogen isotope data for scat samples……………….170
Appendix 4. Carbon and nitrogen isotope data for vegetation samples………..178
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Acknowledgements
There are a large number of people without whom this dissertation would never
have been written. First, I want to thank my advisor, Hank Shugart, and all of my
committee members, especially Paolo D’Odorico, Mike Erwin, and Henry Wilbur, for
their unfailing support and ever excellent advice. Thank you also to the following people
at UVa for their assistance and advice: Katie Burke, Dave Carr, Eric Elton, Rachel
Michaels, Tami Ransom, Dave Richardson, Keir Soderberg, and Mike Tuite.
There are many people associated with the Sevilleta National Wildlife Refuge and
LTER who provided field, logistical, financial, and/or moral support. Special thanks to:
Amanda Boutz, Scott Collins, John Craig, John DeWitt, Michael Donovan, Jon Erz, Mike
Friggens, Jennifer Johnson, Terri Koontz, Mike Parker, Dennis Prichard, and Matt
Spinelli. There are several undergraduate students, Kelly Bowman, Cesar Coronado,
Adrianna Foster, Jeffrey Freiberg, Damon Lowery, and Kelsey Wicks, who helped me in
the field or did projects to which I referred when writing this dissertation.
I am greatly indebted to Dr. Lisette Waits and her students at the University of
Idaho. Without the training that they gave me and expertise that they shared, the genetics
portion of my dissertation would never have been completed. Special thanks to the
following students: Marta DeBarba, Kara Gebhardt, Caren Goldberg, Matt Mumma,
Carisa Stansbury, and Claudia Wultsch. Thank you to Andrew Ouimette at the University
of New Hampshire for running all of my samples for carbon isotope analysis.
Finally, I owe my sanity and happiness over the past 5 and a half years to my
parents, Tom and Teresa Seamster, and my friends, Almea, Erwin, Ian, Jackie, James,
Jon, Karles, Nina, Peter, Sahu, Sujith, and Yo.
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Overview of chapters
Woody plant encroachment is characterized by a proliferation of woody plants in
a grass-dominated environment. This process has been occurring over the last 50 to 200
years in dryland areas on six of the seven continents. Very little is known about the
impacts that this widespread shift in habitat, from grass- to woody plant-dominated, has
on the ecology of predatory mammals. Mammalian predators often play a crucial role in
local ecosystem structure and function and are also sensitive to changes that occur at the
base of the food chain. The central question asked in this dissertation is: What are the
consequences of woody plant encroachment for the ecology of mammalian predators?
Chapter 1 is a review of the causes and consequences, especially the biotic
consequences, of woody plant encroachment. This chapter also addresses the question:
How many mammalian carnivores are present in areas affected by woody plant
encroachment globally?
The four remaining chapters focus on assessing the impacts of woody plant
encroachment on the feeding ecology of an abundant, generalist predator that is found
throughout North America. Chapter 2 is a detailed description of the noninvasive genetic
sampling and carbon isotope techniques used to assess the feeding ecology of the coyote
(Canis latrans) population at the Sevilleta National Wildlife Refuge (NWR) and Long
Term Ecological Research Site in New Mexico, USA. Creosote bush (Larrea tridentata)
has been spreading into grama-(Bouteloua spp.) dominated grasslands at the Sevilleta
NWR over the past century. Noninvasive genetic sampling was used to identify
individual coyotes and carbon isotope techniques were used to assess the feeding ecology
of the identified individuals. In particular, the use of carbon isotopes allows for
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identification of the base of the coyote food chain as native C4 grasses or encroaching C3
woody plants. Chapter 3 presents the results of the noninvasive genetic sampling scheme
that is described in the second chapter and applied in Chapters 4 and 5. Information
regarding coyote family groups, genetic structure of the coyote population, and habitat
use patterns of related individuals are all included in this third chapter.
Chapter 4 considers the impacts of both long-term habitat change, associated
with the spread of C3 woody plants in a native C4 grassland, and seasonal shifts in habitat
characteristics on coyote feeding ecology. The Sevilleta NWR is located in an arid
environment where there is a strong seasonality to local rainfall patterns. Seasonal
variation in habitat is characterized by pulses of C4 grass production in response to
summer rains. Chapter 4 addresses the following two questions: 1) Is there a difference in
the base of the coyote food chain between native grassland and woody plant-encroached
shrubland habitats? and 2) Is there seasonal variation in the base of the coyote food chain
in an area impacted by woody plant encroachment? It was expected that the base of the
coyote food chain would differ between grassland and woody plant-encroached habitats
such that the percent of the coyote diet coming indirectly from encroaching C3 woody
plants would be higher in the woody plant-encroached habitat. It was also expected that
there would be a seasonal shift in the base of the food chain with percent coyote diet
coming indirectly from native C4 grasses increasing from the spring to the summer and
fall in both grassland and woody plant-encroached habitats.
Chapter 5 considers the effect of spatial scale on the strength of the relationship
between coyote feeding ecology and habitat characteristics associated with woody plant
encroachment. The spatial scale at which habitat is characterized can have a dramatic
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effect on the observed relationship between various aspects of animal ecology and the
local environment. A consideration of spatial scale is especially pertinent in studies of the
ecology of wide-ranging animals such as the coyote. The following question is addressed:
Does a) the size of the difference in the base of the coyote food chain between grassland
and woody plant-encroached shrubland habitats, or b) the strength of the linear
relationship between percent coyote diet from C3 woody plants and percent available
shrubland habitat, change with the spatial scale at which the habitat variables are
assessed? Given the wide-ranging nature of coyotes, it was expected that the difference in
coyote feeding ecology between habitat types would become larger, and the association
between coyote diet and percent shrubland habitat would become stronger, as the spatial
scale of the analysis increased and approached the size of an area that coyotes typically
use (i.e., the size of a coyote home range). It was also expected that the strength of the
relationship between coyote feeding ecology and habitat characteristics would decline at
spatial scales larger than the area typically used by a coyote (i.e., larger than a coyote
home range).

Chapter 1: Causes and consequences of woody plant encroachment
Abstract
Woody plant encroachment, the proliferation of woody plants in grassland or
savanna areas, affects semiarid and arid landscapes around the world. Many studies have
considered the potential drivers, as well as the abiotic and economic consequences, of
this phenomenon. This widespread shift in habitat type also has profound bottom-up
effects on local faunal communities, which are less studied and in need of both review
and further attention. The causes and consequences, especially the biotic consequences,
of woody plant encroachment are reviewed here. Consequences include changes in
resource availability, microclimate, habitat structure, species richness, and predator-prey
interactions. Implications for the ecology and conservation of top predators are
emphasized. These species often are threatened by environmental change and play a
critical role in ecosystem function. The ecological impacts of woody plant encroachment
are varied, wide reaching, and in need of further study.
Definition, importance, and extent of woody plant encroachment
Definition
Woody plant encroachment is defined here as the increase in abundance, density,
or cover of one or more native species of woody plants through time (based on Van
Auken 2000, Roques et al. 2001). Woody plants include trees (> 5 m tall), shrubs (0.5 to
5 m tall), and sub-shrubs (< 0.5 m tall; USDA, NRCS 2010). This review focuses on the
spread of woody plants in grassland and savanna ecosystems located in dryland areas
(i.e., arid, semiarid, dry sub-humid) that receive between 100 and 1200 mm of rain per
year (D’Odorico and Porporato 2006). Of particular interest are habitat shifts from
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grassland to shrubland (e.g., Gill and Burke 1999) or from savanna to woodland (e.g.,
Archer 1989). In many cases, this habitat change takes place over the course of roughly
100-200 years (Gill and Burke 1999, Archer 1995, Archer et al. 1995, van Auken 2000).
However, analyses of historical aerial photographs and satellite imagery have detected
increases in woody plant cover over much shorter time periods (e.g., 20-40 years; Hudak
and Wessman 2001, Silva et al. 2001). There are a variety of terms, other than woody
plant encroachment, that refer to the spread of woody plants in areas previously
dominated by grasses and which, for the purposes of this paper, are interchangeable. The
term used depends on the woody plant species being studied, as well as the geographic
region of interest. These terms include shrub encroachment (e.g., Prosopis glandulosa
encroachment in the southwestern United States; Goslee et al. 2003), and bush
encroachment (e.g., Acacia spp. and Dichrostachys cinerea encroachment in southern
Africa; Hudak and Wessman 2001, Muntifering et al. 2006).
Importance and extent
In some regions, woody plant encroachment is associated with a decline in
livestock carrying capacity (e.g., Rappole et al. 1986, Bester 1996) or with desertification
(e.g., Schlesinger et al. 1990, Huenneke et al. 2002, Li et al. 2006). Woody plant
encroachment can reduce local cattle carrying capacities by up to 80% (Bester 1996).
While it is possible to remove the undesirable woody vegetation, treatment may have to
be repeated every 2 to 15 years (Rappole et al. 1986). Desertification is defined as
degradation of land in dryland areas that is driven primarily by anthropogenic forcings
(UNEP 1992, UNCCD 1994, Maestre et al. 2006). This process leads to a decline in food
production in agricultural areas and has the potential to negatively impact roughly 16% of

6
the global human population. Approximately 60,000 km 2 (0.1%) of dryland area is
degraded each year and, while Asia contains the largest total area of degraded dryland,
the continents in which the highest percentages of total dryland area have been degraded
are North America and Africa (UNEP 1992).
Woody plant encroachment is a global phenomenon. Drylands, specifically arid,
semiarid and dry sub-humid areas, cover roughly 40% of the earth’s terrestrial surface
(UNEP 1992), while grasslands and savannas, the ecosystems impacted by woody plant
encroachment, account for approximately 20% (Ojima et al. 1996). Drylands are found in
the western United States, the southern and western part of South America, the northern
and southern-most parts of Africa, eastern Europe, western and central Asia and much of
Australia (UNEP 1992). Grassland and savanna ecosystems on six of the seven
continents have been affected by the encroachment of a variety of woody plant species
(Archer 1995, Ravi et al. 2009; Figure 1 and Table 1).
Figure 1. Map of woody plant-encroached sites. This map shows 34 locations (black
circles; Table 1, Appendix 1) around the world where woody plant encroachment has
been documented in grassland and savanna ecosystems that receive less than 1200 mm/yr
of rain (dark gray regions; Olson et al. 2001). See Appendix 1 for further details.

Table 1. Studies of woody plant encroachment. Summary of 25 studies that document woody plant encroachment around the world.
Average annual rainfall values (mm/yr; Appendix 1) were used to determine the dryland zone (arid = 100-250 mm/yr; semiarid = 250-
600 mm/yr; dry sub-humid = 600-1200 mm/yr; D’Odorico and Porporato 2006). Encroaching species are woody plants that are
classified as one of the following: sub-shrubs, shrubs or trees. For continents and countries/states, N = North, S = South. See
Appendix 1 for further details.
Continent Country/State Dryland zone Original habitat Encroaching species Reference
Africa S. Africa arid/semiarid savanna Acacia spp., Boscia albitrunca, Grewia Tews et al. 2004, Palmer
flava, Rhigozum trichotomum
and van Rooyen 1998
Swaziland dry sub-humid savanna Dichrostachys cinerea Roques et al. 2001
Asia China arid/semiarid grassland/steppe Artemisia spp., Caragana spp., Li et al. 2006, Li et al. 2004,
Ceratoides lateens
Chen et al. 2005, Jin et al.
2009
Australia New S. Wales dry sub-humid grassland Acacia sophorae Costello et al. 2000
Europe Greece semiarid grassland Quercus coccifera Zarovali et al. 2007
N. America Alaska semiarid grassland Betula nana, Ledum palustre Knapp et al. 2008
Arizona semiarid grassland Prosopis velutina Wheeler et al. 2007
California semiarid meadow Artemisia rothrockii Berlow et al. 2002
Canada semiarid grassland Populus tremuloides Steinaker and Wilson 2008
Kansas dry sub-humid grassland Cornus drummondii Knapp et al. 2008
Mexico semiarid
semiarid/dry
grassland Ephedra trifurca, Prosopis glandulosa Ceballos et al. 2010
Minnesota sub-humid prairie Juniperus virginiana Pierce and Reich 2010
Goslee et al. 2003,
Hochstrasser and Peters
New Mexico arid grassland Larrea tridentata, Prosopis glandulosa
2004
Brown and Archer 1999,
Ansley et al. 2001,
Texas dry sub-humid savanna Juniperus ashei, Prosopis glandulosa Schwinning 2008
Virginia dry sub-humid grassland Morella cerifera Knapp et al. 2008
Wyoming semiarid grassland Artemisia tridentate Knapp et al. 2008
S. America Argentina semiarid/dry grassland/steppe Austrocedrus chilensis, Chuquiraga Ghermandi et al. 2010,
sub-humid
avellanedae, Fabiana imbricata, Kitzberger et al. 2000, de
Prosopis caldenia
Villalobos et al. 2005,
Beeskow et al. 1995
Uruguay dry sub-humid grassland Baccharis spp., Eupatorium buniifolium Altesor et al. 2006
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The areas affected by woody plant encroachment are very large. Various species of the
woody plant mesquite (Prosopis spp.) are found on over 380,000 km 2 (9%) of the
semiarid ecosystems of North America (UNEP 1992, Van Auken 2000). Approximately
350,000 km 2 (29%) of a semiarid area in South America has been overgrazed and has
turned into a dense shrubland (Abril and Bucher 2001). In Southern Africa, at least
280,000 km 2 (~10%) of the land has been affected by woody plant encroachment (Bester
1996, Moleele et al. 2002, Hagenah et al. 2009).
Causes of woody plant encroachment
The widespread nature and often negative implications of woody plant
encroachment have lead to extensive study of the factors that drive this process. Proposed
drivers for woody plant encroachment include: changes in fire frequency (Buffington and
Herbel 1965, Archer 1995, Roques et al. 2001); fertilization associated with rising carbon
dioxide concentrations (Archer et al. 1995, Bond and Midgley 2000); nitrogen deposition
(Köchy and Wilson 2001, Wigley et al. 2010); overgrazing by cattle (Walker et al. 1981,
Archer 1989, Archer 1995, Van Auken 2000); seed dispersal by livestock (Buffington
and Herbel 1965, Brown and Carter 1998) and rodents (Buffington and Herbel 1965); soil
erosion (Buffington and Herbel 1965, Walker et al. 1981, McGlynn and Okin 2006);
decreased infiltration of water to the topsoil (Walker et al. 1981, Schlesinger et al. 1990);
and climatic change, including changes in rainfall patterns and temperature (Buffington
and Herbel 1965, Neilson 1986, Schlesinger et al. 1990, Fensham et al. 2005, He et al.
2010). The mechanisms for several of these drivers relate to resource competition
between grasses and woody plants. In particular, conditions that favor the growth of

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woody plants, or hinder the growth of grasses, will facilitate the process of woody plant
encroachment.
A reduction in fire frequency, above average rainfall, or the elevation of
atmospheric carbon dioxide levels are examples of processes that can facilitate the
growth and subsequent spread of shrubs and other woody vegetation (Buffington and
Herbel 1965, Archer 1995, Archer et al. 1995, Van Auken 2000, Fensham et al. 2005).
Shrub seedlings are prone to fire-caused mortality (Buffington and Herbel 1965) and, if
fires occur too often, shrubs are not able to produce seeds and grow to a size at which
they are more resistant to the effects of fire (Van Auken 2000). Multiple year periods of
higher-than-average rainfall can lead to the spread of woody vegetation, especially when
initial woody plant density is relatively low (Fensham et al. 2005). Elevated carbon
dioxide concentrations can lead to a fertilization effect, whereby plants are able to fix
more carbon, produce more photosynthate and thus grow faster than was previously
possible (Bazzaz 1990, Shugart 1998). Due to differences in their photosynthetic
pathways, many woody plant species (i.e., C3 plants) may benefit more from any such
fertilization effect, and subsequently grow faster and accumulate more biomass, than
many of the grasses (i.e., C4 species) found in semiarid regions (Idso 1992, Johnson et al.
1993, Archer et al. 1995, Van Auken 2000). In particular, C3 plants are less efficient at
taking up carbon dioxide and more prone to the loss of photosynthate via
photorespiration than C4 plants, and their photosynthetic rates are not saturated at current
atmospheric carbon dioxide concentrations (Johnson et al. 1993, Chapin et al. 2002). It
has also been proposed that elevated carbon dioxide levels increase the probability of
trees in savanna ecosystems growing to a size at which they are no longer prone to fire-

10
induced mortality (Bond and Midgley 2000), thus facilitating an increase in woody plant
biomass.
Grazing, drought, erosion, and reduced water infiltration to the soil are examples
of processes hindering grass growth (Buffington and Herbel 1965, Walker et al. 1981). In
particular, heavy grazing by livestock and periods of low rainfall decrease local grass
density and allows shrub species to become established (Buffington and Herbel 1965).
Furthermore, the reduction in grass cover associated with overgrazing and drought leads
to an increase in erosion of the exposed soil patches (Buffington and Herbel 1965,
Walker et al. 1981, Schlesinger et al. 1990). This erosion negatively impacts the
establishment and growth of grasses through the loss of topsoil and the deposition of soil
on, and subsequent damage to, extant grasses (Buffington and Herbel 1965). The removal
of grasses and creation of bare soil patches associated with overgrazing and drought also
reduces water infiltration to the top soil layers (Walker et al. 1981), especially when
combined with soil compaction associated with livestock movements (Schlesinger et al.
1990). Since grasses tend to draw water from layers near the soil surface and grass
biomass is positively associated with infiltration of water into the topsoil (Walker et al.
1981), this reduction in infiltration can be detrimental to local grass populations.
There is no single cause for woody plant encroachment. The drivers for this
process work at different spatial scales, vary geographically, and often work together.
Elevated carbon dioxide concentrations affect areas around the globe but overgrazing is a
region-specific process (Wigley et al. 2010). Overgrazing and an associated change in
fire frequency are cited as drivers in Southern Africa, where ranching plays an important
part in the local economy (Bester 1996). Drought is one of the drivers of woody plant

11
encroachment in the southwestern United States (Buffington and Herbel 1965) while the
occurrence of higher-than-average rainfall in multiple years is reported as a driver of the
spread of woody plants in Australian savannas (Bowman et al. 2001, Fensham et al.
2005). Finally, there are cases where multiple woody plant encroachment drivers work in
concert with one another (e.g., grazing, seed dispersal, soil erosion and drought in the
southwestern United States; Buffington and Herbel 1965).
Abiotic consequences of woody plant encroachment
The abiotic consequences of the land cover shifts associated with woody plant
encroachment are varied and include changes in patterns of resource availability
(Schlesinger et al. 1990, Lett and Knapp 2003, Li et al. 2006). An increase in woody
plant cover reduces the availability of light (Lett and Knapp 2003), nutrients (e.g.,
nitrogen, phosphorous, and potassium), and water (Schlesinger et al. 1990, Li et al. 2006)
for other plants, especially grasses. However, the abundance of resources, especially
nitrogen and water, and the rates of nutrient fluxes, specifically of nitrogen
mineralization, are often elevated under woody plants relative to the surrounding grassy
or bare patches (Sánchez et al. 1997, Schlesinger et al. 1990).
Woody plant encroachment can also have an impact on the local microclimate and
hydrological cycle (Kidron 2009, Moran et al. 2009), and on the hydrological properties
of the soil (Schlesinger et al. 1990, Li et al. 2006). In arid regions where shrubs are
surrounded by bare ground, areas under shrubs tend to have reduced rates of evaporation
and lower temperatures relative to more exposed areas between the shrubs (Kidron 2009).
However, when comparing grassland and shrubland areas, grassland areas tend to have a
higher ratio of transpiration to total evapotranspiration (Moran et al. 2009), which

12
indicates that the proportion of water lost via evaporation is higher in shrubland areas.
Water infiltration rates may be reduced by the conversion of grassland to shrubland,
especially when grass cover is initially reduced by grazing and the exposed soil is
compacted by the movements of the grazers. In this case, infiltration rates are high
underneath encroaching woody plants but low in the areas that surround them
(Schlesinger et al. 1990). In addition, a decline in soil water-holding capacity can occur
when there is a transition from grass to shrub-dominated landscapes (Li et al. 2006).
In summary, woody plant encroachment can have a significant impact on the
distribution and cycling of important resources. Furthermore, the abiotic consequences of
woody plant encroachment can combine with the drivers of this shift in habitat to
generate a feedback between the environment and vegetation, which results in further
spread of woody plants and, in some cases, further degradation of the local habitat
(Buffington and Herbel 1965, Walker et al. 1981, Schlesinger et al. 1990, Maestre et al.
2006). As an example of such a feedback, grazing and drought reduce grass cover, which
leads to elevated soil erosion (Buffington and Herbel 1965, Walker et al. 1981). The
reduction in grass biomass opens up space for shrubs to grow and reduces the probability
of fires hot enough to kill shrub seedlings (Buffington and Herbel 1965). Soil erosion
causes further grass death through the loss of topsoil and nutrients and soil deposition on
previously established grass plants (Buffington and Herbel 1965, Schlesinger et al. 1990).
Dust generated by erosion of bare soil patches leads to the formation of clouds with a
high percentage of small droplets, which results in a reduction in local rainfall (UNEP
1992, Rosenfeld et al. 2001). This decline in rainfall, combined with reduced water
infiltration rates associated with both trampling by livestock and lower grass cover,

13
decreases water availability in the topsoil and further retards grass growth (Walker et al.
1981, Schlesinger et al. 1990, Rosenfeld et al. 2001). Over time, nutrients and water
accumulate under the encroaching shrubs and allow for shrub regeneration while a lack
of suitable topsoil, nutrients and water in inter-shrub spaces prevents grass re-growth
(Buffington and Herbel 1965, Schlesinger et al. 1990, Bhark and Small 2003).
Biotic consequences of woody plant encroachment
Changes in habitat characteristics
The spread of woody plants, especially shrubs or trees, in a previously grass-
dominated environment leads to changes in the structure, temporal stability, and quality
of the habitat (Rappole et al. 1986, Brown et al. 1997, Hernández et al. 2005, Blaum et al.
2006, Báez and Collins 2008). Woody plant encroachment implies the spread of plants
that are taller, wider, and, in the case of shrubs, more impenetrable than the native
grasses. In some cases, woody plants form clusters that grow over time (Archer 1989).
This introduction of woody vegetation leads to an increase in the complexity of the local
habitat structure and transition from a single to multiple-stratum environment (Archer et
al. 1988). In general, shrublands are likely to have a greater diversity of microhabitats
than grasslands (Hernández et al. 2005). Furthermore, shrubs provide shade, cover, or
ideal burrow sites for some organisms (Blaum et al. 2006, Blaum et al. 2007a) while
presenting obstacles to the activities of others (Broomhall 2001, Mills et al. 2004, Blaum
et al. 2007a). Yellow mongoose (Cynictis penicillata) burrows are often found
underneath large Acacia shrubs as the shrubs help to moderate temperature fluctuations
and provide protection against trampling by herbivores and predation by raptors (Blaum
et al. 2007a). On the other hand, increased woody vegetation cover may hinder the high-

14
speed hunting technique of the cheetah (Acinonyx jubatus, Bertram 1979, Broomhall
2001, Mills et al. 2004).
Woody plant encroachment has varied effects on the stability of the local habitat.
In semiarid regions, primary productivity and resource availability tend to be more stable
in shrubland than grassland habitats. In particular, grasses have short-term spikes in
growth and seed production in response to rainfall events. These spikes are followed by
drying of the above-ground tissues and a decline in the availability of food resources,
especially seeds, to various primary consumers. On the other hand, some shrub species
are evergreen and, if there are a variety of species present, edible fruits may be available
in all seasons (Hernández et al. 2005). As a result, transformation from grassland to
shrubland via woody plant encroachment may increase the stability of food resources in
the local environment. However, woody plant encroachment decreases the stability of the
composition of the local plant community, with greater changes in community
composition occurring over a decade in areas with higher percent shrub cover (Báez and
Collins 2008).
An increase in woody plant cover can reduce the quality of the local habitat for
both domesticated and wild animal populations. The woody plant encroachment that has
occurred on many ranches and commercial farms has led to a reduction in local livestock
carrying capacity (e.g., Rappole et al. 1986, Bester 1996). In particular, the spread of
shrubs leads to a decline in the production and nutritional value of grasses and legumes
(Zarovali et al. 2007). Studies of wild animal populations have shown a decline in the
abundance or density of a variety of organisms, including insects, rodents, ungulates, and
carnivores, in areas affected by woody plant encroachment (Brown et al. 1997, Marker

15
2002, Blaum et al. 2007b). This decline was driven by a reduction in the availability of
various food resources (e.g., seeds, insects and rodents; Brown et al. 1997, Blaum et al.
2007b).
Habitat degradation has a variety of impacts on local fauna, especially on
predators. Carnivores living in degraded habitats typically have larger home ranges and
need to expend more energy in the course of caring for their offspring than organisms
found in higher quality habitats (Sunquist and Sunquist 2001). This has been observed for
predators living in areas impacted by woody plant encroachment. In particular, cheetahs
in a woody plant-encroached area in southern Africa had home ranges that were at least
three times larger than those of cheetahs inhabiting more open, grassland habitat (Marker
2002). This provides further evidence that woody plant encroachment reduces local
habitat quality.
Changes in species richness and diversity
Increasing woody plant cover is often associated with a decline in local species
richness (Blaum et al. 2006, Blaum et al. 2007b, Báez and Collins 2008, Blaum et al.
2009, Sirami et al. 2009). This decline has been observed across trophic levels, although
several studies have focused on only one trophic level. One study reported a decline in
plant species richness that coincided with an invasion by creosote bush (Larrea
tridentata; Báez and Collins 2008). Another study documented a negative relationship
between rodent species richness and percent shrub cover (Blaum et al. 2006).
The relationship between woody plant cover and species richness or diversity
becomes more complicated when multiple trophic levels are considered simultaneously
(e.g., Blaum et al. 2007c, Ceballos et al. 2010). In South Africa, there is a nonlinear,

16
parabolic relationship between percent shrub cover and the diversity, assessed using the
Shannon index, of both small carnivores and their prey (Blaum et al. 2007c). The initial
rise in species diversity is driven by the increase in niche diversity, which accompanies a
small increase in shrub cover. However, as shrubs become more prevalent, there is a
transition to a relatively homogenous, shrub-dominated environment and thus a decline in
the diversity of both habitat niches and fauna. More specifically, small carnivore diversity
peaks in areas with between 10 and 15% shrub cover and prey item diversity peaks in
areas with 12.5 to 17.5% shrub cover (Blaum et al. 2007c). In Mexico, it was found that
species richness of birds, reptiles and small mammals was higher, but the richness of
carnivores lower, in shrubland than grassland habitat (Ceballos et al. 2010).
Changes in animal ecology and predator-prey interactions
Trophic relationships are also affected by an increase in woody plant cover.
Habitat use patterns of organisms from different trophic levels have been studied in
woody plant-encroached areas. Tree-dwelling lizards were found to use live trees in
savanna areas and dead trees in bush-encroached sites as the dead trees provided hiding
places and prey not available on live members of the encroaching woody plant
populations (Meik et al. 2002). Cheetahs in a more open, plains habitat tend to locate
their territories in areas that contain woody plant cover (e.g., woodland patches, wooded
drainages; Caro 1994), while cheetahs in woody plant-encroached sites tend to use
habitat patches characterized by longer distances of unobstructed vision and more grass
cover (Muntifering et al. 2006). This suggests a shift in habitat use patterns in response to
woody plant encroachment.

17
Woody plant encroachment can have bottom-up effects on the diet of the local
fauna. A study of coyote (Canis latrans) ecology in the southwestern United States
provides an example of a shift in diet in response to an increase in woody plant cover.
Over the course of two decades, two woody, fruit-bearing plants (persimmons, Diospyros
texana and agarito barberries, Berberis trifoliolata) became more prevalent and, during
the same time period, the percent of coyote diet composed of persimmons and barberries
increased (Andelt et al. 1987). These results indicate the potential for the diets of
consumers, especially omnivores like the coyote, to change in response to the
introduction of new food resources that accompanies the spread of woody plants.
The spread of woody vegetation can also have an effect on predator-prey
interactions. Cheetahs are able to hunt in the open and in more wooded areas (Eaton
1974, Hamilton 1986). However, there is evidence that the percentage of attempted hunts
that are successful is higher in more open areas (Mills et al. 2004) and that dense woody
plant cover reduces the availability of prey to the local cheetah population (Marker 2002).
There is also a change in the relationship between cheetahs, vegetation cover and prey
between open and wooded habitats (Marker 2002). In particular, in open, plains habitat
tall vegetation provides cheetahs with shade and cover when stalking prey (Caro 1994,
Fitzgibbon 1990), while in bush-encroached areas, woody plants hinder cheetah
movement and their ability to detect prey (Muntifering et al. 2006) and even cause injury
to the cheetahs, especially to their eyes (Bauer 1998, Marshall 2006).
There is evidence that the hunting strategies of predators that are less specialized
than the cheetah are also affected by the presence of woody vegetation. In particular,
although they are now found across the United States (Whitaker 2000) and in a variety of

18
habitats (e.g., grassland with scattered shrubs, Hernández et al. 2002; forest, Dibello et al.
1990), coyotes originated in open, grassland habitats (Young and Jackson 1951) and are
not able to hunt as efficiently in thickly vegetated forests, in spite of relatively high prey
abundance in these habitats. These factors lead to lower coyote densities and body
reserves in forested landscapes than more open, agricultural areas (Richer et al. 2002).
Given these observations, it is possible that the conversion of grassland to shrubland or
savanna to forest via woody plant encroachment could have a detrimental impact on
coyote hunting success, which could in turn impact coyote body condition and fitness.
Carnivores impacted by woody plant encroachment
Locations where woody plant encroachment has been observed (Figure 1) were
combined with information on the global distributions of carnivores in order to assess the
magnitude of the impact that woody plant encroachment could have on animals,
especially predators, worldwide (Figure 2, Table 2). In particular, a global mammal
dataset compiled by the International Union for Conservation of Nature (IUCN 2008,
Schipper et al. 2008) was used to obtain lists of species in the order Carnivora whose
distributions overlapped 34 sites where woody plant encroachment has been documented
(Figure 1). Predators are of particular interest as a result of their position at the top of the
food chain and thus their tendency to be affected by changes in the local habitat, to
impact the populations of animals in lower trophic levels, and to play an important role in
the local ecosystem (Paine 1969, Estes and Duggins 1995, Crooks and Soule 1999,
Gittleman et al. 2001, Smith and Smith 2003).

Figure 2. Numbers of carnivores in woody plant-encroached areas. Map showing
number of species in the order Carnivora which are found in 34 different locations where
woody plant encroachment has been observed (Figure 1).
A total of 97 species from 9 different families in the order Carnivora are found in
woody plant-encroached sites. The sites with the highest species richness are located in
southern Africa (Figure 2). The continent with the largest number of species potentially
affected by the habitat changes associated with the spread of woody plants is North
America (34 species), though this is closely followed by Africa (33 species; Table 2).
These species include a variety of canids and felids. In addition to coyotes and cheetahs
(discussed above), there are foxes (e.g., red fox, Vulpes vulpes and kit fox, Vulpes
macrotis), gray wolves (Canis lupus), caracals (Caracal caracal), leopards (Panthera
pardus), lions (Panthera leo) and pumas (Puma concolor), to name only a few, present in
woody plant-encroached sites in either North America or Africa. When considering the
geographic distribution of the sites where woody plant encroachment has been observed,
there is a definite bias towards North America (Figure 2). The studies included in this
review are intended as a representative sample, not a comprehensive list, of all studies
that document woody plant encroachment. As a result, there are many locations, and thus
19

20
many carnivores, that are affected by woody plant encroachment but were not considered
here. In particular, information is needed for areas covered by grassland or savanna in the
northern and central portions of both Africa and South America, and in the western and
central part of Asia (Figures 1 and 2).
Table 2. Summary of carnivores in woody plant-encroached areas. This table
provides a summary of carnivores whose distributions overlap at least one of 34 sites that
have been affected by woody plant encroachment (Figures 1 and 2). See Appendix 1 for a
complete list of sites. The number of species and genera in each family in the order
Carnivora that is present in one or more of the sites affected by woody plant
encroachment are given for 6 continents. The total number of studies from which
information on woody plant-encroached sites was drawn is also presented for each
continent.
Continent
Number of
species
Number of
genera Family
Number of
studies
Africa 4 3 Canidae -
7 5 Felidae -
11 9 Herpestidae -
3 3 Hyaenidae -
5 5 Mustelidae -
3 2 Viverridae -
Total 33 27 6 3
Asia 6 4 Canidae -
5 5 Felidae -
9 5 Mustelidae -
1 1 Ursidae -
Total 21 15 4 4
Australia 0 0 - -
Total 0 0 0 1
Europe 3 2 Canidae -
1 1 Felidae -
5 4 Mustelidae -
Total 9 7 3 1
North America 6 4 Canidae -
5 3 Felidae -
6 3 Mephitidae -
12 7 Mustelidae -
3 3 Procyonidae -
2 1 Ursidae -
Total 34 21 6 12
South America 3 1 Canidae -
5 2 Felidae -
2 1 Mephitidae -
4 3 Mustelidae -
Total 14 7 4 5

In spite of these limitations, these results indicate that the habitat shift associated
with woody plant encroachment has strong conservation implications for carnivores
globally (Table 2). A need for woody plant removal and associated improvement in local
habitat quality has been recognized; a bush removal program has been implemented for a
cheetah population in southern Africa (Marker 2002, Muntifering et al. 2006). Further
study of carnivore populations in woody plant-encroached areas is likely to lead to the
development of similar habitat restoration projects for other populations and predators.
Conclusion
Woody plant encroachment has been observed, and has led to significant
environmental changes, in grassland and savanna ecosystems around the world. In some
regions, this increase in woody vegetation is associated with habitat degradation and a
decline in the production of food, especially beef, for human consumption. The factors
that drive woody plant encroachment vary depending on the scale and geographic region
of interest and many of them are related to human endeavor (e.g., overgrazing by
livestock, reduction in fire frequency as a result of fire suppression). The effects of
woody plant encroachment are diverse and impact organisms in all trophic levels. These
effects include 1) a shift from a homogenous to a patchy distribution of water, nutrients
and plant biomass across the landscape; 2) an increase in the structural complexity of the
local habitat; 3) declines in plant and animal species richness; 4) a reduction in the
quality and availability of food for local animal populations; and 5) changes in the diet,
habitat use patterns and hunting success of local predators.
21

22
At least 97 carnivores are found in areas where woody plant encroachment has
been observed. Evidence that the ecology of these animals is affected by woody plant
encroachment, and that this habitat shift has implications for the conservation and
management of predator populations, is mounting. Habitat restoration efforts, including
woody plant removal, would benefit some populations. However, detailed studies have
been performed for only a small number of the mammalian predators found in woody
plant-encroached areas. Much more research is needed regarding the species-specific and
community-wide effects of woody plant encroachment and the effectiveness of habitat
restoration efforts for local predators.

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Appendix 1. Woody plant encroachment references.
Study Latitude Longitude Continent Country/State Rainfall (mm/yr) Original habitat
Tews et al. 2004 -28.6167 24.8 AF South Africa 417 savanna
Palmer and van Rooyen 1998 -26.125 20.125 AF South Africa 160-240 savanna
Roques et al. 2001 -26.275 31.89167 AF Swaziland 675 savanna
Li et al. 2006 35.7 100.83 AS China 367.9 grassland/steppe
Li et al. 2004 37.483 104.767 AS China 180.2 grassland
Chen et al. 2005 43.9583 116.367 AS China 250-400 grassland
Jin et al. 2009 38.65 109.4167 AS China 345 grassland/steppe
Costello et al. 2000 -36.0125 150.161 AU New South Wales 968 grassland
Costello et al. 2000 -35.99583 150.161 AU New South Wales 968 grassland
Zarovali et al. 2007 40.783 23.2 EU Greece 586 grassland
Knapp et al. 2008 68.3 -106.8 NA AK 291 grassland
Wheeler et al. 2007 31.9 -110.8 NA AZ 275-450 grassland
Berlow et al. 2002 36 -118 NA CA 500 montane meadow
Steinaker and Wilson 2008 50.467 -104.367 NA Canada 388 grassland
Knapp et al. 2008 39.1 -96.4 NA KS 859 grassland
van Auken 2000 18.5 -100 NA Mexico 230-600 grassland/savanna
Ceballos et al. 2010 30.83 -108.4 NA Mexico 287 grassland
Pierce and Reich 2010 44.505 -92.485 NA MN > 590 prairie
Pierce and Reich 2010 44.185 -91.99 NA MN > 590 prairie
Goslee et al. 2003 32.53 -106.84 NA NM 230 grassland
Hochstrasser and Peters 2004 34.35 -106.883 NA NM 232 grassland
Brown and Archer 1999 27.67 -98.2 NA TX 720 savanna
Ansley et al. 2001 33.85 -99.43 NA TX 665 savanna
Schwinning 2008 29.94167 -98.12 NA TX 800 savanna
van Auken 2000 33 -104.5 NA TX,NM,AZ 230-600 grassland/savanna
Knapp et al. 2008 37.3 -75.9 NA VA 1065 grassland
Knapp et al. 2008 41.2 -107.2 NA WY 259 grassland
Ghermandi et al. 2010 -41.05 -71.0167 SA Argentina 582 grassland
Ghermandi et al. 2010 -41.167 -70.683 SA Argentina 308 grassland
Kitzberger et al. 2000 -41.1 -71.23 SA Argentina 1100 steppe
Kitzberger et al. 2000 -41.7167 -71 SA Argentina 900 steppe
32

Appendix 1. Woody plant encroachment references.
Study Latitude Longitude Continent Country/State Rainfall (mm/yr) Original habitat
de Villalobos et al. 2005 -38.75 -63.75 SA Argentina 400 rangeland
Beeskow et al. 1995 -43 -64.5 SA Argentina 254 steppe
Altesor et al. 2006 -31.9 -58.25 SA Uruguay 1099 grassland
Appendix 1. The references listed in this Appendix were the sources of the locations shown on the map in Figure 1 and a subset of
these references (25 total; all but van Auken 2000) were used to generate the summary provided in Table 1. First, a list of 57
references compiled by Sujith Ravi and utilized in Ravi et al. (2009) were obtained via web of science and checked for the
requirements outlined below. Then a search was performed on web of science (key word “woody plant encroachment”, time period
2008-2010, 43 studies returned, “study” shown in bold). Any study that was not available via web of science or that dealt with the
spread of either exotic or non-native species, rather than species that were native and had been present in the region historically, were
omitted. Studies that did not indicate that woody plants had been increasing in the area where the study was conducted were also
omitted. Furthermore, studies had to indicate that the documented increase in woody plant cover was occurring in either a grassland or
savanna habitat in an area characterized by one of the following climates: arid, semiarid, dry sub-humid. Studies that dealt with
woodland thickening, succession, or reforestation of pasture or that had been performed in an area with a climate that was wetter than
dry sub-humid (> 1200 mm/yr) were omitted. Finally, studies that did not contain information on any of the variables listed in Table 1,
did not provide coordinates or information on average annual rainfall for the study area, or were performed at the same site (e.g.,
estate, ranch, refuge, research area/center, etc.) as another study that was already listed were omitted. Continent abbreviations are as
follows: AF = Africa, AS = Asia, AU = Australia, EU = Europe, NA = North America, SA = South America. State abbreviations are
as follows: AK = Alaska, AZ = Arizona, CA = California, KS = Kansas, MN = Minnesota, NM = New Mexico, TX = Texas, VA =
Virginia, WY = Wyoming. Rainfall (mm/yr) = average annual rainfall in millimeters. For Figure 1, please note that the grassland and
savanna regions are based on a Terrestrial Ecoregions dataset from the World Wildlife Fund (Olson et al. 2001). In particular,
ecoregions for which annual rainfall data were available, receive less than 1200 mm of rain per year, and are associated with one of
four biomes, each of which had the word “grassland” or “savanna” in their name, were included on the map (i.e., Tropical and
subtropical grasslands, savannas and shrublands; Temperate grasslands, savannas and shrublands; Flooded grasslands and savannas;
Montane grasslands and shrublands).
33

Chapter 2: Dissertation methods
This chapter provides a detailed description of the methods used to produce the
results that are presented in later chapters of this dissertation. Given the diversity of field,
laboratory and computer-based techniques employed in this dissertation, this chapter is a
useful reference for subsequent chapters. One of the goals of this dissertation is to
determine whether woody plant encroachment, or the transition from grassland to
shrubland habitat, has led to a shift in the feeding ecology of one mammalian predator,
the coyote (Canis latrans). The coyote is one of the top predators at a refuge in the
southwestern US where shrubs have been moving northward into a grassland habitat over
the past century. Techniques described in this chapter were used to assess and compare
the base of the coyote food chain between grassland and shrubland sites at this refuge,
thereby evaluating the impact that woody plant encroachment has had on coyote feeding
ecology.
Study site
The fieldwork for this dissertation was carried out at the Sevilleta National
Wildlife Refuge (NWR) and Long Term Ecological Research (LTER) site in central New
Mexico. The refuge encompasses 1,000 km 2 (Hernández et al. 2002), has roughly 200
miles of road, and contains grassland, shrubland and woodland habitat types (Figure 1).
The refuge is bounded by private, ranchland to the north and land owned by the Bureau
of Land Management to the east. There are small communities to the north and south and
a mixture of land ownership along the Rio Grande, which flows south, through the center
of the refuge. The river divides the refuge into eastern and western parts that have
distinct plant communities.
34

Figure 1. Study site map. Map of the Sevilleta NWR and LTER study site in central
New Mexico, USA (based on Muldavin et al. 1998). Black = shrubland, gray = grassland,
white = other land cover types. The path of the Rio Grande through the center of the
refuge is indicated by a thick black line, while the boundary of the refuge is shown by a
light gray line.
On the eastern site of the refuge, and thus to the east of the Rio Grande, the grassland is
dominated by grama grass (Bouteloua spp.), with threeawn (Aristida spp.), muhly
(Muhlenbergia spp.), James’ galleta (Pleuraphis jamesii), burro (Scleropogon
brevifolius), and dropseed (Sporobolus spp.) grasses also present. The grass cover on the
western side is sparse relative to the eastern side. The grasses present in the grassland on
the western side include low woollygrass (Dasyochloa pulchella) and representatives of
all of the genera listed for the eastern side except Mulenbergia spp.
The shrubland on the eastern side of the refuge is dominated by creosote (Larrea
tridentata), while honey mesquite (Prosopis glandulosa) is dominant on the western side.
Desert willow (Chilopsis linearis), Torrey’s jointfir (Ephedra torreyana), pale desert-
35

36
thorn (Lycium pallidum), and yucca (Yucca spp.) are also present on the eastern side,
while fourwing saltbush (Atriplex canescens), creosote, and broom dalea (Psorothamnus
scoparius) are present on the western side. Broom snakeweed (Gutierrezia sarothrae),
tree cholla (Cylindropuntia imbricata), and winterfat (Kraschenlnnikovia lanata) are
present on both the eastern and western sides of the refuge.
In general, grassland is more prevalent in the northern half of the refuge and
shrubland in the southern half. Juniper (Juniperus spp.) savanna or pinyon-juniper (Pinus
spp.-Juniperus spp.) woodland is present in higher elevation areas that include the
mountain ranges running along the eastern and western-most edges of the refuge (Figure
1). Shrubland areas on the eastern side of the refuge have grasses growing between and
sometimes beneath the shrubs. Grass is very sparse on the southwestern side of the refuge
and there are large, honey mesquite-covered sand dunes. In this way, the northeastern
(dominated by C4 grasses) and southwestern (dominated by C3 woody plants) parts of the
refuge are most distinct in terms of their species composition and habitat structure.
Approximately 72% of the refuge is covered by either grassland or shrubland and
there is an active transition zone between grama grassland and creosote shrubland areas
on the eastern side of the refuge (Figure 1). More specifically, creosote shrubs have been
moving into grama grassland areas over the past century (Gill and Burke 1999). These
characteristics make the refuge an ideal setting for an assessment of the impact that
woody plant encroachment has on coyote ecology.

Field data collection
Field work for this study consists of two main parts: 1) coyote scat identification
and collection along 1 mile long, road-based transects; and 2) surveys of habitat variables
within circular (diameter = 30 m) vegetation plots.
Scat surveys
Three scat surveys were carried out at the Sevilleta NWR and LTER from June to
July, 2008. Each survey involved the collection of carnivore scat along 20 road-based
transects. Half of these transects were located in grassland habitat and half in shrubland
habitat in order to allow for a comparison of coyote ecology between habitats (Figure 2).
Figure 2. Scat transect map. Map of scat transect locations along roads (dotted light
gray lines) at the Sevilleta NWR (refuge boundary in solid light gray). The two transects
marked by stars were only surveyed in 2009. Half (n = 10) of the transects surveyed in
2008 were located in grassland (dark gray) and half (n = 10) were located in shrubland
(black) habitat.
Each scat transect was one mile (1.6 km) long and was separated from all other transects
by at least one mile (1.6 km) in an effort to ensure independence of scat samples
collected on different transects (Roughton and Sweeny 1982). These transects cover
37

38
roughly 10% of all of the roads on the refuge. The beginning and end of each scat
transect were marked with a wooden stake to ensure that all surveys were performed
along the same road segments. In an effort to standardize the period over which the scats
were deposited on a given transect, old scats were removed from all transects before the
surveys began and surveys of a given transect were separated by 7 to 16 days.
Surveys were carried out by driving along a given transect at slow speed (1-10
mi/hr; 1.6-16.1 km/hr; Hernández et al. 2002, Parmenter 2004). When an item could not
be identified from within the field vehicle, the observer would get out of the vehicle.
Each scat sample encountered was identified and photographed and the latitude and
longitude of the location of the sample were recorded. Scat was identified to species
based on length, the average of two measurements of maximum diameter (based on Reed
et al. 2004) and morphology. A sample was identified as being from a coyote if its
average maximum diameter was between 1.80 and 3.30 cm, the length of the longest
piece in the sample was between 6.4 and 22.9 cm and at least one end of one of the pieces
in the sample was tapered (Green and Flinders 1981, Murie 1982, Halfpenny 2000). If a
sample did not meet one of these three criteria, it was labelled as being “maybe coyote”
and if it met only one or none of these criteria it was labelled as “not coyote.” Samples
for which accurate length and diameter measurements could not be obtained, for example
because all of the pieces in the sample had been run over by a vehicle or degraded by
insects or the elements, were labelled as “unknown” and were photographed and their
coordinates were recorded. Finally, a rough assessment of the freshness of each scat
sample was made (Reed et al. 2004, Stenglein et al. 2010b). The assumption was that
samples that were still soft and had little to no color variation were very fresh and

39
samples that were mostly white and hardened were old. Samples of intermediate
freshness had some color variation and were hardened, but were not mostly white.
Once identified and photographed, a small subsample (roughly 0.4 mL) of the
fecal material on the outside of each sample was placed in a 2 mL tube containing DETs
(DMSO, EDTA, Tris, salt) buffer (Frantzen et al. 1998) in order to preserve it for genetic
analysis (see genetics section below). Taking samples from the outside of the scats
enhanced the probability of collecting material that contained epithelial cells, and
therefore DNA, from the lining of the gut of the predator that had deposited the scat and
increased the likelihood that the genetic analysis of the samples would be successful
(Stenglein et al. 2010a). DETs buffer was prepared and scat subsamples were collected in
accordance with protocols developed by the Laboratory for Conservation and Ecological
Genetics (LCEG) at the University of Idaho. For samples consisting of multiple pieces, a
small amount of the fecal material from each piece was put into the 2 mL tube. This was
done to increase the probability that mixed samples, or samples deposited by more than
one individual, would be identified and eliminated from further analysis. All 2 mL tubes
were placed in a light reflecting Styrofoam container in order to reduce the rate of DNA
degradation. The tweezers used to collect the subsamples were cleaned with an alcohol
swab and then flame sterilized in order to mitigate cross-sample contamination. The
remainder of each scat sample was dried in a drying oven for 24 hrs at 70 o C and stored in
a plastic bag with a desiccant until the stable carbon isotope signature of any bone, hair or
vegetation in the scat could be determined (see carbon stable isotopes section below).
A running tally was kept of the following: 1) all scats that were torn or very small
relative to other coyote scat samples and were therefore identified as “incomplete”, 2)

40
scats that were worn down, often to the point that they were composed entirely of hair
from a prey item, and appeared to be “old,” such that they had been missed when the
transects were cleared at the beginning of the field season, 3) scats that could have been
deposited during the survey period but did not appear to have any fecal material that
might contain epithelial cells, and therefore DNA, from the lining of the predators’ gut
that could be collected for genetic analysis (i.e., “no DNA”), and 4) scats that were
clearly from a predator other than a coyote, especially those containing insects and which
appeared to be from a reptile. These four types of scat were removed from the transect
but no further information was recorded. It was assumed that the incomplete scats would
not provide a fully representative sample of the coyote diet. Omitting the “old” scats from
those sampled for genetic analysis improved the chances that the minimum counts of
individuals obtained for each scat transect (see genetics section below) were based on
samples that had accumulated over a known period of time (i.e., the time since the last
survey) and were thus more comparable.
In 2009, two surveys of all scat transects were carried out in each of three
seasons: spring, summer and fall. This was done in order to collect further data on inter-
habitat differences in the base of the coyote food chain and address the issue of seasonal
variation in resource use by both coyotes and their prey. Previous studies have shown that
coyote diet, in terms of frequency of occurrence of different prey items (e.g., rodents,
arthropods), varies seasonally (e.g., spring to fall, Hernández et al. 2002) and that there is
seasonal variation (pre monsoon to monsoon) in the use of C3 versus C4 plants by
potential coyote prey species (Warne et al. 2010). This diet shift from C3 to C4 plants
should be reflected in the stable carbon isotope signature of some of the components,

41
especially vegetation and small mammal hair, that were taken from the coyote scat
samples and run through a stable isotope analysis (see carbon stable isotopes section
below).
The monsoon, or rainy, season typically starts between late June and early August
and ends in September or October. Scat samples were collected before (April), during
(July), and after (October) the peak of the monsoon season (Figure 3).
Average precipitation (mm)
70
60
50
40
30
20
10
0
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
Month
Figure 3. Seasonal variation in rainfall at the study site. Average monthly rainfall at
the Sevilleta NWR as recorded by a meteorological station located in the northeastern
part of the NWR (data from Moore 2009). Error bars correspond to 95% confidence
intervals. On average, August is the month with the most rainfall. The spring scat surveys
corresponded to a low rainfall month (April), the summer surveys to a fairly high rainfall
month (July), and the fall surveys to a month (October) after the peak of the monsoon
(i.e., rainy) season.
Scat surveys were carried out along the 20 road-based transects described previously as
well as on two new transects located in the northwestern part of the refuge (Figure 2). In
2008, the three transects on the western side were all located in shrubland habitat. The
purpose of adding two new transects in 2009 was to allow for sampling of the coyote
population in the grassland area on the western side of the refuge. This population was

42
expected to contain different individuals from the population in the grassland on the
eastern side since the two sides of the refuge are separated by a heavily travelled,
interstate highway (I-25). As a result, sampling in the grassland on the western side was
expected to increase sample size. Furthermore, given the floristic differences between the
two sides of the refuge, it seemed important to be able to compare the ecology, especially
the base of the food chain, of coyotes in the shrubland on the western side to that of
coyotes in the grassland on the western side. End points for 3 of the transects surveyed in
2008 were moved slightly in spring 2009 either because the wooden stakes that marked
the end points were not found or because the transects were found to be slightly less than
a mile in length.
Old scats were cleared from all transects at the beginning of each season and
surveys of a given transect were separated by 7 to 19 days. The results of the genetic
analysis of samples collected in 2008 were used to change the criteria for identifying a
sample as being from a coyote. In particular, the range of values for average maximum
diameter and length used to identify coyote samples in 2009 were: 0.90 to 3.20 cm and
2.5 to 19.0 cm, respectively. Another change that was made from the procedures used in
2008 was that three rather than four categories were used for scats that were tallied and
not measured or sampled. In particular, scats were no longer categorized as “old”, only as
“incomplete”, “no DNA”, or being from a predator other than a coyote. This meant that
any scats that were old and worn down enough that they did not have any fecal material
that contained epithelial cells from the predator that deposited them were now included in
the “no DNA” category. The purpose of this change was to reduce the subjectivity of
these categories while still omitting samples that would provide incomplete information

43
on coyote diet (“incomplete”) or did not appear to have material that could be sampled
for genetic analysis (“no DNA”). It was also assumed that samples that were incomplete
or had no DNA were likely to have been missed when the scat transects were cleared at
the beginning of the season. This assumption was based on the fact that these samples
were often smaller and had either a less distinct shape or lighter color which made them
harder to detect and also made it more likely that these samples were old. As a result, the
omission of these samples from the genetic analysis improved the likelihood that the
minimum counts of individuals obtained for each transect (see genetics section below)
were based on samples that had accumulated since the last scat survey and were thus
comparable.
Vegetation surveys
Vegetation surveys provided a quantitative assessment of the habitat surrounding
each scat transect and therefore used by the coyotes being surveyed. This assessment
augmented the qualitative classification of the habitat near the scat transects as grassland
or shrubland that was used when the transect end points were first marked. In July and
August 2008, vegetation was characterized in 40 circular vegetation plots, two per scat
transect (Figure 4). Each plot was 30 m in diameter and was located 30 to 100 m from a
scat transect. The distance from the beginning of the scat transect, the side of the road,
and the distance from the road at which the plot was located were all randomized.
Vegetation variables relevant to woody plant-encroached landscapes and coyote
foraging patterns were assessed in each plot. Specifically, information was collected on:
1) percent live woody plant cover, 2) average size of live woody plants ≥ 0.5 m tall, 3)
average inter-plant distance for live woody plants that are at least 0.5 m tall, 4) dominant

44
grass genera. The first three variables were measured for live woody plants (i.e., plants
with green leaves on some part) that intersected two 30 m line intercept transects. These
transects were perpendicular to one another and were oriented north-south and east-west
such that they intersected at the center of the plot. The fourth variable was determined
using 5 randomly placed 1 m 2 quadrats per plot. These quadrats were located at a random
distance and angle from the center of a given plot.
Figure 4. Vegetation plot map. Map of vegetation plot locations for 2008 and 2009.
Vegetation plots surveyed in both 2008 and 2009 are shown with asterisks and plots
surveyed only in 2008 are shown with crosses. Two plots were only surveyed in 2009 and
are indicated with a black circle. For 2008, 20 plots were located in grassland (gray)
habitat and the other 20 plots were in shrubland (black) habitat. There were 12 plots in
grassland areas (gray) and 10 plots in shrubland areas (black) in 2009.
Each live woody plant that crossed the line intercept transects was identified to
species, a height category (< 0.5 m or ≥ 0.5 m) was noted, and the beginning and ending
points at which the plant intercepted the tape were recorded to the nearest 0.1 m. These
interception points were later used to calculate values for total percent live woody plant
cover. In this calculation, a correction was made for overlap between woody plants of

45
different species or different size classes. This correction ensured that each segment of
the line intercept transects was only counted once in the calculation of percent live woody
plant cover.
Woody plant size was determined by measuring the height, the longest axis and
the axis perpendicular to the longest axis for each live woody plant that crossed one of
the line intercept transects and was at least 0.5 m tall. All of these measurements were
made to the nearest 0.1 m. When no woody plant 0.5 m or taller intersected the line
intercept transects, these plant size measurements were recorded for any plants between
which nearest neighbor distances (see below) were measured. When there was only one
woody plant greater than 0.5 m tall within a plot (i.e., percent woody plant cover is zero
and no interplant distances were measured), plant size measurements were taken for this
plant.
For the measurement of average interplant distance, up to four live woody plants
with a height of at least 0.5 m were selected in each plot. In most plots these selected
plants intersected one of the line intercept transects and were the closest to the center of
the plot. When no live woody plants with a height of at least 0.5 m crossed either line
intercept transect, then the woody plant that was in the plot and was closest to the first 1
m 2 quadrat that was surveyed was used instead. The interplant distances were measured
from the centers of these selected plants to the centers of the 5 closest live woody plants
that were also at least 0.5 m tall and were within the plot. Fewer than 5 distances were
measured when there were fewer than 6 live woody plants (height ≥ 0.5 m) found within
the plot boundaries.

46
For each 1 m 2 quadrat, the grass genus that had the highest percent cover was
recorded as the “dominant” grass. When two genera had similar values for percent cover,
the quadrat was considered to have “co-dominant” grasses and both genera were
recorded. Samples of the dominant grass genera were collected from within the quadrats.
Samples of all woody plant species (excluding cactus) that were found in the plot were
also collected. These samples were then dried at 60 o C for 24 hours and stored in a plastic
bag with desiccant in preparation for carbon stable isotope analysis (see carbon stable
isotopes section below).
To ensure that the two new scat transects (Figure 2) had habitat data comparable
to that collected in 2008, four vegetation plots, two per transect, were surveyed in
summer, 2009. These four plots were located 30 to 100 m from the road at randomly
selected points along the new scat transects. The surveys of these plots included
measurements of the following: percent live woody plant cover for plants with a height of
at least 0.5 m; woody plant size (height, longest axis, perpendicular axis); and interplant
distances. Dominant grass species were determined using five randomly placed 1 m 2
quadrats per plot for the two plots that were also surveyed in spring and fall 2009 (see
description below).
During the spring, summer and fall of 2009, a subset of the habitat variables
measured in 2008 was resurveyed, with some methodological changes, to assess seasonal
variation in the percent live cover of, and therefore availability of resources associated
with, C3 (forbs, woody plants) versus C4 (grasses) plants. Of particular interest was any
shift in availability of live grasses as a food resource from spring to fall. Any such shift

47
should be detected in the carbon isotope signatures of coyote prey items (see carbon
stable isotopes section below).
In 2009, vegetation measurements were performed in 22 plots (Figure 4), one per
scat transect (Figure 2), in each season for a total of three surveys per plot. Specifically,
one of the two vegetation plots surveyed along each of the scat transects during summer
2008 was randomly selected and its center point was marked with a wooden stake to
ensure that the line intercept transects intersected at the same point for each 2009 survey
of the plot. Percent live woody vegetation, grass and forb cover were measured, to the
nearest 0.1m, along 2 x 30 m line intercept transects and height was recorded for each
woody plant that intersected the transects and was at least 0.5 m tall. It is important to
note that data on percent live grass and forb cover were collected in 2009, while only
percent live woody plant cover was assessed along the line intercept transects in 2008.
Also, in 2009, an effort was made to measure percent cover for only the live portions of
plants that intersected transects. More specifically, if a stalk or seed head or leaf were
green, then it was considered to be "alive.” If only part of a given plant was green, then
only that part was included in the calculation of percent live vegetation cover.
Small samples of the dominant woody plant species (excluding cactus species,
except in the fall surveys) and one of the dominant grass genera found within the plot
were collected. Dominant species and genera were determined using the vegetation data
collected in 2008 and the following criteria: the woody plant species that was at least 0.5
m in height and had the highest value for percent cover within the plot; the grass genus
with the highest percentage cover in at least one of five 1 m 2 quadrats that were surveyed
in each plot. If a different species of woody plant was identified as being dominant, using

48
the criteria outlined above, during one of the 2009 surveys, then an attempt was made to
sample both the species that was dominant in 2008 and the species dominant in 2009. A
representative sample of forbs was also collected if any live forbs intersected one or both
of the line intercept transects for a given plot. All of these vegetation samples were dried
for 24 hrs at 60 o C and stored in labeled plastic bags with desiccant for future carbon
stable isotope analysis (see carbon stable isotopes section below).
Laboratory work
Laboratory work consisted primarily of the following: 1) genetic analysis of
coyote scat collected at the Sevilleta NWR, 2) carbon stable isotope analysis of
components separated from these scat samples.
Genetics
Genetic analyses were run at the Laboratory for Conservation and Ecological
Genetics (LCEG) at the University of Idaho to confirm the field-based species
identification of the scat samples collected at the Sevilleta NWR and determine the
minimum number of individuals represented by all coyote scat samples. Segments of
mitochondrial (mtDNA) and nuclear DNA in scat subsamples that were collected and
preserved in DETs buffer in the field were extracted using the QIAamp DNA Stool Mini
Kit from Qiagen Inc. Extractions were performed in a laboratory space that was separate
from the area where DNA was amplified and a negative control was processed with each
set of extracted samples. These procedures helped to reduce contamination of the low
quality DNA from the scat samples with amplified DNA, and to identify cross sample
contamination during the extraction process, respectively (Taberlet et al. 1999, Onorato et
al. 2006).

49
Once extracted, the mtDNA was amplified using polymerase chain reaction
(PCR) techniques and primers designed for a species identification test. This test was
performed primarily to confirm that the collected scat samples were deposited by coyotes
and not other canids. In particular, two primers (Table 1, Murphy et al. 2000) that
amplify a segment of the mtDNA control region (Onorato et al. 2006) were used. The
length of this segment of the control region varies among some mammalian carnivore
species. Specifically, the LCEG has identified size ranges for segments amplified from
coyotes and red wolves, other canids (including gray wolves and domestic dogs), black
bears, brown bears and lynx (Table 2).
Table 1. Species ID primers. Names and sequences for primers used for species
identification (Murphy et al. 2000). Please note the following two revisions from the
information presented by Murphy et al. (2000): 1) primer name is SIDL rather than IDL
and 2) the H16145 sequence has been modified from 5’- AGG AAG AAG CAA CAG
TCT C-3’. 6-FAM is a dye and A = adenine, C = cytosine, G = guanine, T = thymine.
Primer name Primer sequence
SIDL 5'-/6-FAM/TCT ATT TAA ACT ATT CCC TGG-3'
H16145 5'-GGG CAC GCC ATT AAT GCA CG-3'
Table 2. Size ranges for mtDNA amplified from different carnivore species. Sizes
listed in terms of number of base pairs (bp). All values from LCEG.
Species Latin name Fragment size range (bp)
Coyote/Red wolf Canis latrans/Canis rufus 114.9 – 120.1
Lynx Lynx canadensis
Canis lupus/Canis lupus
121.6 – 122.4
Gray wolf/Domestic dog familiaris 123 – 128.2
Brown bear Ursus arctos 146.9 – 153.5
Black bear Ursus americanus 158.1 – 164.5
For the PCR, 1.8 µL of extracted DNA was mixed with 3.5µL of the 2X Qiagen
Master Mix, 0.7µL of 5X Q solution, 0.14µL of each 10 µM primer, and 0.72µL of sterile
water. The reagents from Qiagen contain the enzymes and nucleotides necessary to

50
replicate the DNA, as well as compounds that block PCR-inhibiting chemicals that could
be present in the DNA extract. A segment of the control region of the mtDNA in this
mixture was amplified using the following set of steps programmed into a DNA Engine
Tetrad 2 Peltier Thermal Cycler: 1) 95°C for 15 minutes, 2) 94°C for 30 seconds, 3) 44°C
for 1.5 minutes, 4) 72°C for 1 minute, 5) 60°C for 30 minutes. The second through fourth
steps were performed 40 times. 1µL of amplified mtDNA was then mixed with 0.33 µL
of GeneScan TM 500 LIZ size standard (Applied Biosystems) and 9.67 µL of formamide.
In order for the DNA to denature, the mixture was placed on a thermocycler at 95°C for
2-3 minutes, put on ice for 2 minutes, and then the DNA fragments were separated via
electrophoresis on an Applied Biosystems 3130xl capillary machine.
All DNA fragment length and quantity data produced by the capillary machine
were analyzed using GeneMapper 3.7 and samples were categorized as follows: coyote,
maybe coyote, not coyote, unknown. Coyote samples had a high quantity of DNA
fragments in the following size range: 114.89 to 120.13bp. Samples that were “maybe
coyote” had a smaller quantity of DNA fragments in the coyote size range or had DNA
fragments that were at the lower end, or just outside, of this range. Samples that were
“not coyote” had DNA fragments in size ranges used to identify other species (Table 2)
and “unknown” samples had too little DNA in the coyote size range or were samples for
which no DNA was amplified in the PCR.
To determine the number of different coyotes that were sampled, nuclear DNA
from all scat samples identified as “coyote” or “maybe coyote” was amplified using PCR
techniques and canid-specific primers for 8 microsatellite loci (Ostrander et al. 1993,
Ostrander et al. 1995, Francisco et al. 1996, Table 3). For the PCR, 1.4 µL of extracted

51
DNA was mixed with 3.5µL of 2X Qiagen Master Mix, 0.7µL of 5X Q solution, 0.93µL
of sterile water, and volumes of each primer needed to attain specific primer
concentrations (Table 3). DNA at the 8 microsatellite loci of interest were amplified
using the following set of steps programmed into a DNA Engine Tetrad 2 Peltier Thermal
Cycler: 1) 95°C for 15 minutes, 2) 94°C for 30 seconds, 3) 63°C for 1.5 minutes, 4) 72°C
for 1 minute, 5) 94°C for 30 seconds, 6) 55°C for 1.5 minutes, 7) 72°C for 1 minute, 8)
60°C for 30 minutes. The second through fourth steps were performed 16 times, with the
temperature in step three (63°C) declining by 0.5°C in each cycle. Steps 5 through 7 were
performed 31 times.
Table 3. Microsatellite primer sequences. Sequences of and final PCR concentrations
for the 8 primers used to amplify microsatellite loci as part of the process of identifying
individual coyotes (Ostrander et al. 1993, Ostrander et al. 1995, Francisco et al. 1996). F
= forward primer, R = reverse primer, T = thymine, C = cytosine, A = adenine, G =
guanine.
Primer
Final
concentration
name Primer sequence
F: 5'- TTGATTTCCCCTGTAGCTTA -3'
for PCR (µM)
CXX119 R: 5'- GATGTAAAGAATGAGAGAGG -3'
F: 5’-ATCCAGGTCTGGAATACCCC-3’
0.26
CXX173 R: 5’-TCCTTTGAATTAGCACTTGGC-3’
F: 5’-TTAGTTAACCCAGCTCCCCCA-3’
0.05
CXX250 R: 5’-TCACCCTGTTAGCTGCTCAA-3’
F: 5’-ACGTGTTGATGTACATTCCTGC-3’
0.06
CXX377 R: 5’-CCACCCAGTCACACAATCAG-3’
F: 5’-TCCTCCTCTTCTTTCCATTGG-3’
0.04
FH2001 R: 5’-TGAACAGAGTTAAGGATAGACACG-3’
F: 5’-AAATGGAACAGTTGAGCATGC-3’
0.06
CXX2010 R: 5’-CCCCTTACAGCTTCATTTTCC-3’
F: 5’-GCCTTATTCATTGCAGTTAGGG-3’
0.04
FH2054 R: 5’-ATGCTGAGTTTTGAACTTTCCC-3’
F: 5’-CCCTCTGCCTACATCTCTGC-3’
0.05
FH2088 R: 5’-TAGGGCATGCATATAACCAGC-3’ 0.06

52
The amplified nuclear DNA was then prepared for, and separated via
electrophoresis on, a capillary machine using the same procedure described above for the
mtDNA. GeneMapper 3.7 was used to analyze the resulting fragment size data and
determine which alleles were present at each locus in each sample. Per locus
amplification success rates were calculated based on this initial microsatellite screening
run for all samples that were identified as “coyote” or “maybe coyote” in the species
identification test. A locus was considered to be “successful” if any alleles were
amplified in the PCR and identified using GeneMapper 3.7.
The PCR and electrophoresis steps were repeated two more times for samples for
which DNA was successfully amplified at 5 or more microsatellite loci in the initial
nuclear DNA PCR. The 3 replicate PCRs for a given sample were compared in an
attempt to obtain a consensus genotype for each locus. For a homozygous locus, one
allele, and only that allele, had to be seen in all three replicate PCRs while, for a
heterozygous locus, each allele had to be seen in two PCRs. Samples for which a
consensus genotype was obtained at only 5 loci were screened two more times. Once
these additional PCRs were assessed, all samples with consensus genotypes at 6 or more
loci were analyzed in the programs Gimlet 1.3.3 (Valière 2002) and GenAlEx 6 (Peakall
and Smouse 2006). These programs matched the consensus genotypes and determined the
minimum number of individuals (i.e., different coyotes) from which samples had been
collected. These programs also identified samples that had consensus genotypes which
differed from those of one or more other samples by only 1 or 2 loci. If the genotypes of
these samples were incomplete or were otherwise uncertain, then they were run through
the microsatellite analysis two more times and the results of these reruns were compared

53
to those of previous PCRs. The updated consensus genotypes were then reanalyzed in
Gimlet 1.3.3 and GenAlEx 6. If the updated genotypes still differed from those of one or
more samples at one or two loci and if the difference(s) could be the result of a single
instance of allelic dropout or the presence of a single false allele (see definition below),
then the updated genotypes were excluded from further analysis. Finally, the reliability of
unique consensus genotypes that were observed only once was tested using the program
RELIOTYPE (Miller et al. 2002) and a reliability criteria of 95%. If the reliability of a
unique genotype was confirmed in the initial RELIOTYPE test, or following 2 reruns of
the microsatellite analysis, it was retained in the dataset. Samples that were still identified
as unreliable after a total of 7 microsatellite screenings, or, based on the RELIOTYPE
results, would need more than a total of 7 screenings to be considered reliable, were
excluded from further analysis.
Per locus error (false allele and allelic dropout) rates and per sample probability
of identity values were calculated in order to assess the quality of the microsatellite data.
Error rates and probability of identity values were based on data from all samples that
had consensus genotypes at 6 or more loci and which were not excluded from further
analysis as described above. More specifically, error rates were determined using the first
two successful PCRs, or PCRs for which one or more alleles were identified, for each
sample at each locus. For each sample, loci that did not have at least two successful PCRs
were excluded from this calculation. For a given sample, false alleles were alleles that
appeared in one of the first two successful microsatellite screenings for a particular locus
but did not appear in the consensus genotype for that locus and were thus seen in only
one of the three or more replicate PCRs for that locus. Allelic dropout was noted for a

54
sample when one of the two alleles in the consensus genotype for a heterozygous locus in
that sample failed to amplify in one or more of the first two successful PCRs for that
locus (Waits and Paetkau 2005). Per sample probability of identity (PID) for both
unrelated individuals and siblings was calculated using unbiased per locus values for
unrelated individuals and per locus values for siblings obtained via Gimlet 1.3.3 (Waits et
al. 2001, Valière 2002). In particular, for each sample, the per locus PID values for either
unrelated individuals or siblings for all loci for which consensus had been reached were
multiplied together. PID values provide information regarding the probability that either
two unrelated individuals or two siblings in the population will have the same genotype.
These values are affected by the number and allelic diversity of loci being used to
differentiate individuals (Waits et al. 2001, Waits 2004).
The consensus genotypes of unique individuals, as determined using Gimlet 1.3.3
and GenAlEx 6 and confirmed with RELIOTYPE, were tested using the programs
GENEPOP 4.0.10 (Raymond and Rousset 1995, Rousset 2008), Structure 2.3.1 (Pritchard
et al. 2000), and BAPS 5 (Corander et al. 2008a, Corander et al. 2008b). GENEPOP
4.0.10 was used to determine if the genotypes were in Hardy-Weinberg (HW)
equilibrium and if they met the assumption of linkage equilibrium (LE). The null
hypothesis for the LE test is that the genotype at one locus is independent of the
genotypes at the 7 other loci. Bonferroni corrected p-values were used to assess the
significance of the results of both the HW equilibrium (significant p-value = 0.006 or
0.05 divided by the number of loci used) and LE (significant p-value = 0.002 or 0.05
divided by the number of locus pairs) tests (based on Sacks et al. 2004). Significant
deviations from HW equilibrium or rejection of the null hypothesis for the LE test may

55
be an indication that errors were made in the process of determining consensus genotypes
(Sacks et al. 2004).
Structure 2.3.1 and BAPS 5 were used to determine whether the sampled
individuals appeared to come from one or multiple populations or genetic groups. The
presence of multiple genetic groups could indicate the presence of either more than one
species, and thus errors in the species identification test, or a barrier to gene flow within
the coyote population. The consensus genotypes were tested to see if there were between
1 and 20 genetic groups present in the study area. For Structure 2.3.1, the burnin period
was set to 100,000, the number of Markov Chain Monte Carlo repetitions used was
1,000,000 (based on Pritchard et al. 2000, Wilson et al. 2009) and the program was run
20 times for each value of K (i.e., number of genetic groups). Individuals were assigned
to different genetic groups based on membership coefficients (i.e., Q-values; Pritchard et
al. 2009) produced by the program. Genetic group membership was determined using the
following two approaches: 1) individuals were assigned to the genetic group for which
they obtained the largest Q-value; 2) individuals were assigned to a genetic group only if
they obtained a Q-value larger than 0.8 (based on Sacks et al. 2004). For BAPS 5, the
fixed K clustering function was used in order to run the program 20 times for each K
value of interest (2 to 20) for the spatial clustering of individuals analysis. The program
was also run 380 times (19 x 20) with the fixed K function disabled in order to obtain
information on the optimal number of genetic groups and optimal genetic group
assignment for each individual. Each of the coordinates used for the spatial analysis in
BAPS 5 represents the center point of all locations where a single individual was
sampled.

56
The “kinship” function in Gimlet 1.3.3, as well as the “relationship” function in
ML-Relate (Kalinowski et al. 2006), were used to identify individuals that were related to
one another, in particular individuals that had two parents among the animals that were
sampled (Gimlet) or were in a parent-offspring relationship with another individual
among those sampled (ML-Relate). The HW equilibrium and LE tests, as well as the
analyses of genetic group number, were rerun once two out of every three individuals that
were identified as being in a parent-offspring relationship by Gimlet 1.3.3. were removed.
The presence of related individuals can influence the results of the HW and LE tests, as
well as the analyses of genetic group number. The results of the Gimlet 1.3.3 kinship and
ML-Relate relationship analyses were also used to determine which individuals were in
the same “family” group. In particular, a family group was defined by starting with either
2 (ML-Relate) or 3 (Gimlet) individuals identified as being in a parent-offspring
relationship and then adding all relatives (i.e., mates, offspring, and parents) of 1) these
2-3 individuals and 2) all mates, offspring, and parents of these 2-3 individuals.
Once the reliability of the consensus genotypes had been evaluated, they were
used to determine the minimum number of coyotes that were detected on each road-based
transect in each of 9 scat surveys. These counts were then averaged 1) across all 9
surveys and 2) across each pair of surveys that was conducted in a particular season. Both
of these sets of average minimum counts were then averaged across all transects within
the same habitat type (grassland versus shrubland) to obtain habitat-specific counts.
These habitat-specific counts were analyzed to determine if there were any significant
difference between habitats in the number of coyotes sampled 1) over the entire study

57
period (Student’s t-test; PROC TTEST SAS 9.1), and 2) among seasons (two-way
ANOVA and Duncan’s test; PROC GLM SAS 9.1).
Carbon stable isotopes
The purpose of performing carbon stable isotope analyses was to assess the base
of the food chain for individual coyotes sampled in grassland and shrubland habitats in
different seasons. More specifically, the carbon isotope signatures of components of
coyote scat samples provide information on the percentage of the coyote diet that comes
directly (seeds and fruits) or indirectly (rodents and other prey) from grasses (C4 plants)
versus woody plants (C3 plants). The first scat sample collected for each individual
coyote in each of four field seasons (summer 2008, or spring, summer and fall 2009) was
prepared for carbon isotope analysis (see description below). This was done in order to
maximize sample size, since samples from the same individual are not independent of
one another, while minimizing the cost of the carbon stable isotope analysis.
To prepare samples for carbon stable isotope analysis, scat samples that were
dried at 70°C for 24 hours were first flattened and spread out using a mortar and pestle
and forceps. For individuals that were just sampled in one season, small quantities of
bone were then separated from the rest of the scat components and cleaned with ethanol.
For individuals that were sampled in more than one season in 2009, small amounts of
bone, hair and vegetation (woody plant or forb berries or seeds) were separated from the
rest of the scat sample and cleaned with ethanol. The bones and berries or seeds were
then crushed into a powder. The bones were expected to pick up long-term shifts (i.e.,
those related to habitat changes associated with woody plant encroachment; Ambrose and
DeNiro 1986) in the base of the coyote food chain while hair and vegetation were

58
expected to pick up short-term (i.e., seasonal) variation (Tieszen et al. 1983). Samples of
woody plants, forbs and grasses that had been collected in the field and then dried at 60 o C
for 24 hours were ground using a Wiley Mill until they passed through a 20 mesh filter.
These ground samples included grasses and woody plants found to be dominant in one or
more of the vegetation plots and forbs that were collected in plots located in different
parts of the refuge (northeast, southeast, northwest, southwest). They also included two
woody plants that produce seeds or fruits that coyotes eat (V. Seamster, unpublished
data).
Scat samples obtained from a group of captive Mexican wolves (Canis lupus
baileyi) that are housed at the Sevilleta NWR, as well as small samples of the meat and
kibble that these wolves are fed, were also prepared for carbon stable isotope analysis.
The purpose of analyzing these samples was to obtain a correction factor that could be
used to correct carbon isotope signatures of canid scat components (i.e., bone or hair) for
diet to scat fractionation (see below). This correction factor was calculated by subtracting
the average carbon stable isotope signature of the food samples from the signature of
each of the scats and then averaging the differences. Scats and food samples were dried at
70°C for 24 hours and ground to a powder using a mortar and pestle.
Appropriate masses (Table 4) of the cleaned scat components (i.e., bone or
vegetion powder and hair), the milled vegetation samples, and the crushed wolf scat and
food samples were then placed in small tin cups in preparation for analysis on a coupled
elemental analyzer (Costech 4010) and isotope ratio mass spectrometer (DeltaPlux XP) at
the Stable Isotope Laboratory at the University of New Hampshire (UNH).

59
Table 4. Masses for stable isotope analysis. Masses used for weighing out samples of
different scat components and standards for stable isotope analysis.
Component Mass (mg)
bolete (known unknown) 1.8-2.2
bone 2.8-3.2
citrus (known unknown) 3.8-4.2
hair 0.8-1.2
NIST 1515/1575a (standards) 4.0-4.5
tuna (standard) 0.8-1.2
vegetation 3.8-4.2
wolf scat/food 3.8-4.2
4-5 out of every 58-65 samples was replicated. In addition, 3 standards (NIST 1515 and
1575a, tuna) and one known unknown (bolete; Table 4) were run for every 10-12
samples. 20 tins containing a second known unknown (citrus; Table 4) were mixed in
with the first 277 samples run.
The carbon isotope signatures (i.e., δ 13 C values) derived from the mass
spectrometer were corrected for shifts both over time and in response to variation in
sample weight (A. Ouimette, UNH, 2010, personal communication). The signatures for
scat components, specifically bone and hair, were also corrected for diet to tissue and diet
to scat fractionation (Table 5) and used to calculate the percentage of coyote diet that
came indirectly from grasses (C4) versus woody plants (C3) in grassland versus shrubland
areas (Equation 1, based on Faure and Mensing 2005).
Where
δ 13 Cscat = δ 13 CC 3 (fC 3 ) + δ 13 CC 4 (1-fC 3 ) (1)
δ 13 Cscat = the carbon isotope signature of a coyote scat component (i.e., bone or hair);
δ 13 CC 3 = the average carbon isotope signature of C3 woody plants; fC 3 = the fraction of

coyote diet that comes indirectly from C3 woody plants; δ 13 CC 4 = the average carbon
isotope signature of C4 grasses.
The average δ 13 C values calculated from the woody plant and grass samples that
were collected in the field represented the “end members” (i.e., δ 13 CC 3 , δ 13 CC 4 ) for this
calculation such that the carbon isotope signatures of the coyote scat components were
expected to fall between these values and represent a mixture of grass and woody plant
food resources (Faure and Mensing 2005; Equation 1).
Table 5. Fractionation correction factors. Values used to correct for diet to tissue and
diet to scat fractionation. Please note that these values were subtracted from the δ 13 C
values for the appropriate scat components and that no correction for fractionation was
applied to any of the standards or vegetation samples.
Component
Correction
factor ( o /oo) Correction type Source
bone 1* diet to tissue DeNiro and Epstein 1978
hair 1.0 diet to tissue Tieszen et al. 1983
bone/hair 0 ± 2.0** diet to scat This study
* This value was chosen over species-specific correction factors for bone collagen
because whole bone was used in this study and collagen extraction was not performed.
As a result, it is not known whether, or how much, collagen is present in the bone
samples used.
** Value presented as average ± 1 standard deviation. The average value was used as the
correction factor.
GIS analysis
GIS techniques were used to determine whether the relationship between coyote
feeding ecology and the local habitat changed as the spatial scale at which the habitat was
characterized increased (see Chapter 5). The results of the noninvasive genetic sampling
techniques described above, as well as previously published information on coyote home
range size (Windberg et al. 1997), was used in determining the range of spatial scales
considered.
60

Assessing coyote home range size and patterns of habitat use
The location information for scat samples collected from unique individuals at the
Sevilleta NWR was used to determine the average size of coyote home ranges. In
particular, the location information was analyzed using the freeware program ABODE v.
2 (Laver 2005) and ArcGIS 9.2 (ESRI). A minimum convex polygon was generated to
represent the movements of each individual and the size of each polygon was determined.
The average size of these polygons was taken to represent the average home range size
for a coyote at the Sevilleta NWR. This value was compared to a previously published
value for coyote home range size which was based on radiotelemetry data (Windberg et
al. 1997). Both the noninvasive and previously published datasets were collected in arid
environments (average annual rainfall 230-232mm; Goslee et al. 2003, Hochstrasser and
Peters 2004) and the two study sites (Sevilleta NWR and LTER and Jornada LTER) are
within 200 miles of one another.
Information on the habitat in which the scat samples were collected was used to
determine whether individuals had moved between habitats or stayed in one habitat type.
The home range sizes were naturally log transformed and an ANOVA and Duncan’s test
(PROC ANOVA, SAS 9.1) were performed to determine whether average home range
size differed among individuals that used one (grassland or shrubland) or both habitat
types. Finally, two chi square analyses were used to determine whether individuals that
stayed within one habitat type used the same habitat as, or a different habitat than, either
one or two individuals to which they were related. The chi square analysis was performed
twice for pairs of related individuals and twice for trios. The first analysis of both pairs
and trios of related individuals was based on relationship information provided by Gimlet
61

62
1.3.3 and the second was based on relationships identified by ML-Relate. The probability
that an individual or their relative was sampled in either the grassland or the shrubland
habitat was calculated based on the number of pair members (for pairs) or pairs and third
individuals (for trios) that were sampled in either habitat type.

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Chapter 3: Genetic results
The purpose of this chapter is to provide detailed information regarding the results
of the genetic analyses of noninvasively collected scat samples that were described in
Chapter 2. This chapter also serves as a reference for Chapters 4 and 5, which present the
results of these genetic analyses in abbreviated form. Several calculations were
performed and maps were generated to assess the quality of the genetic data and thus the
reliability of the individual identification information that was derived from these data.
This individual identification information is presented and applied in later chapters.
Results and discussion
Species identification
More than two-thirds of collected scat samples came from coyotes. The use of a
genetic-based species identification test increased the number of samples identified as
being from coyotes and decreased the number of samples with an “unknown” identity
relative to the field-based identification technique (Figure 1).
Number of samples
700
600
500
400
300
200
100
0
Coyote Maybe
Coyote
Sample identification
Not Coyote Unknown
Field
Genetics
Figure 1. Species ID results. Number of samples identified as coyote, maybe coyote, not
coyote or unknown either in the field (black) or based on a genetic analysis of species
(gray).
67

Individual identification
There were a total of 81 individuals, 17 of which were only detected once. The
consensus genotypes of all individuals that were only detected once were analyzed using
RELIOTYPE and had estimated reliabilities between 0.99 and 1. The largest number of
samples collected for a single individual was 25 (Figure 2). The average nearest distance
between scat samples collected from a single individual ranged from 1 to 166 m.
Frequency
18
16
14
12
10
8
6
4
2
0
1 3 5 7 9 11 13 15 17 19 21 23 25
Number of scats/individual
Figure 2. Scat samples per individual. Distribution of number of samples per
individual, with a total of 520 samples and 81 individuals.
Per locus amplification success rates were all above 75 percent and ranged from
78 to 88 percent. With one exception (allelic dropout at locus 2010), error rates were all
below 10% (Table 1). These success rates are as high and error rates as low as those
found in other, recent studies that have utilized microsatellite markers (e.g., Murphy et al.
2003, Adams et al. 2007, Adams and Waits 2007, De Barba and Waits 2010, Stenglein et
68

69
al. 2010a, Stenglein et al. 2010b). Genotyping success rate, calculated as the number of
scat samples for which a consensus genotype was obtained at 6 or more loci (n = 520)
divided by the total number of samples for which individual identification was attempted
(n = 795), was roughly 65%.
Table 1. Microsatellite success and error rates. Per locus success and error (allelic
dropout, false allele) rates. Success rate indicates success of amplification via polymerase
chain reaction (PCR) and is based on the first microsatellite PCR of 761 samples
previously identified as “coyote” or “maybe coyote” via a genetic-based species
identification test (Figure 1). Error rates are based on the first two successful PCRs for
520 samples for which consensus was reached at 6 or more loci. The largest allelic
dropout and false allele rates are shown in bold.
Amplification Allelic
Locus Success rate dropout False alleles
CXX119 0.88 0.05 0.01
CXX173 0.85 0.04 0.01
CXX250 0.83 0.08 0.01
CXX377 0.83 0.05 0.03
FH2001 0.79 0.10 0.04
CXX2010 0.78 0.13 0.01
FH2054 0.80 0.09 0.05
FH2088 0.86 0.05 0.01
Per sample values for probability of identity (PID) for unrelated individuals
ranged from 1.5 x 10 -11 to 2.9 x 10 -8 with an average of 5.4 x 10 -10 for 520 samples for
which a consensus genotype was obtained for 6 to 8 loci. Per sample PID values for
siblings for these same 520 samples ranged from 2.4 x 10 -4 to 2.3 x 10 -3 with an average
of 4.5 x 10 -4 . Per sample PID values for siblings ranged from 1.4 x 10 -3 to 2.3 x 10 -3 for
41 samples for which consensus was obtained at 6 loci (i.e., the minimum number of loci
required for individual identification). All PID values, both for unrelated individuals and
siblings, are within or lower than the recommended range of values (1 x 10 -2 to 1 x 10 -4 ).
This indicates that the probability of two siblings or two unrelated individuals having the
same consensus genotype is low (Waits et al. 2001). Three out of eight loci have

70
significant p-values (below 0.05) for the Hardy-Weinberg (HW) exact test and are thus
out of HW equilibrium. If a Bonferroni corrected p-value is used (0.006), then only one
locus (CXX173) is out of HW equilibrium (Table 2A). When 31 related individuals, as
identified using the kinship analysis in Gimlet 1.3.3., were removed from the population,
either one locus (FH2088; p < 0.05) was, or no loci (p < 0.006) were, out of equilibrium
(Table 2B). This indicates that a high degree of relatedness among some of the sampled
individuals, rather than genotyping errors or locus-specific selection (Sacks et al. 2004),
was likely causing the CXX173 locus to be out of HW equilibrium.
Table 2. Hardy-Weinberg test results. P- and S.E. values for the Hardy-Weinberg
(HW) exact test in GENEPOP 4.0.10. The p-values shown in bold are less than 0.05. An
asterisk denotes a value below the Bonferroni corrected p-value of 0.006 (Sacks et al.
2004). A) These are the values obtained when all sampled coyotes are included in the
analysis (n = 81; 700 batches, S.E. < 0.01). B) These are the values obtained after related
individuals were removed (n = 50, 600 batches, S.E. < 0.01). Related individuals were
identified using the kinship analysis in Gimlet 1.3.3. Only one of every three individuals
identified as being in a parent/offspring relationship by Gimlet was retained in the HW
analysis.
A) B)
Locus p-value S.E.
CXX119 0.562 0.008
CXX173 0.000* 0.000
CXX250 0.551 0.009
CXX377 0.179 0.008
FH2001 0.086 0.005
CXX2010 0.017 0.001
FH2054 0.099 0.005
FH2088 0.022 0.002
Locus p-value S.E.
CXX119 0.266 0.008
CXX173 0.085 0.006
CXX250 0.673 0.009
CXX377 0.178 0.009
FH2001 0.450 0.009
CXX2010 0.182 0.003
FH2054 0.302 0.008
FH2088 0.021 0.002
Results of a linkage equilibrium (LE) test showed that, for eight out of 28 inter-
locus comparisons (29%), the genotypes of the two loci being considered were not
independent of one another (p < 0.05; Table 3A). Most of these significant comparisons
were associated with at least one of the three loci found to be out of HW equilibrium

71
(Table 2A). However, when a Bonferroni corrected p-value (0.002) was used, the null
hypothesis of the LE test was rejected for only two comparisons, one of which was
associated with the locus found to be out of HW equilibrium when a Bonferroni corrected
p-value was used (CXX173; Tables 2A and 3A). When the same 31 related individuals
that were removed from the HW equilibrium test were removed from the LE test, there
was one significant comparison (p < 0.05) or zero significant comparisons (p < 0.002;
Table 3B). The one significant comparison is not associated with the locus found to be
out of HW equilibrium after the related individuals were removed (FH2088; Table 2B).
Table 3. Linkage equilibrium test results. P-values for linkage equilibrium (LE)
analysis in GENEPOP 4.0.10. The p-values shown in bold are less than 0.05. An asterisk
denotes a value below the Bonferroni corrected p-value of 0.002 (Sacks et al. 2004). A)
P-values obtained when all sampled coyotes are retained in the LE analysis (n = 81; 600
batches; S.E. < 0.02). B) P-values obtained when related individuals, as identified using
Gimlet 1.3.3 (see Table 2 for details), are removed from the analysis (n = 50, 600
batches, S.E. < 0.02).
A)
Locus CXX119 CXX173 CXX250 CXX377 FH2001 CXX2010 FH2054 FH2088
CXX119 x 0.046 0.238 0.156 0.059 0.229 0.673 0.001*
CXX173 x 0.04 0.109 0.007 0.341 0.007 0.000*
CXX250 x 0.1 0.125 0.276 0.126 0.053
CXX377 x 0.196 0.303 0.609 0.119
FH2001 x 0.014 0.045 0.238
CXX2010 x 0.194 0.243
FH2054 x 0.105
FH2088
B)
x
Locus CXX119 CXX173 CXX250 CXX377 FH2001 CXX2010 FH2054 FH2088
CXX119 x 0.357 0.050 0.540 0.503 0.729 0.963 0.605
CXX173 x 0.898 0.708 0.389 0.289 0.188 0.389
CXX250 x 0.025 0.641 0.060 0.748 0.519
CXX377 x 0.446 0.500 0.899 0.325
FH2001 x 0.101 0.078 0.866
CXX2010 x 0.291 0.163
FH2054 x 0.648
FH2088 x

72
As was seen for the HW equilibrium test, the high degree of relatedness among some of
the sampled individuals, rather than genotyping errors or physical linkage between one or
more pairs of loci (Sacks et al. 2004), appears to be the primary factor leading to a
rejection of the null hypothesis of the LE test for some loci. A comparison of values for
observed and expected heterozygosity indicated that the deviation from HW equilibrium
(Tables 2A and 2B) might be due to the presence of null alleles at one locus (FH2088;
Tables 4A and 4B).
Table 4. Observed and expected heterozygosity. Observed and expected numbers of
heterozygous individuals (generated by GENEPOP 4.0.10) were divided by the number
of individuals genotyped at a particular locus to determine observed (Hobs) and expected
(Hexp) heterozygosity. Hexp > Hobs and p-value for HW exact test < 0.05 (Table 2) for
locus shown in bold. A) Values for all coyotes sampled (n = 81); B) values once related
individuals are removed (n = 50).
A)
B)
Locus
Expected #
heterozygotes
Observed #
heterozygotes
# of individuals
genotyped Hexp Hobs
CXX119 69.956 68 80 0.87 0.85
CXX173 70.559 73 81 0.87 0.9
CXX250 69.3665 68 81 0.86 0.84
CXX377 71.677 70 81 0.89 0.86
FH2001 65.6273 60 81 0.81 0.74
CXX2010 57.7453 66 81 0.71 0.82
FH2054 62.2795 61 81 0.77 0.75
FH2088 66.4969 60 81 0.82 0.74
Locus
Expected #
heterozygotes
Observed #
heterozygotes
# of individuals
genotyped Hexp Hobs
CXX119 42.9175 41 49 0.88 0.84
CXX173 43.7576 44 50 0.88 0.88
CXX250 42.8182 43 50 0.86 0.86
CXX377 44.9293 43 50 0.9 0.86
FH2001 40.7273 38 50 0.81 0.76
CXX2010 35.7576 40 50 0.72 0.8
FH2054 39.5051 44 50 0.79 0.88
FH2088 40.798 33 50 0.82 0.66

73
More specifically, expected was larger than observed heterozygosity for this locus,
indicating that there were many more samples with a homozygous genotype at this locus
than expected. Since the occurrence of allelic dropout is fairly low for the FH2088 locus
(Table 1), it is possible that this high level of homozygosity is due to the presence of one
or more null alleles (i.e., alleles that can no longer be amplified due to the occurrence of a
mutation). However, if Bonferroni corrected p-values are used to assess the significance
of the HW exact test, then this locus is no longer found to be out of HW equilibrium.
Furthermore, to my knowledge, no other studies have reported the presence of null alleles
at this locus in coyotes, so it is possible that my observations regarding this locus are due
to another cause. For example, this locus (FH2088) may be linked to another locus that is
under selection (L.Waits, University of Idaho, personal communication).
Related individuals and population structure
A qualitative assessment of the spatial organization of groups of related
individuals, or family groups, at the Sevilleta National Wildlife Refuge (NWR) shows
that related individuals were often sampled either on only one scat transect, and thus on a
mile-long stretch of road, or on multiple, contiguous transects. There is one notable
exception; one family was found on transects in both the northeastern and southeastern
parts of the refuge (family 3; Figure 3) and even extended to the western side of the
refuge (family 3; Figure 4). There was a good deal of similarity in the number and spatial
distribution of families identified using the results of the kinship analysis in Gimlet 1.3.3
(Figure 3) and the relationship analysis in ML-Relate (Figure 4).

Figure 3. Gimlet family group map. Map of family groups based on the results of a
kinship analysis in Gimlet 1.3.3. The members of each family group were determined by
starting with a single offspring and its two parents and adding in all mates, parents, and
offspring of 1) each of these three individuals, 2) each of the mates, parents and offspring
of these three individuals.
Figure 4. ML-Relate family group map. Map of family groups based on the results of a
relationship analysis in ML-Relate. The members of each family group were determined
by starting with a single offspring-parent pair and adding in all mates, parents, and
offspring of 1) both of these individuals, 2) each of the mates, parents, and offspring of
these two individuals. Families that share at least one individual with a family group
determined using the kinship analysis in Gimlet 1.3.3 are shown with the same symbols
as in Figure 3.
74

In particular, the results of one program identified 8 and the other one 9 families and the
membership and spatial distribution of several families overlap and are thus shown with
the same symbols in Figures 3 and 4. There are however some differences between the
results of the two programs. In particular, one family group (family 3) identified based on
ML-Relate results has a much larger spatial distribution than the comparable family group
identified using Gimlet (Figures 3 and 4). Furthermore, there are 5 families that either do
not match any family group, or are subsumed by a larger family group, identified by the
other program (Gimlet families 6, 7, and 8 Figure 3; ML-Relate families 6 and 7 Figure
4).
An analysis of the population structure of all 81 individuals shows that there are
either 7 (Figure 5 Structure 2.3.1; aspatial) or 10 (Figure 6; BAPS 5; spatial) different
genetic groups present at the study site. For Structure 2.3.1, 7 was the number of genetic
groups with the largest average value for natural log probability of data (Figure 5). For
BAPS 5, 10 was the optimal number of genetic groups identified in each of 20
independent runs and had the largest log (ml) value (Figure 6). When only one of every
three individuals identified as being related by the kinship analysis in Gimlet 1.3.3 are
retained in the population structure analysis, the number of genetic groups declines to 1
(Figure 5; Structure 2.3.1) or 2 (Figure 6; BAPS 5). The number of genetic groups
identified in the population structure analyses (7 to 10) is similar to the number of family
groups (8 to 9) present at the study site. Furthermore, the number of genetic groups
identified declines significantly once related individuals are removed from the population
structure analyses.
75

Ln P(D)
-1250
-1750
-2250
-2750
-3250
-3750
K
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Structure_n=81
Structure_n=50
Figure 5. Structure analysis results. Average natural log probability of data as
determined using Structure 2.3.1 for all individuals (n = 81) and once related individuals
have been removed from the analysis (n = 50). Related individuals were removed by
taking out two of every three individuals identified as being in a parent-offspring
relationship by the kinship analysis in Gimlet 1.3.3. K = the number of genetic groups
and error bars represent 95% confidence intervals across 20 independent runs for each K,
except where outliers were removed.
Log(ml)_n = 81
-2620
-2630
-2640
-2650
-2660
-2670
-2680
-2690
-2700
-2710
-2720
K
1 2 3 4 5 6 7 8 9 1011121314151617181920
-1660
-1680
-1700
-1720
-1740
-1760
-1780
-1800
-1820
Log(ml)_n = 50
BAPS_n=81_spatial
BAPS_n=50_spatial
Figure 6. BAPS analysis results. Average log(ml) values as determined using the spatial
clustering of individuals analysis in BAPS 5 for all individuals (n = 81) and once related
individuals have been removed (n = 50). Coordinates used for this analysis are the midpoints
of all locations obtained for a particular individual. K = the number of genetic
groups and error bars represent 95% confidence intervals across 10 independent runs for
every K except K = 2.
76

77
These results indicate that relatedness among individuals accounts for most of the
population structure identified by both an aspatial (Structure 2.3.1) and a spatial (BAPS
5) analysis.
A)
B)
Figure 7. Genetic groups generated by Structure. Genetic groups generated by
Structure 2.3.1 (K = 7). A) Individuals were assigned to genetic groups based on the
highest average membership coefficient (Q-value). B) Individuals were assigned to
genetic groups only if they had an average Q-value of 0.8 or higher. For A and B, Qvalues
were averaged across 20 independent runs and genetic groups that share multiple
individuals with family groups determined by both Gimlet 1.3.3 and ML-Relate are
shown with the same symbols as in Figures 3 and 4.

78
There are some similarities between genetic groups identified using both Structure
2.3.1 (Figure 7) and BAPS 5 (Figure 8) and the family groups based on results from
Gimlet and ML-Relate (Figures 3 and 4). In particular, when individuals are assigned to
genetic groups based on the largest average membership coefficient (Figure 7A), the
composition of each of 4 out of 7 genetic groups identified by Structure overlaps that of
one of the families identified by both Gimlet and ML-Relate and a fifth genetic group is
similar to a family group identified by ML-Relate. One of the remaining genetic groups
(genetic group 3) is primarily composed of individuals that were not assigned to a family
group. When only large (0.8 or above) membership coefficients are considered (Figure
7B), the compositions of 2 of the 3 genetic groups to which individuals are assigned
correspond well with those of 2 family groups identified by Gimlet and ML-Relate.
For the results of the BAPS 5 population structure analysis (Figure 8), the
composition of each of 5 out of 10 genetic groups overlaps that of one of the family
groups identified by both Gimlet and ML-Relate and a sixth genetic group matches a
family identified by Gimlet. The 4 remaining genetic groups largely contain individuals
that were not assigned to any family group. All of these similarities between genetic
groups and family groups provide further support to the conclusion that most of the
population structure identified using the programs Structure and BAPS is the result of
relatedness among individuals rather than actual barriers to gene flow or the presence of
more than one species in the dataset. This observed lack of barriers to gene flow is
slightly surprising given the presence of both an interstate highway and a river that run
north-south and divide the study site roughly in half. Other studies have found that roads
can act as gene flow barriers for large mammals (Epps et al. 2005, bighorn sheep; Riley

79
et al. 2006, bobcats and coyotes; Dixon et al. 2007, black bear; Pérez-Espona et al. 2008,
red deer; Rashleigh et al. 2008, coyotes; but see Gula et al. 2009).
Figure 8. Genetic groups generated by BAPS (K = 10). Genetic groups generated by
spatial clustering of individuals analysis in BAPS 5 (K = 10). BAPS analysis included all
81 individuals sampled at the study site. Genetic groups which contain several of the
same individuals as family groups identified using Gimlet and ML-Relate are shown with
the same symbols as in Figures 3 and 4.
Once related individuals are removed from the BAPS 5 population structure
analysis, most individuals are placed in the same genetic group (Figure 9). One individual
was identified as being in a separate group in both BAPS 5 analyses (Figures 8 and 9).
This individual was placed in a genetic group with other individuals in the Structure 2.3.1
analysis (genetic group 3, Figure 7A), so it is unlikely that this individual is a member of
a non-target species.

Figure 9. Genetic groups generated by BAPS (K = 2). Genetic groups generated by
spatial clustering of individuals analysis in BAPS 5 (K = 2). This BAPS analysis was
based on 50 individuals not identified by Gimlet 1.3.3 as being in a parent-offspring
relationship with other individuals included in the analysis. The genetic group which
contains the same individual as a genetic group identified by BAPS 5 when related
individuals were included in the analysis (n = 81) is shown with the same symbol as in
Figure 8.
Coyote count variation between habitats and among transects and seasons
The largest number of individuals were sampled on two shrubland scat transects,
one on the eastern side and one on the western side of the Sevilleta NWR. There are
many individuals in the north-eastern part of the study area, which is characterized by a
grassland habitat, and the south-western part, which is covered by a honey mesquite
(Prosopis glandulosa) dominated shrubland (Figure 10). There was no significant
difference between grassland and shrubland habitat types in the number of individuals
sampled (t = 0.67, d.f. = 20, p = 0.51; Figure 11 and F = 3.59, d.f. = 1, 60, p = 0.06;
Figure 12) and the interaction between habitat type and season did not represent a
significant source of variation in the number of individuals sampled (F = 0.21, d.f. = 2,
60, p = 0.81; Figure 12).
80

Figure 10. Map of individuals per scat transect. Total number of individuals sampled
on each scat transect during summer 2008 and spring, summer, and fall 2009. The
boundary of the Sevilleta NWR is shown in solid light gray, the roads in dotted light
gray, the scat transects in grassland areas in dark gray and the transects in shrubland areas
in black.
Average number of individuals
3
2.5
2
1.5
1
0.5
0
Grassland Shrubland
Habitat type
Figure 11. Inter-habitat differences in number of individuals. Average number of
individuals sampled along scat transects in grassland (n = 12) and shrubland (n = 10)
habitats. Habitat-specific averages are based on counts averaged across 6-9 surveys of
each of twenty-two scat transects.
More coyotes were sampled in the spring than in the summer and fall (F = 9.06,
d.f. = 2, 60, p = 0.0004; Figure 12). The decline in the number of individuals sampled in
the summer and fall may be due to the increased rainfall during these seasons and thus an
81

82
increased likelihood of samples being washed away or degraded past recognition
(Cavallini 1994). Dispersal of juvenile coyotes in the spring or fall may also be a
contributing factor (Berg and Chesness 1978, Gese et al. 1996, Larrucea et al. 2006).
Average number of individuals
4
3.5
3
2.5
2
1.5
1
0.5
0
Spring Summer Fall
Season
Grassland
Shrubland
Figure 12. Seasonal variation in number of individuals. Average number of
individuals sampled along scat transects in grassland (n = 12) versus shrubland (n = 10)
habitat in each of three seasons (spring, summer, fall) in 2009. Habitat-specific averages
are based on counts averaged across two surveys that were performed within each season
for each scat transect. Significantly more coyotes were sampled in the spring (a) than in
summer and fall (b; F = 9.06, d.f. = 2, 60, p = 0.0004).
Home range analysis
a
b
The average home range size for coyotes sampled 3 or more times among all
surveys of the road-based scat transects is 1.7 km 2 (n = 58; Figure 13). This value is
based on the average size of minimum convex polygons generated using scat sample
collection locations (see Chapter 2 for more details). Roughly 9% of all individuals
sampled (n = 81), and 11% of all individuals sampled two or more times (n = 64), used
road-based scat transects both grassland and shrubland habitat types, indicating that most
b

83
individuals stayed in only one of these two habitat types. There is a significant difference
in average home range size between coyotes that used one habitat type versus both
grassland and shrubland habitat (F = 7.17, d.f. = 2, 55, p = 0.002; Figure 13). In
particular, coyotes that used both habitat types had, on average, larger home ranges (6.7
km 2 ) than coyotes that used only grassland (1.3 km 2 ) or only shrubland (0.7 km 2 ) habitat.
Roughly 51% of all individuals sampled (n = 81), and 64% of all individuals sampled 2
or more times (n = 64), moved between different scat transects at least once during the
time period over which scat samples were collected.
Area (km 2 )
12
10
8
6
4
2
0
a
Both Grassland Shrubland
Habitat used
Figure 13. Inter-habitat variation in home range size. Average size of coyote home
ranges, as defined by minimum convex polygons, for individuals that stayed in only one
habitat type (grassland versus shrubland, n = 51) or moved between habitats (both, n = 7).
Error bars correspond to 95% confidence intervals. There is a significant difference in
average home range size between coyotes that used both habitat types (a) and those that
only used one habitat type (b; F = 7.17, d.f. = 2, 55, p = 0.002). Please note that the
ANOVA was performed on home range areas that were natural log transformed..
b
b

Habitat selection patterns of related individuals
There is evidence that the type of habitat used by a given individual is not
independent of the type of habitat use by related individuals. In particular, based on the
results of the kinship analysis in Gimlet 1.3.3, the relationship analysis in ML-Relate
(Figure 4), and information on the habitat use patterns of individual coyotes, more pairs
of related individuals than expected consisted of individuals that used the same habitat
type and fewer pairs of related individuals than expected consisted of individuals that
used different habitat types (Table 5; Χ 2 = 33.95, d.f. = 2, p < 0.05, Gimlet; Χ 2 = 25.40,
d.f. = 2, p < 0.05, ML-Relate).
Table 5. Data for chi square analysis of pairs of related individuals. Observed and
expected counts of pairs of individuals for which both individuals use the same habitat
(Both G or Both S) or each individual uses a different habitat type (One G, One S; G =
Grassland, S = Shrubland). Probability of occurrence was calculated based on the number
of pair members that used a particular habitat type and the total number of pair members
(i.e., the number of pairs multiplied by 2). Observed counts are higher than expected for
pairs of individuals for which both individuals used the same type of habitat. Observed
counts are lower than expected for pairs of individuals for which each individual used a
different habitat type.
Category
Observed
count
Probability of
occurrence
Expected
count
Program used to
assess family
Both G 20 0.17 9 Gimlet
One G, One S 6 0.48 27 Gimlet
Both S 30 0.35 19 Gimlet
Both G 20 0.19 11 ML-Relate
One G, One S 9 0.49 28 ML-Relate
Both S 27 0.32 18 ML-Relate
A similar analysis of trios of related individuals showed that more trios than expected
consisted of individuals that all used the same habitat type (Table 6, Χ 2 = 21.0, d.f. = 1, p
< 0.05, Gimlet; Χ 2 = 9.14, d.f. = 1, p < 0.05, ML-Relate). Furthermore, all individuals
used the same habitat type as all of their relatives in at least 50% of the family groups
84

85
identified using both Gimlet 1.3.3 (6 out of 9 family groups; Figures 3 and 14A) and ML-
Relate (4 out of 8 family groups; Figures 4 and 14B). Regardless of the program used to
determine family group (Gimlet 1.3.3 versus ML-Relate), most of the individuals that
used the same habitat as all the relatives in their family group were found either on the
western side of the refuge or in the central part of the eastern side of the refuge. Overall,
these results are similar to those obtained in a study of coyotes in California which
showed that coyote population structure was related to different habitat “bioregions,”
such that individuals that were more closely related were found in the same habitat
(Sacks et al. 2004).
Table 6. Data for chi square analysis of trios of related individuals. Observed and
expected counts of trios of related individuals for which all three individuals use the same
habitat (All 3 G or All 3 S) or the third individual uses a different habitat type from the
other two (2 G 1 S, 2 S 1 G; G = Grassland, S = Shrubland). Probability of occurrence
was calculated based on the number of pairs of individuals that both used the same
habitat type and the number of “third” individuals that used either grassland or shrubland
habitats. Observed counts are higher than expected for trios of individuals for which all
three individuals used the same type of habitat. Observed counts are lower than expected
for trios of individuals for which the third individual used a different habitat type than the
other two individuals.
Category
Observed
count
Probability of
occurrence
Expected
count
Program used to
assess family
All 3 G 21 0.17 10 Gimlet
2 G 1 S 11 0.23 14 Gimlet
All 3 S 23 0.35 20 Gimlet
2 S 1 G 4 0.25 15 Gimlet
All 3 G 16 0.13 10 ML-Relate
2 G 1 S 16 0.29 21 ML-Relate
All 3 S 34 0.39 29 ML-Relate
2 S 1 G 7 0.18 13 ML-Relate

A)
B)
Figure 14. Map of family groups relative to habitat use. Maps of family groups
determined by A) Gimlet and B) ML-Relate. Families in which all individuals used the
same habitat type are shown with dark symbols (circles, A; diamonds, B). Families in
which individuals used different habitat types from some of their relatives are shown with
white symbols (squares, A; triangles, B).
86

Conclusion
A high percentage of collected scat samples were from coyotes and a minimum of
81 different coyotes were sampled. Success rates associated with the individual
identification process were high and error rates were low. Relatedness among individuals
appears to account for most other irregularities in the data and for most of the population
structure identified by two different programs. There was no difference in the number of
coyotes sampled in grassland versus shrubland habitat, but more individuals were
sampled in the spring than in either the summer or fall seasons. Most coyotes that were
sampled stayed in either the grassland or the shrubland habitat and did not appear to
move often between the two habitat types. On average, the few coyotes that moved
between habitat types used a larger area than the coyotes that used only one habitat type.
There is some evidence that both pairs and trios of related individuals tend to use the
same habitat type and thus there appears to be habitat fidelity within family groups.
87

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the red wolf (Canis rufus) experimental population area using a spatially targeted
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Adams, J.R. and L.P. Waits. 2007. An efficient method for screening faecal DNA
genotypes and detecting new individuals and hybrids in the red wolf (Canis rufus)
experimental population area. Conservation Genetics. 8: 123-131.
Berg, W.E. and R.A. Chesness. 1978. Ecology of coyotes in Northern Minnesota. In M.
Bekoff (ed). Coyotes: Biology, Behavior, and Management. New York: Academic Press,
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Cavallini, P. 1994. Faeces count as an index of fox abundance. Acta Theriologica. 39(4):
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De Barba, M. and L.P. Waits. 2010. Multiplex pre-amplification for noninvasive genetic
sampling: is the extra effort worth it? Molecular Ecology Resources. 10: 659-665.
Dixon, J.D., M.K. Oli, M.C. Wooten, T.H. Eason, J.W. McCown, and M.W.
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Epps, C.W., P.J. Palsboll, J.D. Wehausen, G.K. Roderick, R.R. Ramey II, and D.R.
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Pérez-Espona, S., F.J. Pérez-Barberia, J.E. McLeod, C.D. Jiggins, I.J. Gordon, and J.M.
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Rashleigh, R.M., R.A. Krebs, and H. Van Keulen. 2008. Population structure of coyote
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249-256.

90
Chapter 4: Bottom-up effects of seasonal and long-term habitat change
on predator feeding ecology
Abstract
Mammalian carnivores are susceptible to bottom-up effects associated with
changes in the local environment. Woody plant encroachment, the spread of woody
plants in grass-dominated landscapes, is one of many processes leading to habitat change
on a global scale. This process occurs in dryland areas which tend to have strong seasonal
variation in rainfall and primary productivity. This study examines the effects of both
woody plant encroachment and seasonal change in grass-derived resource availability on
the feeding ecology, specifically the base of the food chain, of a top predator. Seasonal
changes in live grass cover were measured in grassland and woody plant-encroached
shrubland habitats at the Sevilleta National Wildlife Refuge in central New Mexico,
USA. Non-invasive genetic sampling of feces was used to identify individuals in the local
coyote (Canis latrans) population. Stable carbon isotope analyses of pieces of bone and
hair removed from the coyote scats were used to assess 1) the impacts of woody plant
encroachment on coyote feeding ecology by comparing the percent of the coyote diet that
came indirectly from C3 woody plants between grassland and shrubland habitats and 2)
seasonal variation in the percent of the coyote diet that came indirectly from C4 grasses.
Percent live grass cover increased from the spring to the summer and fall. A total of 81
coyotes were sampled and there were no significant differences in percent coyote diet
from C3 woody plants between grassland and shrubland habitats or in percent diet from
C4 grasses among 3 seasons. However, percent coyote diet from C3 woody plants was
large relative to the percentage of total live cover accounted for by woody plants in

91
grassland areas. Additionally, percent coyote diet from C4 grasses followed different
trends in grassland versus shrubland habitats; percentages declined slightly from the
spring to the summer in grassland areas but increased slightly in shrubland areas. Based
on these results, woody plant encroachment and seasonal variation in live grass cover do
not appear to lead to significant shifts in the base of the coyote food chain. However, C3
plants are an important food resource for coyote prey in grassland areas in this arid
ecosystem. Additionally, in the shrubland habitat, percent coyote diet from C4 grasses
follows a trend somewhat similar to that of percent live grass cover among seasons. This
study highlights the need for further investigation of the bottom-up effects of woody
plant encroachment, and variation in the availability of C3 versus C4 plants at the base of
the food chain, on the ecology and fitness of top predators, especially specialized
predators, and their prey.
Introduction
Predators play an important role in many ecosystems (e.g., Paine 1969, McLaren
and Peterson 1994, Estes and Duggins 1995, Crooks and Soule 1999, Schmitz et al. 2000,
Ripple and Beschta 2004) and are also prone to bottom-up effects associated with
changes in the abundance or productivity of organisms in lower trophic levels (Brand et
al. 1976, White 1978, Todd et al. 1981, King 1983, Jaksic et al. 1997, Stenseth et al.
1997, Previtali et al. 2009). Pulses in primary production and plant-derived food
resources associated with either rainfall in an arid environment (Jaksic et al. 1997) or tree
masting in a forested environment (King 1983, Ostfeld and Keesing 2000) lead to spikes
in both primary and secondary consumer populations (King 1983, Jaksic et al. 1997). The
populations of some predators, including coyotes and lynx (Lynx canadensis), have been

92
shown to closely track the abundance of prey species, with peaks in prey population size
leading to peaks in predator abundance and population growth (Brand et al. 1976, Todd et
al. 1981, O’Donoghue et al. 1997, Hone et al. 2007). Overall, the decline of a top
predator can have significant effects on a local community (e.g., Paine 1969, Estes and
Duggins 1995, Crooks and Soule 1999, Ripple and Beschta 2004), and top predators are
particularly sensitive to changes that occur at the base of the food chain. These factors
make top predators ideal focal organisms for investigations of the impacts of habitat
change on biotic communities.
Woody plant encroachment is a widespread process of habitat change occurring in
dryland areas around the world (Archer 1995, Ravi et al. 2009), yet relatively little is
known about its effects on local, top predators (Blaum et al. 2007a). This habitat change
is characterized by the local proliferation of one or more native woody plant species in a
grass-dominated habitat that results in a shift from grassland to shrubland or savanna to
woodland habitat over a period of 50-200 years (Archer 1989, Archer 1995, Gill and
Burke 1999, Van Auken 2000). Drivers of woody plant encroachment include:
overgrazing by livestock (Bester 1996, Van Auken 2000), changes in local fire frequency
(Archer 1995, Roques et al. 2001), elevated levels of carbon dioxide (Bond and Midgley
2000), and nitrogen deposition (Köchy and Wilson 2001, Wigley et al. 2010). This
habitat shift has many implications for the local biological community, especially
predators. Woody plant encroachment leads to changes in habitat structure that can
hinder predator movement and the ability of predators to detect prey (Muntifering et al.
2006) and reduce both their hunting success (Mills et al. 2004) and prey availability
(Marker 2002, Blaum et al. 2007b). A large number and diversity of mammalian

93
carnivores are present in areas affected by woody plant encroachment, and more research
regarding the bottom-up effects of this habitat shift on predators is needed (Blaum et al.
2007a, Chapter 1).
Woody plant encroachment occurs in grassland and savanna habitats in dryland
areas globally (i.e., arid, semi-arid and dry-subhumid regions; Archer 1995, Van Auken
2000, D’Odorico and Porporato 2006, Ravi et al. 2009, Chapter 1). These areas have low
annual rates of precipitation (100-1200 mm/yr; D’Odorico and Porporato 2006). In many
dryland areas, droughts are common and rainfall events are concentrated during a
relatively short winter or summer rainy season (Noy-Meir 1973, D’Odorico and
Porporato 2006). This seasonal climatic variation has bottom-up effects on the local
biotic community as a result of the tight coupling between water availability and various
ecological processes, including grass primary production (Noy-Meir 1973, Ernest et al.
2000, Schwinning and Sala 2004, Muldavin et al. 2008, Warne et al. 2010b). Seasonal
variation in both prey availability and consumer diet has been observed in dryland areas
(Andelt et al. 1987, Hernández et al. 2002, Hernández et al. 2005, Warne et al. 2010b).
Of particular relevance is the observation of a sharp increase in net primary production of
grasses and a corresponding increase in their use during the summer monsoon by
consumers in a woody plant-encroached area (Báez and Collins 2008, Muldavin et al.
2008, Warne et al. 2010b). This highlights the impact that seasonal variation in primary
productivity can have on the feeding ecology of organisms in higher trophic levels.
The coyote is an ideal focal species for a study on the impacts of both woody
plant encroachment and seasonal habitat change on mammalian predator feeding ecology.
Woody plant encroachment has been documented at many sites in North America

94
(Archer 1995, Ravi et al. 2009, Chapter 1) and the distribution of the coyote overlaps the
majority of these sites as it encompasses most of the United States, Canada and Mexico
(IUCN 2010). The coyote is a common, generalist species (IUCN 2010) that can also be a
top predator and play an important role in the dynamics of the local community (Crooks
and Soule 1999). Coyote diet varies geographically (e.g., Hidalgo-Mihart et al. 2001,
Samson and Crete 1997) and in response to both short- and long-term changes in food
resource availability (Todd et al. 1981, Hamlin et al. 1984, Andelt et al. 1987, Windberg
and Mitchell 1990, Young et al. 2006). Of particular interest is the observation that
coyote diet tracks changes in the availability of fruits from different woody plants both
seasonally and over a period of nearly twenty years (Andelt et al. 1987). Additionally,
two potential prey species of the coyote have been found to shift their diet in response to
an increase in grass primary production during the summer monsoon season (Muldavin et
al. 2008, Warne et al. 2010b). All of these factors indicate that the feeding ecology of
coyotes found in an arid, woody plant-encroached area is likely to be affected both by the
increase in woody plant-derived resources associated with woody plant encroachment
and the spike in grass-derived resources that is driven by the seasonal variation in rainfall
typical of arid environments.
Shifts in consumer feeding ecology in response to habitat changes associated with
both woody plant encroachment and seasonal variation in rainfall can be analyzed using
stable carbon isotope techniques. C3 and C4 plants have distinct carbon isotope signatures
as a result of differences in their photosynthetic pathways (Bender 1971, Farquhar 1983,
Sternberg et al. 1984, Marshall et al. 2007, Warne et al. 2010b). Encroaching, woody
plants use the C3 photosynthetic pathway while grasses at the same sites are often C4

95
plants (e.g., Boutton et al. 1998, Gill and Burke 1999, McKinley and Blair 2008,
Muldavin et al. 2008). Carbon isotope techniques have been used to determine whether
various herbivores are browsers that eat C3 plants, or grazers that forage on C4 grasses
(Ambrose and DeNiro 1986, Codron et al. 2005, Codron et al. 2007, Wallington et al.
2007). These techniques have also shown that both primary and secondary consumers in
a woody plant-encroached area shift their diet from C3- to C4-derived resources during
the summer monsoon season (Muldavin et al. 2008, Warne et al. 2010b).
My primary objective is to use stable carbon isotope techniques to assess the
impact that long-term habitat change associated with woody plant encroachment and
short-term habitat change driven by seasonal climatic variation has on the feeding
ecology of a common, generalist predator. The facet of coyote feeding ecology that the
use of stable carbon isotopes enables me to assess is the base of the coyote food chain
and thus whether coyote prey are feeding on C4 grasses or C3 plants. My specific
questions are: 1) Is there a difference in the base of the coyote food chain between native
grassland and woody plant-encroached shrubland habitats? and 2) Is there seasonal
variation in the base of the coyote food chain in an area impacted by woody plant
encroachment? I expected that the base of the coyote food chain would differ between
grassland and shrubland habitats such that the percent of the coyote diet coming
indirectly from C3 woody plants would be higher for coyotes sampled in the shrubland
habitat. I also expected that there would be a seasonal shift in the base of the food chain
with percent coyote diet coming indirectly from C4 grasses increasing from the spring to
the summer and fall in both grassland and shrubland habitats.

Methods
Study site
The fieldwork for this study was performed at the Sevilleta National Wildlife
Refuge (NWR) and Long Term Ecological Research (LTER) site in central New Mexico
(34.32°N, 106.81°W). The refuge encompasses 1,000 km 2 (Hernández et al. 2002), has
roughly 200 miles of road, and contains grassland, shrubland and woodland habitat types
(Figure 1). The average annual rainfall from 1988-2008 was 235 mm (based on Moore
2009). The Rio Grande divides the refuge into eastern and western parts that have
distinct plant communities. The grassland on the eastern side of the refuge, and thus to
the east of the Rio Grande, is dominated by grama grass (Bouteloua spp.) and the
shrubland is dominated by creosote bush (Larrea tridentata), which has grasses growing
in gaps among the shrubs. The grass cover on the western side is sparse relative to the
eastern side. Honey mesquite (Prosopis glandulosa), which often grows on top of sand
dunes, dominates the shrubland on the western side.
Figure 1. Map of study site. Map of the Sevilleta NWR and LTER study site in central
New Mexico, USA. Black = shrubland, gray = grassland, white = other land cover types.
The path of the Rio Grande through the center of the refuge is indicated by a thick black
line, while the boundary of the refuge is shown by a light gray line.
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Approximately 72% of the refuge is covered by either grassland or shrubland.
Grassland is more prevalent in the northern half of the refuge and shrubland in the
southern half. The eastern side of the refuge contains an active transition zone between
grama grassland and creosote shrubland. More specifically, creosote shrubs have been
moving into grama grassland areas at the refuge over the past century (Gill and Burke
1999). These characteristics make the refuge an ideal setting for an assessment of the
impact that woody plant encroachment has on coyote ecology.
Scat surveys
All field and laboratory methods outlined below are described in more detail in
Chapter 2. Carnivore scat samples were collected between June 2008 and November
2009. 3 surveys of 20 road-based transects (10 in grassland, 10 in shrubland) were
performed in the summer (June-July) of 2008. 6 surveys of 22 transects (12 in grassland,
10 in shrubland) were conducted in 2009, two in each of three seasons: spring (April-
May), summer (July), and fall (October-November). Each transect was 1 mile (1.6 km)
long and was separated from all other transects by at least a mile (1.6 km). Transects
were driven at slow speed (< 16.1 km/hr) and each carnivore scat that was encountered
was measured and sub-sampled for genetic analysis (see below). Measurements included
length and maximum diameter. Roughly 0.4 mL of the fecal material on the outside of
each sample was placed in a 2 mL tube containing DETs (DMSO, EDTA, Tris, salt)
buffer (Frantzen et al. 1998). A GPS coordinate was recorded before the remainder of the
sample was collected for carbon isotope analysis (see below).

Genetic analyses
All sub-samples stored in DETs buffer were extracted using QIAamp DNA Stool
Mini Kits from Qiagen Inc. Extractions were performed in a laboratory space that was
separate from the area where DNA was amplified and a negative control was processed
with each set of extracted samples (Onorato et al. 2006). Primers (Murphy et al. 2000)
that amplify a segment of the mitochondrial DNA (mtDNA) control region (Onorato et
al. 2006) and polymerase chain reaction (PCR) techniques (see Chapter 2 for PCR
profiles) were used to identify the species that had deposited each scat sample and screen
out any samples deposited by canids other than the coyote. All samples found to be from
coyotes were run through a microsatellite analysis in order to determine the minimum
number of individual coyotes that had been sampled. In particular, coyote samples were
screened using primers for 8 canid-specific microsatellite loci (CXX173 and CXX250,
Ostrander et al. 1993; CXX377, Ostrander et al. 1995; FH2001, CXX2010, FH2054, and
FH2088, Francisco et al. 1996; CXX119) and per locus success rates were calculated
based on the results. Samples for which 5 or more loci were successfully amplified were
screened 2-6 more times with all 8 primers. Replicate microsatellite PCR results were
compared in order to obtain a consensus genotype for each sample. For a homozygous
locus, one allele, and only that allele, had to be seen in all replicate PCRs while, for a
heterozygous locus, each allele had to be seen at least twice. All PCR products were
separated via electrophoresis on an Applied Biosystems 3130xl capillary machine and all
microsatellite data was viewed using GeneMapper 3.7.
All samples for which a consensus genotype was obtained at 6 or more loci were
analyzed using Gimlet 1.3.3. (Valière 2002) in order to determine the minimum number
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of individuals from which samples had been collected. The reliability of genotypes that
were unique in the population was tested using RELIOTYPE (Miller et al. 2002) and a
reliability criteria of 95%. Per locus error rates, for both allelic dropout and false alleles,
were determined using the first two successful microsatellite PCRs for samples that were
included in the Gimlet analysis and were not excluded following analysis with
RELIOTYPE. GENEPOP 4.0.10 (Raymond and Rousset 1995, Rousset 2008) was used
to determine if the genotypes were in Hardy-Weinberg (HW) equilibrium and if they met
the assumption of linkage equilibrium (LE). Deviation from HW equilibirum or rejection
of the null hypothesis of the LE test could indicate that there were errors in the consensus
genotypes used to determine the minimum number of individual coyotes sampled (Sacks
et al. 2004). Structure 2.3.1 (Pritchard et al. 2000) and BAPS 5 (Corander et al. 2008a,
Corander et al. 2008b) and the kinship analysis in Gimlet 1.3.3 were used to determine
whether there was any population structure that could not be explained in terms of
relatedness among individuals.
Vegetation surveys
Data on percent live grass and woody plant cover were collected in 22 circular
plots, one per scat transect. Each plot was 30 m in diameter and was located at a
randomly selected site within 100 m of one of the road-based scat transects. Vegetation
surveys were performed in each of the three seasons in 2009 in which scat surveys were
conducted (spring, summer, fall). For each survey, percent live cover data were collected
in each circular plot along 2, orthogonal line intercept transects that were each 30 m long.
Points at which live vegetation intersected transects were recorded to the nearest 0.1 m.
Vegetation was considered to be alive if its leaves (woody plants and grasses) or stems

100
(grasses) were green. Small samples of the dominant woody plant species and one of the
dominant grass genera found within each plot were collected in each season for carbon
isotope analysis (see below).
Carbon isotope analyses
Prior to performing carbon isotope analysis, all scat (70 o C) and vegetation (60 o C)
samples were oven-dried for 24 hours. Vegetation samples were ground with a Wiley
Mill until they passed through a 20 mesh filter. Small pieces of bone and hair were
removed from the first scat sample collected from each individual in the summer of 2008
and in each of the three seasons that surveys were performed in 2009. Individuals that
moved between scat transects that were in different habitat types at any point during the
study were excluded from this analysis. Hair was only removed if a given individual had
been sampled in more than one season in 2009. To test the validity of this approach and
ensure that it did not overlook any impact of intra-individual variation in diet, bone and
hair were removed from all samples collected within a single season from 6 (bone) or 7
(hair) individuals sampled in different parts of the study area (Appendix 1). Each of the
individuals used only one habitat type (grassland or shrubland).
Bone and hair pieces were cleaned with ethanol and bone was crushed to a
powder. Small masses of each item (vegetation, 4mg; hair, 1mg; bone, 3mg) were
weighed into 5x9mm tin capsules. These weighed samples were analyzed on a coupled
elemental analyzer (Costech 4010) and isotope ratio mass spectrometer (DeltaPlux XP) at
the Stable Isotope Laboratory at the University of New Hampshire (UNH). In general, 4
out of every 58 samples were replicated and 3 standards (NIST 1515 and 1575a, tuna)
and one known “unknown” (bolete) were run for every 10 samples. 20 tins containing a

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second known unknown (citrus) were mixed in with the first 277 samples run. The
carbon isotope signatures (i.e., δ 13 C values) derived from the mass spectrometer were
corrected for shifts both over time and in response to variation in sample weight (A.
Ouimette, UNH, 2010, personal communication). The signatures for bone and hair
samples were also corrected for diet to tissue (1 o /oo, bone, DeNiro and Epstein 1978; 1.0
o /oo, hair, Tieszen et al. 1983) and diet to scat (0 o /oo, both bone and hair, Chapter 2)
fractionation. The corrected signatures were used to calculate the percentage of coyote
diet of each individual in each season that came indirectly from C3 woody plants
(Equation 1, based on Faure and Mensing 2005). These percentages were subtracted from
100% to obtain the percentage of coyote diet that came indirectly from C4 grasses.
Where
δ 13 Cscat = δ 13 CC 3 (fC 3 ) + δ 13 CC 4 (1-fC 3 ) (1)
δ 13 Cscat = the carbon isotope signature of a coyote scat component (i.e., bone, hair);
δ 13 CC 3 = the average carbon isotope signature of C3 woody plants; fC 3 = the fraction of
coyote diet that comes indirectly from C3 woody plants; δ 13 CC 4 = the average carbon
isotope signature of C4 grasses.
Statistical analyses
Vegetation survey data was used to assess differences in habitat characteristics
between grassland and shrubland habitats both on average and among seasons. Percent
live grass and woody plant cover values were averaged across seasons, arcsine
transformed to meet the assumption of normality, and analyzed using a two-way
ANOVA (PROC GLM, SAS 9.1), with habitat and cover type as independent variables.
Two a-priori contrasts were performed as part of this two-way ANOVA. These contrasts

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compared percent live grass and percent live woody plant cover between grassland and
shrubland habitats. Percent live grass values were analyzed using a second two-way
ANOVA with season and habitat as independent variables. A Student’s t-test (PROC
TTEST, SAS 9.1) was used to assess differences in the percent of coyote diet coming
indirectly from C3 woody plants for individuals sampled in native grassland versus
woody plant-encroached shrubland habitats. The coyote diet values used for this analysis
were derived from the stable carbon isotope signatures of bones. Bones represent a
dietary record that can span the life of an organism, while hair integrates dietary
information for shorter periods of time (Ambrose and DeNiro 1986, Crawford et al.
2008). The stable carbon isotope signatures of bone are thus more likely than those of
hair to reflect any long-term differences in foraging pattern between grassland and
shrubland habitats, while hair is likely to pick up shorter term variation. A repeated
measures ANOVA (PROC MIXED, SAS 9.1) was used to assess seasonal variation in
the percent of the coyote diet coming indirectly from C4 grasses for individuals sampled
in grassland versus shrubland habitats. Carbon isotope signatures of hair samples were
used to calculate the diet values for this analysis.
Results
Scat surveys and genetic analyses
A total of 935 scat samples were sub-sampled for genetic analysis. The mtDNA
species identification test indicated that just over two thirds of these samples had been
deposited by coyotes. Analysis of the 520 scats (65% of 795 samples for which
individual identification was attempted) for which consensus genotypes were obtained at
6 or more microsatellite loci in Gimlet 1.3.3 indicated that a minimum of 81 individuals

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had been sampled. Per locus success rates for the microsatellite analysis ranged from 0.78
to 0.88. Per locus error rates ranged from 0.04 to 0.13 for allelic dropout and from 0.01 to
0.05 for false alleles. Results of the Hardy-Weinberg (HW) exact test and linkage
equilibrium analysis in GENEPOP 4.0.10 indicated that, once relatedness among
individuals was accounted for, one locus was out of HW equilibrium (FH2088, p < 0.05)
and genotypes at one pair of loci (CXX250, CXX377, p < 0.05) were not independent of
one another. If Bonferroni corrected p-values are used (0.05/number of comparisons;
based on Sacks et al. 2004), then no loci were out of HW equilibrium and genotypes at all
loci were independent of one another. Results from Structure 2.3.1 and BAPS 5 indicated
that, when all 81 individuals were included, there were 7-10 genetic groups at the study
site. When 31 related individuals were removed from the Structure 2.3.1 and BAPS 5
analyses, the number of genetic groups declined to only 1 or 2 (see Chapter 3 for further
details).
Vegetation surveys and stable carbon isotope analyses
The two-way ANOVA analysis of arcsine transformed values of live percent
grass and woody plant cover between habitat types indicated that, while there were no
significant differences between habitat types, there were significant differences between
cover types and there was a habitat*cover interaction (Table 1, Figure 2A).
Table 1. Two-way ANOVA of percent live cover. Results of a two-way ANOVA for
arcsine transformed values of percent live cover with habitat and cover type as
independent variables (d.f. = degrees of freedom).
Source d.f. F-value p-value
Habitat 1 0.17 0.69
Cover 1 60.95 < 0.01
Habitat*Cover 1 11.37 < 0.01
Error 40 . .

The results of two a-priori contrasts comparing percent live grass and percent live woody
plant cover between habitat types were evaluated at the Bonferroni corrected p-value of
0.025. These contrasts indicated that percent live grass cover did not differ significantly
between habitats (F = 4.40, p = 0.04, d.f. = 1, 40) but that percent woody plant cover was
higher in shrubland areas (F = 7.14, p = 0.01, d.f. = 1, 40; Figure 2A). Results of the two-
way ANOVA and Duncan’s test of percent live grass cover indicates that there are
differences between habitat types and between the spring and the summer and fall
seasons but that there is no habitat*season interaction (Table 2, Figure 2B).
104
Table 2. Two-way ANOVA of percent live grass cover. Results of a two-way ANOVA
for untransformed values of percent live grass cover with habitat and season as
independent variables (d.f. = degrees of freedom).
Source d.f. F-value p-value
Habitat 1 4.59 0.04
Season 2 9.73 < 0.01
Habitat*Season 2 0.54 0.58
Error 58 . .
Average differences (± 1 standard deviation, i.e., s.d.) between observed and true
values for the two known unknowns were small (0 ± 0.1 o /oo, bolete; 0.3 ± 0.1 o /oo, citrus).
The standard deviation of all carbon isotope signatures obtained was 0.1 o /oo for each of
the three standards (NIST 1515 and 1575a, tuna). The δ 13 C values for 18 samples of
dominant grasses, representing species from 6 genera, were used to calculate an average
(± 1 s.d.) carbon isotope signature for C4 grasses (-14.8 o /oo ± 0.3, Appendix 2). The δ 13 C
values for 14 samples of 5 dominant woody plant species, as well as 4 samples of two
woody plant species which produce fruit or seeds eaten by coyotes (V. Seamster,

105
unpublished data), were used to calculate an average (± 1 s.d.) carbon isotope signature
for C3 woody plants (-24.9 o /oo ± 0.7, Appendix 2).
A) B)
% live cover
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Habitat type
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Woody
C) D)
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Woody
% live grass cover
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Spring Summer Fall
Season
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Figure 2. Inter-habitat and seasonal differences in live cover and coyote diet. A)
There are significant differences between percent live grass and woody plant cover types
and there is a significant habitat*cover interaction (Table 1). Please note that statistical
analyses were performed on arcsine transformed values and untransformed percentages
are shown here. B) Percent live grass cover is significantly different between habitats and
between the spring (a) and the summer and fall (b; Table 2). C) There is no significant
difference in percent coyote diet obtained indirectly from C3 woody plants or C4 grasses
between habitat types (see text). D) Percent of the coyote diet from C4 grasses does not
vary between habitats or among seasons, but there is a significant habitat*season effect at
α = 0.1 (Table 3). For all graphs, error bars represent 95% confidence intervals.
a
b b

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Carbon isotope signatures of bone pieces taken from 62 scat samples, each
obtained from a different individual, were used to calculate values for percent coyote diet
coming indirectly from C3 woody plants in grassland versus shrubland areas (Figure 2C,
Appendix 2). Bone pieces were taken from scat samples collected in 2009 for 58 of the
62 individuals. There was no significant difference in percent coyote diet from C3 woody
plants between grassland and shrubland habitats (t = 0.94, d.f. = 60, p = 0.35; Figure 2C).
However, average percent coyote diet from C3 woody plants was 32% or higher in both
habitat types, while woody plants, on average, represented 30% of total live cover in
shrubland habitats and less than 10% in grassland areas (Figure 2A and C). The average
(± 1 s.d.) difference between δ 13 C values of replicate samples of bone pieces taken from a
single scat was small (0 ± 0.3 o /oo).
Carbon isotope signatures of hair taken from 70 scat samples obtained from 30
individuals were used to calculate values for percent coyote diet coming indirectly from
C4 grasses (Figure 2D, Appendix 2). There were no significant differences between
habitat types or among seasons in percent coyote diet from C4 grasses, but the
habitat*season interaction was significant at α = 0.1 (Table 3).
Table 3. Repeated measures ANOVA of coyote diet. Results of a repeated measures
ANOVA for percent of the coyote diet from C4 grasses with habitat and season as
independent variables (d.f. = degrees of freedom).
Effect Numerator d.f. Denominator d.f. F p
Habitat 1 28 0.08 0.78
Season 2 36 0.06 0.94
Habitat*Season 2 36 2.56 0.09
With the exception of diet values for coyotes sampled in grassland habitat in the summer,
values for percent coyote diet from C4 grasses followed a somewhat similar pattern as

107
percent live grass cover values both between habitats and among seasons (Figure 2B and
D). Additionally, average percent coyote diet from C4 grasses was 53% or higher in both
habitats and all three seasons (Figure 2D). The average (± 1 s.d.) difference between δ 13 C
values of replicate samples of hair pieces taken from a single scat was small (0 ± 0.5 o /oo).
Discussion
Scat surveys and genetic analyses
The number of individuals sampled in this study is within the range of sample
sizes (30-122) obtained in other studies of canid species that used noninvasive genetic
sampling techniques to identify individuals (e.g., Kohn et al. 1999, Smith et al. 2006,
Stenglein et al. 2010b). There are several studies of canid populations and canid ecology
that utilized invasive techniques (e.g., radio-telemetry) and were based on much smaller
numbers of individuals (n = 12 to 65; Koehler and Hornocker 1991, Windberg et al.
1997, Kitchen et al. 2000, Kamler et al. 2005, Young et al. 2006, Boisjoly et al. 2010).
The per locus success rates obtained in this study are as high, and error rates as low, as
those found in multiple, recent studies that have utilized microsatellite primers to amplify
DNA extracted from fecal samples (Murphy et al. 2003, De Barba and Waits 2010,
Stenglein et al. 2010a, Stenglein et al. 2010b). The majority of the population structure
identified by the programs Structure 2.3.1 and BAPS 5 was explained by the presence of
closely related individuals. This conclusion is supported by the observation that Structure
can overestimate the number of populations or genetic groups when related individuals
are included in the analysis (Anderson and Dunham 2008). It is thus likely that all 81
individuals sampled in this study are part of a panmictic coyote population with no
physical barriers to gene flow.

Vegetation surveys and stable carbon isotope analyses
108
Percent live grass cover is higher than percent live woody plant cover in both
grassland and shrubland habitats. This reflects field-based observations of grass patches
both in the inter-shrub spaces and even beneath shrubs in the woody plant-encroached
shrubland habitat. The difference between the percentages for these two cover types is
larger in grassland than in shrubland areas. This indicates that grass is the dominant life
form in grassland areas but that woody plants and grasses are closer to being co-dominant
in woody plant-encroached shrubland habitats. Additionally, though there is no
significant difference in percent live cover between habitat types, percent live woody
plant cover is significantly higher in the shrubland habitat. These inter-habitat differences
are to be expected and indicate that the landscape surrounding the road-based,
“grassland” scat transects has different habitat characteristics from that surrounding the
“shrubland” transects. Seasonal variation in percent live grass cover follows an expected
trend in both habitat types, with percent cover values increasing significantly between the
spring and the summer and remaining high in the fall. This matches other observations at
the Sevilleta NWR of increased net primary production of C4 plants between the spring
and summer (Warne et al. 2010b).
Contrary to expectations, there was no significant shift in the base of the coyote
food chain between grassland and woody plant-encroached shrubland habitats and thus in
response to woody plant encroachment. More specifically, there was not a statistically
significant increase in percent coyote diet from C3 woody plants between grassland and
shrubland areas and thus the null hypothesis of no difference in the base of the food chain
between habitat types could not be rejected. However, C3 plants accounted for a

109
surprisingly large percentage of coyote diet in grassland areas (32% on average) when the
availability of woody plants, quantified in terms of percent of total live cover (8% on
average in grassland habitat), is considered. This highlights the importance of C3 plants
as a food resource for coyote prey that use grassland areas in this ecosystem. Other
researchers have found that C3 plants are an important source of energy for consumers in
this arid ecosystem (Warne et al. 2010b) and indicated that C3 plants tend to be a higher
quality food resource than C4 plants (Caswell et al. 1973, Ehleringer et al. 2002,
Barbehenn et al. 2004a). In particular, C3 plants tend to have higher protein, nonstructural
carbohydrate, and water content while having lower carbon to nitrogen ratios, less fiber,
and being overall less tough than C4 plants (Ehleringer et al. 2002, Barbehenn et al.
2004a). It is important to note that woody plants are not the only C3 plants present at the
Sevilleta NWR. Several small mammal species found at the study site eat the seeds of
various forbs (Hope and Parmenter 2007). Many forbs have carbon isotopic signatures
that are very close in value to those of woody plants (forbs = -26.1 o /oo ± 1.1; woody
plants = 24.9 o /oo ± 0.7; values shown as average ± 1 s.d.; Appendix 2) and average
percent live forb cover (4%) is comparable to average percent live woody plant cover
(3%) in grassland areas (Figure 2A and Appendix 3).
My expectation that there would be a shift in the base of the food chain from C3
woody plants to C4 grasses from spring to summer and fall in both grassland and
shrubland habitats was not met as there was no significant seasonal variation in percent
coyote diet from C4 grasses. Furthermore, percent coyote diet from C4 grasses followed
different trends between the grassland and shrubland habitats. However, grass did
account for a fairly high percentage of coyote diet (53-71%) and total live cover (52-94

110
%) in both habitats and all seasons. Additionally, percent coyote diet from C4 grasses did
follow a similar trend as percent live grass cover in the shrubland habitat. These results
indicate that seasonal changes in habitat characteristics do not appear to lead to
significant changes in coyote feeding ecology, specifically in the base of the coyote food
chain. However, grass is a consistent food resource for coyote prey regardless of habitat
type and season, though its importance is not always proportional to its availability in the
ecosystem. These results do not mirror the definitive increase in C4-derived food resource
use from spring to summer documented by other researchers for both primary
(arthropods) and secondary (lizards) consumers at the Sevilleta NWR (Warne et al.
2010b). However, Warne et al. (2010b) did observe that, in a year of high C4 plant
production, percent of C4-derived carbon in the blood of secondary consumers (40%) was
low relative to C4 plant availability (87% of total annual net primary production).
There are a variety of factors that may account for the observed lack of significant
variation in coyote feeding ecology, specifically the base of the coyote food chain,
between habitat types and the high percentage of C3 plants in the coyote diet in grassland
habitats. The habitat at the Sevilleta NWR is quite heterogeneous, especially in the
southern and western parts of the refuge (Figure 1) and there are many places where
patches of grassland and shrubland habitat interdigitate. Even though individuals that
moved between road-based scat transects located in different habitat types were excluded
from the statistical analyses performed in this study, coyotes are wide ranging animals
(e.g., Roy and Dorrance 1985, Windberg et al. 1997, Kamler et al. 2005, Boisjoly et al.
2010) and it is likely that many of them passed between patches of different habitat types
while foraging. This could account for the high percentage of C3 plants in the diets of

111
coyotes sampled in grassland areas. Furthermore, many of the bone pieces used to assess
percent coyote diet in different habitat types were obtained from scat samples collected in
the spring season. Though bone is thought to integrate information on diet over the life of
an animal (Ambrose and DeNiro 1986, Hobson and Clark 1992), it is possible that the
coyotes preyed on juvenile rodents or other small mammals that had only been alive for a
few weeks or months. Various small mammals found at the Sevilleta NWR, including
Dipodomys merriami, Perognathus flavus, and Neotoma albigula, can breed during the
late winter or early spring months (Whitaker 1996, Friggens 2008). In that case, the
signature of the bones would represent resource use over a much shorter time period,
especially the period during which the bones were growing (Hobson and Clark 1992).
Given the greater availability of C3 relative to C4 plants in the spring (Warne et al.
2010b), this use of a large number of scat samples collected during the spring could
partially account for the high percentage of C3 plants in the coyote diet observed in the
grassland habitat. Additionally, there is a good deal of intra-individual variation in coyote
diet within a single season (Appendix 1). The analysis of inter-habitat differences in
coyote diet presented here is based on the carbon isotope signatures of bone pieces taken
from one scat sample collected from each individual. It is possible that inclusion of more
samples per individual would provide different results that better represent the total
variation in coyote diet.
There are several possible explanations for the lack of significant seasonal
variation in the base of the coyote food chain and the surprising, marginally significant
interaction between habitat type and season for values of percent coyote diet from C4
grasses observed in this study. It is possible that the analysis of hair pieces obtained from

112
one scat sample from each individual in each season does not account for all of the
variation in coyote diet (Appendix 1). It is also possible that the hair of the rodents and
other mammals consumed by coyotes at the Sevilleta NWR does not turn over rapidly
enough for inter-seasonal variation in diet to be detected. The half-life of carbon in hair
ranges from 47.5 days (gerbils, Tieszen et al. 1983) up to 136 days (horses, Ayliffe et al.
2004), and can be as high as 537 days (bats, Voigt et al. 2003) for some species. If the
half-life of carbon in the hair of mammals at the Sevilleta NWR is close to 47.5 days,
then any diet shifts should be detectable between the spring and the fall (April to
October) and possibly from the spring to the summer (April to July) and the summer to
the fall (July to October). However, if the half life is closer to 136 days, or longer, diet
shifts from the spring to the fall might be detectable but the difference would probably be
small as only about half of the carbon in the hair would have turned over between
sampling events. The study that detected a shift in the use of C4-derived carbon by
secondary consumers between spring and summer at the Sevilleta NWR utilized samples
of blood plasma (Warne et al. 2010b) and blood plasma has a faster turnover time (25-44
days, Warne et al. 2010a) than hair. The moderately significant habitat type by season
interaction is driven by opposing trends in percent coyote diet from C4 grasses between
the spring and the summer in the two different habitat types. In grassland areas, percent
coyote diet from C4 grasses declines from the spring to the summer while in shrubland
areas, it increases. The increase from spring to summer in shrubland areas matches my
expectations. The decline in percent C4 grasses in coyote diet from spring to summer in
grassland areas may be due to a shift in diet from C4 grass to C3 forb seeds or other
tissues on the part of the coyote prey. For some small mammal species at the Sevilleta

113
NWR, the consumption of forb seeds increases between the spring and the summer in
grassland areas (Hope and Parmenter 2007). There is a similar decline in percent C4 grass
in coyote diet from summer to fall in shrubland areas. This could also be driven by an
increase in the consumption of C3 forb seeds by coyote prey as there is an increase in the
availability of forbs from the summer to the fall in shrubland areas (Appendix 3).
Management implications and future work
The impact that continued woody plant encroachment may have on the ecology
and fitness of top predators, such as the coyote, and their prey is uncertain. There is no
significant difference in the base of the coyote food chain between habitats and the results
presented here indicate that woody plant encroachment has had limited impact on coyote
feeding ecology. Given the apparent importance of C3 plants as a food resource in
grassland areas, woody plant encroachment may be beneficial for coyote prey. However,
these observations are tempered by several observations. First of all, grass cover is
relatively high in the woody plant-encroached habitats considered in this study and there
are woody plants present in the grassland areas, as well as patches of shrubland within
grassland habitats and vice versa. It would be necessary to compare grassland and woody
plant-encroached habitats with a greater difference in grass availability and to survey
predator populations in grasslands that are either devoid of woody vegetation or are
further removed from shrubland areas to be certain that this habitat shift has no impact on
predator feeding ecology. Second, the use of C4 grasses as a food resource is both high
and consistent between habitats and among seasons and it is not possible to differentiate
the use of C3 woody plants and forbs with the techniques used in this study. Third, the
coyote is a generalist species capable of using a variety of food resources. An evaluation

114
of the feeding ecology of a suite of predators, including more specialized feeders, would
provide more information regarding the importance of C3 versus C4 plants in the local
food web and the probable impact of continued woody plant encroachment on local
predator populations.
Another factor to consider is that only one facet of coyote feeding ecology was
evaluated in this study. The use of stable carbon isotopes allowed for an assessment of
the base of the coyote food chain and differentiation between native C4 grasses and
encroaching C3 woody plants. However, this approach did not account for other aspects
of coyote feeding ecology that may have been affected by woody plant encroachment. In
particular, it did not account for habitat-related shifts in prey availability or in the use of
different prey species by the coyote. Surveys conducted in spring and fall of 2008 in
grassland and shrubland habitats at the Sevilleta NWR indicate that there are inter-habitat
differences in the species composition of the small mammal community (Friggens 2008,
Appendix 4) and thus in the coyote’s potential prey base. The 4 species found to be most
abundant in these surveys were: Dipodomys merriami, Dipodomys ordii, Dipodomys
spectabilis, and Perognathus flavus (Friggens 2008, Appendix 4). The typical diet of
these four species is quite varied and includes seeds, green vegetation, and insects. The
seeds that these small mammals eat come from C3 (forbs, shrubs), C4 (grasses), and CAM
(cacti) plants (Hope and Parmenter 2007, Appendices 2 and 4). It is important to note that
the average (± 1 s.d.) carbon isotope signature of CAM plants (-12.0 ± 0.1 o /oo) was
indistinguishable from that of C4 grasses (-14.8 o /oo ± 0.3; Appendix 2) in this study.
These observations lend support to my finding that coyote prey consume both C3 and C4

115
plants and reinforce my previous statements regarding the likelihood that coyote prey are
using both C3 forbs and C3 woody plants.
Information on the body condition and fitness of both primary and secondary
consumers with plants of different photosynthetic pathways and functional types at the
base of their food chain (C4 versus C3 and C3 woody plant versus C3 forb) would help to
clarify the impact that the shift in plant dominance from C4 grasses to C3 woody plants
associated with woody plant encroachment has on local mammalian predator populations.
Several studies have assessed the use of C3- versus C4-derived food resources by primary
and secondary consumers (e.g., Fry et al. 1978, Ambrose and DeNiro 1986, Magnusson
et al. 1999, Smith et al. 2002, Codron et al. 2007, Warne et al. 2010b) and considered the
nutritional quality of the diets of herbivores that eat C3 versus C4 plants (Codron et al.
2005). In contrast and to my knowledge, very little is known regarding the effect that
differences in the quality of these two food resources can have on the growth and
reproductive rates of mammalian predators (but see Barbehenn et al. 2004b for
information on insects). In addition, studies of the impact of woody plant encroachment
on the availability and nutritional quality of C3 forbs (e.g., Zarovali et al. 2007, Báez and
Collins 2008), when combined with extant information on the foraging patterns of
various primary consumers (e.g., Bradley and Mauer 1971, Alcoze and Zimmerman
1973, Flake 1973, Soholt 1973, Dial 1988, Hope and Parmenter 2007, Appendix 4),
would further elucidate the likely impact of woody plant encroachment on key prey
species and thus on mammalian predator populations.

Conclusion
116
Overall, long term (woody plant encroachment) and seasonal changes in habitat
characteristics do not appear to have strong bottom-up effects on the coyote population at
the Sevilleta NWR. However, C3 plants, which include both forbs and woody plants,
appear to be an important food resource in grassland areas in this arid environment. This
is likely the result of these plants being of higher nutritional quality for coyote prey than
C4 grasses. Additionally, the base of the coyote food chain shows different patterns of
seasonal variation between grassland and shrubland habitat types. This difference is
likely driven by the summer use of C3 forbs by coyote prey in grassland areas. Woody
plant encroachment does not appear to have had a significant impact on coyote feeding
ecology, specifically on the base of the coyote food chain, and may even be beneficial
given the importance of C3 food resources in this ecosystem. However, more information
regarding the effects of woody plant encroachment on different facets of coyote feeding
ecology, and the effects of different food resources (C4 versus C3 forb versus C3 woody
plant) on the fitness and ecology of both generalist and more specialized primary and
secondary consumers, is needed.

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Appendix 1. Intra-individual variation in diet.
A)
B)
% coyote diet from woody plants
% coyote diet from grass
100
90
80
70
60
50
40
30
20
10
0
100
90
80
70
60
50
40
30
20
10
0
1 15 16 30 38 55
Individual #
1 14 15 16 30 38 55
Individual #
126
Appendix 1. Percent diet values determined via the carbon isotope analysis of A) bone
and B) hair pieces removed from scat samples deposited by 6 (bone) or 7 (hair)
individuals. All samples were collected during spring 2009. Each individual deposited 3-
8 samples along transects located in either grassland or shrubland habitat. Error bars
represent 95% confidence intervals.

Appendix 2. δ 13 C values for vegetation samples and scat components.
-30 -25 -20 -15 -10
delta 13 C
Grass
Woody
Forb
Cactus
Bone
Hair
127
Appendix 2. δ 13 C values for plant samples and hair and bone components of scat samples
collected at the Sevilleta NWR. The δ 13 C values for grasses and woody plants shown here
were used to calculate average carbon isotope signatures for C3 woody plants and C4
grasses (Equation 1). Each point on this graph represents either a single sample or the
average of 2 replicates of a sample (scat components, forbs, cacti) or the average of 1-3
samples for a single genus (grasses) or species (woody plants). For the forbs and cacti,
each sample is from a different location. For the bones, each sample is from a scat from a
different individual. The bone values shown here were used to calculate average values
for percent coyote diet from C4 grasses and C3 woody plants in grassland versus
shrubland habitats (Figure 2C). For the hair, each sample is from either a different
individual or from the same individual but a different season. Hair values shown here
were used to calculate average values for percent coyote diet from C4 grasses in grassland
versus shrubland areas in spring, summer, and fall (Figure 2D). The values for bone and
hair samples shown here have not been corrected for either diet to tissue or diet to scat
fractionation.

Appendix 3. Inter-habitat and seasonal differences in forb cover.
A)
B)
% live forb cover
% live forb cover
100
100
90
80
70
60
50
40
30
20
10
0
90
80
70
60
50
40
30
20
10
0
Grassland Shrubland
Habitat type
Spring Summer Fall
Season
Grassland
Shrubland
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Appendix 3. A) Inter-habitat differences and B) seasonal variation in percent live cover
of forbs. Percent live forb cover was measured using the same techniques and in the same
circular vegetation plots (n = 22) in which percent live grass and woody plant cover were
assessed in spring, summer, and fall of 2009.

Appendix 4. Composition and diet of the small mammal community.
A)
Habitat type Latin name Common name Count
Grassland Dipodomys ordii Ord’s kangaroo rat 120
Perognathus flavus Silky pocket mouse 48
Dipodomys spectabilis Banner-tailed kangaroo rat 15
Peromyscus boylii Brush mouse 7
Neotoma albigula White-throated woodrat 3
Onychomys arenicola Mearn’s grasshopper mouse 3
Shrubland Dipodomys merriami Merriam’s kangaroo rat 198
Dipodomys spectabilis Banner-tailed kangaroo rat 94
Dipodomys ordii Ord’s kangaroo rat 54
Perognathus flavus Silky pocket mouse 48
Peromyscus eremicus Cactus mouse 3
Peromyscus leucopus White-footed mouse 3
Reithrodontomys megalotis Western harvest mouse 2
Neotoma albigula White-throated woodrat 1
Onychomys arenicola Mearn’s grasshopper mouse 1
B)
Species Item in diet
Average %
volume Habitat type
Dipodomys merriami Seed (F,G) 69 Shrubland
Plant 11
Arthropod 21
Dipodomys ordii Seed (C,F,G,S) 60 Grassland
Plant 33
Arthropod 7
Dipodomys spectabilis Seed (C,F) 7 Grassland/Shrubland
Plant 60
Arthropod 3
Unidentified 30
Perognathus flavus Seed (C,F,G,S) 96 Grassland
Arthropod 5
Appendix 4. A) Abundance of small mammals surveyed at the Sevilleta NWR during the
spring and fall of 2008 (based on Hope and Parmenter 2007, Friggens 2008). B) Diet
(based on Hope and Parmenter 2007) of the 4 most abundant species of small mammals
in grassland and shrubland habitats at the Sevilleta NWR (based on Appendix 4A,
Friggens 2008). Diet data was collected in 1998 and was averaged across seasons. C =
cactus, F = forb, G = grass, S = shrub; Plant = green vegetation/plant matter.
129

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Chapter 5: The impact of spatial scale on the relationship between
coyote feeding ecology and local habitat characteristics
Abstract
Many ecological patterns are scale-dependent and consideration of spatial scale is
important in studies of the ecology of wide-ranging animals. Little is known about the
impacts of woody plant encroachment, a shift in habitat type from grass- to woody plant-
dominated habitats, on the ecology of local predators. The purpose of this study is to
determine whether the strength of the relationship between predator feeding ecology and
local habitat characteristics in a woody plant-encroached area varies with spatial scale.
The coyote (Canis latrans) is an abundant, top predator at the Sevilleta National Wildlife
Refuge in central New Mexico, USA. Individual coyotes were identified using
noninvasive genetic sampling of feces and stable carbon isotope techniques were used to
assess coyote feeding ecology in native grassland and woody plant-encroached shrubland
areas. Habitat characteristics were evaluated at 30 different spatial scales using circular
buffers centered on scat collection sites and two different land cover maps. Buffer size
ranged from 0.03 to 28.3 km 2 . The difference in percent coyote diet that came indirectly
from C3 woody plants between grassland and shrubland habitats and the linear
relationship between coyote diet and percent shrubland habitat were evaluated at each
spatial scale. A significant difference in coyote diet between habitat types was observed
at a small spatial scale (buffer area = 0.03 km 2 ). There was no significant or strong linear
relationship between coyote diet and percent shrubland habitat at any spatial scale. There
were scale-driven break points in the p-values associated with coyote diet differences
between habitat types. These break points occurred at transitions between the following

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buffer sizes: 0.03 to 0.1 km 2 , 2.0 to 2.5 km 2 , 4.5 to 5.3 km 2 , 10.2 to 11.3 km 2 , and 15.2 to
16.6 km 2 . Woody plant encroachment appears to have a significant impact on coyote
feeding ecology, specifically on the base of the coyote food chain, but only when habitat
characteristics are evaluated at a small spatial scale. The size of the difference in coyote
diet between habitat types declines rapidly when the spatial scale of the analysis exceeds
previously published values for the sizes of both estimated core and total coyote home
ranges in an arid environment. Overall, these results indicate that spatial scale impacts the
strength of the relationship between coyote ecology and local habitat characteristics, and
that coyotes may be foraging in an area that is quite small relative to the size of a typical
coyote home range.
Introduction
Spatial scale can play an important role in assessments of ecological patterns
(Wiens 1989, Levin 1992, Ludwig et al. 2000, Trani (Griep) 2002), particularly in studies
of animal ecology (Senft et al. 1987, McLoughlin et al. 2004, Bowyer and Kie 2006,
Mayor et al. 2009, van Beest et al. 2010). Results of studies of mammalian ecology,
especially of habitat use and selection patterns, can vary greatly with the spatial scale at
which habitat variables are evaluated (e.g., Pereira and Itami 1991, Powell 1994, Carr et
al. 2002, Apps et al. 2004, Jiang et al. 2010, Pedersen et al. 2010, van Beest et al. 2010,
but see Vanak and Gompper 2010). Furthermore, the significance of the relationship
between animal ecology and habitat characteristics can vary with spatial scale such that
strong relationships are observed at one spatial scale of habitat assessment and not at
another (Quinn 1997, Kie et al. 2002, McLoughlin et al. 2004, Wilson and Nielsen 2007).
The concept of scale-dependency in animal-habitat relationships is particularly relevant

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in studies of wide-ranging species. Many studies that consider the importance of scale
have focused on large herbivores. Far fewer studies have dealt with mammalian
carnivores (Hobbs 2003, Bowyer and Kie 2006). Many mammalian carnivores are wide
ranging (Sunquist and Sunquist 2001) and their home ranges can be as large as several
hundred (several species, Lindstedt et al. 1986, Woodroffe and Ginsberg 1998; wild dog,
Claridge et al. 2009) to over a thousand square kilometers (grizzly bear, Blanchard and
Knight 1991; cheetah, Marker et al. 2008). These observations highlight the importance
of considering multiple spatial scales when assessing the relationship between habitat
characteristics and the ecology of mammalian predators.
A large number and diversity of mammalian carnivores are present in areas
affected by woody plant encroachment (Chapter 1), a widespread process of habitat
change that is occurring on six of the seven continents (Archer 1995, Ravi et al. 2009).
Relatively little is known about the effects of woody plant encroachment on local
mammalian predators (Blaum et al. 2007a). This habitat change is characterized by a shift
from grassland to shrubland, or savanna to woodland, habitat over a period of 50-200
years (Archer 1989, Archer 1995, Gill and Burke 1999, Van Auken 2000). Drivers of
woody plant encroachment include: overgrazing by cattle (Bester 1996, Van Auken
2000), changes in local fire frequency (Archer 1995, Roques et al. 2001), elevated levels
of carbon dioxide (Bond and Midgley 2000), and nitrogen deposition (Köchy and Wilson
2001, Wigley et al. 2010). This habitat shift has many implications for the ecology of
local predator populations. Woody plant encroachment leads to changes in habitat
structure that can hinder predator movement and the ability of predators to detect prey
(Muntifering et al. 2006) and reduce both hunting success (Mills et al. 2004) and prey

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availability (Marker 2002, Blaum et al. 2007b). More research regarding the effects of
this habitat shift on predator ecology is needed (Blaum et al. 2007a, Chapter 1).
Secondary consumer diet and feeding ecology varies in response to changes in
lower trophic levels (e.g., Brand et al. 1976, Todd et al. 1981, Andelt et al. 1987, Dibello
et al. 1990, Genovesi et al. 1996, Warne et al. 2010). Stable isotope techniques have been
used to evaluate the feeding ecology of herbivores and carnivores (Botha and Stock 2005,
Codron et al. 2005, Codron et al. 2007a, Codron et al. 2007b, Bowman et al. 2010). C3
and C4 plants have distinct carbon stable isotope signatures as a result of differences in
their photosynthetic pathways (Bender 1971, Farquhar 1983, Sternberg et al. 1984,
Marshall et al. 2007, Warne et al. 2010). Encroaching, woody plants use the C3
photosynthetic pathway while grasses at woody plant-encroached sites are often C4 plants
(e.g., Boutton et al. 1998, Gill and Burke 1999, McKinley and Blair 2008, Muldavin et al.
2008). As a result, stable carbon isotope techniques are particularly useful for assessing
the effects of the encroachment of C3 woody plants into C4 native grassland areas on the
feeding ecology of predators.
The coyote (Canis latrans) is a wide ranging (Boisjoly et al. 2010) and abundant
generalist predator (IUCN 2010). The size of home ranges of resident coyotes varies
widely (Windberg et al. 1997, Young et al. 2006, Gehrt et al. 2009, Boisjoly et al. 2010)
but can be as large as 121 km 2 (Canada; Boisjoly et al. 2010). Average home range size
for resident coyotes in an arid environment comparable to that considered in this study is
12.6 km 2 (Windberg et al. 1997). Even areas of high use, or coyote core home ranges,
may be relatively large (e.g., 1.73-5.6 km 2 ; New Mexico, Windberg et al. 1997;
Mississippi, Chamberlain et al. 2000; Florida, Thornton et al. 2004). However, some

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studies in the southwestern United States report small core areas (0.04-0.05 km 2 ; Young
et al. 2006, Young et al. 2008). Coyote diet varies geographically (e.g., Hidalgo-Mihart et
al. 2001, Samson and Crete 1997) and over time (Andelt et al. 1987). Of particular
interest is a study showing that coyote diet tracked changes in the availability of fruits
from different woody plants over a period of nearly twenty years (Andelt et al. 1987).
Woody plant encroachment has been documented at many sites in North America
(Archer 1995, Ravi et al. 2009, Chapter 1), and the distribution of the coyote overlaps
most of these sites (IUCN 2010). These factors make coyotes an ideal focal species for a
study of the impacts of woody plant encroachment on predator feeding ecology. Coyotes
are capable of shifting their foraging habits and diet in response to a change in habitat
characteristics (i.e., woody plant encroachment), and they use large enough areas that an
assessment of this animal ecology-habitat relationship at multiple spatial scales is both
appropriate and advisable.
My primary objective is to determine whether the relationship between predator
feeding ecology and local habitat characteristics changes with the spatial scale of the
analysis. My emphasis is on determining whether there is a shift in the base of the coyote
food chain between native grassland and woody plant-encroached habitats and whether
the spatial scale at which habitat characteristics are assessed impacts the strength of this
shift. In particular, I address the following question: Does a) the size of the difference in
the base of the coyote food chain between grassland and woody plant-encroached
shrubland habitats, or b) the strength of the linear relationship between percent coyote
diet from C3 woody plants and percent available shrubland habitat, change with the
spatial scale at which the habitat variables are assessed? The stable carbon isotope data

135
used in this study has been utilized previously to assess the relationship between coyote
feeding ecology and woody plant encroachment (Chapter 4). This previous assessment
was based on an evaluation of habitat characteristics at a small spatial scale (i.e., 30 m
diameter vegetation plots). Given the wide-ranging nature of coyotes, I expected that the
difference in coyote feeding ecology between habitat types would become larger, and the
association between coyote diet from C3 woody plants and percent shrubland habitat
would become stronger, as the spatial scale of the analysis increased and approached the
size of an area that coyotes typically use (i.e., the size of a coyote home range). I also
expected that the strength of the relationship between coyote feeding ecology and habitat
characteristics would decline at spatial scales larger than the area typically used by a
coyote (i.e., larger than a coyote home range).
Methods
Study site
The study site, field data collection and laboratory analyses described here are
presented in much greater detail in Chapter 2. Briefly, the fieldwork for this study was
performed at the Sevilleta National Wildlife Refuge (NWR) and Long Term Ecological
Research (LTER) site in central New Mexico (34.32°N, 106.81°W). The refuge
encompasses 1,000 km 2 (Hernández et al. 2002) and has roughly 200 miles of road. The
average annual rainfall from 1988-2008 was 235 mm (based on Moore 2009).
Approximately 72% of the refuge is covered by either grassland or shrubland habitat.
Grassland is more prevalent in the northern half of the refuge and shrubland in the
southern half. The eastern side of the refuge contains an active transition zone between
grama (Bouteloua spp.) grassland and creosote (Larrea tridentata) shrubland. More

136
specifically, creosote shrubs have been moving into grama grassland areas over the past
century (Gill and Burke 1999). The coyote is one of several predators present at the
Sevilleta NWR and the foraging strategy of the local coyote population has been studied
previously (Hernández et al. 2002). These characteristics make the refuge an ideal setting
for an assessment of the impact that woody plant encroachment has on coyote feeding
ecology.
Field data collection
Carnivore scat samples were collected between June 2008 and November 2009. 3
surveys of 20 road-based transects (10 in grassland, 10 in shrubland) were performed in
the summer (June-July) of 2008. 6 surveys of 22 transects (12 in grassland, 10 in
shrubland) were conducted in 2009, two in each of three seasons: spring (April-May),
summer (July), and fall (October-November). Each transect was 1 mile (1.6 km) long and
was separated from all other transects by at least a mile (1.6 km). Transects were driven
at slow speed (< 16.1 km/hr) and each carnivore scat that was encountered was sub-
sampled for genetic analysis (see below). Roughly 0.4 mL of the fecal material on the
outside of each sample was placed in a 2 mL tube containing DETs (DMSO, EDTA, Tris,
salt) buffer (Frantzen et al. 1998). A GPS coordinate was recorded before the remainder
of the sample was collected for carbon isotope analysis (see below).
In July and August 2008, vegetation was characterized in a total of 40 circular
vegetation plots, two per scat transect. Each plot was 30 m in diameter and was located a
randomly selected distance, between 30 and 100 m, from a scat transect. Vegetation
variables relevant to woody plant-encroached landscapes were assessed in each plot.
Specifically, data were collected on: 1) percent woody plant cover, 2) woody plant size

137
and 3) inter-woody plant distance. These variables were measured to the nearest 0.1 m for
live woody plants (i.e., plants with green leaves on some part) that intersected two,
orthogonal, 30 m line intercept transects. Woody plant size was determined by measuring
the height, the longest axis and the axis perpendicular to the longest axis for each woody
plant that was at least 0.5 m tall. For the measurement of average inter-plant distance, up
to four woody plants with a height of at least 0.5 m were selected in each plot. The inter-
plant distances were measured from the centers of these selected plants to the centers of
the 5 closest woody plants that were also at least 0.5 m tall and were within the plot. In
2009, small samples of the dominant woody plant species and one of the dominant grass
genera found within each of 22 circular, 30 m diameter plots, one per scat transect, were
collected in each season that scat surveys were conducted. These vegetation samples
were then prepared for carbon isotope analysis (see below).
Laboratory analyses
All scat sub-samples stored in DETs buffer were extracted using QIAamp DNA
Stool Mini Kits from Qiagen Inc. Primers (Murphy et al. 2000) that amplify a segment of
the mitochondrial DNA (mtDNA) control region (Onorato et al. 2006) and polymerase
chain reaction (PCR) techniques (see Chapter 2 for PCR profiles) were used to identify
the species that had deposited each scat sample. This species identification test was done
to screen out any samples that had been deposited by canids other than the coyote. All
samples found to be from coyotes were run through a microsatellite analysis to determine
the minimum number of different individuals that had been sampled. In particular, coyote
samples were screened using primers for 8 canid-specific microsatellite loci (CXX173
and CXX250, Ostrander et al. 1993; CXX377, Ostrander et al. 1995; FH2001, CXX2010,

138
FH2054, and FH2088, Francisco et al. 1996; CXX119) All PCR products were separated
via electrophoresis on an Applied Biosystems 3130xl capillary machine and all
microsatellite data was viewed using GeneMapper 3.7. Samples for which 5 or more loci
were successfully amplified were screened 2-6 more times with all 8 primers. Replicate
microsatellite PCR results were compared to obtain a consensus genotype for each
sample. All samples for which a consensus genotype was obtained at 6 or more loci were
analyzed using Gimlet 1.3.3. (Valière 2002) to determine the minimum number of
individuals from which samples had been collected.
Prior to performing carbon isotope analysis, all scat (70 o C) and vegetation (60 o C)
samples were oven-dried for 24 hours. Vegetation samples were ground with a Wiley
Mill until they passed through a 20 mesh filter. Small pieces of bone were removed from
the first scat sample collected from each individual in the summer of 2008 and in each of
the three seasons that surveys were performed in 2009. Individuals that moved between
scat transects that were in different habitat types at any point during the study were
excluded from this analysis. Bone pieces were cleaned with ethanol and crushed to a
powder. Small masses of each item (vegetation, 4mg; bone, 3mg) were weighed into
5x9mm tin capsules. These weighed samples were analyzed on a coupled elemental
analyzer (Costech 4010) and isotope ratio mass spectrometer (DeltaPlux XP) at the Stable
Isotope Laboratory at the University of New Hampshire (UNH). The carbon isotope
signatures (i.e., δ 13 C values) derived from the mass spectrometer for the bone samples
were corrected for diet to tissue (1 o /oo, DeNiro and Epstein 1978) and diet to scat (0 o /oo,
Chapter 2) fractionation. The corrected signatures were used to calculate the percentage

139
of the diet of each individual in each season that came indirectly from C3 woody plants
(Equation 1, based on Faure and Mensing 2005).
Where
δ 13 Cbone = δ 13 CC 3 (fC 3 ) + δ 13 CC 4 (1-fC 3 ) (1)
δ 13 Cbone = the carbon isotope signature of bone taken from a coyote scat; δ 13 CC 3 = the
average carbon isotope signature of C3 woody plants; fC 3 = the fraction of coyote diet that
comes indirectly from C3 woody plants; δ 13 CC 4 = the average carbon isotope signature of
C4 grasses.
Multiple spatial scale analysis
Field collected vegetation data on percent live woody plant cover, woody plant
size, and inter-woody plant distance were used to perform a discriminant function
analysis (PROC DISCRIM, SAS 9.1) and identify key habitat variables that differentiated
grassland and shrubland habitats. For this analysis, vegetation plots, and thus the
vegetation data collected in those plots, were initially assigned to the same habitat type as
the nearest scat transect. Scat transects were assigned to habitat types based on a visual
assessment of the vegetation visible from the road. Values were extracted from 6 Landsat
7 ETM+ bands (1-5 and 7) for pixels (30 m resolution) corresponding to the locations of
the field surveyed vegetation plots (30 m diameter) which were correctly classified as
grassland or shrubland habitat in the discriminant function analysis. These raw digital
numbers were converted to reflectance values (Appendix 1) and run through a second
discriminant function analysis to determine which Landsat 7 ETM+ bands were most
useful in differentiating grassland and shrubland habitats. The results of this second
discriminant function analysis and the Landsat 7 ETM+ image, which was taken on

140
March 31 st , 2009 and obtained from the U.S. Geological Survey
(), were used to generate a map of grassland and shrubland
habitat across the Sevilleta NWR. An image taken in the spring was considered more
appropriate for distinguishing grass- and shrub-dominated habitats as some shrub species
at the field site are evergreen and most of the grass biomass does not green up until the
beginning of the summer rainy season.
Both the Landsat 7 ETM+-derived map of grassland and shrubland habitats
(Figure 1B) and a previously developed land cover map (Muldavin et al. 1998; Figure
1A) were used to assess habitat characteristics at various spatial scales. In particular, data
on the land cover types of the areas encompassed by circles (radius = 100 m, area = 0.03
km 2 ) that were centered on scat sample collection sites were extracted using ArcGIS 9.2.
Only scats for which data on percent coyote diet from C3 woody plants had been obtained
were included in this analysis. Percent area covered by each land cover type was
calculated for each circle and thus for the habitat within a particular distance of each scat
sample. Each scat sample, and thus each individual coyote, was assigned to the land
cover type (grassland versus shrubland) with the highest percent area within the circle
centered on the scat’s collection site. This analysis was repeated for circles with radii that
increased to 3000 m (buffer area = 28.3 km 2 ) in increments of 100 m. The following
analyses were performed at each spatial scale (i.e., circle size) considered: 1) a Student’s
t-test (PROC TTEST, SAS 9.1) that assessed the difference in percent diet from C3
woody plants between coyotes assigned to grassland versus shrubland areas, and 2) a
linear regression analysis that evaluated the relationship between the percent of the

141
coyote diet that comes indirectly from C3 woody plants and percent area covered by
shrubland habitat (PROC REG, SAS 9.1).
The upper bound of 28.3 km 2 for the size of the circular buffers, and thus the area
in which habitat characteristics were assessed, was based on information regarding
average coyote home range size. The average size of a coyote home range as determined
using noninvasive genetic sampling techniques and scat samples collected at the Sevilleta
NWR and LTER (Chapters 2 and 3) is 1.7 km 2 . Another study, which used
radiotelemetry data to evaluate home range size and was performed in an arid
environment comparable to the Sevilleta NWR, indicates that, for territorial coyotes, the
average size of a core home range is 5.6 km 2 , while the average size of a total home range
is 12.6 km 2 (Windberg et al. 1997). By using circles that range in size from 0.03 to 28.3
km 2 , habitat characteristics were assessed in areas that are much smaller and notably
larger than an average coyote home range for an arid environment.
Results
Field data collection and laboratory analyses
A total of 935 carnivore scat samples were sub-sampled for genetic analysis. The
mtDNA species identification test indicated that 69% of these samples had been
deposited by coyotes. Analysis in Gimlet 1.3.3 of the 520 scats for which consensus
genotypes were obtained at 6 or more microsatellite loci indicated that a minimum of 81
individuals had been sampled (see Chapter 3 for further details). The δ 13 C values for
samples (n = 14) of 5 dominant woody plant species, as well as 4 samples of two woody
plant species which produce fruit or seeds eaten by coyotes (V. Seamster, unpublished
data), were used to calculate an average (± 1 standard deviation, i.e., s.d.) carbon isotope

142
signature for C3 woody plants (-24.9 o /oo ± 0.7). The δ 13 C values for samples (n = 18) of
grass species from 6 genera were used to calculate an average carbon isotope signature
for C4 grasses (-14.8 o /oo ± 0.3). Carbon isotope signatures of bone pieces taken from 62
scat samples, each obtained from a different individual, were used to calculate values for
percent coyote diet coming indirectly from C3 woody plants. These percent diet values
were used in all t-test and regression analyses described below (see Chapter 4 for further
details).
Multiple spatial scale analysis
The discriminant function analysis of field collected vegetation measurements
was based on data from 37 plots and the association between different habitat types
(grassland versus shrubland) and the measured vegetation variables was significant (F =
5.83, d.f. = 5, 31, p = 0.0007). The squared canonical correlation was 0.48, and 6 of 37
plots (16%) were misclassified. The discriminant function was positively associated with
percent woody plant cover and plant size and negatively associated with inter-plant
distance (Appendix 2). The mean discriminant function value for grassland plots was
-1.02 and for shrubland plots was 0.87. The discriminant function analysis of 6 Landsat 7
ETM+ bands was based on reflectance values for 24 pixels corresponding to vegetation
plot locations. The 6 plot locations misclassified in the discriminant function analysis of
vegetation measurements were excluded from the Landsat analysis. Locations that fell in
areas with missing data (Figure 1B) were also excluded. The association between
different habitat types (grassland versus shrubland) and reflectance values from Landsat 7
ETM+ bands was significant (F = 7.04, d.f. = 6, 17, p = 0.0007). The squared canonical
correlation was 0.71 and 3 of 24 plots (13%) were misclassified. The discriminant

143
function was negatively associated with all Landsat 7 ETM+ bands except bands 3 (red)
and 4 (near infrared) and was most strongly associated with reflectance values from
bands 4 and 5 (middle infrared, Jensen 2007; Appendix 3). The mean discriminant
function value for grassland locations was -1.51 and for shrubland locations was 1.51.
The results of the linear discriminant function analysis (Appendix 3) were used to
generate a map of grassland and shrubland habitat across the study site (Figure 1B).
There was no significant difference (p > 0.05) in percent coyote diet from C3
woody plants between grassland and shrubland areas at any spatial scale when habitat
was evaluated using a previously developed map of the study site (Figures 1A and 2A).
However, there was a significant difference in coyote diet between habitat types at small
spatial scales (p < 0.05, buffer size = 0.03 km 2 ) when a Landsat 7 ETM+-derived land
cover map was used to evaluate habitat characteristics (Figures 1B and 2B). There were
multiple sharp increases in the p-values at transitions between different buffer sizes. For
the previously developed map, these sharp increases occurred when the spatial scale of
the analysis increased from a buffer area of 0.03 to 0.1 km 2 and from 4.5 to 5.3 km 2
(Figure 2A). For the Landsat 7 ETM+-derived map, sharp increases occurred when the
buffer area increased from 2.0 to 2.5 km 2 , 10.2 to 11.3 km 2 , and from 15.2 to 16.6 km 2
(Figure 2B). Regardless of the land cover map used to evaluate habitat characteristics,
there were no strong or statistically significant relationships between percent coyote diet
from C3 woody plants (dependent variable) and percent shrubland habitat (independent
variable) at any spatial scale (R 2 < 0.05; d.f. = 1, 60; p > 0.05 for all spatial scales except
buffer area 0.03 km 2 where d.f. = 1, 59).

A)
B)
144
Figure 1. Maps of land cover at the study site. Two land cover maps used to assess
habitat characteristics at the Sevilleta NWR. A) Previously developed map described by
Muldavin et al. (1998). The original map was based on an analysis of Landsat Thematic
Mapper images taken between 1987 and 1993 and had 13 land cover types which were
simplified to 3 for the purposes of this study. Black = shrubland, gray = grassland, white
= other land cover types including savanna, woodland, barren areas or water. B) Map
developed for this study using a Landsat 7 ETM+ image taken on March 31 st , 2009.
Black = shrubland, gray = grassland. White lines are the result of missing data. Light
gray line represents the study site boundary for both maps.

A)
B)
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T-value
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Figure 2. Results of t-tests at multiple spatial scales. Results of t-tests of the difference
in percent coyote diet from C3 woody plants for individuals sampled in grassland versus
shrubland areas. Habitat type was evaluated using circular buffers centered on the
locations at which coyote scats were sampled and one of two different land cover maps.
The two land cover maps used were: A) a previously developed map of the study site
(Figure 1A, Muldavin et al. 1998), B) a map of grassland and shrubland habitat generated
from a Landsat 7 ETM+ image taken in March, 2009 (Figure 1B). d.f. = 60 for all tests
except for buffer size of 0.03 km 2 in B, where d.f. = 59. Gray line indicates p = 0.05.

Discussion
Multiple spatial scale analysis
146
The results of the discriminant function analysis of field measured vegetation
variables indicate that woody plant-encroached shrubland areas had higher percent
woody plant cover and larger woody plants while grassland areas were characterized by
larger inter-woody plant distances. These results show that the vegetation plots, and thus
the scat transects with which they were associated, were located in areas with structurally
different habitats. The discriminant function analysis of 6 Landsat 7 ETM+ bands
indicates that the reflectance of all bands, except bands 3 (red) and 4 (near infrared), was
lower in shrubland than grassland areas. This is not surprising given the tendency of
plants to reflect near infrared light (band 4), but is somewhat surprising given the
tendency of plants to absorb red light (band 3). Many vegetation indices developed for
the analysis of satellite images depend on the difference in reflectance of red and near
infrared radiation to detect variation in green, photosynthetically active plant biomass or
in the leaf area indices of local vegetation (Tucker 1979, Running et al. 1994, Jensen
2007). It is however important to note that the strength of the positive relationship
between reflectance and the discriminant function was stronger for band 4 than band 3, so
it is likely that reflectance of red light did not increase very much between grassland and
shrubland areas.
The vegetation map developed for this study differs from the land cover map
developed by other researchers for the Sevilleta NWR (Muldavin et al. 1998). Though the
general vegetation patterns are similar, with grassland prevalent in the northeastern part
of the study area, the map developed for this study appears to overestimate the

147
occurrence of shrubland habitat, especially on the western side of the refuge. Part of this
discrepancy between the two land cover maps can be explained by differences in the
number of land cover categories considered. The map developed by Muldavin et al.
(1998) had a total of 13 land cover types and was thus more sensitive to local habitat
heterogeneity. Approximately 19% of the areas classified as shrubland in the Landsat 7
ETM+-derived map were classified as woodland or savanna by Muldavin et al. (1998).
This indicates that the approach taken in this study is more appropriate for differentiating
grass- and woody plant-dominated areas rather than grassland and shrubland habitats and
does not account for finer distinctions between habitat types or fully represent the
diversity of habitats present at the Sevilleta NWR. Differences between the images and
procedures used to construct the two land cover maps should also be considered. Images
used to create the map developed by Muldavin et al. (1998) were taken between
September 1 st , 1987 and September 30 th , 1993 during the spring, summer, and fall
seasons while the map generated for this study is based on a single image acquired on
March 31 st , 2009. It is probable that some habitat changes have occurred in the 15+ years
between the acquisition dates of these two sets of images. Given that creosote shrubs
have been spreading into grassland areas at the Sevilleta NWR over the past century (Gill
and Burke 1999, Báez and Collins 2008), it is likely that some areas classified as
grassland by Muldavin et al. (1998) have since turned into shrubland. Furthermore,
Muldavin et al. (1998) used a much more complicated procedure, which involved
unsupervised image classification techniques followed by extensive field surveys for the
purpose of ground truthing, to develop their map. If the image classification approach
utilized in this study were more similar to that used by Muldavin et al. (1998), then the

148
two land cover maps (Muldavin et al. 1998 and this study) would likely be more
comparable.
Contrary to expectations, the size of the difference in coyote diet between habitat
types and the strength of the linear relationship between coyote diet and percent
shrubland habitat neither increased consistently with spatial scale nor peaked at a spatial
scale that approximates the size of an average coyote home range. The only significant
relationship between coyote feeding ecology and habitat characteristics was observed at
the smallest spatial scale when a Landsat 7 ETM+-derived land cover map was used to
evaluate habitat and a t-test was used to assess the size of the difference in diet between
habitat types. There were no strong or significant linear relationships between coyote diet
and percent shrubland habitat at any spatial scale for either land cover map. Regardless of
the land cover map used, the size of all differences in coyote diet between habitat types
declined as the size of the area in which habitat was evaluated increased. The results of
the habitat analysis based on a previously developed land cover map (Muldavin et al.
1998) provide further support for the conclusion presented in Chapter 4 that woody plant
encroachment does not appear to have a significant impact on coyote feeding ecology,
specifically on the base of the coyote food chain. However, the results of the habitat
analysis based on the Landsat 7 ETM+-derived map developed for this study indicate that
woody plant encroachment does have a significant impact on coyote feeding ecology
when habitat is evaluated at a small spatial scale (roughly 0.03 km 2 ). This could indicate
that coyotes are foraging in relatively small areas close to the locations where scat
samples were collected. This observation of the importance of habitat characteristics
measured on a small spatial scale mirrors findings of other studies of carnivore ecology

149
(Ray 1998, Wilson and Nielsen 2007). For example, microhabitat characteristics have
been found to be important in predicting the location of raccoon (Procyon lotor) daytime
resting sites (Wilson and Nielsen 2007).
There were five sharp increases or “break points” in p-values for the tests of
differences in coyote diet between habitat types, two for the analyses based on the land
cover map developed by Muldavin et al. (1998) and 3 for the land cover map developed
for this study. The first break point for the analysis that utilized the map developed by
Muldavin et al. (1998) provides further support for the conclusion that woody plant
encroachment appears to affect coyote feeding ecology only when habitat characteristics
are evaluated at small spatial scales. For this break point, the size of the difference in
average coyote diet between habitat types declined as the spatial scale increased from a
buffer size of 0.03 to 0.1 km 2 . The other four break points can be partially explained in
terms of information on average coyote home range size presented in this dissertation and
in previously published studies performed in arid environments comparable to my study
site (i.e., Windberg et al. 1997). The first break point in the analysis based on the land
cover map developed for this study occurred when the spatial scale of the analysis
exceeded the average size of a coyote home range presented in this dissertation. In
particular, the break point occurred when buffer size increased from 2.0 to 2.5 km 2 and
the average size of a coyote home range is 1.7 km 2 (Chapters 2 and 3). The second break
point for the analysis based on a land cover map developed by Muldavin et al. (1998)
occurred when the evaluation of habitat approached the spatial scale of a core home range
of a territorial coyote. In particular, the break point occurred when buffer size increased
from 4.5 to 5.3 km 2 , and the average size of a core home range for territorial coyotes

150
found in an arid environment is 5.6 km 2 (Windberg et al. 1997). The second and third
break points for the Landsat 7 ETM+-based analysis occurred when the spatial scale of
the habitat assessment approached and exceeded the size of a resident coyote home range,
respectively. More specifically, the break points occurred at the transitions from buffers
of size 10.2 to 11.3 km 2 and 15.2 to 16.6 km 2 . The average size of a home range of a
territorial coyote in an arid environment is 12.6 km 2 (Windberg et al. 1997). These break
points match my expectation that the strength of the relationship between coyote feeding
ecology and local habitat characteristics would decline when habitat is evaluated across
areas larger than those that coyotes typically use.
There are several possible reasons why a statistically significant relationship
between coyote feeding ecology and local habitat characteristics was observed only at a
small spatial scale (buffer area = 0.03 km 2 ). Other studies have found that the specific
habitat variables associated with carnivore movement patterns vary with spatial scale
(e.g., Powell 1994, Carr et al. 2002, Constible et al. 2006, Pedersen et al. 2010). For
example, a study of Asiatic black bears (Ursus thibetanus) showed that, at a smaller
spatial scale, variables related to food availability were important in explaining habitat
use patterns while, at the larger spatial scale, bears selected areas that had minimal
agricultural development (Carr et al. 2002). This emphasizes the possibility that, at larger
spatial scales (buffer area > 0.03 km 2 ), there is a relationship between coyote feeding
ecology and habitat characteristics other than the ones considered here. Since I evaluated
the same habitat characteristic at each spatial scale in each analysis performed (i.e.,
habitat type for the t-test and percent shrubland habitat for the linear regression), I
precluded the possibility of finding significant relationships between feeding ecology and

151
different habitat characteristics at different spatial scales. Additionally, the results of
various studies provide evidence that coyotes may forage on a relatively small spatial
scale (Laundré and Keller 1981, Reichel 1991). In particular, coyote movement rates are
lower when they are hunting as opposed to just moving through their home range
(Laundré and Keller 1981) and there is evidence that coyotes seek out small patches with
high prey densities (Reichel 1991). Given my emphasis on feeding ecology, my results
are strongly affected by the spatial scale at which coyotes forage, and thus by a process
that may occur at a relatively fine scale. Furthermore, a high percentage of coyote
movements may be unrelated to foraging and coyotes may hunt primarily when they
encounter a prey item in the course of performing other activities (Sacks and Neale
2002). Given the utility of trails and roads as movement corridors for carnivores
(Macdonald 1980, Mahon et al. 1998, Harmsen et al. 2010) and my use of road-based
scat transects, it is possible that many of the scats I collected had been deposited by
coyotes that traveled along roads and foraged whenever they encountered prey on or in
the vicinity of the road. Finally, my use of circular buffers centered on scat collection
locations to characterize the habitat in which the scats were deposited, and thus the areas
potentially used by coyotes while foraging, is very simple. This approach does not
account for the possibility that scat samples were deposited at the periphery, rather than
the center, of a coyote’s movements (Gese and Ruff 1997), nor does it account for the
asymmetrical shape of the areas used by a coyote (e.g., a coyote home range;
Chamberlain et al. 2000, Young et al. 2008).
The conclusions presented in Chapter 4 highlight another important factor
contributing to the paucity of statistically significant results obtained in this study; woody

152
plants are not the only C3 plants present at the study site. Forbs are present in both
grassland and shrubland habitats at the Sevilleta NWR (Báez and Collins 2008, Chapter
4) and several species of small mammals found at the refuge are known to eat forb seeds
(Hope and Parmenter 2007). The techniques used in this study do not allow me to
differentiate between C3 woody plants and C3 forbs in my evaluation of the base of the
coyote food chain. However, my analysis of habitat characteristics only provides
information on the availability of C3 woody plants. If I had been able to consider the
availability of both C3 forbs and C3 woody plants in my evaluation of the relationship
between percent coyote diet from C3 plants and local habitat characteristics, it is likely
that I would have observed stronger relationships at several spatial scales.
Conclusion
Overall, the magnitude of the observed shift in coyote feeding ecology between
native grassland and woody plant-encroached areas does vary with spatial scale. A
significant shift is only observed when habitat characteristics are assessed at a small
spatial scale (buffer area = 0.03 km 2 ). This could indicate that coyotes are foraging in
fairly small areas that are close to where their scats were sampled along road-based
transects. Despite the lack of statistically significant relationships at larger spatial scales,
the results presented here do demonstrate an expected decline in the size of the difference
in coyote diet between habitat types as the scale of the habitat analysis exceeds the size of
areas typically used by coyotes (buffer area = 12.6 km 2 ). An evaluation of habitat
variables other than the ones considered here might lead to the observation of a
significant relationship between coyote feeding ecology and habitat characteristics at
intermediate spatial scales (0.03 km 2 < buffer area < 12.6 km 2 ). A habitat assessment that

153
provides information on the availability of all C3 plants, which include both woody plants
and forbs, or that addresses the non-circular shape of the areas typically used by coyotes,
might also lead to the observation of significant relationships across a wider range of
spatial scales.

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Appendix 1. Converting Landsat 7 ETM+ pixel values to reflectance values.
162
All of the equations and values in this Appendix are based on NASA (2009) or NOAA
(2010). Pixel values, in digital number (DN), were first converted to values for spectral
radiance (Equation 2) and then to values for planetary reflectance (Equation 3).
Lλ = ((LMAXλ-LMINλ)/(QCALMAX-QCALMIN))*(QCAL-QCALMIN)+LMINλ (2)
Where
Lλ = spectral radiance (watts/m 2 *sr*µm)
LMAXλ = spectral radiance scaled to QCALMAX (watts/m 2 *sr*µm; Appendix 1A)
LMINλ = spectral radiance scaled to QCALMIN (watts/m 2 *sr*µm; Appendix 1A)
QCALMAX = maximum Landsat 7 ETM+ pixel value (255 DN)
QCALMIN = minimum Landsat 7 ETM+ pixel value (1 DN)
QCAL = Landsat 7 ETM+ pixel value
Where
ρp = (π*Lλ*d 2 )/(ESUNλ*cosθs) (3)
ρp = planetary reflectance (unitless)
Lλ = spectral radiance (watts/m 2 *sr*µm)
d = earth-sun distance (0.99897 astronomical units)
ESUNλ = mean solar exoatmospheric irradiances (watts/m 2 *µm; Appendix 1B)
cosθs = cosine of solar zenith angle (0.3977; NOAA 2010)
A)
Landsat band LMINλ LMAXλ
B)
1 -6.2 293.7
2 -6.4 300.9
3 -5 234.4
4 -5.1 241.1
5 -1 47.57
7 -0.35 16.54
Landsat band ESUNλ
1 1997
2 1812
3 1533
4 1039
5 230.8
7 84.9
Appendix 1. A) Values for LMAXλ and LMINλ in watts/m 2 *sr*µm used for Equation 2.
B) Values for ESUNλ in watts/m 2 *µm used for Equation 3.

163
Appendix 2. Discriminant function analysis of vegetation variables.
A)
Vegetation variable Correlation coefficient
Percent woody plant cover 0.75
Inter-plant distance -0.61
Long axis 0.45
Short axis 0.40
Height 0.50
B)
Variable Grassland Shrubland
Constant -5.59 -8.01
Percent woody plant cover 0.12 0.20
Inter-plant distance 0.92 0.47
Long axis 9.16 6.96
Short axis -10.99 -8.17
Height 7.19 11.27
Appendix 2. A) Correlation coefficients showing the strength of the association between
field-measured vegetation variables and the discriminant function. B) Results of the
linear discriminant function analysis.

164
Appendix 3. Discriminant function analysis of reflectance values from a Landsat 7
ETM+ image.
A)
Landsat band Correlation coefficient
1 (blue) -0.07
2 (green) -0.05
3 (red) 0.03
4 (near infrared) 0.13
5 (middle infrared) -0.24
7 (middle infrared) -0.12
B)
Variable Grassland Shrubland
Constant -849.36 -883.48
Band 1 11597 11767
Band 2 -9171 -9238
Band 3 -2419 -2666
Band 4 2479 2779
Band 5 -1347 -1523
Band 7 3588 3710
Appendix 3. A) Correlation coefficients showing the strength of the association between
reflectance values for each Landsat 7 ETM+ band and the discriminant function.
Information on Landsat band wavelengths taken from Jensen (2007). B) Results of the
linear discriminant function analysis. These coefficients were used to classify the Landsat
7 ETM+ image and generate a map of grassland and shrubland land cover types across
the Sevilleta NWR (Figure 1B).

Conclusion
165
This dissertation addressed several questions relating to the consequences of
woody plant encroachment for mammalian predators. The first question posed was: How
many mammalian carnivores are present in areas affected by woody plant encroachment
globally? According to a dataset on the global distribution of mammalian carnivores and
a map of woody plant-encroached sites, there are at least 97 different carnivores found in
areas affected by woody plant encroachment. Given what is currently known about how
carnivores respond to woody plant encroachment, many of these species could be
negatively affected by continued spread of woody plants into native grassland areas. In
particular, some carnivore populations appear to decline in abundance once a critical
threshold of woody plant cover is reached. Other carnivores are physically hindered by
the presence of woody plants; they are less successful at hunting, experience a reduction
of prey availability, and are even injured by contact with woody plants as they move
through the landscape. All of these observations highlight the need for further
investigation of the impact of woody plant encroachment on the ecology of mammalian
predators.
The remaining three questions posed in this dissertation dealt with the ecology of
one predator, the coyote (Canis latrans), that is abundant throughout North America and
is a top predator at the Sevilleta National Wildlife Refuge (NWR) in New Mexico, USA.
In particular, the following questions were addressed: 1) Is there a difference in the base
of the coyote food chain between native grassland and woody plant-encroached
shrubland habitats?; 2) Is there seasonal variation in the base of the coyote food chain in
response to pulses of grass productivity in an area impacted by woody plant

166
encroachment?; 3) Does a) the size of the difference in the base of the coyote food chain
between grassland and woody plant-encroached shrubland habitats, or b) the strength of
the linear relationship between percent coyote diet from C3 woody plants and percent
available shrubland habitat, change with the spatial scale at which the habitat variables
are assessed? The answer to the first two questions is no; woody plant encroachment and
seasonal variation in grass productivity in a woody plant-encroached area do not lead to
significant shifts in the base of the coyote food chain. However, the answer to the first
question is tempered by the answer to the third question; a statistically significant shift in
the base of the coyote food chain between habitat types is observed when habitat
characteristics are assessed at a small spatial scale. Thus, the assessment of the
relationship between coyote feeding ecology and habitat characteristics is a scale-
dependent process.
There are several additional conclusions regarding the feeding ecology of coyotes
in a woody plant-encroached environment. First, C3 plants, which include woody plants
and forbs, appear to be an important food resource in the local food web in grassland
areas relative to their availability across the landscape. This matches observations by
other researchers that these plants are of higher nutritional quality than C4 plants. Percent
coyote diet that comes indirectly from C4 plants follows different seasonal trends in
grassland versus shrubland habitats, especially between the spring and summer seasons.
In shrubland areas, coyote diet from C4 grasses follows a trend that matches expectations
and mirrors the spike in grass productivity in response to summer rains. In grassland
areas, it is likely that consumption of C3 forbs by coyote prey increases from the spring to

167
the summer. Finally, coyotes appear to forage in areas that are small relative to the size of
a typical coyote home range.
Overall, the consequences of woody plant encroachment for mammalian predators
are varied and significant. Further research regarding the impacts of woody plant
encroachment on the ecology and fitness of mammalian predators is needed, especially in
areas where the decline of the local grass population in response to woody plant
encroachment is more severe than it has been to date at the Sevilleta NWR. Of particular
interest are studies of the nutritional quality of C3 versus C4 plants and the impacts of a
shift from C4 grasses to C3 woody plants at the base of the food chain on the survival and
fitness of both primary and secondary consumers. Of equal importance are studies of
facets of predator feeding ecology other than the base of the food chain, and of the
ecology of more specialized mammalian predators found in woody plant-encroached
areas. The predator emphasized in this dissertation is a generalist in terms of its diet and
patterns of habitat use. This ecological flexibility is ideal for detecting shifts in ecology
between habitat types and in response to seasonal variation. However, other species with
more specialized habitat and diet requirements are less likely to be able to shift their
resource use patterns in response to woody plant encroachment. Populations of these
more specialized species are likely to decline and may eventually go locally extinct in
woody plant-encroached areas.

Isotope appendices
Appendix 1. Seasonal variation in coyote diet.
A) B)
% coyote diet from grass
100
90
80
70
60
50
40
30
20
10
0
Spring Summer Fall
Season
C) D)
% coyote diet from grass
100
90
80
70
60
50
40
30
20
10
0
Spring Summer Fall
Season
% coyote diet from grass
100
100
90
80
70
60
50
40
30
20
10
90
80
70
60
50
40
30
20
10
0
0
Spring Summer Fall
Season
Spring Summer Fall
Season
Appendix 1. Seasonal variation in percent coyote diet from grass for individuals sampled
in A) grassland habitat in 3 seasons (n = 3); B) shrubland habitat in 3 seasons (n = 7); C)
grassland habitat in 2 seasons (n = 13); D) shrubland habitat in 2 seasons (n = 7). The
data from all of these individuals was used to assess average percent coyote diet from C4
grasses in both grassland and shrubland habitats in the spring, summer, and fall (Figure
2D; Chapter 4). The individuals for which intra-seasonal variation is shown in Appendix
1, Chapter 4 are shown in this appendix as follows: Individual 1, Appendix 1C, white
diamonds; Individual 14, Appendix 1C, gray triangles; Individual 15, Appendix 1B, gray
diamonds; Individual 16, Appendix 1C, black x’s; Individual 30, Appendix 1D, gray
diamonds; Individual 55, Appendix 1B, gray crosses.
% coyote diet from grass
168

Appendix 2. Assessing variation in vegetation end-member values.
A) B)
% coyote diet from woody plants
C)
% coyote diet from grass
100
90
80
70
60
50
40
30
20
10
0
100
90
80
70
60
50
40
30
20
10
0
Grassland Shrubland
Habitat type
Spring Summer Fall
Season
Average
Narrow range
Wide range
Average
Narrow range
Wide range
% coyote diet from grass
100
90
80
70
60
50
40
30
20
10
0
Spring Summer Fall
Season
169
Average
Narrow range
Wide range
Appendix 2. Average values for percent coyote diet from: A) woody plants in grassland
and shrubland habitats, B) grasses in grassland areas, and C) grasses in shrubland areas.
Values for coyote diet calculated based on: average end member values for C3 woody
plants and C4 grasses; values calculated by adding or subtracting a standard deviation
from the average end member values and using the C3 and C4 end member values with
the smallest (narrow range) or largest (wide range) difference between them. Error bars
represent 95% confidence intervals. The same set of A) bone or B) and C) hair samples
was used for the calculations for each series (average, narrow range, wide range). The
values in the “average” series in each of these graphs were used to generate figures
(Figure 2C and D) and run statistical analyses (Table 3) in Chapter 4.

170
Appendix 3. Carbon and nitrogen isotope data for scat samples.
Month Day Year Habitat Lab_ID Individual Component Wt (mg)
15
N N%
13
C C%
6 24 2008 G 1 1 hair 1.023 8.6 14.8 -14.2 43.0
6 24 2008 G 1 1 bone 3.141 6.3 3.9 -17.5 15.6
6 24 2008 G 12 3 hair 0.893 5.4 14.5 -20.3 43.3
6 24 2008 G 12 3 bone_rep 2.049 4.4 2.6 -20.6 8.0
6 24 2008 G 12 3 bone 2.09 4.5 2.9 -20.4 9.7
6 24 2008 G 16 2 bone 2.214 4.7 3.3 -14.2 10.9
6 24 2008 G 31 5 bone 2.965 2.1 3.7 -17.8 12.6
6 24 2008 G 32 6 hair 0.936 4.2 14.0 -18.5 41.2
6 24 2008 S 38 7 bone 3.118 7.0 3.4 -15.9 10.9
6 24 2008 S 40 8 bone 3.076 4.1 3.4 -17.0 12.1
6 26 2008 G 42 9 hair 1.023 6.6 14.4 -17.9 41.9
6 26 2008 G 42 9 bone_rep 2.173 7.9 4.0 -18.2 13.5
6 26 2008 G 42 9 bone 2.14 8.3 3.6 -18.3 12.5
6 27 2008 S 44 10 bone 3.157 5.2 3.4 -15.7 11.1
7 8 2008 G 47 12 hair 1.081 5.1 14.0 -20.4 43.1
7 8 2008 G 47 12 bone 3.125 6.3 11.1 -20.6 41.8
7 23 2008 G 51 14 hair 1.064 8.8 13.6 -17.1 39.8
7 23 2008 G 51 14 bone 3.094 7.4 2.9 -17.4 9.5
7 7 2008 S 52 15 hair 1.112 4.1 14.5 -20.5 42.4
7 7 2008 S 52 15 bone 2.922 4.7 3.8 -18.6 13.5
7 7 2008 G 53 16 hair 0.938 3.4 13.9 -18.8 42.7
7 7 2008 G 53 16 bone 1.986 3.5 4.0 -19.3 15.3
7 8 2008 G 54 17 hair 0.959 6.2 13.7 -17.4 41.4
7 8 2008 G 54 17 bone 2.223 7.2 4.1 -16.3 15.1
7 8 2008 G 57 2 bone 2.995 5.2 4.1 -15.2 13.3
6 25 2008 S 63 19 hair 0.89 3.9 13.9 -17.3 41.5
6 25 2008 S 63 19 bone 1.949 4.6 3.3 -18.8 10.5
6 25 2008 S 64 20 bone 3.062 8.9 3.5 -13.8 11.2
6 25 2008 G 67 21 hair 0.633 7.2 13.4 -20.1 40.9
6 25 2008 G 73 23 hair 1.05 5.0 14.5 -19.7 42.9
6 25 2008 G 73 23 bone 1.9 4.4 3.8 -18.6 14.8
6 25 2008 G 75 24 hair 0.955 6.1 15.0 -10.3 44.1
6 25 2008 G 75 24 bone 2.869 6.5 3.8 -13.5 13.0
6 25 2008 G 76 25 bone 2.852 4.4 3.3 -21.1 11.4
6 25 2008 G 76 25 hair 0.113 8.3 4.2 -16.9 29.7
6 25 2008 G 78 26 hair 0.855 7.9 14.9 -17.6 50.9
6 25 2008 G 78 26 bone 3.08 9.1 3.5 -15.2 12.6
6 26 2008 G 92 27 hair 1.103 2.8 13.8 -18.3 40.8
6 26 2008 G 92 27 bone 2.852 3.1 3.3 -17.8 13.8
6 26 2008 G 93 27 bone 2.822 3.5 3.3 -14.9 10.9
6 26 2008 G 94 28 hair 0.328 6.5 1.9 -24.1 44.1
6 26 2008 G 95 2 bone 1.656 3.2 2.5 -17.8 8.9
6 26 2008 G 96 29 hair 0.875 4.3 14.5 -17.7 43.6
6 26 2008 G 96 29 bone 3.073 3.9 3.4 -15.5 11.4
6 27 2008 S 106 30 hair 0.164 6.2 13.6 -18.7 34.3
6 27 2008 G 115 31 hair 0.969 2.3 12.5 -22.9 42.9
6 27 2008 G 115 31 bone_rep 2.887 3.6 3.8 -21.7 14.6
6 27 2008 G 115 31 bone 3.112 3.6 3.9 -21.7 15.2

Month Day Year Habitat Lab_ID Individual Component Wt (mg)
15
N N%
13
C
171
C%
6 28 2008 S 123 33 hair 0.918 3.1 14.2 -19.4 43.3
6 28 2008 S 123 33 bone 2.988 3.3 3.9 -18.7 15.1
6 28 2008 S 124 10 bone 3.012 2.5 3.8 -18.6 13.4
6 28 2008 S 128 34 hair 1.103 7.4 14.2 -22.0 42.7
6 28 2008 S 128 34 bone 2.917 5.1 3.5 -20.6 11.6
6 28 2008 S 133 35 hair 1.201 3.5 14.3 -16.5 42.7
6 28 2008 S 133 35 bone 2.884 4.1 3.7 -18.5 12.8
6 28 2008 S 138 36 hair 0.87 5.1 14.3 -19.4 43.9
6 28 2008 S 138 36 bone 2.984 4.1 2.1 -19.7 7.8
6 28 2008 S 139 37 hair_rep 1.041 3.8 18.9 -21.4 55.0
6 28 2008 S 139 37 hair 0.935 3.9 14.4 -21.1 42.9
6 28 2008 S 139 37 bone 2.877 3.0 3.1 -20.0 11.2
6 28 2008 S 146 38 hair_rep 0.898 5.3 13.6 -20.5 42.5
6 28 2008 S 146 38 hair 0.933 5.3 13.3 -20.5 41.9
6 28 2008 S 146 38 bone 2.922 6.8 3.4 -17.3 10.6
6 28 2008 S 147 37 bone 2.93 6.2 4.1 -19.7 13.7
7 7 2008 G 161 2 bone 2.86 5.7 3.5 -20.3 13.9
7 7 2008 G 162 39 hair 1.019 4.1 12.0 -20.6 37.7
7 7 2008 G 162 39 bone 3.033 4.0 3.2 -17.2 11.5
7 7 2008 G 168 27 bone 1.942 3.6 4.2 -18.9 14.6
7 7 2008 S 170 40 bone 2.832 5.2 2.6 -19.0 8.7
7 7 2008 S 170 40 hair 1.13 6.7 3.5 -20.4 22.9
7 7 2008 S 177 41 hair 0.998 4.0 13.6 -17.9 40.8
7 7 2008 S 177 41 bone 1.901 2.8 3.5 -17.4 11.3
7 8 2008 G 199 5 bone 2.806 4.7 1.3 -17.4 4.0
7 8 2008 G 201 5 bone 2.916 6.7 3.5 -18.1 11.9
7 8 2008 G 204 42 bone 2.907 5.0 3.8 -17.0 13.4
7 8 2008 G 207 27 bone 2.942 5.5 4.2 -21.7 15.7
7 8 2008 G 211 2 bone 2.926 4.5 3.6 -17.8 12.8
7 8 2008 G 214 2 bone 3.151 5.3 4.1 -15.7 13.7
7 9 2008 S 216 30 bone 2.964 6.5 3.8 -20.4 13.4
7 9 2008 S 217 30 bone 3.172 7.0 3.9 -18.7 12.5
7 9 2008 S 217 30 bone_rep 2.87 7.1 3.3 -19.3 10.6
7 20 2008 G 248 2 bone 2.156 5.3 3.9 -18.5 15.0
7 21 2008 S 255 43 hair 1.117 2.5 14.5 -18.7 43.9
7 21 2008 S 255 43 bone 3.131 5.8 3.2 -15.6 10.7
7 21 2008 G 267 17 bone 2.065 6.0 3.9 -21.2 14.5
7 22 2008 G 273 5 bone 1.189 6.6 9.6 -18.0 31.8
7 23 2008 S 279 44 hair 1.172 4.1 12.8 -19.2 38.3
7 23 2008 S 279 44 bone 2.801 2.8 3.3 -16.9 10.4
7 23 2008 S 280 30 bone 2.991 7.1 3.1 -19.3 10.1
7 23 2008 G 282 45 hair 0.984 5.8 14.5 -17.6 42.6
7 23 2008 G 282 45 bone 3.017 5.0 3.5 -19.3 12.7
7 24 2008 S 289 37 bone 3.057 3.4 3.4 -15.4 11.5
7 24 2008 S 295 37 bone 2.897 5.7 4.5 -14.5 15.0
4 13 2009 G 298 1 hair 0.955 4.9 13.9 -18.4 40.9
4 13 2009 G 298 1 bone 2.123 4.9 3.8 -17.3 14.4
4 13 2009 G 300 1 hair 1.002 5.7 13.0 -16.2 40.8
4 13 2009 G 300 1 bone_rep 2.887 5.6 3.8 -15.5 13.3

Month Day Year Habitat Lab_ID Individual Component Wt (mg)
15
N N%
13
C
172
C%
4 13 2009 G 300 1 bone 2.853 5.2 3.8 -15.7 13.6
4 13 2009 G 301 46 hair 1.051 4.8 13.9 -19.7 41.5
4 13 2009 G 301 46 bone 1.941 3.9 3.6 -16.2 12.2
4 13 2009 G 303 2 bone 2.2 4.1 3.1 -14.9 10.3
4 13 2009 G 304 3 bone 0.352 6.8 2.9 -13.4 9.4
4 13 2009 G 307 39 bone 2.872 8.6 3.7 -16.5 12.0
4 13 2009 G 309 29 bone 3.025 8.0 2.4 -11.5 8.3
4 13 2009 G 309 29 hair 0.082 6.4 4.6 -20.7 48.2
4 13 2009 S 312 19 hair 1.076 5.8 13.6 -20.6 40.5
4 13 2009 S 312 19 bone 2.912 4.3 3.9 -20.0 16.2
4 13 2009 S 313 47 bone 2.097 6.0 4.1 -17.0 14.6
4 13 2009 S 315 43 hair 0.862 5.0 14.4 -19.8 42.7
4 13 2009 S 315 43 bone 2.113 4.0 3.7 -18.7 12.8
4 13 2009 S 317 41 hair 0.41 8.1 14.4 -15.4 39.1
4 13 2009 S 317 41 bone 2.198 9.0 2.4 -13.8 8.2
4 13 2009 S 324 15 hair 0.903 7.8 14.0 -13.1 42.3
4 13 2009 S 324 15 bone 2.044 6.7 5.5 -17.6 20.6
4 13 2009 S 325 15 hair 0.89 5.9 14.6 -19.2 47.7
4 13 2009 S 325 15 bone 3.067 4.6 2.5 -20.3 8.9
4 13 2009 S 331 15 hair 0.872 6.1 12.9 -14.8 39.1
4 13 2009 S 331 15 bone 3.028 5.8 3.0 -15.8 10.6
4 14 2009 S 333 15 hair 0.882 7.1 17.7 -17.2 52.7
4 14 2009 S 333 15 bone 2.98 5.3 3.8 -18.3 14.0
4 14 2009 G 338 16 hair 1.154 4.7 13.8 -16.3 42.7
4 14 2009 G 338 16 bone_rep 2.906 3.4 3.2 -16.6 11.1
4 14 2009 G 338 16 bone 3.274 2.8 1.2 -16.3 4.2
4 14 2009 G 341 16 hair_rep 1.003 5.0 14.5 -15.9 41.4
4 14 2009 G 341 16 hair 0.899 5.0 14.4 -16.6 43.3
4 14 2009 G 341 16 bone 2.884 5.0 3.3 -18.3 12.3
4 14 2009 G 343 17 hair 1.173 8.2 13.9 -12.8 43.1
4 14 2009 G 343 17 bone 2.917 5.0 2.2 -17.9 8.9
4 14 2009 G 344 16 hair 0.987 5.0 14.0 -15.9 44.0
4 14 2009 G 344 16 bone 2.846 4.1 3.7 -17.4 14.1
4 14 2009 G 345 16 hair 1.03 4.2 14.4 -17.1 43.2
4 14 2009 G 345 16 bone 3.204 2.8 3.0 -14.7 10.1
4 14 2009 G 346 16 hair 1.075 8.7 14.4 -17.2 43.0
4 14 2009 G 346 16 bone 0.36 4.9 4.4 -15.7 20.0
4 14 2009 G 347 16 bone 2.896 3.9 2.1 -13.6 7.5
4 14 2009 G 351 24 hair 1.037 5.9 14.5 -17.0 42.4
4 14 2009 G 351 24 bone_rep 3.142 6.2 4.3 -16.0 14.8
4 14 2009 G 351 24 bone 2.912 5.9 3.5 -16.2 12.3
4 14 2009 G 352 48 bone 2.916 6.3 3.4 -17.5 13.1
4 14 2009 G 355 25 hair 0.915 7.0 14.8 -16.1 45.0
4 14 2009 G 357 12 bone 2.861 5.2 3.7 -17.4 11.8
4 15 2009 S 360 30 hair 1.111 4.7 13.8 -18.2 42.9
4 15 2009 S 360 30 bone 2.92 4.8 3.3 -17.4 12.5
4 15 2009 S 361 30 hair 1.016 2.1 14.2 -19.4 43.0
4 15 2009 S 361 30 bone 2.834 1.8 4.5 -14.6 14.7
4 15 2009 S 363 49 hair 0.997 4.3 14.1 -21.2 44.2

Month Day Year Habitat Lab_ID Individual Component Wt (mg)
15
N N%
13
C
173
C%
4 15 2009 S 363 49 bone 3.198 3.8 3.2 -20.1 11.3
4 15 2009 S 364 30 hair 1.076 5.6 13.7 -22.2 42.8
4 15 2009 S 364 30 bone 3.077 5.6 3.2 -18.5 10.9
4 15 2009 S 366 44 hair 1.11 5.2 13.9 -19.2 43.6
4 15 2009 S 366 44 bone 3.144 5.3 2.9 -17.1 10.3
4 15 2009 G 367 45 hair 0.869 4.5 14.0 -16.4 43.8
4 15 2009 G 367 45 bone 2.995 3.9 3.3 -12.2 11.2
4 15 2009 G 368 14 hair 0.847 7.5 14.0 -17.4 43.2
4 15 2009 G 368 14 bone 2.884 5.7 3.6 -15.5 13.0
4 15 2009 G 371 31 hair 0.867 5.8 13.4 -14.3 41.7
4 15 2009 G 371 31 bone 3.06 4.5 2.6 -15.2 8.6
4 15 2009 G 372 50 hair_rep 1.154 3.6 13.8 -21.6 43.2
4 15 2009 G 372 50 hair 0.858 3.9 13.4 -20.9 43.8
4 16 2009 S 382 52 hair 0.831 6.9 14.5 -12.5 42.8
4 16 2009 S 387 55 hair 0.958 4.7 13.8 -21.4 41.7
4 16 2009 S 387 55 bone 2.094 3.8 3.3 -18.6 12.2
4 16 2009 S 388 55 hair 0.939 6.1 14.6 -17.0 42.9
4 16 2009 S 388 55 bone 3.183 9.7 3.6 -15.1 11.4
4 16 2009 S 393 55 hair 1.168 7.0 14.8 -16.2 43.1
4 16 2009 S 393 55 bone 3.2 5.6 3.5 -16.6 12.8
4 16 2009 S 394 55 hair 1.136 4.8 14.8 -22.4 43.8
4 16 2009 S 394 55 bone 3.162 5.5 3.7 -17.3 12.3
4 17 2009 G 396 56 bone 1.964 10.6 6.6 -20.2 21.3
4 17 2009 G 397 27 hair 0.996 6.0 13.9 -15.5 41.2
4 17 2009 G 397 27 bone 2.02 5.3 4.2 -13.8 13.9
4 17 2009 G 398 57 hair 1.147 6.1 13.3 -15.0 43.0
4 17 2009 G 398 57 bone 1.933 7.7 4.1 -16.3 15.5
4 17 2009 G 399 27 bone 3.106 6.4 3.5 -18.1 12.4
4 17 2009 G 402 1 hair 0.957 5.8 13.0 -16.2 39.4
4 17 2009 G 402 1 bone 2.915 5.7 3.4 -13.1 11.7
4 17 2009 G 404 6 bone 2.167 6.1 3.6 -17.9 13.0
4 20 2009 G 407 1 hair 0.816 6.5 14.6 -14.8 43.9
4 20 2009 G 407 1 bone 2.883 4.6 3.2 -17.0 11.6
4 20 2009 G 408 1 hair 1.111 7.6 14.7 -16.4 43.1
4 20 2009 G 408 1 bone 3.071 6.7 2.4 -17.4 9.0
4 20 2009 G 414 1 hair 1.019 4.6 14.0 -16.2 42.0
4 20 2009 G 414 1 bone 2.981 4.3 4.1 -15.5 13.2
4 20 2009 G 415 13 bone 2.091 3.9 2.7 -17.8 10.2
4 20 2009 G 416 28 hair 0.817 4.2 14.3 -14.9 41.7
4 20 2009 G 416 28 bone 1.964 3.3 3.8 -19.0 14.3
4 20 2009 G 417 1 hair 0.967 5.7 14.2 -15.0 43.7
4 20 2009 G 417 1 hair_rep 0.997 5.7 14.1 -15.3 44.0
4 20 2009 G 417 1 bone 2.899 5.3 3.1 -15.9 10.2
4 20 2009 S 422 35 hair 1.052 6.7 13.7 -19.0 42.8
4 20 2009 S 422 35 bone 2.216 7.2 4.1 -16.8 14.3
4 20 2009 S 423 58 bone 2.956 6.1 3.5 -22.4 12.9
4 20 2009 S 425 59 bone 2.267 3.7 3.2 -14.8 10.7
4 20 2009 S 427 60 bone 1.386 5.5 3.5 -20.7 12.2
4 20 2009 S 429 36 hair 1.181 3.2 14.0 -16.1 43.2

Month Day Year Habitat Lab_ID Individual Component Wt (mg)
15
N N%
13
C
174
C%
4 20 2009 S 429 36 bone 1.994 2.8 3.7 -18.3 14.5
4 20 2009 S 430 61 bone 2.889 3.7 3.6 -14.7 12.0
4 21 2009 S 434 37 hair 1.123 5.5 13.4 -21.3 41.9
4 21 2009 S 434 37 bone 2.044 6.4 4.4 -14.9 14.4
4 21 2009 S 436 37 bone 2.921 7.8 2.6 -16.9 8.2
4 21 2009 G 440 62 hair 1.192 5.7 13.7 -23.1 43.2
4 22 2009 G 447 2 bone 3.124 5.0 2.8 -16.3 9.6
4 22 2009 G 447 2 bone_rep 3.119 5.0 2.5 -16.7 8.8
4 22 2009 G 448 2 bone 3.153 3.0 3.4 -15.4 11.0
4 22 2009 S 459 40 hair 1.068 6.0 14.6 -17.8 42.8
4 22 2009 S 459 40 bone_rep 2.096 4.7 2.8 -21.0 8.5
4 22 2009 S 459 40 bone 2.152 4.6 2.6 -21.3 9.0
4 22 2009 S 467 15 hair 0.893 5.4 13.2 -16.7 43.2
4 22 2009 S 467 15 bone 2.851 6.2 3.1 -16.0 10.2
4 22 2009 S 468 15 hair 1.016 5.3 13.7 -16.2 43.6
4 23 2009 S 473 7 bone 1.916 6.3 3.0 -17.2 9.3
4 23 2009 S 475 43 bone 3.171 4.8 3.7 -19.8 12.3
4 23 2009 S 478 15 hair 0.916 6.0 14.0 -15.8 42.6
4 23 2009 S 478 15 bone 2.921 5.6 3.3 -16.1 10.8
4 23 2009 S 479 43 bone 2.929 3.7 4.8 -17.2 15.7
4 23 2009 S 481 15 hair 1.163 7.2 13.3 -14.7 43.2
4 23 2009 S 481 15 bone 2.91 7.1 3.8 -14.9 12.6
4 23 2009 G 483 16 hair 0.934 5.1 15.0 -17.5 44.6
4 23 2009 G 483 16 bone 2.985 5.9 2.0 -14.5 6.5
4 23 2009 G 484 16 hair 1.178 6.3 14.8 -16.6 44.0
4 23 2009 G 484 16 bone 3.052 6.3 3.5 -13.9 11.4
4 23 2009 G 485 22 hair 0.845 9.4 13.0 -16.2 44.1
4 23 2009 G 485 22 bone 3.165 9.4 3.3 -17.0 11.6
4 23 2009 G 489 23 hair 0.843 8.5 14.0 -15.8 44.8
4 23 2009 G 490 21 bone 2.949 2.8 3.3 -16.5 11.6
4 24 2009 G 497 26 bone 1.389 9.1 4.3 -14.4 15.7
4 24 2009 S 502 30 hair 0.837 3.0 14.9 -19.5 43.6
4 24 2009 S 502 30 bone 2.988 2.4 4.1 -19.2 15.9
4 28 2009 G 506 14 hair 0.995 10.7 13.7 -17.1 43.5
4 28 2009 G 507 14 hair 1.128 7.4 14.2 -14.0 42.5
4 28 2009 G 507 14 bone 0.968 5.9 4.0 -12.5 12.9
4 28 2009 G 517 10 bone 3.151 4.4 3.1 -18.6 10.9
4 28 2009 G 521 10 bone 2.987 3.9 3.8 -18.5 14.9
4 29 2009 S 528 10 bone 3.168 4.1 3.7 -18.3 13.6
4 29 2009 S 530 65 hair 1.09 6.5 12.6 -16.8 43.3
4 29 2009 S 530 65 bone_rep 3.16 5.6 3.8 -16.1 13.6
4 29 2009 S 530 65 bone 2.951 5.1 3.6 -16.1 13.2
4 29 2009 S 537 66 bone 2.904 6.7 2.3 -15.8 7.5
4 29 2009 S 542 55 hair 1.079 5.9 14.5 -16.1 43.8
4 29 2009 S 542 55 bone 2.817 5.8 4.2 -17.8 16.7
4 29 2009 S 543 33 hair 1.184 2.8 14.7 -16.5 43.3
4 29 2009 S 543 33 bone 3.08 3.3 3.5 -15.1 11.4
4 29 2009 S 545 55 hair 0.918 3.7 14.6 -17.9 43.3
4 29 2009 S 545 55 bone 3.08 4.3 4.0 -17.9 13.9

Month Day Year Habitat Lab_ID Individual Component Wt (mg)
15
N N%
13
C
175
C%
4 29 2009 S 545 55 bone_rep 2.916 4.5 4.0 -18.3 14.2
5 6 2009 G 569 27 bone 3.094 3.9 3.1 -16.9 10.3
5 6 2009 G 571 1 hair 1.061 6.0 14.7 -17.4 44.1
5 6 2009 G 571 1 bone 2.823 8.0 1.7 -16.1 5.4
5 6 2009 G 573 67 bone 3.087 5.3 3.7 -18.4 12.3
5 6 2009 G 576 27 bone 3.109 4.9 3.3 -16.9 11.3
5 7 2009 S 589 38 hair 1.036 4.5 15.2 -17.4 42.4
5 7 2009 S 589 38 bone 2.955 6.5 2.8 -16.2 9.2
5 7 2009 S 592 70 hair 1.225 4.9 13.3 -18.8 43.8
5 7 2009 S 593 38 hair 1.105 4.8 14.6 -18.4 44.4
5 7 2009 S 593 38 bone 2.888 4.5 3.3 -16.6 11.8
5 7 2009 S 595 38 hair 0.887 5.8 12.7 -18.5 47.7
5 7 2009 S 595 38 bone 2.97 6.2 4.9 -18.9 22.3
5 7 2009 S 596 34 hair 1.209 9.9 14.4 -17.5 43.9
5 7 2009 S 596 34 bone 1.875 9.9 3.5 -17.0 11.7
5 8 2009 G 605 71 bone 3.046 4.1 3.9 -20.4 13.3
7 6 2009 G 612 46 hair 0.894 7.8 14.6 -15.2 44.2
7 6 2009 G 612 46 hair_rep 0.812 7.6 14.1 -15.4 42.5
7 6 2009 G 612 46 bone 3.177 8.5 2.5 -15.1 8.3
7 6 2009 S 614 72 hair 1.155 4.5 14.5 -18.2 42.5
7 6 2009 S 614 72 bone 2.931 4.4 3.3 -19.2 11.0
7 6 2009 S 615 73 hair 1.144 3.9 15.0 -16.2 44.0
7 6 2009 S 615 73 bone 3.186 4.3 3.5 -16.6 11.8
7 6 2009 S 616 43 hair 0.131 9.5 12.4 -13.1 37.2
7 6 2009 S 616 43 bone 1.426 9.5 3.4 -15.3 13.0
7 7 2009 S 620 15 hair 1.03 5.5 14.8 -17.1 43.7
7 7 2009 S 620 15 bone 3.081 6.9 3.9 -20.5 14.0
7 7 2009 S 621 43 bone_rep 2.988 4.6 3.4 -16.3 10.9
7 7 2009 S 621 43 bone 3.184 4.6 3.3 -16.4 10.9
7 7 2009 G 626 16 hair 0.888 5.0 13.8 -16.2 42.7
7 7 2009 G 626 16 bone_rep 3.035 5.2 3.7 -18.3 14.7
7 7 2009 G 626 16 bone 3.021 5.3 3.1 -19.3 14.8
7 7 2009 G 628 23 bone 2.945 4.6 3.5 -16.0 11.7
7 7 2009 G 629 25 hair 0.844 5.6 13.2 -19.8 40.5
7 7 2009 G 629 25 bone 3.058 4.9 3.1 -15.1 9.8
7 8 2009 G 631 57 bone 0.634 10.2 2.7 -13.5 7.7
7 8 2009 G 631 57 hair 1.026 6.7 14.3 -18.2 44.0
7 13 2009 G 641 14 hair 0.84 8.2 13.6 -19.7 44.6
7 13 2009 G 641 14 bone 2.906 7.8 3.6 -14.9 11.7
7 13 2009 G 643 50 hair 0.835 4.9 14.2 -21.9 43.8
7 13 2009 G 643 50 bone 3.19 2.8 3.0 -20.5 10.1
7 13 2009 G 646 31 hair 0.932 10.6 13.5 -16.0 42.8
7 13 2009 G 646 31 bone 3.008 3.7 3.3 -19.1 11.5
7 14 2009 S 657 65 hair 0.835 5.6 14.3 -14.8 45.1
7 14 2009 S 657 65 bone 3.079 6.2 3.5 -14.2 12.7
7 14 2009 S 660 52 hair 0.994 6.1 14.8 -16.2 43.9
7 14 2009 S 660 52 bone 3.144 6.6 2.6 -17.4 8.8
7 15 2009 S 666 55 hair 1.244 4.4 14.3 -20.5 44.0
7 15 2009 S 666 55 bone 3.137 3.1 3.3 -20.0 10.7

Month Day Year Habitat Lab_ID Individual Component Wt (mg)
15
N N%
13
C
176
C%
7 15 2009 S 670 37 hair 0.933 7.2 14.3 -15.8 42.6
7 15 2009 S 670 37 bone 3.049 7.4 4.1 -17.1 15.1
7 15 2009 S 673 35 hair 0.854 4.1 12.9 -17.8 42.5
7 15 2009 S 673 35 bone 2.822 4.6 4.3 -18.4 17.1
7 16 2009 S 686 36 hair 0.395 4.3 13.4 -15.0 41.4
7 16 2009 S 686 36 bone 2.953 3.5 3.5 -15.0 11.4
7 17 2009 G 691 76 bone 3.162 4.0 4.3 -17.6 16.1
7 20 2009 G 693 1 hair 0.908 4.4 15.1 -18.6 44.4
7 20 2009 G 693 1 bone 3.091 5.2 2.3 -17.0 7.7
7 21 2009 S 705 40 bone 3.088 7.0 3.3 -18.3 10.6
7 21 2009 G 719 17 hair 1.054 4.8 14.2 -15.7 42.5
7 21 2009 G 719 17 bone 3.148 5.4 2.3 -18.5 8.0
7 22 2009 G 721 17 bone 3.032 5.0 2.0 -13.1 6.6
7 23 2009 G 723 27 hair 0.851 6.2 14.0 -16.6 44.4
7 23 2009 G 723 27 bone 2.926 4.3 3.4 -16.1 11.6
7 27 2009 G 734 45 hair 0.822 7.0 14.2 -21.5 44.4
7 27 2009 G 734 45 bone 3.167 7.2 2.9 -19.5 9.6
7 27 2009 G 747 10 bone 3.18 5.4 3.6 -18.2 11.3
7 28 2009 S 755 77 bone 3.168 7.2 3.5 -17.8 11.8
7 29 2009 S 759 33 hair 0.809 4.4 14.0 -16.0 43.0
7 29 2009 S 759 33 bone 3.243 1.6 2.9 -15.6 9.5
7 30 2009 S 767 34 hair_rep 0.923 5.0 14.1 -16.2 41.2
7 30 2009 S 767 34 hair 0.876 4.9 13.8 -16.1 41.9
7 30 2009 S 767 34 bone 3.24 4.1 3.1 -19.7 10.3
7 31 2009 S 769 37 bone 2.839 5.0 3.7 -17.0 12.2
10 13 2009 G 770 39 bone 2.82 5.5 4.4 -18.2 14.9
10 13 2009 G 773 3 hair 1.163 4.6 14.1 -15.8 44.6
10 13 2009 G 773 3 bone 2.948 4.2 3.0 -15.9 11.1
10 13 2009 G 773 3 bone_rep 2.933 4.4 2.4 -16.0 8.9
10 13 2009 G 775 29 hair 0.86 6.2 14.0 -16.9 43.7
10 14 2009 S 779 5 bone 3.103 5.3 3.4 -13.6 11.7
10 14 2009 S 780 73 hair_rep 0.97 4.5 14.2 -20.3 44.0
10 14 2009 S 780 73 hair 1.193 4.3 13.9 -21.2 44.0
10 14 2009 S 783 5 bone 2.92 6.3 3.6 -16.3 13.4
10 14 2009 S 784 43 bone 3.05 6.9 3.8 -14.6 13.3
10 14 2009 S 785 15 hair 0.924 4.5 14.0 -14.4 42.8
10 14 2009 S 785 15 bone 2.945 6.2 3.7 -13.6 12.1
10 14 2009 G 792 16 hair 1.05 3.9 14.4 -13.1 43.2
10 14 2009 G 792 16 bone 2.945 3.4 3.4 -13.0 11.1
10 17 2009 G 802 57 bone 3.109 4.3 4.6 -18.7 14.1
10 17 2009 G 802 57 hair 1.023 4.6 14.0 -21.7 43.7
10 17 2009 G 806 17 bone 3.063 3.4 3.5 -15.2 13.6
10 19 2009 G 808 27 hair 1.219 7.6 14.1 -19.0 43.6
10 19 2009 G 808 27 bone 3.092 7.1 5.1 -16.8 16.4
10 19 2009 G 809 27 bone 0.182 8.8 5.5 -17.8 17.5
10 19 2009 G 810 28 hair 0.722 6.3 13.5 -16.5 44.0
10 19 2009 G 810 28 bone 3.127 9.3 1.6 -12.7 28.5
10 19 2009 G 815 27 bone 2.82 7.3 4.7 -16.1 14.7
10 19 2009 S 816 30 hair 0.816 5.3 14.3 -20.3 43.7

Month Day Year Habitat Lab_ID Individual Component Wt (mg)
15
N N%
13
C
177
C%
10 22 2009 S 829 35 hair 1.015 4.8 13.7 -22.0 42.1
10 22 2009 S 829 35 bone 2.949 5.1 5.0 -21.0 15.7
10 22 2009 S 829 35 bone_rep 2.956 4.7 4.5 -20.9 14.7
10 22 2009 S 830 36 hair 1.172 4.2 14.5 -16.9 43.4
10 22 2009 S 830 36 bone 3.159 3.5 3.8 -17.6 14.8
10 23 2009 S 833 34 hair 0.707 8.2 14.3 -20.8 41.8
10 23 2009 S 833 34 bone 3.121 8.4 3.2 -19.7 10.6
10 23 2009 S 834 38 bone 2.899 6.2 2.7 -17.5 9.3
10 23 2009 S 835 37 hair 1.02 6.6 14.0 -15.4 42.9
10 23 2009 S 835 37 bone 2.903 6.5 4.8 -13.8 14.8
10 23 2009 S 839 37 bone 3.02 3.4 3.0 -19.0 9.5
10 23 2009 G 840 62 hair 1.033 5.2 14.2 -17.4 43.1
10 23 2009 G 840 62 bone 3.101 4.3 3.7 -17.5 12.4
10 26 2009 S 845 10 bone 3.139 4.9 2.7 -20.0 9.5
10 26 2009 S 857 55 hair 1.047 4.8 13.9 -17.6 41.6
10 26 2009 S 857 55 bone 3.075 4.3 3.7 -17.9 15.5
10 27 2009 S 863 43 bone 3.105 6.1 4.0 -14.0 13.0
10 27 2009 S 867 43 bone 2.975 4.5 3.2 -17.4 11.8
10 27 2009 S 871 41 hair 1.141 5.8 14.2 -18.1 44.3
10 27 2009 S 871 41 bone 3.01 2.9 3.5 -19.3 14.3
10 28 2009 S 873 43 bone 3.176 4.8 3.1 -16.1 10.7
10 28 2009 S 874 43 bone 3.111 4.4 3.3 -16.7 11.1
10 28 2009 S 875 43 bone 3.138 4.0 3.1 -16.8 10.4
10 28 2009 S 876 72 hair 1.024 5.2 14.5 -16.3 45.2
10 28 2009 S 876 72 bone 2.837 5.1 4.4 -14.7 14.1
10 29 2009 G 892 24 hair 0.864 6.2 14.5 -14.9 43.0
10 29 2009 G 892 24 bone 3.133 5.2 4.0 -16.5 12.9
10 30 2009 S 899 74 hair 1.103 6.4 14.4 -22.5 43.9
10 30 2009 S 899 74 bone 2.919 3.1 3.0 -16.8 10.9
10 30 2009 S 903 70 hair 0.96 3.7 14.1 -17.8 43.7
10 30 2009 S 903 70 bone 3.023 2.4 3.7 -18.6 14.0
11 2 2009 G 910 48 hair 1.168 7.7 14.4 -11.7 44.4
11 2 2009 G 910 48 bone 2.804 7.7 3.9 -10.9 12.6
11 3 2009 G 915 27 bone_rep 2.924 4.9 4.6 -14.2 14.2
11 3 2009 G 915 27 bone 3.072 5.0 4.5 -14.3 14.3
11 3 2009 S 916 30 bone 2.986 3.2 3.8 -16.2 13.6
11 3 2009 S 917 44 hair 1.144 5.8 14.1 -16.2 43.7
11 3 2009 S 917 44 bone 3.148 6.3 3.8 -14.2 12.4
11 4 2009 G 920 50 bone 4.078 2.0 0.5 -21.4 48.4
11 6 2009 S 931 10 bone 2.829 5.0 3.0 -18.4 10.3
11 6 2009 S 934 33 hair 1.224 2.1 14.4 -17.5 43.0
11 6 2009 S 934 33 bone 3.093 2.4 3.6 -18.9 13.5
Appendix 3. δ 13 C and δ 15 N values for hair and bone samples taken from coyote scats
collected at the Sevilleta National Wildlife Refuge. Each sample has a unique lab
identification number (Lab_ID) and each individual has a unique individual identification
number (Individual). G = grassland habitat, S = shrubland habitat, rep = replicate sample
of pieces of hair or bone taken from a single scat sample. Month, day and year
correspond to the date that the scat sample was collected in the field.

Appendix 4. Carbon and nitrogen isotope data for vegetation samples
Month Day Year Identifier Weight (mg)
15
N N%
13
C C%
4 19 2009 Aristida 4.153 0.18 0.87 -13.51 44.12
7 12 2009 Aristida 3.817 1.70 1.36 -14.36 44.19
10 16 2009 Aristida 3.806 1.05 1.27 -14.39 44.70
4 25 2009 ATCA1 4.012 7.30 2.24 -14.85 41.89
7 19 2009 ATCA1 4.126 6.85 3.20 -16.50 41.19
10 24 2009 ATCA2 4.057 4.47 1.11 -14.97 42.10
4 4 2009 Bouteloua 4.118 0.36 0.90 -14.95 46.26
7 11 2009 Bouteloua 4.19 -0.12 0.76 -15.03 44.22
10 11 2009 Bouteloua 4.11 -1.99 1.45 -14.46 44.61
4 4 2009 Bouteloua_rep 4.066 -0.06 0.73 -14.99 46.28
10 16 2009 CYIMI1_F 3.899 5.58 2.79 -12.05 50.86
10 16 2009 CYIMI1_F 3.858 4.61 2.22 -11.91 49.74
4 12 2009 Dasyochloa 4.042 2.44 1.24 -14.79 42.69
7 18 2009 Dasyochloa 3.971 2.14 1.67 -15.37 43.63
10 10 2009 Dasyochloa 3.985 2.04 1.89 -15.07 42.55
7 18 2009 EPTO1 4.157 2.34 1.49 -25.33 47.28
10 24 2009 EPTO2 4.135 1.06 0.91 -25.01 46.34
7 11 2009 Forb1 4.044 2.49 2.84 -26.10 42.20
10 11 2009 Forb1 3.809 3.83 2.34 -20.62 41.32
7 11 2009 Forb2 4.009 1.39 1.71 -26.45 45.81
10 11 2009 Forb2 3.969 0.82 1.56 -26.31 44.51
10 11 2009 Forb3 3.94 2.25 3.36 -26.80 41.32
10 11 2009 Forb3_rep 4.139 2.18 3.22 -26.34 40.97
7 19 2009 Forb4 3.912 6.18 4.05 -14.02 38.24
10 24 2009 Forb5 4.184 -1.48 2.11 -26.32 42.93
4 25 2009 Forb6 3.951 0.43 1.39 -26.21 45.05
4 25 2009 Forb6_rep 3.876 0.40 1.33 -25.76 44.01
4 19 2009 Forb7 3.81 0.55 1.70 -27.37 43.07
7 25 2009 Forb7 4.092 -0.03 2.49 -26.65 44.79
10 31 2009 Forb7 3.869 0.86 2.73 -27.40 42.07
4 19 2009 Forb8 3.947 2.52 2.39 -25.32 42.64
4 18 2009 Forb9 4.156 -0.33 2.43 -26.94 43.45
7 18 2009 Forb10 3.939 0.68 2.67 -26.89 46.08
4 26 2009 Forb11 3.878 2.42 2.89 -25.10 39.68
7 26 2009 Forb11 4.124 1.52 3.40 -25.96 41.07
10 25 2009 Forb12 3.818 -0.73 3.19 -27.11 44.81
4 25 2009 GUSA 4.196 0.61 1.97 -25.87 47.71
7 12 2009 GUSA 4.035 0.10 1.20 -25.65 46.69
10 25 2009 GUSA 4.035 1.32 2.16 -25.15 49.79
10 10 2009 JUMO 3.969 -1.12 1.20 -23.61 55.79
10 10 2009 JUMO_F 3.999 -0.26 1.89 -20.25 52.02
10 10 2009 JUMO_rep 3.838 -1.15 1.23 -23.43 53.52
4 19 2009 KRLA 3.836 5.42 2.14 -25.30 42.28
7 12 2009 KRLA 4.073 4.96 2.05 -25.34 42.17
10 16 2009 KRLA 4.117 5.87 2.27 -24.20 42.46
4 25 2009 LATR 4.101 4.19 1.64 -24.89 48.40
7 19 2009 LATR 4.09 5.87 1.79 -24.36 48.92
178

Month Day Year Identifier Weight (mg)
15
N N%
13
C C%
10 24 2009 LATR 3.936 4.79 1.72 -25.10 49.00
4 19 2009 Pleuraphis 3.802 1.83 0.70 -14.43 44.31
7 25 2009 Pleuraphis 3.95 1.45 1.18 -15.02 43.29
10 31 2009 Pleuraphis 4.147 0.81 1.46 -15.10 42.85
10 24 2009 PRGL1_F 4.026 6.34 7.88 -23.98 44.20
4 25 2009 PRGL2 4.125 1.27 2.88 -24.30 46.39
7 19 2009 PRGL2 3.873 1.09 2.77 -24.72 46.65
10 24 2009 PRGL2 4.121 1.57 2.52 -25.21 46.30
7 19 2009 PRGL2_F 4.113 3.35 4.30 -23.19 44.76
10 24 2009 PRGL2_F 3.911 3.81 4.75 -23.96 45.25
10 10 2009 PRGL3_F 3.952 3.23 3.74 -25.69 44.60
4 18 2009 Scleropogon 3.902 5.36 1.57 -14.74 43.19
7 25 2009 Scleropogon 4.078 5.48 1.89 -14.63 43.59
10 31 2009 Scleropogon 3.833 5.15 2.32 -14.94 42.71
4 25 2009 Sporobolus 3.801 0.27 0.75 -13.90 42.83
7 19 2009 Sporobolus 4.175 2.40 1.33 -15.27 43.15
10 24 2009 Sporobolus 3.907 1.43 1.84 -15.62 42.71
4 4 2009 YUGL 3.833 2.28 1.68 -24.52 50.56
7 11 2009 YUGL 4.152 2.24 1.37 -26.87 51.30
10 11 2009 YUGL 4.021 1.43 1.58 -25.63 51.57
Appendix 4. δ 13 C and δ 15 N values for vegetation samples collected at the Sevilleta
National Wildlife Refuge. Month, day, and year correspond to dates that the samples
were collected in the field. Identifiers are species codes for woody plants, genera names
for grasses, functional type for forbs. F = a sample of a plant that coyotes eat (food) and
that was cleaned with ethanol prior to being prepared for isotope analysis; rep = replicate
sample; different numbers within a given identifier (e.g., Forb1, Forb2, etc.) indicate
samples that were collected at different locations. ATCA = Atriplex canescens, CYIMI =
Cylindropuntia imbricata, EPTO = Ephedra torreyana, GUSA = Gutierrezia sarothrae,
JUMO = Juniperus monosperma, KRLA = Krascheninnikovia lanata, LATR = Larrea
tridentata, PRGL = Prosopis glandulosa, YUGL = Yucca glauca.
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