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RESPONSE OF PUMAS (Puma concolor) TO THE MIGRATION OF GUANACOS (Lama guanicoe) IN PATAGONIA
By
MARIA LAURA GELIN SPESSOT
A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
UNIVERSITY OF FLORIDA
2016
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©2016 Maria Laura Gelin Spessot
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ACKNOWLEDGMENTS
This study was financed by the Programa de becas BEC.AR Argentina, The
School of Natural Resources and Environment of the University of Florida, the
Conservation Food and Health Foundation, the Rufford Foundation, the Pittsburgh Zoo
& PPG Aquarium, Idea Wild, the Tropical Conservation and Development Program – U.
Florida, and the Sackler Institute for Comparative Genomics at the American Museum
of Natural History. The Argentinian program of the Wildlife Conservation Society and the
Dirección de Recursos Naturales Renovables de Mendoza graciously provided logistical
support.
I am deeply grateful to my advisor, Lyn Branch, for her helping and supporting
me every step of the way during my stay in Gainesville, sharing her knowledge and
making such valuable contributions to my academic training, and above all, for showing
me infinite patience. I want to thank my committee members, Dan Thornton and John
Blake, who provided valuable comments and suggestions to improve my work.
I am also grateful to Mariel, Maco and Andres Novaro, for giving me the
opportunity to work with them in such a beautiful landscape, and for their help and
support during field work. Special thanks to Andres for his valuable contributions to this
work, and to the Park rangers of Malargue, who opened the door to their home and
made my stay in “La Payunia” so comfortable. I would also like to thank my friend Nata
for being with me during the hardest stage of my fieldwork, as well as all of my field
assistants who withstood every difficulty during the fieldwork.
I would also like to acknowledge Oscar Murillo, Jose Soto, Andrew Noss and
Matt Gould for their important contributions to this Project, as well as Daniela Gomez,
Jose Priotto and Silvia Valdano for their helpful letters of support everytime I need them.
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My heartfelt thanks to my friends Juliana, Diego, Harry and Claudio for the thousands of
“mates” that we shared, and to Maca and Audrey for being the best roommates one
could ask for.
Finally, to Lucas for his unconditional love and support, and to my beloved family
for being always there and giving me the strength to never give up.
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TABLE OF CONTENTS
page
ACKNOWLEDGMENTS.............................................................................................................. 3
LIST OF TABLES ......................................................................................................................... 7
LIST OF FIGURES....................................................................................................................... 8
ABSTRACT ................................................................................................................................... 9
CHAPTER
1 INTRODUCTION................................................................................................................. 11
2 MATERIALS AND METHODS .......................................................................................... 14
Study Area............................................................................................................................ 14 Camera-trap Surveys ......................................................................................................... 15
Identification of Individual Pumas ..................................................................................... 16 Puma Density Estimates .................................................................................................... 16
Collection of Puma Scats................................................................................................... 20 Diet Analysis ........................................................................................................................ 21
3 RESULTS ............................................................................................................................. 25
Identification of Individual Pumas ..................................................................................... 25 Puma Density Estimates .................................................................................................... 25
Diet Analysis ........................................................................................................................ 27
4 DISCUSSION ...................................................................................................................... 29
APPENDIX
A PUMA CAPTURE HISTORIES ......................................................................................... 34
B ADDITIONAL MODELS EXAMINED WITH COVARIATES FOR DETECTION
PROBABILITY ..................................................................................................................... 40
C COUNTS OF GUANACOS AND PUMAS IN BOTH STUDY GRIDS ......................... 41
Index of guanaco abundance ............................................................................................ 41
D PLOT GENERATED BY SPATIAL MARK- RESIGHT TO DETERMINE APPROPIATE BUFFER SIZE........................................................................................... 44
E ADDITIONAL MODELS EXAMINED FOR DENSITY ................................................... 45
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LIST OF REFERENCES ........................................................................................................... 46
BIOGRAPHICAL SKETCH ....................................................................................................... 51
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LIST OF TABLES
Table page
2-1 Top five spatial mark-resight (SMR) models for density of pumas. Models
were ranked using Akaike’s Information Criterion adjusted for small sample sizes (AICc). .................................................................................................................... 24
2-2 Percent of occurrence of prey items in puma scats in summer (n =25 scats)
and winter (n =14 scats) in the north of La Payunia Reserve ................................. 24
3-1 Number of pumas identified to the individual level (n*), total number of puma
photographs that were labeled as individually identifiable (ID) and unidentifiable (non-ID). Distances moved and recaptures....................................... 28
3-2 Density estimates for pumas obtained from the five highest ranked spatial
mark-resight models ..................................................................................................... 28
4-1 Estimates of puma densities from other studies in Central and South America
and from studies in the western US with habitat similar to La Payunia. ................ 33
A-1 Capture histories for pumas individually identified on the north and south grids. ................................................................................................................................. 34
A-2 Capture histories for non-identified individuals for the north and south grids.. .... 36
B-1 Model comparison table to determine best fitting model for detection
probability parameter in multi-session density models using Akaike’s Information Criterion adjusted for small sample sizes (AICc). ................................ 40
B-2 Model comparison table to determine best-fitting model for detection
probability parameter with density constant. Ranking is based on Akaike’s Information Criterion adjusted for small sample sizes (AICc). ................................ 40
E-1 Model comparison table to determine best fitting model for density with constant detection probability, using Akaike’s Information Criterion adjusted for small sample sizes (AICc) ....................................................................................... 45
E-2 Model comparison table to determine influence of season in North Grid, using Akaike’s Information Criterion adjusted for small sample sizes (AICc)....... 45
E-3 Model comparison table to determine influence of season in South Grid, using Akaike’s Information Criterion adjusted for small sample sizes (AICc).. .... 45
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LIST OF FIGURES
Figure page
2-1 La Payunia Reserve....................................................................................................... 22
2-2 Grid layout and camera station locations (dots) in the La Payunia Reserve........ 23
C-1 Counts of guanacos in the north and south grids using 1 photograph per camera station per day as an independent record.. ................................................. 42
C-2 Counts of pumas in the north and south grids using 1 photograph per camera station per day as an independent record.................................................................. 43
D-1 Capture or detection probability as a function of distance from the center of a home range for the best-fitting SMR model. .............................................................. 44
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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science
RESPONSE OF PUMAS (Puma concolor) TO THE MIGRATION OF GUANACOS (Lama guanicoe) IN PATAGONIA
By
Maria Laura Gelin Spessot
December 2016
Chair: Lyn C. Branch
Major: Interdisciplinary Ecology
Large-scale migrations can generate changes in the availability of prey for top
predators. In response to prey migration, predators can either track prey movements by
moving with them or they can modify their foraging behavior to focus on alternative
prey. My study was conducted in La Payunia Reserve, Argentina, where the puma
(Puma concolor) is the largest predator, and the guanaco (Lama guanicoe) is the
largest native ungulate. Guanacos exhibit a seasonal extensive migration at this site.
The goal of my project was to determine whether pumas in this site respond to
migration of guanacos by moving with the guanacos or by switching prey. Based on
data collected from camera traps, I used spatial mark–resight (SMR) models to estimate
density of pumas in summer and winter ranges of guanacos and analyzed the changes
in their density in response to the migration. Also, I analyzed puma scats to assess
changes in prey consumption in response to guanaco migration.
My results indicate that pumas do not follow the migration of guanacos in La
Payunia Reserve nor do they switch to alternative prey. Density estimates of pumas did
not increase significantly in the winter and summer range of guanacos when guanacos
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migrated to these areas. Analysis of puma diet showed that pumas feed on guanacos
throughout the year and do not switch to alternative prey when guanaco availability is
lower. Density estimates obtained in this study were within the range of estimates from
other sites with a similar landscape.
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CHAPTER 1 INTRODUCTION
Large-scale ungulate migrations, associated with changes in the availability of
resources and risk of predation (Fryxell and Sinclair 1988), are widespread phenomena
that influence mammal community composition and prey abundance for top predators.
Predators can either track prey movements by undertaking migrations with them or they
can modify their foraging behavior to focus on alternative prey sources (i.e., prey
switching; Bergerud 1983; Giroux et al. 2012; Elbroch et al. 2013). Either response can
have strong effects at the community level. For example, if predators move with prey,
the distribution of carcasses for scavengers and decomposers also shifts (Elbroch et al.
2013). On the other hand, if predators remain relatively stationary and switch prey in
response to the migration, they may have significant impacts on populations of
alternative prey (Ballard et al. 1997; Keehener et al. 2015). Migration of native prey also
may affect the frequency of predation on livestock and increase human-predator
conflict, if livestock serve as alternative prey sources for predators (Morehouse and
Boyce 2011).
The behavioral responses of carnivores to prey migration have been documented
in a variety of ecosystems for North America (Pierce et al. 1999; Giroux et al. 2012;
Nelson et al. 2012), but herbivore migrations and responses of top predator to these
migrations are poorly studied in South America. One predator that spans both of these
continents is the puma (Puma concolor), which has the largest geographic range of any
mammal in the western hemisphere and is a top predator from Canada to southern
South America. Although pumas rely on non-migratory prey in much of their range,
studies in the US have reported migration by pumas (Pierce et al. 1999) and prey
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switching (Elbroch et al. 2013) in systems where large ungulates, their primary prey,
migrate.
In the Patagonian region of southern South America, pumas prey heavily on
guanacos (Lama guanicoe), the largest native ungulate, at least in areas where
guanacos are still common (Martinez et al. 2012; Iriarte et al. 1991; Ortega and Franklin
1995). Although guanaco populations have declined throughout their range and
movement patterns likely have been altered, the longest known terrestrial migration for
mammals in South America is the seasonal migration of guanacos in Patagonia (Puig et
al. 2003; Mueller et al. 2011; Schroeder et al. 2014). The response of pumas to this
migration is unknown.
In this study, I examined the response of pumas to seasonal migration of
guanacos in La Payunia Reserve in northern Patagonia, Argentina, which is the site of
this extensive guanaco migration. Although La Payunia Reserve is one of the few sites
in South America where ungulate migrations have been well documented, shorter
seasonal movements of guanacos are known to occur in other areas (e.g., Tierra del
Fuego, Moraga et al. 2015). Migrations and other seasonal movements may have been
more common in the past when guanaco populations were large and fences were less
common.
I hypothesized that pumas respond to the migration of guanacos by switching to
alternative prey rather than migrating with them because pumas generally are territorial,
have generalist food habits (Logan and Sweanor 2001), and alternative prey are not
scarce in La Payunia and are available throughout the year. Also, I expected pumas to
include more livestock as well as native prey in their diet when guanacos migrated.
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Ranching of goats and cattle occurs within the reserve and on neighboring lands. A
higher frequency of attacks from pumas on livestock has been reported by local herders
in areas where the abundance of guanacos is lower, but this predation has not been
explicitly linked to the guanaco migration (Bolgeri and Novaro 2015).
To test my hypothesis, I examined density of pumas in the summer and winter
ranges of guanacos and analyzed the diet of pumas through analysis of scats to
determine whether pumas shift their diet when guanacos migrate. Alternatively, if
pumas move with guanacos, I expected to see changes in density of pumas that parallel
changes in abundance of guanacos with migration. I derived density estimates of
pumas from camera trapping data analyzed with spatial mark-resight models (SMR).
Spatial mark-resight models are a new technique developed to overcome important
limitations of capture-recapture models (CR) related to incorporation of unidentified
individuals in density estimates and the need to better define the effective area sampled
(Efford 2004; Chandler and Royle 2013; Rich et al. 2014; Efford 2016).
In addition to examining the response of pumas to prey migration, this study
provided an opportunity to compare densities of pumas from large open rangelands in
southern South America to estimates from similar habitats in North America (Logan et
al. 1996, Seidensticker et al. 1973, Lindzey et al. 1994, Logan et al. 1986). With the
exception of one study in Chile (Elbroch and Wittmer 2012), density estimates of pumas
from South America are limited to tropical and subtropical forests (Kelly et al. 2008;
Paviolo et al. 2009; Quiroga et al. 2016).
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CHAPTER 2 MATERIALS AND METHODS
Study Area
My study was conducted in La Payunia Reserve (36º10’S, 68º50’W, Figure 2-1),
a provincial reserve (6641 km2) in northern Patagonia, Mendoza Province, Argentina.
La Payunia Reserve has one of the largest remaining populations of guanacos (>
25,000 individuals, Puig et al. 2003; Schroeder et al. 2014; Bolgeri and Novaro 2015).
Guanacos in La Payunia move up to 70 km from north to south during the winter season
and return to the north of the reserve in the summer (Novaro et al. 2006). The reserve is
characterized by a high density (> 800) of ancient volcanoes (1200 - 2000 m above sea
level) dispersed across broad open plains. The low, open vegetation (~ 50 % cover) of
the area comprises shrubs interspersed with grasses. Dominant species include:
Neosparton aphyllum, Chuquiraga erinacea, Larrea divaricata, Cassia aphila, Panicum
urvilleanum, Poa spp., and Stipa spp. (Candia et al. 1993; Puig et al. 2003). Annual
precipitation averages 255 mm, occurring mostly during the summer months, and mean
seasonal temperatures range from 6ºC in winter to 20ºC in summer (Puig et al. 1996).
Approximately 55% of La Payunia Reserve is under government protection; the
rest is managed by private ranchers whose main economic activity is extensive
livestock ranching, principally of goats (Candia et al. 1993, Bolgeri and Novaro 2015).
Despite the protected status of the area, the reserve suffers from hunting pressure on a
variety of wildlife species, especially guanacos, the lesser rhea (Rhea pennata) and
pichi armadillo (Zaedyus pichiy), which also are potential prey for pumas (Pers. comm.
Lucas Aros, Park Ranger).
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Camera-trap Surveys
I conducted field surveys with remotely-triggered cameras (Browning BTC-5 HD
Strike Force) on two grids (each ~720 km2) from July 23, 2015- January 3, 2016, which
represents mid-winter 2015 to mid-summer 2016. For my study, I considered winter as
July 23 – October 5, 2015 and summer as October 6, 2015 – January 3, 2016. One grid
was located in the summer range of guanacos (north grid, Figure 2-2) and one grid was
located in the winter range (south grid). Each grid was divided into 17 cells of 6 x 6 km2.
I placed two camera-trap stations (hereafter stations) in each cell, except in one cell in
the south grid where I could not get permission from the landowner to access his land.
Instead, I placed four camera stations in the adjacent cell to the west (see Figure 2-2 for
empty cell and adjacent cell with four stations). Also one extra station was placed in the
north grid to maximize use of available cameras. Stations within a cell were separated
by at least 1 km and were placed in sites pumas were likely to use (e.g., drainages,
hills, etc.). I recorded the location of camera stations with a GPS (Garmin GPSMAP 64).
Each station consisted of two infrared cameras separated by an average of 5 meters.
Cameras were oriented facing each other to obtain photographs of both sides of
animals to facilitate identification of individuals from natural markings and for
redundancy in case of camera failure (Silver 2004; Silver et al. 2004; Rich et al. 2014).
Cameras were tied to wooden stakes approximately 50 cm off the ground and parallel to
it (Silver 2004; Negrões et al. 2010). To improve the quantity and quality of the
photographs of pumas in each station, I placed perfume (Obsession by Calvin Klein) as
an attractant on a stake approximately 50 cm tall between the two cameras (Marker and
Dickman 2003; Arroyo-Arce et al. 2014). Cameras were programmed to function 24
h/day, with an interval of five seconds between photographs.
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Cameras were checked every 1-2 months to verify that they were functioning
properly and to change memory cards, batteries and to replenish perfume. Pictures
were downloaded as jpeg files with the program FastStone Image Viewer 5.5. Data from
photographs (camera number, date and time) were extracted from the jpeg files to a
database using the camtrapR package (Niedballa et al. 2016) implemented in R.
Identification of Individual Pumas
Individual pumas were identified based on unique natural marks along the flanks,
scars, kinked tails and ear nicks (Kelly et al. 2008; Negrões et al. 2010). When possible,
I also recorded the sex of the individual. Identification of individual pumas from
photographs is more difficult than for species such as tigers (Panthera tigris) or jaguars
(Panthera onca) with strong natural markings (Kelly et al. 2008; Negrões et al. 2010).
Also, the long distances at which some animals were detected from the camera, poor
angles, and incomplete images hindered individual identification (Rich et al. 2014;
Thornton and Pekins 2015). Individuals could be identified in 43 % of the photographs.
Photographs of both identified and unidentified individuals were included in analyses.
Puma Density Estimates
In order to estimate puma density, I used spatial mark-resight models (SMR)
with a likelihood analysis framework (implemented in the R package ‘secr’, Efford 2016).
SMR models were developed as an alternative to capture-recapture models and as an
extension of spatially-explicit capture-recapture models (SECR) in cases where only
part of the population can be identified to the individual level (Efford 2004; Chandler and
Royle 2013; Rich et al. 2014). Unidentified individuals are incorporated in the SMR
models and treated as independent of identified sightings (Efford 2016), whereas
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standard capture-recapture models and SECR are used only with data sets where all
the individuals can be identified.
Another advantage of both SMR and SECR over earlier capture-recapture
models is that spatial data on captures informs density models. The spatial coordinates
of the camera traps where individuals are captured are used to provide information on
the location of the animal’s activity center (Chandler and Royle 2013; Rich et al. 2014).
The probability of an individual being captured is modeled as a function of distance
between the survey location and the animal’s activity center. The probability of capturing
an animal whose activity center is close to the camera will be higher than that of an
individual whose activity center is further away. This approach addresses one of the
primary criticisms of other methods of estimating density from camera trapping which is
the need to calculate a reliable ‘effective trapping area’ (Noss et al. 2012; Thornton and
Pekins 2015).
Input files for SMR include information on animal captures (i.e., photographs),
trap (i.e., camera) deployment, and potential home-range centers for the target species.
For photographs of identified individuals, I created encounter histories that included
individual animal identification, encounter occasion, and camera station in which the
individual was detected. To reduce processing time, I collapsed daily camera-trap data
into 10 time intervals (i.e., 10 encounter occasions), each comprising a 19-day sampling
period. I used one photograph per day per station as an independent record for
identified individuals and also for unidentified pumas. Then I summed counts of these
independent records separately for identified and unidentified animals for all days within
an occasion. If, for example, an individual was detected two times during the same
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occasion at the same station, this was registered twice in the capture history (e.g.,
puma4 in occasion 7 detected at site N7_27 twice for north_summer; Table A-1). For
unidentified individuals, encounter histories were similar but here the captures were
registered with the same identification name (i.e., UN), encounter occasion, and station
of animal detection (Table A-2).
Other input files necessary for analysis in SMR are the detector layout data (trap
locations and days the traps were active) and a grid of potential home range centers
(equally spaced point locations) for the study area. This grid is called the ‘mask’, and
each point represents a grid cell of potentially occupied habitat. The mask is
constructed by placing a buffer around the outer-most camera locations. The width of
the buffer is defined by a distance that ensures that no animals outside that distance
could be captured within the camera grid (Thornton and Pekins 2015, Efford 2016). I
used a buffer of 12 km. This buffer size was obtained from the function suggest.buffer
from package secr, which determines a suitable buffer width for the mask. I then
checked if this buffer was sufficiently large (i.e., detection probability was near 0 at the
edge of the buffer), using function esa.plot in SECR (Efford 2016). I generated potential
home range centers within this buffered area as a regular grid of points with 2-km
spacing.
SMR models contain parameters for each of the following: animal density (D),
detection (λ0- probability of detection at the center of the home range), and a spatial
scale parameter generated by SMR (σ- related to animal movement) (Royle et al. 2009;
Rich et al. 2014). I fit several models for the detection parameter λ0 including constant
detection (.), models with several covariates automatically generated, and one user-
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defined covariate (Table 2-1, Table B-1). For the automatically generated covariates, I
used a time factor model (‘t’, where probability of detection changes linearly with
occasion), a site learned response model (‘k’, where detection probability at the site
changes after an animal is recorded at that location), and a learned response model (‘b’,
where detection of an individual at all sites changes, dependent on previous capture)
(Efford 2016). I included an additional covariate to determine if the site where the station
was located (i.e., drainage or open plain) influenced detection probability. I expected
cameras in drainages to have higher detection probability because the ravine would
channel pumas between the two cameras.
To examine whether density of pumas changed seasonally in response to
guanaco migration, I fit a series of models of density, and compared the models with
Akaike’s Information Criteria corrected for finite sample size (AICc) (Thornton and
Pekins; Rich et al. 2014; Efford 2016). Models included a multi-session model where
each session was a different combination of trapping grid and season (north
grid_summer, north grid_winter, south grid_summer, south grid_winter) and reduced
models with single factors (season or grid), and a base model with no factors (Table 2-
1). Some models also included covariates for detection probability. Each season or
session contained 5 encounter occasions (i.e., 95 trapping days) and likely fulfilled the
assumption of a closed population during the survey (Kelly et al. 2008; Negrões et al.
2010; Quiroga et al. 2016). Models with grid only or the base model with no factors
generated density estimates based on 190 trapping days (i.e., all 10 occasions) and
may violate assumptions of closure.
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Collection of Puma Scats
Puma scats were collected along standardized transects as well as
opportunistically in drainages and other suitable puma habitat in the reserve. Collection
began prior to camera trapping but occurred in areas where grids eventually were
established. Eight 4-km transects were walked in the area occupied by each camera
grid repeatedly in mid-late summer 2015 (January-April) and winter 2015 (May-
September). From mid-winter 2015 through mid-summer 2016 (July 2015 - January
2016), I also searched for puma scats along the paths used when I was installing or
checking cameras. Puma scats were distinguished from those of other sympatric
predators (e.g., foxes and small cats) by their size and shape; identification was later
confirmed by DNA analysis. Scats were air-dried and stored with silica gel in labelled
paper envelopes, following the protocol of American Museum of Natural History (AMNH)
for preservation of scats for DNA analysis (conducted by AMNH). A small piece of each
sample was selected for DNA analysis and the remainder of the samples were washed
and sundried for lab analysis of hair and bones of consumed prey.
Prey items in scats were identified to species using identification keys for
mammalian hair for species from Patagonia (Chehébar and Martín 1989) and by
comparing hairs to known hair samples obtained from specimens from the Florida
Museum of Natural History and from dead animals found in the reserve. Hair samples
were analyzed macroscopically and microscopically. First, I characterized hairs by
shape, coloration, and presence of bands using a magnifying glass. Second, hair shafts
were observed under the microscope and dark hairs were cleared with commercial hair
clearer when necessary in order to observe the medulla. Third, I analyzed hair cuticles
(scale pattern) under the microscope. The scale patterns were obtained by pressing the
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hair on slides painted with transparent nail polish and observing the negative cast
(Weingart 1973; Rau and Jiménez 2010; Juárez-Sánchez et al. 2007).
Diet Analysis
To test for changes in the diet of pumas with the guanaco migration, prey items
in scats were divided in two major groups: guanacos and alternative prey, including
small and medium-sized mammals and livestock (Table 2-2). Because of the small
number of scats, particularly in the south, analyses were limited to scats collected in the
northern part of the study area (i.e., summer range of guanacos). I compared frequency
of prey items consumed between winter and summer using a Fisher’s Exact test (Zar
1999; Soto-Shoender and Giuliano 2011).
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Figure 2-1. La Payunia Reserve. A) Location of the reserve in Argentina, and B)
boundaries of the reserve and approximate layout of north and south grids within the reserve (red boxes).
A B
48 Km
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Figure 2-2. Grid layout and camera station locations (dots) in the La Payunia Reserve.
The north grid is located at the summer range of guanacos, and the south grid is located at their winter range. Each grid cell is 6 x 6 km.
Su
mm
er
ran
ge
of g
ua
na
co
s
Win
ter
ran
ge
of g
ua
na
co
s
29 Km
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Table 2-1. Top five spatial mark-resight (SMR) models for density of pumas. Models were ranked using Akaike’s Information Criterion adjusted for small sample
sizes (AICc). See text for explanation of covariates. Δ AICc= differences in AICc; σ = spatial scale parameter; K= number of parameters in the model; λ0 = probability of individual detection.
Table 2-2. Percent of occurrence of prey items in puma scats in summer (n =25 scats)
and winter (n =14 scats) in the north of La Payunia Reserve. Numbers in parentheses correspond to counts of prey items.
Model AICc Δ AICc AICc Weight σ (SE) K λ0
D(grid) λ0(site), σ (.) 1167 0 0.99 1485 (75) 5 0.011
D(.) λ0(site), σ (.) 1202 35 <0.01 1496 (76) 4 0.010
D(season) λ0(.), σ (.) 1305 138 <0.01 1326 (141) 4 0.029
D(season) λ0(site), σ (.) 1310 142 <0.01 1514 (180) 5 0.010
D(session) λ0(site), σ (.) 1432 256 <0.01 755 (28) 7 0.019
Percent of occurrence
Prey Winter Summer
Guanaco
Lama guanicoe 85.71 (12) 64 (16)
Alternative Prey
Lagostomus maximus 0 20 (5)
Lepus europaeus 14.29 (2) 12 (3)
Zaedius pichii 7.14 (1) 4 (1)
Leopardus geoffroy 0 4 (1)
Microcavia australis 0 4 (1)
Other small mammals 0 8 (2)
Cow (Bos taurus) 7.14 (1) 0
Goat (Capra hircus) 0 4 (1)
Unidentified mammal 0 12 (3)
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CHAPTER 3 RESULTS
Identification of Individual Pumas
I obtained 149 photographs of pumas on the north grid from which I identified 12
adult individuals (5 males, 6 females and 1 unsexed) (Table 3-1). These individuals
occurred in 59 of 149 photographs. For the south grid, I obtained 59 records of pumas
and identified 6 adults (4 males and 2 females). These individuals occurred in 23 of 59
photographs. Only one male was detected in both the north and south grids.
Puma Density Estimates
Density estimates from SMR models do not provide strong support for movement
of pumas with migration of guanacos. Puma density estimates for the north grid in
summer were about 19% higher than estimates for winter (Table 3-2), when many
guanacos had migrated to the winter range to the south, and counts of pumas in
photographs (uncorrected for detectability) peaked at the time guanaco abundance was
highest in the north grid (Figures C-1 and C-2). However, in the south grid, estimates of
puma density also were slightly higher for summer than in winter, opposite to the
expected pattern. Sample sizes were low, particularly in the south grid, and confidence
intervals were large in all cases. The model that included both season and grid (i.e.,
session) was not among the top-ranked models for density (Table 2-1).
The top-ranked model for density (i.e., the model with the lowest AICc value) was
one where density varied between grids, and capture probability was associated with
the location of the camera station (i.e., site in a drainage versus open habitat; Table 2-
1). However, because data from 190 days were used in the density estimate for each
grid, this model may violate the assumption of demographic closure. This problem also
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may occur in the second ranked model, which was the base model with no factors. The
next three models, which modeled density separately for winter and summer seasons
(95 days each season), likely do not violate the assumption of demographic closure if
pumas breed and disperse on an annual cycle. Based on AICc, the highest ranked
model that most certainly does not violate demographic closure included season, but
not grid, and included no effect of camera location on capture probability. However,
confidence intervals are high from this model. The poor fit of the model that included
season and grid (the 5th ranked model) was somewhat surprising for two reasons: 1) A
model with density as a function of grid was the highest ranked model, and raw counts
of photographs with pumas appears to support differences in grids (Figure C-2); 2)
Camera site is important in determining capture probability in all other models.
However, sample sizes, especially recaptures, were low when photographs were
divided by two grids and two seasons, and likely contributed to the poor fit of this model
(Table 3-1).
Density estimates of pumas ranged from 1.63/100 km2 (+0.52 SE) in the south
grid to 3.27/100 km2 (+0.71 SE) in the north grid for the best-fitting model, and from
1.50/100 km2 (+0.30 SE) to 1.93/100 km2 (+0.38 SE) in winter versus summer for the
best model restricted to a sampling period of 95 days (Table 3-2). Inclusion of site as a
covariate for capture probability only resulted in slightly higher density estimates, even
though site was an important factor in most models (Table 2-1 and Table 3-2). The 12-
km buffer incorporated around camera stations was sufficient; probability of detection
declined to near 0 by 12 km (Figure D-1).
27
Diet Analysis
I collected a total of 129 puma scats. However, only 43 scats (39 from north grid,
4 from south grid) were fairly fresh and thus could be clearly identified as being
deposited during the period when guanacos were present or absent in the area.
Because of the small sample size from south grid, I only analyzed scats collected in the
north grid.
I detected a total of 49 prey items corresponding to 9 different mammal species
(Table 2-2). In contrast to my prediction, pumas do not switch to alternative prey during
winter when guanaco availability is lower in the north grid (Fisher’s Exact test, p =
0.072, n = 39 scats). Remains of guanaco were found in 85% of puma scats during the
winter (n = 16) and only in 64% of scats during the summer (n = 33). Overall, guanacos
were the most common item in scats (71.8% of the scats), followed in order of
importance by plain vizcachas (Lagostomus maximus) and European hares (Lepus
europaeus), dwarf armadillos (Zaedyus pichiy), and small rodents. Livestock remains
(cow, Bos taurus and goat, Capra hircus) were found only in one scat in summer and
one in winter.
28
Table 3-1. The number of pumas identified to the individual level (n*), the total number
of puma photographs that were labeled as individually identifiable (ID) and unidentifiable (non-ID) per season and grid. MMDM= Mean Maximum
Distance Moved; RDM= Range Distance Moved; MNR= Mean Number of Recaptures; RNR= Range Number of Recaptures.
Table 3-2. Density estimates for pumas obtained from the five highest ranked spatial mark-resight models (i.e., models with the lowest AICc values).
Model
Density [pumas/100 km2 (SE)]
Season Grid
Grids
combined North South
D(grid) λ0(site), σ (.) Seasons combined 3.27 (0.71) 1.63 (0.52)
D(.) λ0(site), σ (.) Seasons combined 2.81 (0.46)
D(season) λ0(.), σ (.) Summer 1.93 (0.38)
Winter 1.50 (0.30)
D(season) λ0(site), σ (.) Summer 2.09 (0.43)
Winter 1.61 (0.34)
D(session) λ0(site), σ (.) Summer 4.27 (1.06) 2.25 (0.81)
Winter 3.59 (0.92) 1.80 (0.72)
n* ID non-ID MMDM (km) RDM (km) MNR RNR
North Grid 6.23 1.60 – 21.03 3.83 1 - 9
Summer 11 39 51
Winter 9 20 39
South Grid 6.84 2.17 – 11.13 3 1 - 5
Summer 5 12 20
Winter 4 11 16
29
CHAPTER 4 DISCUSSION
My results indicate that most pumas are not following the migration of guanacos
in La Payunia Reserve or switching to alternative prey. Density estimates of pumas did
not increase significantly in the winter and summer range of guanacos when guanacos
migrated to these areas, although counts of pumas in the summer range increased
slightly during the period when guanacos were most abundant and giving birth to
offspring. Only one of the 18 pumas individually identified was detected in the north and
south of the reserve. This puma was detected only once in the north grid in late
September and then was recorded 45 km away in the south grid in December. This
movement was in the opposite direction that would be expected if this puma was
following the guanaco migration. Although my sample of fresh scats was small, pumas
clearly feed primarily on guanacos throughout the year and, contrary to my predictions,
do not switch to alternative native prey or livestock when guanacos migrate. They
consume some small and medium-sized mammals in both winter and summer, and
livestock occurred at a very low frequency in the diet of pumas (5%) during both
seasons.
Migrations of large ungulates and other prey for top predators may include
movement of all individuals between seasonal ranges, or only part of the prey
population may migrate (Elbroch et al. 2013; Bolgeri and Novaro 2015). These two
patterns have very different implications for prey availability for predators. Even though
the majority of guanacos in La Payunia migrate south in winter (Ruiz Blanco and
Novaro, unpublished data), part of the population does not move (Bolgeri and Novaro
2015). These resident guanacos, together with some alternative prey, apparently
30
provide sufficient food so that pumas do not have to follow the migration of their primary
prey. In addition, pumas may specialize on killing guanacos in La Payunia, as has been
reported for southern Chile (Elbroch and Wittmer 2012), and, thus, may be efficient at
taking guanacos even when guanaco density is low.
Data on responses of pumas to migratory prey are still scarce but suggest that
pumas exhibit considerable flexibility (Fryxell and Sinclair 1988; Ballard et al. 1997;
Pierce et al. 1999; Elbroch et al. 2013). Similar to my study, pumas in Yellowstone
remain relatively stationary during seasonal migrations of their ungulate prey. However,
in Yellowstone, pumas have the option of feeding on a variety of ungulates and switch
prey as prey density changes with migrations (Elbroch et al. 2013). In contrast, in
California pumas move seasonally with their primary prey, mule deer (Odocoileus
hemionus), perhaps because few deer remain resident throughout the year and other
prey are scarce (Kucera or Weyhausen, personal communication – to be confirmed).
Density of pumas was lower in the southern part of my study area compared to
north. Several factors may explain this pattern, and landscape level factors that
potentially influence puma density (e.g., presence of volcanoes, distance from roads
and settlements, etc.) will be explored more thoroughly in future models by
incorporating density covariates. On the one hand, the habitat in the south and the north
of the reserve is somewhat different. The north grid has more rocky outcrops and large
volcanos than the south grid. In the south, volcanoes have gentler slopes with relatively
low height to diameter ratio (Llambías 2008). Also, vegetation cover is lower in the
south. Together these habitat differences may mean fewer refuges for pumas and less
cover for pumas stalking prey. Another potential explanation for the differences in puma
31
density is that human-induced mortality is higher for pumas in the south. This area
includes large amounts of private land and is poorly protected compared to northern
part of the reserve. This is cause for concern and points to the need to monitor puma
populations with methods that can detect mortality (e.g., telemetry) and to work with
private landowners in the south on puma conservation.
Density estimates for pumas obtained in this study (Table 3-2) are similar to the
only previous estimate from Patagonia and parts of the western US with large expanses
of open habitat as in Patagonia (e.g., Idaho and Wyoming; Table 4-1), even though
density estimates in these studies were based on very different methods (e.g., capture-
recapture and telemetry studies; Seidensticker et al. 1973; Logan et al. 1986; Elbroch
and Wittmer 2012). My estimates on puma density in the north of La Payunia, the area
with highest protection, are more than four times that reported for sites in northern
Argentina with high poaching pressure (Atlantic Forest, Kelly et al. 2008; semi-arid
Chaco, Quiroga et al. 2016; Table 4-1), but closer to estimates reported for sites with
low poaching pressure in the Atlantic Forest (Paviolo et al. 2009). Even though
poaching occurs in La Payunia, likely poaching is much lower than in most of the
Atlantic Forest and Chaco, which have much larger human populations (Núñez-
Regueiro et al. 2015; Altrichter et al. 2006; Kelly et al. 2008). Also, habitat in Payunia is
not fragmented as in the Atlantic forest and Chaco.
In addition to increasing understanding of the role of migration and predator-prey
dynamics in this system, baseline data on densities of pumas is important for
conservation and management. Pumas are heavily controlled outside protected areas in
Patagonia because of their impact on livestock, and sport hunting occurs in some
32
regions of Argentina (Novaro and Walker 2005). For elusive species such as pumas,
obtaining density estimates can be challenging. In my case, the lack of trails or other
features to guide pumas between cameras, made captures with cameras difficult. Many
of the photographs were taken with pumas at long distances from the camera and often
photos did not include both sides of the puma, which made detection of natural marks
difficult and hindered individual identification. The use of SMR helps overcome some of
these difficulties because unidentified individuals can be included in models. To date,
only one study has used SMR models to estimate densities for pumas. This study found
that different statistical techniques produce different estimates and that SMR models
increased precision compared to other analyses (Rich et al. 2014). Although obtaining
sufficiently large samples for robust estimates still remains a challenge, my study
demonstrates that the combination of camera traps and SMR analysis appears to be a
promising tool to help estimate population numbers for such elusive species, even in
open, non-forested habitat.
33
Table 4-1. Estimates of puma densities from other studies in Central and South America and from studies in the western US with habitat similar to La Payunia.
Country Habitat Adult pumas/
100 km2
Method
used Analysis
Argentina1 Atlantic Forest with high poaching pressure
0.47-0.87 Camera trapping
Capture
Argentina2 Atlantic Forest with high poaching
pressure
0.6 Camera trapping
SMR
Argentina3 semi-arid Chaco <1 Camera trapping
SECR- Capture
Argentina4 Atlantic Forest with low poaching
pressure
2.22 Camera trapping
Capture. Model Mh
Bolivia1 Chaco dry forest 5.3-8.3 Camera trapping
Capture
Bolivia2 Chaco dry forest 11.22 Camera trapping
SMR
Belize1 Tropical forest 2.12-4.72 Camera trapping
Capture
Belize2 Tropical forest 1.42 Camera
trapping SMR
Chile5 Patagonian steppe
and forest 3.44
radio-
telemetry
Home range
analysis
USA, New
Mexico6
San Andres Mountains,
Chihuahuan desert
1.7-3.9 radio-
telemetry
Home range
analysis
USA, Idaho7
Ponderosa pine
and desert shrublands
1.7-3.5 radio-
telemetry
Home range
analysis
USA, Utah8 Pinyon pine and
desert shrublands 0.58-1.4
radio-
telemetry
Home range
analysis
USA,
Wyoming9
Ponderosa pine
and desert grass and shrublands
3.5-4.6 radio-
telemetry
Overlapping
home ranges
1 Kelly et al. 2008, 2 Rich et al. 2014, 3 Quiroga et al. 2016, 4 Paviolo et al. 2009, 5 Elbroch and Wittmer
2012, 6 Logan et al. 1996, 7 Seidensticker et al. 1973, 8 Lindzey et al. 1994, 9 Logan et al. 1986.
34
APPENDIX A PUMAS CAPTURE HISTORIES
Table A-1. Capture histories for pumas individually identified on the north and south
grids. Data are in the format as used to model density by session. Session – grid location and sampling season; IndID – label for individually identified pumas; Occasion – time interval of animal capture; SiteID – identification
label for camera station.
#Session IndID Occasion SiteID
north_summer puma1 5 N5_18
north_summer puma1 6 N5_18
north_summer puma7 6 N7_27
north_summer puma7 7 N7_27
north_summer puma4 7 N7_27
north_summer puma4 7 N7_27
north_summer puma11 7 N14_88
north_summer puma5 7 N7_27
north_summer puma7 7 N7_27
north_summer puma3 7 N7_27
north_summer puma9 7 N7_27
north_summer puma1 7 N1_142
north_summer puma4 8 N7_27
north_summer puma5 8 N7_27
north_summer puma5 8 N7_27
north_summer puma1 8 N5_18
north_summer puma10 8 N7_27
north_summer puma4 8 N7_27
north_summer puma4 8 N7_27
north_summer puma9 8 N7_27
north_summer puma10 8 N7_61
north_summer puma8 8 N6_74
north_summer puma4 8 N7_27
north_summer puma5 8 N11_69
north_summer puma6 8 N7_27
north_summer puma2 8 N5_95
north_summer puma6 9 N7_27
north_summer puma1 9 N5_18
north_summer puma6 9 N11_69
north_summer puma6 9 N7_27
north_summer puma5 9 N7_27
north_summer puma6 9 N7_27
north_summer puma6 9 N7_27
35
Tabla A-1. Continued
#Session IndID Occasion SiteID
north_summer puma2 10 N5_95
north_summer puma11 10 N10_101
north_summer puma11 10 N14_88
north_summer puma11 10 N14_88
north_summer puma8 10 N6_74
north_summer puma1 10 N5_18
north_winter puma1 1 N1_142
north_winter puma10 1 N7_61
north_winter puma12 2 N14_16
north_winter puma3 2 N7_27
north_winter puma12 2 N14_16
north_winter puma3 2 N7_27
north_winter puma1 3 N5_18
north_winter puma3 3 N7_27
north_winter puma7 3 N8_29
north_winter puma4 4 N8_29
north_winter puma4 4 N9_99
north_winter puma4 4 N8_29
north_winter puma11 4 N18_126
north_winter puma11 4 N18_126
north_winter puma4 4 N7_27
north_winter puma5 5 N7_27
north_winter puma8 5 N14_88
north_winter puma8 5 N14_88
north_winter puma8 5 N14_16
north_winter puma17 5 N6_20
south_summer puma16 6 S5_124
south_summer puma16 6 S6_64
south_summer puma14 6 S5_124
south_summer puma18 7 S6_107
south_summer puma13 8 S6_64
south_summer puma16 8 S6_64
south_summer puma16 8 S13_11
south_summer puma13 9 S5_124
south_summer puma17 9 S14_21
south_summer puma13 9 S6_64
south_summer puma14 10 S5_124
south_summer puma17 10 S14_104
south_winter puma13 2 S6_64
south_winter puma13 2 S6_64
south_winter puma15 3 S6_64
36
Table A-1. Continued
#Session IndID Occasion SiteID
south_winter puma14 3 S5_124
south_winter puma18 3 S14_21
south_winter puma18 4 S14_21
south_winter puma18 4 S14_21
south_winter puma18 4 S14_21
south_winter puma15 4 S2_91
south_winter puma18 5 S14_21
south_winter puma14 5 S5_124
Table A-2. Capture histories for non-identified individuals for the north and south grids. Data are in the format as used to model density by session. Session – grid
location and sampling season; IndID – UN for unidentified pumas; Occasion – time interval of animal capture; SiteID – identification label for camera station.
#Session IndID Occasion SiteID
north_summer UN 6 N10_101
north_summer UN 6 N5_18
north_summer UN 6 N14_88
north_summer UN 6 N7_27
north_summer UN 6 N10_48
north_summer UN 6 N7_61
north_summer UN 6 N15_58
north_summer UN 6 N13_141
north_summer UN 6 N15_58
north_summer UN 6 N7_27
north_summer UN 6 N10_48
north_summer UN 6 N4_96
north_summer UN 7 N5_18
north_summer UN 7 N13_90
north_summer UN 7 N7_27
north_summer UN 7 N5_18
north_summer UN 7 N5_18
north_summer UN 7 N7_61
north_summer UN 7 N7_27
north_summer UN 7 N10_48
north_summer UN 7 N13_141
north_summer UN 7 N12_92
north_summer UN 8 N10_48
north_summer UN 8 N7_27
north_summer UN 8 N7_61
north_summer UN 8 N14_16
37
Table A-2. Continued
#Session IndID Occasion SiteID
north_summer UN 8 N7_27
north_summer UN 8 N7_61
north_summer UN 8 N7_27
north_summer UN 8 N12_92
north_summer UN 8 N7_27
north_summer UN 8 N11_69
north_summer UN 8 N13_141
north_summer UN 9 N11_4
north_summer UN 9 N13_90
north_summer UN 9 N7_27
north_summer UN 9 N7_27
north_summer UN 9 N14_88
north_summer UN 9 N6_20
north_summer UN 9 N7_61
north_summer UN 9 N3_89
north_summer UN 9 N7_27
north_summer UN 10 N14_88
north_summer UN 10 N10_48
north_summer UN 10 N12_92
north_summer UN 10 N7_27
north_summer UN 10 N5_18
north_summer UN 10 N7_27
north_summer UN 10 N6_74
north_summer UN 10 N5_18
north_summer UN 10 N14_16
north_winter UN 1 N10_48
north_winter UN 1 N7_61
north_winter UN 1 N10_48
north_winter UN 1 N7_61
north_winter UN 2 N11_69
north_winter UN 2 N13_141
north_winter UN 2 N13_90
north_winter UN 2 N6_74
north_winter UN 2 N14_88
north_winter UN 2 N10_48
north_winter UN 2 N14_16
north_winter UN 2 N10_101
north_winter UN 2 N11_69
north_winter UN 2 N7_27
north_winter UN 2 N10_48
38
Table A-2. Continued
#Session IndID Occasion SiteID
north_winter UN 2 N11_4
north_winter UN 2 N14_88
north_winter UN 3 N10_48
north_winter UN 3 N13_141
north_winter UN 3 N18_35
north_winter UN 3 N10_48
north_winter UN 3 N10_48
north_winter UN 3 N14_88
north_winter UN 3 N7_27
north_winter UN 4 N14_16
north_winter UN 4 N10_48
north_winter UN 4 N14_16
north_winter UN 4 N14_88
north_winter UN 4 N7_27
north_winter UN 4 N7_61
north_winter UN 4 N18_35
north_winter UN 4 N7_27
north_winter UN 5 N7_27
north_winter UN 5 N7_27
north_winter UN 5 N5_95
north_winter UN 5 N14_88
north_winter UN 5 N5_18
north_winter UN 5 N6_20
north_winter UN 5 N7_61
south_summer UN 6 S6_64
south_summer UN 6 S10_52
south_summer UN 7 S14_21
south_summer UN 7 S9_65
south_summer UN 7 S10_52
south_summer UN 7 S10_52
south_summer UN 7 S13_11
south_summer UN 7 S9_65
south_summer UN 7 S6_64
south_summer UN 7 S2_15
south_summer UN 8 S2_91
south_summer UN 8 S9_65
south_summer UN 8 S2_15
south_summer UN 8 S6_64
south_summer UN 9 S14_39
south_summer UN 9 S12_71
39
Table A-2. Continued
#Session IndID Occasion SiteID
south_summer UN 9 S14_39
south_summer UN 9 S14_21
south_summer UN 9 S6_64
south_summer UN 10 S13_11
south_winter UN 2 S9_87
south_winter UN 2 S4_1
south_winter UN 2 S2_91
south_winter UN 2 S14_21
south_winter UN 2 S2_15
south_winter UN 3 S14_39
south_winter UN 3 S5_124
south_winter UN 4 S14_42
south_winter UN 4 S5_124
south_winter UN 4 S4_1
south_winter UN 5 S14_21
south_winter UN 5 S6_64
south_winter UN 5 S6_64
south_winter UN 5 S6_64
south_winter UN 5 S10_97
south_winter UN 5 S6_64
40
APPENDIX B ADDITIONAL MODELS EXAMINED WITH COVARIATES FOR DETECTION PROBABILITY
Table B-1. Model comparison table to determine best fitting model for detection probability parameter in multi -session
density models using Akaike’s Information Criterion adjusted for small sample sizes (AICc). See text for
explanation of covariates. Δ AICc= differences in AICc; σ= spatial scale parameter; K= number of parameters in the model; λ0= probability of individual detection.
Table B-2. Model comparison table to determine best-fitting model for detection probability parameter with density
constant. Ranking is based on Akaike’s Information Criterion adjusted for small sample sizes (AICc). See text for explanation of covariates. Δ AICc= differences in AICc; σ= spatial scale parameter; K= number of parameters in the model; λ0= probability of individual detection.
Model AICc Δ AICc σ (SE) K λ0
D(session) λ0(site), σ(.) 1432 0.00 755 (28) 7 0.019
D(session) λ0(.), σ(.) 1433 0.30 695 (20) 6 0.007
D(session) λ0(b), σ(.) 1551 118 733 (23) 7 0.001
D(session) λ0(t), σ(.) 1564 131 791 (24) 16 0.001
D(session) λ0(k), σ(.) 1719 287 1766 (187) 7 0.0000007
Model AICc Δ AICc σ (SE) K λ0
D(.) λ0(site), σ(.) 1203 0.00 771 (28) 4 0.001
D(.) λ0(.), σ(.) 1549 346 727 (24) 3 0.005
D(.) λ0(t), σ(.) 1624 421 834 (84) 13 0.001
D(.) λ0(b), σ(.) 1926 723 772 (25) 4 0.0007
D(.) λ0(k), σ(.) 2183 980 1712 (176) 4 0.0000007
41
APPENDIX C COUNTS OF GUANACOS AND PUMAS IN BOTH STUDY GRIDS
Index of guanaco abundance
Although the general pattern of guanaco migration is known for the reserve
(Schroeder et al. 2014) and additional seasonal censuses are under way (Ruiz Blanco
and Novaro pers. comm), data on seasonal changes in guanaco density were not
available for my grids. Therefore, I derived an index of guanaco abundance for each
grid in winter and summer based on photographs of guanacos at my camera stations.
From the database of photographic records obtained with camtrapR, I conducted a
count of the number of times guanacos were detected by cameras using 1
photograph/station/day as an independent record. Counts were summed for 14-day
intervals. I did not attempt to count the number of guanacos in photographs because I
was not certain that this would give me a good estimate of the real number of guanacos
in the site (photographs likely contained parts of large herds) and also due to time
constraints.
In summer, the number of photographic detections of guanacos increased in the
north of the reserve and decreased in the south during the same period, following the
observed seasonal migration of this species in the area (Figure C-1). A peak occurred in
detections of guanacos in the north during the period when guanacos give birth to their
offspring (11/22-12/4). In the winter season, detections of guanacos were much lower in
the north grid than during summer, but number of photographs with guanacos did not
increase greatly in the south. Detection may have underestimated guanaco abundance
in winter because guanacos are more aggregated in herds during this period.
42
Figure C-1. Counts of guanacos in the north and south grids using 1 photograph per camera station per day as an independent record. Counts were summed for
intervals of 14 days. The numbers above the columns correspond to the percentage of cameras in operation at that period. Periods with no number
above the columns had 100% of the cameras in operation.
0
10
20
30
40
50
60
70
80
90
100
Gu
an
aco
co
un
t
Count period
NORTH GRID SOUTH GRID
Winter Summer
9
88
70
94
20
43
Figure C-2. Counts of pumas in the north and south grids using 1 photograph per
camera station per day as an independent record. Solid bars represent
individuals that could be identified individually (ID). Bars with patterns indicate pumas that could not be identified individually (non-ID). Counts were summed
over 14-day intervals. The numbers above the columns correspond to the percentage of cameras in operation at that period. Periods with no number above the columns had 100% of the cameras in operation.
0
5
10
15
20
25P
um
a c
ou
nts
Count period
NORTH GRID ID NORTH GRID nonID SOUTH GRID ID SOUTH GRID nonID
9
88
70
94
20
Winter Summer
44
APPENDIX D PLOT GENERATED BY SPATIAL MARK- RESIGHT TO DETERMINE APPROPIATE
BUFFER SIZE
Figure D-1. Capture or detection probability as a function of distance from the center of a home range for the best-fitting SMR model. Note that capture probability is near 0 at 12000 m, which represents the edge of the buffer used in my
models.
45
APPENDIX E ADDITIONAL MODELS EXAMINED FOR DENSITY
Table E-1. Model comparison table to determine best fitting model for density with constant detection probability, using Akaike’s Information Criterion adjusted for small sample sizes (AICc). Δ AICc= differences in AICc; σ= spatial scale parameter; K= number of parameters in the model; λ0= probability of individual detection.
Model AICc Δ AICc σ (SE) K λ0
D(grid) λ0(.), σ (.) 1174.41 0.00 1393 (67) 4 0.003
D(season) λ0 (.), σ (.) 1305.49 131.08 1326 (141) 4 0.002
D(session) λ0(.), σ (.) 1433.22 258.81 695 (20) 6 0.007
D(.) λ0(.), σ (.) 1549.20 374.79 727 (23) 3 0.005
Table E-2. Model comparison table to determine influence of season in North Grid, using Akaike’s Information Criterion adjusted for small sample sizes (AICc). Δ AICc= differences in AICc; σ= spatial scale parameter; K= number of parameters in the model; λ0= probability of individual detection; D= density estimates obtained for summer (s)
and winter (w).
Model AICc Δ AICc σ (SE) K λ0 D
D(.) λ0(.), σ(.) 867.91 0.00 1187 (142) 3 0.003 2.45
D(season) λ0(.), σ (.) 869.42 1.51 1185 (142) 4 0.003 s= 2.64 w=2.23
Table E-3. Model comparison table to determine influence of season in South Grid, using Akaike’s Information Criterion adjusted for small sample sizes (AICc). Δ AICc= differences in AICc; σ= spatial scale parameter; K= number of parameters in the model; λ0= probability of individual detection; D= density estimates obtained for summer (s)
and winter (w).
Model AICc Δ AICc σ (SE) K λ0 D
D(.) λ0(.), σ(.) 750.96 0.00 1480 (296) 3 0.000002 6.70
D(season) λ0(.), σ (.) 764.25 13.29 1480 (296) 4 0.000002 s= 7.41 w=5.93 Note: The detectability (λ) is extremely low and the density estimate is unrealistically high.
46
LIST OF REFERENCES
Altrichter, M. 2006. Wildlife in the life of local people of the semi-arid Argentine Chaco.
Human Exploitation and Biodiversity Conservation. Springer Netherlands 379-396.
Arroyo-Arce, S., J. Guilder, and R. Salom-Pérez. 2014. Habitat features influencing
jaguar Panthera onca (Carnivora: Felidae) occupancy in Tortuguero National
Park, Costa Rica. Revista de Biología Tropical 62:1449-1458.
Ballard, W. B., L. A. Ayres, P. R Krausman, D. J. Reed, and S. G. Fancy. 1997. Ecology of wolves in relation to a migratory caribou herd in northwest Alaska. Wildlife Monographs 3-47.
Bergerud, A. T. 1983. Prey switching in a simple ecosystem. Scientific American
249:130-141. Bolgeri, M. J., and A. J. Novaro. 2015. Variación espacial en la depredación por puma
(Puma concolor) sobre guanacos (Lama guanicoe) en La Payunia, Mendoza, Argentina. Mastozoología Neotropical 22:255-264.
Candia, R., S. Puig, and A. Dalmasso. 1993. Diseño del plan de manejo para la
Reserva Provincial La Payunia (Malargue, Mendoza). Multequina 2:5-87.
Chandler, R. B., and J. A. Royle. 2013. Spatially explicit models for inference about
density in unmarked or partially marked populations. Annals of Applied Statistics 7:936-954.
Chehébar, C., and S. Martín. 1989. Guía para el reconocimiento microscópico de los pelos de los mamíferos de la Patagonia. Doñana Acta Vertebrata 16:247-291.
Efford, M. 2004. Density estimation in live-trapping studies. Oikos 106:598–610.
Efford, M. 2016. secr 2.10-spatially explicit capture–recapture in R.
Elbroch, L. M., and H. U. Wittmer. 2012. Puma spatial ecology in open habitats with aggregate prey. Mammalian Biology-Zeitschrift für Säugetierkunde 77(5):377-384.
Elbroch, L.M., P.E. Lendrum, J. Newby, H. Quigley, and D. Craighead. 2013. Seasonal
foraging ecology of non-migratory cougars in a system with migrating prey. PLoS ONE 8:e83375.
Fryxell, J. M., and A. R. E. Sinclair. 1988. Causes and consequences of migration by large herbivores. Trends in Ecology & Evolution 9:237-241.
47
Giroux, M. A., D. Berteaux, N. Lecomte, G. Gauthier, G. Szor, and J. Bêty. 2012. Benefiting from a migratory prey: Spatio-temporal patterns in allochthonous
subsidization of an arctic predator. Journal of Animal Ecology 3:533-542.
Iriarte, J. A., W. E. Johnson, and W. L. Franklin. 1991. Feeding ecology of the Patagonia puma in southernmost Chile. Revista Chilena de Historia Natural 64:145-156.
Juárez-Sánchez, A. D., C. G Estrada, M. Bustamante, Y. Quintana, and J. E. López.
2007. Guía ilustrada de pelos para la identificación de mamíferos medianos y mayores de Guatemala. Dirección General de Investigación, Universidad de San Carlos de Guatemala 28.
Keehner, J. R., R. B. Wielgus, and A. M. Keehner. 2015. Effects of male targeted
harvest regimes on prey switching by female mountain lions: Implications for apparent competition on declining secondary prey. Biological Conservation 192:101-108.
Kelly, M. J., A. J. Noss, M. S. Di Bitetti, L. Maffei, R. L. Arispe, A. Paviolo, C.D. De
Angelo, and Y.E. Di Blanco. 2008. Estimating puma densities from camera trapping across three study sites: Bolivia, Argentina, and Belize. Journal of Mammalogy 89(2):408-418.
Llambías, E. J. 2008. El distrito volcánico de la Payunia: un paisaje lunar en nuestro
planeta. Sitios de interés geológico de la República Argentina. Buenos Aires 263-280.
Lindzey, F. G., W. D. Van Sickle, B. B. Ackerman, D. Barnhurst, T. P. Hemker, and S. P. Laing. 1994. Cougar population dynamics in southern Utah. The Journal of
Wildlife Management 619-624. Logan, K. A., and L. L. Sweanor. 2001. Desert puma: evolutionary ecology and
conservation of an enduring carnivore. Island Press, Washington, USA.
Logan, K. A., L. I. Larry, and R. Skinner. 1986. Characteristics of a hunted mountain lion population in Wyoming. The Journal of wildlife management 648-654.
Logan, K. A., L. L. Sweanor, T. K. Ruth, and M. G. Hornocker. 1996. Cougars of the San Andres Mountains, New Mexico. Final Report, Federal Aid in Wildlife
Restoration Project W-128-R. New Mexico Department of Game and Fish, Santa Fe, NM.
Marker, L. and A. Dickman. 2003. Conserving cheetahs outside protected areas: an example from Namibian farmlands. Cat News 38:24-25.
48
Martínez, J. I. Z., A. Travaini, S. Zapata, D. Procopio, and M. A. Santillán. 2012. The ecological role of native and introduced species in the diet of the puma Puma
concolor in southern Patagonia. Oryx 46(01):106-111.
Morehouse, A. T., and M. S. Boyce. 2011. From venison to beef: Seasonal changes in wolf diet composition in a livestock grazing landscape. Frontiers in Ecology and the Environment 9:440-445.
Moraga, C., M. C. Funes, J. C. Pizarro, C. Briceño, A. J. and Novaro. 2015. Effects of
livestock on guanaco density, movements and habitat selection in a forest-grassland mosaic in Tierra del Fuego, Chile. Oryx 1:30-4.
Mueller, T., K. A. Olson, G. Dressler, P. Leimgruber, T. K. Fuller, C. Nicolson, A. J. Novaro, M. J. Bolgeri, D. Wattles, S. DeStefano, and J. M. Calabrese. 2011. How landscape dynamics link individual‐to population‐level movement patterns: a
multispecies comparison of ungulate relocation data. Global Ecology and
Biogeography 20(5):683-694.
Negrões, N., P. Sarmento, J. Cruz, C. Eira, E. Revilla, C. Fonseca, R. Sollmann, N. M. Torres, M. M. Furtado, A. T. Jácomo, and L. Silveira. 2010. Use of camera‐trapping to estimate puma density and influencing factors in central Brazil. The Journal of Wildlife Management 74:1195-1203.
Nelson, A., M. J. Kauffman, A. D. Middleton, M. Jimenez, D. McWhirter, J. Barber, and K. Gerow. 2012. Elk migration patterns and human activity influence wolf habitat
use in the Greater Yellowstone Ecosystem. Ecological Applications 22:2293-2307.
Niedballa, J., R. Sollmann, A. Courtiol, and A. Wilting. 2016. camtrapR: an R package for efficient camera trap data management. Methods in Ecology and Evolution.
Noss, A. J., B. Gardner, L. Maffei, E. Cuéllar, R. Montaño, A. Romero‐Muñoz, R.
Sollman, and A. F. O'Connell. 2012. Comparison of density estimation methods for mammal populations with camera traps in the Kaa‐Iya del Gran Chaco
landscape. Animal Conservation 15(5):527-535.
Novaro, A. J. and R. S. Walker. 2005. Human-induced changes in the effect of top carnivores on biodiversity in the Patagonian Steppe. Pp. 268-288 in Large
Carnivores and the Conservation of Biodiversity. Island Press, Washington. Novaro, A. J., S. Walker, M. J. Bolgeri, J. Berg, L. Rivas, and P. Carmanchahi. 2006.
Tercer informe de avance: Movimientos estacionales en la población de guanacos de la Payunia. Wildlife Conservation Society, Argentina. Dirección de
Recursos Naturales Renovables de Mendoza, Provincia de Mendoza, Argentina.
49
Núñez-Regueiro, M.M., L. Branch, R. J. Fletcher, G. A. Marás, E. Derlindati, and A. Tálamo. 2015. Spatial patterns of mammal occurrence in forest strips surrounded
by agricultural crops of the Chaco region, Argentina. Biological Conservation 187:19-26.
Ortega, I. M., and W. L. Franklin. 1995. Social organization, distribution and movements
of a migratory guanaco. Revista Chilena de Historia Natural 68:489-500.
Paviolo, A., Y. E. Di Blanco, C. D. De Angelo, and M. S. Di Bitetti. 2009. Protection
affects the abundance and activity patterns of pumas in the Atlantic Forest. Journal of mammalogy 90(4): 926-934.
Pierce, B. M., V. C. Bleich, J. D. Wehausen, and R. T. Bowyer. 1999. Migratory patterns of mountain lions: implications for social regulation and conservation. Journal of
Mammalogy 80: 986-992. Puig, S., F. Videla, S. Monge, and V. Roig. 1996. Seasonal variations in guanaco diet
(Lama guanicoe, Müller 1776) and food availability in Northern Patagonia, Argentina. Journal of Arid Environments 34(2):215-224.
Puig, S., G. Ferraris, M. Superina, and F. Videla. 2003. Distribución de densidades de
guanacos (Lama guanicoe) en el norte de la Reserva La Payunia y su área de
influencia (Mendoza, Argentina). Multequina 12(2):37-48.
Quiroga, V. A., A. J. Noss, A. Paviolo, G. I. Boaglio, and M. S. Di Bitetti. 2016. Puma density, habitat use and conflict with humans in the Argentine Chaco. Journal for Nature Conservation 31:9-15.
Rau, J. R., and J. E. Jiménez. 2002. Diet of puma (Puma concolor, Carnivora: Felidae)
in coastal and Andean ranges of southern Chile. Studies on Neotropical Fauna and Environment 37(3):201-205.
Rich, L. N., M. J. Kelly, R. Sollmann, A. J. Noss, L. Maffei, R. L. Arispe, A. Paviolo, C. D. De Angelo, Y. E. Di Blanco, and M. E. Di Bitetti. 2014. Comparing capture–
recapture, mark–resight, and spatial mark–resight models for estimating puma densities via camera traps. Journal of Mammalogy 95:382-391.
Royle, J. A., J. D. Nichols, K. U. Karanth, and A. M. Gopalaswamy. 2009. A hierarchical model for estimating density in camera‐trap studies. Journal of Applied Ecology
46(1):118-127.
Schroeder, N. M., S. D. Matteucci, P. G. Moreno, P. Gregorio, R. Ovejero, P. Taraborelli, and P. D. Carmanchahi. 2014. Spatial and seasonal dynamic of abundance and distribution of guanaco and livestock: Insights from using density
surface and null models. PLoS ONE 9:e85960.
50
Seidensticker, J. C., M. G. Hornocker, W. V. Wiles, and J. P. Messick. 1973. Mountain lion social organization in the Idaho Primitive Area. Wildlife Monographs 35:3-60.
Silver, S., and Jaguar Survey Coordinator. 2004. Assessing jaguar abundance using
remotely triggered cameras. Wildlife Conservation Society, New York, USA. Silver, S. C., L. E. Ostro, L. K. Marsh, L. Maffei, A. J. Noss, M. J. Kelly, R. B. Wallace,
H. Gómez, and G. Ayala. 2004. The use of camera traps for estimating jaguar Panthera onca abundance and density using capture/recapture analysis. Oryx
38:148-154. Soto-Shoender, J. R., and W. M. Giuliano. 2011. Predation on livestock by large
carnivores in the tropical lowlands of Guatemala. Oryx 45(4):561-568.
Thornton, D. H., and C. E. Pekins. 2015. Spatially explicit capture–recapture analysis of bobcat (Lynx rufus) density: implications for mesocarnivore monitoring. Wildlife Research 42(5):394-404.
Walker, S., and A. Novaro. 2010. The world's southernmost pumas in Patagonia and
the southern Andes. Cougar, Ecology and Conservation. Pp. 91-99 in Cougar: Ecology and Conservation. M Hornocker y S Negri, eds. University of Chicago Press, Chicago.
Weingart, E. L. 1973. A simple technique for revealing hair scale patterns. American
Midland Naturalist 508-509. Zar, J. H. 1999. Biostatistical analysis. Pp. 929. 4th edition. New Jersey, USA.
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BIOGRAPHICAL SKETCH
Maria Laura earned her Bachelor of Science in biology from Universidad
Nacional de Rio Cuarto, Argentina in 2011. Laura was supported by BEC.AR in
Argentina to begin her master’s degree in interdisciplinary ecology at the University of
Florida in August 2014 and graduated in December 2016.