Landscape Genetics of Mountain Lions (Puma concolor) in Southwestern Arizona

Transcription

1 Landscape Genetics of Mountain Lions (Puma concolor) in Southwestern Arizona FINAL REPORT February 2014 Project Number: HPC Mountain Lion Management in SW AZ (minimum population size, food habits, connectivity patterns, prey species-bs, MD response) Submitted to Arizona Game & Fish Department Habitat Partnership Committee Investigators Ashwin Naidu, PhD Candidate School of Natural Resources and the Environment, University of Arizona Robert Fitak, PhD Candidate Graduate Interdisciplinary Program in Genetics, University of Arizona Melanie Culver, Assistant Professor; Assistant Unit Leader School of Natural Resources and the Environment, University of Arizona Arizona Cooperative Fish and Wildlife Research Unit, US Geological Survey

2 Introduction The projected growth in human population, urbanization, and expansion of highways could pose a major threat to the future survival of big game species in Arizona. Fragmentation of habitat can restrict gene flow and create small populations leading to inbreeding, loss in genetic diversity and local extinction (Frankham et al. 2004). Wildlife managers, stakeholders, and conservationists in Arizona are concerned about the impact of urbanization, particularly interstate highways, on habitat connectivity for big game species populations (AGFD 2009). Additionally, recent documentation of mountain lions (Puma concolor) and a concurrent decline in desert bighorn sheep (Ovis canadensis mexicana) numbers in southwestern Arizona (Smythe 2008, USFWS 2009, Naidu et al. 2011) has led to investigating the potential source of these mountain lions and the impact of predation, among other factors affecting prey species such as disease, drought, and hunting. In light of management of mountain lions in Arizona, several questions yet remain unanswered: What is the population size of mountain lions in southwestern Arizona? What is the effect of anthropogenic modifications to the landscape on mountain lions? How are mountain lion subpopulations connected across the landscape? And what proportion of their diet is composed of vulnerable prey species such as desert bighorn sheep, particularly in southwestern Arizona? Answering these questions requires a comprehensive understanding of species biology through extensive surveys and the use of best available scientific techniques. Of the available scientific resources to date, genetic techniques such as conservation and forensic genetics have gained immense popularity in wildlife management and law enforcement over the last decade because of their strong scientific underpinnings (Ogden et al. 2009). Recent advances in genetics such as the development of high-throughput DNA extraction, nextgeneration DNA sequencing technologies, and identification of novel genetic markers such as Single Nucleotide Polymorphisms (SNPs), are providing a means to accurately analyze genetic variation at the species, population, and individual levels (Allendorf et al. 2010; Ogden 2011). Studies using these techniques and markers can provide us with insight on population structuring, kinship, ancestry, and robust evidence for investigative questions requiring species, individuals, and source population identification. Wildlife managers and stakeholders in Arizona are interested in the current numbers and population genetic status of mountain lions, the impact of highways on mountain lions and their prey populations (particularly desert bighorn sheep), and how current and future adaptive management actions will benefit from information generated through the application of recent scientific advances in genetics. With this project, we attempt to provide insight on the population size, genetic structure, and the potential source of mountain lions occurring in southwestern Arizona. We also describe food habits (or diet) of mountain lions

3 occurring on Kofa National Wildlife Refuge. Data from this project are based on a five-year sampling effort on mountain lions ( ) in Arizona, and are placed in the context of mountain lions sampled from California, southern Nevada, central New Mexico, and northern Sonora, Mexico. Information from this project will be critical for regional and statewide predator management decisions, and can be used in preparing adaptive management approaches to more effectively manage predators and their prey (e.g. AGFD 2010). Objectives 1. Estimates of the minimum number and/or population size of mountain lions on Kofa National Wildlife Refuge (Kofa NWR), and continual monitoring of mountain lion abundance on Kofa NWR (Y2009 through Y2012) 2. Information on the source population/s for mountain lions currently on Kofa NWR and potential mountain lion influx corridors for mountain lions connecting to Kofa NWR 3. Estimates of the levels of relatedness of Kofa mountain lions to the larger regional mountain lion population/s 4. Mountain lion diet profiles and understanding of food habits through estimates of seasonal prey selection and/or prey preference trends (if any detected) by individual mountain lions on Kofa NWR. Research Methods and Results 1. Minimum number of mountain lions occurring on Kofa NWR METHODS We obtained a total of 59 opportunistically Methods used for species identification and collected, suspected mountain lion scat obtaining genotypes for individual identification samples from Kofa NWR during from scat samples were performed for this We were able to obtain amplifiable DNA study, as in Naidu et al from 26 of the 59 scat samples and confirm their species identity as mountain lion. We genotyped these scat samples with 12 well-known

4 Felis catus (FCA) microsatellite markers (or loci), and used genotypic information from a minimum of five of these microsatellite loci to match them against scat and tissue/blood samples previously genotyped for a minimum number estimate of mountain lions on Kofa NWR (Naidu et al. 2011). RESULTS We obtained a total of 10 unique genotypes (Table 1). Hence, the minimum number of mountain lions occurring on Kofa NWR during is 10. Table 1 Unique genotypes obtained and identified from scat, tissue and blood samples* collected from mountain lions on Kofa NWR during Tissue and blood samples not identified as scat are located at the bottom four rows of the table. Sample0ID Collection0Date BINNED GENOTYPE DATA FOR 9 CODOMINANT FCA MARKERS Indiv. # Scat-KS-13M 23-Mar Scat-KS-1c 5-Oct Scat-K-4 16-Feb Scat-K Apr Scat-K May Scat-K Apr Scat-K Mar Scat-KS-7Mb 4-Oct Scat-KS-7Ma 4-Oct Scat-K-29 1-Jan KM06 28-Jan KF01 18-Feb KF02 4-Mar KF03 5-Feb *Samples that failed to yield PCR amplifiable DNA and/or a composite genotype with at least five felid microsatellite markers were excluded from our analysis. CONCLUSIONS This estimate corroborates the previous minimum number estimate of mountain lions (n = 11) occurring on Kofa NWR during (Naidu et al. 2011). This estimate will be useful in understanding whether mountain lions on Kofa NWR are resident or transient, however, due to limited success in genotyping scat samples (reviewed in Waits & Paetkau 2005) and a low sample size, we are currently unable to produce minimum number per year estimates that could speak to transiency or residency.

5 2. Source population(s) of mountain lions currently occurring on Kofa NWR and potential influx corridors for mountain lions connecting to Kofa NWR And 3. Levels of relatedness of Kofa mountain lions to the larger regional mountain lion population(s) We collectively address objectives 2 and 3 of this project since they are related to each other, and to the population structure of mountain lions occurring at the regional level. METHODS Samples and markers To estimate connectivity of mountain lions occurring on Kofa NWR (and southwestern Arizona) with the larger regional population, we obtained genotypes of 529 samples from surrounding regions in Arizona, California, Methods used for species identification from scats and genotyping scat DNA for individual identification were performed for this study, as in Naidu et al Nevada, New Mexico and Sonora, Mexico (including 15 scat samples from Kofa NWR and 10 scat samples from northern Sonora, Mexico). After eliminating individuals with missing microsatellite genotype data for more than 5 microsatellite markers, the final data set contained genotype data for 466 mountain lion samples across 10 felid microsatellite markers known to be highly polymorphic in Puma concolor. The original 15 microsatellite markers were chosen from among those developed for the domestic cat (Felis catus; FCA) and included FCA26, FCA35, FCA43, FCA52, FCA57, FCA77, FCA82, FCA90, FCA96, FCA132, FCA144, FCA176, FCA 221, FCA229, and FCA290). This marker set was previously used to examine population structure of mountain lions across Utah, Colorado, Arizona and New Mexico (McRae et al. 2005). Microsatellite genotyping using PCR amplification for this project has also been described in the report for the AGFD - Habitat Partnership Committee (HPC) Project #

6 Population genetics A variety of population genetic statistics were calculated among populations for the final set of 10 felid microsatellite loci to determine the reliability of the genotypic data. We used GenAlEx v6.5 to calculate Hardy-Weinberg Equilibrium for each locus, within each population, and we decided to include all loci in further analyses because we observed no consistent pattern of loci significantly deviating from Hardy-Weinberg Equilibrium across all populations; P< , after Bonferroni correction. We used GenePop v4.2 (Raymond & Rousset 1995) to estimate linkage-disequilibrium to test for independence among microsatellite loci. We found five pairs of loci linked in the SWAZ population, and one pair of loci linked in the WNCR population. There was no consistent pattern of linked loci across all populations, and so we included all loci in further analyses. The high number of linked loci in SWAZ could be due to low genetic diversity within this population, or a high level of relatedness among individuals within this population. The statistical association of linked loci can increase also due to the low sample size from this population, but in general is associated with population bottlenecks, low genetic diversity, and high relatedness within a population. We used GenAlEx v6.5 to also estimate average heterozygosity for each population. With the exception of SWAZ, which had the lowest average heterozygosity estimate of 0.546, average heterozygosity ranged from to in all populations. Population structure analysis To perform population structure analyses (to determine if population structure is evident among the geographic areas represented by these samples) we grouped samples into six predefined populations based on expert-opinion of potential barriers to gene flow across the landscape. These six populations are: 1. SWAZ bounded by the Lower Colorado River on the west, Interstate-10 on the north, Interstate-8 on the south, and Interstate-10-East on the east 2. N10W bounded by the Lower Colorado River on the west, Interstate-40 on the north, Interstate-10 on the south, and Interstate-17 on the east 3. N10E bounded by Interstate-17 on the west, Interstate-40 on the north, and Interstate- 10 on the south

7 4. N40E bounded by the Lower Colorado River and the Grand Canyon on the west, and Interstate-40 on the south 5. S10M bounded by Interstate-10 and Interstate-8 on the north 6. WNCR the area to the west of the Colorado River and north of the Grand Canyon (including mountain lions sampled in California) Many of the barriers proposed are interstate highways, which is based on movement data gathered through GPS-collared mountain lions in Arizona (AGFD 2009), where interstate highways seemed to be restricting movement of mountain lions across the landscape. We then used the following programs for analyses on elucidating population structure: GenAlEx v6.5 (Peakall & Smouse 2012) Calculation of F ST and Genetic Distance to estimate amount of genetic differentiation. STRUCTURE v2.3 (Pritchard et al. 2000; Pritchard 2010) Bayesian assignment, or clustering, of individual genotypes into populations to estimate populations structure, and equally importantly, to investigate how many significant clusters occur in the geographic area under study. CLUMPP v1.1.2 and DISTRUCT v 1.1 (Rosenberg 2004; Jakobsson & Rosenberg 2007) Statistical interpretation and visualization of the results from STRUCTURE. RESULTS The genetic distance estimates between SWAZ and the other five populations (or geographic regions) showed the lowest distance between SWAZ and S10M, which includes Mexico, and the greatest distance between SWAZ and WNCR, across the Colorado River (Figure 1).

8 Mean Genetic Distance - SWAZ vs. Other regions Mean Genotypic Distance WNCR N10W N10E N40E S10M Figure 1 Codominant genotypic distance, mean pairwise genetic distance between southwestern Arizona (SWAZ) and 5 other surrounding subpopulations. The estimates of subdivision among populations as determined by F ST calculations for SWAZ compared to the other five populations (or geographic regions), showed a similar result. SWAZ had the lowest pairwise F ST value with S10M, which includes Mexico, and the highest pairwise F ST value with WNCR, across the Colorado River (Figure 2; Table 2). F ST values of SWAZ vs. Other regions N10W N40E WNCR 0.08 F ST 0.06 N10E 0.04 S10M REGIONS Figure 2 Pairwise F ST values between SWAZ and other subpopulations showing lowest differentiation with S10M and N10E, and highest differentiation with N10W and WNCR.

9 Table 2 Pairwise F ST values between predefined subpopulations is shown below the diagonal, and Probability, P (rand >= data) based on 999 permutations is shown above diagonal. N10E N10W N40E S10M SWAZ WNCR N10E N10W N40E S10M SWAZ WNCR We additionally examined population structure with an alternate Bayesian statistical approach, which assigns or clusters individuals to populations based on multi-locus genotype data. We examined population structure by varying K from 1 through 7 and placed each observation of K in a biological context, i.e., in relation to potential barriers to gene flow based on location data of the six pre-defined populations as a prior in the software program STRUCTURE v2.3. We obtained maximum statistical support for number of populations (K) equal to 4 as shown by the highest Delta K peak at 4 (Figure 3). Figure 3 (A) Delta K (ΔK) of Evanno et al. (2005) across 10 replicates of STRUCTURE, where K = 4 is shown as the best fit of the data for the highest level of hierarchical genetic structure followed by K = 2 at a lower level of structuring. (B) The mean lnp(d K) and SD of 10 replicates of STRUCTURE runs for each K where the model of K = 4 is indicated as the best fit.

10 When the software programs CLUMPP v1.1.2 and DISTRUCT v1.1 were used to visualize results from the software STRUCTURE v2.3, we inferred that the four genetic groups were fairly consistent with our pre-defined populations (Figure 4) with two exceptions: WNCR and N40E were not genetically distinguishable; and N10E and S10M were not genetically distinguishable either (Figures 4 and 5). Furthermore there is a noticeable connection between SWAZ and S10M as the genotypes found in SWAZ are observed almost exclusively in S10M and SWAZ. Figure 4 Bar plots from STRUCTURE showing individual assignments to predefined populations (K=4).

11 WNCR N40E N10W SWAZ N10E GENOTYPE ASSIGNMENTS TO SUBPOPULATIONS S10M Figure 5 Map of genotyped samples from predefined populations in Arizona and surrounding areas assigned to populations. Colors designated in map according to results of population structure analysis (K=4). Relatedness of Kofa mountain lions to other nearby regions To further elucidate genetic relatedness of mountain lions occurring in SWAZ, which includes samples from Kofa NWR, to those of S10M, we split the S10M populations into two groups: W19 Samples occurring west of Interstate-19 and north of the US-Mexico border, and S10E Samples occurring east of Interstate-19 in Arizona, and south of the US-Mexico border.

12 We then repeated the genetic distance test, now using seven populations instead of the previous six populations (Figure 6). The results of this test showed SWAZ with the lowest genetic distance to the W19 subpopulation, conversely SWAZ and W19 have the highest genetic similarity among the seven populations. N10E, N40E, and S10E together have similar and the next highest level of genetic distance relative to SWAZ in this analysis, and WNCR again has the highest genetic distance compared to SWAZ. Mean Genetic Distance: SWAZ vs. Other regions Mean Genotypic Distance WNCR N10W N10E N40E S10E W WNCR N10W N10E N40E S10E W19 REGION Figure 6 Mean pairwise genetic distance between southwestern Arizona (SWAZ) and 6 other surrounding subpopulations, including W19. CONCLUSIONS The final set of 10 microsatellite markers was informative in our samples and we were able to identify a significant level of population structuring among mountain lions in our study area. Further, our estimates of genetic distance and pairwise F ST values corroborate results from the software programs STRUCTURE, CLUMPP and DISTRUCT, affording greater confidence to the findings. From our observation of genetic structure, we infer four populations to be the most appropriate description of population differentiation and gene flow across the landscape in our study area. The following barriers to gene flow loosely define the four populations: Interstate 10, Interstate-17, Interstate-40, and the Lower Colorado River (Figure 5). Interestingly, because we did not find support for all of our six pre-defined populations, we

13 infer that based on our data set, the Upper Colorado River and the Grand Canyon are not significant barriers to gene flow for mountain lions. Likewise, Interstate-10 east of Tucson and Interstate-8 west of Tucson are not significant barriers to gene flow for mountain lions. We also identified that mountain lions occurring of Kofa NWR are most closely related to mountain lions occurring south of Interstate-10 and northern Sonora, Mexico. Our conclusion is based on observations of relative genetic distance of mountain lions in southwestern Arizona (Figure 1) and pairwise F ST values between populations (Figures 1, 2, 6, and Table 2). Based on the assumption that mountain lions occurring on Kofa NWR are a newly founded population, we can be fairly certain from the data in this study that mountain lions in SWAZ may have undergone a bottleneck within the last years and appear to stem from a founder event originating from the S10M pre-defined population. Our preliminary result obtained from subdividing the S10M population into S10E and W19, and further investigation of pairwise relatedness between individuals, may provide further insight into the potential influx corridors for mountain lions connecting S10M to Kofa NWR. FUTURE DIRECTIONS In the upcoming months, we will be using landscape genetic approaches, involving the use of ArcGIS software and advanced analysis tools (e.g. GENELAND and CIRCUITSCAPE software programs), to create maps of habitat corridors based on population structure and genetic relatedness among individual mountain lions sampled in the landscape. We are in the process of synthesizing landcover data (vegetation, water, elevation, urban structures, etc.) in a GIS to create a habitat-suitability map for mountain lions. Movement data collected via GPS-collaring of mountain lions in southwestern Arizona (AGFD ) are deemed necessary for the creation of a robust habitat suitability map for mountain lions in southwestern Arizona. We will compare genetic data on mountain lion population structure and connectivity to the GIS-based habitat suitability model for mountain lions. This analysis will provide us with an estimate of the likely habitat corridors connecting mountain lion occupied areas in southwestern Arizona.

14 Performance of SNP vs. microsatellite markers on scat DNA During this project s progress, we also compared the results of microsatellite and SNP markers (see PumaPlex, reported in AGFD-HPC Project #10-705) on 46 mountain lion scat samples. In cases where microsatellites had marginal success SNPs outperformed microsatellites (Figure 7). This analysis demonstrated that SNPs might have a significant benefit over microsatellite markers when analyzing degraded DNA from non-invasively collected samples such as scats. Therefore, SNPs can also be used for obtaining individual genotypes and population size (minimum number) estimates from scat samples collected in a particular area, such as the Kofa NWR; or for obtaining data for population structure where the primary sample type was scat, such as for Kofa NWR and for Sonora, Mexico. Figure 7 Performance of 12 FCA microsatellite markers and SNP markers in the PumaPlex on 46 DNA samples from mountain lion scats, repeated 3X each. This is a plot of the genotyping success (proportion of successfully called genotypes) for SNPs (X-axis) vs. microsatellites (Yaxis). Each black circle represents a single scat sample. The dashed line indicates the 1:1 ratio expected if no difference between markers was observed. Those scats to the right of the line performed better for SNPs than microsatellites and vice-versa. Overall, genotyping success was significantly greater in SNPs than microsatellites for the same 46 scat samples (Wilcoxon signed-rank test; p= )

15 4. Mountain lion diet on Kofa NWR and seasonal prey selection/preference trends (food habits) Several studies have observed that learned and specialist behavior of predation by individual mountain lions on bighorn sheep can occur (Ross et al. 1997, Festa-Bianchet et al. 2006; B. Henry, AGFD, personal communication). Predation by mountain lions has primarily been interpreted as the proportion of prey population lost due to predation (e.g. Wehausen 1996, Hayes et al. 2000, Rominger et al. 2004). Kill rates can also be used to describe predation pressure, especially in the case of individual mountain lions exhibiting learned or specialist behavior (e.g. Festa-Bianchet et al. 2006). Individual-based kill rates are realized to be important especially during implementation of predator management action plans (e.g. AGFD 2010) because one individual specializing and selecting for one prey species could have a significant impact on a vulnerable prey population. As Henry et al. (unpublished data) report from Kofa NWR, one GPS-collared mountain lion (KM04; referenced in Figure 8) was observed to switch prey between mule deer and desert bighorn sheep, and eventually selected only for desert bighorn sheep killing at the rate of 1 sheep per 10.5 days. It is important for wildlife managers to understand diet (or food habits) of mountain lions, especially in southwestern Arizona, to assess the impact of predation on vulnerable prey species such as desert bighorn sheep. The diet of mountain lions also sheds light on their ecological role as apex predators in the northern Sonoran desert ecosystem. Predation by mountain lions on desert bighorn sheep in Kofa NWR is supported by data following a ~50% decline in desert bighorn sheep numbers during (see USFWS 2009, AGFD 2010). The concurrent documentation of a possible resident mountain lion population (Smythe 2008, Naidu et al. 2011) led to further investigating the impact of predation by mountain lions on desert bighorn sheep in Kofa NWR. A predation model (G. Harris, L. Smythe, R. Thompson et al., unpublished data) based on GPS-collar data predicted a decline in desert bighorn sheep population by additive mortality due to predation by mountain lions occurring on Kofa NWR (Figure 8). These data suggest that mountain lions could be the primary factor in the decline of desert bighorn sheep in Kofa NWR. However, other factors on bighorn sheep such as translocations, hunting, disease, drought and dispersal to other mountain ranges cannot be ignored. An estimate of mountain lion food habits (or diet), and identification of seasonal patterns in their selection of major prey items (i.e. mule deer and desert bighorn sheep) can be used to reinvestigate such predation models.

16 Estimated Annual Bighorn Mortality on Kofa NWR Yearling Recruitment KM04 KM males + 1 female 3 males + 1 female Figure 8 Modeled impact of an individual male mountain lion (KM04) and additional individual mountain lions on desert bighorn sheep occurring on Kofa NWR (G. Harris, L. Smythe, R. Thompson et al., unpublished data). Genetic data, particularly the identification of diet from non-invasively collected scats in the field, can significantly augment prey species data (obtained from GPS-tracking of mountain lions and identification of their kills) (see Naidu 2009). During , an analysis of GPScollar (point-clusters/kill-site) data by B. Henry, L. Smythe, R. Thompson, B. Jansen et al. (unpublished data), and non-invasive genetics by Naidu (2009) revealed desert bighorn sheep to be a major prey species, comprising approximately 25% of mountain lion diet, next to mule deer, which comprised approximately 60% of their diet. METHODS To further expand data on mountain lion diet (and/or food habits), we recorded a comprehensive dataset on the diet of mountain lions occurring on Kofa NWR based on mitochondrial DNA sequence-based identification of prey remains recovered from scats. A total of 77 scats opportunistically collected during Methods used for species identification of prey remains (bone and tissue fragments) recovered from scat samples were performed for this study, as in Naidu were positively identified as puma scats and containing prey remains for diet analysis. These samples included scats analyzed by Naidu (2009).

17 RESULTS We analyzed this up-to-date data set to produce a more robust estimate of mountain lion diet on Kofa NWR (Figure 9). In the decreasing order of majority, mountain lion diet consisted of mule deer Odocoileus hemionus (44.2%), desert bighorn sheep Ovis canadensis (27.3%), American badger Taxidea taxus (6.5%), puma Puma concolor (6.5%), domestic sheep Ovis aries (2.6%), gray fox Urocyon cinereoargenteus (1.3%), human Homo sapiens (1.3%), and other ambiguous species* (10.4%) Mountain lion diet profile on Kofa NWR Number of scats Mule deer Bighorn sheep American badger Puma Domestic sheep Gray Fox Human Other* Figure 9 Occurrence of prey species identified from 77 mountain lion scat samples analyzed for diet. *These species include ambiguously identified Cephalophus spp., Nyctereutes spp., Ciprinus carpio, and Insecta. Ambiguity in species identification is a result of low query-to-reference DNA-sequence alignment scores and/or lack of appropriate reference sequences in the DNA database (GenBank, NCBI; see Naidu et al. 2012). To identify seasonal trends in prey selection by mountain lions, we divided this dataset into two and three annual seasons with close reference to the desert bighorn sheep lambing season, which peaks around late winter (February-April) and can extended from winter to summer (January-June)*. To account for a sufficient sample size and an even distribution of sampling throughout the calendar year, we plotted only the major prey species as part of this analysis (Figure 10).

18 Prey selection (two seasons) Prey selection (three seasons) Number of scats Jan - Jun Jul - Dec Number of scats Jan - Apr May - Aug Sep - Dec 0 Mule deer Bighorn 0 Mule deer Bighorn Figure 10 Seasonal patterns of prey selection by mountain lions on Kofa NWR*. Desert bighorn sheep lambing season coincides with January June in the plot for two seasons, and January April in the plot for three seasons. *We make an assumption that scat samples included in this analysis were defecated by mountain lions within the defined seasons. Predation by mountain lions could have occurred several days or weeks prior to collection of samples. Our assumption is based on the fact that our success in DNA extraction and identification was negatively correlated with age and exposure of scat sample in the field to environmental variables affecting DNA recovery (Vynne et al. 2012). Scat samples that failed to yield DNA for predator identification were not included. In the two-season and three-season plot of prey selection, the lambing seasons of desert bighorn sheep is potentially associated with heightened predation of bighorn sheep by mountain lions. In the three-season plot of prey selection a strong inverse relationship is observed between mule deer predation and bighorn sheep predation (Figure 10). CONCLUSIONS Such data can be used in the creation of prey availability indices and can be used to model impact of predators on prey species. Although genetic analyses are effective, becoming increasingly available, and getting cheaper each year, the time and resources needed to perform such analyses limit their use by wildlife managers who may require quick examination of on-the-ground situations to implement adaptive management action plans. Moreover, obtaining genetic data to examine predation at the individual level can take several months or years (depending on ability to successfully obtain a significant number of scat samples from each individual) compared to obtaining similar data with the use of GPS-collars and

19 movement tracking. A study comparing the cost-benefit analysis of diet at the population and individual levels for the two methods, DNA-based diet identification and GPS-collartracking, can benefit wildlife biologists in making decisions for implementing research for short or long-term adaptive management action plans. Overall Conclusions Our estimated minimum number of 10 individual mountain lions occurring on Kofa NWR during corroborates the previous estimate of 11 individual mountain lions occurring on Kofa NWR during (Naidu et al. 2011). Interstate highways (particularly Interstate-10 west of Phoenix, Interstate-17 and Interstate-40) and the lower Colorado River are the most potential barriers restricting gene flow among mountain lions sampled for this study. Mountain lions occurring on Kofa NWR are most closely related to the mountain lions occurring south of Interstate-10 and northern Sonora, Mexico; specifically most closely related to mountain lions occurring west of Interstate-19. Further investigation of pairwise relatedness between individuals along with the use of a GIS can potentially elucidate the movement/influx corridors connecting mountain lions in southwestern Arizona, southern Arizona, and Sonora, Mexico. Our investigation of mountain lion diet revealed mule deer and desert bighorn sheep to be their major prey. With seasonal division of diet, we observed a potential correlation between mountain lion predation and the desert bighorn sheep lambing season, and an inverse relationship between mule deer and desert bighorn sheep predation. These data can be used in modeling the impact of predation and vulnerability of prey species. We conclude that data generated from this study will be useful for wildlife managers, stakeholders, and conservation planners in making regional or statewide management decisions for mountain lions, designating wildlife corridors, and facilitating collaborative research between individuals, laboratories, and agencies through shared genetic databases (e.g. see report on AGFD-HPC Project #10-705). Acknowledgments We would like to thank Linsday Smythe, Susanna Henry, and Grant Harris of the USFWS for providing us with the preliminary information and impetus to conduct this study, and financial support from the USFWS to start this project. Many thanks to Ron Thompson and Reuben Terán for facilitating the funding of this proposal through the Habitat Partnership Committee, in partnership with the Arizona Desert Bighorn Sheep Society (ADBSS) for Special Big Game Tag funds. Many thanks to the AGFD Game Branch for collection of hunter harvested

20 samples, and to Ron Thompson and Ron Day for delivering these samples to the Culver Conservation Genetics Laboratory at University of Arizona. We would like to thank Robert Henry, Brian Jansen, Dave Conrad and Ron Thompson from the AGFD for providing us with the necessary insight on mountain lion biology in southwestern Arizona. Our deepest thanks to Sophia Amirsultan and Alex Ochoa for their assistance with data scoring and analysis, and to John Clemons from the Arizona Desert Bighorn Sheep Society, who spent nearly two years learning conservation genetics and performing laboratory work on scat samples for mountain lion diet analysis as his retirement occupation. We also thank John Clemons for facilitating scholarship funds through the ADBSS, and providing the much-needed moral support, to Ashwin Naidu for his dissertation. Finally, we would like to thank the members of the Culver Conservation Genetics Laboratory for reviewing this project during its course. Literature Cited Allendorf, F.W., Hohenlohe, P. A. & Luikart, G Genomics and the future of conservation genetics. Nature Reviews Genetics, 11: Arizona Game and Fish Department (AGFD) Mountain Lion Depredation Harvests in Arizona, 1976 to Hunt Arizona. Arizona Game and Fish Department, Phoenix. Arizona Game and Fish Department (AGFD) Mountain Lion And Bear Conservation Strategies Report. Arizona Game and Fish Department, Phoenix. Arizona Game and Fish Department (AGFD) Kofa Mountains complex adaptive predation management plan. ( y2010_ pdf, accessed March 15, 2013). Evanno, G., Regnaut, S. & Goudet, J Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology, 14: Festa-Bianchet, M., Coulson T., Gaillard J., Hogg J.T. & Pelletier, F Stochastic predation events and population persistence in bighorn sheep. Proceedings of the Royal Society Biological Sciences, 273: Frankham, R., Ballou, J.D. & Briscoe, D.A A Primer of Conservation Genetics. Oryx. Hayes, C. L., Rubin E. S., Jorgensen M. C., Botta R. A. & Boyce, W. M Mountain Lion Predation of Bighorn Sheep in the Peninsular Ranges, California. The Journal of Wildlife Management, 64(4):

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