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Genetic diversity and spatial structures of snow leopards (Panthera uncia) reveal proxies of connectivity across Mongolia and northwestern China

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Abstract

Understanding landscape connectivity and population genetic parameters is imperative for threatened species management. However, such information is lacking for the snow leopard (Panthera uncia). This study sought to explore hierarchical snow leopard gene flow patterns and drivers of genetic structure in Mongolia and China. A total of 97 individuals from across Mongolia and from the north-eastern edge of the Qinghai-Tibetan Plateau in Gansu Province to the middle of Qinghai Province in China were genotyped across 24 microsatellite loci. Distance-based frameworks were used to determine a landscape scenario best explaining observed genetic structure. Spatial and non-spatial methods were used to investigate fine-scale autocorrelation and similarity patterns as well as genetic structure and admixture. A genetic macro-division between populations in China and Mongolia was observed, suggesting that the Gobi Desert is a substantial barrier to gene flow. However, admixture and support for a resistance-based mode of isolation suggests connective routes that could facilitate movement. Populations in Mongolia had greater connectivity, indicative of more continuous habitat. Drivers of genetic structure in China were difficult to discern, and fine-scale sampling is needed. This study elucidates snow leopard landscape connectivity and helps to prioritize conservation areas. Although contact zones may have existed and occasional crossings can occur, establishing corridors to connect these areas should not be a priority. Focus should be placed on maintaining the relatively high connectivity for snow leopard populations within Mongolia and increasing research efforts in China.

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Data availability

The dataset generated during and/or analyzed during the current study is available in the DRYAD repository, https://doi.org/10.5061/dryad.cvdncjt7j.

References

  • Atzeni L, Cushman SA, Bai D, Wang J, Chen P, Shi K, Riordan P (2020) Meta-replication, sampling bias, and multi-scale model selection: A case study on snow leopard (Panthera uncia) in western China. Ecol and Evol 10:7686–7712

    Article  Google Scholar 

  • Atzeni L, Cushman SA, Wang J, Riordan P, Shi K, Bauman D (2021) Evidence of spatial genetic structure in a snow leopard population from Gansu, China. Hered 127:522–534

    Article  CAS  Google Scholar 

  • Balkenhol N, Cushman SA, Storfer AT, Waits LP (2016) Landscape Genetics: Concepts, Methods Applications. Wiley, Oxford

    Google Scholar 

  • Bauman D, Drouet T, Dray S, Vleminckx J (2018b) Disentangling good from bad practices in the selection of spatial or phylogenetic eigenvectors. Ecography 41:1638–1649

    Article  Google Scholar 

  • Bauman D, Drouet T, Fortin M, Dray S (2018a) Optimizing the choice of a spatial weighting matrix in eigenvector-based methods. Ecology 99:2159–2166

    Article  PubMed  Google Scholar 

  • Bayandonoi G, Lkhagvajav P, Alexander JS, Durbach I, Borchers D, Munkhtsog B, Munkhtogtokh O, Chimeddorj B, Sergelen E, Koustubh S (2021a) Nationwide Snow Leopard Population Assessment of Mongolia Key Findings. Summary Report. WWF-Mongolia, Ulaanbaatar, Mongolia. Divers Distrib 27:2441–2453. https://doi.org/10.1111/ddi.13412

  • Bayandonoi G, Sharma K, Alexander J, Lkhagvajav P, Durbach I, Buyanaa C, Munkhtsog B et al. (2021b) Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia. Divers Distrib 27:2441–2453

    Article  Google Scholar 

  • Bjornstad ON, Cai J (2016) Package ‘ncf’: Spatial nonparametric covariance functions

  • Blanchet FG, Legendre P, Borcard D (2008) Forward selection of explanatory variables. Ecology 89:2623–2632

    Article  PubMed  Google Scholar 

  • Caragiulo A, Amato G, Weckworth B (2016) Conservation genetics for snow leopards. In: Nyhus P, McCarthy T, Mallon D (eds) Snow Leopards, 1st edn. Academic Press, London, pp 368–374

    Google Scholar 

  • Clarke RT, Rothery P, Raybould AF (2002) Confidence limits for regression relationships between distance matrices: estimating gene flow with distance. J Agric, Biol, Environ Stat 7:361–372

    Article  Google Scholar 

  • Cushman SA, Landguth EL (2012a) Multi-taxa population connectivity in the northern rocky mountains. Ecol Model 231:101–112

    Article  Google Scholar 

  • Cushman SA, McRae B, Adriaensen F, Beier P, Shirley M, Zeller KA (2013a) Biological corridors and connectivity. In: MacDonald D, Willis K (eds) Key topics in Conservation Biology, 2nd edn. Blackwell, Oxford, pp 384–404

    Chapter  Google Scholar 

  • Cushman SA, McRae BH, McGarigal K (2016) Basics of landscape ecology: an introduction to landscapes and population processes for landscape geneticists. In: Balkhenol N, Cushman S, Storfer A, Waits L (eds) Landscape Genetics: Concepts, Methods, Applications, 1st edn. Wiley, Oxford, pp 11–34

    Google Scholar 

  • Cushman SA, Shirk A, Landguth EL (2012b) Separating the effects of habitat area, fragmentation and matrix resistance on genetic differentiation in complex landscapes. Landsc Ecol 27:369–380. https://doi.org/10.1007/s10980-011-9693-0

    Article  Google Scholar 

  • Cushman SA, Shirk AJ, Landguth EL (2013b) Landscape genetics and limiting factors. Conserv Genet 14:263–274. https://doi.org/10.1007/s10592-012-0396-0

    Article  Google Scholar 

  • Diniz MF, Cushman SA, Machado RB, Júnior PDM (2020) Landscape connectivity modeling from the perspective of animal dispersal. Landsc Ecol 35:41–58

    Article  Google Scholar 

  • Dray S, Bauman D, Blanchet G, Borcard D, Clappe S, Guenard G, Jombart T, et al. (2020) adespatial: multivariate multiscale spatial analysis. R package version 0.3–8

  • Dray S (2008) On the number of principal components: a test of dimensionality based on measurements of similarity between matrices. Comput Stat Data Anal 52:2228–2237

    Article  Google Scholar 

  • Dray S (2011) A new perspective about Moran’s coefficient: spatial autocorrelation as a linear regression problem. Geogr Anal 43:127–141

    Article  Google Scholar 

  • Dray S, Dufour A (2007) The ade4 package: implementing the duality diagram for ecologists. J Stat Softw 22:1–20

    Article  Google Scholar 

  • Dray S, Legendre P, Peres-Neto PR (2006) Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol Model 196:483–493

    Article  Google Scholar 

  • Earl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: a website and program visualizing STRUCTURE output and implementing the Evanno method. Conserv Genetic Resour 4:359–361

    Article  Google Scholar 

  • Epps CW, Wasser SJ, Keim JL, Mutayoba BM, Brashares JS (2013) Quantifying past and present connectivity illuminates a rapidly changing landscape for the African elephant. Mol Eco 22:1574–1588

    Article  Google Scholar 

  • Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Eco 1:2611–2620

    Article  Google Scholar 

  • Evans JS (2020) spatialEco_. R package version 1.3–1

  • Fox JL, Chundawat RS (2016) What is a snow leopard? Behavior and ecology. In: Nyhus P, McCarthy T, Mallon D (eds) Snow Leopards, 1st edn. Academic Press, London, pp 13–21

    Chapter  Google Scholar 

  • François O, Durand E (2010) Spatially explicit Bayesian clustering models in population genetics. Mol Ecol Resour 10:773–784

    Article  PubMed  Google Scholar 

  • Goslee SC, Urban DL (2007) The ecodist package for dissimilarity-based analysis of ecological data. J Statal Softw 22:1–19

    Google Scholar 

  • Graves TA, Wasserman TN, Ribeiro MC, Landguth EL, Spear SF, Balkenhol N, Higgins CB, Fortin MJ, Cushman SA, Waits LP (2012) The influence of landscape characteristics and home-range size on the quantification of landscape-genetics relationships. Landsc Ecol 27:253–266

    Article  Google Scholar 

  • Guillot G, Leblois R, Coulon A, Frantz AC (2009) Statistical methods in spatial genetics. Mol Ecol 18:4734–4756

    Article  PubMed  Google Scholar 

  • Hijmans RJ, Phillips S, Leathwick J, Elith J (2020) dismo: Species Distribution Modeling. R package version 1.3–3

  • Janecka JE, Jackson R, Munkhtsog B, Murphy WJ (2014) Characterization of 9 microsatellites and primers in snow leopards and a species-specific PCR assay for identifying noninvasive samples. Conserv Genet Resour 6:369–373

    Article  Google Scholar 

  • Janecka JE, Jackson R, Zhang Y, Li D, Munkhtsog B, Buckley-Beason V, Murphy WJ (2008) Population monitoring of snow leopards using noninvasive collection of scat samples: a pilot study. Animal Conserv 11:401–411

    Article  Google Scholar 

  • Janecka JE, Munkhtsog B, Jackson RM, Naranbaatar G, Mallon DP, Murphy WJ (2011) Comparison of noninvasive genetic and camera-trapping techniques for surveying snow leopards. J Mammal 92:771–783

    Article  Google Scholar 

  • Janecka JE, Zhang Y, Li D, Munkhtsog B, Bayaraa M, Galsandorj N, Wangchuk TR et al. (2017) Range-wide snow leopard phylogeography supports three subspecies. J of Hered 108:597–607

    Article  Google Scholar 

  • Johnson WE, Onorato DP, Roelke ME, et al (2010) Genetic restoration of the Florida panther. Science 329:1641–1645. https://doi.org/10.1126/science.1192891

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jombart T (2017) An introduction to adegenet 2.1.0. https://github.com/thibautjombart/adegenet/wiki/Tutorials

  • Jombart T (2008) Adegenet: An R package for the multivariate analysis of genetic markers. Bioinformatics 24:1402–1405

    Article  Google Scholar 

  • Jombart T, Devillard S, Balloux (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet 11:94

    Article  PubMed  PubMed Central  Google Scholar 

  • Karmacharya DB, Thapa K, Shrestha R, Dkakal M, Janecka JE (2011) Noninvasive genetic population survey of snow leopards (Panthera uncia) in Kangchenjunga conservation area, Shey Phoksundo National Park and surrounding buffer zones of Nepal. BMC Res Note 4:516–523

    Article  Google Scholar 

  • Kaszta Ż, Cushman SA, Htun S, et al (2020) Simulating the impact of Belt and Road initiative and other major developments in Myanmar on an ambassador felid, the clouded leopard, Neofelis nebulosa. Landsc Ecol 35:727–746. https://doi.org/10.1007/s10980-020-00976-z

    Article  Google Scholar 

  • Keeley ATH, Beier P, Gagnon JW (2016) Estimating landscape resistance from habitat suitability: effects of data source and nonlinearities. Landsc Ecol 31:2151–2162

    Article  Google Scholar 

  • Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I (2015) Clumpak: a program for identifying clustering models and packing inferences across K. Mol Eco Resour 15:1179–1191

    Article  CAS  Google Scholar 

  • Korablev M, Poyarkov A, Karnaukhov A, Zvychainaya E, Kuskin A, Malykh S, Istomov SV et al. (2021) Large-scale and fine-grain population structure and genetic diversity of snow leopards (Panthera uncia Schreber, 1776) from the northern and western parts of the range with an emphasis on the Russian population. Conserv Genet 22:397–410

    Article  CAS  Google Scholar 

  • Landguth EL, Cushman SA, Schwartz MK, McKelvey KS, Murphy M, Luikart G (2010) Quantifying the lag time to detect barriers in landscape genetics. Mol Ecol 19:4179–4191

    Article  CAS  PubMed  Google Scholar 

  • Landguth EL, Fedy BC, Oyler-McCance SJ, Garey AL, Emel SL, Mumma M, Wagner HH, Fortin MJ, Cushman SA (2012) Effects of sample size, number of markers, and allelic richness on the detection of spatial genetic pattern. Mol Eco Resour 12:276–284. https://doi.org/10.1111/j.1755-0998.2011.03077.x

    Article  Google Scholar 

  • Legendre P, Legendre L (2012) Numerical Ecology, 3rd edn. Elsevier, Amsterdam

    Google Scholar 

  • Levins R (1969) Some demographic and genetic consequences of environmental heterogeneity for biological control. Am Entomologist 15:237–240

    Google Scholar 

  • Li J, McCarthy TM, Wang H, Weckworth B, Schaller GB, Mishra C, Lua Z, Beissinger SR (2016) Climate refugia of snow leopards in high Asia. Biol Conserv 203:188–196

    Article  Google Scholar 

  • Li J, Weckworth BV, McCarthy TM, Liang X, Liu Y, Xing R, Li D et al. (2020) Defining priorities for global snow leopard conservation landscapes. Biol Conserv 241:108387

    Article  Google Scholar 

  • Li J, Xue Y, Hacker CE, Zhang Y, Li Y, Cong W, Jin L et al (2021) Projected impacts of climate change on snow leopard habitat in Qinghai Province, China. Ecol Evol 11:17202–17218

    Article  PubMed  PubMed Central  Google Scholar 

  • Li X, Ma L, Hu D, Ma D, Li R, Sun Y, Gao E (2022) Potential range shift of snow leopard in future climate change scenarios. Sustainability 14:1115

    Article  Google Scholar 

  • Manel S, Holderegger R (2013) Ten years of landscape genetics. Trends Ecol Evol 28:614–621

    Article  PubMed  Google Scholar 

  • Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18:189–197

    Article  Google Scholar 

  • Matte EM, Castilho CS, Miotto RA, Sana DA, Johnson WE, O’Brien SJ, de Treitas TRO, Eizirik E (2013) Molecular evidence for a recent demographic expansion in the puma (Puma concolor) (Mammalia, Felidae). Genet and Mol Biol 36:586–597

    Article  CAS  Google Scholar 

  • Mateo-Sánchez MC, Balkenhol N, Cushman S, et al (2015) Estimating effective landscape distances and movement corridors: comparison of habitat and genetic data. Ecosphere 6:1–16. https://doi.org/10.1890/ES14-00387.1

    Article  Google Scholar 

  • McCarthy TM (2000) Ecology and conservation of snow leopards, Gobi brown bears and wild Bactrian camels in Mongolia. Dissertation, University of Massachusetts

  • McCarthy TM, Mallon D, Jackson R, Zahler P, McCarthy K (2017) Panthera uncia. IUCN Red List Threatened Species, 2017: e.T22732A50664030, https://doi.org/10.2305/IUCN.UK.2017-2.RLTS.T22732A50664030.en

  • McCarthy TM, Fuller TK, Munkhtsog B (2005) Movements and activities of snow leopards in southwestern Mongolia. Biol Conserv 124:527–537

    Article  Google Scholar 

  • McCarthy TM, Mallon D, Sanderson EW, Zahler P, Fisher K (2016) What is a Snow Leopard? Biogeography and Status Overview. In: Nyhus P, McCarthy T, Mallon D (eds) Snow Leopards, 1st edn. Academic Press, London, pp 23–42

    Chapter  Google Scholar 

  • McGarigal K, Wan HY, Zeller KA, Timm BC, Cushman SA (2016) Multi-scale habitat selection modeling: a review and outlook. Landsc Ecol 31:1161–1175

    Article  Google Scholar 

  • McNeely JA, Miller KR, Reid WV, Mittermeier RA, Werner TB (1990) Conserving the World’s Biological Diversity, IUCN, World Resources Institute, Conservation International, WWF-US and the World Bank, Washington, DC

  • McRae BH (2006) Isolation by resistance. Evolution 60:1551–1561

    PubMed  Google Scholar 

  • Montgelard C, Zenboudji S, Ferchaud AL, Arnal V, van Vuuren Jansen B (2014) Landscape genetics in Mammals. Mammalia 78:139–157

    Article  Google Scholar 

  • Mullen L (2016) mullenMisc: Miscellaneous Functions. R package version 0.2.0.9000, https://rdrr.io/github/lmullen/mullenMisc

  • Naidu A (2019) Where mountain lions traverse: insights from landscape genetics in southwestern United States and northwestern Mexico. Dissertation, University of Arizona

  • Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR et al. (2019) vegan: community ecology package. R package version 2.5–6

  • Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research – an update. Bioinform 28:2537–2539

    Article  CAS  Google Scholar 

  • Peterman WE (2018) ResistanceGA: an R package for the optimization of resistance surfaces using genetic algorithms. Method in Ecol and Evol 9:1638–1647

    Article  Google Scholar 

  • Peterman WE, Pope NS (2021) The use and misuse of regression models in landscape genetic analyses. Mol Ecol 30:37–47

    Article  PubMed  Google Scholar 

  • Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259

    Article  Google Scholar 

  • Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genet 155:945–959

    Article  CAS  Google Scholar 

  • Putman AI, Carbone I (2014) Challenges in analysis and interpretation of microsatellite data for population genetic studies. Ecol and Evol 22:4399–4428

    Article  Google Scholar 

  • R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

  • Raymond M, Rousset F (1995) GENEPOP (v 1.2): A population genetics software for exact tests and ecumenicism. J of Hered 86:248–249

    Article  Google Scholar 

  • Reddy P, Puyravaud JP, Cushman S, Segu H (2019) Spatial variation in the response of tiger gene flow to landscape features and limiting factors. Anim Conserv 22:472–480

    Article  Google Scholar 

  • Reding DM, Cushman SA, Gosselink TE, Clark WR (2013) Linking movement behavior and fine-scale genetic structure to model landscape connectivity for bobcats (Lynx rufus). Landsc Ecol 28:471–486

    Article  Google Scholar 

  • Riordan P, Cushman S, Mallon D, Shi E, Hughes J (2015) Predicting global population connectivity and targeting conservation action for snow leopard across its range. Ecography 38:1–8

    Google Scholar 

  • Riordan P, Shi K (2016) China: Current State of Snow Leopard Conservation in China. In: Nyhus P, McCarthy T, Mallon D (eds) Snow Leopards, 1st edn. Academic Press, London, pp 523–531

    Chapter  Google Scholar 

  • Robinson HS, Weckworth B (2016) Landscape ecology: linking landscape metrics to ecological processes. In: Nyhus P, McCarthy T, Mallon D (eds) Snow Leopards, 1st edn. Academic Press, London, pp 395–402

    Chapter  Google Scholar 

  • Rousset F (2008) Genepop’007: a complete reimplementation of the genepop software for Windows and Linux. Mol Ecol Resour 8:103–106

    Article  PubMed  Google Scholar 

  • RStudio Team (2021) RStudio: Integrated Development Environment for R. http://www.rstudio.com/

  • Rudnick D, Ryan SJ, Beier P, Cushman SA, Dieffenbach F, Trombulak SC (2012) The role of landscape connectivity in planning and implementing conservation and restoration priorities. Issue Ecol 16:1–20

    Google Scholar 

  • Ruiz-Gonzalez A, Cushman SA, Madeira MJ et al. (2015) Isolation by distance, resistance and/or clusters? Lessons learned from a forest-dwelling carnivore inhabiting a heterogeneous landscape. Mol Eco 24:5110–5129. https://doi.org/10.1111/mec.13392

    Article  Google Scholar 

  • Schaller GB (1998) Wildlife of the Tibetan steppe. University of Chicago Press, Chicago

    Google Scholar 

  • Schwartz MK, McKelvey KS (2008) Why sampling scheme matters: the effect of sampling scheme on landscape genetic results. Conserv Genet 10:441–452

    Article  Google Scholar 

  • Shirk AJ, Cushman SA (2011) sGD: software for estimating spatially explicit indices of genetic diversity. Mol Eco Resour 11:922–934. https://doi.org/10.1111/j.1755-0998.2011.03035.x

    Article  CAS  Google Scholar 

  • Shirk AJ, Cushman SA (2014) Spatially-explicit estimation of Wright’s neighborhood size in continuous populations. Front Ecol Evol 2:62

    Article  Google Scholar 

  • Shirk AJ, Cushman SA, Landguth EL (2012) Simulating pattern-process relationships to validate landscape genetic models. Int J Ecol 2012:1–8. https://doi.org/10.1155/2012/539109

    Article  Google Scholar 

  • Shirk AJ, Landguth EL, Cushman SA (2017) A comparison of regression methods for model selection in individual-based landscape genetic analysis. Mol Ecol Resour 18:55–67

    Article  PubMed  Google Scholar 

  • Shirk AJ, Landguth EL, Cushman SA (2020) The effect of gene flow from unsampled demes in landscape genetic analysis. Mol Ecol Resour. https://doi.org/10.1111/1755-0998.13267

    Article  PubMed  Google Scholar 

  • Shirk AJ, Wallin DO, Cushman SA, Rice CG, Warheit KI (2010) Inferring landscape effects on gene flow: a new model selection framework. Mol Ecol 19:3603–3619

    Article  CAS  PubMed  Google Scholar 

  • Slatkin M (1987) Gene flow and the geographic structure of natural populations. Sci 236:787–792

    Article  CAS  Google Scholar 

  • Spear SF, Balkenhol N, Fortin M, McRae BH, Scrier K (2010) Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis. Mol Ecol 19:3576–3591

    Article  PubMed  Google Scholar 

  • Storfer A, Murphy MA, Evans JS, Goldberg CS, Robinson S, Spear SF, Dezzani R et al. (2006) Putting the ‘landscape’ in landscape genetics. Hered 98:128–142

    Article  Google Scholar 

  • Suryawanshi KR, Khanyari M, Sharma K, Lkhagvajav P, Mishra C (2019) Sampling bias in snow leopard population estimation studies. Popul Ecol 61:268–276

    Article  Google Scholar 

  • Taubmann J, Sharma K, Uulu KZ, Hines JE, Mishra C (2016) Status assessment of the endangered snow leopard panthera uncia and other large mammals in the Kyrgyz alay, using community knowledge corrected for imperfect detection. Oryx 50:220–230

    Article  Google Scholar 

  • Van Den Wollenberg AL (1977) Redundancy analysis. An alternative for canonical correlation analysis. Psychometrika 42:207–219

    Article  Google Scholar 

  • Vergara M, Cushman SA, Ruiz-González A (2017) Ecological differences and limiting factors in different regional contexts: landscape genetics of the stone marten in the Iberian Peninsula. Landsc Ecol 32:1269–1283. https://doi.org/10.1007/s10980-017-0512-0

    Article  Google Scholar 

  • Vignali S, Barras AG, Arlettaz R, Braunisch V (2020) SDMtune: An R package to tune and evaluate species distribution models. Ecol Evol 10:11488–11506

    Article  PubMed  PubMed Central  Google Scholar 

  • Wagner HH, Fortin MJ (2012) A conceptual framework for the spatial analysis of landscape genetic data. Conserv Genet 142:53–261

    Google Scholar 

  • Wahlund S (1928) Zusammensetzung von population und korrelationserscheinung vom stand-punkt der vererbungslehre aus betrachtet. Hered 11:65–106

    Article  Google Scholar 

  • Wasserman TN, Cushman SA, Schwartz MK, Wallin DO (2010) Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho. Landsc Ecol 25:1601–1612. https://doi.org/10.1007/s10980-010-9525-7

    Article  Google Scholar 

  • Wang J (2021) A multiscale assessment of snow leopard distribution, habitat-use and landscape connectivity in a new national park in China. PhD Thesis, Manchester Metropolitan University, Manchester, United Kingdom

  • Waples RS (2015) Testing for Hardy-Weinberg proportions: have we lost the plot? J of Hered 106:1–19

    Article  Google Scholar 

  • Weckworth B (2021) Snow Leopard (Panthera uncia) genetics: the knowledge gaps, needs, and implications for conservation. J of the Indian Inst Sci 101:279–290

    Article  Google Scholar 

  • Weir BS (1996) Genetic Data Analysis II. Sinauer, Sunder

  • Wright S (1946) Isolation by distance under diverse systems of mating. Genet 31:39–59

    Article  CAS  Google Scholar 

  • Xinhua News Agency (2021) Snow leopard appears again in Helan Mountain. http://static.cms.xinhua-news.cn/c/2021-05-28/4442235.shtml. Accessed 15 April 2022

  • Zeller KA, Jennings MK, Vickers TW, Ernest HB, Cushman SA, Boyce WM (2018) Are all data types and connectivity models created equal? Validating common connectivity approaches with dispersal data. Divers and Distrib 24:868–879

    Article  Google Scholar 

  • Zeller KA, McGarigal K, Whiteley AR (2012) Estimating landscape resistance to movement: a review. Landsc Ecol 27:777–797

    Article  Google Scholar 

  • Zhang Y, Hacker CE, Zhang Y, Xue Y, Wu L, Dai Y, Ping L et al. (2019) The genetic structure of snow leopard population in Sanjiangyuan and Qilianshan National Parks. Acta Theriologica Sinica 39:442–444

    Google Scholar 

  • Hein C, Moniem HEA, Wagner HH (2021) Can we Compare effect size of spatial genetic structure between studies and species using Moran eigenvector maps? Frontiers Ecol Evol 9:612718:1–10. https://doi.org/10.3389/fevo.2021.612718

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Acknowledgements

The authors would like to thank the Snow Leopard Conservancy, the Britton Foundation, and the Sacramento Zoo for the support of this work.

Funding

Snow Leopard Conservancy (G2000019), Cleveland Metroparks Zoo Asia Seeds Grant (G1800082), Panthera Corporation & The Andrew Sabin Family Foundation (G1900011, G2000017), and the Chicago Zoological Society Chicago Board of Trade Endangered Species Fund (G1900014).

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CEH, JEJ, CG, and RJ conceived study, CEH and LA wrote manuscript and created figures. BM, MB, NG, YZ, YL, CB, GB, MO, MJ, Yu Z, LW, WC, DL, and JEJ collected scat samples or assisted in coordinating scat sampling collection efforts. CEH, CW, Yu Z, and JEJ processed scat samples. CEH and JEJ genotyped samples. CEH and LA conducted non-spatial analyses. LA conducted spatial analyses. CEH, LA, JJ, and RJ conducted data interpretation. JEJ, RJ, and CG provided edits to manuscript drafts.

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Correspondence to Jan E. Janecka.

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Hacker, C., Atzeni, L., Munkhtsog, B. et al. Genetic diversity and spatial structures of snow leopards (Panthera uncia) reveal proxies of connectivity across Mongolia and northwestern China. Landsc Ecol 38, 1013–1031 (2023). https://doi.org/10.1007/s10980-022-01573-y

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