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Landscape resistance to gene flow in a snow leopard population from Qilianshan National Park, Gansu, China

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Abstract

Context

The accurate estimation of landscape resistance to movement is important for ecological understanding and conservation applications. Rigorous estimation of resistance requires validation and optimization. One approach uses genetic data for the optimization or validation of resistance models.

Objectives

We used a genetic dataset of snow leopards from China to evaluate how landscape genetics resistance models varied across genetic distances and spatial scales of analysis. We evaluated whether landscape genetics models were superior to models of resistance derived from habitat suitability or isolation-by-distance.

Methods

We regressed genetically optimized, habitat-based, and isolation-by-distance hypotheses against genetic distances using mixed effect models. We explored all subset combinations of genetically optimized variables to find the most supported resistance scenario for each genetic distance.

Results

Genetically optimized models always outperformed habitat-based and isolation-by-distance hypotheses. The choice of genetic distances influenced the apparent influence of variables, their spatial scales and their functional response shapes, producing divergent resistance scenarios. Gene flow in snow leopards was largely facilitated by areas of intermediate ruggedness at intermediate elevations corresponding to small-to-large valleys within and between the mountain ranges.

Conclusions

This study highlights that landscape genetics models provide superior estimation of functional dispersal than habitat surrogates and suggests that optimization of genetic distance should be included as an optimization routine in landscape genetics, along with variables, scales, effect size and functional response shape. Furthermore, our study provides new insights on the ecological conditions that promote gene flow in snow leopards, which expands ecological knowledge, and we hope will improve conservation planning.

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Data availability

The genetic data that support the findings of this study are property of the Government of the People’s Republic of China and managed through the National Forestry and Grassland Administration. Restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the National Forestry and Grassland Administration of the People’s Republic of China.

References

  • Ahlborn G, Jackson R (1988) Marking in free-ranging snow leopards in west Nepal: a preliminary assessment. In: Freeman H (ed) Proceedings of 5th international snow leopard symposium, pp 25–49. International Snow Leopard Trust, Seattle and Wildlife Institute of India, Dehradun

  • Aryal A, Shrestha UB, Ji W, Ale SB, Shrestha S, Ingty T, Maraseni, TN, Cockfield G, Raubenheimer D (2016) Predicting the distributions of predator (snow leopard) and prey (blue sheep) under climate change in the Himalaya. Ecol Evol 6:4065–4075

  • Ash E, Cushman SA, Redford T et al (2022) Tigers on the edge: mortality and landscape change dominate individual-based spatially explicit simulations of a small tiger population. Landsc Ecol. https://doi.org/10.1007/s10980-022-01494-w

  • Ashrafzadeh MR, Khosravi R, Adibi MA et al (2020) A multi-scale, multi-species approach for assessing effectiveness of habitat and connectivity conservation for endangered felids. Biol Conserv 245:108523

    Article  Google Scholar 

  • 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 Evol. https://doi.org/10.1002/ece3.6492

    Article  PubMed  PubMed Central  Google Scholar 

  • Atzeni L, Cushman SA, Wang J et al (2021) Evidence of spatial genetic structure in a snow leopard population from Gansu, China. Heredity. https://doi.org/10.1038/s41437-021-00483-0

  • Aziz MA, Smith O, Barlow A et al (2018) Do rivers influence fine-scale population genetic structure of tigers in the Sundarbans? Conserv Genet 19:1137–1151

    Article  CAS  Google Scholar 

  • Bai D, Chen P, Atzeni L et al (2018) Assessment of habitat suitability of the snow leopard (Panthera uncia) in Qomolangma National Nature Reserve based on MaxEnt modeling. Zool Res 39:373–386

    Google Scholar 

  • Balkenhol N, Cushman S, Storfer A, Waits L (2016) Landscape genetics: concepts, methods, applications. Wiley, Oxford

    Google Scholar 

  • Bauder JM, Peterman WE, Spear SF et al (2021) Multiscale assessment of functional connectivity: landscape genetics of eastern indigo snakes in an anthropogenically fragmented landscape in central Florida. Mol Ecol 30:3422–3438

    Article  PubMed  Google Scholar 

  • Bowcock AM, Ruiz-Linares A, Tomfohrde J, Minch E, Kidd JR, Cavalli-Sforza LL (1994) High resolution of human evolutionary trees with polymorphic microsatellites. Nature 368:455

    Article  CAS  PubMed  Google Scholar 

  • Burgess SM, Garrick RC (2020) Regional replication of landscape genetics analyses of the Mississippi slimy salamander, Plethodon mississippi. Landsc Ecol 35(2):337–351

    Article  Google Scholar 

  • Burgess SM, Garrick RC (2021) The effect of sampling density and study area size on landscape genetics inferences for the Mississippi slimy salamander (Plethodon mississippi). Ecol Evol. https://doi.org/10.1002/ece3.7481

    Article  PubMed  PubMed Central  Google Scholar 

  • Cameron AC, Page RB, Watling JI et al (2019) Using a comparative approach to investigate the relationship between landscape and genetic connectivity among woodland salamander populations. Conserv Genet 20:1265–1280

    Article  CAS  Google Scholar 

  • CAS, Chinese Academy of Science (2018) Layer of rivers of China (2018), Resource and Environment Science and Data Center, Chinese Academy of Sciences. http://www.resdc.cn/DOI/doiList.aspx

  • Castillo JA, Epps CW, Davis AR, Cushman SA (2014) Landscape effects on gene flow for a climate-sensitive montane species, the American pika. Mol Ecol 23(4):843–856

    Article  PubMed  Google Scholar 

  • Chen J, Ban Y, Li S (2014) China: open access to Earth land-cover map. Nature 514(7523): 434–434

  • 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(3):361

    Article  Google Scholar 

  • Cozzi G, Broekhuis F, McNutt JW, Schmid B (2013) Comparison of the effects of artificial and natural barriers on large African carnivores: implications for interspecific relationships and connectivity. J Anim Ecol 82:707–715

    Article  PubMed  Google Scholar 

  • Cushman SA, Landguth EL (2010a) Spurious correlations and inference in landscape genetics. Mol Ecol 19(17):3592–3602

    Article  PubMed  Google Scholar 

  • Cushman SA, Landguth EL (2010b) Scale dependent inference in landscape genetics. Landsc Ecol 25(6):967–979

    Article  Google Scholar 

  • Cushman SA, Lewis JS (2010) Movement behavior explains genetic differentiation in American black bears. Landsc Ecol 25:1613–1625

    Article  Google Scholar 

  • Cushman SA, Lewis JS, Landguth EL (2014) Why did the bear cross the road? Comparing the performance of multiple resistance surfaces and connectivity modeling methods. Diversity 6:844–854

    Article  Google Scholar 

  • Cushman SA, McKelvey KS, Hayden J, Schwartz MK (2006) Gene flow in complex landscapes: testing multiple hypotheses with causal modeling. Am Nat 168(4):486–499

    Article  PubMed  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. Wiley, Oxford, pp 384–404

  • Cushman SA, Shirk A, Landguth EL (2012) Separating the effects of habitat area, fragmentation and matrix resistance on genetic differentiation in complex landscapes. Landsc Ecol 27(3):369–380

    Article  Google Scholar 

  • Cushman SA, Shirk AJ, Landguth EL (2013b) Landscape genetics and limiting factors. Conserv Genet 14(2):263–274

    Article  Google Scholar 

  • Dalapicolla J, do Prado JR, Percequillo AR, Knowles LL (2021) Functional connectivity in sympatric spiny rats reflects different dimensions of Amazonian Forest-Association. J Biogeogr 48:3196–3209

    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 

  • Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G et al (2012) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36(1):27–46

    Article  Google Scholar 

  • Eisenring M, Best RJ, Zierden MR, Cooper HF, Norstrem MA, Whitham TG, Grady K, Allan GJ, Lindroth RL (2022) Genetic divergence along a climate gradient shapes chemical plasticity of a foundation tree species to both changing climate and herbivore damage. Glob Change Biol 28:4684–4700

    Article  CAS  Google Scholar 

  • Elliot NB, Cushman SA, Loveridge AJ et al (2014) Movements vary according to dispersal stage, group size, and rainfall: the case of the african lion. Ecology 95:2860–2869

    Article  Google Scholar 

  • Evans JS (2021) spatialEco. R package version 1.3-6. https://github.com/jeffreyevans/spatialEco

  • Evans JS, Oakleaf J, Cushman SA (2014) An ArcGIS toolbox for surface gradient and geomorphometric modeling, version 2.0-0. https://github.com/jeffreyevans/GradientMetrics

  • Fattebert J, Robinson HS, Balme G et al (2015) Structural habitat predicts functional dispersal habitat of a large carnivore: how leopards change spots. Ecol Appl 25:1911–1921

    Article  PubMed  Google Scholar 

  • Fox JL, Chundawat RS (2016) What is a snow leopard? Behavior and ecology. In: McCarthy T, Mallon D (eds) Snow leopards of the world. Elsevier, Academic Press, Amsterdam/New York, pp 13–21

  • Frankham R (2005) Genetics and extinction. Biol Conserv 126(2):131–140

    Article  Google Scholar 

  • Frantz AC, Pope LC, Etherington TR et al (2010) Using isolation-by-distance-based approaches to assess the barrier effect of linear landscape elements on badger (Meles meles) dispersal. Mol Ecol 19:1663–1674

    Article  CAS  PubMed  Google Scholar 

  • Gessler PE, Moore ID, McKenzie NJ, Ryan PJ (1995) Soil-landscape modeling and spatial prediction of soil attributes. Int J GIS 9(4):421–432

    Google Scholar 

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

    Article  Google Scholar 

  • Hacker C, Atzeni L, Munkhtsog B et al (2022) Genetic diversity and spatial structures of snow leopards (Panthera uncia) reveal proxies of connectivity across Mongolia and northwestern China. Landsc Ecol. https://doi.org/10.1007/s10980-022-01573-y

  • Hameed S, Din J, Ali H, Kabir M, Younas M, ur Rehman E et al (2020) Identifying priority landscapes for conservation of snow leopards in Pakistan. PLoS ONE 15(11):e0228832

  • Hartmann SA, Steyer K, Kraus RHS et al (2013) Potential barriers to gene flow in the endangered European wildcat (Felis silvestris). Conserv Genet 14:413–426

    Article  Google Scholar 

  • Hearn AJ, Cushman SA, Goossens B et al (2018) Evaluating scenarios of landscape change for Sunda clouded leopard connectivity in a human dominated landscape. Biol Conserv 222:232–240

    Article  Google Scholar 

  • Hearn AJ, Ross J, Bernard H et al (2019) Responses of Sunda clouded leopard Neofelis diardi population density to anthropogenic disturbance: refining estimates of its conservation status in Sabah. Oryx 53:643–653

    Article  Google Scholar 

  • Holt C, Nevin O, Smith D, Convery I (2018) Environmental niche overlap between snow leopard and four prey species in Kazakhstan. Ecol Inf 48:97–103

    Article  Google Scholar 

  • Jackson R (1996) Home range, movements and habitat use of snow leopard in Nepal. PhD Thesis, University of London, pp 1–255

  • Jackson R, Ahlborn G (1989) Snow leopards (Panthera uncia) in Nepal—home range and movements. Natl Geogr Res 5:161–175

    Google Scholar 

  • Jackson RM, Mishra C, McCarthy TM, Ale SB (2010) Snow leopards: conflict and conservation. In: MacDonald DW, Loveridge AJ (eds) The biology and conservation of wild felids. Oxford University Press, Oxford, pp 417–430

    Google Scholar 

  • Jaeger B (2017) r2glmm: computes R squared for mixed (multilevel) models. R package version.1.2. https://CRAN.R-project.org/package=r2glmm

  • Jaeger BC et al (2016) An R squared statistic for fixed effects in the generalized linear mixed model. J Appl Stat. https://doi.org/10.1080/02664763.2016.1193725

  • Janecka J, Jackson R, Yuquang Z et al (2008) Population monitoring of snow leopards using noninvasive collection of scat samples: a pilot study. Anim Conserv 11:401–411

    Article  Google Scholar 

  • Janecka JE, Yu-Guang Z, Di-Qiang L, Munkhtsog B, Bayaraa M et al (2017) Range-wide snow leopard phylogeography supports three subspecies. J Heredity. https://doi.org/10.1093/jhered/esx044

    Article  Google Scholar 

  • Jarvis A, Reuter HI, Nelson A, Guevara E (2008) Hole-filled SRTM for the globe, version 4. CGIAR-CSI SRTM 90m Database. http://srtm.csi.cgiar.org

  • Johansson Ö, Ausilio G, Low M et al (2021) The timing of breeding and independence for snow leopard females and their cubs. Mamm Biol 101:173–180

    Article  Google Scholar 

  • Johansson Ö, Koehler G, Rauset G, Samelius G, Andrén H, Mishra C, Lhagvasuren P, McCarthy T, Low M (2018) Sex-specific seasonal variation in puma and snow leopard home range utilization. Ecosphere 9(8):e02371

    Article  Google Scholar 

  • Johansson O, McCarthy T, Samelius G, Andrén H, Tumursukh L, Mishra C (2015) Snow leopard predation in a livestock dominated landscape in Mongolia. Biol Conserv 184:251–258

    Article  Google Scholar 

  • Johansson Ö, Rauset GR, Samelius G, McCarthy T, Andrén H, Tumursukh L, Mishra C (2016) Land sharing is essential for snow leopard conservation. Biol Conserv 203:1–7. https://doi.org/10.1016/j.biocon.2016.08.034

    Article  Google Scholar 

  • Jombart T (2008) Adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405

    Article  CAS  PubMed  Google Scholar 

  • Kanine JM, Kierepka EM, Castleberry SB et al (2018) Influence of landscape heterogeneity on the functional connectivity of Allegheny woodrats (Neotoma magister) in Virginia. Conserv Genet 19:1259–1268

    Article  CAS  Google Scholar 

  • Kaszta Ż, Cushman SA, Hearn AJ, Burnham D, Macdonald EA, Goossens B et al (2019) Integrating Sunda clouded leopard (Neofelis diardi) conservation into development and restoration planning in Sabah (Borneo). Biol Conserv 235:63–76

    Article  Google Scholar 

  • Kaszta Ż, Cushman SA, Htun S, Naing H, Burnham D, Macdonald DW (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(3):727–746

    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 

  • Kimmig SE, Beninde J, Brandt M, Schleimer A, Kramer-Schadt S, Hofer H et al (2020) Beyond the landscape: resistance modelling infers physical and behavioural gene flow barriers to a mobile carnivore across a metropolitan area. Mol Ecol 29(3):466–484

    Article  PubMed  Google Scholar 

  • Koen EL, Bowman J, Garroway CJ et al (2012) Landscape resistance and american marten gene flow. Landsc Ecol 27:29–43

    Article  Google Scholar 

  • Korablev MP, Poyarkov AD, Karnaukhov AS, Zvychaynaya EYu, Kuksin AN, Malykh 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. https://doi.org/10.1007/s10592-021-01347-0

  • Landguth EL, Cushman SA (2010) Cdpop: a spatially explicit cost distance population genetics program. Mol Ecol Resour 10(1):156–161

    Article  CAS  PubMed  Google Scholar 

  • Landguth EL, Hand BK, Glassy J, Cushman SA, Sawaya MA (2012) UNICOR: a species connectivity and corridor network simulator. Ecography 35(1):9–14

    Article  Google Scholar 

  • Li J, McCarthy TM, Wang H et al (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 et al (2020) Defining priorities for global snow leopard conservation landscapes. Biol Conserv 241:108387

    Article  Google Scholar 

  • Li Y, Zhang Y, Xue Y, Zhang Y, Zhang Y, Gao Y, Li D (2022) Analysis of conservation gaps and landscape connectivity for snow leopard in Qilian Mountains of China. Sustainability 14:1638

    Article  Google Scholar 

  • Manel S, Holderegger R (2013) Ten years of landscape genetics. Trends Ecol Evol 28(10):614–621

    Article  PubMed  Google Scholar 

  • Mateo-Sánchez MC, Balkenhol N, Cushman S, Pérez T, Domínguez A, Saura S (2015) A comparative framework to infer landscape effects on population genetic structure: are habitat suitability models effective in explaining gene flow? Landsc Ecol. https://doi.org/10.1007/s10980-015-0194-4

  • McCarthy TM, Chapron G (2003) Snow leopard survival strategy. ISLT and SLN, Seattle

    Google Scholar 

  • McCarthy TM, Fuller TK, Munkhtsog B (2005) Movements and activities of snow leopards in Southwestern Mongolia. Biol Conserv 124(4):527–537

    Article  Google Scholar 

  • McCarthy T, Mallon D, Jackson R, Zahler P, McCarthy K (2017) Panthera uncia. The IUCN Red list of threatened species 2017:e.T22732A50664030. https://doi.org/10.2305/IUCN.UK.2017-2.RLTS.T22732A50664030.en. Downloaded on 29 June 2019

  • McGarigal K, Cushman SA, Ene E (2012) FRAGSTATS v4: Spatial pattern analysis program for categorical and continuous maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. http://www.umass.edu/landeco/research/fragstats/fragstats.html

  • McGarigal K, Wan HY, Zeller KA, Timm BC, Cushman SA (2016) Multi-scale habitat selection modeling: a review and outlook. Landsc Ecol 31(6):1161–1175

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  • McRae BH, Dickson BG, Keitt TH, Shah VB (2008) Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89:2712–2724

    Article  PubMed  Google Scholar 

  • Minnie L, Zalewski A, Zalewska H, Kerley GIH (2018) Spatial variation in anthropogenic mortality induces a source–sink system in a hunted mesopredator. Oecologia 186:939–951

    Article  PubMed  Google Scholar 

  • Mohammadi A, Almasieh K, Nayeri D et al (2022) Comparison of habitat suitability and connectivity modelling for three carnivores of conservation concern in an iranian montane landscape. Landsc Ecol 37:411–430

    Article  Google Scholar 

  • Mukherjee S, Mukherjee S, Garg RD et al (2013) Evaluation of topographic index in relation to terrain roughness and DEM grid spacing. J Earth Syst Sci 122:869–886

    Article  Google Scholar 

  • Murphy MA, Evans JS, Storfer A (2010) Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics. Ecology 91(1):252–261

    Article  PubMed  Google Scholar 

  • Naimi B, Hamm NAS, Groen TA, Skidmore AK, Toxopeus AG (2014) Where is positional uncertainty a problem for species distribution modelling? Ecography 37:191–203

    Article  Google Scholar 

  • O’Grady JJ, Brook BW, Reed DH, Ballou JD, Tonkyn DW, Frankham R (2006) Realistic levels of inbreeding depression strongly affect extinction risk in wild populations. Biol Conserv 133(1):42–51

    Article  Google Scholar 

  • Peterman WE (2018) ResistanceGA: an R package for the optimization of resistance surfaces using genetic algorithms. Methods Ecol Evol 9(6):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 

  • Peterman WE, Winiarski KJ, Moore CE et al (2019) A comparison of popular approaches to optimize landscape resistance surfaces. Landsc Ecol 34:2197–2208

    Article  Google Scholar 

  • Purvis A, Gittleman JL, Cowlishaw G, Mace GM (2000) Predicting extinction risk in declining species. Proc R Soc Lond Ser Biol Sci 267(1456):1947–1952

  • R Core Team (2020) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/

  • Reddy PA, Cushman SA, Srivastava A, Sarkar MS, Shivaji S (2017) Tiger abundance and gene flow in Central India are driven by disparate combinations of topography and land cover. Divers Distrib 23(8):863–874

    Article  Google Scholar 

  • Reddy P, Puyravaud J-P, Cushman S, Segu H (2019) Spatial variation in the response of tiger gene flow to landscape features and limiting factors. Anim Conserv 22(5):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(3):471–486

    Article  Google Scholar 

  • Riley SJ, DeGloria SD, Elliot R (1999) A terrain ruggedness index that quantifies topographic heterogeneity. Intermt J Sci 5:1–4

    Google Scholar 

  • Riordan P, Cushman SA, Mallon D, Shi K, Hughes J (2016) Predicting global population connectivity and targeting conservation action for snow leopard across its range. Ecography 39(5):419–426

    Article  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, Edition: 1, chapter: 29, Biodiversity of the world: conservation from genes to landscapes. Elsevier, Amsterdam, pp 395–402

  • Row JR, Oyler-McCance SJ, Fike JA et al (2015) Landscape characteristics influencing the genetic structure of greater sage-grouse within the stronghold of their range: a holistic modeling approach. Ecol Evol 5:1955–1969

    Article  PubMed  PubMed Central  Google Scholar 

  • R Studio Team (2020) RStudio: integrated development for R. RStudio, PBC, Boston. 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. Issues 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 Ecol 24:5110–5129

    Article  PubMed  Google Scholar 

  • Rutten A, Cox K, Scheppers T, Broecke BV, Leirs H, Casaer J (2019) Analysing the recolonisation of a highly fragmented landscape by wild boar using a landscape genetic approach. Wildl Bio. https://doi.org/10.2981/wlb.00542

  • Sartor CC, Wan HY, Pereira JA et al (2022) Landscape genetics outperforms habitat suitability in predicting landscape resistance for congeneric cat species. J Biogeogr 49:2206–2217

    Article  Google Scholar 

  • Schwalm D, Waits LP, Ballard WB (2014) Little fox on the prairie: genetic structure and diversity throughout the distribution of a grassland carnivore in the United States. Conserv Genet 15:1503–1514

    Article  Google Scholar 

  • Shirk A (2015) sGD: spatially explicit estimation of genetic diversity indices and Wright’s neighborhood size (NS). R package version 2.11

  • 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, Landguth EL, Cushman SA (2017a) A comparison of individual-based genetic distance metrics for landscape genetics. Mol Ecol Resour. https://doi.org/10.1111/1755-0998.12684

    Article  PubMed  Google Scholar 

  • Shirk AJ, Landguth EL, Cushman SA (2017b) A comparison of regression methods for model selection in individual-based landscape genetic analysis. Mol Ecol Resour. https://doi.org/10.1111/1755-0998.12709

    Article  PubMed  Google Scholar 

  • Shirk AJ, Schroeder MA, Robb LA, Cushman SA (2015) Empirical validation of landscape resistance models: insights from the Greater sage-grouse (Centrocercus urophasianus). Landsc Ecol 30:1837–1850

    Article  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(17):3603–3619

    Article  CAS  PubMed  Google Scholar 

  • Short Bull RA, Cushman SA, Mace R, Chilton T, Kendall KC, Landguth EL et al. (2011) Why replication is import-ant in landscape genetics: american black bear in the Rocky Mountains. Mol Ecol 20:1092–1107

    Article  Google Scholar 

  • Shrestha B, Kindlmann P (2020) Implications of landscape genetics and connectivity of snow leopard in the nepalese himalayas for its conservation. Sci Rep 10(1):19853

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Snow Leopard Network (2014) Snow Leopard Survival Strategy. Revised 2014 Version. Snow Leopard Network, Seattle

  • 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(17):3576–3591

    Article  PubMed  Google Scholar 

  • Spiess AN (2018) qpcR: Modelling and Analysis of Real-Time PCR Data. R package version 1.4-1. https://CRAN.R-project.org/package=qpcR

  • Storfer A, Murphy MA, Evans JS, Goldberg CS, Robinson S, Spear SF et al. (2006) Putting the ‘landscape’ in landscape genetics. Heredity 98(3):128–142

    Article  PubMed  Google Scholar 

  • Strien MJV, Keller D, Holderegger R (2012) A new analytical approach to landscape genetic modelling: least-cost transect analysis and linear mixed models. Mol Ecol 21(16):4010–4023

    Article  PubMed  Google Scholar 

  • Sunquist M, Sunquist F (2002) Wild cats of the World. University of Chicago Press, Chicago

    Book  Google Scholar 

  • Thatte P, Chandramouli A, Tyagi A, Patel K, Baro P, Chhattani H, Ramakrishnan U (2020) Human footprint differentially impacts genetic connectivity of four wide-ranging mammals in a fragmented landscape. Divers Distrib 26(3):299–314

    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(6):1269–1283

    Article  Google Scholar 

  • Waits L, Storfer A (2016) Basics of population genetics: quantifying neutral and adaptive genetic variation for landscape genetic studies. Landscape genetics: concepts, methods, applications. Wiley, Oxford, pp 35–37

    Google Scholar 

  • Wan HY, Cushman SA, Ganey JL (2019) Improving habitat and connectivity model predictions with multi-scale resource selection functions from two geographic areas. Landsc Ecol 34:503–519

    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(10):1601–1612

    Article  Google Scholar 

  • Wasserman TN, Cushman SA, Littell JS, Shirk AJ, Landguth EL (2012a) Population connectivity and genetic diversity of american marten (Martes americana) in the United States northern Rocky Mountains in a climate change context. Conserv Genet 14(2):529–541

    Article  Google Scholar 

  • Wasserman TN, Cushman SA, Shirk AS, Landguth EL, Littell JS (2012b) Simulating the effects of climate change on population connectivity of American marten (Martes americana) in the northern Rocky Mountains, USA. Landsc Ecol 27(2):211–225

    Article  Google Scholar 

  • Watts SM, McCarthy TM, Namgail T (2019) Modeling potential habitat for snow leopards (Panthera uncia) in Ladakh, India. PLoS ONE 14:e0211509

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Weckworth B (2021) Snow leopard (Panthera uncia) genetics: the knowledge gaps, needs, and implications for conservation. J Indian Inst Sci. https://doi.org/10.1007/s41745-021-00236-2

  • Wiens JA (2001) The landscape context of dispersal. In: Clobert J, Danchin E, Dhondt AA, Nichols JD (eds) Dispersal: individual, population, and community. Oxford University Press, Oxford, pp 96–109

  • Winiarski KJ, Peterman WE, Whiteley AR, McGarigal K (2020) Multiscale resistant kernel surfaces derived from inferred gene flow: an application with vernal pool breeding salamanders. Mol Ecol Resour 20:97–113

    Article  CAS  PubMed  Google Scholar 

  • Wright S (1943) Isolation by distance. Genetics 28:114–138

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 Distrib 24(7):868–879

    Article  Google Scholar 

  • Zeller KA, McGarigal K, Whiteley AR (2012) Estimating landscape resistance to movement: a review. Landsc Ecol 27(6):777–797

    Article  Google Scholar 

  • Zeller KA, Vickers TW, Ernest HB, Boyce WM (2017) Multi-level, multi-scale resource selection functions and resistance surfaces for conservation planning: Pumas as a case study. PLoS ONE 12(6):e0179570

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang YG, Hacker C, Zhang Y, Xue Y, Wu L, Dai Y, Luo P, Xierannima, Janecka JE, Li D (2019) An analysis of genetic structure of snow leopard populations in Sanjiang-Yuan and Qilianshan National Parks. Acta Theriol Sin 39:442–449

    Article  Google Scholar 

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Acknowledgements

We acknowledge support from the Second National Survey of Terrestrial Wildlife in China, and National Park Management Office, National Forestry and Grassland Administration of China (NFGA), and National Natural Science Foundation of China (Grant No. 31470567). We thank the Jiuquan Branch of Gansu Provincial Administration of Giant Panda & Qilianshan National Park authorities, especially Mr. Wuliji, Mr. Dazhan, Ms Yang Hairong, Mr. Wan Shengqi, Mr. Dou Zhigang, and Mr. Pei Wen, for their help and support with the fieldwork in Gansu Province, China. We thank the Zhangye Branch of Gansu Provincial Administration of Giant Panda & Qilianshan National Park authorities, especially Mr. A Cheng, Mr. Ma Duifeng and Mr. Liao Kongtai for their support with fieldwork. We thank all the rangers and staff of the Nature Reserves who assisted with samples collection. Atzeni Luciano thanks Deng Zhixiong, Ma Bing, Zhang Chengcheng and Bai Defeng, for early help with DNA extraction, species identification, and genotyping.

Funding

National Natural Science Foundation of China (Grant No. 31470567).

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Authors and Affiliations

  1. Department of Biology, University of Oxford, Oxford, UK

    Luciano Atzeni & Samuel A. Cushman

  2. Wildlife Institute, School of Ecology and Nature Conservation, Beijing Forestry University, Beijing, China

    Luciano Atzeni, Jun Wang, Philip Riordan & Kun Shi

  3. Department of Natural Sciences, Manchester Metropolitan University, Manchester, UK

    Jun Wang

  4. Marwell Wildlife, Winchester, UK

    Philip Riordan

  5. School of Biological Sciences, University of Southampton, Southampton, UK

    Philip Riordan

  6. Eco-Bridge Continental, Beijing, China

    Kun Shi

Authors
  1. Luciano Atzeni
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  2. Jun Wang
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  3. Philip Riordan
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  4. Kun Shi
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  5. Samuel A. Cushman
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Contributions

LA, SK, PR, and SC conceived the study. LA and SK supervised laboratory analyses. LA conducted laboratory analyses. LA and SC supervised modelling analyses. LA and WJ conducted modelling analyses. LA wrote the manuscript. All authors provided contributions through comments and editing of the final version.

Corresponding author

Correspondence to Kun Shi.

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Competing interests

All the authors declare no competing interests involved with this manuscript. The authors disclose no competing interests associated with this manuscript. No live animal was involved in the research presented in this study. The manuscript presented here is a modified version of a draft submitted previously to another journal and withdrawn after rejection.

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Atzeni, L., Wang, J., Riordan, P. et al. Landscape resistance to gene flow in a snow leopard population from Qilianshan National Park, Gansu, China. Landsc Ecol 38, 1847–1868 (2023). https://doi.org/10.1007/s10980-023-01660-8

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