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Moving beyond landscape resistance: considerations for the future of connectivity modelling and conservation science

Unnithan Kumar, Siddharth, Turnbull, Jonathon, Hartman Davies, Oscar, Hodgetts, Timothy, Cushman, Samuel Alan·2022·Landscape Ecology
DOI 10.1007/s10980-022-01504-x
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Abstract

Landscape connectivity, the extent to which a landscape facilitates the flow of ecological processes such as organism movement, has emerged as a central focus of landscape ecology and conservation science. Connectivity modelling now encompasses an enormous body of work across ecological theory and application. The dominant connectivity models in use today are based on the framework of ‘landscape resistance’, which is a way of measuring how landscape structure influences movement patterns. However, the simplistic assumptions and high degree of reductionism inherent to the landscape resistance paradigm severely limits the ability of connectivity algorithms to account for many fundamental aspects of animal movement, and thus greatly reduces the effectiveness and relevance of connectivity models for conservation theory and practice. In this paper, we first provide an overview of the development of connectivity modelling and resistance surfaces. We then discuss several key drivers of animal movement which are absent in resistance-based models, with a focus on spatiotemporal variation, human and interspecies interactions, and other context-dependent effects. We look at a range of empirical studies which highlight the strong impact these effects have on movement and connectivity predictions. But we also provide promising avenues of future research to address this: we discuss newly emerging technologies and interdisciplinary work, and look to developing methodologies, models and conversations which move beyond the limiting framework of landscape resistance, so that connectivity models can better reflect the complexities and richness of animal movement.

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  • Emerging technologies and methodologies are making the integration of spatiotemporal variation, interactions, and other important contextual details into connectivity models increasingly possible.

    “Despite the complexities involved in developing connectivity models which attend to spatiotemporal variation, interactions, and other important contextual details, emerging technologies and methodologies are making this integration increasingly possible15.”

    From Connectivity modelling in conservation science: a comparative evaluation (2022) · cites this paper as [15]

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Research works printed but not linked: 131

  1. Abram D (1996) The spell of the sensuous. Vintage Books, New York
  2. Abram D (2010) Becoming animal. Vintage Books, New York · DOI
  3. Adams WB, Mulligan M (2012) Decolonizing nature: strategies for conservation in a post-colonial era. Routledge, London · DOI · Paper page
  4. Adams W M, Aveling R, Brockington D, Dickson B, Elliott J, Hutton J, Roe D, Vira B, Wolmer W (2004). Biodiversity conservation and the eradication of poverty. Science 306(5699):1146–1149 · DOI
  5. Adriaensen F, Chardon JP, De Blust G, Swinnen E, Villalba S, Gulinck H, Matthysen E (2003) The application of ‘least-cost’modelling as a functional landscape model. Landsc Urban Plan 64(4):233–247 · DOI · Paper page
  6. Arts K, van der Wal R, Adams WM (2015) Digital technology and the conservation of nature. Ambio 44(4):661–673 · DOI
  7. Ash E, Cushman SA, Macdonald DW, Redford T, Kaszta Ż (2020) How important are resistance, dispersal ability, population density and mortality in temporally dynamic simulations of population connectivity? A case study of tigers in Southeast Asia. Land 9(11):415 · DOI
  8. Barua M (2014a) Bio-geo-graphy: Landscape, dwelling, and the political ecology of human-elephant relations. Environ Plan D 32(5):915–934 · DOI · Paper page
  9. Barua M (2014b) Volatile ecologies: towards a material politics of human—animal relations. Environ Plan A 46(6):1462–1478 · DOI
  10. Beier P, Majka DR, Spencer WD (2008) Forks in the road: choices in procedures for designing wildland linkages. Conserv Biol 22(4):836–851 · DOI · Paper page
  11. Beier P, Majka DR, Newell SL (2009) Uncertainty analysis of least-cost modeling for designing wildlife linkages. Ecol Appl 19(8):2067–2077 · DOI · Paper page
  12. Bennett NJ, Roth R, Klain SC, Chan K, Christie P, Clark DA et al (2017) Conservation social science: understanding and integrating human dimensions to improve conservation. Biol Conserv 205:93–108 · DOI
  13. Benson ES (2014) Minimal animal: surveillance, simulation, and stochasticity in wildlife biology. Antennae 30:39
  14. Benson ES (2016) Movement ecology and the minimal animal. LA+ Simul 4:30–33
  15. Bergman C (2005) Inventing a beast with no body: radio-telemetry, the marginalization of animals, and the simulation of ecology. Worldviews: Glob Relig C Ecol 9(2):255–270
  16. Bergman CM, Schaefer JA, Luttich SN (2000) Caribou movement as a correlated random walk. Oecologia 123(3):364–374 · DOI
  17. Berkes F, Colding J, Folke C (eds) (2008) Navigating social-ecological systems: building resilience for complexity and change. Cambridge University Press, Cambridge
  18. Bocedi G, Palmer SC, Malchow AK, Zurell D, Watts K, Travis JM (2021) RangeShifter 2.0: an extended and enhanced platform for modelling spatial eco-evolutionary dynamics and species’ responses to environmental changes. Ecography 44(10):1453–1462 · DOI · Paper page
  19. Boettiger AN, Wittemyer G, Starfield R, Volrath F, Douglas-Hamilton I, Getz WM (2011) Inferring ecological and behavioral drivers of African elephant movement using a linear filtering approach. Ecology 92(8):1648–1657 · DOI · Paper page
  20. Bograd SJ, Block BA, Costa DP, Godley BJ (2010) Biologging technologies: new tools for conservation. Introduction. Endanger Spec Res 10:1–7 · DOI
  21. Bolliger J, Silbernagel J (2020) Contribution of connectivity assessments to green infrastructure (GI). ISPRS Int J Geo Inf 9(4):212 · DOI
  22. Boyce MS, Vernier PR, Nielsen SE, Schmiegelow FK (2002) Evaluating resource selection functions. Ecol Model 157(2–3):281–300 · DOI
  23. Broekhuis F, Cozzi G, Valeix M, McNutt JW, Macdonald DW (2013) Risk avoidance in sympatric large carnivores: reactive or predictive? J Anim Ecol 82(5):1098–1105 · DOI
  24. Broekhuis F, Madsen EK, Keiwua K, Macdonald DW (2019) Using GPS collars to investigate the frequency and behavioural outcomes of intraspecific interactions among carnivores: a case study of male cheetahs in the Maasai Mara, Kenya. PLoS ONE 14(4):e0213910 · DOI
  25. Bunnefeld N, Nicholson E, Milner-Gulland EJ (eds) (2017) Decision-making in conservation and natural resource management: models for interdisciplinary approaches (vol 22). Cambridge University Press, Cambridge
  26. Burnham KP, Anderson DR (2004) Multimodel inference: understanding AIC and BIC in model selection. Sociol Methods Res 33(2):261–304 · DOI · Paper page
  27. Campbell LM (2005) Overcoming obstacles to interdisciplinary research. Conserv Biol 19(2):574–577 · DOI
  28. Cantor M, Wedekin LL, Guimaraes PR, Daura-Jorge FG, Rossi-Santos MR, Simoes-Lopes PC (2012) Disentangling social networks from spatiotemporal dynamics: the temporal structure of a dolphin society. Anim Behav 84(3):641–651 · DOI
  29. Compton BW, McGarigal K, Cushman SA, Gamble LR (2007) A resistant‐kernel model of connectivity for amphibians that breed in vernal pools. Conserv Biol 21(3):788–799 · DOI · Paper page
  30. Cooke SJ, Hinch SG, Donaldson MR, Clark TD, Eliason EJ, Crossin GT et al (2012) Conservation physiology in practice: how physiological knowledge has improved our ability to sustainably manage Pacific salmon during up-river migration. Philos Trans R Soc B 367(1596):1757–1769 · DOI
  31. Cooke SJ, Sack L, Franklin CE, Farrell AP, Beardall J, Wikelski M, Chown SL (2013) What is conservation physiology? Perspectives on an increasingly integrated and essential science. Conserv Physiol 1(1):cot0001 · DOI
  32. Cooke SJ, Blumstein DT, Buchholz R, Caro T, Fernandez-Juricic E, Franklin CE et al (2014) Physiology, behavior, and conservation. Physiol Biochem Zool 87(1):1–14 · DOI
  33. Cronon W (1996) The trouble with wilderness: or, getting back to the wrong nature. Environ Hist 1(1):7–28 · DOI · Paper page
  34. Cushman SA (2006) Effects of habitat loss and fragmentation on amphibians: a review and prospectus. Biol Cons 128(2):231–240 · DOI
  35. Cushman SA (2010a) Animal movement data: GPS telemetry, autocorrelation and the need for path-level analysis. In: Spatial complexity, informatics, and wildlife conservation. Springer, Tokyo, pp 131–149 · DOI · Paper page
  36. Cushman SA (2010b) Space and time in ecology: noise or fundamental driver?. In Spatial complexity, informatics, and wildlife conservation. Springer, Tokyo, pp 19–41 · DOI
  37. Cushman SA, Huettmann F (eds) (2010) Spatial complexity, informatics, and wildlife conservation. Springer, Tokyo, pp 349–368
  38. Cushman SA, Landguth EL (2010) Spurious correlations and inference in landscape genetics. Mol Ecol 19(17):3592–3602 · DOI
  39. Cushman SA, Lewis JS (2010) Movement behavior explains genetic differentiation in American black bears. Landscape Ecol 25(10):1613–1625 · DOI
  40. Cushman SA, Chase M, Griffin C (2005) Elephants in space and time. Oikos 109(2):331–341 · DOI
  41. Cushman SA, McKelvey KS, Schwartz MK (2009) Use of empirically derived source-destination models to map regional conservation corridors. Conserv Biol 23(2):368–376 · DOI
  42. Cushman SA, Raphael MG, Ruggiero LF, Shirk AS, Wasserman TN, O’Doherty EC (2011) Limiting factors and landscape connectivity: the American marten in the Rocky Mountains. Landscape Ecol 26(8):1137–1149 · DOI
  43. Cushman SA, Shirk A, Landguth EL (2012) Separating the effects of habitat area, fragmentation and matrix resistance on genetic differentiation in complex landscapes. Landscape Ecol 27(3):369–380 · DOI
  44. Cushman SA, McRae B, Adriaensen F, Beier P, Shirley M, Zeller K (2013a) Biological corridors and connectivity. In: Macdonald DW, Willis KJ (eds) Key topics in conservation biology 2. Hoboken, NJ, Wiley-Blackwell, pp 384–404 · DOI · Paper page
  45. Cushman SA, Shirk AJ, Landguth EL (2013b) Landscape genetics and limiting factors. Conserv Genet 14(2):263–274 · DOI
  46. 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(4):844–854 · DOI
  47. DeAngelis DL, Mooij WM (2005) Individual-based modeling of ecological and evolutionary processes. Annu Rev Ecol Evol Syst 36:147–168 · DOI
  48. Dray S, Royer-Carenzi M, Calenge C (2010) The exploratory analysis of autocorrelation in animal-movement studies. Ecol Res 25(3):673–681 · DOI · Paper page
  49. Dunning JB Jr, Stewart DJ, Danielson BJ, Noon BR, Root TL, Lamberson RH, Stevens EE (1995) Spatially explicit population models: current forms and future uses. Ecol Appl 5(1):3–11 · DOI
  50. Elliot NB, Cushman SA, Macdonald DW, Loveridge AJ (2014a) The devil is in the dispersers: predictions of landscape connectivity change with demography. J Appl Ecol 51(5):1169–1178 · DOI · Paper page
  51. Elliot NB, Cushman SA, Loveridge AJ, Mtare G, Macdonald DW (2014b) Movements vary according to dispersal stage, group size, and rainfall: the case of the African lion. Ecology 95(10):2860–2869 · DOI
  52. Ellis RD, McWhorter TJ, Maron M (2012) Integrating landscape ecology and conservation physiology. Landscape Ecol 27(1):1–12 · DOI
  53. Fahrig L (2003) Effects of habitat fragmentation on biodiversity. Annu Rev Ecol Evol Syst 34(1):487–515 · DOI · Paper page
  54. Farina A (2008) Principles and methods in landscape ecology: towards a science of the landscape (vol 3). Springer Science & Business Media, New York
  55. Frank B, Glikman JA, Marchini S (eds) (2019) Human–wildlife interactions: turning conflict into coexistence (vol 23). Cambridge University Press, Cambridge
  56. Gibbs JP (1998) Amphibian movements in response to forest edges, roads, and streambeds in southern New England. J Wildl Manag 62:584–589 · DOI · Paper page
  57. Gorini L, Linnell JD, May R, Panzacchi M, Boitani L, Odden M, Nilsen EB (2012) Habitat heterogeneity and mammalian predator–prey interactions. Mammal Rev 42(1):55–77 · DOI
  58. Hargrove WW, Hoffman FM, Efroymson RA (2005) A practical map-analysis tool for detecting potential dispersal corridors. Landsc Ecol 20(4):361–373 · DOI
  59. Hearn AJ, Cushman SA, Ross J, Goossens B, Hunter LT, Macdonald DW (2018) Spatio-temporal ecology of sympatric felids on Borneo. Evidence for resource partitioning? PLoS ONE 13(7):e0200828 · DOI
  60. Heinrichs JA, Lawler JJ, Schumaker NH (2016) Intrinsic and extrinsic drivers of source–sink dynamics. Ecol Evol 6(4):892–904 · DOI
  61. Hilty JA, Lidicker WZ Jr, Merenlender AM (2012) Corridor ecology: the science and practice of linking landscapes for biodiversity conservation. Island Press, Washington, DC
  62. Hodgetts T (2018) Connectivity as a multiple: in, with and as “nature.” Area 50(1):83–90 · DOI · Paper page
  63. Hodgetts T, Lorimer J (2015) Methodologies for animals’ geographies: cultures, communication and genomics. Cult Geogr 22(2):285–295 · DOI
  64. Hodgetts T, Lorimer J (2020) Animals’ mobilities. Prog Hum Geogr 44(1):4–26 · DOI
  65. Holyoak M, Casagrandi R, Nathan R, Revilla E, Spiegel O (2008) Trends and missing parts in the study of movement ecology. Proc Natl Acad Sci 105(49):19060–19065 · DOI
  66. Hooks B (2001) All about love: new visions. Harper Perennial, New York
  67. Ingold T (1993) The temporality of the landscape. World Archaeol 25(2):152–174 · DOI · Paper page
  68. Ingold T (2000) The perception of the environment: essays on livelihood, dwelling and skill. Routledge, London
  69. Ingold T (2011) Being alive: essays on movement, knowledge and description. Routledge, London · DOI · Paper page
  70. Jeltsch F, Bonte D, Pe’er G, Reineking B, Leimgruber P, Balkenhol N (2013) Integrating movement ecology with biodiversity research-exploring new avenues to address spatiotemporal biodiversity dynamics. Mov Ecol 1(1):1–13 · DOI
  71. Jennings MK, Zeller KA, Lewison RL (2020) Supporting adaptive connectivity in dynamic landscapes. Land 9(9):295 · DOI
  72. Johnson JT, Howitt R, Cajete G, Berkes F, Louis RP, Kliskey A (2016) Weaving Indigenous and sustainability sciences to diversify our methods. Sustain Sci 11(1):1–11 · DOI
  73. Kareiva PM, Shigesada N (1983) Analyzing insect movement as a correlated random walk. Oecologia 56(2):234–238 · DOI · Paper page
  74. Kareiva P, Marvier M (2012) What is conservation science? Bioscience 62(11):962–969 · DOI · Paper page
  75. 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 Cons 235:63–76 · DOI · Paper page
  76. Kaszta Ż, Cushman SA, Macdonald DW (2020) Prioritizing habitat core areas and corridors for a large carnivore across its range. Anim Conserv 23(5):607–616 · DOI
  77. Kaszta Ż, Cushman SA, Slotow R (2021) Temporal non-stationarity of path-selection movement models and connectivity: an example of African Elephants in Kruger National Park. Front Ecol Evol 9:207 · DOI · Paper page
  78. Keeley AT, Beier P, Keeley BW, Fagan ME (2017) Habitat suitability is a poor proxy for landscape connectivity during dispersal and mating movements. Landsc Urban Plan 161:90–102 · DOI · Paper page
  79. Levin SA (1992) The problem of pattern and scale in ecology: the Robert H. MacArthur award lecture. Ecology 73(6):1943–1967 · DOI
  80. Lewis MA, Maini PK, and Petrovskii SV (2013) Dispersal, individual movement and spatial ecology. Lecture notes in mathematics (mathematics bioscience series), 2071. · DOI
  81. Lorimer J (2015) Wildlife in the Anthropocene: conservation after nature. University of Minnesota Press, Minnesota · DOI · Paper page
  82. Manly BFL, McDonald L, Thomas DL, McDonald TL, Erickson WP (2007) Resource selection by animals: statistical design and analysis for field studies. Springer Science & Business Media, New York
  83. Mateo-Sánchez MC, Balkenhol N, Cushman S, Pérez T, Domínguez A, Saura S (2015a) A comparative framework to infer landscape effects on population genetic structure: are habitat suitability models effective in explaining gene flow? Landsc Ecol 30(8):1405–1420 · DOI
  84. Mateo-Sánchez MC, Balkenhol N, Cushman S, Pérez T, Domínguez A, Saura S (2015b) Estimating effective landscape distances and movement corridors: comparison of habitat and genetic data. Ecosphere 6(4):1–16 · DOI
  85. Mathur N (2021) Crooked cats: beastly encounters in the anthropocene. University of Chicago Press, Chicago · DOI
  86. McGarigal K, Cushman S (2005) The gradient concept of landscape structure [Chapter 12]. In: Wiens JA, Moss MR (eds) Issues and perspectives in landscape ecology. Cambridge University Press, Cambridge, pp 112–119 · DOI
  87. Moilanen A (2011) On the limitations of graph-theoretic connectivity in spatial ecology and conservation. J Appl Ecol 48:1543–1547 · DOI · Paper page
  88. Moller H, Berkes F, Lyver POB, Kislalioglu M (2004) Combining science and traditional ecological knowledge: monitoring populations for co-management. Ecol Soc 9(3) · DOI
  89. Nathan R, Getz WM, Revilla E, Holyoak M, Kadmon R, Saltz D, Smouse PE (2008) A movement ecology paradigm for unifying organismal movement research. Proc Natl Acad Sci 105(49):19052–19059 · DOI
  90. Hanh TN (1993) Transformation and healing: sutra on the four establishments of mindfulness. Rider, London
  91. Ntuli H, Jagers SC, Linell A, Sjöstedt M, Muchapondwa E (2019) Factors influencing local communities’ perceptions towards conservation of transboundary wildlife resources: the case of the Great Limpopo Trans-frontier Conservation Area. Biodivers Conserv 28(11):2977–3003 · DOI
  92. Osipova L, Okello MM, Njumbi SJ, Ngene S, Western D, Hayward MW, Balkenhol N (2019) Using step-selection functions to model landscape connectivity for African elephants: accounting for variability across individuals and seasons. Anim Conserv 22(1):35–48 · DOI · Paper page
  93. Paquet PC, Darimont CT (2010) Wildlife conservation and animal welfare: two sides of the same coin. Anim Welf 19(2):177–190 · DOI
  94. Parathian HE, McLennan MR, Hill CM, Frazão-Moreira A, Hockings KJ (2018) Breaking through disciplinary barriers: human–wildlife interactions and multispecies ethnography. Int J Primatol 39(5):749–775 · DOI · Paper page
  95. Parreñas JS (2018) Decolonizing extinction. Duke University Press, Durham · DOI · Paper page
  96. Paltsyn MY, Gibbs JP, Mountrakis G (2019) Integrating traditional ecological knowledge and remote sensing for monitoring rangeland dynamics in the Altai Mountain region. Environ Manag 64(1):40–51 · DOI
  97. Patel R, Moore JW (2017) Cheap nature. In: A history of the world in seven cheap things: a guide to capitalism, nature, and the future of the planet. University of California Press · DOI
  98. Penjor U, Astaras C, Cushman SA, Kaszta Ż, Macdonald DW (2022) Contrasting effects of human settlement on the interaction among sympatric apex carnivores. Proc R Soc B 289(1973):20212681 · DOI · Paper page
  99. Peterson MN, Birckhead JL, Leong K, Peterson MJ, Peterson TR (2010) Rearticulating the myth of human–wildlife conflict. Conserv Lett 3(2):74–82 · DOI
  100. Pinto N, Keitt TH (2009) Beyond the least-cost path: evaluating corridor redundancy using a graph-theoretic approach. Landsc Ecol 24(2):253–266 · DOI · Paper page
  101. Pooley SP, Mendelsohn JA, Milner-Gulland EJ (2014) Hunting down the chimera of multiple disciplinarity in conservation science. Conserv Biol 28(1):22–32 · DOI
  102. Pooley S, Barua M, Beinart W, Dickman A, Holmes G, Lorimer J et al (2017) An interdisciplinary review of current and future approaches to improving human–predator relations. Conserv Biol 31(3):513–523 · DOI · Paper page
  103. Powell RA, Mitchell MS (2012) What is a home range? J Mammal 93(4):948–958 · DOI
  104. Rayfield B, Fortin MJ, Fall A (2010) The sensitivity of least-cost habitat graphs to relative cost surface values. Landsc Ecol 25(4):519–532 · DOI · Paper page
  105. Reddy PA, Puyravaud JP, Cushman SA, Segu H (2019) Spatial variation in the response of tiger gene flow to landscape features and limiting factors. Anim Conserv 22(5):472–480 · DOI · Paper page
  106. 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 · DOI
  107. Rostro-García S, Tharchen L, Abade L, Astaras C, Cushman SA, Macdonald DW (2016) Scale dependence of felid predation risk: identifying predictors of livestock kills by tiger and leopard in Bhutan. Landsc Ecol 31(6):1277–1298 · DOI
  108. Rudnick D, Ryan SJ, Beier P, Cushman SA, Dieffenbach F, Epps C et al (2012) The role of landscape connectivity in planning and implementing conservation and restoration priorities. Issues Ecol 16:1–20
  109. Salmón E (2000) Kincentric ecology: indigenous perceptions of the human–nature relationship. Ecol Appl 10(5):1327–1332
  110. Schumaker NH (1996) Using landscape indices to predict habitat connectivity. Ecology 77:1210–1225 · DOI · Paper page
  111. Schumaker NH, Brookes A (2018) HexSim: a modeling environment for ecology and conservation. Landsc Ecol 33(2):197–211 · DOI · Paper page
  112. Schwartz MK, Copeland JP, Anderson NJ, Squires JR, Inman RM, McKelvey KS et al (2009) Wolverine gene flow across a narrow climatic niche. Ecology 90(11):3222–3232 · DOI · Paper page
  113. Sekar N, Shiller D (2020) Engage with animal welfare in conservation. Science 369(6504):629–630 · DOI
  114. Sherub S, Fiedler W, Duriez O, Wikelski M (2017) Bio-logging, new technologies to study conservation physiology on the move: a case study on annual survival of Himalayan vultures. J Comp Physiol A 203(6):531–542 · DOI
  115. 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 · DOI · Paper page
  116. Shirk AJ, Schroeder MA, Robb LA, Cushman SA (2015) Empirical validation of landscape resistance models: insights from the Greater Sage-Grouse (Centrocercus urophasianus). Landscape Ecol 30(10):1837–1850 · DOI
  117. Shirk AJ, Landguth EL, Cushman SA (2018) A comparison of regression methods for model selection in individual-based landscape genetic analysis. Mol Ecol Resour 18(1):55–67 · DOI
  118. Stronen AV, Schumaker NH, Forbes GJ, Paquet PC, Brook RK (2012) Landscape resistance to dispersal: simulating long-term effects of human disturbance on a small and isolated wolf population in southwestern Manitoba, Canada. Environ Monitor Assess 184(11):6923–6934 · DOI
  119. Thurfjell H, Ciuti S, Boyce MS (2014) Applications of step-selection functions in ecology and conservation. Mov Ecol 2(1):1–12 · DOI
  120. Tischendorf L, Fahrig L (2000) How should we measure landscape connectivity? Landscape Ecol 15(7):633–641 · DOI
  121. Tree I (2018) Wilding: the return of nature to a British farm. Pan Macmillan, UK
  122. Unnithan Kumar S, Kaszta Ż, Cushman SA (2022a) Pathwalker: a new individual-based movement model for conservation science and connectivity modelling. ISPRS Int J Geo Inf 11(6):329 · DOI
  123. 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 · DOI · Paper page
  124. Wallach AD, Bekoff M, Batavia C, Nelson MP, Ramp D (2018) Summoning compassion to address the challenges of conservation. Conserv Biol 32(6):1255–1265 · DOI
  125. 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 · DOI
  126. Webster MS, Marra PP, Haig SM, Bensch S, Holmes RT (2002) Links between worlds: unraveling migratory connectivity. Trends Ecol Evol 17(2):76–83 · DOI
  127. Wiens JA (1989) Spatial scaling in ecology. Funct Ecol 3(4):385–397 · DOI · Paper page
  128. Wilson AD, Wikelski M, Wilson RP, Cooke SJ (2015) Utility of biological sensor tags in animal conservation. Conserv Biol 29(4):1065–1075 · DOI
  129. Zeller KA, McGarigal K, Whiteley AR (2012) Estimating landscape resistance to movement: a review. Landsc Ecol 27(6):777–797 · DOI · Paper page
  130. Zeller KA, McGarigal K, Beier P, Cushman SA, Vickers TW, Boyce WM (2014) Sensitivity of landscape resistance estimates based on point selection functions to scale and behavioral state: pumas as a case study. Landsc Ecol 29(3):541–557 · DOI · Paper page
  131. Zeller KA, Lewsion R, Fletcher RJ, Tulbure MG, Jennings MK (2020) Understanding the importance of dynamic landscape connectivity. Land 9(9):303 · DOI · Paper page

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Metadata

Authors
Unnithan Kumar, S., Turnbull, J., Hartman Davies, O., Hodgetts, T., & Cushman, S. A.
Venue
Landscape Ecology
Publisher
Springer Science and Business Media LLC
Published
2022-08-13
Volume
37
Issue
10
Pages
2465-2480
ISSN
  • 0921-2973
  • 1572-9761
DOI
10.1007/s10980-022-01504-x
Cited by
71
References
4
Language
en
Licence
Funders
  • Oxford University
  • Pembroke College, University of Oxford

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