Abram, D. The spell of the sensuous: Perception and language in a more-than-human world. Vintage (2012).
Ingold, T. Being alive: Essays on movement, knowledge and description. Routledgehttps://doi.org/10.4324/9780203818336 (2011).
Google Scholar
Kimmerer, R.W. Braiding sweetgrass: Indigenous wisdom, scientific knowledge and the teachings of plants (Milkweed editions, 2013).
Tucker, M. A. et al. Moving in the Anthropocene: Global reductions in terrestrial mammalian movements. Science 359(6374), 466–469 (2018).
Google Scholar
Gibbs, J.P. Amphibian movements in response to forest edges, roads, and streambeds in southern New England. in The Journal of Wildlife Management (1998), pp. 584–589. https://doi.org/10.2307/3802333.
Moller, H., Berkes, F., O’Brian Lyver, P., & Kislalioglu, M. Combining science and traditional ecological knowledge: Monitoring populations for co-management. in Ecology and society (2004).
Lorimer, J. Wildlife in the Anthropocene: conservation after nature. (U of Minnesota Press, 2015).
Wiens, J. A. Spatial scaling in ecology. Funct. Ecol. 3(4), 385–397 (1989).
Google Scholar
Abram, D. Becoming animal: An earthly cosmology. Vintage (2010).
Nathan, R. et al. A movement ecology paradigm for unifying organismal movement research. Proc. Natl. Acad. Sci. 105(49), 19052–19059 (2008).
Google Scholar
Tischendorf, L. & Fahrig, L. On the usage and measurement of landscape connectivity. Oikos 90(1), 7–19. https://doi.org/10.1034/j.1600-0706.2000.900102.x (2000).
Google Scholar
Rudnick, D., Ryan, S.J., Beier, P., Cushman, S.A., Dieffenbach, F., Epps, C., Gerber, L.R., Hartter, J.N., Jenness, J.S., & Kintsch, J. et al. The role of landscape connectivity in planning and implementing conservation and restoration priorities. Issues in Ecology (2012).
Hilty, J.A., Lidicker, W.Z., & Merenlender, A.M. Corridor Ecology: The Science and Practice of Linking Landscapes for Biodiversity Conservation (Island Press, 2012).
Cushman, S.A., McRae, B.H., Adriaensen, F., Beier, P., Shirley, M., & Zeller, K. Biological corridors and connectivity [Chapter 21]. in Key Topics in Conservation Biology 2nd ed. (eds Macdonald, D.W., Willis, K.J.) pp. 384–404 (Hoboken, NJ: Wiley-Blackwell, 2013).
Unnithan Kumar, S., Turnbull, J., Hartman Davies, O., Hodgetts, T., & Cushman, S.A. Moving beyond landscape resistance: Considerations for the future of connectivity modelling and conservation science. in Landscape Ecology (2022).
Zeller, K. A., McGarigal, K. & Whiteley, A. R. Estimating landscape resistance to movement: a review. Landscape Ecol. 27(6), 777–797 (2012).
Google Scholar
Adriaensen, F. et al. The application of ‘least-cost’ modelling as a functional landscape model. Landsc. Urban Plan. 64(4), 233–247 (2003).
Google Scholar
Cushman, S. A. & McKelvey, K. S. Use of empirically derived source-destination models to map regional conservation corridors. Conserv. Biol. 23(2), 368–376. https://doi.org/10.1111/j.1523-1739.2008.01111.x (2009).
Google Scholar
Moilanen, A. On the limitations of graph-theoretic connectivity in spatial ecology and conservation. J. Appl. Ecol. pp. 1543–1547 (2011).
Compton, B. W., McGarigal, K., Cushman, S. A. & Gamble, L. R. A resistant kernel model of connectivity for amphibians that breed in vernal pools. Conserv. Biol. 21(3), 788–799. https://doi.org/10.1111/j.1523-1739.2007.00674.x (2007).
Google Scholar
McRae, B. H., Dickson, B. G., Keitt, T. H. & Shah, V. B. Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89(10), 2712–2724. https://doi.org/10.1890/07-1861.1. (2008).
Google Scholar
Zeller, K. A. et al. Are all data types and connectivity models created equal? Validating common connectivity approaches with dispersal data. Divers. Distrib. 24(7), 868–879. https://doi.org/10.1111/ddi.12742. (2018).
Google Scholar
Pullinger, M. G. & Johnson, C. J. Maintaining or restoring connectivity of modified landscapes: evaluating the least-cost path model with multiple sources of ecological information. Landscape Ecol. 25(10), 1547–1560 (2010).
Google Scholar
Sawyer, S. C., Clinton, W. E. & Brashares, J. S. Placing linkages among fragmented habitats: do least-cost models reflect how animals use landscapes?. J. Appl. Ecol. 48(3), 668–678 (2011).
Google Scholar
Laliberté, J. & St-Laurent, M.-H. Validation of functional connectivity modeling: The Achilles’ heel of landscape connectivity mapping. Landsc. Urban Plan. 202, 103878 (2020).
Google Scholar
Landguth, E. L. & Cushman, S. A. CDPOP: A spatially explicit cost distance popula tion genetics program. Mol. Ecol. Resour. 10(1), 156–161. https://doi.org/10.1111/j.1755-0998.2009.02719.x. (2010).
Google Scholar
Landguth, E. L. et al. Quantifying the lag time to detect barriers in landscape genetics. Mol. Ecol. 19(19), 4179–4191. https://doi.org/10.1111/j.1365-294X.2010.04808.x (2010).
Google Scholar
Cushman, S. A. & Landguth, E. L. Scale dependent inference in landscape genetics. Landsc. Ecol. 25(6), 967–979 (2010).
Google Scholar
Cushman, S. A., Shirk, A. J. & Landguth, E. L. Separating the effects of habitat area, fragmentation and matrix resistance on genetic differentiation in complex landscapes. Landscape Ecol. 27(3), 369–380. https://doi.org/10.1007/s10980-011-9693-0 (2012).
Google Scholar
Macdonald, E. A. et al. Simulating impacts of rapid forest loss on population size, connectivity and genetic diversity of Sunda clouded leopards (Neofelis diardi) in Borneo. PLoS ONE 13(9), e0196974 (2018).
Google Scholar
Schumaker, N. H. et al. Mapping sources, sinks, and connectivity using a simulation model of northern spotted owls. Landscape Ecol. 29(4), 579–592 (2014).
Google Scholar
Unnithan Kumar, S., Kaszta, Ż & Cushman, S. A. Pathwalker: A new individual-based movement model for conservation science and connectivity modelling. ISPRS Int. J. Geo Inf. 11(6), 329 (2022).
Google Scholar
Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17(3), 261–272 (2020).
Google Scholar
Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14(6), 927–930 (2003).
Google Scholar
Dray, S., Royer-Carenzi, M. & Calenge, C. The exploratory analysis of autocorrelation in animal-movement studies. Ecol. Res. 25(3), 673–681. https://doi.org/10.1007/s11284-010-0701-7 (2010).
Google Scholar
Cushman, S.A. Animal movement data: GPS telemetry, autocorrelation and the need for path-level analysis. in Spatial Complexity, Informatics, and Wildlife Conservation (Springer, 2010), pp. 131-149.
Zeller, K. A. et al. Sensitivity of landscape resistance estimates based on point selection functions to scale and behavioral state: pumas as a case study. Landscape Ecol. 29(3), 541–557 (2014).
Google Scholar
Kareiva, P. M. & Shigesada, N. Analyzing insect movement as a correlated random walk. Oecologia 56(2), 234–238 (1983).
Google Scholar
Schumaker, N.H. Using landscape indices to predict habitat connectivity. Ecology (1996), pp. 1210–1225.
Schumaker, N. H. & Brookes, A. HexSim: A modeling environment for ecology and conservation. Landscape Ecol. 33(2), 197–211 (2018).
Google Scholar
Bocedi, G., Palmer, S. C. F., Malchow, A.-K., Zurell, D. & Watts, K. 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 (2021).
Google Scholar
Kaszta, Ż, Cushman, S. A. & Slotow, R. Temporal non-stationarity of path- selection movement models and connectivity: An example of African elephants in Kruger national park. Front. Ecol. Evol. 9, 207 (2021).
Google Scholar
Osipova, L. et al. Using step-selection functions to model landscape connectivity for African elephants: Accounting for variability across individuals and seasons. Anim. Conserv. 22(1), 35–48 (2019).
Google Scholar
Vergara, M., Cushman, S. A. & Ruiz-González, A. Ecological differences and limiting factors in different regional contexts: landscape genetics of the stone marten in the Iberian Peninsula. Landscape Ecol. 32(6), 1269–1283 (2017).
Google Scholar
Reddy, P. A., Puyravaud, J.-P., Cushman, S. A. & Segu, H. Spatial variation in the response of tiger gene ow to landscape features and limiting factors. Anim. Conserv. 22(5), 472–480 (2019).
Google Scholar
Zeller, K. A., Lewsion, R., Fletcher, R. J., Tulbure, M. G. & Jennings, M. K. Understanding the importance of dynamic landscape connectivity. Land 9(9), 303. https://doi.org/10.3390/land9090303 (2020).
Google Scholar
Cronon, W. The trouble with wilderness: or, getting back to the wrong nature. Environ. Hist. 1(1), 7–28 (1996).
Google Scholar
Ingold, T. The Perception of the Environment: Essays on Livelihood, Dwelling and Skill (Routledge, 2021).
Boettiger, A. N. et al. Inferring ecological and behavioral drivers of African elephant movement using a linear filtering approach. Ecology 92(8), 1648–1657 (2011).
Google Scholar
Pooley, S. et al. An interdisciplinary review of current and future approaches to improving human-predator relations. Conserv. Biol. 31(3), 513–523. https://doi.org/10.1111/cobi.12859 (2017).
Google Scholar
Benson, E. S. Minimal animal: Surveillance, simulation, and stochasticity in wildlife biology. Antennae 30, 39 (2014).
Kaszta, Ż et al. Integrating Sunda clouded leopard (Neofelis diardi) conservation into development and restoration planning in Sabah (Borneo). Biol. Cons. 235, 63–76 (2019).
Google Scholar
Penjor, U., Astaras, C., Cushman, S. A., Kaszta, Ż & Macdonald, D. W. Contrasting effects of human settlement on the interaction among sympatric apex carnivores. Proc. R. Soc. B 289(1973), 20212681 (2022).
Google Scholar
Barua, M. Bio-geo-graphy: Landscape, dwelling, and the political ecology of human-elephant relations. Environ. Plann. D Soc. Space 32(5), 915–934 (2014).
Google Scholar
Elliot, N. B., Cushman, S. A., Macdonald, D. W. & Loveridge, A. J. The devil is in the dispersers: Predictions of landscape connectivity change with demography. J. Appl. Ecol. 51(5), 1169–1178 (2014).
Google Scholar
Kareiva, P. & Marvier, M. What is conservation science?. Bioscience 62(11), 962–969 (2012).
Google Scholar
Bennett, N. J. et al. Conservation social science: Understanding and integrating human dimensions to improve conservation. Biol. Conserv. 205, 93–108 (2017).
Google Scholar
Bunnefeld, N., Nicholson, E., & Milner-Gulland, E.J. Decision-Making in Conservation and Natural Resource Management: Models for Interdisciplinary Approaches. (Vol. 22, Cambridge University Press, 2017).
Parathian, H. E., McLennan, M. R., Hill, C. M., Fraza o-Moreira, A. & Hockings, K. J. Breaking through disciplinary barriers: Human-wildlife interactions and multispecies ethnography. Int. J. Primatol. 39(5), 749–775 (2018).
Google Scholar
Hodgetts, T. Connectivity as a multiple: In with and as “nature’’. Area 50(1), 83–90. https://doi.org/10.1111/area.12353 (2018).
Google Scholar
Berkes, F. Sacred ecology (Routledge, 2017). https://doi.org/10.4324/9781315114644.
Parrenas, J.S. Decolonizing Extinction: The Work of Care in Orangutan Rehabilitation (Duke University Press, 2018).
Bill Adams, W., & Mulligan, M. Decolonizing Nature: Strategies for Conservation in a Post-Colonial Era (Routledge, 2012).
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