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Deterministic, random, or in between? Inferring the randomness level of wildlife movements
Abstract
Background: When assessing connectivity, it is crucial to rely on accurate modeling frameworks that consider species movement preferences and patterns. One important aspect is the level of randomness or unpredictability in the route selection. In this respect, traditional approaches (based on least-cost path or circuit theory) consider species movements unrealistically as totally deterministic or as totally random. A recent approach (randomized shortest path) advocates for choosing intermediate levels of randomness through a single parameter. This parameter may be optimized by validating connectivity surfaces developed from different levels of randomness against observed movement data. However, connectivity models are seldom validated, and it is still unclear how to approach this task. To address this knowledge gap, this paper aims at comparing different validation methods to infer the optimal randomness level in connectivity studies. Additionally, we aimed to disentangle the practical consequences of applying traditional connectivity approaches versus using an optimized level of movement randomness when delineating corridors. Methods: These objectives were accomplished through the study case of the Iberian lynx, an endangered species whose maintenance and recovery depend on the current connectivity among its population nuclei. We firstly determined a conductance surface based on point selection functions accounting for the behavioral state (territorial or exploratory) of individuals. Secondly, we identified the level of randomness that better fits lynxes’ movements with independent GPS locations and different validation techniques. Lastly, we delineated corridors between lynx population nuclei through a) the randomized shortest path approach and the extreme and optimal levels of randomness of each validation method, and b) the traditional connectivity approaches. Results: According to all used validation methodologies, models with intermediate levels of randomness outperformed those with extreme randomness levels representing totally deterministic or random movements. We found differences in the optimal randomness level among validation methods but similar results in the delineation of corridors. Our results also revealed that models with extreme randomness levels (deterministic and random walk) of the randomized path approach provided equivalent corridor networks to those from traditional approaches. Moreover, these corridor networks calculated with traditional approaches showed notable differences in patterns from the corridor network calculated with an optimized randomness level. Conclusions: Here we presented a connectivity model with a solid biological basis that calibrates the level of movement randomness and is supported by comprehensive validation methods. It is thus a step forward in the search and evaluation of connectivity approaches that lead to improved, efficient, and successful management actions.
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The Random Shortest Path model was formally proposed to more realistically simulate individual migration routes.
“为更加真实地模拟个体迁徙路线, 随机最短路径 (Random Shortest Path, 简称“RSP” )模型被正式提出 [93,94]”
From Progress and prospects of quantitative simulation research on ancient human migration paths (2025) · cites this paper as [93]
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USING CIRCUIT THEORY TO MODEL CONNECTIVITY IN ECOLOGY, EVOLUTION, AND CONSERVATION
McRae, B. H., Dickson, B. G., Keitt, T. H., & Shah, V. B. (2008) · Ecology · doi:10.1890/07-1861.1 · Cited by 2,244
Circuitscape in Julia: High Performance Connectivity Modelling to Support Conservation Decisions
(2020) · JuliaCon Proceedings · doi:10.21105/jcon.00058 · Cited by 163
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Research works printed but not linked: 64
- Taylor PD, Fahrig L, Henein K, Merriam G, Taylor PD, Fahrig L. Connectivity is a vital element of landscape structure. Oikos. 1993;68(3):571–3. https://doi.org/10.2307/3544927. · DOI · Paper page
- Crooks KR, Sanjayan M. Connectivity conservation. In: Kevin R, Crooks MS, editors. Connectivity Conservation. Cambridge; 2006. p. 1–20. Available from: http://ebooks.cambridge.org/ref/id/CBO9780511754821A008. Accessed 24 Jan 2017. · DOI
- Correa Ayram CA, Mendoza ME, Etter A, Pérez Salicrup DR. Habitat connectivity in biodiversity conservation : a review of recent studies and applications. Prog Phys Geogr. 2015;40(1):1–32. · DOI
- Keeley ATH, Ackerly DD, Cameron DR, Heller NE, Huber PR, Schloss CA, et al. New concepts , models , and assessments of climate-wise connectivity New concepts , models , and assessments of climate-wise connectivity. Environ Res Lett. 2018;13:1–18. · DOI
- Gurrutxaga M, Saura S. Prioritizing highway defragmentation locations for restoring landscape connectivity. Environ Conserv. 2013;41(2):157–64. · DOI
- de la Fuente B, Mateo-Sánchez MC, Rodríguez G, Gastón A. Pérez de Ayala R, Colomina-Pérez D, et al. Natura 2000 sites, public forests and riparian corridors: the connectivity backbone of forest green infrastructure. Land Use Policy. 2018;75(March 2017):429–41. https://doi.org/10.1016/j.landusepol.2018.04.002 Available from: http://linkinghub.elsevier.com/retrieve/pii/S0264837717302764. · DOI
- McClure ML, Hansen AJ, Inman RM. Connecting models to movements: testing connectivity model predictions against empirical migration and dispersal data. Landsc Ecol. 2016;31(7):1419–32. https://doi.org/10.1007/s10980-016-0347-0. · DOI · Paper page
- Laliberté J, St-Laurent MH. Validation of functional connectivity modeling: The Achilles’ heel of landscape connectivity mapping. Landsc Urban Plan. 2020;202(June):1–11. · DOI · Paper page
- Zeller KA, McGarigal K, Whiteley AR. Estimating landscape resistance to movement: a review. Landsc Ecol. 2012;27(6):777–97. https://doi.org/10.1007/s10980-012-9737-0. · DOI · Paper page
- Manly BF, McDonald L, Thomas DL, McDonald TL, Erickson WP. In: Springer Science & Business Media, editor. Resource selection by animals: statistical design and analysis for field studies: USA: Springer Science & Business Media; 2007.
- Pullinger MG, Johnson CJ. Maintaining or restoring connectivity of modified landscapes: evaluating the least-cost path model with multiple sources of ecological information. Landsc Ecol. 2010;25(10):1547–60. https://doi.org/10.1007/s10980-010-9526-6. · DOI
- Zeller KA, McGarigal K, Beier P, Cushman SA, Vickers TW, Boyce WM. Sensitivity of landscape resistance estimates based on point selection functions to scale and behavioral state: pumas as a case study. Landsc Ecol. 2014;29(3):541–57. https://doi.org/10.1007/s10980-014-9991-4. · DOI · Paper page
- Zeller KA, Jennings MK, Vickers TW, Ernest HB, Cushman SA, Boyce WM. Are all data types and connectivity models created equal? Validating common connectivity approaches with dispersal data. Divers Distrib. 2018;24(7):868–79. https://doi.org/10.1111/ddi.12742. · DOI
- Abrahms B, Sawyer SC, Jordan NR, McNutt JW, Wilson AM, Brashares JS. Does wildlife resource selection accurately inform corridor conservation? J Appl Ecol. 2017;54(2):412–22. https://doi.org/10.1111/1365-2664.12714. · DOI
- Gastón A, Blázquez-Cabrera S, Ciudad C, Mateo Sánchez MC, Simón MA, Saura S. The role of forest canopy cover in habitat selection: insights from the Iberian lynx. Eur J Wildl Res. 2019;65(2):1–10. · DOI
- Tischendorf L, Fahrig L. On the usage and measurement of landscape connectivity. Oikos. 2000;1(January):7–19. · DOI
- Elliot NB, Cushman SA, Macdonald DW, Loveridge AJ. The devil is in the dispersers: predictions of landscape connectivity change with demography. J Appl Ecol. 2014;51(5):1169–78. https://doi.org/10.1111/1365-2664.12282. · DOI · Paper page
- Maiorano L, Boitani L, Chiaverini L, Ciucci P. Uncertainties in the identification of potential dispersal corridors: the importance of behaviour, sex, and algorithm. Basic Appl Ecol. 2017;21:66–75. https://doi.org/10.1016/j.baae.2017.02.005. · DOI · Paper page
- Gastón A, Blázquez-Cabrera S, Garrote G, Mateo-Sánchez MC, Beier P, Simón MA, et al. Response to agriculture by a woodland species depends on cover type and behavioural state: insights from resident and dispersing Iberian lynx. J Appl Ecol. 2016;53(3):814–24. https://doi.org/10.1111/1365-2664.12629. · DOI
- Cushman SA, McKelvey KS, Schwartz MK. Use of empirically derived source-destination models to map regional conservation corridors. Conserv Biol. 2009;23(2):368–76. https://doi.org/10.1111/j.1523-1739.2008.01111.x. · DOI
- Adriaensen F, Chardon JP, De Blust G, Swinnen E, Villalba S, Gulinck H, et al. The application of “least-cost” modelling as a functional landscape model. Landsc Urban Plan. 2003;64(4):233–47. · DOI · Paper page
- Etherington TR. Least-cost modelling and landscape ecology: concepts, applications, and opportunities. Curr Landsc Ecol Reports. 2016;1(1):40–53. https://doi.org/10.1007/s40823-016-0006-9. · DOI · Paper page
- Fahrig L. Non-optimal animal movement in human-altered landscapes. Funct Ecol. 2007;21(6):1003–15. https://doi.org/10.1111/j.1365-2435.2007.01326.x. · DOI
- Castilho CS, Hackbart VCS, Pivello VR, dos Santos RF. Evaluating landscape connectivity for Puma concolor and Panthera onca among Atlantic Forest protected areas. Environ Manag. 2015;55(6):1377–89. https://doi.org/10.1007/s00267-015-0463-7. · DOI
- Mateo Sánchez MC, Balkenhol N, Cushman S, Pérez T, Domínguez A, Saura S. Estimating effective landscape distances and movement corridors : comparison of habitat and genetic data. Ecosphere. 2015;6(4):1–16. https://doi.org/10.1890/ES14-00387.1. · DOI
- Dilts TE, Weisberg PJ, Leitner P, Matocq MD, Inman RD, Nussear KE, et al. Multiscale connectivity and graph theory highlight critical areas for conservation under climate change. Ecol Appl. 2016;26(4):1223–37. https://doi.org/10.1890/15-0925. · DOI
- Coulon AA, Aben J, Palmer SF, Stevens VM, Callens T, Strubbe D, et al. A stochastic movement simulator improves estimates of landscape connectivity. Ecology. 2015;96(8):2203–13. https://doi.org/10.1890/14-1690.1. · DOI
- Panzacchi M, Van Moorter B, Strand O, Saerens M, Kivimäki I, St. Clair CC, et al. Predicting the continuum between corridors and barriers to animal movements using step selection functions and randomized shortest paths. J Anim Ecol. 2016;85(1):32–42. https://doi.org/10.1111/1365-2656.12386. · DOI
- Van Moorter B, Kivimäki I, Panzacchi M, Saerens M. Defining and quantifying effective connectivity of landscapes for species’ movements. Ecography (Cop). 2021;44(6):1–15. · DOI
- Saerens M, Achbany Y, Fouss F, Yen L. Randomized shortest-path problems: two related models. Neural Comput. 2009;21(8):2363–404. https://doi.org/10.1162/neco.2009.11-07-643. · DOI
- Kivimäki I, Shimbo M, Saerens M. Developments in the theory of randomized shortest paths with a comparison of graph node distances. Physica A. 2014;393:600–16. https://doi.org/10.1016/j.physa.2013.09.016. · DOI
- Taylor PD, Fahrig L, With KA. Landscape connectivity: a return to the basics. In: Connectivity conservation. Cambridge University Press: Cambridge; 2006. · DOI
- Rudnick DA, Ryan SJ, Beier P, Cushman SA, Dieffenbach F, Epps CW, et al. The role of landscape connectivity in planning and implementing conservation and restoration priorities. Issues Ecol. 2012;Fall(16):1–20.
- Fullman TJ, Joly K, Ackerman A. Effects of environmental features and sport hunting on caribou migration in northwestern Alaska. Mov Ecol. 2017;5(1):1–11. · DOI
- Long JA. Estimating wildlife utilization distributions using randomized shortest paths. Landsc Ecol. 2019;34(11):2509–21. https://doi.org/10.1007/s10980-019-00883-y. · DOI
- Brennan A, Hanks EM, Merkle JA, Cole EK, Dewey SR, Courtemanch AB, et al. Examining speed versus selection in connectivity models using elk migration as an example. Landsc Ecol. 2018;33(6):955–68. https://doi.org/10.1007/s10980-018-0642-z. · DOI
- Peck CP, VanManen FT, Costello CM, Haroldson MA, Landenburger LA, Roberts LL, et al. Potential paths for male-mediated gene flow to and from an isolated grizzly bear population. Ecosphere. 2017;8(10):e01969. · DOI
- Driezen K, Adriaensen F, Rondinini C, Doncaster CP, Matthysen E. Evaluating least-cost model predictions with empirical dispersal data: a case-study using radiotracking data of hedgehogs (Erinaceus europaeus). Ecol Model. 2007;209(2–4):314–22. https://doi.org/10.1016/j.ecolmodel.2007.07.002. · DOI
- Poor EE, Loucks C, Jakes A, Urban DL. Comparing habitat suitability and connectivity modeling methods for conserving pronghorn migrations. PLoS One. 2012;7(11):e49390. · DOI · Paper page
- Stevenson CD, Ferryman M, Nevin OT, Ramsey AD, Bailey S, Watts K. Using GPS telemetry to validate least-cost modeling of gray squirrel (Sciurus carolinensis) movement within a fragmented landscape. Ecol Evol. 2013;3(7):2350–61. https://doi.org/10.1002/ece3.638. · DOI
- Trainor AM, Walters JR, Morris WF, Sexton J, Moody A. Empirical estimation of dispersal resistance surfaces : a case study with red-cockaded woodpeckers. Landsc Ecol. 2013;28(4):755–67. https://doi.org/10.1007/s10980-013-9861-5. · DOI
- Cushman SA, Lewis JS, Landguth EL. Why did the bear cross the road? Comparing the performance of multiple resistance surfaces and connectivity modeling methods. Diversity. 2014;6(4):844–54. https://doi.org/10.3390/d6040844. · DOI
- Osipova L, Okello MM, Njumbi SJ, Ngene S, Western D, Hayward MW, et al. Validating movement corridors for African elephants predicted from resistance-based landscape connectivity models. Landsc Ecol. 2019;34(4):865–78. https://doi.org/10.1007/s10980-019-00811-0. · DOI
- Rodríguez A, Calzada J. The IUCN red list of threatened species; 2015. https://doi.org/10.2305/IUCN.UK.2015-2.RLTS.T12520A174111773.en. · DOI
- Simón MA, Gil-Sánchez JM, Ruiz G, Garrote G, Mccain EB, Fernández L, et al. Reverse of the decline of the endangered Iberian Lynx. Conserv Biol. 2012;26(4):731–6. https://doi.org/10.1111/j.1523-1739.2012.01871.x. · DOI
- Rodríguez A, Delibes M. Current range and status of the Iberian lynx Felis pardina Temminck, 1824 in Spain. Biol Conserv. 1992;61(3):189–96. https://doi.org/10.1016/0006-3207(92)91115-9. · DOI
- Palomares F, Delibes M, Ferreras P, Fedriani JM, Calzada J, Revilla E. Iberian lynx in a fragmented landscape: Predispersal, dispersal, and postdispersal habitats. Conserv Biol. 2000;14(3):809–18. https://doi.org/10.1046/j.1523-1739.2000.98539.x. · DOI
- Palomares F. Vegetation structure and prey abundance requirements of the Iberian lynx: implications for the design of reserves and corridors. J Appl Ecol. 2001;38(1):9–18. https://doi.org/10.1046/j.1365-2664.2001.00565.x. · DOI
- Blazquez-Cabrera S, Gastón A, Beier P, Garrote G, Simón MÁ, Saura S. Influence of separating home range and dispersal movements on characterizing corridors and effective distances. Landsc Ecol. 2016;31(10):2355–66. https://doi.org/10.1007/s10980-016-0407-5. · DOI
- Getz WM, Fortmann-Roe S, Cross PC, Lyons AJ, Ryan SJ, Wilmers CC. LoCoH: Nonparameteric kernel methods for constructing home ranges and utilization distributions. PLoS One. 2007;2(2):1–11. · DOI
- Huck M, Davison J, Roper TJ. Comparison of two sampling protocols and four home-range estimators using radio-tracking data from urban badgers Meles meles. Wildl Biol. 2008;14(4):467–77. https://doi.org/10.2981/0909-6396-14.4.467. · DOI
- Podgórski T, Baś G, Jędrzejewska B, Sönnichsen L, Śnieżko S, Jędrzejewski W, et al. Spatiotemporal behavioral plasticity of wild boar ( Sus scrofa ) under contrasting conditions of human pressure : primeval forest and metropolitan area. J Mammal. 2013;94(1):109–19. https://doi.org/10.1644/12-MAMM-A-038.1. · DOI
- Getz WM, Wilmers CC. A local nearest-neighbor convex-hull construction of home ranges and utilization distributions. Ecography (Cop). 2004;27(4):489–505. https://doi.org/10.1111/j.0906-7590.2004.03835.x. · DOI
- Ministerio de Fomento. Plan Nacional de Ortofotografía Aérea: Instituto Geográfico Nacional (PNOA); 2015. http://pnoa.ign.es/. Accessed 24 Jan 2017.
- Ribatet M. POT: generalized Pareto distribution and peaks over threshold: R package; 2011.
- Zeller KA, McGarigal K, Cushman SA, Beier P, Vickers TW, Boyce WM. Using step and path selection functions for estimating resistance to movement: pumas as a case study. Landsc Ecol. 2016;31(6):1319–35. · DOI
- Pearce J, Ferrier S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model. 2000;133(3):225–45. https://doi.org/10.1016/S0304-3800(00)00322-7. · DOI
- Roberts DR, Bahn V, Ciuti S, Boyce MS, Elith J, Guillera-Arroita G, et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography (Cop). 2017;40(8):913–29. https://doi.org/10.1111/ecog.02881. · DOI · Paper page
- van Etten J. R package gdistance: Distances and routes on geographical grids. J Stat Softw. 2017;76(1):1–21. · DOI · Paper page
- Horne JS, Garton EO, Krone SM, Lewis JS. Analyzing animal movements using Brownian bridges. Ecology. 2007;88(9):2354–63. https://doi.org/10.1890/06-0957.1. · DOI
- Landguth EL, Hand BK, Glassy J, Cushman SA, Sawaya MA. UNICOR : a species connectivity and corridor network simulator. Ecography (Cop). 2012;(August 2011):9–14. · DOI
- Cushman SA, Mcrae B, Adriaensen F, Beier P, Shirley M, Zeller K. Biological corridors and connectivity. In: Key Topics in Conservation Biology 2; 2013. p. 384–404. · DOI · Paper page
- Garrote G, Bueno JF, Ruiz M, de Lillo S, Martin JM, Moral M, et al. Breaking barriers: Iberian Lynx Lynx pardinus Temminck, 1827 (Mammalia: Carnivora: Felidae) colonizing olive groves Germán. J Threat Taxa. 2020;12(2):15221–8. https://doi.org/10.11609/jott.4829.12.2.15221-15228. · DOI
- Keeley ATH, Beier P, Gagnon JW. Estimating landscape resistance from habitat suitability: effects of data source and nonlinearities. Landsc Ecol. 2016;31(9):2151–62. https://doi.org/10.1007/s10980-016-0387-5. · DOI · Paper page
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Progress and prospects of quantitative simulation research on ancient human migration paths
Wang, L., Peng, P., Liu, X., & Chen, F. (2025) · Chinese Science Bulletin · doi:10.1360/tb-2025-0148
Metadata
- Authors
- Goicolea, T., Gastón, A., Cisneros-Araujo, P., García-Viñas, J. I., & Mateo-Sánchez, M. C.
- Venue
- Movement Ecology
- Published
- 2021-06-29
- Volume
- 9
- Issue
- 1
- ISSN
- 2051-3933
- PMC
- PMC8244215
- Cited by
- 19
- References
- 2
- Language
- en
- Funders
- WOODNET
- Universidad Politécnica de Madrid
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