Abstract
Hindrance in the ecological connectivity and gene transfer in the ecosystem affects numerous animals from large mammals to small invertebrates. For foraging, mating, and dispersal, many animal species need wide-ranging habitats such as ungulates—deer, elk, as well as bears, wolves, mountain lions, elephants and tigers. Their access to suitable habitats might be restricted by fragmented landscapes, which can also block essential movement corridors. Moreover, increased human inhabitants and population shift towards the edge of forests provides animals with very less or no scope of living in the wilderness thereby isolating the population. As a result, ecological connectivity analysis and landscape planning are integral part of one another. This paper gives a scoping review of the modelling techniques used to address the ecological connectivity in a landscape. The literature on existing modelling technique, highlighting its uses, advantages, limitations, and developments, is analysed and summarised in the paper. An exhaustive discussion on modelling techniques such as graph theoretic approaches (least cost path analysis, network analysis, etc.), circuit theoretic approaches, agent-based models and machine learning-based approach is compiled for improved decision-making. This review paper aims to support evidence-based decision-making by synthesising the current state of knowledge, identifying research gaps, and providing insights into future directions for advancing connectivity modelling.
Similar content being viewed by others
References
Adriaensen, F., Chardon, J. P., De Blust, G., Swinnen, E., Villalba, S., Gulinck, H., & Matthysen, E. (2003). The application of ‘least-cost’ modeling as a functional landscape model. Landscape and Urban Planning, 64(4), 233–247.
Ament, R., Callahan, R., McClure, M., Reuling, M., & Tabor, G. (2014). Wildlife Connectivity: Fundamentals for conservation action. Bozeman: Center for Large Landscape Conservation. https://doi.org/10.13140/RG.2.1.3958.0561
Balaji, G., & Sharma, G. (2022). Forest cover in India: A victim of technicalities. Ecological Economics, 193, 107306.
Barlow, T. J. (1989). Sites of significance for nature conservation in the Werribee Corridor. Western Region Commission.
Barnes, J. A., & Harary, F. (1983). Graph theory in network analysis. Social Networks, 5(2), 235–244.
Bastille-Rousseau, G., & Wittemyer, G. (2021). Characterizing the landscape of movement to identify critical wildlife habitat and corridors. Conservation Biology, 35(1), 346–359.
Biewener, A. A., Bomphrey, R. J., Daley, M. A., & Ijspeert, A. J. (2022). Stability and manoeuvrability in animal movement: Lessons from biology, modelling and robotics. Proceedings of the Royal Society B, 289(1967), 20212492.
Bond, M. L., Bradley, C. M., Kiffner, C., Morrison, T. A., & Lee, D. E. (2017). RESEARCH ARTICLE A multi-method approach to delineate and validate migratory corridors. Landscape Ecology, 32(8), 1705–1721. https://doi.org/10.1007/s10980-017-0537-4
Bunn, A. G., Urban, D. L., & Keitt, T. H. (2000). Landscape connectivity: A conservation application of graph theory. Journal of Environmental Management, 59(4), 265–278. https://doi.org/10.1006/jema.2000.0373
Can, Ö. E., D’Cruze, N., Garshelis, D. L., Beecham, J., & Macdonald, D. W. (2014). Resolving human-bear conflict: A global survey of countries, experts, and key factors. Conservation Letters, 7(6), 501–513.
Cantwell, M. D., & Forman, R. T. (1993). Landscape graphs: Ecological modeling with graph theory to detect configurations common to diverse landscapes. Landscape Ecology, 8(4), 239–255.
Carroll, C., & Miquelle, D. G. (2006). Spatial viability analysis of Amur tiger Panthera tigris altaica in the Russian Far East: The role of protected areas and landscape matrix in population persistence. Journal of Applied Ecology, 43(6), 1056–1068.
Castillo, M. G., Jaime Hernández, H., & Estades, C. F. (2018). Effect of connectivity and habitat availability on the occurrence of the Chestnutthroated Huet-Huet (Pteroptochos castaneus, Rhinocryptidae) in fragmented landscapes of central Chile. Landscape Ecology, 33, 1061–1068.
Chen, H., Qi, Z., & Shi, Z. (2021). Remote sensing image change detection with transformers. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–14.
Clements, S. J., Ballard, B. M., Eccles, G. R., Sinnott, E. A., & Weegman, M. D. (2022). Trade‐offs in performance of six lightweight automated tracking devices for birds. Journal of Field Ornithology.
Cochran, W. W., & Lord Jr, R. D. (1963). A radio-tracking system for wild animals. The Journal of Wildlife Management, 9–24.
Compton, B. W., McGarigal, K., Cushman, S. A., & Gamble, L. R. (2007). A resistant-kernel model of connectivity for amphibians that breed in vernal pools. Conservation Biology, 21(3), 788–799.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297.
Fahrig, L. (2003). Effects of habitat fragmentation on biodiversity. Annual review of ecology, evolution, and systematics, 34(1), 487–515.
Crowley, M. A., & Cardille, J. A. (2020). Remote sensing’s recent and future contributions to landscape ecology. Current Landscape Ecology Reports, 5, 45–57.
Cushman, S. A., McKelvey, K. S., & Schwartz, M. K. (2009). Use of empirically derived source‐destination models to map regional conservation corridors. Conservation Biology, 23(2), 368–376.
Cushman, S. A., Elliot, N. B., Bauer, D., Kesch, K., Bahaa-El-Din, L., Bothwell, H., et al. (2018). Prioritizing core areas, corridors and conflict hotspots for lion conservation in southern Africa. PLoS ONE, 13(7), e0196213.
Cushman, S. A., Mcrae, B., Adriaensen, F., Beier, P., Shirley, M., & Zeller, K. (2013). Biological corridors and connectivity. Key Topics in Conservation Biology, 2, 384–404. https://doi.org/10.1002/9781118520178.ch21
DeAngelis, D. L., & Diaz, S. G. (2019). Decision-making in agent-based modeling: A current review and future prospectus. Frontiers in Ecology and Evolution, 6, 237.
de Weerd, N., van Langevelde, F., van Oeveren, H., Nolet, B. A., Kölzsch, A., Prins, H. H., & de Boer, W. F. (2015). Deriving animal behaviour from high-frequency GPS: Tracking cows in open and forested habitat. PLoS ONE, 10(6), e0129030.
Downs, J., Horner, M., Lamb, D., Loraamm, R. W., Anderson, J., & Wood, B. (2018). Testing time-geographic density estimation for home range analysis using an agent-based model of animal movement. International Journal of Geographical Information Science, 32(7), 1505–1522.
Drielsma, M., Ferrier, S., & Manion, G. (2007). A raster-based technique for analysing habitat configuration: The cost–benefit approach. Ecological Modeling, 202(3–4), 324–332.
Dumont, B., & Hill, D. R. (2004). Spatially explicit models of group foraging by herbivores: What can Agent-Based Models offer? Animal Research, 53(5), 419–428.
Dupras, J., Marull, J., Parcerisas, L., Coll, F., Gonzalez, A., Girard, M., et al. (2016). The impacts of urban sprawl on ecological connectivity in the Montreal Metropolitan Region. Environ Sci Pol., 58, 61–73.
Dutta, T., Sharma, S., Mcrae, B. H., Sarathi, P., & Defries, R. (2016). Connecting the dots: Mapping habitat connectivity for tigers in central India. Regional Environmental Change, 16(1), 53–67. https://doi.org/10.1007/s10113-015-0877-z
Fust, P., & Schlecht, E. (2018). Integrating spatio-temporal variation in resource availability and herbivore movements into rangeland management: RaMDry—An agent-based model on livestock feeding ecology in a dynamic, heterogeneous, semi-arid environment. Ecological Modelling, 369, 13–41.
Ghahramani, Z. (2003). Unsupervised learning. In Summer school on machine learning (pp. 72–112). Springer.
Gillanders, S. N., Coops, N. C., Wulder, M. A., Gergel, S. E., & Nelson, T. (2008). Multitemporal remote sensing of landscape dynamics and pattern change: Describing natural and anthropogenic trends. Progress in Physical Geography, 32(5), 503–528.
Gross, J. L., & Yellen, J. (Eds.). (2003). Handbook of graph theory. CRC press.
Grünewälder, S., Broekhuis, F., Macdonald, D. W., Wilson, A. M., McNutt, J. W., Shawe-Taylor, J., & Hailes, S. (2012). Movement activity based classification of animal behaviour with an application to data from cheetah (Acinonyx jubatus). PLoS ONE, 7(11), 1–11. https://doi.org/10.1371/journal.pone.0049120
Guild, L. S., Cohen, W. B., & Kauffman, J. B. (2004). Detection of deforestation and land conversion in Rondonia, Brazil using change detection techniques. International Journal of Remote Sensing, 25(4), 731–750.
Guillera-Arroita, G., Lahoz-Monfort, J. J., Elith, J., Gordon, A., Kujala, H., Lentini, P. E., et al. (2015). Is my species distribution model fit for purpose? Matching data and models to applications. Global Ecology and Biogeography, 24(3), 276–292.
Hilty, J. A., Lidicker, W. Z., Jr., & Merenlender, A. M. (2012). Corridor ecology: The science and practice of linking landscapes for biodiversity conservation. Island Press.
Howard, W. E. (1960). Innate and environmental dispersal of individual vertebrates. American Midland Naturalist, 152–161.
Jongman, R. H., & Pungetti, G. (2004). Introduction: Ecological networks and greenways. In Ecological networks and greenways; concept, desing, implementation (pp. 1–6). Cambridge University Press.
Jonsen, I. D., Myers, R. A., & James, M. C. (2007). Identifying leatherback turtle foraging behaviour from satellite telemetry using a switching state-space model. Marine Ecology Progress Series, 337, 255–264.
Kalsi, R. S. (2022). Roadmap for wildlife research and conservation in India. In Advances in animal experimentation and modeling (pp. 297–306). Academic Press.
Kays, R., Crofoot, M. C., Jetz, W., & Wikelski, M. (2015). Terrestrial animal tracking as an eye on life and planet. Science, 348(6240), aaa2478.
Koen, E. L., Bowman, J., Sadowski, C., & Walpole, A. A. (2014). Landscape connectivity for wildlife: Development and validation of multispecies linkage maps. Methods in Ecology and Evolution, 5(7), 626–633. https://doi.org/10.1111/2041-210X.12197
Komarek, R. (1963). Fire and the changing wildlife habitat. In Proceedings of the 2nd annual tall timbers fire ecology conference (pp. 35–43). Tall Timbers Research Station.
Krosby, M., Breckheimer, I., John Pierce, D., et al. (2015). Focal species and landscape “naturalness” corridor models offer complementary approaches for connectivity conservation planning. Landscape Ecology, 30, 2121–2132.
Kuipers, K. J., Hilbers, J. P., Garcia-Ulloa, J., Graae, B. J., May, R., Verones, F., et al. (2021). Habitat fragmentation amplifies threats from habitat loss to mammal diversity across the world’s terrestrial ecoregions. One Earth, 4(10), 1505–1513.
Kumar, D., & Jakhar, S. D. (2022). Artificial intelligence in animal surveillance and conservation. In Impact of artificial intelligence on organizational transformation (pp. 73–85).
Lalechère, E., & Bergès, L. (2021). A validation procedure for ecological corridor locations. Land, 10(12), 1320. https://doi.org/10.3390/land10121320
Landguth, E. L., Hand, B. K., Glassy, J., Cushman, S. A., & Sawaya, M. A. (2012). UNICOR: A species connectivity and corridor network simulator. Ecography, 35(1), 9–14.
LaPoint, S., Gallery, P., Wikelski, M., & Kays, R. (2013). Animal behavior, cost-based corridor models, and real corridors. Landscape Ecology, 28(8), 1615–1630.
LaRue, M. A., & Nielsen, C. K. (2008). Modelling potential dispersal corridors for cougars in midwestern North America using least-cost path methods. Ecological Modelling, 212(3–4), 372–381. https://doi.org/10.1016/j.ecolmodel.2007.10.036
Lee, P. C., & Graham, M. D. (2006). African elephants Loxodonta africana and human-elephant interactions: Implications for conservation. International Zoo Yearbook, 40(1), 9–19.
Leos‐Barajas, V., Photopoulou, T., Langrock, R., Patterson, T. A., Watanabe, Y. Y., Murgatroyd, M., & Papastamatiou, Y. P. (2017). Analysis of animal accelerometer data using hidden Markov models. Methods in Ecology and Evolution, 8(2), 161-173.
Lewis, R. J., Ph, D., & Street, W. C. (2000). An introduction to classification and regression tree ( CART ) analysis. In 2000 Annual meeting of the society for academic emergency medicine, 310, 14. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.95.4103&rep=rep1&type=pdf
Martiskainen, P., Järvinen, M., Skön, J. P., Tiirikainen, J., Kolehmainen, M., & Mononen, J. (2009). Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines. Applied Animal Behaviour Science, 119(1–2), 32–38.
McClure, M. L., Hansen, A. J., & Inman, R. M. (2016). Connecting models to movements: Testing connectivity model predictions against empirical migration and dispersal data. Landscape Ecology, 31, 1419–1432.
McLane, A. J., Semeniuk, C., McDermid, G. J., & Marceau, D. J. (2011). The role of agent-based models in wildlife ecology and management. Ecological Modelling, 222(8), 1544–1556.
McRae, B. H., Dickson, B. G., Keitt, T. H., & Shah, V. B. (2008a). Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology, 89(10), 2712–2724.
Mcrae, B., Shah, V., & Mohapatra, T. (2014). CIRCUITSCAPE User Guide. 2006.
Mengle, G. S., & Hussain, Z. (2018). Tigress Avni shot dead in late night encounter. The Hindu.
Messner, W. (2022). Advancing our understanding of cultural heterogeneity with unsupervised machine learning. Journal of International Management, 28(2), 100885.
Murphy, K. J., Ciuti, S., & Kane, A. (2020). An introduction to agent‐based models as an accessible surrogate to field‐based research and teaching. Ecology and evolution, 10(22), 12482–12498.
Nathan, R., Spiegel, O., Fortmann-Roe, S., Harel, R., Wikelski, M., & Getz, W. M. (2012). Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: General concepts and tools illustrated for griffon vultures. Journal of Experimental Biology, 215(6), 986–996.
Odun-Ayo, I., Ananya, M., Agono, F., & Goddy-Worlu, R. (2018). Cloud computing architecture: A critical analysis. In 2018 18th international conference on computational science and applications (ICCSA) (pp. 1–7). IEEE.
Ogburn, M. B., Harrison, A. L., Whoriskey, F. G., Cooke, S. J., Mills Flemming, J. E., & Torres, L. G. (2017). Addressing challenges in the application of animal movement ecology to aquatic conservation and management. Frontiers in Marine Science, 4, 70.
Osipova, L., Okello, M. M., Njumbi, S. J., Ngene, S., Western, D., Hayward, M. W., & Balkenhol, N. (2019a). Using step-selection functions to model landscape connectivity for African elephants: Accounting for variability across individuals and seasons. Animal Conservation, 22(1), 35–48.
Osipova, L., Okello, M. M., Njumbi, S. J., Ngene, S., Western, D., Hayward, M. W., & Balkenhol, N. (2019b). Validating movement corridors for African elephants predicted from resistance-based landscape connectivity models. Landscape Ecology, 34(4), 865–878. https://doi.org/10.1007/s10980-019-00811-0
Ovenden, T. S., Palmer, S. C., Travis, J. M., & Healey, J. R. (2019). Improving reintroduction success in large carnivores through individual-based modelling: How to reintroduce Eurasian lynx (Lynx lynx) to Scotland. Biological Conservation, 234, 140–153.
Pallara, A. (1992). Binary decision trees approach to classification. In Statisca applicata (Vol. 4, Issue 3, p. 255).
Parks, S. A., Mckelvey, K. S., & Schwartz, M. K. (2013). Effects of weighting schemes on the identification of wildlife corridors generated with least-cost abstract : April 2018. https://doi.org/10.2307/23360342
Parry, H. R., Topping, C. J., Kennedy, M. C., Boatman, N. D., & Murray, A. W. (2013). A Bayesian sensitivity analysis applied to an agent-based model of bird population response to landscape change. Environmental Modelling and Software, 45, 104–115.
Phillips, B. S. J. (2017). A Brief tutorial on maxent.
Phillips, S. J., Williams, P., Midgley, G., & Archer, A. (2008). Optimizing dispersal corridors for the Cape Proteaceae using network flow. Ecological Applications, 18(5), 1200–1211.
Qian, L., Luo, Z., Du, Y., & Guo, L. (2009). Cloud computing: An overview. In Cloud Computing: First International Conference, CloudCom 2009, Beijing, China, December 1–4, 2009. Proceedings 1 (pp. 626–631). Springer.
Rather, T. A., Kumar, S., & Khan, J. A. (2020). Multi-scale habitat modelling and predicting change in the distribution of tiger and leopard using random forest algorithm. Scientific Reports, 10(1), 1–19. https://doi.org/10.1038/s41598-020-68167-z
Riggio, J., Foreman, K., Freedman, E., Gottlieb, B., Hendler, D., Radomille, D., et al. (2022). Predicting wildlife corridors for multiple species in an East African ungulate community. PLoS ONE, 17(4), e0265136.
Roever, C. L., Beyer, H. L., Chase, M. J., & Van Aarde, R. J. (2014). The pitfalls of ignoring behaviour when quantifying habitat selection. Diversity and Distributions, 20(3), 322–333.
Roy, A., Devi, B. S. S., Debnath, B., & Murthy, M. S. R. (2010). Geospatial modeling for identification of potential ecological corridors in Orissa. Journal of the Indian Society of Remote Sensing, 38(3), 387–399.
Roy, P. S., & Tomar, S. (2000). Biodiversity characterization at landscape level using geospatial modelling technique. Biological Conservation, 95(1), 95–109.
Sanderson, G. C. (1966). The study of mammal movements: A review. The Journal of Wildlife Management, 215–235.
Senbel, S., Kasinak, J. M. E., & Mattei, J. (2021). A Random forest regression model for predicting the movement of horseshoe crabs in Long Island sound. In Computational science and its applications–ICCSA 2021: 21st international conference, Cagliari, Italy, September 13–16, 2021, proceedings, part IV 21 (pp. 107–119). Springer International Publishing.
Sethi, S. (2022). Insights into illegal wildlife hunting by forest guards of selected tiger reserves in Central India. European Journal of Wildlife Research, 68(1), 1–12.
Singh, A., Thakur, N., & Sharma, A. (2016). A review of supervised machine learning algorithms. In 2016 3rd international conference on computing for sustainable global development (INDIACom) (pp. 1310–1315). IEEE.
Singleton, P. H. (2002). Landscape permeability for large carnivores in Washington: A geographic information system weighted-distance and least-cost corridor assessment (Vol. 549). US Department of Agriculture, Forest Service, Pacific Northwest Research Station.
Tang, W., & Bennett, D. A. (2010). Agent‐based modeling of animal movement: A review. Geography Compass, 4(7), 682–700.
Therrien, J. F., Pinaud, D., Gauthier, G., Lecomte, N., Bildstein, K. L., & Bety, J. (2015). Is pre-breeding prospecting behaviour affected by snow cover in the irruptive snowy owl? A test using state-space modelling and environmental data annotated via Movebank. Movement Ecology, 3(1), 1–8.
Turner, M. G. (1989). Landscape ecology: The effect of pattern on process. Annual Review of Ecology and Systematics, 20(1), 171–197.
Urban, D., & Keitt, T. (2001). Landscape connectivity: A graph‐theoretic perspective. Ecology, 82(5), 1205–1218.
Vasudev, D., Fletcher Jr, R. J., Goswami, V. R., & Krishnadas, M. (2015). From dispersal constraints to landscape connectivity: Lessons from species distribution modeling. Ecography, 38(10), 967–978.
Wade, A. A., McKelvey, K. S., & Schwartz, M. K. (2015). Resistance-surface-based wildlife conservation connectivity modeling: Summary of efforts in the united states and guide for practitioners. USDA Forest Service General Technical Report RMRS-GTR, 2015(333), 1–93.
Wang, C., Chen, H., Zhang, X., & Meng, C. (2016). Evaluation of a laying-hen tracking algorithm based on a hybrid support vector machine. Journal of Animal Science and Biotechnology, 7(1), 1–10.
Wang, G. (2019). Machine learning for inferring animal behavior from location and movement data. Ecological Informatics, 49, 69–76. https://doi.org/10.1016/j.ecoinf.2018.12.002
Watanabe, S., Izawa, M., Kato, A., Ropert-Coudert, Y., & Naito, Y. (2005). A new technique for monitoring the detailed behaviour of terrestrial animals: A case study with the domestic cat. Applied Animal Behaviour Science, 94(1–2), 117–131. https://doi.org/10.1016/j.applanim.2005.01.010
Wierzchowski, J., Kučas, A., & Balčiauskas, L. (2019). Application of least-cost movement modeling in planning wildlife mitigation measures along transport corridors: Case study of forests and moose in Lithuania. Forests, 10(10), 831. https://doi.org/10.3390/f10100831
Yasuhara, M., Hunt, G., Breitburg, D., Tsujimoto, A., & Katsuki, K. (2012). Human-induced marine ecological degradation: Micropaleontological perspectives. Ecology and Evolution, 2(12), 3242–3268.
Acknowledgements
The first author would like to express her sincere, gratitude, to the PhD supervisors, Dr. Sameer Saran and Dr. Kirti Avishek for helping in designing the conceptual framework of the paper.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
No conflict of interest declared by the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Tiwari, A., Saran, S. & Avishek, K. A Scoping Review of Modelling Techniques for Ecological Connectivity in Heterogeneous Landscape. J Indian Soc Remote Sens 51, 2143–2158 (2023). https://doi.org/10.1007/s12524-023-01758-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-023-01758-1