The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
USING CIRCUIT THEORY TO MODEL CONNECTIVITY IN ECOLOGY, EVOLUTION, AND CONSERVATION
Abstract
Connectivity among populations and habitats is important for a wide range of ecological processes. Understanding, preserving, and restoring connectivity in complex landscapes requires connectivity models and metrics that are reliable, efficient, and process based. We introduce a new class of ecological connectivity models based in electrical circuit theory. Although they have been applied in other disciplines, circuit-theoretic connectivity models are new to ecology. They offer distinct advantages over common analytic connectivity models, including a theoretical basis in random walk theory and an ability to evaluate contributions of multiple dispersal pathways. Resistance, current, and voltage calculated across graphs or raster grids can be related to ecological processes (such as individual movement and gene flow) that occur across large population networks or landscapes. Efficient algorithms can quickly solve networks with millions of nodes, or landscapes with millions of raster cells. Here we review basic circuit theory, discuss relationships between circuit and random walk theories, and describe applications in ecology, evolution, and conservation. We provide examples of how circuit models can be used to predict movement patterns and fates of random walkers in complex landscapes and to identify important habitat patches and movement corridors for conservation planning.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Circuit theory connectivity models are generally seen to be robust to coarsening of input data [53], which can enable computation across very large areas at coarse pixel resolution [28], [57].”
From Applying circuit theory for corridor expansion and management at regional scales: tiling, pinch points, and omnidirectional connectivity (2014) · cites this paper as [28]
In the citing paper
“It has already been demonstrated that across long time scales at which genetic mutations can be measured, resistance distance is strongly correlated to the differences between populations [28], [29], [42].”
From Applying circuit theory for corridor expansion and management at regional scales: tiling, pinch points, and omnidirectional connectivity (2014) · cites this paper as [28]
In the citing paper
“In that way, potential movement between different locations in a region is modelled omnidirectionally, as a flow gradient, rather than as a single optimal line, generating corridors beyond optimality whose amplitude and narrowness depend on the conditions of the terrain.”
From Exploring the inherent affordability for movement in past landscapes (2027) · cites this paper as McRae et al., 2008
In the citing paper
“GIS technologies and least-cost analysis tools, such as Linkage Mapper [19] and Circuitscape [20], have demonstrated their potential in diverse fields, from biodiversity conservation to the study of ecological networks [21–25].”
From Tracing neanderthal mobility through the romanian carpathians: a GIS-based least-cost connectivity model (2025) · cites this paper as [21]
In the citing paper
“McRae et al. (2008) illustrated how circuit theory could be used to identify multiple (i.e., redundant) movement pathways or habitat corridors and reveal, for example, critical ‘pinch points’ constraining potential flow between focal areas.”
From Circuit‐theory applications to connectivity science and conservation (2019) · cites this paper as McRae et al. (2008)
In the citing paper
“Finally, McRae et al. (2008) introduced many readers to the key concepts and metrics used in electrical circuit theory, and how they could be applied to model and map the process of connectivity over extensive landscape, habitat, or population networks (using georeferenced raster grids).”
From Circuit‐theory applications to connectivity science and conservation (2019) · cites this paper as McRae et al. (2008)
In the citing paper
“The development of accompanying software, such as the open-source program Circuitscape (McRae et al. 2008, 2013; www.circuitscape.org), has provided accessible means of implementing circuit theory concepts across a range of projects and disciplines.”
From Circuit‐theory applications to connectivity science and conservation (2019) · cites this paper as McRae et al. 2008
In the citing paper
“Circuit theory’s basis in random walk theory results in an implicit assumption that individuals moving across a landscape have no knowledge of relative resistance beyond their immediate surroundings, making it particularly appropriate for modeling natal dispersal paths (McRae et al. 2008).”
From Circuit‐theory applications to connectivity science and conservation (2019) · cites this paper as McRae et al. 2008
In the citing paper
“Additionally, several assumptions and caveats still need to be addressed, such as modeling long-distance dispersal events (McRae 2006) and asymmetric migration (McRae et al. 2008; but see Hanks 2017).”
From Circuit‐theory applications to connectivity science and conservation (2019) · cites this paper as McRae et al. 2008
In the citing paper
“In the circuittheoretic approach, the landscape is abstracted as a network of current sources, grounds, and resistors.The resulting current flow through the electrical network is then related to the movement or flow intensity of the ecological process of interest.”
From Omniscape.jl: software to compute omnidirectional landscape connectivity (2021) · cites this paper as McRae et al., 2008
In the citing paper
“The circuit-theoretic approach to connectivity modeling was a more recent innovation, and it has been gaining popularity over the past decade (Dickson et al., 2019;McRae, 2006;McRae et al., 2008).”
From Omniscape.jl: software to compute omnidirectional landscape connectivity (2021) · cites this paper as McRae et al., 2008
In the citing paper
“Similarly, McRae et al. (2008) modelled functional connectivity based on electrical circuit theory, which models connectivity across a resistance surface as electric current moving through a circuit.”
From Landscape connectivity for wildlife: development and validation of multispecies linkage maps (2014) · cites this paper as McRae et al. 2008
In the citing paper
“Finally, some methods work by connecting source and destination sites (McRae et al. 2008;Schwartz et al. 2009), which may not be appropriate for all types of questions (Ferrarini 2013).”
From Landscape connectivity for wildlife: development and validation of multispecies linkage maps (2014) · cites this paper as McRae et al. 2008
In the citing paper
“Several authors have alluded to the idea that artificial boundaries might influence the results of least cost path and landscape resistance models [2], [11], [15]–[18], but to our knowledge, no one has directly addressed the issue.”
From The effect of map boundary on estimates of landscape resistance to animal movement (2010) · cites this paper as [11]
In the citing paper
“One of the reasons circuit theory is appealing as a model of gene flow and individual movement is that the incorporation of random walkers on networks to estimate landscape resistance allows resistance to decrease with increasing connectivity, path width, and path redundancy [11], [14].”
From The effect of map boundary on estimates of landscape resistance to animal movement (2010) · cites this paper as [11]
In the citing paper
“Connectivity modelling is an alternate approach to spatial connectivity modelling that is widely used in ecological research (McRae et al., 2008).”
From A spatial connectivity approach to landscapes of conflict: julius caesar and the assault to puig ciutat (NE iberian peninsula) (2022) · cites this paper as McRae et al., 2008
In the citing paper
“Developed by conservation scientist Brad McRae (with a background in electrical engineering) and computer scientist Viral Shah, Circuitscape allows users to apply the logic and mathematics of electrical circuit theory to questions of how genes, animals, or processes flow across heterogeneous landscapes [22,31].”
From Circuitscape in julia: empowering dynamic approaches to connectivity assessment (2021) · cites this paper as [22]
In the citing paper
“However, once the surface is created and used as input for a connectivity analysis, many modeling workflows share the trait of being constrained by computational demand [15,22,25,26], as representing spatial patterns in a cumulative process like connectivity requires extensive calculations, often across millions of pixels.”
From Circuitscape in julia: empowering dynamic approaches to connectivity assessment (2021) · cites this paper as [22]
In the citing paper
“To date, Circuitscape has been most widely applied in landscape genetics, where modelers calculate”
From Circuitscape in julia: empowering dynamic approaches to connectivity assessment (2021) · cites this paper as [22]
In the citing paper
“The Circuitscape algorithm tracks where”
From Circuitscape in julia: empowering dynamic approaches to connectivity assessment (2021) · cites this paper as [22]
In the citing paper
“In contrast, in an animal movement context, producing current flow maps is the goal, with current magnitudes representing the relative use of different pixels by animals (or ecological processes) moving across the landscape (resistance surface) between focal nodes (habitat patches), following the assumption of a random walk [22].”
From Circuitscape in julia: empowering dynamic approaches to connectivity assessment (2021) · cites this paper as [22]
In the citing paper
“The first version [22] was written in Java, followed by version 2.0 in MATLAB.”
From Circuitscape in julia: empowering dynamic approaches to connectivity assessment (2021) · cites this paper as [22]
In the citing paper
“To find Circuitscape applications, we reviewed 1280 papers identified through Web of Science searches (end date for publication, including”
From Circuitscape in julia: empowering dynamic approaches to connectivity assessment (2021) · cites this paper as [22]
In the citing paper
“Furthermore, as noted in21, Circuitscape assumes a directional symmetry of resistance between adjacent pixels, despite movement from an area of higher to lower resistance being much more likely than the reverse.”
From Connectivity modelling in conservation science: a comparative evaluation (2022) · cites this paper as [21]
In the citing paper
“Furthermore, as noted in21, Circuitscape assumes a directional symmetry of resistance between adjacent pixels, despite movement from an area of higher to lower resistance being much more likely than the reverse.”
From Connectivity modelling in conservation science: a comparative evaluation (2022) · cites this paper as [21]
Citations
Cites
Harary, F. (1969) · Cited by 10,739
The Ecology of Natural Disturbance and Patch Dynamics.
Proctor, J., Pickett, S. T. A., & White, P. S. (1986) · Journal of Applied Ecology · doi:10.2307/2403105 · Cited by 3,948
The Matrix Matters: Effective Isolation in Fragmented Landscapes
Ricketts, T. (2001) · American Naturalist · doi:10.1086/320863 · Cited by 1,539
LANDSCAPE CONNECTIVITY: A GRAPH-THEORETIC PERSPECTIVE
Urban, D. L., & Keitt, T. H. (2001) · Ecology · doi:10.1890/0012-9658(2001)082[1205:lcagtp]2.0.co;2 · Cited by 1,485
Self-citationKlein, D. J., & Randić, M. (1993) · Journal of Mathematical Chemistry · doi:10.1007/bf01164627 · Cited by 1,441
The application of ‘least-cost’ modelling as a functional landscape model
Adriaensen, F., Chardon, J. P., De Blust, G., Swinnen, E., Villalba, S., Gulinck, H., & Matthysen, E. (2003) · Landscape and Urban Planning · doi:10.1016/s0169-2046(02)00242-6 · Cited by 1,342
On the usage and measurement of landscape connectivity
Tischendorf, L., & Fahrig, L. (2000) · Oikos · doi:10.1034/j.1600-0706.2000.900102.x · Cited by 1,285
McRae, B. H. (2006) · Evolution · doi:10.1111/j.0014-3820.2006.tb00500.x · Cited by 1,081
Self-citationRandom Walks and Electric Networks
Doyle, P., & Snell, J. (1984) · Carus Mathematical Monographs · doi:10.5948/upo9781614440222 · Cited by 1,061
Circuit theory predicts gene flow in plant and animal populations
McRae, B. H., & Beier, P. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706568104 · Cited by 945
Self-citation
Research works printed but not linked: 1
- 10.1086/587070 · DOI
Cited by
Sorted by citations
Items 1,101–1,200 of 2,245
Momigliano, P., Harcourt, R. G., & Stow, A. (2015) · Frontiers in Marine Science · doi:10.3389/fmars.2015.00016 · Cited by 13
Linnell, M. A., & Lesmeister, D. B. (2019) · Animal Conservation · doi:10.1111/acv.12496 · Cited by 13
Memory at your feet: Modeling the agency of past trails
Llobera, M. (2020) · Journal of Archaeological Science: Reports · doi:10.1016/j.jasrep.2019.102177 · Cited by 6
Espinosa-Zaragoza, S., Nidia Bélgica Pérez-De la, O., Aguirre-Medina, J. F., & López‐Martínez, V. (2021) · Insects · doi:10.3390/insects12050450 · Cited by 13
Daele, F. V., Honnay, O., & Kort, H. D. (2021) · Diversity and Distributions · doi:10.1111/ddi.13367 · Cited by 13
Gao, C., Huang, C., Wang, J., & Li, Z. (2021) · Remote Sensing · doi:10.3390/rs14010145 · Cited by 13
Tan, J., Li, H., & Lin, W. (2022) · Frontiers in Environmental Science · doi:10.3389/fenvs.2022.862310 · Cited by 13
The importance of energetics in archaeological least cost analysis
Field, S., Glowacki, D. M., & Gettler, L. T. (2023) · Journal of Archaeological Method and Theory · doi:10.1007/s10816-022-09564-8 · Cited by 13
Clark, J. N. (2022) · Journal of Intervention and Statebuilding · doi:10.1080/17502977.2022.2084237 · Cited by 13
Climate Change and Its Effects
Hansen, L. J., & Hoffman, J. R. (2011) · Island Press/Center for Resource Economics eBooks · doi:10.5822/978-1-59726-988-9_2 · Cited by 13
Luck, G., Spooner, P. G., Watson, D. M., Watson, S. J., & Saunders, M. E. (2014) · Ecological Management & Restoration · doi:10.1111/emr.12082 · Cited by 13
Genetic differentiation and habitat connectivity across towhee hybrid zones in Mexico
Kingston, S. E., Navarro-Sigüenza, A. G., García-Trejo, E. A., Vázquez‐Miranda, H., Fagan, W. F., & Braun, M. J. (2013) · Evolutionary Ecology · doi:10.1007/s10682-013-9673-8 · Cited by 13
Habitat suitability and connectivity for an endangered brown bear population in the Iranian Caucasus
Habibzadeh, N., & Ashrafzadeh, M. R. (2018) · Wildlife Research · doi:10.1071/wr17175 · Cited by 13
Phylogenetic dynamics of Tropical Atlantic Forests
Diniz, É. S., Gastauer, M., Thiele, J., & Meira‐Neto, J. A. A. (2021) · Evolutionary Ecology · doi:10.1007/s10682-020-10094-6 · Cited by 13
A multidirectional model for studying mobility affordance of past landscapes
Kohut, L. E. (2018) · Journal of Archaeological Science: Reports · doi:10.1016/j.jasrep.2018.02.031 · Cited by 12
Counter-Mapping Maroon Cartographies: GIS and Anticolonial Modeling in St. Croix
Dunnavant, J. P., Wernke, S. A., & Kohut, L. E. (2023) · ACME · doi:10.7202/1107310ar · Cited by 12
Potential cougar habitats and dispersal corridors in Eastern North America
Winkel, B. M., Nielsen, C. K., Hillard, E. M., Sutherland, R. W., & Larue, M. A. (2022) · Landscape Ecology · doi:10.1007/s10980-022-01538-1 · Cited by 12
Mosquera-Guerra, F., Barreto, J. S., Moreno‐Niño, N., González, T. M., & Armenteras, D. (2024) · Mammalian Biology · doi:10.1007/s42991-024-00404-8 · Cited by 12
Urbina, L., Lehmann, A., Huber, L., & Fischer, C. (2024) · Journal for Nature Conservation · doi:10.1016/j.jnc.2024.126654 · Cited by 12
Multi-scenario simulation prediction of land use in Nanchang based on network robustness analysis
Wei, X., Zhao, L., Zhang, F., & Xia, Y. (2024) · Ecological Indicators · doi:10.1016/j.ecolind.2024.112599 · Cited by 12
Miao, Z., Yu, H., Jiang, R., Wang, C., & Cao, J. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.177055 · Cited by 12
Regional ecosystem health improvement pathway design based on ecological network optimization
Shi, J., Zhang, D., Yin, J., Wang, Z., Feng, H., Liu, Y., & Shi, P. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2025.113377 · Cited by 12
Serva, D., Krofel, M., Cerasoli, F., Biondi, M., & Iannella, M. (2025) · Diversity and Distributions · doi:10.1111/ddi.70024 · Cited by 12
ERIKSSON, A., Low, M., & Berggren, Å. (2013) · European Journal of Entomology · doi:10.14411/eje.2013.010 · Cited by 12
Sagot, M., Rodríguez‐Herrera, B., & Stevens, R. D. (2013) · Biotropica · doi:10.1111/btp.12034 · Cited by 12
Kilbane, S. J., Weller, R., & Hobbs, R. J. (2017) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2017.05.001 · Cited by 12
Perović, D. J., Gámez‐Virués, S., Landis, D. A., Tscharntke, T., Zalucki, M. P., Saura, S., Furlong, M. J., Desneux, N., Sciarretta, A., Balkenhol, N., et al. (2021) · Advances in ecological research/Advances in Ecological Research · doi:10.1016/bs.aecr.2021.10.003 · Cited by 12
Rana, D., Samad, I., & Rastogi, S. (2022) · Journal for Nature Conservation · doi:10.1016/j.jnc.2022.126225 · Cited by 12
Dunford, C. E., Martins, Q. E., Mann, G. K. H., Spalton, J. A., Hikmani, H. A., Robinson, N., Almalki, A. S., Gallacher, E., Balme, G. A., & Robinson, H. S. (2022) · Journal for Nature Conservation · doi:10.1016/j.jnc.2022.126233 · Cited by 12
Bolom‐Huet, R., Pacheco, X. P., Alonso, L. A. M., & Sunny, A. (2022) · Environmental Management · doi:10.1007/s00267-022-01700-0 · Cited by 12
Habitat suitability and landscape connectivity for an expanding population of bobcats
Cerreta, A. L., McCarthy, K. P., & Fowles, G. (2023) · Landscape Ecology · doi:10.1007/s10980-023-01639-5 · Cited by 12
Modelling plant community dynamics in changing forest ecosystems: a review
Decocq, G., Regnault, P., Lenoir, J., Paccaut, F., Menza, L. D., Delvoye, G., Janvresse, É., Closset‐Kopp, D., & Goubet, O. (2023) · Botany Letters · doi:10.1080/23818107.2023.2231045 · Cited by 12
Vlková, K., Zýka, V., Papp, C. R., & Romportl, D. (2023) · Journal of Maps · doi:10.1080/17445647.2023.2290858 · Cited by 12
Yan, M., Duan, J., Li, Y., Yu, Y., Wang, Y., Zhang, J., & Qiu, Y. F. (2024) · Sustainability · doi:10.3390/su16083290 · Cited by 12
Borrett, S. R., Freeze, M., & Salas, A. K. (2011) · Ecological Modelling · doi:10.1016/j.ecolmodel.2011.04.003 · Cited by 12
Grid‐induced biases in connectivity metric implementations that use regular grids
Dunn, A. G. (2009) · Ecography · doi:10.1111/j.1600-0587.2009.05980.x · Cited by 12
Latent spatial models and sampling design for landscape genetics
Hanks, E. M., Hooten, M. B., Knick, S. T., Oyler‐McCance, S. J., Fike, J. A., Cross, T. B., & Schwartz, M. K. (2016) · The Annals of Applied Statistics · doi:10.1214/16-aoas929 · Cited by 12
Geographical Relationship between Ungulates, Human Pressure and Territory
Riccioli, F., Boncinelli, F., Roberto, F., Pierre, E. A. J., & Casini, L. (2018) · Applied Spatial Analysis and Policy · doi:10.1007/s12061-018-9272-8 · Cited by 12
Su, X., Han, W., & Liu, G. (2019) · Journal of Geographical Sciences · doi:10.1007/s11442-019-1654-6 · Cited by 12
Diniz, É. S., do Amaral, C. H., Sardinha, S. T., Thiele, J., & Meira‐Neto, J. A. A. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.112172 · Cited by 12
Estimation of Ecological Connectivity in a City Based on Land Cover and Urban Habitat Maps
Kim, D., Shin, W., Choi, H., Kim, J., & Song, Y. (2020) · Sustainability · doi:10.3390/su12229529 · Cited by 12
A framework for collaborative wolverine connectivity conservation
Carroll, K. A., Inman, R. M., Hansen, A. J., Lawrence, R. L., & Barnett, K. (2021) · iScience · doi:10.1016/j.isci.2021.102840 · Cited by 12
Gálvez‐Reyes, N., Arribas, P., Andújar, C., Emerson, B. C., Piñero, D., & Mastretta‐Yanes, A. (2021) · Molecular Ecology · doi:10.1111/mec.16195 · Cited by 12
O’Brien, D., Hall, J., Miró, A., O'Brien, K., Falaschi, M., & Jehle, R. (2021) · Journal for Nature Conservation · doi:10.1016/j.jnc.2021.126077 · Cited by 12
Hernandez-Castro, L. E., Villacís, A. G., Jacobs, A., Cheaib, B. A., Day, C. C., Ocaña-Mayorga, S., Yumiseva, C. A., Bacigalupo, A., Andersson, B., Matthews, L., et al. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010019 · Cited by 12
Dense residential areas promote gene flow in dengue vector mosquito Aedes albopictus
Yeo, H., Tan, H. Z., Tang, Q., Tan, T. R. H., Puniamoorthy, N., & Rheindt, F. E. (2023) · iScience · doi:10.1016/j.isci.2023.107577 · Cited by 12
Watson, S. J., Watson, D. M., Luck, G., & Spooner, P. G. (2014) · Landscape Ecology · doi:10.1007/s10980-014-0065-4 · Cited by 12
Yurek, S., DeAngelis, D. L., Trexler, J. C., Klassen, J. A., & Larsen, L. G. (2016) · Ecological Complexity · doi:10.1016/j.ecocom.2016.08.004 · Cited by 12
Falquina, R., & Gallardo, C. (2017) · Atmospheric Research · doi:10.1016/j.atmosres.2017.06.031 · Cited by 12
Understanding the impacts of temporal variability on estimates of landscape connectivity
Simpkins, C., & Perry, G. L. W. (2017) · Ecological Indicators · doi:10.1016/j.ecolind.2017.08.008 · Cited by 12
BOURGEOIS, M., Cossart, É., & Fressard, M. (2017) · Géomorphologie relief processus environnement · doi:10.4000/geomorphologie.11895 · Cited by 12
Zhao, Q., Diao, Y., Weng, Y., Huang, Z., Gu, B., Wu, Y., Wang, Y., Zhao, Q., & Wang, F. (2022) · Environmental Research Letters · doi:10.1088/1748-9326/ac9491 · Cited by 11
Estimating scale-specific and localized spatial patterns in allele frequency
Lasky, J. R., Takou, M., Gamba, D., & Keitt, T. H. (2024) · Genetics · doi:10.1093/genetics/iyae082 · Cited by 11
Self-citationAbedin, I., Mukherjee, T., Abedin, J., Kim, H., & Kundu, S. (2024) · Biology · doi:10.3390/biology13090667 · Cited by 11
Li, X., Tao, H., Wang, J., Zhang, B., Liu, Z., Liu, Z., & Li, J. (2025) · Land · doi:10.3390/land14010205 · Cited by 11
Yu, H., Chen, X., Gu, X., Yang, Y., Qiu, H., & Zhang, L. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106635 · Cited by 11
Yu, Y., Yu, B., Zhang, X., Ma, L., Caijun, Z., Jia, Y., Hou, S., Han, X., Li, K., Chen, X., et al. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2025.113962 · Cited by 11
Zhang, F., Liu, X., & Jia, Y. (2025) · Ecological Modelling · doi:10.1016/j.ecolmodel.2025.111364 · Cited by 11
Liu, J., Tian, Y., Yang, Y., Zhang, T., Deng, Y., Chen, W., Yin, L., & Zhang, B. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.114090 · Cited by 11
(2013) · DOAJ (DOAJ: Directory of Open Access Journals)
Greenspan, E., Montgomery, C., Stokes, D., Wantai, S., & Moo, S. S. B. (2021) · Landscape Ecology · doi:10.1007/s10980-021-01316-5 · Cited by 11
On the resistance diameters of graphs and their line graphs
Xu, S.-A., Li, Y., Hua, H., & Pan, X.-F. (2021) · Discrete Applied Mathematics · doi:10.1016/j.dam.2021.09.033 · Cited by 11
A Quantitative Framework for Identifying Patterns of Route-Use in Animal Movement Data
Alavi, S. E., Vining, A. Q., Caillaud, D., Hirsch, B. T., Havmøller, R. W., Havmøller, L. W., Kays, R., & Crofoot, M. C. (2022) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2021.743014 · Cited by 11
Martínez‐Cillero, R., Siggery, B., Murphy, R., Perez‐Diaz, A., Christie, I., & Chimbwandira, S. J. (2022) · Journal of Environmental Management · doi:10.1016/j.jenvman.2022.116857 · Cited by 11
Hughes, J. S., Lucet, V., Barrett, G., Moran, S., Manseau, M., Martin, A. E., Naujokaitis‐Lewis, I., Dastis, J. O. N., & Pither, R. (2023) · Landscape Ecology · doi:10.1007/s10980-023-01619-9 · Cited by 11
Modeling red deer functional connectivity at a regional scale in a human-dominated landscape
Urbina, L., Fischer, C., Ray, N., & Lehmann, A. (2023) · Frontiers in Environmental Science · doi:10.3389/fenvs.2023.1198168 · Cited by 11
Liu, Z., Yin, H., Wang, Y., Cheng, Q., & Wang, Z. (2024) · Frontiers in Environmental Science · doi:10.3389/fenvs.2023.1133879 · Cited by 11
Impacts of land-use change on the habitat suitability and connectivity of giant panda
Hu, L., Feng, B., Zhang, J., Dong, X., Tang, J., Zhou, C., Qi, D., & Bai, W. (2024) · Global Ecology and Conservation · doi:10.1016/j.gecco.2024.e03019 · Cited by 11
Hu, J., Qing, G., Wang, Y., Qiu, S., & Luo, N. (2024) · Sustainability · doi:10.3390/su16135777 · Cited by 11
Ruiz-Reyes, J., Heredia-Bobadilla, R. L., Ávila-Akerberg, V., Tejocote-Pérez, M., Gómez‐Ortiz, Y., Domínguez-Vega, H., Ramírez‐Corona, F., Álvarez‐Lopeztello, J., Bolom‐Huet, R., & Sunny, A. (2024) · European Journal of Wildlife Research · doi:10.1007/s10344-024-01838-8 · Cited by 11
Xu, D., Peng, J., Liu, M., Jiang, H., Tang, H., Dong, J., & Meersmans, J. (2024) · Geography and sustainability · doi:10.1016/j.geosus.2024.08.012 · Cited by 11
Kauer, L., Imholt, C., Jacob, J., & Kuehn, R. (2024) · Landscape Ecology · doi:10.1007/s10980-024-02021-9 · Cited by 11
Xu, Q., Ma, X., Ding, Z., & Wang, H. (2025) · Urban forestry & urban greening · doi:10.1016/j.ufug.2025.128817 · Cited by 11
Fine-scale genetic structure of the ringtail (Bassariscus astutus) in a Sky Island mountain range
Lonsinger, R. C., Schweizer, R. M., Pollinger, J. P., Wayne, R. K., & Roemer, G. W. (2015) · Journal of Mammalogy · doi:10.1093/jmammal/gyv050 · Cited by 11
Bernier, A., & Théau, J. (2013) · VertigO · doi:10.4000/vertigo.14105 · Cited by 11
Refining hernando de soto's route using electric circuit theory and CircuitScape
Thayn, J. B., Sampeck, K., & Spaccapaniccia, M. (2016) · The Professional Geographer · doi:10.1080/00330124.2015.1124787 · Cited by 11
Wüest, R. O., Boucher, F. C., Bouchenak‐Khelladi, Y., Karger, D. N., & Linder, H. P. (2019) · Journal of Biogeography · doi:10.1111/jbi.13625 · Cited by 11
Williamson, M. A., Creech, T. G., Carnwath, G. C., Dixon, B., & Kelly, V. (2019) · Conservation Science and Practice · doi:10.1111/csp2.155 · Cited by 11
Ibsen, P. C., Borowy, D., Rochford, M. E., Swan, C. M., & Jenerette, G. D. (2020) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2020.501502 · Cited by 11
Khanghah, S. S., Moameri, M., Ghorbani, A., Mostafazadeh, R., & Biswas, A. (2022) · Environmental Monitoring and Assessment · doi:10.1007/s10661-021-09716-5 · Cited by 11
Genetic structure in neotropical birds with different tolerance to urbanization
Rodríguez-Bardía, M., Fuchs, E. J., Barrantes, G., Madrigal‐Brenes, R., & Sandoval, L. (2022) · Scientific Reports · doi:10.1038/s41598-022-09961-9 · Cited by 11
With, K. A. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780198838388.003.0005 · Cited by 11
Isola, F., Leone, F., & Zoppi, C. (2022) · Sustainability · doi:10.3390/su14116588 · Cited by 11
Liang, G., Liu, J., Niu, H., & Ding, S. (2022) · Ecology and Evolution · doi:10.1002/ece3.9429 · Cited by 11
Mapping urban cool air connectivity in a megacity
Park, C. Y., Choe, H., & Thorne, J. H. (2022) · Urban Climate · doi:10.1016/j.uclim.2022.101362 · Cited by 11
Wang, Z., Zhang, J., Chen, J., Gao, H., Li, J., & Li, M. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1087588 · Cited by 11
Boreal Forest Landscape Restoration in the Face of Extensive Forest Fragmentation and Loss
Svensson, J. S., Mikusiński, G., Bubnicki, J. W., Andersson, J. D., & Jonsson, B. G. (2023) · Advances in global change research · doi:10.1007/978-3-031-15988-6_19 · Cited by 11
Atzeni, L., Wang, J., Riordan, P., Shi, K., & Cushman, S. A. (2023) · Landscape Ecology · doi:10.1007/s10980-023-01660-8 · Cited by 11
Implementation resistance and the human dimensions of connectivity planning
Williamson, M. A., Parrott, L., Carter, N., & Ford, A. T. (2023) · People and Nature · doi:10.1002/pan3.10525 · Cited by 11
Wildlife habitat connectivity in the changing climate of New York's Hudson Valley
Howard, T. G., & Schlesinger, M. (2013) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12172 · Cited by 11
Urbanization and Raptors: Trends and Research Approaches
Cooke, R., Hogan, F., Isaac, B., Weaving, M., & White, J. (2018) · Island Press/Center for Resource Economics eBooks · doi:10.5822/978-1-61091-841-1_5 · Cited by 11
A topoclimate model for Quaternary insular speciation
García‐Olivares, V., Patiño, J., Overcast, I., Salces‐Castellano, A., de Heredia, U. L., Mora‐Márquez, F., Machado, A., Hickerson, M. J., & Emerson, B. C. (2019) · Journal of Biogeography · doi:10.1111/jbi.13689 · Cited by 11
An Introduction to Landscape Ecology
With, K. A. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780198838388.003.0001 · Cited by 11
A statistical framework for modelling migration corridors
Nuñez, T. A., Hurley, M. A., Graves, T. A., Ortega, A. C., Sawyer, H., Fattebert, J., Merkle, J. A., & Kauffman, M. J. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13969 · Cited by 10
Zhang, S., Chen, Y., Li, J., Ye, Q., Chen, W., Wang, X., Wei, H., Jiang, Q., & Guo, L. (2024) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2024.1480198 · Cited by 10
Li, S. X., Chen, Y., Hou, C., & Peng, W. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.147323 · Cited by 10
Liu, S., Xie, Y., Wang, N., Tong, Z., Liu, Y., & Liu, Y. (2025) · Environmental Impact Assessment Review · doi:10.1016/j.eiar.2025.108184 · Cited by 10
Zhang, L., Yu, H., Zhong, Q., Zhang, G., Wang, Z., & Zhang, Q. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2025.114224 · Cited by 10
Hu, C., & Zeng, H. (2026) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2026.105725 · Cited by 10
Plant responses to global change: next generation biogeography
Johnson, J. S., Gaddis, K. D., Cairns, D. M., Lafon, C. W., & Krutovsky, K. V. (2016) · Physical Geography · doi:10.1080/02723646.2016.1162597 · Cited by 10
Metadata
- Authors
- McRae, B. H., Dickson, B. G., Keitt, T. H., & Shah, V. B.
- Affiliations
- National Center for Ecological Analysis and Synthesis (US)
- Northern Arizona University (US)
- The University of Texas at Austin (US)
- University of California, Santa Barbara (US)
- Corresponding author
- McRae, Brad H.
- Venue
- Ecology
- Publisher
- Wiley
- Published
- 2008-10
- Volume
- 89
- Issue
- 10
- Pages
- 2712-2724
- ISSN
- 0012-9658 (ISSN-L)
- OpenAlex
- W2091202128
- Cited by
- 2,244
- References
- 44
- Citations by year
- 2026: 258 · 2025: 233 · 2024: 226 · 2023: 166 · 2022: 209 · 2021: 177 · 2020: 148 · 2019: 148 · 2018: 96 · 2017: 130 · 2016: 93 · 2015: 89 · 2014: 65 · 2013: 69 · 2012: 49
- FWCI
- 21.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ecology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Ecology › Wildlife-Road Interactions and Conservation
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Physical Sciences › Environmental Science › Global and Planetary Change › Land Use and Ecosystem Services
- Keywords
- ecological connectivity, circuit theory, random walk hypothesis, resistance surfaces, gene flow, animal movement, dispersal routes, habitat patches, movement corridors, conservation planning, landscape connectivity, population networks
- MeSH terms
- Algorithms, Animals, Conservation of Natural Resources, Ecology, Biological Evolution, Models, Biological, Models, Theoretical, Population Density, Population Dynamics, Ecosystem
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
