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 801–900 of 2,245
Human Dispersal in the Atlantic Slope of Patagonia and the Role of Lithic Availability
Franco, N. V., Borrero, L. A., & Lucero, G. F. (2019) · PaleoAmerica · doi:10.1080/20555563.2019.1582128 · Cited by 23
Cameron, A. C., Page, R. B., Watling, J. I., Hickerson, C. M., & Anthony, C. D. (2019) · Conservation Genetics · doi:10.1007/s10592-019-01207-y · Cited by 23
Winiarski, K. J., Peterman, W. E., Whiteley, A. R., & McGarigal, K. (2019) · Molecular Ecology Resources · doi:10.1111/1755-0998.13089 · Cited by 23
Yu, H., Huang, J., Ji, C., & Li, Z. (2021) · Land · doi:10.3390/land10040344 · Cited by 23
Vasudev, D., Goswami, V. R., Srinivas, N., Syiem, B. L. N., & Sarma, A. (2021) · Diversity and Distributions · doi:10.1111/ddi.13419 · Cited by 23
Wang, Z., Liu, Y., Xie, X., Wang, X., Lin, H., Xie, H., & Liu, X. (2022) · Land · doi:10.3390/land11091496 · Cited by 23
Cushman, S. A., McRae, B. H., & McGarigal, K. (2015) · Wiley eBooks · doi:10.1002/9781118525258.ch02 · Cited by 23
Self-citationPrioritization of landscape connectivity for the conservation of Peary caribou
Mallory, C. D., & Boyce, M. S. (2019) · Ecology and Evolution · doi:10.1002/ece3.4915 · Cited by 23
A multi-objective scheme for sustainable ecological conservation on the Qinghai–Tibet Plateau
Wang, H., Zhang, H., Zhan, J., Wang, C., Liu, W., Teng, Y., & Yang, Z. (2025) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2025.108276 · Cited by 22
Planning for carnivore recolonization by mapping sex-specific landscape connectivity
Gantchoff, M. G., Conlee, L., & Belant, J. L. (2019) · Global Ecology and Conservation · doi:10.1016/j.gecco.2019.e00869 · Cited by 22
Mountain frog species losing out to climate change around the Sichuan Basin
Yang, S., Wang, X., & Hu, J. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.150605 · Cited by 22
Xiao, H., Guo, Y., Wang, Y., Xu, Y., & Liu, D. (2022) · Applied Sciences · doi:10.3390/app12199600 · Cited by 22
Xie, J., Xie, B., Zhou, K., Li, J., Xiao, J., Liu, C., & Zhang, X. (2023) · Ecological Indicators · doi:10.1016/j.ecolind.2023.110771 · Cited by 22
Lan, Y., Wang, J., Liu, Q., Liu, F., Liu, L., Li, J., & Luo, M. (2023) · Ecological Indicators · doi:10.1016/j.ecolind.2023.111337 · Cited by 22
Lumia, G., Modica, G., & Cushman, S. A. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.120073 · Cited by 22
Zhao, Y., Zhang, M., Zhao, D., Duo, L., & Lu, C. (2024) · Environmental Science and Pollution Research · doi:10.1007/s11356-024-32271-8 · Cited by 22
Xie, Y., Zou, J., Chen, Y., Li, F., & Jiang, Q. (2024) · Ecological Indicators · doi:10.1016/j.ecolind.2024.111785 · Cited by 22
Gray, M. E., & Dickson, B. G. (2015) · Ecological Applications · doi:10.1890/14-0367.1 · Cited by 21
Self-citationCowley, D. J., Johnson, O., & Pocock, M. J. O. (2015) · Landscape Ecology · doi:10.1007/s10980-015-0168-6 · Cited by 22
Forest cover mediates genetic connectivity of northwestern cougars
Warren, M. J., Wallin, D. O., Beausoleil, R. A., & Warheit, K. I. (2016) · Conservation Genetics · doi:10.1007/s10592-016-0840-7 · Cited by 22
Angulo, D. F., Amarilla, L. D., Anton, A. M., & Sosa, V. (2017) · PLoS ONE · doi:10.1371/journal.pone.0168933 · Cited by 22
Ofori, B. Y., Stow, A., Baumgartner, J. B., & Beaumont, L. J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184193 · Cited by 22
Centeno‐Cuadros, A., Hulva, P., Romportl, D., Santoro, S., Stříbná, T., Shohami, D., Evin, A., Tsoar, A., Benda, P., Horáček, I., et al. (2017) · Molecular Ecology · doi:10.1111/mec.14365 · Cited by 22
Examining speed versus selection in connectivity models using elk migration as an example
Brennan, A., Hanks, E. M., Merkle, J. A., Cole, E. K., Dewey, S. R., Courtemanch, A. B., & Cross, P. C. (2018) · Landscape Ecology · doi:10.1007/s10980-018-0642-z · Cited by 22
Ramirez, S. S., Rico, Y., Berry, K. H., Edwards, T., Karl, A. E., Henen, B. T., & Murphy, R. W. (2018) · Scientific Reports · doi:10.1038/s41598-018-29395-6 · Cited by 22
An, Y., Liu, S., Sun, Y., Shi, F., & Zhao, S. (2019) · Agricultural Systems · doi:10.1016/j.agsy.2019.102766 · Cited by 22
Chubaty, A. M., Galpern, P., & Doctolero, S. (2020) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13350 · Cited by 22
Multiscale landscape genetics of American marten at their southern range periphery
Aylward, C. M., Murdoch, J. D., & Kilpatrick, C. W. (2020) · Heredity · doi:10.1038/s41437-020-0295-y · Cited by 22
Howey, M. C. L., Sullivan, F. B., Burg, M. B., & Palace, M. (2020) · Journal of Field Archaeology · doi:10.1080/00934690.2020.1713435 · Cited by 22
A novel quantitative framework for riverscape genetics
White, S. L., Hanks, E. M., & Wagner, T. (2020) · Ecological Applications · doi:10.1002/eap.2147 · Cited by 22
Jennings, M. K., Haeuser, E., Foote, D., Lewison, R. L., & Conlisk, E. E. (2020) · Land · doi:10.3390/land9100341 · Cited by 22
Goswami, V. R., Vasudev, D., Joshi, B., Hait, P., & Sharma, P. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.144000 · Cited by 22
Impact of Road Fencing on Ungulate–Vehicle Collisions and Hotspot Patterns
Kučas, A., & Balčiauskas, L. (2021) · Land · doi:10.3390/land10040338 · Cited by 22
Analysis of Ecological Blockage Pattern in Beijing Important Ecological Function Area, China
Xu, J., Wang, J., Xiong, N., Chen, Y., Sun, L., Wang, Y., & An, L. (2022) · Remote Sensing · doi:10.3390/rs14051151 · Cited by 22
Ihlow, F., Bonke, R., Hartmann, T., Geißler, P., Behler, N., & Rödder, D. (2014) · Aquatic Conservation Marine and Freshwater Ecosystems · doi:10.1002/aqc.2473 · Cited by 22
Harrisson, K. A., Pavlova, A., Amos, J. N., Radford, J. Q., & Sunnucks, P. (2013) · Journal of Animal Ecology · doi:10.1111/1365-2656.12172 · Cited by 22
Modeling Spatial Covariance Using the Limiting Distribution of Spatio-Temporal Random Walks
Hanks, E. M. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2016.1224714 · Cited by 22
Correia, D. L. P., Raulier, F., Filotas, É., & Bouchard, M. (2016) · Ecological Indicators · doi:10.1016/j.ecolind.2016.08.033 · Cited by 22
Forest Connectivity Regions of Canada Using Circuit Theory and Image Analysis
Pelletier, D., Lapointe, M.-É., Wulder, M. A., White, J. C., & Cardille, J. A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0169428 · Cited by 22
Health promotion behaviors of private practice dental hygienists.
McConaughy, F. L., Lukken, K. M., & Toevs, S. E. (1991) · PubMed · Cited by 22
Resistance is futile: effects of landscape features on gene flow of the northern bobwhite
Berkman, L. K., Nielsen, C. K., Roy, C. L., & Heist, E. J. (2013) · Conservation Genetics · doi:10.1007/s10592-013-0471-1 · Cited by 21
Davis, K. J., Chadès, I., Rhodes, J. R., & Bode, M. (2019) · Journal of Applied Ecology · doi:10.1111/1365-2664.13425 · Cited by 21
Antunes, B., Figueiredo‐Vázquez, C., Dudek, K., Liana, M. K., Pabijan, M., Zieliński, P., & Babik, W. (2022) · Molecular Ecology · doi:10.1111/mec.16543 · Cited by 21
Guo, N., Liang, X., & Meng, L. (2022) · Sustainability · doi:10.3390/su14137744 · Cited by 21
Wilcox, M. A., Jeffery, N. W., DiBacco, C., Bradbury, I., Lowen, B., Wang, Z., Beiko, R. G., & Stanley, R. R. E. (2023) · Landscape Ecology · doi:10.1007/s10980-023-01690-2 · Cited by 21
Hou, W., Liu, J., & Walz, U. (2023) · Journal of Environmental Management · doi:10.1016/j.jenvman.2023.119084 · Cited by 21
Triana, D. L., Ayram, C. A. C., & Velásquez‐Tibatá, J. (2023) · Global Ecology and Conservation · doi:10.1016/j.gecco.2023.e02713 · Cited by 21
High‐resolution habitat modeling with airborne LiDAR for red tree voles
Johnston, A. N., & Moskal, L. M. (2016) · Journal of Wildlife Management · doi:10.1002/jwmg.21173 · Cited by 21
Crist, M. R., Knick, S. T., & Hanser, S. E. (2017) · Ornithological Applications · doi:10.1650/condor-16-60.1 · Cited by 21
Zero, V. H., Barocas, A., Jochimsen, D. M., Pelletier, A., Giroux‐Bougard, X., Trumbo, D. R., Vardaro, J. A. C., Mack, D. E., Linnell, M. A., Pigg, R. M., et al. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00081 · Cited by 21
Rocca, F. D., Bogliani, G., & Milanesi, P. (2017) · Nature Conservation · doi:10.3897/natureconservation.19.12457 · Cited by 21
Methods for studying earthworm dispersal
Mathieu, J., Caro, G., & Dupont, L. (2017) · Applied Soil Ecology · doi:10.1016/j.apsoil.2017.09.006 · Cited by 21
Herzog, C., Honegger, A., Hegglin, D., Wittwer, R., de Ferron, A., Verbruggen, E., Jeanneret, P., Schloter, M., Banerjee, S., & van der Heijden, M. G. A. (2019) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2019.106596 · Cited by 21
O’Donnell, M. S., Edmunds, D. R., Aldridge, C. L., Heinrichs, J. A., Coates, P. S., Prochazka, B. G., & Hanser, S. E. (2019) · Ecosphere · doi:10.1002/ecs2.2872 · Cited by 21
Crops and their wild progenitors recruit beneficial and detrimental soil biota in opposing ways
Martín‐Robles, N., García‐Palacios, P., Rodríguez, M., Rico, D., Vigo, R., Sánchez‐Moreno, S., Deyn, G. B. D., & Milla, R. (2020) · Plant and Soil · doi:10.1007/s11104-020-04703-0 · Cited by 21
Zhang, J., Jiang, F., Cai, Z., Dai, Y., Liu, D., Song, P., Hou, Y., Gao, H., & Zhang, T. (2021) · Sustainability · doi:10.3390/su13041656 · Cited by 21
Barik, S., Saha, G. K., & Mazumdar, S. (2021) · Wetlands · doi:10.1007/s13157-021-01433-6 · Cited by 21
Quantitative Analysis of Ecological Networks
Dale, M. R. T., & Fortin, M. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108649018 · Cited by 21
Friesen, S. K., Rubidge, E. M., Martone, R., Hunter, K. L., Peña, M. A., & Ban, N. C. (2021) · Ocean & Coastal Management · doi:10.1016/j.ocecoaman.2021.105776 · Cited by 21
Shi, X., Qin, M., Li, B., & Zhang, D. (2021) · Sustainability · doi:10.3390/su131810053 · Cited by 21
Lim, J. Y., Patiño, J., Noriyuki, S., Cayetano, L., Gillespie, R. G., & Krehenwinkel, H. (2021) · Molecular Ecology · doi:10.1111/mec.16323 · Cited by 21
Liu, W., Xu, H., Zhang, X., & Jiang, W. (2022) · Forests · doi:10.3390/f13050735 · Cited by 21
Environmental niche divergence between genetically distant lineages of an endangered water beetle
Sánchez‐Fernández, D., Lobo, J. M., Abellán, P., & Millán, A. (2011) · Biological Journal of the Linnean Society · doi:10.1111/j.1095-8312.2011.01668.x · Cited by 21
Immune function trade-offs in response to parasite threats
Kirschman, L. J., Quade, A. H., Zera, A. J., & Warne, R. W. (2017) · Journal of Insect Physiology · doi:10.1016/j.jinsphys.2017.01.009 · Cited by 21
Yin, Y., Liu, S., Sun, Y., Zhao, S., An, Y., Dong, S., & Coxixo, A. (2019) · Journal of Geographical Sciences · doi:10.1007/s11442-019-1655-5 · Cited by 21
Shen, Z., Liao, C., Cao, F., Zhang, Q., Yang, D., Zhang, Q., Shi, Y., & Yang, J. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106727 · Cited by 20
Changes and Characteristics of Green Infrastructure Network Based on Spatio-Temporal Priority
Chen, X., Xu, L., Zhu, R., Ma, Q., Shi, Y., & Lu, Z. (2022) · Land · doi:10.3390/land11060901 · Cited by 20
Making the Case for Parks: Construction of an Ecological Network of Urban Parks Based on Birds
Yang, Y., Zhou, Y., Feng, Z., & Wu, K. (2022) · Land · doi:10.3390/land11081144 · Cited by 20
Barik, S., Saha, G. K., & Mazumdar, S. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101833 · Cited by 20
Zhuo, Y., Wang, M., Liu, Z., Xu, W., Abdulnazar, A., Rajabi, A. M., Темирбекович, Д. А., Haider, J., Khan, M. Z., Loik, N., et al. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.169298 · Cited by 20
Ke, L., Zhao, Y., Wang, Q., Yin, S., & Liu, W. (2024) · Ocean & Coastal Management · doi:10.1016/j.ocecoaman.2024.107204 · Cited by 20
Do not only connect: a model of infiltration‐excess overland flow based on simulation
Kirkby, M. J. (2014) · Earth Surface Processes and Landforms · doi:10.1002/esp.3556 · Cited by 20
Luo, Z., Yu, H., Pu, Y., Yang, J., Mei, H., Wang, D., Zhu, Z., Zhao, M., & Wu, H. (2016) · International Journal of Primatology · doi:10.1007/s10764-016-9910-4 · Cited by 20
Payn, R. A., Hall, R. O., Kennedy, T. A., Poole, G. C., & Marshall, L. A. (2017) · Limnology and Oceanography Methods · doi:10.1002/lom3.10204 · Cited by 20
Harihar, A., Ghosh‐Harihar, M., & MacMillan, D. C. (2017) · Oryx · doi:10.1017/s0030605317001156 · Cited by 20
Zárrate-Charry, D. A., Massey, A. L., González-Maya, J. F., & Betts, M. G. (2018) · Biodiversity and Conservation · doi:10.1007/s10531-018-1605-z · Cited by 20
Potential distribution and habitat connectivity of Crotalus triseriatus in Central Mexico
Sunny, A., Gandarilla-Aizpuro, F. J., Monroy‐Vilchis, O., & Zarco-González, M. M. (2019) · Herpetozoa · doi:10.3897/herpetozoa.32.e36361 · Cited by 20
Spatial drivers of composition and connectivity across endangered tropical dry forests
Balzotti, C. S., Asner, G. P., Adkins, E. D., & Parsons, E. W. (2020) · Journal of Applied Ecology · doi:10.1111/1365-2664.13632 · Cited by 20
Harnessing paleo‐environmental modeling and genetic data to predict intraspecific genetic structure
Yannic, G., Hagen, O., Leugger, F., Karger, D. N., & Pellissier, L. (2020) · Evolutionary Applications · doi:10.1111/eva.12986 · Cited by 20
Forecasting Seasonal Habitat Connectivity in a Developing Landscape
Zeller, K. A., Wattles, D. W., Bauder, J. M., & DeStefano, S. (2020) · Land · doi:10.3390/land9070233 · Cited by 20
Hui-hui, W., Li, H., Miao-miao, X., Xu, M., Shao-ling, L., & Zhong-ke, B. (2020) · 自然资源学报 · doi:10.31497/zrzyxb.20200114 · Cited by 20
Ouellet, J., Michaud, R., Moisan, M., & Lesage, V. S. (2021) · Ecosphere · doi:10.1002/ecs2.3560 · Cited by 20
Deterministic, random, or in between? Inferring the randomness level of wildlife movements
Goicolea, T., Gastón, A., Cisneros-Araujo, P., García-Viñas, J. I., & Mateo-Sánchez, M. C. (2021) · Movement Ecology · doi:10.1186/s40462-021-00273-7 · Cited by 19
Genetic diversity and functional connectivity of a critically endangered salamander
Sunny, A., López‐Sánchez, M., Ramírez‐Corona, F., Suárez‐Atilano, M., & González‐Fernández, A. (2021) · Biotropica · doi:10.1111/btp.13025 · Cited by 20
Zárrate-Charry, D. A., González-Maya, J. F., Arias‐Alzate, A., Jiménez-Alvarado, J. S., Arias, J. D. R., Armenteras, D., & Betts, M. G. (2022) · Royal Society Open Science · doi:10.1098/rsos.201154 · Cited by 20
Yu, F., Zhang, B., Yao, Y., Wang, J., Zhang, X., Liu, J., & Li, J. (2022) · Sustainability · doi:10.3390/su14084377 · Cited by 20
Landscape ecology meets landscape science
Helfenstein, J., Bauer, L., Clalüna, A., Bolliger, J., & Kienast, F. (2014) · Landscape Ecology · doi:10.1007/s10980-014-0055-6 · Cited by 20
A connectivity and wildlife management conflict in isolated desert waters
McIntyre, N. E., Drake, J. C., & Griffis‐Kyle, K. L. (2016) · Journal of Wildlife Management · doi:10.1002/jwmg.1059 · Cited by 20
Thompson, J. J., & Velilla, M. (2017) · Endangered Species Research · doi:10.3354/esr00840 · Cited by 20
Model explorations of ecological network performance under conditions of global change
Bakker, M., Opdam, P. F. M., Jongman, R. H. G., & van den Brink, A. (2015) · Landscape Ecology · doi:10.1007/s10980-015-0181-9 · Cited by 20
Optimization of landscape connectivity: A directed dynamic ecological network topology perspective
Li, R., Du, Z., Yan, Y., Wu, S., Zhang, H., & Ma, K. (2026) · Ecological Informatics · doi:10.1016/j.ecoinf.2026.103818 · Cited by 19
Trading connectivity improvement for area loss in patch-based biodiversity reserve networks
Dalang, T., & Hersperger, A. M. (2012) · Biological Conservation · doi:10.1016/j.biocon.2012.01.042 · Cited by 19
Burger, J. R., Anderson, R. P., Balk, M. A., & Fristoe, T. S. (2019) · Frontiers of Biogeography · doi:10.21425/f5fbg44383 · Cited by 19
Jacobsen, C. D., Brown, D. J., Flint, W. D., Pauley, T. K., Buhlmann, K. A., & Mitchell, J. C. (2019) · Global Ecology and Conservation · doi:10.1016/j.gecco.2019.e00883 · Cited by 19
Connectivity of priority areas for the conservation of large carnivores in northern Mexico
Balbuena‐Serrano, Á., Zarco-González, M. M., Carreón-Arroyo, G., Carrera-Treviño, R., Amador-Alcalá, S. A., & Monroy‐Vilchis, O. (2021) · Journal for Nature Conservation · doi:10.1016/j.jnc.2021.126116 · Cited by 19
Lecis, R., Dondina, O., Orioli, V., Biosa, D., Canu, A., Fabbri, G., Iacolina, L., Cossu, A., Bani, L., Apollonio, M., et al. (2022) · Ecology and Evolution · doi:10.1002/ece3.8804 · Cited by 19
Bi, X., Wu, Y., Meng, L., Wu, J., Li, Y., Zhou, S., & Pan, X. (2022) · Ocean & Coastal Management · doi:10.1016/j.ocecoaman.2022.106334 · Cited by 19
Isola, F., Lai, S., Leone, F., & Zoppi, C. (2022) · Sustainability · doi:10.3390/su14159788 · Cited by 19
Xie, Y., Wang, Q., Xie, M., & Shibata, S. (2023) · Landscape and Ecological Engineering · doi:10.1007/s11355-023-00547-9 · Cited by 19
Environmental suitability and potential range expansion of the Eurasian beaver in Italy
Falaschi, M., Ficetola, G. F., Viviano, A., Mazza, G., & Mori, E. (2023) · Animal Conservation · doi:10.1111/acv.12910 · Cited by 19
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