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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.
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Circuit theory connectivity models are generally seen to be robust to coarsening of input data, which can enable computation across very large areas at coarse pixel resolution.
“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]
Across long time scales at which genetic mutations can be measured, resistance distance is strongly correlated to the differences between populations.
“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]
Circuit theory models potential movement between locations omnidirectionally as a flow gradient, generating corridors beyond optimality whose amplitude and narrowness depend on terrain conditions.
“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
GIS technologies and least-cost analysis tools such as Linkage Mapper and Circuitscape have demonstrated potential in diverse fields, from biodiversity conservation to ecological networks.
“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]
McRae et al. (2008) illustrated how circuit theory could be used to identify multiple (i.e., redundant) movement pathways or habitat corridors and reveal critical 'pinch points' constraining potential flow between focal areas.
“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)
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.
“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)
The open-source program Circuitscape has provided accessible means of implementing circuit theory concepts across a range of projects and disciplines.
“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
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.
“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
Circuit theory has assumptions and caveats that still need to be addressed, including modeling long-distance dispersal events and asymmetric migration.
“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 circuit-theoretic approach, the landscape is abstracted as a network of current sources, grounds, and resistors, and the resulting current flow is related to the movement or flow intensity of the ecological process of interest.
“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
The circuit-theoretic approach to connectivity modeling has been gaining popularity over the past decade.
“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
Circuit theory models connectivity across a resistance surface as electric current moving through a circuit.
“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
Some methods work by connecting source and destination sites, which may not be appropriate for all types of questions.
“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
Several authors have alluded to the idea that artificial boundaries might influence the results of least cost path and landscape resistance models, but no one has directly addressed the issue.
“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]
Circuit theory is an appealing model because it uses random walkers to model overall resistance to movement rather than discrete paths.
“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]
Connectivity modelling is widely used in ecological research.
“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
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.
“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]
Many modeling workflows for connectivity analysis are constrained by computational demand.
“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]
Circuitscape has been most widely applied in landscape genetics, where modelers calculate resistance distances between spatially referenced pairs of genetic samples.
“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]
The Circuitscape algorithm tracks where current flows across a resistance surface, following current flow from source nodes to ground nodes, and quantifies spatial patterns of current accumulation as higher resistance areas and barriers shift current flow into pathways with lower resistance.
“The Circuitscape algorithm tracks where”
From Circuitscape in julia: empowering dynamic approaches to connectivity assessment (2021) · cites this paper as [22]
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 between focal nodes, following the assumption of a random walk.
“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]
The first version of Circuitscape was written in Java, followed by version 2.0 in MATLAB.
“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]
The authors reviewed 1280 papers identified through Web of Science searches that cited key foundational papers or a Circuitscape user guide.
“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]
Movement from an area of higher to lower resistance is much more likely than the reverse.
“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]
Circuitscape assumes a directional symmetry of resistance between adjacent pixels.
“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-citationMcRae, B. H. (2006) · Evolution · doi:10.1554/05-321.1 · Cited by 900
Self-citationPascual-Hortal, L., & Saura, S. (2006) · Landscape Ecology · doi:10.1007/s10980-006-0013-z · Cited by 774
SIMPLE CONNECTIVITY MEASURES IN SPATIAL ECOLOGY
Moilanen, A., & Nieminen, M. (2002) · Ecology · doi:10.1890/0012-9658(2002)083[1131:scmise]2.0.co;2 · Cited by 709
Detecting Critical Scales in Fragmented Landscapes
Keitt, T. H., Urban, D. L., & Milne, B. T. (1997) · Conservation Ecology · doi:10.5751/es-00015-010104 · Cited by 598
Self-citationConnecting landscape patterns to ecosystem and population processes
Kareiva, P., & Wennergren, U. (1995) · Nature · doi:10.1038/373299a0 · Cited by 581
Spatially Explicit Population Models: Current Forms and Future Uses
Dunning, J. B., Stewart, D. J., Danielson, B. J., Noon, B. R., Root, T. L., Lamberson, R. H., & Stevens, E. E. (1995) · Ecological Applications · doi:10.2307/1942045 · Cited by 534
Scaling of ?landscapes? in landscape ecology, or, landscape ecology from a beetle's perspective
Wiens, J. A., & Milne, B. T. (1989) · Landscape Ecology · doi:10.1007/bf00131172 · Cited by 507
Forks in the Road: Choices in Procedures for Designing Wildland Linkages
BEIER, P., MAJKA, D. R., & SPENCER, W. D. (2008) · Conservation Biology · doi:10.1111/j.1523-1739.2008.00942.x · Cited by 489
Moilanen, A., Franco, A. M. A., Early, R. I., Fox, R., Wintle, B., & Thomas, C. D. (2005) · Proceedings of the Royal Society B: Biological Sciences · doi:10.1098/rspb.2005.3164 · Cited by 480
Minimizing Effective Resistance of a Graph
Ghosh, A., Boyd, S., & Saberi, A. (2008) · SIAM Review · doi:10.1137/050645452 · Cited by 477
Corridors Increase Plant Species Richness at Large Scales
Damschen, E. I., Haddad, N. M., Orrock, J. L., Tewksbury, J. J., & Levey, D. J. (2006) · Science · doi:10.1126/science.1130098 · Cited by 407
Using Landscape Indices to Predict Habitat Connectivity
Schumaker, N. H. (1996) · Ecology · doi:10.2307/2265590 · Cited by 405
A comparison-shopper's guide to connectivity metrics
Calabrese, J. M., & Fagan, W. F. (2004) · Frontiers in Ecology and the Environment · doi:10.1890/1540-9295(2004)002[0529:acgtcm]2.0.co;2 · Cited by 394
How should we measure landscape connectivity?
Tischendorf, L., & Fahrig, L. (2000) · Landscape Ecology · doi:10.1023/a:1008177324187 · Cited by 384
GRAPH THEORY AS A PROXY FOR SPATIALLY EXPLICIT POPULATION MODELS IN CONSERVATION PLANNING
Minor, E. S., & Urban, D. L. (2007) · Ecological Applications · doi:10.1890/06-1073.1 · Cited by 331
The electrical resistance of a graph captures its commute and cover times
Chandra, A. K., Raghavan, P., Ruzzo, W. L., Smolensky, R., & Tiwari, P. (1996) · Computational Complexity · doi:10.1007/bf01270385 · Cited by 328
Designing Large‐Scale Conservation Corridors for Pattern and Process
Rouget, M., Cowling, R. M., Lombard, A. T., Knight, A. T., & Kerley, G. I. H. (2006) · Conservation Biology · doi:10.1111/j.1523-1739.2006.00297.x · Cited by 305
HABITAT-SPECIFIC MOVEMENT PARAMETERS ESTIMATED USING MARK–RECAPTURE DATA AND A DIFFUSION MODEL
Ovaskainen, O. (2004) · Ecology · doi:10.1890/02-0706 · Cited by 220
An introduction to electric circuits
Bird, J. (2003) · Routledge eBooks · doi:10.4324/9780080474311-38 · Cited by 178
pathmatrix : a geographical information system tool to compute effective distances among samples
Ray, N. (2004) · Molecular Ecology Notes · doi:10.1111/j.1471-8286.2004.00843.x · Cited by 169
Jordán, F., Báldí, A., Orci, K. M., Rácz, I. A., & Varga, Z. (2003) · Landscape Ecology · doi:10.1023/a:1022958003528 · Cited by 156
Exploring the functional connectivity of landscapes using landscape networks
Theobald, D. M. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511754821.019 · Cited by 129
Planning for robust reserve networks using uncertainty analysis
Moilanen, A., Runge, M. C., Elith, J., Tyre, A. J., Carmel, Y., Fegraus, E., Wintle, B. A., Burgman, M. A., & Ben‐Haim, Y. (2006) · Ecological Modelling · doi:10.1016/j.ecolmodel.2006.07.004 · Cited by 112
Quantifying connectivity: balancing metric performance with data requirements
Fagan, W. F., & Calabrese, J. M. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511754821.014 · Cited by 106
Uncertainty Analysis for Regional‐Scale Reserve Selection
Moilanen, A., Wintle, B. A., Elith, J., & Burgman, M. A. (2006) · Conservation Biology · doi:10.1111/j.1523-1739.2006.00560.x · Cited by 106
Quantifying the network connectivity of landscape mosaics: a graph-theoretical approach
Ricotta, C., Stanisci, A., Avena, G. C., & Blasi, C. (2000) · Community Ecology · doi:10.1556/comec.1.2000.1.12 · Cited by 102
Moilanen, A., & Wintle, B. A. (2007) · Conservation Biology · doi:10.1111/j.1523-1739.2006.00625.x · Cited by 100
Vertebrate Responses to Environmental Patchiness in Arid and Semiarid Ecosystems
Wiens, J. A. (1985) · Elsevier eBooks · doi:10.1016/b978-0-08-050495-7.50015-6 · Cited by 100
Arellano, L., León‐Cortés, J. L., & Ovaskainen, O. (2007) · Landscape Ecology · doi:10.1007/s10980-007-9165-8 · Cited by 82
An Empirical Test of a Diffusion Model: Predicting Clouded Apollo Movements in a Novel Environment
Ovaskainen, O., Luoto, M., Ikonen, I., Rekola, H., Meyke, E., & Kuussaari, M. (2008) · The American Naturalist · doi:10.2307/30119661 · Cited by 73
A Practical Map-Analysis Tool for Detecting Potential Dispersal Corridors
Hargrove, W. W., Hoffman, F. M., & Efroymson, R. A. (2005) · Landscape Ecology · doi:10.1007/s10980-004-3162-y · Cited by 69
Beier, P., Penrod, K. L., Luke, C., Spencer, W. D., & Cabañero, C. (2006) · Connectivity Conservation · doi:10.1017/cbo9780511754821.025 · Cited by 48
O’Hanley, J. R., Church, R. L., & Gilless, J. K. (2006) · Biological Conservation · doi:10.1016/j.biocon.2006.10.004 · Cited by 16
Introduction: evaluating and quantifying the conservation dividends of connectivity
Kareiva, P. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511754821.013 · Cited by 9
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Sorted by citations
Linking ecosystem services and circuit theory to identify ecological security patterns
Peng, J., Yang, Y., Liu, Y., Hu, Y., Du, Y., Meersmans, J., & Qiu, S. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.06.292 · Cited by 789
Visualizing spatial population structure with estimated effective migration surfaces
Petkova, D., Novembre, J., & Stephens, M. (2015) · Nature Genetics · doi:10.1038/ng.3464 · Cited by 649
SPEAR, S. F., BALKENHOL, N., FORTIN, M., MCRAE, B. H., & SCRIBNER, K. (2010) · Molecular Ecology · doi:10.1111/j.1365-294x.2010.04657.x · Cited by 598
Self-citationVillarrubia-Gómez, P., Cornell, S. E., & Fabres, J. (2018) · Marine Policy · doi:10.1016/j.marpol.2017.11.035 · Cited by 588
R Package gdistance : Distances and Routes on Geographical Grids
van Etten, J. (2017) · Journal of Statistical Software · doi:10.18637/jss.v076.i13 · Cited by 439
Saura, S., & Rubio, L. (2010) · Ecography · doi:10.1111/j.1600-0587.2009.05760.x · Cited by 558
Graph models of habitat mosaics
Urban, D. L., Minor, E. S., Treml, E. A., & Schick, R. S. (2009) · Ecology Letters · doi:10.1111/j.1461-0248.2008.01271.x · Cited by 539
Losses, inefficiencies and waste in the global food system
Alexander, P., Brown, C., Arneth, A., Finnigan, J., Moran, D., & Rounsevell, M. D. A. (2017) · Agricultural Systems · doi:10.1016/j.agsy.2017.01.014 · Cited by 524
Circuit‐theory applications to connectivity science and conservation
Dickson, B. G., Albano, C. M., Anantharaman, R., Beier, P., Fargione, J., Graves, T. A., Gray, M. E., Hall, K. R., Lawler, J. J., Leonard, P. B., et al. (2019) · Conservation Biology · doi:10.1111/cobi.13230 · Cited by 461
Self-citationSaura, S., Estreguil, C., Mouton, C., & Rodríguez-Freire, M. (2010) · Ecological Indicators · doi:10.1016/j.ecolind.2010.06.011 · Cited by 441
Habitat connectivity in biodiversity conservation
Ayram, C. A. C., Mendoza, M. E., Andrés Etter, R., & Pérez‐Salicrup, D. R. (2015) · Progress in Physical Geography Earth and Environment · doi:10.1177/0309133315598713 · Cited by 440
Foraging theory upscaled: the behavioural ecology of herbivore movement
Owen‐Smith, N., Fryxell, J. M., & Merrill, E. H. (2010) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2010.0095 · Cited by 431
Remote sensing of terrestrial plant biodiversity
Wang, R., & Gamon, J. A. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111218 · Cited by 410
Population connectivity: recent advances and new perspectives
Kool, J., Moilanen, A., & Treml, E. A. (2012) · Landscape Ecology · doi:10.1007/s10980-012-9819-z · Cited by 396
Placing linkages among fragmented habitats: do least-cost models reflect how animals use landscapes?
Sawyer, S. C., Epps, C. W., & Brashares, J. S. (2011) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2011.01970.x · Cited by 392
Connectivity for conservation: a framework to classify network measures
Rayfield, B., Fortin, M.-J., & Fall, A. (2011) · Ecology · doi:10.1890/09-2190.1 · Cited by 391
Landscape ecology and biogeography
Kupfer, J. A. (2012) · Progress in Physical Geography Earth and Environment · doi:10.1177/0309133312439594 · Cited by 368
Dai, L., Liu, Y., & Luo, X. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.141868 · Cited by 364
Where to restore ecological connectivity? Detecting barriers and quantifying restoration benefits
McRae, B. H., Hall, S. A., Beier, P., & Theobald, D. M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0052604 · Cited by 361
Self-citationResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms
Peterman, W. E. (2018) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12984 · Cited by 280
Incorporating climate change into systematic conservation planning
Groves, C. R., Game, E. T., Anderson, M. G., Cross, M. S., Enquist, C. A. F., Ferdaña, Z., Girvetz, E. H., Gondor, A., Hall, K. R., HIGGINS, J. V., et al. (2012) · Biodiversity and Conservation · doi:10.1007/s10531-012-0269-3 · Cited by 329
Mastretta‐Yanes, A., Moreno‐Letelier, A., Piñero, D., Jorgensen, T. H., & Emerson, B. C. (2015) · Journal of Biogeography · doi:10.1111/jbi.12546 · Cited by 328
Ecological connectivity research in urban areas
LaPoint, S. D., Balkenhol, N., Hale, J., Sadler, J. P., & van der Ree, R. (2015) · Functional Ecology · doi:10.1111/1365-2435.12489 · Cited by 321
Galpern, P., Manseau, M., & Fall, A. (2011) · Biological Conservation · doi:10.1016/j.biocon.2010.09.002 · Cited by 318
A software tool dedicated to the modelling of landscape networks
Foltête, J., Clauzel, C., & Vuidel, G. (2012) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2012.07.002 · Cited by 306
Mistaking geography for biology: inferring processes from species distributions
Warren, D. L., Cardillo, M., Rosauer, D. F., & Bolnick, D. I. (2014) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2014.08.003 · Cited by 303
Thompson, P. L., Rayfield, B., & Gonzalez, A. (2016) · Ecography · doi:10.1111/ecog.02558 · Cited by 302
Landscape connectivity for wildlife: development and validation of multispecies linkage maps
Koen, E. L., Bowman, J., Sadowski, C., & Walpole, A. A. (2014) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12197 · Cited by 293
Li, Y., Liu, W., Feng, Q., Zhu, M., Yang, L., Zhang, J., & Yin, X. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.158940 · Cited by 290
An, Y., Liu, S., Sun, Y., Shi, F., & Beazley, R. E. (2020) · Landscape Ecology · doi:10.1007/s10980-020-01027-3 · Cited by 286
Keesstra, S. D., Nunes, J. P., Saco, P., Parsons, T., Pöppl, R. E., Masselink, R. J. H., & Cerdà, A. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.06.342 · Cited by 279
Use of Land Facets to Plan for Climate Change: Conserving the Arenas, Not the Actors
Beier, P., & Brost, B. M. (2010) · Conservation Biology · doi:10.1111/j.1523-1739.2009.01422.x · Cited by 272
Functional connectivity of the world’s protected areas
Brennan, A., Naidoo, R., Greenstreet, L., Mehrabi, Z., Ramankutty, N., & Kremen, C. (2022) · Science · doi:10.1126/science.abl8974 · Cited by 270
Carroll, C., McRae, B. H., & Brookes, A. F. (2011) · Conservation Biology · doi:10.1111/j.1523-1739.2011.01753.x · Cited by 266
Self-citationDale, M. R. T., & Fortin, M.-J. (2010) · Annual Review of Ecology, Evolution, and Systematics · doi:10.1146/annurev-ecolsys-102209-144718 · Cited by 265
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Keeley, A. T. H., Beier, P., & Jenness, J. S. (2021) · Biological Conservation · doi:10.1016/j.biocon.2021.109008 · Cited by 243
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LaPoint, S., Gallery, P., Wikelski, M., & Kays, R. (2013) · Landscape Ecology · doi:10.1007/s10980-013-9910-0 · Cited by 203
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Watling, J. I., Nowakowski, A. J., Donnelly, M. A., & Orrock, J. L. (2010) · Global Ecology and Biogeography · doi:10.1111/j.1466-8238.2010.00586.x · Cited by 238
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Tipton, L., Müller, C. L., Kurtz, Z. D., Huang, L., Kleerup, E. C., Morris, A., Bonneau, R., & Ghedin, E. (2018) · Microbiome · doi:10.1186/s40168-017-0393-0 · Cited by 234
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BEIER, P., SPENCER, W., BALDWIN, R. F., & McRAE, B. H. (2011) · Conservation Biology · doi:10.1111/j.1523-1739.2011.01716.x · Cited by 229
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Dörfler, F., Simpson-Porco, J. W., & Bullo, F. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2821924 · Cited by 225
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Harmon‐Threatt, A. (2020) · Annual Review of Entomology · doi:10.1146/annurev-ento-011019-024955 · Cited by 219
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Wilmers, C. C., Wang, Y., Nickel, B. A., Houghtaling, P., Shakeri, Y. N., Allen, M. L., Kermish-Wells, J., Yovovich, V., & Williams, T. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0060590 · Cited by 208
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Least-Cost modelling and landscape ecology: concepts, applications, and opportunities
Etherington, T. R. (2016) · Current Landscape Ecology Reports · doi:10.1007/s40823-016-0006-9 · Cited by 160
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Aguirre‐Liguori, J. A., Ramírez‐Barahona, S., & Gaut, B. S. (2021) · Nature Ecology & Evolution · doi:10.1038/s41559-021-01526-9 · Cited by 206
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Brodie, J. F., Giordano, A. J., Dickson, B. G., Hebblewhite, M., Bernard, H., Mohd‐Azlan, J., Anderson, J., & Ambu, L. N. (2014) · Conservation Biology · doi:10.1111/cobi.12337 · Cited by 205
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Lawler, J. J., Ruesch, A. S., Olden, J. D., & McRae, B. H. (2013) · Ecology Letters · doi:10.1111/ele.12132 · Cited by 179
Self-citationMcClure, M. L., Hansen, A. J., & Inman, R. M. (2016) · Landscape Ecology · doi:10.1007/s10980-016-0347-0 · Cited by 166
Dyer, R. J., Nason, J. D., & Garrick, R. C. (2010) · Molecular Ecology · doi:10.1111/j.1365-294x.2010.04748.x · Cited by 198
Powell, J. A., & Bentz, B. (2013) · Ecological Modelling · doi:10.1016/j.ecolmodel.2013.10.034 · Cited by 196
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Nie, W., Xu, B., Yang, F., Shi, Y. Y., Liu, B., Wu, R., Lin, W., Pei, H., & Bao, Z. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.160262 · Cited by 196
Dong, J., Peng, J., Liu, Y., Qiu, S., & Han, Y. (2020) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2020.103815 · Cited by 191
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Bernatchez, L., Ferchaud, A., Berger, C. S., Venney, C. J., & Xuereb, A. T. J. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00657-y · Cited by 191
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Antonelli, A., Ariza, M., Albert, J. S., Andermann, T., Azevedo, J. A. R., Bacon, C. D., Faurby, S., Guedes, T., Hoorn, C., Lohmann, L. G., et al. (2018) · PeerJ · doi:10.7717/peerj.5644 · Cited by 188
Vicente, M. G. S., Rubio, L., & Saura, S. (2011) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2011.02.036 · Cited by 185
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Urban, M. C., Zarnetske, P. L., & Skelly, D. K. (2013) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12184 · Cited by 179
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Huang, L., Wang, J., Fang, Y., Zhai, T., & Cheng, H. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.102865 · Cited by 170
Gao, J., Du, F., Zuo, L., & Jiang, Y. (2020) · Landscape Ecology · doi:10.1007/s10980-020-01100-x · Cited by 169
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Xiao, S., Wu, W., Guo, J., Ou, M., Pueppke, S. G., Ou, W., & Tao, Y. (2020) · Landscape Ecology · doi:10.1007/s10980-020-01113-6 · Cited by 167
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(2020) · JuliaCon Proceedings · doi:10.21105/jcon.00058 · Cited by 163
Self-citationMateo-Sánchez, M. C., Balkenhol, N., Cushman, S. A., Pérez, T., Domínguez, A., & Saura, S. (2015) · Ecosphere · doi:10.1890/es14-00387.1 · Cited by 160
Pelletier, D., Clark, M., Anderson, M. G., Rayfield, B., Wulder, M. A., & Cardille, J. A. (2014) · PLOS One · doi:10.1371/journal.pone.0084135 · Cited by 158
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Belote, R. T., Dietz, M. S., McRae, B. H., Theobald, D. M., McClure, M. L., Irwin, G. H., McKinley, P. S., Gage, J., & Aplet, G. H. (2016) · PLoS ONE · doi:10.1371/journal.pone.0154223 · Cited by 158
Self-citationDivergent landscape effects on population connectivity in two co‐occurring amphibian species
RICHARDSON, J. L. (2012) · Molecular Ecology · doi:10.1111/j.1365-294x.2012.05708.x · Cited by 154
Modica, G., Praticò, S., Laudari, L., Ledda, A., Fazio, S. D., & Montis, A. D. (2021) · Journal of Environmental Management · doi:10.1016/j.jenvman.2021.112494 · Cited by 154
Luque, S. S., Saura, S., & Fortin, M. (2012) · Landscape Ecology · doi:10.1007/s10980-011-9700-5 · Cited by 154
Panzacchi, M., Moorter, B. F. A. V., Strand, O., Saerens, M., Kivimäki, I., Clair, C. C. S., Herfindal, I., & Boitani, L. (2015) · Journal of Animal Ecology · doi:10.1111/1365-2656.12386 · Cited by 153
Predicting species distribution combining multi-scale drivers
Fournier, A., Barbet‐Massin, M., Rome, Q., & Courchamp, F. (2017) · Global Ecology and Conservation · doi:10.1016/j.gecco.2017.11.002 · Cited by 152
The disentangled bank: How loss of habitat fragments and disassembles ecological networks
Gonzalez, A., Rayfield, B., & Lindo, Z. (2011) · American Journal of Botany · doi:10.3732/ajb.1000424 · Cited by 152
Assessing habitat connectivity for ground‐dwelling animals in an urban environment
Braaker, S., Moretti, M., Boesch, R., Ghazoul, J., Obrist, M. K., & Bontadina, F. (2014) · Ecological Applications · doi:10.1890/13-1088.1 · Cited by 121
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Fletcher, R. J., Burrell, N. S., Reichert, B. E., Vasudev, D., & Austin, J. D. (2016) · Current Landscape Ecology Reports · doi:10.1007/s40823-016-0009-6 · Cited by 149
The effect of map boundary on estimates of landscape resistance to animal movement
Koen, E. L., Garroway, C. J., Wilson, P. J., & Bowman, J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0011785 · Cited by 148
Almenar, J. B., Bolowich, A. F., Elliot, T. R., Geneletti, D., Sonnemann, G., & Rugani, B. (2019) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2019.05.004 · Cited by 148
Li, Q., Yong, Z., & Yi, S. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.108723 · Cited by 148
Do metacommunities vary through time? Intermittent rivers as model systems
Sarremejane, R., Cañedo‐Argüelles, M., Prat, N., Mykrä, H., Muotka, T., & Bonada, N. (2017) · Journal of Biogeography · doi:10.1111/jbi.13077 · Cited by 146
Comparing Habitat Suitability and Connectivity Modeling Methods for Conserving Pronghorn Migrations
Poor, E. E., Loucks, C., Jakes, A., & Urban, D. L. (2012) · PLoS ONE · doi:10.1371/journal.pone.0049390 · Cited by 120
Li, Y., Zhang, Y., Jiang, Z., Cheng-xuan, G., Zhao, M., Yang, Z., Guo, M., Wu, B.-Y., & Chen, Q.-L. (2021) · Ecological Processes · doi:10.1186/s13717-021-00332-2 · Cited by 144
Rayfield, B., Pelletier, D., Dumitru, M., Cardille, J. A., & Gonzalez, A. (2015) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12470 · Cited by 143
Phillipsen, I. C., Kirk, E. E. H., Bogan, M. T., Mims, M. C., Olden, J. D., & Lytle, D. A. (2014) · Molecular Ecology · doi:10.1111/mec.13003 · Cited by 143
Littlefield, C. E., McRae, B. H., Michalak, J. L., Lawler, J. J., & Carroll, C. (2017) · Conservation Biology · doi:10.1111/cobi.12938 · Cited by 134
Self-citationPaz, A., Ibáñez, R., Lips, K. R., & Crawford, A. J. (2015) · Molecular Ecology · doi:10.1111/mec.13275 · Cited by 142
Invasive alien species and disease risk: An open challenge in public and animal health
Chinchio, E., Crotta, M., Romeo, C., Drewe, J. A., Guitián, J., & Ferrari, N. (2020) · PLoS Pathogens · doi:10.1371/journal.ppat.1008922 · Cited by 142
Landscape connectivity modeling from the perspective of animal dispersal
Diniz, M. F., Cushman, S. A., Machado, R. B., & Júnior, P. D. M. (2019) · Landscape Ecology · doi:10.1007/s10980-019-00935-3 · Cited by 141
A conceptual framework for the spatial analysis of landscape genetic data
Wagner, H. H., & Fortin, M. (2012) · Conservation Genetics · doi:10.1007/s10592-012-0391-5 · Cited by 140
Omniscape.jl: software to compute omnidirectional landscape connectivity
Landau, V., Shah, V., Anantharaman, R., & Hall, K. (2021) · Journal of Open Source Software · doi:10.21105/joss.02829 · Cited by 139
Self-citationOn the limitations of graph-theoretic connectivity in spatial ecology and conservation
Moilanen, A. (2011) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2011.02062.x · Cited by 139
Models of settlement hierarchy based on partial evidence
Bevan, A., & Wilson, A. (2013) · Journal of Archaeological Science · doi:10.1016/j.jas.2012.12.025 · Cited by 87
Gao, M., Hu, Y., & Bai, Y. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.108867 · Cited by 139
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
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