The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Are all data types and connectivity models created equal? Validating common connectivity approaches with dispersal data
Open accessAbstract
Aim: There is enormous interest in applying connectivity modelling to resistance surfaces for identifying corridors for conservation action. However, the multiple analytical approaches used to estimate resistance surfaces and predict connectivity across resistance surfaces have not been rigorously compared, and it is unclear what methods provide the best inferences about population connectivity. Using a large empirical data set on puma (Puma concolor), we are the first to compare several of the most common approaches for estimating resistance and modelling connectivity and validate them with dispersal data. Location: Southern California, USA. Methods: We estimate resistance using presence‐only data, GPS telemetry data from puma home ranges and genetic data using a variety of analytical methods. We model connectivity with cost distance and circuit theory algorithms. We then measure the ability of each data type and connectivity algorithm to capture GPS telemetry points of dispersing pumas. Results: We found that resource selection functions based on GPS telemetry points and paths outperformed species distribution models when applied using cost distance connectivity algorithms. Point and path selection functions were not statistically different in their performance, but point selection functions were more sensitive to the transformation used to convert relative probability of use to resistance. Point and path selection functions and landscape genetics outperformed other methods when applied with cost distance; no methods outperformed one another with circuit theory. Main conclusions: We conclude that path or point selection functions, or landscape genetic models, should be used to estimate landscape resistance for wildlife. In cases where resource limitations prohibit the collection of GPS collar or genetic data, our results suggest that species distribution models, while weaker, may still be sufficient for resistance estimation. We recommend the use of cost distance‐based approaches, such as least‐cost corridors and resistant kernels, for estimating connectivity and identifying functional corridors for terrestrial wildlife.
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Cites
Habitat fragmentation and its lasting impact on Earth’s ecosystems
Haddad, N. M., Brudvig, L. A., Clobert, J., Davies, K. F., Gonzalez, A., Holt, R. D., Lovejoy, T. E., Sexton, J. O., Austin, M. P., Collins, C. D., et al. (2015) · Science Advances · doi:10.1126/sciadv.1500052 · Cited by 4,773
McRae, B. H. (2006) · Evolution · doi:10.1111/j.0014-3820.2006.tb00500.x · Cited by 1,081
Mateo-Sánchez, M. C., Balkenhol, N., Cushman, S., Pérez, T., Domínguez, A., & Saura, S. (2015) · Landscape Ecology · doi:10.1007/s10980-015-0194-4 · Cited by 189
Self-citationBURDETT, C. L., CROOKS, K. R., THEOBALD, D. M., WILSON, K. R., BOYDSTON, E. E., LYREN, L. M., FISHER, R. N., VICKERS, T. W., MORRISON, S. A., & BOYCE, W. M. (2010) · Ecosphere · doi:10.1890/es10-00005.1 · Cited by 89
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Cited by
Validation of functional connectivity modeling: The Achilles’ heel of landscape connectivity mapping
Laliberté, J., & St-Laurent, M.-H. (2020) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2020.103878 · Cited by 86
Connectivity modelling in conservation science: a comparative evaluation
Unnithan Kumar, S., & Cushman, S. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-20370-w · Cited by 83
Self-citationComparison of methods for estimating omnidirectional landscape connectivity
Phillips, P., Clark, M. M., Baral, S., Koen, E. L., & Bowman, J. (2021) · Landscape Ecology · doi:10.1007/s10980-021-01254-2 · Cited by 62
Importance of spatio–temporal connectivity to maintain species experiencing range shifts
Huang, J., Andrello, M., Martensen, A. C., Saura, S., Liu, D., He, J., & Fortin, M. (2020) · Ecography · doi:10.1111/ecog.04716 · Cited by 62
Burke, R. A., Frey, J. K., Ganguli, A., & Stoner, K. E. (2019) · Diversity and Distributions · doi:10.1111/ddi.12950 · Cited by 48
Metadata
- Authors
- Zeller, K. A., Jennings, M. K., Vickers, T. W., Ernest, H. B., Cushman, S. A., & Boyce, W. M.
- Venue
- Diversity and Distributions
- Publisher
- Wiley
- Published
- 2018-03-25
- Volume
- 24
- Issue
- 7
- Pages
- 868-879
- ISSN
- 0967-9952 (ISSN-L)
- 1366-9516
- 1472-4642
- OpenAlex
- W2792563852
- Cited by
- 248
- References
- 4
- FWCI
- 14.12
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Zeller, K. A., Jennings, M. K., Vickers, T. W., Ernest, H. B., Cushman, S. A., & Boyce, W. M. (2018). Are all data types and connectivity models created equal? Validating common connectivity approaches with dispersal data. Diversity and Distributions. https://doi.org/10.1111/ddi.12742
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