Multiscale assessment of functional connectivity: Landscape genetics of eastern indigo snakes in an anthropogenically fragmented landscape in central Florida.
ResistanceGA
connectivity
habitat fragmentation
landscape genetics
multiscale
spatial scale
Journal
Molecular ecology
ISSN: 1365-294X
Titre abrégé: Mol Ecol
Pays: England
ID NLM: 9214478
Informations de publication
Date de publication:
07 2021
07 2021
Historique:
revised:
04
05
2021
received:
02
02
2021
accepted:
05
05
2021
pubmed:
13
5
2021
medline:
29
7
2021
entrez:
12
5
2021
Statut:
ppublish
Résumé
Landscape features can strongly influence gene flow and the strength and direction of these effects may vary across spatial scales. However, few studies have evaluated methodological approaches for selecting spatial scales in landscape genetics analyses, in part because of computational challenges associated with optimizing landscape resistance surfaces (LRS). We used the federally threatened eastern indigo snake (Drymarchon couperi) in central Florida as a case study with which to compare the importance of landscape features and their scales of effect in influencing gene flow. We used genetic algorithms (ResistanceGA) to empirically optimize LRS using categorical land cover surfaces, multiscale resource selection surfaces (RSS), and four combinations of landscape covariates measured at multiple spatial scales (multisurface multiscale LRS). We compared LRS where scale was selected using pseudo- and full optimization. Multisurface multiscale LRS received more empirical support than LRS optimized from categorical land cover surfaces or RSS. Multiscale LRS with scale selected using full optimization generally outperformed those with scale selected using pseudo-optimization. Multiscale LRS with large spatial scales (1200-1800 m) received the most empirical support. Our results highlight the importance of considering landscape features across multiple spatial scales in landscape genetic analyses, particularly broad scales relative to species movement potential. Different effects of scale on home range-level movements and dispersal could explain weak associations between habitat suitability and gene flow in other studies. Our results also demonstrate the importance of large tracts of undeveloped upland habitat with heterogenous vegetation communities and low urbanization for promoting indigo snake connectivity.
Banques de données
figshare
['10.6084/m9.figshare.c.5282129']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
3422-3438Informations de copyright
© 2021 John Wiley & Sons Ltd.
Références
Abrahamson, W. G., Johnson, A. F., Layne, J. N., & Peroni, P. A. (1984). Vegetation of the Archbold Biological Station, Florida: An example of the southern Lake Wales Ridge. Florida Scientist, 47(4), 209-250.
Adamack, A. T., & Gruber, B. (2014). PopGenReport: Simplifying basic population genetic analyses in R. Methods in Ecology and Evolution, 5(4), 384-387. https://doi.org/10.1111/2041-210x.12158.
Anderson, C. D. (2010). Effects of movement and mating patterns on gene flow among overwintering hibernacula of the timber rattlesnake (Crotalus horridus). Copeia, 2010(1), 54-61. https://doi.org/10.1643/ch-08-121
Anderson, C. D., Epperson, B. K., Fortin, M.-J., Holderegger, R., James, P. M. A., Rosenberg, M. S., Scribner, K. T., & Spear, S. (2010). Considering spatial and temporal scale in landscape-genetic studies of gene flow. Molecular Ecology, 19(17), 3565-3575. https://doi.org/10.1111/j.1365-294X.2010.04757.x
Balkenhol, N., Pardini, R., Cornelius, C., Fernandes, F., & Sommer, S. (2013). Landscape-level comparison of genetic diversity and differentiation in a small mammal inhabiting different fragmented landscapes of the Brazilian Atlantic Forest. Conservation Genetics, 14(2), 355-367. https://doi.org/10.1007/s10592-013-0454-2
Banks, S. C., & Peakall, R. (2012). Genetic spatial autocorrelation can readily detect sex-biased dispersal. Molecular Ecology, 21(9), 2092-2105. https://doi.org/10.1111/j.1365-294X.2012.05485.x
Bauder, J. M., & Barnhart, P. (2014). Factors affecting the accuracy and precision of triangulated radio telemetry locations of Eastern Indigo Snakes (Drymarchon couperi). Herpetological Review, 45(4), 590-597.
Bauder, J. M., Breininger, D. R., Bolt, M. R., Legare, M. L., Jenkins, C. L., Rothermel, B. B., & McGarigal, K. (2016a). The influence of sex and season on conspecific spatial overlap in a large, actively-foraging colubrid snake. PLoS One, 11(8), e0160033. https://doi.org/10.1371/journal.pone.0160033
Bauder, J. M., Breininger, D. R., Bolt, M. R., Legare, M. L., Jenkins, C. L., Rothermel, B. B., & McGarigal, K. (2016b). Seasonal variation in eastern indigo snake (Drymarchon couperi) movement patterns and space use in peninsular Florida at multiple temporal scales. Herpetologica, 72(3), 214-226. https://doi.org/10.1655/Herpetologica-D-15-00039.1
Bauder, J. M., Breininger, D. R., Bolt, M. R., Legare, M. L., Jenkins, C. L., Rothermel, B. B., & McGarigal, K. (2018). Multi-level, multi-scale habitat selection by a wide-ranging ranging federally threatened snake. Landscape Ecology, 33(5), 743-763.
Bauder, J. M., Breininger, D. R., Bolt, M. R., Legare, M. L., Jenkins, C. L., Rothermel, B. B., & McGarigal, K. (2020). Movement barriers, habitat heterogeneity or both? Testing hypothesized effects of landscape features on home range sizes in eastern indigo snakes. Journal of Zoology, 311, 204-216. https://doi.org/10.1111/jzo.12777
Biek, R., Akamine, N., Schwartz, M. K., Ruth, T. K., Murphy, K. M., & Poss, M. (2006). Genetic consequences of sex-biased dispersal in a solitary carnivore: Yellowstone cougars. Biology Letters, 2(2), 312-315. https://doi.org/10.1098/rsbl.2005.0437
Blouin-Demers, G., & Weatherhead, P. J. (2002). Implications of movement patterns for gene flow in black rat snakes (Elaphe obsoleta). Canadian Journal of Zoology, 80(7), 1162-1172. https://doi.org/10.1139/z02-096
Bohonak, A. J. (1999). Dispersal, gene flow, and population structure. Quarterly Review of Biology, 74(1), 21-45. https://doi.org/10.1086/392950
Breininger, D. R., Bolt, M. R., Legare, M. L., Drese, J. H., & Stolen, E. D. (2011). Factors influencing home-range sizes of eastern indigo snakes in central Florida. Journal of Herpetology, 45(4), 484-490. https://doi.org/10.1670/10-176.1
Breininger, D. R., Legare, M. L., & Bolt, R. B. (2004). Eastern indigo snakes (Drymarchon couperi) in Florida: Influence of edge on species viability. In H. Akcakaya, M. Burgman, O. Kindvall, C. Wood, P. Sjögren-Gulve, J. Hatfield, & M. McCarthy (Eds.), Species conservation and management: Case studies (pp. 299-311). Oxford University Press.
Breininger, D. R., Mazerolle, M. J., Bolt, M. R., Legare, M. L., Drese, J. H., & Hines, J. E. (2012). Habitat fragmentation effects on annual survival of the federally protected eastern indigo snake. Animal Conservation, 15, 361-368. https://doi.org/10.1111/j.1469-1795.2012.00524.x
Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference. Springer.
Clark, R. W., Brown, W. S., Stechert, R., & Zamudio, K. R. (2008). Integrating individual behaviour and landscape genetics: The population tructure of timber rattlesnake hibernacula. Molecular Ecology, 17(3), 719-730.
Clark, R. W., Brown, W. S., Stechert, R., & Zamudio, K. R. (2010). Roads, interrupted dispersal, and genetic diversity in timber rattlesnakes. Conservation Biology, 24(4), 1059-1069. https://doi.org/10.1111/j.1523-1739.2009.01439.x
Clark, R. W., Marchand, M. N., Clifford, B. J., Stechert, R., & Stephens, S. (2011). Decline of an isolated timber rattlesnake (Crotalus horridus) population: Interactions between climate change, disease, and loss of genetic diversity. Biological Conservation, 144(2), 886-891. https://doi.org/10.1016/j.biocon.2010.12.001
Clarke, R. T., Rothery, P., & Raybould, A. F. (2002). Confidence limits for regression relationships between distance matrices: Estimating gene flow with distance. Journal of Agricultural Biological and Environmental Statistics, 7(3), 361-372. https://doi.org/10.1198/108571102320
Crandall, K. A., Bininda-Emonds, O. R. P., Mace, G. M., & Wayne, R. K. (2000). Considering evolutionary processes in conservation biology. Trends in Ecology & Evolution, 15(7), 290-295. https://doi.org/10.1016/s0169-5347(00)01876-0
Cushman, S. A., & Landguth, E. L. (2010). Scale dependent inference in landscape genetics. Landscape Ecology, 25(6), 967-979. https://doi.org/10.1007/s10980-010-9467-0
Cushman, S. A., McKelvey, K. S., Hayden, J., & Schwartz, M. K. (2006). Gene flow in complex landscapes: Testing multiple hypotheses with causal modeling. The American Naturalist, 168(4), 486-499. https://doi.org/10.1086/506976
DeCesare, N. J., Hebblewhite, M., Schmiegelow, F., Hervieux, D., McDermid, G. J., Neufeld, L., Bradley, M., Whittington, J., Smith, K. G., Morgantini, L. E., Wheatley, M., & Musiani, M. (2012). Transcending scale dependence in identifying habitat with resource selection functions. Ecological Applications, 22(4), 1068-1083. https://doi.org/10.1890/11-1610.1
Dudaniec, R. Y., Wilmer, J. W., Hanson, J. O., Warren, M., Bell, S., & Rhodes, J. R. (2016). Dealing with uncertainty in landscape genetic resistance models: A case of three co-occurring marsupials. Molecular Ecology, 25(2), 470-486. https://doi.org/10.1111/mec.13482
Elliot, N. B., Cushman, S. A., Macdonald, D. W., & Loveridge, A. J. (2014). The devil is in the dispersers: Predictions of landscape connectivity change with demography. Journal of Applied Ecology, 51(5), 1169-1178. https://doi.org/10.1111/1365-2664.12282
Enge, K. M., Stevenson, D. J., Elliot, M. J., & Bauder, J. M. (2013). The historical and current distribution of the eastern indigo snake (Drymarchon couperi). Herpetological Conservation and Biology, 8(2), 288-307.
Fattebert, J., Robinson, H. S., Balme, G., Slotow, R., & Hunter, L. (2015). Structural habitat predicts functional dispersal habitat of a large carnivore: How leopards change spots. Ecological Applications, 25(7), 1911-1921. https://doi.org/10.1890/14-1631.1
Folt, B., Bauder, J., Spear, S., Stevenson, D., Hoffman, M., Oaks, J. R., Wood, P. L., Jenkins, C., Steen, D. A., & Guyer, C. (2019). Taxonomic and conservation implications of population genetic admixture, mito-nuclear discordance, and male-biased dispersal of a large endangered snake, Drymarchon couperi. PLoS One, 14(3), e0214439. https://doi.org/10.1371/journal.pone.0214439
Galpern, P., Manseau, M., Hettinga, P., Smith, K., & Wilson, P. (2012). Allelematch: An R package for identifying unique multilocus genotypes where genotyping error and missing data may be present. Molecular Ecology Resources, 12(4), 771-778. https://doi.org/10.1111/j.1755-0998.2012.03137.x
Galpern, P., Manseau, M., & Wilson, P. (2012). Grains of connectivity: Analysis at multiple spatial scales in landscape genetics. Molecular Ecology, 21(16), 3996-4009. https://doi.org/10.1111/j.1365-294X.2012.05677.x
Garant, D., Forde, S. E., & Hendry, A. P. (2007). The multifarious effects of dispersal and gene flow on contemporary adaptation. Functional Ecology, 21(3), 434-443. https://doi.org/10.1111/j.1365-2435.2006.01228.x
Gaston, A., Blazquez-Cabrera, S., Garrote, G., Mateo-Sanchez, M. C., Beier, P., Simon, M. A., & Saura, S. (2016). Response to agriculture by a woodland species depends on cover type and behavioural state: Insights from resident and dispersing Iberian lynx. Journal of Applied Ecology, 53(3), 814-824. https://doi.org/10.1111/1365-2664.12629
Holland, J. D., Bert, D. D., & Fahrig, L. (2004). Determining the spatial scale of a species’ response to habitat. BioScience, 54(3), 227-233.
Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65-70.
Hyslop, N. L., Meyers, J. M., Cooper, R. J., & Stevenson, D. J. (2014). Effects of body size and sex of Drymarchon couperi (Eastern Indigo Snake) on habitat use, movements, and home range size in Georgia. Journal of Wildlife Management, 78(1), 101-111.
Jackson, H. B., & Fahrig, L. (2015). Are ecologists conducting research at the optimal scale? Global Ecology and Biogeography, 24(1), 52-63. https://doi.org/10.1111/geb.12233
Jackson, N. D., & Fahrig, L. (2014). Landscape context affects genetic diversity at a much larger spatial extent than population abundance. Ecology, 95(4), 871-881. https://doi.org/10.1890/13-0388.1
Johnson, D. H. (1980). The comparison of usage and availability measurements for evaluating resource preference. Ecology, 61(1), 65-71. https://doi.org/10.2307/1937156
Johnson, M. T. J., & Munshi-South, J. (2017). Evolution of life in urban environments. Science, 358(6363), eaam8327. https://doi.org/10.1126/science.aam8327
Johnson, P. C. D. (2014). Extension of Nakagawa & Schielzeth’s R_GLMM² to random slopes models. Methods in Ecology and Evolution, 5, 944-946.
Jombart, T. (2008). adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics, 24(11), 1403-1405. https://doi.org/10.1093/bioinformatics/btn129
Kalinowski, S. T., Taper, M. L., & Marshall, T. C. (2007). Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Molecular Ecology, 16(5), 1099-1106. https://doi.org/10.1111/j.1365-294X.2007.03089.x
Kawula, R. (2014). Florida land cover classification system: Final report. State Wildlife Grant, SWG T-13 (FWRI Grant # 6325), Center for Spatial Analysis, Fish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission.
Keeley, A. T. H., Beier, P., & Gagnon, J. W. (2016). Estimating landscape resistance from habitat suitability: Effects of data source and nonlinearities. Landscape Ecology, 31(9), 2151-2162. https://doi.org/10.1007/s10980-016-0387-5
Keller, D., Holderegger, R., van Strien, M. J., & Bolliger, J. (2015). How to make landscape genetics beneficial for conservation management? Conservation Genetics, 16(3), 503-512. https://doi.org/10.1007/s10592-014-0684-y
Kelly, A. C., Mateus-Pinilla, N. E., Douglas, M., Douglas, M., Brown, W., Ruiz, M. O., Killefer, J., Shelton, P., Beissel, T., & Novakofski, J. (2010). Utilizing disease surveillance to examine gene flow and dispersal in white-tailed deer. Journal of Applied Ecology, 47(6), 1189-1198. https://doi.org/10.1111/j.1365-2664.2010.01868.x
Keyghobadi, N. (2007). The genetic implications of habitat fragmentation for animals. Canadian Journal of Zoology, 85(10), 1049-1064. https://doi.org/10.1139/z07-095
Knight, G. R. (2010). Development of a cooperative land cover map: Final report. Florida's Wildlife Legacy Initiative Project 08009.
Kraemer, P., & Gerlach, G. (2017). Demerelate: Calculating interindividual relatedness for kinship analysis based on codominant diploid genetic markers using R. Molecular Ecology Resources, 17(6), 1371-1377. https://doi.org/10.1111/1755-0998.12666
Landguth, E. L., Cushman, S. A., Schwartz, M. K., McKelvey, K. S., Murphy, M., & Luikart, G. (2010). Quantifying the lag time to detect barriers in landscape genetics. Molecular Ecology, 19(19), 4179-4191. https://doi.org/10.1111/j.1365-294X.2010.04808.x
Levin, S. A. (1992). The problem of pattern and scale in ecology. Ecology, 73(6), 1943-1967. https://doi.org/10.2307/1941447
Madsen, T., Shine, R., Olsson, M., & Wittzell, H. (1999). Restoration of an inbred adder population. Nature, 402(6757), 34-35. https://doi.org/10.1038/46941
Manly, B. F. J., McDonald, L., Thomas, D. L., McDonald, T. L., & Erickson, W. P. (2002). Resource selection by animals: Statistical design and analysis for field studies. Kluwer Academic Publishers.
Martin, A. E. (2018). The spatial scale of a species’ response to the landscape context depends on which biological response you measure. Current Landscape Ecology Reports, 3(1), 23-33. https://doi.org/10.1007/s40823-018-0030-z
Mateo-Sanchez, M. C., Balkenhol, N., Cushman, S., Perez, T., Dominguez, A., & Saura, S. (2015a). A comparative framework to infer landscape effects on population genetic structure: Are habitat suitability models effective in explaining gene flow? Landscape Ecology, 30(8), 1405-1420. https://doi.org/10.1007/s10980-015-0194-4
Mateo-Sanchez, M. C., Balkenhol, N., Cushman, S., Perez, T., Dominguez, A., & Saura, S. (2015b). Estimating effective landscape distances and movement corridors: Comparison of habitat and genetic data. Ecosphere, 6(4), 59. https://doi.org/10.1890/es14-00387.1
McGarigal, K., Cushman, S. A., & Stafford, S. G. (2000). Multivariate statistics for wildlife and ecology research. Springer.
McGarigal, K., Wan, H. Y., Zeller, K. A., Timm, B. C., & Cushman, S. A. (2016). Multi-scale habitat selection modeling: A review and outlook. Landscape Ecology, 31, 1161-1175. https://doi.org/10.1007/s10980-016-0374-x
Miles, L. S., Rivkin, L. R., Johnson, M. T. J., Munshi-South, J., & Verrelli, B. C. (2019). Gene flow and genetic drift in urban environments. Molecular Ecology, 28(18), 4138-4151. https://doi.org/10.1111/mec.15221
Mills, L. S., & Allendorf, F. W. (1996). The one-migrant-per-generation rule in conservation and management. Conservation Biology, 10, 1509-1518. https://doi.org/10.1046/j.1523-1739.1996.10061509.x
Moraga, A. D., Martin, A. E., & Fahrig, L. (2019). The scale of effect of landscape context varies with the species’ response variable measured. Landscape Ecology, 34(4), 703-715. https://doi.org/10.1007/s10980-019-00808-9
Myers, R. L., & Ewel, J. J. (Eds.). (1990). Ecosystems of Florida. University of Florida Press.
Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133-142. https://doi.org/10.1111/j.2041-210x.2012.00261.x
Oyler-McCance, S. J., Fedy, B. C., & Landguth, E. L. (2013). Sample design effects in landscape genetics. Conservation Genetics, 14, 275-285. https://doi.org/10.1007/s10592-012-0415-1
Paradis, E. (2010). pegas: An R package for population genetics with an integrated-modular approach. Bioinformatics, 26(3), 419-420. https://doi.org/10.1093/bioinformatics/btp696
Peakall, R., Ruibal, M., Lindenmayer, D. B., & Tonsor, S. (2003). Spatial autocorrelation analysis offers new insights into gene flow in the Australian bush rat, Rattus fuscipes. Evolution, 57(5), 1182-1195. https://doi.org/10.1554/0014-3820(2003)057[1182:saaoni]2.0.co;2
Peakall, R., & Smouse, P. E. (2006). GenAlEx 6: Genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes, 6(1), 288-295. https://doi.org/10.1111/j.1471-8286.2005.01155.x
Peakall, R., & Smouse, P. E. (2012). GenAIEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research - An update. Bioinformatics, 28(19), 2537-2539. https://doi.org/10.1093/bioinformatics/bts2460
Peterman, W. E. (2018). ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms. Methods in Ecology and Evolution, 9(6), 1638-1647. https://doi.org/10.1111/2041-210x.12984
Pusey, A. E. (1987). Sex-biased dispersal and inbreeding avoidance in birds and mammals. Trends in Ecology & Evolution, 2(10), 295-299. https://doi.org/10.1016/0169-5347(87)90081-4
R Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.R-project.org/
Reding, D. M., Cushman, S. A., Gosselink, T. E., & Clark, W. R. (2013). Linking movement behavior and fine-scale genetic structure to model landscape connectivity for bobcats (Lynx rufus). Landscape Ecology, 28, 471-486. https://doi.org/10.1007/s10980-012-9844-y
Richardson, J. L., Brady, S. P., Wang, I. J., & Spear, S. F. (2016). Navigating the pitfalls and promise of landscape genetics. Molecular Ecology, 25(4), 849-863. https://doi.org/10.1111/mec.13527
Rivera, P. C., Gardenal, C. N., & Chiaraviglio, M. (2006). Sex-biased dispersal and high levels of gene flow among local populations in the Argentine boa constrictor, Boa constrictor occidentalis. Austral Ecology, 31(8), 948-955. https://doi.org/10.1111/j.1442-9993.2006.01661.x
Roffler, G. H., Schwartz, M. K., Pilgrim, K. L., Talbot, S. L., Sage, G. K., Adams, L. G., & Luikart, G. (2016). Identification of landscape features influencing gene flow: How useful are habitat selection models? Evolutionary Applications, 9(6), 805-817. https://doi.org/10.1111/eva.12389
Rousset, F. (2008). GENEPOP'007: A complete re-implementation of the GENEPOP software for Windows and Linux. Molecular Ecology Resources, 8, 103-106. https://doi.org/10.1111/j.1471-8286.2007.01931.x
Row, J. R., Oyler-McCance, S. J., Fike, J. A., O'Donnell, M. S., Doherty, K. E., Aldridge, C. L., Bowen, Z. H., & Fedy, B. C. (2015). Landscape characteristics influencing the genetic structure of greater sage-grouse within the stronghold of their range: A holistic modeling approach. Ecology and Evolution, 5(10), 1955-1969. https://doi.org/10.1002/ece3.1479
Shamblin, B. M., Alstad, T. I., Stevenson, D. J., Macey, J. N., Snow, F., & Nairn, C. J. (2011). Isolation and characterization of microsatellite markers from the threatened eastern indigo snake (Drymarchon couperi). Conservation Genetics Resources, 3, 303-306. https://doi.org/10.1007/s12686-010-9348-5
Shirk, A. J., Landguth, E. L., & Cushman, S. A. (2017). A comparison of individual-based genetic distance metrics for landscape genetics. Molecular Ecology Resources, 17, 1308-1317. https://doi.org/10.1111/1755-0998.12684
Shirk, A. J., Wallin, D. O., Cushman, S. A., Rice, C. G., & Warheit, K. I. (2010). Inferring landscape effects on gene flow: A new model selection framework. Molecular Ecology, 19(17), 3603-3619. https://doi.org/10.1111/j.1365-294X.2010.04745.x
Short bull, R. A., Cushman, S. A., Mace, R., Chilton, T., Kendall, K. C., Landguth, E. L., Schwartz, M. K., Mckelvey, K., Allendorf, F. W., & Luikart, G. (2011). Why replication is important in landscape genetics: American black bear in the Rocky Mountains. Molecular Ecology, 20(6), 1092-1107. https://doi.org/10.1111/j.1365-294X.2010.04944.x
Spear, S. F., Balkenhol, N., Fortin, M.-J., McRae, B. H., & Scribner, K. (2010). Use of resistance surfaces for landscape genetic studies: Considerations for parameterization and analysis. Molecular Ecology, 19, 3576-3591. https://doi.org/10.1111/j.1365-294X.2010.04657.x
Stevenson, D. J., & Hyslop, N. L. (2010). Drymarchon couperi (eastern indigo snake). Long-distance interpopulation movement. Herpetological Review, 41(1), 91-92.
Stuber, E. F., & Gruber, L. F. (2020). Recent methodological solutions to identifying scales of effect in multi-scale modeling. Current Landscape Ecology Reports, 5, 127-139. https://doi.org/10.1007/s40823-020-00055-8
Tassone, E. E., Miles, L. S., Dyer, R. J., Rosenberg, M. S., Cowling, R. M., & Verrelli, B. C. (2021). Evolutionary stability, landscape heterogeneity, and human land-usage shape population genetic connectivity in the Cape Floristic Region biodiversity hotspot. Evolutionary Applications, 14, 1109-1123. https://doi.org/10.1111/eva.13185
Thompson, C. M., & McGarigal, K. (2002). The influence of research scale on bald eagle habitat selection along the lower Hudson River, New York (USA). Landscape Ecology, 17, 569-586.
Turner, W. R., Wilcove, D. S., & Swain, H. M. (2006). Assessing the effectiveness of reserve acquisition programs in protecting rare and threatened species. Conservation Biology, 20(6), 1657-1669. https://doi.org/10.1111/j.1523-1739.2006.00536.x
U. S. Census Bureau. (2016). 2016 TIGER/Line shapefiles technical documentation. Retrieved from https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2016/TGRSHP2016_TechDoc.pdf
U. S. Fish and Wildlife Service. (1978). Endangered and threatened plants: Listing of the eastern indigo snake as a threatened species. Federal Register, 43, 4026-4028.
U. S. Fish and Wildlife Service. (2008). Eastern indigo snake (Drymarchon couperi) 5-year review: Summary and evaluation. United States Fish and Wildlife Service, Southeast region, Mississippi Ecological Services Field Office.
U. S. Fish and Wildlife Service. (2020). National wetlands inventory website. Retrieved from http://www.fws.gov/wetlands/
U. S. Geologic Survey. (2020). Hydrography: National hydrography dataset. Retrieved from http://nhd.usgs.gov/index.html
U. S. Geologic Survey. (2021). The National Geologic Map Database. Retrieved from https://ngmdb.usgs.gov/ngmdb/ngmdb_home.html
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M., & Shipley, P. (2004). MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology Notes, 4(3), 535-538. https://doi.org/10.1111/j.1471-8286.2004.00684.x
Wang, I. J., Savage, W. K., & Shaffer, H. B. (2009). Landscape genetics and least-cost path analysis reveal unexpected dispersal routes in the California tiger salamander (Ambystoma californiense). Molecular Ecology, 18(7), 1365-1374. https://doi.org/10.1111/j.1365-294X.2009.04122.x
Wasserman, T. N., Cushman, S. A., Schwartz, M. K., & Wallin, D. O. (2010). Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho. Landscape Ecology, 25(10), 1601-1612. https://doi.org/10.1007/s10980-010-9525-7
Weekley, C. W., Menges, E. S., & Pickert, R. L. (2008). An ecological map of Florida's Lake Wales Ridge: A new boundary delineation and an assessment of post-Columbian habitat loss. Florida Scientist, 71(1), 45-64.
Wiens, J. A. (1989). Spatial scaling in ecology. Functional Ecology, 3(4), 385-397. https://doi.org/10.2307/2389612
Winiarski, K. J., Peterman, W. E., Whiteley, A. R., & McGarigal, K. (2020). Multiscale resistant kernel surfaces derived from inferred gene flow: An application with vernal pool breeding salamanders. Molecular Ecology Resources, 20(1), 97-113. https://doi.org/10.1111/1755-0998.13089
Zeller, K. A., Jennings, M. K., Vickers, T. W., Ernest, H. B., Cushman, S. A., Boyce, W. M., & Bolliger, J. (2018). Are all data types and connectivity models created equal? Validating common connectivity approaches with dispersal data. Diversity and Distributions, 24(7), 868-879. https://doi.org/10.1111/ddi.12742
Zeller, K. A., McGarigal, K., & Whiteley, A. R. (2012). Estimating landscape resistance to movement: A review. Landscape Ecology, 27, 777-797. https://doi.org/10.1007/s10980-012-9737-0
Zeller, K. A., Vickers, T. W., Ernest, H. B., & Boyce, W. M. (2017). Multi-level, multi-scale resource selection functions and resistance surfaces for conservation planning: Pumas as a case study. PLoS One, 12(6), e0179570. https://doi.org/10.1371/journal.pone.0179570