Abstract
Context
The ability of landscapes to impede species’ movement or gene flow may be quantified by resistance models. Few studies have assessed the performance of resistance models parameterized by expert opinion. In addition, resistance models differ in terms of spatial and thematic resolution as well as their focus on the ecology of a particular species or more generally on the degree of human modification of the landscape (i.e. landscape integrity). The effect of these design decisions on model accuracy is poorly understood.
Objectives
We sought to understand the influence of expert parameterization, resolution, and specificity (i.e. species-specific or landscape integrity) on the fit of resistance model predictions to empirical landscape patterns.
Methods
With genetic and observational data collected from Greater Sage-Grouse (Centrocercus urophasianus) in Washington State, USA, we used landscape genetic analysis and logistic regression to evaluate a range of resistance models in terms of their ability to predict empirical patterns of genetic differentiation and lek occupancy.
Results
We found that species-specific, fine resolution resistance models generally had stronger relationships to empirical patterns than coarse resolution or landscape integrity models, and that the expert models were less predictive than alternative parameterizations.
Conclusions
Our study offers an empirical framework to validate expert resistance models, suggests the need to match the grain of the data to the scale at which the species responds to landscape heterogeneity, and underscores the limitations of landscape integrity models when the species under study does not meet their assumptions.
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References
Baldwin RF, Reed SE, McRae BH, Theobald DM, Sutherland RW (2012) Connectivity restoration in large landscapes: modeling landscape condition and ecological flows. Ecol Restor 30:274–279
Balkenhol N, Waits LP, Dezzani RJ (2009) Statistical approaches in landscape genetics: an evaluation of methods for linking landscape and genetic data. Ecography 32(5):818–830
Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New York
Castillo JA, Epps CW, Davis AR, Cushman SA (2014) Landscape effects on gene flow for a climate-sensitive montane species, the American pika. Mol Ecol 23:843–856
Connelly J, Knick S, Braun C, Baker W, Beever E, Christiansen T, Doherty K, Garton E, Hanser S, Johnson D (2011) Conservation of Greater Sage-Grouse. Stud Avian Biol 38:549–646
Cushman SA, McGarigal K (2002) Hierarchical, multi-scale decomposition of species-environment relationships. Landscape Ecol 17(7):637–646
Cushman SA, McGarigal K (2004) Patterns in the species—environment relationship depend on both scale and choice of response variables. Oikos 105:117–124
Cushman SA, Landguth EL (2010a) Scale dependent inference in landscape genetics. Landscape Ecol 25:967–979
Cushman SA, Landguth EL (2010b) Spurious correlations and inference in landscape genetics. Mol Ecol 19:3592–3602
Cushman SA, McKelvey KS, Hayden J, Schwartz MK (2006) Gene flow in complex landscapes: testing multiple hypotheses with causal modeling. Am Nat 168:486–499
Cushman SA, Raphael MG, Ruggiero LF, Shirk AJ, Wasserman TN, O’Doherty EC (2011) Limiting factors and landscape connectivity: the American marten in the Rocky Mountains. Landscape Ecol 26:1137–1149
Cushman SA, Shirk AJ, Landguth EL (2013a) Landscape genetics and limiting factors. Conserv Genet 14(2):263–274
Cushman SA, Wasserman TN, Landguth EL, Shirk AJ (2013b) Re-evaluating causal modeling with mantel tests in landscape genetics. Diversity 5:51–72
Diniz-Filho JAF, Soares TN, Lima JS, Dobrovolski R, Landeiro VL, Telles MPDC, Bini LM (2013) Mantel test in population genetics. Genet mol biol 36(4):475–485
Dobzhansky T (1940) Speciation as a stage in evolutionary divergence. Am Nat 74:312–321
Epps CW, Wehausen JD, Bleich VC, Torres SG, Brashares JS (2007) Optimizing dispersal and corridor models using landscape genetics. J Appl Ecol 44:714–724
ESRI (2008) ArcGIS Desktop: release 10.0. Environmental Systems Research Institute, Redlands, CA
Frankham R, Briscoe DA, Ballou JD (2002) Introduction to conservation genetics. Cambridge University Press, Cambridge
Galpern P, Manseau M (2013) Finding the functional grain: comparing methods for scaling resistance surfaces. Landscape Ecol 28:1269–1281
Graves TA, Beier P, Royle JA (2013) Current approaches using genetic distances produce poor estimates of landscape resistance to interindividual dispersal. Mol ecol 22(15):3888–3903
Guillot G, Rousset F (2013) Dismantling the Mantel tests. Methods Ecol Evol 4(4):336–344
Jombart T, Devillard S, Dufour AB, Pontier D (2008) Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Heredity 101:92–103
Legendre P, Fortin MJ (2010) Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data. Mol Ecol Resour 10(5):831–844
Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27:209–220
McRae BH (2006) Isolation by resistance. Evolution 60:1551–1561
R Development Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing. R Development Core Team, Vienna
Schroeder MA, Hays DW, Livingston MF, Stream LE, Jacobson JE, Pierce DJ (2000) Changes in the distribution and abundance of sage-grouse in Washington. Northwest Nat 81:104–112
Shirk AJ, Wallin DO, Cushman SA, Rice CG, Warheit KI (2010) Inferring landscape effects on gene flow: a new model selection framework. Mol Ecol 19:3603–3619
Shirk AJ, Wasserman TN, Cushman SA, Raphael MG (2012) Scale dependency of American marten (Martes americana) habitat relations. In: Aubry KB, Zielinski WJ, Raphael MG, Proulx G, Buskirk SW (eds) Biology and conservation of martens, sables, and new synthesis. Cornell University Press, Ithaca
Short Bull RAS, Cushman SA, Mace R, Chilton T, Kendall KC, Landguth EL, Schwartz MK, McKelvey K, Allendorf FW, Luikart G (2011) Why replication is important in landscape genetics: American black bear in the Rocky Mountains. Mol Ecol 20:1092–1107
Smouse PE, Long JC, Sokal RR (1986) Multiple regression and correlation extensions of the Mantel test of matrix correspondence. Syst Zool 35:627–632
Spear SF, Balkenhol N, Fortin MJ, McRae BH, Scribner K (2010) Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis. Mol Ecol 19:3576–3591
WHCWG (2010) Washington connected landscapes project: analysis of the Columbia Plateau ecoregion. Washington Department of Fish and Wildlife and Washington Department of Transportation, Olympia
WHCWG (2012) Washington connected landscapes project: analysis of the Columbia Plateau ecoregion. Washington Department of Fish and Wildlife and Washington Department of Transportation, Olympia
Whiteman K, Vaccaro J, Gonthier J, Bauer H (1994) The hydrogeologic framework and geochemistry of the Columbia Plateau aquifer system. US Government Printing Office, Washington
Wright S (1943) Isolation by distance. Genetics 28:114–138
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Shirk, A.J., Schroeder, M.A., Robb, L.A. et al. Empirical validation of landscape resistance models: insights from the Greater Sage-Grouse (Centrocercus urophasianus). Landscape Ecol 30, 1837–1850 (2015). https://doi.org/10.1007/s10980-015-0214-4
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DOI: https://doi.org/10.1007/s10980-015-0214-4