Empirical validation of landscape resistance models: insights from the Greater Sage-Grouse (Centrocercus urophasianus)
- 489 Downloads
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.
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.
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.
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.
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.
KeywordsCentrocercus urophasianus Greater Sage-Grouse Landscape genetics Lek Resistance Validation
- 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):818–830Google Scholar
- Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New YorkGoogle Scholar
- 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 38:549–646Google Scholar
- 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):475–485Google Scholar
- ESRI (2008) ArcGIS Desktop: release 10.0. Environmental Systems Research Institute, Redlands, CAGoogle Scholar
- 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):3888–3903Google Scholar
- Guillot G, Rousset F (2013) Dismantling the Mantel tests. Methods Ecol Evol 4(4):336–344Google Scholar
- 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):831–844Google Scholar
- R Development Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing. R Development Core Team, ViennaGoogle Scholar
- 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, IthacaGoogle Scholar
- WHCWG (2010) Washington connected landscapes project: analysis of the Columbia Plateau ecoregion. Washington Department of Fish and Wildlife and Washington Department of Transportation, OlympiaGoogle Scholar
- WHCWG (2012) Washington connected landscapes project: analysis of the Columbia Plateau ecoregion. Washington Department of Fish and Wildlife and Washington Department of Transportation, OlympiaGoogle Scholar
- Whiteman K, Vaccaro J, Gonthier J, Bauer H (1994) The hydrogeologic framework and geochemistry of the Columbia Plateau aquifer system. US Government Printing Office, WashingtonGoogle Scholar