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Data from: Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials
datasetposted on 2022-06-11, 04:13 authored by Rachael Y. Dudaniec, Jessica Worthington-Wilmer, Jeffrey O. Hanson, Matthew Warren, Sarah Bell, Jonathan R. Rhodes, Jessica Worthington Wilmer
Landscape genetics lacks explicit methods for dealing with the uncertainty in landscape resistance estimation, which is particularly problematic when sample sizes of individuals are small. Unless uncertainty can be quantified, valuable but small datasets may be rendered unusable for conservation purposes. We offer a method to quantify uncertainty in landscape resistance estimates using multi-model inference as an improvement over single-model based inference. We illustrate the approach empirically using co-occurring, woodland-preferring Australian marsupials within a common study area: two arboreal gliders (Petaurus breviceps, and Petaurus norfolcensis) and one ground-dwelling Antechinus (Antechinus flavipes). First, we use maximum-likelihood and a bootstrap procedure to identify the best-supported isolation by resistance (IBR) model out of 56 models defined by linear and non-linear resistance functions. We then quantify uncertainty in resistance estimates by examining parameter selection probabilities from the bootstrapped data. The selection probabilities provide estimates of uncertainty in the parameters that drive the relationships between landscape features and resistance. We then validate our method for quantifying uncertainty using simulated genetic and landscape data showing that for most parameter combinations it provides sensible estimates of uncertainty. We conclude that small datasets can be informative in landscape genetic analyses provided uncertainty can be explicitly quantified. Being explicit about uncertainty in landscape genetic models will make results more interpretable and useful for conservation decision-making, where dealing with uncertainty is critical.
Usage NotesP_norf_coordsSample coordinates for P. norfolcensis in latitude and longitudeRoussets_matrix_P. brevGenetic distance matrix for P. breviceps (Roussets' a)Roussets_matrix_P.norfGenetic distance matrix for P. norfolcensis (Rousset's a)A.flav_GenepopInputMicrosatellite datafile in genepop format - A.flavipesP.brev_GenepopInputMicrosatellite datafile in genepop format - P.brevicepsP.norf_GenepopInputMicrosatellite datafile in genepop format - P.norfolcensismakeFPCrastersR code to make raster files for %FPC for resistance calculationmakeFPCLCrastersR code to make rasterfiles for %FPC+LC for resistance modelingA.flav_coordsSample geographic coordinates for A. flavipesPbrev_coordsSample geographic coordinates for P. brevicepsA.flav_sample_dataSample and site information including morphological measurements, sampling date and sex for A. flavipes.P.brev_sample_dataSample and site information including morphological measurements, sampling date and sex for P. breviceps.P.norf_sample_dataSample and site information including morphological measurements, sampling date and sex for P. norfolcensis.Roussets_matrix_A.flavRoussets a genetic distance matrix for A. flavipesA.flav_microsatDataMicrosatellite dataset for A. flavipesP. norfolcensis_microsatDataMicrosatellite dataset for P. norfolcensisP.brev_microsatDataMicrosatellite dataset for P. brevicepsdata_set_upR code for constructing fractal landscapes for the simulations.functionsR functions for constructing resistance surfacesgrid_searchR Code for conducting the grid search approach for estimating resistance parametersoptim_searchR code for conducting the optimisation search method for estimating landscape resistance parameters.simulation_script
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