title: Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials.
abstract: 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 data sets may be rendered unusable for conservation purposes. We offer a method to quantify uncertainty in landscape resistance estimates using multimodel 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 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 data sets 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.
authors.affiliation: Department of Biological Sciences, Macquarie University, Sydney, NSW, 2109, Australia.
authors.forename: Rachael Y
authors.initials: RY
authors.lastname: Dudaniec
authors.affiliation: Natural Environments Program, Queensland Museum, PO Box 3300, South Brisbane, Qld, 4101, Australia.
authors.forename: Jessica
authors.initials: J
authors.lastname: Worthington Wilmer
authors.affiliation: School of Biological Sciences, The University of Queensland, Brisbane, Qld, 4072, Australia.
authors.forename: Jeffrey O
authors.initials: JO
authors.lastname: Hanson
authors.affiliation: School of Geography, Planning and Environmental Management, The University of Queensland, Brisbane, Qld, 4072, Australia.
authors.forename: Matthew
authors.initials: M
authors.lastname: Warren
authors.affiliation: School of Biomedical Sciences, The University of Queensland, Brisbane, Qld, 4072, Australia.
authors.forename: Sarah
authors.initials: S
authors.lastname: Bell
authors.affiliation: School of Geography, Planning and Environmental Management, The University of Queensland, Brisbane, Qld, 4072, Australia.
authors.forename: Jonathan R
authors.initials: JR
authors.lastname: Rhodes
mesh_terms: D000818:Animals; D014644:Genetic Variation; D005828:Genetics, Population; D060005:Genotyping Techniques; D016013:Likelihood Functions; D008394:Marsupialia / Q000235:genetics*; D018895:Microsatellite Repeats; D008957:Models, Genetic; D015233:Models, Statistical; D011793:Queensland; D018401:Sample Size; D017422:Sequence Analysis, DNA; D035501:Uncertainty
keywords: Antechinus; Petaurus; conservation; landscape genetics; landscape resistance optimization; simulation; uncertainty