ContextScientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown,and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multi-scale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large.
ObjectivesOur objective was to implement an automated, or Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance.
MethodsWe introduce Bayesian latent indicator scale choice (BLISS),a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, and USA.
ResultsOur method returns accurate descriptions of the explanatory power of multiple spatial scales,and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model choice methods including stepwise and AIC,and reduces runtime by 90%.
ConclusionsGiven the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, and identifying the spatial scales over which species are integrating habitat information is an significant step in understanding species-habitat relationships. BLISS is a widely relevant method for identifying significant spatial scales,propagating scale uncertainty, and testing hypotheses of scaling relationships.
Source: usgs.gov