Investigating niches and distribution of a rare species in a hierarchical framework: Virginia’s Warbler (Leiothlypis virginiae) at its northeastern range limit

Abstract

Ensemble of small models (ESMs) is a technique to overcome the problem of few occurrence points. Applying the ESMs in a spatially hierarchical framework could increase the accuracy of predictions and conclusions by restricting available habitat at sequentially finer spatial scales. Our objective was to show how applying ESMs in a hierarchical habitat selection framework could help to understand rare species’ niches at various scales. We compared the accuracy of ESMs made by committee averaging and weighted averaging methods. We also compared the predictive power of ESMs made by various modeling techniques for Virginia’s warbler (Leiothlypis virginiae) at its northeastern range limit. We defined biologically relevant hierarchical orders of habitat selection for Virginia’s warbler in the Black Hills, U.S.A. We modeled habitat suitabity at the broadest scale as a function of bioclimatic covariates and at finer scales as functions of landcover, soil group and landscape covariates. The performance of modeling techniques varied among scales. Using the committee averaging method led to more accurate results than weighted averaging. At the broadest order, Virginia’s warbler had a narrow climatic niche. The importance of covariates changed across finer orders, such that at broader orders many covariates were important whereas at finer orders certain covariates became more important than others. We conclude that applying ESMs within a hierarchical framework can lead to detailed information about rare species’ niches, limiting factors at each habitat selection order, and potential distribution, which could help inform multiscale management.