Selection of sampling sites for biodiversity inventory: Effects of environmental and geographical considerations

Publication
Methods in Ecology and Evolution 13: 1595–1607

Abstract

  1. Biodiversity inventory is among the major challenges for conservation biology in the face of global change. Species exist in two spaces that are linked in the so-called Hutchinsonian Duality: distributions in geographical space and ecological niches in environmental space. We explore implications of using distinct methods to select locations for biodiversity inventories, based on this idea of two-space distributions.
  2. We combined empirical and statistical methods to facilitate selecting localities for biodiversity inventory based on either or both of geographical and environmental considerations. These approaches were applied to select sites for inventory in four example countries. For one of our examples, we tested how effective distinct methods were in sampling biodiversity.
  3. Random and geographically uniform selections are generally biased towards the most common environments in the regions; selections aiming for uniform sampling of environments are concentrated spatially in areas of high heterogeneity in geographical context. Considering disparate geographical distributions of environments helped to cover geographical areas more broadly when selections were environmentally uniform. Generally, sets of sites selected considering environmental conditions perform better in sampling known biodiversity in regions of interest.
  4. Our results underline the benefits of considering environmental and geographical conditions when selecting sites on the effectiveness of resulting inventories. Our tools, implemented in the R package biosurvey, will help researchers to design biodiversity survey systems taking into account the Hutchinsonian Duality and the crucial considerations that it suggests.

Marlon E. Cobos
Marlon E. Cobos
Postdoctoral Fellow

My research interests include ecology and biogeography, methods and tools for predictive modeling, and evolutionary adaptation.