An integrated framework to map animal distributions in large and remote regions

In this paper we show how new technologies can be incorporated from the gathering of field data on wildlife distribution to the final stage of producing distribution maps. We describe an integrated framework for conducting wildlife censuses to obtain data to build predictive models of species distribution that when integrated in a GIS will produce a distribution map. Field data can be obtained with greater accuracy and at lower costs using a combination of Global Positioning System, Personal Digital Assistant, and specific wildlife recording software. Sampling design benefits from previous knowledge of environmental variability that can be obtained from free remote sensing data. Environmental predictors derived from this remote sensing information alone, combined with automatic procedures for predictor selection and model fitting, can render cost‐effective predictive distribution models for wildlife. We show an example with guanaco distribution in the Patagonian steppes of Santa Cruz province, Argentina.

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