Although the conservation of endangered species often implies the definition of priority areas for conservation, detailed information on their distribution patterns is seldom available over a large geographic range. The present paper explores the performance of an alternative data analysis approach, artificial neural networks, for assessing distribution patterns of endangered mammals when data are scarce and noisy. This approach was applied to identify wolf occupancy in Portugal based on information on wolf d…
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