Logistic Methods for Resource Selection Functions and Presence-Only Species Distribution Models

In order to better protect and conserve biodiversity, ecologists use machine learning and statistics to understand how species respond to their environment and to predict how they will respond to future climate change, habitat loss and other threats. A fundamental modeling task is to estimate the probability that a given species is present in (or uses) a site, conditional on environmental variables such as precipitation and temperature. For a limited number of species, survey data consisting of both presence and absence records are available, and can be used to fit a variety of conventional classification and regression models. For most species, however, the available data consist only of occurrence records — locations where the species has been observed. In two closely-related but separate bodies of ecological literature, diverse special-purpose models have been developed that contrast occurrence data with a random sample of available environmental conditions. The most widespread...

• Type: Journal
• Year: 2011
• Where: AAAI
• Authors: Steven Phillips, Jane Elith
• Keywords: AAAI 2011, Ecological Literature, Intelligent Agents, Occurrence Data, Regression Trees