Abstract
Maximum entropy (Maxent) modeling has great potential for identifying distributions and habitat selection of wildlife given its reliance on only presence locations. Recent studies indicate Maxent is relatively insensitive to spatial errors associated with location data, requires few locations to construct useful models, and performs better than other presence-only modeling approaches. Further advances are needed to better define model thresholds, to test model significance, and to address model selection. Additionally, development of modeling approaches is needed when using repeated sampling of known individuals to assess habitat selection. These advancements would strengthen the utility of Maxent for wildlife research and management.
Highlights
Factors that influence species distributions and habitat selection are of great importance to researchers and managers of wildlife
Wildlife agencies are often tasked with establishing hunting quotas for game species and must rely on information about habitat potential and wildlife distributional patterns to help establish these quotas; wildlife managers and researchers are frequently tasked with delineating current and potential distributions of endangered species to establish protected areas; while in other regions, invasive species are expanding their ranges into new areas requiring rapid identification of these areas to slow down or eliminate this invasion
Maximum entropy (Maxent) does not appear to be strongly influenced by moderate spatial error associated with occurrence data, as location errors up to 5 km appear to have no impact on model performance [19]
Summary
Factors that influence species distributions and habitat selection are of great importance to researchers and managers of wildlife. A number of new approaches (e.g., BIOCLIM, DOMAIN, GARP, Maxent) have been developed over the last decade that utilize only presence locations, thereby eliminating the need for true absence locations. A recently developed Maxent program [5] should be a very useful tool for delineating species distributions and habitat associations. I will: 1) describe how the Maxent method works, as well as output features associated with the most current version of Maxent software [6], 2) describe strengths and potential weaknesses of the Maxent approach for wildlife research, and 3) indicate areas for needed research and development to increase the utility of Maxent for wildlife research and management practices
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