Abstract
The maximum entropy model, a commonly used species distribution model (SDM) normally combines observations of the species occurrence with environmental information to predict the geographic distributions of animal or plant species. However, it only produces point estimates for the probability of species existence. To understand the uncertainty of the point estimates, we analytically derived the variance of the outputs of the maximum entropy model from the variance of the input. We applied the analytic method to obtain the standard deviation of dengue importation probability and Aedes aegypti suitability. Dengue occurrence data and Aedes aegypti mosquito abundance data, combined with demographic and environmental data, were applied to obtain point estimates and the corresponding variance. To address the issue of not having the true distributions for comparison, we compared and contrasted the performance of the analytical expression with the bootstrap method and Poisson point process model which proved of equivalence of maximum entropy model with the assumption of independent point locations. Both Dengue importation probability and Aedes aegypti mosquito suitability examples show that the methods generate comparatively the same results and the analytic method we introduced is dramatically faster than the bootstrap method and directly apply to maximum entropy model.
Highlights
Species distribution models [1,2,3] are commonly used to predict the geographic distributions of animals or plants species
The maximum entropy model, a commonly used species distribution model (SDM) normally combines observations of the species occurrence with environmental information to predict the geographic distributions of animal or plant species
Both Dengue importation probability and Aedes aegypti mosquito suitability examples show that the methods generate comparatively the same results and the analytic method we introduced is dramatically faster than the bootstrap method and directly apply to maximum entropy model
Summary
Species distribution models [1,2,3] are commonly used to predict the geographic distributions of animals or plants species. They are applied in species conservation [4], ecology [5], and other fields. Some SDMs, like the maximum entropy model, are used to predict the probability for the species being present. Like Poisson point process models, are used to model the intensity of the species per unit area. Quantifying the uncertainty of maximum entropy models can help biologists allocate sampling efforts more efficiently. For places with the same probability estimate, different uncertainty estimates can help differentiate the need for further sampling effort. The independence between sample units need to be guaranteed to maintain the independence
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