Sedentary benthic species such as Alcyonacea corals form critical habitat for fishes and invertebrates, that are vulnerable to anthropogenic activities. Assessing risks to these organisms requires unbiased, quantitative species distribution models (SDMs); however, the accuracy of SDM methods is largely unknown. Here I investigated how data and model types affect SDM predictions of Alcyonacea probability of presence. I compared predictions from generalized additive models (GAMs) fitted to presence-absence observations over a stratified-random survey design with predictions from Maxent maximum entropy models fitted to presence-only bycatch records from commercial fisheries. I developed a simulation algorithm to evaluate the direction and magnitude of bias in each model type. I show that presence-only Maxent predictions are overly optimistic based on commonly used diagnostic measures calculated using cross-validation, and produce biased estimates of species distribution. This study demonstrates a need for robust presence-absence SDMs that will better inform management strategies to maximize conservation measures while minimizing economic losses.
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Thesis advisor: Cox, Sean
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