Abstract:
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The distribution and relative abundance of marine birds along the US Northeast and Mid-Atlantic coastlines are of special interest to ocean planners. However, marine bird count data often exhibits excessive zero-inflation and over-dispersion. Modeling of such data typically involves a truncation or censoring technique to avoid the extremely large counts. Our modeling effort incorporates a spatial-temporal double-hurdle model specifically tailored to look at extreme abundances, which is especially important for assessing potential risks of offshore activities to seaducks and other highly aggregative species. The double-hurdle model, which includes negative binomial and generalized Pareto distribution components, is compared to several single-hurdle specifications using Bayesian model selection criterions. Using the best fit model, maps showing areas of high occupancy and persistent aggregation are produced to inform marine spatial planning efforts, such as offshore development and marine protected areas. We discuss the development of these map products, including maps of model uncertainty, that may help lead to better decisions in ocean planning.
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