Statistical methods for predicting the spatial abundance of reef fish species

Understanding the spatial distribution of organism abundance is fundamental to assessing and managing ecological populations. Marine species can be difficult and logistically challenging and expensive to observe. This often results in spatial data containing low detection rates when sampling underwater, biasing spatial predictions from many modeling approaches. We propose a multistage statistical workflow that can use zero inflated sampling data to develop non-linear predictive spatial distributions of reef fish abundance. The workflow includes: (1) an individual-based discrete event simulation which generates simulated survey data under different abundance settings; (2) empirical maximum likelihood analysis to establish the relationship between survey data and abundance from the simulation; (3) a two-step random smoothing method to estimate reliable block spatial abundance around each survey station; (4) an ensemble of different machine learning models which use the estimated abundance from step three as input to compute a stable non-linear prediction of abundance across the entire study area (Gulf of Mexico). Applying our workflow greatly improved the ability to forecast abundance at small spatial scales. The ability to forecast at fine spatial scales is critical when working with species that are patchily distributed. This workflow can apply to many ecological populations to develop abundance maps even if sample data is not well distributed across the study area or is zero inflated.