Abstract
Spatially explicit ecosystem models of all types require an initial allocation of biomass, often in areas where fisheries independent abundance estimates do not exist. A generalized additive modelling (GAM) approach is used to describe the abundance of 40 species groups (i.e. functional groups) across the Gulf of Mexico (GoM) using a large fisheries independent data set (SEAMAP) and climate scale oceanographic conditions. Predictor variables included in the model are chlorophyll a, sediment type, dissolved oxygen, temperature, and depth. Despite the presence of a large number of zeros in the data, a single GAM using a negative binomial distribution was suitable to make predictions of abundance for multiple functional groups. We present an example case study using pink shrimp (Farfantepenaeus duroarum) and compare the results to known distributions. The model successfully predicts the known areas of high abundance in the GoM, including those areas where no data was inputted into the model fitting. Overall, the model reliably captures areas of high and low abundance for the large majority of functional groups observed in SEAMAP. The result of this method allows for the objective setting of spatial distributions for numerous functional groups across a modeling domain, even where abundance data may not exist.
Highlights
The need for ecosystem-based approaches to fisheries management has been widely recognized throughout the world [1]
Generalized additive modeling offers an objective way to predict abundance or biomass according to the known ecology of the animals over broad geographic areas
94% of the pink shrimp harvested in the Gulf of Mexico (GoM) are landed on the west coast of Florida [23] where they are abundant
Summary
The need for ecosystem-based approaches to fisheries management has been widely recognized throughout the world [1]. It is not straightforward to develop biomass distribution grids due to the lack of comprehensive stock assessments outside a handful of commercially valued species and there is a particular lack of spatial distribution data from international waters In most cases, this limits the development of ecosystem models to those areas that are rich in fisheries independent data. GAMs can be used to identify optimal conditions for a given species using environmental variables (e.g., depth and temperature) in order to predict the likelihood that a given species would inhabit a particular environment, or their abundance [5,6,7,8] The outputs of these models are often used to interpolate species distributions at high resolution within coarsely sampled areas [9,5,7]. Despite their acknowledgment as a proven tool for ecological analyses, albeit with some caveats [11], few studies, have applied the method to make predictions outside of a sampled area
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