Generalized additive models used to predict species abundance in the Gulf of Mexico: an ecosystem modeling tool

Michael Drexler; Cameron H Ainsworth

doi:10.1371/journal.pone.0064458

Generalized additive models used to predict species abundance in the Gulf of Mexico: an ecosystem modeling tool

PLoS One. 2013 May 14;8(5):e64458. doi: 10.1371/journal.pone.0064458. Print 2013.

Authors

Michael Drexler¹, Cameron H Ainsworth

Affiliation

¹ College of Marine Science, University of South Florida, Saint Petersburg, Florida, USA. mdrexler@mail.usf.edu

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.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Animals
Aquatic Organisms* / isolation & purification
Conservation of Natural Resources
Ecosystem*
Fisheries
Gulf of Mexico
Models, Statistical*
Population Density
Reproducibility of Results
Spatial Analysis

Grants and funding

The authors would like thank the funding sources: Florida Sea Grant, Gulf of Mexico Research Initiative, C-IMAGE (Center for Integrated Modeling and Analysis of the Gulf Ecosystem) consortium, and the National Marine Fisheries Service/USF College of Marine Science Marine Resource Assessment fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.