Journal
ECOLOGICAL MODELLING
Volume 269, Issue -, Pages 1-8Publisher
ELSEVIER
DOI: 10.1016/j.ecolmodel.2013.07.025
Keywords
Bayesian variable selection; Environmental forcing; Multicollinearity; Overfitting; Phytoplankton
Categories
Funding
- Ace-net post-doctoral fellowship
- Marjorie-Young Bell fund
- NSERC Canada
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The structure of phytoplankton communities is thought to influence total productivity, trophic structure and the export of carbon below the mixed layer. Community structure is determined by a complex interaction between the physiological characteristics of each species, environmental conditions, resource availability, competition among species, and numerous loss terms. This complexity makes it very difficult to predict how changes in environmental conditions will alter the structure of phytoplankton communities. Here we develop a hierarchical Bayesian model with variable selection to identify how temperature, salinity, irradiance, and macronutrient concentrations determine the abundance of the 67 dominant identified species at Station CARIACO in the Caribbean Sea. This approach allows us to overcome the statistical challenge presented by the highly correlated environmental variables. Approximately three-quarters of the variables for each species have little effect on phytoplankton abundance. About half of the species decline in abundance with increasing temperature. Diatom species' abundances are much more likely to respond to changes in irradiance and nitrate concentration than dinoflagellates and dinoflagellate species' abundances are more likely to respond to changes in salinity. (C) 2013 Elsevier B.V. All rights reserved.
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