Data is from Masi, M and Ainsworth, C. (in press) A Probabilistic Representation of Fish Diet Compositions from Multiple Data Sources: A Gulf of Mexico Case Study. Ecological Modelling. April 2014. Trophic ecosystem models are interactive tools that allow decision makers to analyze how a management decision can impact an ecosystem on a multi-species level, and are increasingly being used as a supplement to the current single species approach to fisheries management. The functionality of such a model is dependent upon an accurate representation of the trophic interactions occurring within a study area. Typical methods for developing a diet matrix to be used in ecosystem models often fail to account for uncertainty associated with sampling; this is especially relevant when dealing with small diet data sets. In this case study of the Gulf of Mexico ecosystem, we have conducted a laboratory diet analysis to define predator-prey interactions for non-commercially important predator species resident to the study area, and then expounded on this laboratory data by assimilating two, more robust data sets. By applying a maximum likelihood estimation method, we combine these data sets and produce maximum likelihood estimates (MLEs) and associated error ranges, which describe the likely diet contribution that a given prey item contributes to a predator’s diet. These results will be used to parameterize the availabilities (diet) matrix of an Atlantis ecosystem model of the Gulf of Mexico. Column A: predator name. Column B: prey name. Column C: lower 95% confidence interval. Column D: upper 95% confidence interval. Column E: mode of the maximum likelihood marginal beta distribution (percent).
Diet data parameterizes an Atlantis Ecosystem model.
Diet composition, Stomach sampling, Gut content analysis, Atlantis ecosystem model, Dirichlet distribution
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This work is licensed under a Creative Commons Public Domain Dedication 1.0 License.
Scholar Commons Citation
Ainsworth, Cameron H., "Predator-prey diet linkages with error range for the Gulf of Mexico fitted using maximum likelihood method" (2016). C-IMAGE data. 6.