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Uncertainty analysis in fisheries science—an interdisciplinary approach

  • Author(s): Urbisci, Laura
  • Advisor(s): Gaines, Steve
  • et al.
Abstract

My dissertation is an interdisciplinary approach that combines fisheries science, ecological theory, and applied statistics. My first chapter is a meta-analysis on transfer efficiency that describes and quantifies the variation in transfer efficiency. My second chapter assesses uncertainty in food web models by creating multiple Monte Carlo simulations to test various ecological assumptions about net primary production and transfer efficiency. My final chapter is a comparative analysis of two Bayesian models: a classic Bayesian surplus production model and a Bayesian surplus production model that incorporates ecological information. This chapter examines if the inclusion of ecological information informs and alters fisheries assessment models, with a focus on data-limited fisheries. Ultimately my work bridges the gap between applied statistics and ecological theory and encourages the use of uncertainty analysis to make more robust predictions in food web models.

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