Evaluating Model Uncertainty to Inform Offset Ratios for the Protection of Freshwater Fish Productivity in Canada
Year: 2020
Type: dissertation
Abstract: Ecological models allow resource managers to evaluate human impacts on fish and their habitat. However, quantitative methods that incorporate and assess uncertainty in these models are rare. These models require fish life history information, which is often uncertain, and can thus lead to uncertain predictions and pose risks to either the environment or proponent. I utilized a multispecies size spectrum model to evaluate uncertainty surrounding estimates of three fish life history traits: (1) asymptotic weight; (2) the von Bertalanffy growth coefficient; and (3) weight at maturity. I then applied an existing framework for quantifying compensation ratios to achieve “no net loss” (NNL) in the context of a theoretical development project. The results of this study show that asymptotic size and forage fishes have the greatest influence on predictive uncertainty. I recommend that sampling effort be prioritized for these and demonstrate how to account for uncertainty in a precautionary management framework.
Source: TSpace (University of Toronto)
Author: Laura C. Third
Language: English
Subfield: Nature and Landscape Conservation
Field: Environmental Science
Domain: Physical Sciences
SDG: Life below water
Open Access status: green
Funder: University of Toronto
OpenAlex ID: W3152889956