Keywords
probability network, integrated modelling, population modelling, causal assessment
Start Date
1-7-2004 12:00 AM
Abstract
A Bayesian belief network is described that integrates the various scientific findings of an interdisciplinary research project on brown trout and their habitat in Switzerland. The network is based on a population model for brown trout, which is extended to include the effect of natural and anthropogenic influence factors. Uncertainty is included in the form of conditional probability distributions describing model relationships. The model is applied to brown trout populations at twelve locations in four river basins. Model testing consisted of comparing predictions of juvenile and adult density under current conditions to the results of recent population surveys. The relative importance of the various influence factors was then assessed by comparing various model scenarios, including a hypothetical reference condition. A measure of causal strength was developed based on this comparison, and the major stress factors were ranked according to this measure for each location. Results give an indication of the type of management actions that will be most effective in protecting or restoring brown trout populations.
A Bayesian belief network for modelling brown trout (Salmo trutta) populations in Switzerland
A Bayesian belief network is described that integrates the various scientific findings of an interdisciplinary research project on brown trout and their habitat in Switzerland. The network is based on a population model for brown trout, which is extended to include the effect of natural and anthropogenic influence factors. Uncertainty is included in the form of conditional probability distributions describing model relationships. The model is applied to brown trout populations at twelve locations in four river basins. Model testing consisted of comparing predictions of juvenile and adult density under current conditions to the results of recent population surveys. The relative importance of the various influence factors was then assessed by comparing various model scenarios, including a hypothetical reference condition. A measure of causal strength was developed based on this comparison, and the major stress factors were ranked according to this measure for each location. Results give an indication of the type of management actions that will be most effective in protecting or restoring brown trout populations.
