A decision support system to diagnose factors limiting stream trout fisheries

Abstract

AbstractMaintaining or restoring productive freshwater fisheries is a key challenge for resource managers. However, the inherent uncertainty and complexity of managing fisheries, often based on scant environmental data, make it difficult for managers and the public to reach consensus on appropriate actions. To help deal with this issue, we created a literature‐based decision support system to diagnose limiting factors for stream brown trout fisheries. Once limiting factors are determined, appropriate management actions can be tailored to address them. Our Bayesian belief network (BBN)‐based framework serves 2 functions: (a) It directs users to assemble a parsimonious environmental data set to inform stream fishery management, and (b) it integrates and interrogates these data to generate standardized and testable hypotheses about which environment factors are likely to limit trout productivity. The BBN has been trained on brown trout because among freshwater fish, this species has the richest literature base and is highly valued worldwide. However, the framework could be adapted for other stream fish. We applied our BBN to the Horokiri Stream, a data‐rich catchment in Wellington, New Zealand. The BBN probability outputs were comparable with the conclusions of 5 experienced fishery biologists following their detailed investigation into the factors that led to the loss of the Horokiri brown trout fishery between 1951 and 1990.