A decision support system to diagnose factors limiting stream trout fisheries

Abstract

Maintaining 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.

Straying of brown trout in the catchment of a large New Zealand river evaluated by otolith microchemistry

“…The first liberations of brown trout occurred in the Taieri River catchment in 1869, and they are most widespread fish species inhabiting river system (Jones, & Closs, 2017). Given the negative impacts of brown trout on native fish communities, brown trout is considered to be an invasive species (Jellyman et al, 2017), although brown trout also have high economic significance sustaining valuable recreational fisheries (Holmes et al, 2018). The distribution of brown trout life histories within the catchment is affected by its geomorphological diversity, as the Taieri River flows through steep gorges with cascades of rapids and small waterfalls separating one inland peneplain from another.…”

Accounting for Uncertainty in Regional Flow–Ecology Relationships

“…The next three sets of runs (Fig. 6, Column End) evaluated uncertainty about which variables were most limiting, given that it is difficult to isolate the individual importance of certain variables (Holmes et al 2018).…”

“…For example, disease may become more prevalent across a watershed as streams warm with summer air temperatures (Schaaf et al 2017) and additional environmental stressors, such as nonnative predation, may become more important as invasive species expand throughout the Eel River basin (Moyle et al 2017). Given challenges in isolating limiting variables that impact aquatic species (Holmes et al 2018) and knowing how relationships will hold through time (Horne et al 2019), traditional BN modeling using one set of assumptions risks making incorrect assumptions and drawing inaccurate conclusions.…”

Is recruitment of brown trout in a New Zealand river driven by parental spawning investment, density dependence, or environmental factors?

“…In New Zealand there are few studies of the population dynamics of brown trout (Allen, 1951;Kristensen and Closs, 2008a;Hayes, Olsen and Hay, 2010;Jones, Akbaripasand, Nakagawa and Closs, 2019), and one study has examined their migration dynamics (Holmes et al, 2014). The effects of natural reproduction, food abundance, floods and other environmental factors on trout population dynamics in New Zealand were analyzed by Hobbs (1940Hobbs ( , 1948, Allen (1951), Burnet (1959), Jowett (1989Jowett ( , 1990Jowett ( , 1995 and Hayes (1995), with the importance of different factors in driving brown trout populations in New Zealand being a source of long-standing debate (Huryn, 1996;Holmes et al, 2018).…”