1. Agriculture affects streams worldwide, and multiple stressors are usually at work. The effects of farming intensification in the catchment are likely to interact with flow reduction due to abstraction for irrigation, but their combined effects on fish communities are unknown. 2. In a low-rainfall, agricultural catchment, we sampled instream physicochemistry and fish populations (non-native brown trout, Salmo trutta, and native upland bullies, Gobiomorphus breviceps, dominated the fish communities) at 36 stream sites chosen to cover wide gradients of % Farming Intensity (%FI) (% catchment in 'high-producing exotic grassland') and % Water Abstraction (%WA) (estimated from a published hydrological model). These landscape variables were not well correlated, allowing us to unravel their individual and combined effects. 3. Presence of trout was best described by an additive multiple-stressor model consisting of a unimodal response to %FI and a negative response to %WA. Trout density only showed a negative response to %FI. Upland bullies were unrelated to either landscape variable. 4. When populations were modelled using instream variables, trout presence was negatively related to fine sediment depth, while density was negatively related to both sediment depth and total nitrogen (themselves closely related to %FI). Upland bully presence and density showed unimodal responses to just total nitrogen. Ammonium concentration was the only measured instream variable related to %WA. 5. The final models for instream stressors explained more of the variation in fish density, whereas the final models for landscape stressors explained more of the variation in fish presence. 6. Overall, farming intensity showed stronger negative relationships with fish populations than water abstraction, and fish were absent from stream reaches whose upstream catchments contained more than 40% high-producing exotic grassland. Resource managers considering intensifying water abstraction or agriculture in low-rainfall river catchments should be aware of the interplay between these two agricultural stressors.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.