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A Science‐Based Approach for Identifying Temperature‐Sensitive Streams for Rainbow Trout
Author(s) -
Nelitz Marc A.,
MacIsaac Erland A.,
Peterman Randall M.
Publication year - 2007
Publication title -
north american journal of fisheries management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 72
eISSN - 1548-8675
pISSN - 0275-5947
DOI - 10.1577/m05-146.1
Subject(s) - streams , environmental science , watershed , salvelinus , rainbow trout , habitat , regression , fish <actinopterygii> , trout , hydrology (agriculture) , ecology , statistics , fishery , mathematics , biology , computer science , computer network , geotechnical engineering , machine learning , engineering
To regulate human‐induced changes to fish habitat, resource managers commonly set standards based on maximum allowable changes. For example, new legislation in British Columbia (BC), Canada, calls for restrictions on harvesting of trees and related activities near temperature‐sensitive streams. However, methods for designating such streams are still evolving. Our objective was to help develop such methods by (1) improving understanding of the temperature‐dependent responses of fish and (2) devising improved methods for estimating the effects of forestry‐related activities on stream temperature as well as the chance of exceeding upper temperature limits. Using previously published models, we found that for rainbow trout Oncorhynchus mykiss particular increases in stream temperature led to different effects on juvenile growth rate, egg survival rate, and resistance to mortality from diseases. In a separate analysis, to evaluate the chance that cumulative forestry activities will increase stream temperature by various amounts, we compiled summer temperature data for 104 streams in central BC that reflected different watershed features, contrasting summer climates, and various levels of land use. A classification and regression tree analysis of a summer maximum weekly average temperature (MWAT) index grouped streams into six categories as a function of watershed size, watershed elevation, and air temperature. We then analyzed the remaining unexplained variation among stream temperature indices using Bayesian regression. We found high probabilities that increases in road density and the density of road crossings of streams within watersheds are associated with increases in residual temperature. For instance, a Bayesian regression indicated a 6‐in‐10 chance that the MWAT in our study area will increase by 1.25°C for a road density of 2 km/km 2 of watershed area and by 3.25°C for a road density of 4 km/km 2 . These analyses illustrate some possible ways to help designate temperature‐sensitive streams.