A Science‐Based Approach for Identifying Temperature‐Sensitive Streams for Rainbow Trout

Abstract

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 1.258C for a road density of 2 km/km 2 of watershed area and by 3.258C for a road density of 4 km/km 2 . These analyses illustrate some possible ways to help designate temperature-sensitive streams.

Recent advances in stream and river temperature research

“…Collings, 1973;Mosley, 1982;Webb and Walling, 1986), there has been a renewed interest recently in statistical analysis to tease out the relationships between water temperature metrics and potential controlling factors operating over sizeable areas (e.g. Wehrly et al, 1998;Lewis et al, 2000;Scholz, 2001;Nelitz et al, 2007). A wide range of predictor variables has been used in regression equations, although these often provide indices of catchment scale, macroclimatic context or reach-scale shading by riparian vegetation (Moore, 2005).…”

Stream temperature prediction in ungauged basins: review of recent approaches and description of a new physics-derived statistical model

“…Similarly, both Pratt and Chang (2012) and Hill et al (2013) aimed at estimating mean stream temperature in summer and winter. Very few studies have actually attempted Bogan et al (2003) Eastern USA AE 596 30 Week R 2 = 0.80, σ e = 3.1 • C Chang and Psaris (2013) Western USA MLR, GWR 74 n/a Week, year R 2 = 0.52-0.62, σ e = 2.0-2.3 • C Daigle et al (2010) Western Canada Various 16 0.5 Month σ e = 0.9-2.8 • C DeWeber and Wagner (2014) Eastern USA ANN 1080 31 Day σ e = 1.8-1.9 • C Ducharne (2008) France MLR 88 7 Month R 2 = 0.88-0.96, σ e = 1.4-1.9 Gardner and Sullivan (2004) Eastern USA NKM 72 1 Day σ e = 1.4 • C Garner et al (2014) UK CA 88 18 Month n/a Hawkins et al (1997) Western USA MLR 45 ≥ 1 Year R 2 = 0.45-0.64 Hill et al (2013) Conterminous USA RF ∼ 1000 1/site Season, year σ e = 1.1-2.0 • C Hrachowitz et al (2010) UK MLR 25 1 Month, year R 2 = 0.50-0.84 Imholt et al (2013) UK MLR 23 2 Month R 2 = 0.63-0.87 Isaak et al (2010) Western USA MLR, NKM 518 14 Month, year R 2 = 0.50-0.61, σ e = 2.5-2.8 • C Isaak and Hubert (2001) Western USA PA 26 1/site Season R 2 = 0.82 Johnson (1971) New Zealand ULR 6 1 Month n/a Johnson et al (2014) UK NLR 36 1.5 Day R 2 = 0.67-0.90, σ e = 1.0-2.4 • C Jones et al (2006) Eastern USA MLR 28 3 Year R 2 = 0.57-0.73 Kelleher et al (2012) Eastern USA MLR 47 2 Day, week n/a Macedo et al (2013) Brazil LMM 12 1.5 Day R 2 = 0.86 Mayer (2012) Western USA MLR 104 ≥ 2 Week, month R 2 = 0.72, σ e = 1.8 • C Miyake and Takeuchi (1951) Japan ULR 20 n/a Month n/a Moore et al (2013) Western Canada MLR 418 1/site Year σ e = 2.1 • C Nelitz et al (2007) Western Canada CRT 104 1/site Year n/a Nelson and Palmer (2007) Western USA MLR 16 3 Season R 2 = 0.36-0.88 Ozaki et al (2003) Japan ULR 5 8 Day n/a Pratt and Chang (2012) Western USA MLR, GWR 51 1/site Season R 2 = 0.48-078 Risley et al (2003) Western USA ANN 148 0.25 Hour, season σ e = 1.6-1.8 • C Rivers- Moore et al (2012) South Africa MLR 90 1/site Month, year R 2 = 0.14-0.50 …”

Summer temperature variation and implications for juvenile Atlantic salmon

“…Another approach to evaluating temperature tolerance is to relate field temperature records and fish distribution through statistical analysis (Eaton et al, 1995;Welsh et al, 2001;Nelitz et al, 2007;Wehrly et al, 2007). One method is to estimate the maximum temperatures tolerated by fish species in nature using the 95th percentile of weekly mean temperature records at sites where fish and temperature data cooccur (Eaton et al, 1995).…”

“…In our study, we combined elements of both approaches, i.e., statistical patterns with physiological-based endpoints to examine the relationships between Atlantic salmon and summer distribution and abundance. However, adding modeling to biological benchmarks and statistical analyses, as was used to predict potential responses of temperature sensitive streams to anthropogenic development (Nelitz et al, 2007), could provide for more detailed predictions.…”