Ryan S. Butryn
Donna M. Rizzo
Donna L. Parrish
2012
<p><span>We developed a methodology to predict brook trout (</span><i class="EmphasisTypeItalic ">Salvelinus fontinalis</i><span>) distribution using summer temperature metrics as predictor variables. Our analysis used long-term fish and hourly water temperature data from the Dog River, Vermont (USA). Commonly used metrics (e.g., mean, maximum, maximum 7-day maximum) tend to smooth the data so information on temperature variation is lost. Therefore, we developed a new set of metrics (called event metrics) to capture temperature variation by describing the frequency, area, duration, and magnitude of events that exceeded a user-defined temperature threshold. We used 16, 18, 20, and 22°C. We built linear discriminant models and tested and compared the event metrics against the commonly used metrics. Correct classification of the observations was 66% with event metrics and 87% with commonly used metrics. However, combined event and commonly used metrics correctly classified 92%. Of the four individual temperature thresholds, it was difficult to assess which threshold had the “best” accuracy. The 16°C threshold had slightly fewer misclassifications; however, the 20°C threshold had the fewest extreme misclassifications. Our method leveraged the volumes of existing long-term data and provided a simple, systematic, and adaptable framework for monitoring changes in fish distribution, specifically in the case of irregular, extreme temperature events.</span></p>
application/pdf
10.1007/s10750-012-1336-1
en
Springer Netherlands
Summer temperature metrics for predicting brook trout (Salvelinus fontinalis) distribution in streams
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