Summer temperature metrics for predicting brook trout (Salvelinus fontinalis) distribution in streams

Hydrobiologia
By: , and 

Links

Abstract

We developed a methodology to predict brook trout (Salvelinus fontinalis) 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.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Summer temperature metrics for predicting brook trout (Salvelinus fontinalis) distribution in streams
Series title Hydrobiologia
DOI 10.1007/s10750-012-1336-1
Volume 703
Issue 1
Year Published 2012
Language English
Publisher Springer Netherlands
Contributing office(s) Coop Res Unit Leetown
Description 11 p.
First page 47
Last page 57
Online Only (Y/N) N
Additional Online Files (Y/N) N