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Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Date of Award

Fall 2010

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Department of Biology

Abstract

Predicting coldwater fisheries distributions under various climate scenarios is of interest to many fisheries managers and researchers. Larger scale models have been useful in highlighting the potential large scale threat. However, the error associated with these models makes predictions of the persistence of individual cold water fisheries problematic. Most of this error is associated with predicted air and water temperatures which typically are simple elevation and location (latitude/longitude) models with simple caveats such as 1°C increase in air temperature equals 0.8°C increase in water temperatures. I directly measured paired air and water temperatures in watersheds containing reproducing populations of brook trout in Virginia during the critical summer period (July 1 to September 30) in both 2009 and 2010. I developed a classification system using sensitivity (change in the daily maximum water temperature from a 1°C increase in the daily maximum air temperature) and exposure metrics (frequency; duration; and magnitude of daily maximum water temperatures > 21°C) that classified brook trout populations into four categories: High Sensitivity-High Exposure; High Sensitivity-Low Exposure; Low Sensitivity-High Exposure and Low Sensitivity- Low Exposure. I found that my paired air and water temperature relationships were highly variable among sites and were a useful metric for classifying the sensitivity and exposure of individual brook trout populations to various climate change scenarios. I identified many (25%) Low Sensitivity- Low Exposure brook trout populations that appear to be resilient to climate change. The median sensitivity (0.39°C) in this study was much lower than the assumed rate (0.80°C) used in many regional models that predicted a complete extirpation of brook trout in Virginia. Several GIS generated metrics (sample area; % riparian canopy; solar insolation ; % groundwater; elevation; % watershed in forest cover) were useful for predicting (accuracy approximately 75%) sensitivity and exposure values. Directly measuring paired air and water temperature relationships can reduce the error of large scale models. I recommend that managers making investment decisions in protecting and restoring brook trout use my direct measurement approach when they cannot afford to make a Type I or Type II error.

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