Ryan S. Butryn | Search USGS

USGS2012

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

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.

Hydrobiologia↗

USGS2011

Summer stream water temperature models for Great Lakes streams: New York

Temperature is one of the most important environmental influences on aquatic organisms. It is a primary driver of physiological rates and many abiotic processes. However, despite extensive research and measurements, synoptic estimates of water temperature are not available for most regions, limiting our ability to make systemwide and large-scale assessments of aquatic resources or estimates of aquatic species abundance and biodiversity. We used subwatershed averaging of point temperature measurements and associated multiscale landscape habitat conditions from over 3,300 lotic sites throughout New York State to develop and train artificial neural network models. Separate models predicting water temperature (in cold, cool, and warm temperature classes) within small catchment–stream order groups were developed for four modeling units, which together encompassed the entire state. Water temperature predictions were then made for each stream segment in the state. All models explained more than 90% of data variation. Elevation, riparian forest cover, landscape slope, and growing degree-days were among the most important model predictors of water temperature classes. Geological influences varied among regions. Predicted temperature distributions within stream networks displayed patterns of generally increasing temperature downstream but were patchy due to the averaging of water temperatures within stream size-classes of small drainages. Models predicted coldwater streams to be most numerous and warmwater streams to be generally associated with the largest rivers and relatively flat agricultural areas and urban areas. Model predictions provide a complete, georeferenced map of summer daytime mean stream temperature potential throughout New York State that can be used for planning and assessment at spatial scales from the stream segment class to the entire state.

New York↗