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NorEast - Presentations
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NorEaST Workshop - December 2014
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NALCC Temperature Data and Modeling Meeting
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Aquatic Technical Team Meeting information.
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2012 Stream Temperature Data and Modeling (Meeting I)
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Stream Temperature Data and Modeling Meeting Notes
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NALCC Aquatic Technical Team Meeting, 2012
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2012 Stream Temperature Data and Modeling (Meeting I)
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Forecasting changes in stream flow, temperature, and salmonid populations in Eastern U.S. as a result of climate change
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Presentation by Ben Letcher. One of the slides near the end is entitled: Papers where he lists many relevant publications
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Brook Trout and Stream Temperature Workshop Information
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Predicting Brook Trout Occurrence in Stream Reaches throughout their Native Range in the Eastern United States
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Abstract
The Brook Trout Salvelinus fontinalis is an important species of conservation concern in the eastern USA. We
developed a model to predict Brook Trout population status within individual stream reaches throughout the
species’ native range in the eastern USA. We utilized hierarchical logistic regression with Bayesian estimation to
predict Brook Trout occurrence probability, and we allowed slopes and intercepts to vary among ecological
drainage units (EDUs). Model performance was similar for 7,327 training samples and 1,832 validation samples
based on the area under the receiver operating curve (»0.78) and Cohen’s kappa statistic (0.44). Predicted water
temperature had a strong negative effect on Brook Trout occurrence probability at the stream reach scale and was
also negatively associated with the EDU average probability of Brook Trout occurrence (i.e., EDU-specific
intercepts). The effect of soil permeability was positive but decreased as EDU mean soil permeability increased.
Brook Trout were less likely to occur in stream reaches surrounded by agricultural or developed land cover, and an
interaction suggested that agricultural land cover also resulted in an increased sensitivity to water temperature.
Our model provides a further understanding of how Brook Trout are shaped by habitat characteristics in the
region and yields maps of stream-reach-scale predictions, which together can be used to support ongoing
conservation and management efforts. These decision support tools can be used to identify the extent of potentially
suitable habitat, estimate historic habitat losses, and prioritize conservation efforts by selecting suitable stream
reaches for a given action. Future work could extend the model to account for additional landscape or habitat
characteristics, include biotic interactions, or estimate potential Brook Trout responses to climate and land use
changes.
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A regional neural network ensemble for predicting mean daily river water temperature
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Abstract: Water temperature is a fundamental property of river habitat and often a key aspect of river resource
management, but measurements to characterize thermal regimes are not available for most streams
and rivers. As such, we developed an artificial neural network (ANN) ensemble model to predict mean
daily water temperature in 197,402 individual stream reaches during the warm season (May–October)
throughout the native range of brook trout Salvelinus fontinalis in the eastern U.S. We compared four
models with different groups of predictors to determine how well water temperature could be predicted
by climatic, landform, and land cover attributes, and used the median prediction from an ensemble of 100
ANNs as our final prediction for each model. The final model included air temperature, landform attributes
and forested land cover and predicted mean daily water temperatures with moderate accuracy
as determined by root mean squared error (RMSE) at 886 training sites with data from 1980 to 2009
(RMSE = 1.91 C). Based on validation at 96 sites (RMSE = 1.82) and separately for data from 2010
(RMSE = 1.93), a year with relatively warmer conditions, the model was able to generalize to new stream
reaches and years. The most important predictors were mean daily air temperature, prior 7 day mean air
temperature, and network catchment area according to sensitivity analyses. Forest land cover at both
riparian and catchment extents had relatively weak but clear negative effects. Predicted daily water temperature
averaged for the month of July matched expected spatial trends with cooler temperatures in
headwaters and at higher elevations and latitudes. Our ANN ensemble is unique in predicting daily temperatures
throughout a large region, while other regional efforts have predicted at relatively coarse time
steps. The model may prove a useful tool for predicting water temperatures in sampled and unsampled
rivers under current conditions and future projections of climate and land use changes, thereby providing
information that is valuable to management of river ecosystems and biota such as brook trout.
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Brook Trout and Stream Temperature Workshop Information
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2012 Stream Temperature Data and Modeling (Meeting I)
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Resource Materials: Previous Workshops
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Brook Trout and Stream Temperature Workshop Information
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Current Research (2015)
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Links to relevant conservation research:
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Brook Trout and Stream Temperature Workshop Information
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Spatial and Temporal Dynamics in Brook Trout Density: Implications for Population Monitoring
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T.Wagner et al., Abstract
Many potential stressors to aquatic environments operate over large spatial scales, prompting the need to assess and
monitor both site-specific and regional dynamics of fish populations. We used hierarchical Bayesian models to evaluate
the spatial and temporal variability in density and capture probability of age-1 and older Brook Trout Salvelinus
fontinalis from three-pass removal data collected at 291 sites over a 37-year time period (1975–2011) in Pennsylvania
streams. There was high between-year variability in density, with annual posterior means ranging from 2.1 to 10.2
fish/100 m2
; however, there was no significant long-term linear trend. Brook Trout density was positively correlated
with elevation and negatively correlated with percent developed land use in the network catchment. Probability
of capture did not vary substantially across sites or years but was negatively correlated with mean stream width.
Because of the low spatiotemporal variation in capture probability and a strong correlation between first-pass CPUE
(catch/min) and three-pass removal density estimates, the use of an abundance index based on first-pass CPUE could
represent a cost-effective alternative to conducting multiple-pass removal sampling for some Brook Trout monitoring
and assessment objectives. Single-pass indices may be particularly relevant for monitoring objectives that do not
require precise site-specific estimates, such as regional monitoring programs that are designed to detect long-term
linear trends in density.
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