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Link to Reprint: A Model for Evaluating Stream Temperature Response to Climate Change in Wisconsin
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USGS report on stream temperature modeling and climate change in Wisconsin
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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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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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Paired stream-air temperature measurements reveal fine-scale thermal heterogeneity within headwater brook trout stream networks.
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Authors:
Y. Kanno,J. C. Vokoun and B. H. Letcher
Keywords:
climate change;fish conservation;groundwater;Salmonidae;stream discharge;water temperature
ABSTRACT
Previous studies of climate change impacts on stream fish distributions commonly project the potential patterns of habitat loss and fragmentation due to elevated stream temperatures at a broad spatial scale (e.g. across regions or an entire species range). However, these studies may overlook potential heterogeneity in climate change vulnerability within local stream networks. We examined fine-scale stream temperature patterns in two headwater brook trout Salvelinus fontinalis stream networks (7.7 and 4.4 km) in Connecticut, USA, by placing a combined total of 36 pairs of stream and air temperature loggers that were approximately 300 m apart from each other. Data were collected hourly from March to October 2010. The summer of 2010 was hot (the second hottest on record) and had well below average precipitation, but stream temperature was comparable with those of previous 2 years because streamflow was dominated by groundwater during base-flow conditions. Nonlinear regression models revealed stream temperature variation within local stream networks, particularly during warmest hours of the day (i.e. late afternoon to evening) during summer. Thermal variability was primarily observed between stream segments, versus within a stream segment (i.e. from confluence to confluence). Several cold tributaries were identified in which stream temperature was much less responsive to air temperature. Our findings suggested that regional models of stream temperature would not fully capture thermal variation at the local scale and may misrepresent thermal resilience of stream networks. Groundwater appeared to play a major role in creating the fine-scale spatial thermal variation, and characterizing this thermal variation is needed for assessing climate change impacts on headwater species accurately.
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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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Relevant reprints
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As referenced in Ben Letcher's 2014 Presentation Slides (partial list)
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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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Detecting Temporal Trends in Freshwater Fisheries Surveys: Statistical Power and the Important Linkages between Management Questions and Monitoring Objectives
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by T.Wagner et al., ABSTRACT: Monitoring to detect temporal trends in biological
and habitat indices is a critical component of fisheries
management. Thus, it is important that management objectives
are linked to monitoring objectives. This linkage requires a
definition of what constitutes a management-relevant “temporal
trend.” It is also important to develop expectations for the
amount of time required to detect a trend (i.e., statistical power)
and for choosing an appropriate statistical model for analysis.
We provide an overview of temporal trends commonly encountered
in fisheries management, review published studies that
evaluated statistical power of long-term trend detection, and
illustrate dynamic linear models in a Bayesian context, as an
additional analytical approach focused on shorter term change.
We show that monitoring programs generally have low statistical
power for detecting linear temporal trends and argue that
often management should be focused on different definitions of
trends, some of which can be better addressed by alternative
analytical approaches.
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Fall and Early Winter Movement and Habitat Use of Wild Brook Trout
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Abstract
Brook Trout Salvelinus fontinalis populations face a myriad of threats throughout the species’ native range in
the eastern United States. Understanding wild Brook Trout movement patterns and habitat requirements is essential
for conserving existing populations and for restoring habitats that no longer support self-sustaining populations.
To address uncertainties related to wild Brook Trout movements and habitat use, we radio-tracked 36 fish in a
headwater stream system in central Pennsylvania during the fall and early winter of 2010–2011. We used generalized
additive mixed models and discrete choice models with random effects to evaluate seasonal movement and habitat
use, respectively. There was variability among fish in movement patterns; however, most of the movement was
associated with the onset of the spawning season and was positively correlated with fish size and stream flow. There
was heterogeneity among fish in selection of intermediate (0.26–0.44 m deep) and deep (0.44–1.06 m deep) residual
pools, while all Brook Trout showed similar selection for shallow (0.10–0.26 m) residual pools. There was selection for
shallow residual pools during the spawning season, followed by selection for deep residual pools as winter approached.
Brook Trout demonstrated a threshold effect for habitat selection with respect to pool length, and selection for pools
increased as average pool length increased up to approximately 30 m, and then use declined rapidly for pool habitats
greater than 30 m in length. The heterogeneity and nonlinear dynamics of movement and habitat use of wild Brook
Trout observed in this study underscores two important points: (1) linear models may not always provide an accurate
description of movement and habitat use, which can have implications for management, and (2) maintaining stream
connectivity and habitat heterogeneity is important when managing self-sustaining Brook Trout populations.
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Modeling spatially varying landscape change points in species occurrence thresholds
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by T. Wagner and S. Miday, Abstract. Predicting species distributions at scales of regions to continents is often necessary, as largescale
phenomena influence the distributions of spatially structured populations. Land use and land cover
are important large-scale drivers of species distributions, and landscapes are known to create species
occurrence thresholds, where small changes in a landscape characteristic results in abrupt changes in
occurrence. The value of the landscape characteristic at which this change occurs is referred to as a change
point. We present a hierarchical Bayesian threshold model (HBTM) that allows for estimating spatially
varying parameters, including change points. Our model also allows for modeling estimated parameters in
an effort to understand large-scale drivers of variability in land use and land cover on species occurrence
thresholds. We use range-wide detection/nondetection data for the eastern brook trout (Salvelinus
fontinalis), a stream-dwelling salmonid, to illustrate our HBTM for estimating and modeling spatially
varying threshold parameters in species occurrence. We parameterized the model for investigating
thresholds in landscape predictor variables that are measured as proportions, and which are therefore
restricted to values between 0 and 1. Our HBTM estimated spatially varying thresholds in brook trout
occurrence for both the proportion agricultural and urban land uses. There was relatively little spatial
variation in change point estimates, although there was spatial variability in the overall shape of the
threshold response and associated uncertainty. In addition, regional mean stream water temperature was
correlated to the change point parameters for the proportion of urban land use, with the change point value
increasing with increasing mean stream water temperature. We present a framework for quantify
macrosystem variability in spatially varying threshold model parameters in relation to important largescale
drivers such as land use and land cover. Although the model presented is a logistic HBTM, it can
easily be extended to accommodate other statistical distributions for modeling species richness or
abundance.
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