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Fall and Early Winter Movement and Habitat Use of Wild Brook Trout by Jean Brennan, last updated: Feb 20, 2015 01:15 PM
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.
Detecting Temporal Trends in Freshwater Fisheries Surveys: Statistical Power and the Important Linkages between Management Questions and Monitoring Objectives by Jean Brennan, last updated: Feb 20, 2015 01:15 PM
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.
Spatial and Temporal Dynamics in Brook Trout Density: Implications for Population Monitoring by Jean Brennan, last updated: Feb 20, 2015 01:15 PM
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.
State Stream Temperature Compilation by Jean Brennan, last updated: Feb 20, 2015 12:49 PM
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Mallek, Maritza by Maritza Mallek, last updated: Feb 20, 2015 11:24 AM
 
Current Research (2015) by Jean Brennan, last updated: Feb 20, 2015 08:59 AM
Links to relevant conservation research:
April 2015 Workshop Materials & Logistical Information by Jean Brennan, last updated: Feb 20, 2015 08:46 AM
Include the materials for workshop participants to review or complete in advance of the workshop.
Aquatic Resource Management & Conservation Planning by Jean Brennan, last updated: Feb 20, 2015 08:25 AM
Tools, indices, and other resource materials etc. for prioritization, management, and decision-making by resource managers.
Relevant reprints by Jean Brennan, last updated: Feb 20, 2015 08:23 AM
As referenced in Ben Letcher's 2014 Presentation Slides (partial list)
2014 Lecture: Forecasting changes in stream flow, temperature, and salmonid populations in Eastern U.S. as a result of climate change by Jean Brennan, last updated: Feb 20, 2015 08:21 AM
Ben Letcher
Forecasting changes in stream flow, temperature, and salmonid populations in Eastern U.S. as a result of climate change by Jean Brennan, last updated: Feb 20, 2015 08:17 AM
Presentation by Ben Letcher. One of the slides near the end is entitled: Papers where he lists many relevant publications
A regional neural network ensemble for predicting mean daily river water temperature by Jean Brennan, last updated: Feb 20, 2015 08:17 AM
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.
Resource Materials: Presentations by Jean Brennan, last updated: Feb 20, 2015 08:16 AM
Copies of related background presentations
Paired stream-air temperature measurements reveal fine-scale thermal heterogeneity within headwater brook trout stream networks. by Jean Brennan, last updated: Feb 20, 2015 08:12 AM
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.
Predicting Brook Trout Occurrence in Stream Reaches throughout their Native Range in the Eastern United States by Jean Brennan, last updated: Feb 20, 2015 08:06 AM
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.
NorEast - Presentations by Jean Brennan, last updated: Feb 20, 2015 07:34 AM
Go to link to download all presentations as a zip file
Stream Temperature Data and Modeling Meeting Notes by Jean Brennan, last updated: Feb 20, 2015 07:33 AM
NALCC Aquatic Technical Team Meeting, 2012
NALCC Temperature Data and Modeling Meeting by Jean Brennan, last updated: Feb 20, 2015 07:33 AM
Aquatic Technical Team Meeting information.
2012 Stream Temperature Data and Modeling (Meeting I) by Jean Brennan, last updated: Feb 20, 2015 07:28 AM
 
NorEaST Workshop - December 2014 by Jean Brennan, last updated: Feb 20, 2015 07:23 AM
Co-hosted by the US Geological Survey, Michigan State University and University of Wisconsin – Platteville. This meeting was organized as part of the “NorEaST – Stream Temperature Web Portal Demonstration and Application” project funded by the USGS Northeast Climate Science Center. Presentations are available for download.