Bureau of Water Quality

Muncie, IN, United States

Bureau of Water Quality

Muncie, IN, United States
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Williams M.C.W.,Bureau of Water Quality | Murphy E.W.,U.S. Environmental Protection Agency | McCarty H.B.,Science and Engineering | Snyder B.D.,Tetra Tech Inc. | Crimmins B.S.,Clarkson University
Journal of Great Lakes Research | Year: 2017

This dataset represents the largest collection of fatty acid data from the Great Lakes region to date, summarizing concentrations of omega-3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in fish sampled from the U.S. waters of all 5 Great Lakes and 92 U.S. lakes and rivers. Determining how freshwater fishes' fatty acid content varies across environmental gradients is important in understanding aquatic trophic interactions and to providing comprehensive fish consumption advice. However, there is currently a lack of information on variation in freshwater fish fatty acid content that may hinder human health and fisheries professionals tasked with establishing fish monitoring and analysis programs which capture this variability. To that end, fillet EPA + DHA concentrations were modeled over several biotic and abiotic gradients in order to constrain variability. Recommendations based on model results are then used to suggest starting points for planning future fish sampling efforts (e.g. inland walleye [EPA + DHA] varied with both length and waterbody eutrophication; these gradients should be incorporated into sampling efforts to capture walleye [EPA + DHA] variability). In terms of nutrition, Great Lakes species (all taxonomic families) and inland salmonid fillets contained a daily adequate intake (AI) level of ≥. 250. mg of EPA + DHA per 8-oz. (227. g) fillet, but other taxonomic families from inland waters generally did not. Very few species' fillets, regardless of sampling location or taxonomic family, contained the equivalent weekly AI level of 1750. mg EPA + DHA per 8-oz. fillet. The data presented here can inform both fish sampling efforts and fish consumption risk-benefit analyses. © 2017 The Authors.


Lawrence D.J.,University of Washington | Stewart-Koster B.,University of Washington | Olden J.D.,University of Washington | Ruesch A.S.,Bureau of Water Quality | And 2 more authors.
Ecological Applications | Year: 2014

Predicting how climate change is likely to interact with myriad other stressors that threaten species of conservation concern is an essential challenge in aquatic ecosystems. This study provides a framework to accomplish this task in salmon-bearing streams of the northwestern United States, where land-use-related reductions in riparian shading have caused changes in stream thermal regimes, and additional warming from projected climate change may result in significant losses of coldwater fish habitat over the next century. Predatory, nonnative smallmouth bass have also been introduced into many northwestern streams, and their range is likely to expand as streams warm, presenting an additional challenge to the persistence of threatened Pacific salmon. The goal of this work was to forecast the interactive effects of climate change, riparian management, and nonnative species on stream-rearing salmon and to evaluate the capacity of restoration to mitigate these effects. We intersected downscaled global climate forecasts with a local-scale water temperature model to predict mid- and end-of-century temperatures in streams in the Columbia River basin. We compared one stream that is thermally impaired due to the loss of riparian vegetation and another that is cooler and has a largely intact riparian corridor. Using the forecasted stream temperatures in conjunction with fish-habitat models, we predicted how stream-rearing chinook salmon and bass distributions would change as each stream warmed. In the highly modified stream, end-of-century warming may cause near total loss of chinook salmon-rearing habitat and a complete invasion of the upper watershed by bass. In the less modified stream, bass were thermally restricted from the upstream-most areas. In both systems, temperature increases resulted in higher predicted spatial overlap between stream-rearing chinook salmon and potentially predatory bass in the early summer (two- to fourfold increase) and greater abundance of bass. We found that riparian restoration could prevent the extirpation of chinook salmon from the more altered stream and could also restrict bass from occupying the upper 31 km of salmon-rearing habitat. The proposed methodology and model predictions are critical for prioritizing climate-change adaptation strategies before salmonids are exposed to both warmer water and greater predation risk by nonnative species. © 2014 by the Ecological Society of America.


Doll J.C.,Bureau of Water Quality
Environmental Monitoring and Assessment | Year: 2011

The goal of biological monitoring programs is to determine impairment classification and identify local stressors. Biological monitoring performs well at detecting impairment but when used alone falls short of determining the cause of the impairment. Following detection a more thorough survey is often conducted using extensive biological, chemical, and physical analysis coupled with exhaustive statistical treatments. These methods can be prohibitive for small programs that are limited by time and budget. The objective of this study was to develop a simple and useful model to predict the probability of biological impairment based on routinely collected habitat assessments. Biological communities were assessed with the Index of Biotic Integrity (IBI), and habitat was assessed with the Qualitative Habitat Evaluation Index. Two models were constructed from a validation dataset. The first predicted a binary outcome of impaired (IBI < 35) or non-impaired (IBI ≤ 35) and the second predicted a categorical gradient of impairment. Categories include very poor, poor, fair, good, and excellent. The models were then validated with an independently collected dataset. Both models successfully predicted biological integrity of the validation dataset with an accuracy of 0.84 (binary) and 0.75 (categorical). Based on the binary outcome model, 22 sites were observed to be impaired while the model predicted them to not be impaired. The categorical model misclassified 47 samples while only seven of those were misclassified by two or more categories. The impairment source was subsequently identified by known stressors. The models developed here can be easily applied to other datasets from the Eastern Corn Belt Plain to aid in stressor identification by predicting the probability of observing an impaired fish community based on habitat. Predicted probabilities from the models can also be used to support conclusions that have already been determined. © 2011 Springer Science+Business Media B.V.


Mcginley P.M.,University of Wisconsin - Stevens Point | Freihoefer A.T.,Bureau of Water Quality | Mentz R.S.,University of Wisconsin - Platteville
Journal of the American Water Resources Association | Year: 2013

This study used monitoring in the waterways of agricultural fields to understand the use of the runoff curve number (CN) in continuous simulation models. The CN has a long history as a design tool for estimating runoff volumes for large, single storms on small watersheds, but its use in continuous simulation models to describe runoff from smaller storms and relatively small areas is more recent and controversial. We examined 788 nonwinter rainfall events on four agricultural fields over five years (2004-2008) during which runoff was generated in 87 events. The largest 20 runoff events on each field generated approximately 90% of the total runoff volume. The runoff event CNs showed an inverse correlation with storm depth that could not consistently be explained by previous precipitation. We review how small areas of higher runoff generation within larger areas will systematically increase the apparent CN of the larger area as the storm size decreases. If this variation is not incorporated into a model explicitly, continuous simulation modelers must understand that when source areas are aggregated or when runoff generation is spatially variable, the overall CN is not unique when smaller storms are included in the calibration set. © 2013 American Water Resources Association.


PubMed | Bureau of Water Quality
Type: Journal Article | Journal: Environmental monitoring and assessment | Year: 2011

The goal of biological monitoring programs is to determine impairment classification and identify local stressors. Biological monitoring performs well at detecting impairment but when used alone falls short of determining the cause of the impairment. Following detection a more thorough survey is often conducted using extensive biological, chemical, and physical analysis coupled with exhaustive statistical treatments. These methods can be prohibitive for small programs that are limited by time and budget. The objective of this study was to develop a simple and useful model to predict the probability of biological impairment based on routinely collected habitat assessments. Biological communities were assessed with the Index of Biotic Integrity (IBI), and habitat was assessed with the Qualitative Habitat Evaluation Index. Two models were constructed from a validation dataset. The first predicted a binary outcome of impaired (IBI < 35) or non-impaired (IBI 35) and the second predicted a categorical gradient of impairment. Categories include very poor, poor, fair, good, and excellent. The models were then validated with an independently collected dataset. Both models successfully predicted biological integrity of the validation dataset with an accuracy of 0.84 (binary) and 0.75 (categorical). Based on the binary outcome model, 22 sites were observed to be impaired while the model predicted them to not be impaired. The categorical model misclassified 47 samples while only seven of those were misclassified by two or more categories. The impairment source was subsequently identified by known stressors. The models developed here can be easily applied to other datasets from the Eastern Corn Belt Plain to aid in stressor identification by predicting the probability of observing an impaired fish community based on habitat. Predicted probabilities from the models can also be used to support conclusions that have already been determined.

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