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  • Center for Urban Environmental Studies Northeastern University, Boston, MA 02115

    TECHNICAL REPORT NO. 3

    EVALUATING SINGLE AND MULTIPLE STRESSORS IN WATERSHED RISK ASSESSMENT

    Jessica Keye Brooks, MSc. Research Associate

    Vladimir Novotny, Ph.D., P.E. Primary Investigator

    Project sponsored by the Grant No. R83-0885-010 to Northeastern University from the US EPA/NSF/USDA STAR Watershed Program

    Bernice Smith EPA Project Officer

    Iris Goodman

    EPA Program Director

    Boston, Massachusetts April 2005

  • i

    Abstract The Index Biological Integrity (IBI) can be used as an indicator of overall water body health and may be predicted by using artificial neural networks (ANN). Changes within a watershed, such as urbanization, can affect local water bodies by increasing chemical concentrations or degrading habitat. Risk by single stressors on all fish and benthic invertebrate species as well at key IBI metrics was evaluated using toxicity data and Maximum Species Richness. However, the risk from combined stresses may not be directly additive, as is assumed by these methods. ANN technology was used to consider the overall symbiotic effect of multiple stressors on IBI. Data for the purpose of this study was extracted from large database that combines chemical, habitat, and biological information for streams in Ohio. This database was used to train a multilayer, backpropagation network to estimate the IBI value based on water, sediment, and habitat quality. Inputs to the network included water quality parameters, such as chemical and dissolved oxygen concentrations, sediment quality, expressed by invertebrate community index, and the qualitative habitat evaluation index. Results of IBI prediction may be useful to watershed planners and decision makes evaluating new projects and restoration opportunities.

  • ii

    Acknowledgements The funding for this research was received through a grant from the US Environmental Protection Agency/National Science Foundation/US Department of Agriculture STAR Watershed Program. The findings and conclusions expressed in this thesis are those of the author and not of the funding agencies. The finding has been greatly appreciated.

  • iii

    Table of Content Abstract………………………………………………………………………………... i Acknowledgements…………………………………………………………………… ii Table of Content………………………………………………………………………. iii List of Figures…………………………………………………………………………. iv List of Tables………………………………………………………………………….. v 1 Introduction……………………………………………………………………… 1 1.1 Ecological Integrity……………………………………………………………. 1 1.2 Ecological Risk Assessment…………………………………………………… 4 2. Toxic Response………………….………………………………………………….. 11 2.1 Evaluating Toxicity Data.……………………………………………………… 11 2.2 Estimation of Risk Response Function Using Toxicity Data ….……………… 16 3. Maximum Species Richness Lines………………………………………………… 21 3.1 Developing Maximum Species Richness Lines……………………………….. 21 3.2 Risk Calculation Using Maximum Species Richness Lines…………………… 23 4. Artificial Neural Networks………………………………………………………… 27 4.1 Introduction to Artificial Neural Networks……………………………………. 27 4.2 Network Data……………………………………………...…………………… 29 4.3 Preliminary Network Analysis………………………………………………… 31 5. Conclusions and Recommendations………………………………………………. 37 5.1 Risk Calculation……………………………………………………………….. 37 5.2 Addressing Limitations of Risk Calculation…………………………………… 38 5.3 Artificial Neural Networks…………………………………………………….. 39 5.4 Linking Biology and Watershed ……………………………………….…….... 39 5.5 Recommendations for Future Work ……………………………………..…….. 40 References…………………………………………………………………………….. 43

  • iv

    List of Figures Figure 1.1 Watershed, land use changes and other impacts on water quality (adapted from

    Novotny 2004)…………………………………………………… 1 Figure 1.2 Increased % impervious area in a drainage basin correlates with decreased

    macroinvertebrate quality (Schueler 1994, Novotny 2004)… 2 Figure 1.3 Schematic of multilayer risk propagation through hierarchical model…3 Figure 1.4 Patterns of Maximum Species Richness Lines for various stressors (Novotny

    2004)………………………………………………………… 8 Figure 2.1 Toxic response curve for cadmium evaluated at 50 mg CaCO3/l……... 15 Figure 2.2 Ecological risk from stormwater impacts (Novotny and Witte 1997)…. 16 Figure 3.1 Maximum Species Richness line for lead……………………………… 22 Figure 3.2 Calculating the risk of extinction using maximum species richness…... 23 Figure 3.3 Maximum species richness for lead: Count decreasing with increasing

    concentration……………………...……………………………………. 24 Figure 3.4 Maximum species richness for pH: MSRL displays a peak at pH 7-8… 24 Figure 3.5 Maximum species richness for fish vs zinc: Non-linear response……... 24 Figure 3.6 Maximum species richness for copper and zinc……………………….. 25 Figure 3.7 Comparing cadmium MSRLs for invertebrates and fish………………. 25 Figure 3.8 Comparing responses of intolerant and tolerant fish to copper…………25 Figure 4.1 A model neuron (Demuth and Beale 1992)……………………………. 27 Figure 4.2 A model of a layered network (Demuth and Beale 1992)……………... 28 Figure 4.3 Examples of simulations results for Group 1 parameters……………… 33 Figure 4.4 Clusters resulting from Self Organizing Map analysis of Ohio database from

    Virani (2005)………………………………………………........... 34 Figure 4.5 Calibration and simulation results for network trained with Group 2…. 35 Figure 5.1 Developing risk functions from Maximum Species Richness…………. 38

  • v

    List of Tables Table 2.1 Hardness Conversion Factors………………………………………….. 12 Table 2.2 Data Count for Fish…………………………………………………….. 13 Table 2.3 Data Count for Benthic Invertebrates………………………………….. 13 Table 2.4 Goodness of Fit for Distributions of Cadmium LC50s………………… 15 Table 2.5 Mean and Standard Deviation of Concentrations at Site S17830……… 17 Table 2.6 Risk calculations for fish………………………………………………. 17 Table 2.7 Risk calculations for benthic invertebrates…………………………….. 18 Table 3.1 Description of Data used from Ohio Database………………………… 22 Table 4.1 Habitat Parameters Available in Ohio Database………………………. 30 Table 4.2 Network Training Group 1…………………………………………….. 32 Table 4.3 Network Training Group 2…………………………………………….. 35

  • 1

    1 Introduction 1.1 Ecological Integrity The United States’ Clean Water Act of 1972 was designed to protect the ecological integrity of the Nation’s waters. Ecological integrity can be defined as three parts, chemical, physical, and biological integrity. Physical integrity describes habitat conditions of the water body that would support a balanced biological community. Chemical integrity describes the chemical composition of water and sediments that would not be injurious to the aquatic biota or limit other uses of the water body such as recreation. Biological integrity describes a composition of aquatic organisms that would be balanced and would resemble that of unimpaired water bodies in the same ecoregion (Davis 1995; Karr 1995; Karr and Chu 1999). Although there are three separate measures, chemical and physical integrity are both defined with the purpose of maintaining, to some degree, biological integrity. The physical and chemical conditions within a water body are dependent on the actions within the contributing watershed area. Human activities within a watershed alter water body sources and conditions. The interactions between human alterations and the changes in the water body can become quite complex, see Figure 1.1. Any alteration to a watershed presents a risk that the biological integrity will be reduced.

    Figure 1.1 Watershed, land use changes and other impacts on water quality

    (adapted from Novotny 2003)

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