Robert A. Pastorok

Ecological modeling in risk assessment : chemical effects on populations, ecosystems, and landscapes

Preface, S.E. Jorgensen and R.A. Pastorok Introduction, R.A. Pastorok Methods, R.A. Pastorok and H.R. Akcakaya Results of the Evaluation of Ecological Models: Introduction, R.A. Pastorok Population Models-Scalar Abundance, S. Ferson Population Models-Life History, S. Carroll Population Models-Individual-Based, H.M. Regan Population Models-Metapopulations, H.R. Akcakaya and H. M. Regan Ecosystem Models-Food Webs, S. Carroll Ecosystem Models-Aquatic, S.M. Bartell Ecosystem Models-Terrestrial, C.E. Mackay and R.A. Pastorok Landscape Models-Aquatic and Terrestrial, C.E. Mackay and R.A. Pastorok Toxicity-Extrapolation Models, J.A. Colton Profiles of Selected Models, R.A. Pastorok Enhancing the Use of Ecological Models in Environmental Decision-Making, L.R. Ginzburg and H. R. Akcakaya Conclusions and Recommendations, R.A. Pastorok and L.R. Ginzburg Summary, R.A. Pastorok and H.R. Akcakaya References Appendix A: Fish Population Modeling: Data Needs and Case Study, S.J. Pauwels Appendix B: Classification Systems, K.V. Root Appendix C: Results of the Initial Screening of Ecological Models, Model Analysis Team

Role of Ecological Modeling in Risk Assessment

Ecological models are useful tools for evaluating the ecological significance of observed or predicted effects of toxic chemicals on individual organisms. Current risk estimation approaches using hazard quotients for individual-level endpoints have limited utility for assessing risks at the population, ecosystem, and landscape levels, which are the most relevant indicators for environmental management. In this paper, we define different types of ecological models, summarize their input and output variables, and present examples of the role of some recommended models in chemical risk assessments. A variety of population and ecosystem models have been applied successfully to evaluate ecological risks, including population viability of endangered species, habitat fragmentation, and toxic chemical issues. In particular, population models are widely available, and their value in predicting dynamics of natural populations has been demonstrated. Although data are often limited on vital rates and doseresponse functions needed for ecological modeling, accurate prediction of ecological effects may not be needed for all assessments. Often, a comparative assessment of risk (e.g., relative to baseline or reference) is of primary interest. Ecological modeling is currently a valuable approach for addressing many chemical risk assessment issues, including screening-level evaluations.

Realism and Relevance of Ecological Models Used in Chemical Risk Assessment

Ecological models have been developed and used in management of renewable natural resources, conservation biology, and assessments of ecological risks posed by toxic chemicals and other stressors. Because few models have been developed specifically for use in assessing chemical risks, this study examines the realism and relevance of a wide range of ecological models from the perspective of assessing toxicological risks posed by chemicals. Model realism is evaluated relative to the degree of structural and functional description of the actual (i.e., real-world) ecological entities or systems that is believed necessary for reliable estimation and management of risk. Relevance refers to the usefulness of model outputs in addressing the ecological impacts of interest in either generic or site-specific assessment of risks posed by toxic chemicals. A model becomes increasingly relevant the more closely its outputs correspond to the endpoints of the risk analysis. In addition to the relatively few models (e.g., CASM, IFEM, AQUATOX) that have been designed specifically for ecological risk assessment, we identify several models developed in support of basic research that might be adapted for realistic and relevant risk estimation. Population, ecosystem, and landscape models describe ecological phenomena from different perspectives. Associated with each perspective and resulting modeling approach are hypotheses concerning simplifying assumptions that facilitate the specification of model structure in relation to the ecological topic of interest. The structurally complex system models (e.g., AQUATOX, CASM) attempt to explicitly represent the many biotic and abiotic processes and interactions that are believed to influence the production dynamics of aquatic populations included in the models. Future efforts in ecological risk assessment modeling should focus on identifying the necessary model complexity required to achieve sufficiently accurate and precise estimates of risk, as defined by the needs of risk management and risk-based decision making. Evaluations of model realism, endpoint relevance, flexibility, ease of use, and other characteristics may help to guide model users in their choice of specific models for further development and application to continuing challenges in assessing ecological risks. Clear definition of specific model capabilities and corresponding risk assessment endpoints can help to ensure that applications of existing ecological models to chemical risk assessments are appropriately customized to the needs of environmental risk assessors, managers, and decision-makers.

Modeling wildlife exposure to toxic chemicals: Trends and recent advances

Abstract Modeling wildlife exposure to toxic chemicals and associated risk is a practical approach to ecological risk assessments at hazardous waste sites. However, screening‐level models to estimate wildlife exposure typically assume that the receptor spends all of its foraging time at the site, that chemical concentrations and habitats are stable over space and time, and that chemicals are 100% bioavailable. Currently, many ecological risk analysts are developing new methods for exposure analysis that lead to more realistic estimates of risk (e.g., spatial analysis, which includes quantifying the distribution of receptors in various habitats in relation to the distribution of chemicals; toxicokinetic modeling to estimate body burdens of chemicals; Monte Carlo analysis to derive probabilistic estimates of risk; interspecies extrapolation based on allometric models for exposure variables [e.g., feeding rate, metabolic rate]; and validation of exposure models at specific sites). To better support risk mana...

Metals that Drive Health-Based Remedial Decisions for Soils at U.S. Department of Defense Sites

ABSTRACT This study was undertaken to establish which metals are most likely to drive the risk-based remedial decision-making process at those U.S. Department of Defense (DoD) sites that are affected by metals in site soils. Our approach combined queries of various databases, interviews with U.S. Environmental Protection Agency (USEPA) experts in each Region, and communication with database administrators and DoD personnel. The databases that were used were comprehensive for DoD sites, yet sometimes contained inaccuracies. Metal concentration data for various DoD facilities were screened against established regulatory criteria for both human health and ecological endpoints. Results from this analysis were compared against the information gleaned from the interviews. This preliminary analysis indicates that the five metals that most frequently exceeded risk-based screening criteria for potential human health concerns at DoD sites, in descending order of frequency, are lead, arsenic, cadmium, chromium, and antimony. The metals that most frequently exceeded ecological screening criteria, in order, are lead, cadmium, mercury, zinc, arsenic, chromium, and selenium. Although the majority of USEPA personnel interviewed indicated that human health risk, rather than ecological endpoints, generally drives remedial decision-making, the data indicated that ecological screening thresholds were exceeded more often than human health standards.

Introduction: Improving Chemical Risk Assessments through Ecological Modeling

Despite several decades of progress in prevention and cleanup of environmental pollution, there are still many complex issues to address in solving toxic chemical problems. Further progress toward managing chemically contaminated sites as well as effective stewardship of new chemical products depends on developing more relevant assessments of ecological risks. Many ecologists have recommended integrating ecological modeling with toxicology to assess risks to populations or higher levels of biological organization and thereby achieve more relevance in ecological risk assessments (e.g., Barnthouse et al. 1986; Landis 2000; Pastorok et al. 2002). Although many ecological models have been developed and applied in other fields (e.g., basic ecological research and conservation biology), their use for chemical risk assessment has been relatively limited. As defined by Pastorok et al. (2002), ecological models are mathematical expressions to estimate the effects of environmental factors, including toxic chemicals, on attributes of populations, ecosystems, or landscapes (i.e., ecological endpoints above the level of the individual organism). Many ecologists recognize that population and ecosystem modeling is essential for quantitatively estimating risks to ecological systems (e.g., Barnthouse et al. 1986; Emlen 1989; Bartell et al. 1992; Barnthouse 1998; Forbes and Calow 1999; Snell and Serra 2000). However, most current assessments of toxic chemicals do not use such models and focus only on endpoints for individual organisms, such as survival, growth, or reproductive potential (e.g., fecundity). Many assessments done in support of environmental regulatory programs rely on simplistic approaches and fail to incorporate basic ecological information and modeling capabilities. Typically, an ecological risk assessment for a contaminated site or a new chemical (e.g., pesticide) relies on comparison of some exposure estimate for the chemical of interest with a corresponding toxicity threshold in a deterministic hazard quotient. This approach was originally intended only as a screening method (Barnthouse et al. 1986) and may produce misleading results because of compounding conservatism (Burmaster and von Stackelberg 1989; Cullen 1994). In many cases, there is substantial uncertainty in the no-observed-effects level or lowest-observed-effects level used as the toxicity threshold (Chapman et al. 1998), and the complete dose-response curve is unknown.

Future directions in modeling wildlife exposure to toxic chemicals

Abstract Predictive modeling of wildlife exposure to toxic chemicals continues to present new challenges for ecologists and modelers. Despite the variety of approaches developed within the past two decades, new methods are needed to achieve realistic estimates of exposure in practical ways. Promising areas for further development of modeling methods include (1) enhancing the accuracy of food webs, (2) developing a framework for application of wildlife exposure models, (3) incorporating concepts from landscape ecology into wildlife exposure models to better characterize habitat heterogeneity and interactions among patches, (4) developing practical individual‐based models, (5) developing approaches to verify and validate wildlife exposure models, and (6) linking exposure models with population and community models.