Regional Scale Ecological Risk Assessment (eBook)

Regional Scale Ecological Risk Assessment: Using the Relative Risk Model is a collaborative summary and guidebook of the development, methods, and application of the Relative Risk Model (RRM) to meet the need for regional assessments with multiple stressors from diverse sources and numerous desired endpoints. Amenable to additional iterations as new information becomes available, this assessment model is ideal for designing a dynamic management plan that can be customized to meet specific ecological needs.Driven to expand ecological risk assessment to reflect the structure, function, and scale of the environment, this book offers a new approach to evaluating risks at regional scales, assessing a variety of potential stressors within a dynamic landscape. The book proposes that using data on land use, hydrology, types of contaminants, the distribution of species, and the history of disturbance can result in a computation of risk gradients with in the system being managed. The calculation uses a process of setting ranks for a broad range of sources, stressors, and habitats with filters to denote exposure and effect to look at a combined risk at large geographical scales. The book provides numerous real world examples of how this process works, how it is tested, and how it is used for making management decisions.Regional Scale Ecological Risk Assessment demonstrates the capabilities of RRM using nine case studies in the Pacific Northwest, Pennsylvania, Brazil, and Tasmania. This book provides detailed descriptions for each step of RRM-from the determination of assessment goals to documentation, evaluation, and communication with decision-makers-that can benefit practitioners in environmental management, remediation, and related fields worldwide.

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As debates over how relative risk can be used to shape landscape-scale environmental management intensify, Regional-Scale Risk Assessment demonstrates the capabilities of RRM using nine case studies in the Pacific Northwest, Pennsylvania, Brazil, and Tasmania. The authors use a process of ranking and filters to interrelate different kinds of risks