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Population-Level Ecological Risk Assessment (Paperback)
Loot Price: R1,767
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Population-Level Ecological Risk Assessment (Paperback)
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Most ecological risk assessments consider the risk to individual
organisms or organism-level attributes. From a management
perspective, however, risks to population-level attributes and
processes are often more relevant. Despite many published calls for
population risk assessment and the abundance of available
scientific research and technical tools assessing risks to
populations, risk assessors worldwide still have difficulty
determining how population level considerations can be integrated
into environmental decision-making. Population-Level Ecological
Risk Assessment establishes a framework for goals, methods, and
data needs for different assessment applications and for
integrating population-level risk assessment into risk management
decisions. Beginning with a summary of legal, regulatory, business,
and other contexts, the book presents population-level ecological
risk assessment as an internationally recognized, science-based
tool and offers specific recommendations for using this tool to
support environmental management decisions. It gives clear,
explicit, operational population assessment definitions and
explains the relevance of density dependence, genetics, and spatial
considerations, as well as applicable lessons from conservation
biology and natural resource management. The authors provide a
"tool box" of empirical and modeling methods and describe the
general approaches, assumptions, data requirements, strengths, and
limitations of each method. They establish a working foundation for
designing and conducting population-level ecological risk
assessments consistent with North American, European, and Japanese
risk management approaches. The book concludes by highlighting key
considerations needed to improve the scientific quality and
interpretation of assessments. Detailed appendices include examples
of population-level assessment approaches applicable to specific
environmental management contexts, a modeling case study, and a
supplemental r
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