Multiple Stressors in the Environment: Assessing Ecological Effects in Complex Systems - Tapa dura

Acerca del autor

LAWRENCE A. KAPUSTKA, PhD, is an Emeritus Senior Ecologist and SETAC Fellow with experience as a professor, government research scientist, and environmental consultant.

WAYNE G. LANDIS, PhD, is Research Professor Emeritus at Western Washington University, where he directed the Institute of Environmental Toxicology and Chemistry for over three decades.

DAVID J. OSTRACH, PhD, is Chief Scientist at Ostrach Consulting, specializing in aquatic ecosystems, aquatic toxicology, and risk assessment. He founded the Pathobiology, Conservation and Population Biology Laboratory at UC Davis.

CAMERON IRVINE is an Associate Environmental Scientist and Ecotoxicologist with Robertson-Bryan, Inc. He chairs the ASTM International E50.47 subcommittee developing international toxicity testing standards, has been an active SETAC member for over 25 years.

De la contraportada

Assess ecological effects of co-occurring stressors in complex environmental systems

Organisms face simultaneous chemical, physical, biological, and societal stressors whose interactions produce ecological effects that single-stressor assessments cannot predict. Multiple Stressors in the Environment: Assessing Ecological Effects in Complex Systems, edited by a team of environmental scientists and ecotoxicologists each with decades of research and consulting experience, delivers practical frameworks for evaluating these mixed-stressor conditions across freshwater, estuarine, marine, and terrestrial systems.

Multiple Stressors traces the state of the science from the modern environmental era through current analytical developments in biology, chemistry, and ecology. Case studies drawn from North America, Europe, and Australia address diverse biomes and social structures, including urban, rural, and Indigenous Community settings. Statistical methods for complex data analysis, Bayesian network modeling for ecological risk estimation, and applications of artificial intelligence and machine learning are presented.

Readers will also find:

• Examinations of how biological, chemical, physical, and societal stressors interact to produce effects invisible to single-stressor evaluation approaches
• Case studies spanning freshwater, estuarine, marine, and terrestrial systems set within urban, rural, and Indigenous Community contexts
• Bayesian network applications for modeling multiple stressor systems and estimating ecological risk across diverse geographic regions
• Techniques for eliciting stakeholder values and applying conflict resolution methods to reach agreed environmental practices and policies
• Adaptive management strategies that integrate emerging computational methods with established ecological risk assessment frameworks

Designed for scientists, practitioners, regulators, and academics in environmental fields, this reference equips readers with the analytical approaches and case-based models needed to assess and manage ecological effects in systems where stressors co-occur and interact.