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Investigating cumulative effects across ecological scales
Authors:Emma E Hodgson  Benjamin S Halpern
Institution:1. Department of Biological Sciences, Simon Fraser University, 8888 University Way, Burnaby, BC, V5A 1S6 Canada;2. National Center for Ecological Analysis and Synthesis, University of California, 735 State Street #300, Santa Barbara, CA 93101 U.S.A.

Bren School of Environmental Science and Management, University of California, Santa Barbara, Santa Barbara, CA 93106 U.S.A.

Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, SL57PY U.K.

Abstract:Species, habitats, and ecosystems are increasingly exposed to multiple anthropogenic stressors, fueling a rapidly expanding research program to understand the cumulative impacts of these environmental modifications. Since the 1970s, a growing set of methods has been developed through two parallel, sometimes connected, streams of research within the applied and academic realms to assess cumulative effects. Past reviews of cumulative effects assessment (CEA) methods focused on approaches used by practitioners. Academic research has developed several distinct and novel approaches to conducting CEA. Understanding the suite of methods that exist will help practitioners and academics better address various ecological foci (physiological responses, population impacts, ecosystem impacts) and ecological complexities (synergistic effects, impacts across space and time). We reviewed 6 categories of methods (experimental, meta-analysis, single-species modeling, mapping, qualitative modeling, and multispecies modeling) and examined the ability of those methods to address different levels of complexity. We focused on research gaps and emerging priorities. We found that no single method assessed impacts across the 4 ecological foci and 6 ecological complexities considered. We propose that methods can be used in combination to improve understanding such that multimodel inference can provide a suite of comparable outputs, mapping methods can help prioritize localized models or experimental gaps, and future experiments can be paired from the outset with models they will inform.
Keywords:cumulative effects assessment  meta-analysis  multispecies modeling  population modeling  qualitative modeling  review  evaluación de efectos acumulativos  meta-análisis  modelado cualitativo  modelado multi-especie  modelado poblacional  revisión  累积效应评估  集合分析  定性建模  种群建模  多物种建模  综述
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