Decision-making under uncertainty for species introductions into ecological networks.

Autor: Van Kleunen LB; Department of Computer Science, University of Colorado, Boulder, Colorado, USA., Peterson KA; National Socio-Environmental Synthesis Center, Annapolis, Maryland, USA., Hayden MT; Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA., Keyes A; Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA., Schwartz AJ; Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA., Li H; Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA., Dee LE; Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA.
Jazyk: angličtina
Zdroj: Ecology letters [Ecol Lett] 2023 Jun; Vol. 26 (6), pp. 983-1004. Date of Electronic Publication: 2023 Apr 10.
DOI: 10.1111/ele.14212
Abstrakt: Ecological communities are increasingly subject to natural and human-induced additions of species, as species shift their ranges under climate change, are introduced for conservation and are unintentionally moved by humans. As such, decisions about how to manage ecosystems subject to species introductions and considering multiple management objectives need to be made. However, the impacts of gaining new species on ecological communities are difficult to predict due to uncertainty in introduced species characteristics, the novel interactions that will be produced by that species, and the recipient ecosystem structure. Drawing on ecological and conservation decision theory, we synthesise literature into a conceptual framework for species introduction decision-making based on ecological networks in high-uncertainty contexts. We demonstrate the application of this framework to a theoretical decision surrounding assisted migration considering both biodiversity and ecosystem service objectives. We show that this framework can be used to evaluate trade-offs between outcomes, predict worst-case scenarios, suggest when one should collect additional data, and allow for improving knowledge of the system over time.
(© 2023 John Wiley & Sons Ltd.)
Databáze: MEDLINE
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