000			03129 am a22003013u 4500
042			_adc
100	1	0	_avan Moorsel, Sofia J. _eauthor _92779
700	1	0	_aThébault, Elisa _eauthor _92780
700	1	0	_aRadchuk, Viktoriia _eauthor _92781
700	1	0	_aNarwani, Anita _eauthor _92782
700	1	0	_aMontoya, José M. _eauthor
700	1	0	_aDakos, Vasilis _eauthor _92783
700	1	0	_aHolmes, Mark _eauthor _92784
700	1	0	_aDe Laender, Frederik _eauthor _92785
700	1	0	_aPennekamp, Frank _eauthor _92786
245	0	0	_aPredicting effects of multiple interacting global change drivers across trophic levels
260			_bJohn Wiley and Sons Inc., _c2022-12-21.
500			_a/pmc/articles/PMC7614140/
500			_a/pubmed/36461630
520			_aGlobal change encompasses many co‐occurring anthropogenic drivers, which can act synergistically or antagonistically on ecological systems. Predicting how different global change drivers simultaneously contribute to observed biodiversity change is a key challenge for ecology and conservation. However, we lack the mechanistic understanding of how multiple global change drivers influence the vital rates of multiple interacting species. We propose that reaction norms, the relationships between a driver and vital rates like growth, mortality, and consumption, provide insights to the underlying mechanisms of community responses to multiple drivers. Understanding how multiple drivers interact to affect demographic rates using a reaction‐norm perspective can improve our ability to make predictions of interactions at higher levels of organization-that is, community and food web. Building on the framework of consumer-resource interactions and widely studied thermal performance curves, we illustrate how joint driver impacts can be scaled up from the population to the community level. A simple proof‐of‐concept model demonstrates how reaction norms of vital rates predict the prevalence of driver interactions at the community level. A literature search suggests that our proposed approach is not yet used in multiple driver research. We outline how realistic response surfaces (i.e., multidimensional reaction norms) can be inferred by parametric and nonparametric approaches. Response surfaces have the potential to strengthen our understanding of how multiple drivers affect communities as well as improve our ability to predict when interactive effects emerge, two of the major challenges of ecology today.
540			_a© 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.
540			_ahttps://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
546			_aen
690			_aOpinions _92787
655	7		_aText _2local
786	0		_nGlob Chang Biol
856	4	1	_uhttp://dx.doi.org/10.1111/gcb.16548 _zConnect to this object online.
999			_c466 _d466