Wildcards in climate change biology. Issue 4 (10th August 2021)

• Record Type: Journal Article
• Authors: Srivastava, Diane S.; Coristine, Laura; Angert, Amy L.; Bontrager, Megan; Amundrud, Sarah L.; Williams, Jennifer L.; Yeung, Alex C. Y.; de Zwaan, Devin R.; Thompson, Patrick L.; Aitken, Sally N.; Sunday, Jennifer M.; O'Connor, Mary I.; Whitton, Jeannette; Brown, Norah E. M.; MacLeod, Colin D.; Parfrey, Laura Wegener; Bernhardt, Joey R.; Carrillo, Juli; Harley, Christopher D. G.; Martone, Patrick T.; Freeman, Benjamin G.; Tseng, Michelle; Donner, Simon D.
• Is Part Of: Ecological monographs. Volume 91:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-10
• Subjects: contingency -- ecological surprise -- forecast -- insurance -- multiple stressor -- prediction -- rescue -- uncertainty; Ecology -- Periodicals; Ecology; Écologie; Electronic journals; Periodicals; Ressource Internet (Descripteur de forme); Périodique électronique (Descripteur de forme); 577
• DOI: 10.1002/ecm.1471
• Languages: English
• ISSNs: 0012-9615

Abstract

Abstract: Forecasting how climate change will impact biological systems represents a grand challenge for biologists. However, climate change biology lacks an effective framework for anticipating and resolving uncertainty. Here, we introduce the concept of climate change wildcards: biological or bioclimatic processes with a high degree of uncertainty and a large impact on our ability to address the biotic consequences of climate change. Wildcards may occur at multiple points in the progression of research—from understanding, to predicting, to forecasting biological responses. Our understanding of biological responses is limited by the components and processes we exclude to make research tractable. Our ability to predict biological responses often requires integration between biological levels of organization, across multiple stressors, and from specific cases to general systems. However, these types of integration can be dramatically affected by, respectively, differences between biological levels in their critical points, nonadditivity of the effects of different stressors, and historical and geographic contingency. Finally, our ability to forecast biological responses to climate change requires incorporating climatic projections in bioclimatic models. Such forecasts are vulnerable to the compounding of biological and climatic uncertainty, especially when biological responses occur in novel areas of bioclimatic parameter space. Both biological responses and climate change are dynamic processes; the potential of biological systems to be buffered against or rescued from the effects of climate change depends on the relative timing of biological and climatic effects—one of the least predictable aspects of both systems. In sum, our framework identifies stress points in the research process where we should anticipate and forestall wildcards. Focusing on universal currencies, like energy and elements, and universal structures, like functional traits and ecological networks, will improve our ability to generalize results. Most importantly, by modeling and communicating uncertainty, climate change biology can identify critical foci for future research.