2021
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Abstract: The ability of mechanistic models to reliably extrapolate to novel conditions could position them as the gold standard in understanding the impacts of global change, but exactly how mechanistic models can be used most effectively remains to be determined. In this issue, Desforges et al. present a mechanistic physiological model to understand the drivers of muskox population dynamics. We took this as an opportunity to discuss the potential for, and challenges of, using mechanistic models to predict ecological r… Show more

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Cited by 5 publications
(4 citation statements)
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References 9 publications
(18 reference statements)
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“…We also found that the direction of extreme event impacts in the Arctic sometimes differed within and between biota and across extreme events (Figure 2). Although this highlights that the predictability of local responses to extreme events is low, and more empirical data is clearly required to better understand the mechanisms and processes involved, we echo the view that predictive modelling efforts are a useful addition to the toolkit required to tackle some of the current knowledge gaps (Sillmann et al, 2017;Boult and Evans, 2021). Empirical data derived from long-term, science-based monitoring programmes combined with e.g., remote sensing products greatly facilitates the development of hindcasting and forecasting models to assess previous and future conditions over large geographic areas and temporal scales.…”
Section: Consider Predictive Modelling and Ecosystem-level Impactsmentioning
confidence: 77%
“…We also found that the direction of extreme event impacts in the Arctic sometimes differed within and between biota and across extreme events (Figure 2). Although this highlights that the predictability of local responses to extreme events is low, and more empirical data is clearly required to better understand the mechanisms and processes involved, we echo the view that predictive modelling efforts are a useful addition to the toolkit required to tackle some of the current knowledge gaps (Sillmann et al, 2017;Boult and Evans, 2021). Empirical data derived from long-term, science-based monitoring programmes combined with e.g., remote sensing products greatly facilitates the development of hindcasting and forecasting models to assess previous and future conditions over large geographic areas and temporal scales.…”
Section: Consider Predictive Modelling and Ecosystem-level Impactsmentioning
confidence: 77%
“…Linking community patterns with detailed mechanisms, such as context‐dependent growth rates, is a major challenge and currently has been attempted only at small scales (e.g., White et al, 2020). In future, modelling efforts might be better able to connect species physiology to large‐scale patterns observed for butterflies by using standardized schemes (Johnston et al, 2019), but this would require tighter integration of the cycle of data collection, theory, modelling and testing than is typically practised (Boult & Evans, 2021; Dietze et al, 2018). Finally, our data might contain observational errors, which have been shown to explain variation in community stability (de Mazancourt et al, 2014).…”
Section: Discussionmentioning
confidence: 99%
“…As time is running out maintaining and restoring biodiversity using a case-by-case approach for each species is unlikely to support achieving these goals ( Franklin, 1993 ; Lindenmayer et al , 2007 ). Additionally, given the variety of stressors and their potential interactions, mechanistic approaches that rely on unified principles, rather than empirical approaches, should facilitate extrapolating knowledge from species to species ( Denny and Helmuth, 2009 ; Boult and Evans, 2021 ). Research in ecophysiology and metabolic theories provide the fundamental knowledge that explains the functioning of organisms, populations and ecosystems under the full range of environmental conditions ( Cooke et al , 2013 ; Kearney et al , 2021 ).…”
Section: Introductionmentioning
confidence: 99%