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Global environmental change (GEC) is a significant concern. However, forecasting the outcomes of this change for species and ecosystems remains a major challenge. In particular, predicting specific changes in systems where initial conditions, instabilities, and model errors have large impacts on the outcome is problematic. Indeed, predictive community ecology has been deemed unworthy of pursuit or an unreachable goal. However, new developments in large-scale biology provide ways of thinking that might substantially improve forecasts of local and regional impacts of climate change. Most notably, these are the explicit recognition of the regional and landscape contexts within which populations reside, the matrix approach that can be used to investigate the consequences of population variation across space and within assemblages, and the development of macrophysiology, which explicitly seeks to understand the ecological implications of physiological variation across large spatial and temporal scales. Here we explore how a combination of these approaches might promote further understanding and forecasting of the effects of global climate change and perhaps other GEC drivers on biodiversity. We focus on the population level, examining the ways in which environmental variation might be translated through performance and its plasticity to variation in demography.

Population responses within a landscape matrix: a macrophysiological approach to understanding climate change impacts