Integrating Antarctic Environmental and Biological Predictability to Obtain Optimal Forecasts
Overview: We aim to provide the most detailed investigation to date of the factors that influence predictability of Antarctic climate, the coupling of climate to penguins populations, and the integration of the two to optimize ecological forecasts. This integrated understanding is critical for guiding future ecological and climate research, prioritizing bio-physical monitoring efforts, and informing conservation decision-making. Our study will reveal the influence of climate system dynamics on ecological predictability across a range of scales and will examine how this role differs among ecological processes, species and regions of Antarctica. Intellectual Merit: Many biophysical processes will change in the coming century. Yet, the mechanisms controlling the predictability of many climate processes are still poorly understood, limiting progress in climate forecasting. In parallel, ecological forecasting remains a nascent discipline. In particular, comparative assessments of predictability, both within and among species, are critically needed to understand the factors that allow (or prevent) useful ecological forecasts. While important for ecological science generally, this need is particularly pressing in Antarctica where the environment is highly dynamic, strongly coupled to biological processes, and likely to change in the future. Improved ecological forecasting therefore requires interdisciplinary efforts to understand the causes of predictability in climate, and in tandem, how climate influences the predictability of natural populations. This proposed research will examine the predictability of Antarctic climate and its influence on penguin demographic response predictability at various temporal and spatial scales using the longest datasets available for two penguin species. Specifically, the PI will 1) identify the physical mechanisms giving rise to climate predictability in Antarctica, 2) identify the relationships between climate and ecological processes at a range of scales, and 3) reveal the factors controlling ecological predictability across a range of scales (e.g., those relevant for short-term adaptive management versus those relevant at end-of-century timescales). These objectives will be achieved using the analysis of existing climate data and Atmosphere-Ocean Global Circulation Models (AGOCMs), with coupled analysis of existing long-term demographic data for multiple seabird species that span a range of ecological niches, life histories, and study sites across the continent.
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