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Detecting climate signals in populations across life histories
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International audience. Climate impacts are not always easily discerned in wild populations as our ability to detect climate change signals in populations is challenged by stochastic noise associated with climate natural variability; biotic and abiotic processes variability in ecosystem; and observation error in demographic processes. In addition, population responses to climate variability and change can be contrasted and differ among life histories, affecting the detection of anthropogenic forced change across species. To detect the impact of climate change on populations, climate-driven signals in population should be distinguished from stochastic noise. The time of emergence (ToE) identifies when the signal of anthropogenic climate change can be quantitatively distinguished from natural climate variability. This concept has been applied extensively in the climate sciences, but has not yet formally been explored in the context of population dynamics. Here, we outline a new direction for detecting climate-driven signals in population by characterizing whether climate changes are potentially beyond the year-specific stochastic variations of populations. Specifically, we present a theoretical assessment of the time of emergence of climate-driven signals in population dynamics (ToEpop) to detect climate signals in populations. We identify the dependence of ToEpop on the magnitude of climate trends and variability and explore the demographic controls on ToEpop. We demonstrate that different life histories (fast species vs. slow species), demographic processes (survival, reproduction) and functional relationships between climate and demographic rates, yield population dynamics that filter trends and variability in climate differently. We illustrate empirically how to detect the point in time when anthropogenic signals in populations emerge from stochastic noise for a species threatened by climate change: the emperor penguin. Finally, we propose six testable hypotheses and a road map for future research.
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