Abstract : 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 associ-
ated 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, affect-
ing the detection of anthropogenic forced change across species. To detect the impact
of climate change on populations, climate-driven signals in population should be distin-
guished 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 out-
line 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 pop-
ulations. We identify the dependence of ToEpop on the magnitude of climate trends and
variability and explore the demographic controls on ToEpop. We demonstrate that dif-
ferent life histories (fast species vs. slow species), demographic processes (survival, re-
production) 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