Coste
Christophe F. D.
Coste
Christophe F. D.
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ArticleDetecting climate signals in populations across life histories(Wiley, 2021-12-20) Jenouvrier, Stephanie ; Long, Matthew C. ; Coste, Christophe F. D. ; Holland, Marika M. ; Gamelon, Marlène ; Yoccoz, Nigel G. ; Saether, Bernt-ErikClimate impacts are not always easily discerned in wild populations as detecting climate change signals in populations is challenged by stochastic noise associated with natural climate variability, variability in biotic and abiotic processes, and observation error in demographic rates. Detection of the impact of climate change on populations requires making a formal distinction between signals in the population associated with long-term climate trends from those generated by 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 been explored in the context of population dynamics. Here, we outline an approach to detecting climate-driven signals in populations based on an assessment of when climate change drives population dynamics beyond the envelope characteristic of stochastic variations in an unperturbed state. Specifically, we present a theoretical assessment of the time of emergence of climate-driven signals in population dynamics (ToEpop). We identify the dependence of (ToEpop)on the magnitude of both trends and variability in climate and also explore the effect of intrinsic demographic controls on (ToEpop). We demonstrate that different life histories (fast species vs. slow species), demographic processes (survival, reproduction), and the relationships between climate and demographic rates yield population dynamics that filter climate trends and variability 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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ArticleQuantifying fixed individual heterogeneity in demographic parameters: performance of correlated random effects for Bernoulli variables(British Ecological Society, 2021-09-24) Fay, Remi ; Authier, Matthieu ; Hamel, Sandra ; Jenouvrier, Stephanie ; van de Pol, Martijn ; Cam, Emmanuelle ; Gaillard, Jean-Michel ; Yoccoz, Nigel G. ; Acker, Paul ; Allen, Andrew ; Aubry, Lise M. ; Bonenfant, Christophe ; Caswell, Hal ; Coste, Christophe F. D. ; Larue, Benjamin ; Le Coeur, Christie ; Gamelon, Marlène ; Macdonald, Kaitlin R. ; Moiron, Maria ; Nicol-Harper, Alex ; Pelletier, Fanie ; Rotella, Jay J. ; Teplitsky, Celine ; Touzot, Laura ; Wells, Caitlin P. ; Saether, Bernt-Erik1. An increasing number of empirical studies aim to quantify individual variation in demographic parameters because these patterns are key for evolutionary and ecological processes. Advanced approaches to estimate individual heterogeneity are now using a multivariate normal distribution with correlated individual random effects to account for the latent correlations among different demographic parameters occurring within individuals. Despite the frequent use of multivariate mixed models, we lack an assessment of their reliability when applied to Bernoulli variables. 2. Using simulations, we estimated the reliability of multivariate mixed effect models for estimating correlated fixed individual heterogeneity in demographic parameters modelled with a Bernoulli distribution. We evaluated both bias and precision of the estimates across a range of scenarios that investigate the effects of life-history strategy, levels of individual heterogeneity and presence of temporal variation and state dependence. We also compared estimates across different sampling designs to assess the importance of study duration, number of individuals monitored and detection probability. 3. In many simulated scenarios, the estimates for the correlated random effects were biased and imprecise, which highlight the challenge in estimating correlated random effects for Bernoulli variables. The amount of fixed among-individual heterogeneity was frequently overestimated, and the absolute value of the correlation between random effects was almost always underestimated. Simulations also showed contrasting performances of mixed models depending on the scenario considered. Generally, estimation bias decreases and precision increases with slower pace of life, large fixed individual heterogeneity and large sample size. 4. We provide guidelines for the empirical investigation of individual heterogeneity using correlated random effects according to the life-history strategy of the species, as well as, the volume and structure of the data available to the researcher. Caution is warranted when interpreting results regarding correlated individual random effects in demographic parameters modelled with a Bernoulli distribution. Because bias varies with sampling design and life history, comparisons of individual heterogeneity among species is challenging. The issue addressed here is not specific to demography, making this warning relevant for all research areas, including behavioural and evolutionary studies.