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Climatic shifts to warmer and often drier conditions are challenging terrestrial species worldwide. These shifts are occurring more rapidly at higher elevations and latitudes, likely causing disproportionate effects to mammalian hibernators there. While there is some information about how these species' ranges are responding to climatic shifts, we lack an understanding of how climate components are affecting species' life history variation, which is key to individual success and population‐level resilience. We reviewed the literature to identify the direction of life history responses to climate change in mammalian hibernators along three axes: latitudinal, elevational and temporal. We found 39 studies involving 27 species that reported climate effects on our four target life history traits – phenology, body mass/condition and growth, reproduction and survival. We found warmer temperatures are advancing hibernator phenology and increasing reproductive success. By contrast, warming and drying trends are having uncertain effects on body condition, and complex effects on survival – depending on season, age class, latitude and elevation. We found no pattern of significant climate‐trait outcomes by duration or decade of study. More research on drought conditions – particularly in relation to resource availability – would help inform hibernator susceptibility to increased drying trends expected to intensify globally. Notably, our results are highly biased towards small mammal hibernators in Northern Hemisphere alpine/mountain ecosystems, with few long‐term studies conducted on Southern Hemisphere hibernators. This review highlights that phenological shifts constitute one of the most obvious consequences of climate change, yet, the timing of life history events (e.g. timing of migration, reproduction, hibernation) remains poorly understood. Further integration of insights from physiologists, evolutionary biologists and population ecologists working on wild populations will improve our collective understanding of the effects of seasonal climatic shifts on mammalian hibernator life history traits, key drivers of their population‐level persistence. [ABSTRACT FROM AUTHOR]

Maternal characteristics, social dynamics, and environmental factors can all influence reproduction and survival and shape trade‐offs that might arise between these components of fitness. Short‐lived mammals like the golden‐mantled ground squirrel (GMGS; Callospermophilus lateralis) tend to maximize effort toward current reproduction at the expense of survival but may be complicated by other aspects of the species' life history and environment. Here, we use 25 years of data (1995–2020) collected from a population of GMGS at the Rocky Mountain Biological Research Laboratory in Gothic, Colorado, to test the effect of several maternal characteristics (e.g., age, experience, and timing of litter emergence), social context (e.g., litter sex ratio and kin density), and environmental context (e.g., date of bare ground and length of vegetative growing season) on survival of reproductive female GMGS using Cox proportional hazard models. Our results indicated that social dynamics (i.e., density) and environmental conditions (i.e., standardized first day of permanent snow cover and length of growing season) explained significant variation in annual maternal survival, while maternal characteristics did not. A higher density of related breeding females and the total number of females (both related and unrelated to the focal mother) were associated with an increase in the mortality hazard. A later standardized date of the first day of permanent snow cover and a shorter growing season both reduced the maternal mortality hazard. Together, our results suggest that factors extrinsic to the squirrels affect maternal survival and thus may also influence local population growth and dynamics in GMGS and other short‐lived, territorial mammal species. We used 25 years of longitudinal data collected from golden‐mantled ground squirrel females to test the effects of individual characteristics, the social context, and the environment on female mortality using Cox proportional hazard models. Both female kin density and the environmental context explained significant variation in female mortality, while individual characteristics did not. Our results suggest that both kin density and climate metrics related to growing season phenology swamp individual characteristics in explaining variability in maternal mortality. This is an important finding in a rather short‐lived species where every additional active season survived has a lot of weight in improving lifetime reproduction. [ABSTRACT FROM AUTHOR]

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.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.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.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. [ABSTRACT FROM AUTHOR]

Understanding the processes behind change in reproductive state along life-history trajectories is a salient research program in evolutionary ecology. Two processes, state dependence and heterogeneity, can drive the dynamics of change among states. Both processes can operate simultaneously, begging the difficult question of how to tease them apart in practice. The Neutral Theory for Life Histories ( NTLH) holds that the bulk of variations in life-history trajectories is due to state dependence and is hence neutral: Once previous (breeding) state is taken into account, variations are mostly random. Lifetime reproductive success ( LRS), the number of descendants produced over an individual's reproductive life span, has been used to infer support for NTLH in natura. Support stemmed from accurate prediction of the population-level distribution of LRS with parameters estimated from a state dependence model. We show with Monte Carlo simulations that the current reliance of NTLH on LRS prediction in a null hypothesis framework easily leads to selecting a misspecified model, biased estimates and flawed inferences. Support for the NTLH can be spurious because of a systematic positive bias in estimated state dependence when heterogeneity is present in the data but ignored in the analysis. This bias can lead to spurious positive covariance between fitness components when there is in fact an underlying trade-off. Furthermore, neutrality implied by NTLH needs a clarification because of a probable disjunction between its common understanding by evolutionary ecologists and its translation into statistical models of life-history trajectories. Irrespective of what neutrality entails, testing hypotheses about the dynamics of change among states in life histories requires a multimodel framework because state dependence and heterogeneity can easily be mistaken for each other. [ABSTRACT FROM AUTHOR]

Studies of wild vertebrates have provided evidence of substantial differences in lifetime reproduction among individuals and the sequences of life history 'states' during life (breeding, nonbreeding, etc.). Such differences may reflect 'fixed' differences in fitness components among individuals determined before, or at the onset of reproductive life. Many retrospective life history studies have translated this idea by assuming a 'latent' unobserved heterogeneity resulting in a fixed hierarchy among individuals in fitness components. Alternatively, fixed differences among individuals are not necessarily needed to account for observed levels of individual heterogeneity in life histories. Individuals with identical fitness traits may stochastically experience different outcomes for breeding and survival through life that lead to a diversity of 'state' sequences with some individuals living longer and being more productive than others, by chance alone. The question is whether individuals differ in their underlying fitness components in ways that cannot be explained by observable 'states' such as age, previous breeding success, etc. Here, we compare statistical models that represent these opposing hypotheses, and mixtures of them, using data from kittiwakes. We constructed models that accounted for observed covariates, individual random effects (unobserved heterogeneity), first-order Markovian transitions between observed states, or combinations of these features. We show that individual sequences of states are better accounted for by models incorporating unobserved heterogeneity than by models including first-order Markov processes alone, or a combination of both. If we had not considered individual heterogeneity, models including Markovian transitions would have been the best performing ones. We also show that inference about age-related changes in fitness components is sensitive to incorporation of underlying individual heterogeneity in models. Our approach provides insight into the sources of individual heterogeneity in life histories, and can be applied to other data sets to examine the ubiquity of our results across the tree of life. [ABSTRACT FROM AUTHOR]