The cumulative effects of climate warming on herbivore vital rates and population dynamics are hard to predict, given that the expected effects differ between seasons. In the Arctic, warmer summers enhance plant growth which should lead to heavier and more fertile individuals in the autumn. Conversely, warm spells in winter with rainfall (rain-on-snow) can cause 'icing', restricting access to forage, resulting in starvation, lower survival and fecundity. As body condition is a 'barometer' of energy demands relative to energy intake, we explored the causes and consequences of variation in body mass of wild female Svalbard reindeer (Rangifer tarandus platyrhynchus) from 1994 to 2015, a period of marked climate warming. Late winter (April) body mass explained 88% of the between-year variation in population growth rate, because it strongly influenced reproductive loss, and hence subsequent fecundity (92%), as well as survival (94%) and recruitment (93%). Autumn (October) body mass affected ovulation rates but did not affect fecundity. April body mass showed no long-term trend (coefficient of variation, CV = 8.8%) and was higher following warm autumn (October) weather, reflecting delays in winter onset, but most strongly, and negatively, related to 'rain-on-snow' events. October body mass (CV = 2.5%) increased over the study due to higher plant productivity in the increasingly warm summers. Density-dependent mass change suggested competition for resources in both winter and summer but was less pronounced in recent years, despite an increasing population size. While continued climate warming is expected to increase the carrying capacity of the high Arctic tundra, it is also likely to cause more frequent icing events. Our analyses suggest that these contrasting effects may cause larger seasonal fluctuations in body mass and vital rates. Overall our findings provide an important 'missing' mechanistic link in the current understanding of the population biology of a keystone species in a rapidly warming Arctic.

Abstract:We develop an integrated population model for a population of Svalbard reindeer (Rangifer tarandus platyrhynchus), and show how the model succeeds in extracting more information from the data and separating different sources of variability in population estimates. The model combines individual mark-recapture data with population counts and harvesting data within a Bayesian model framework, and accounts for observation error, environmental and demographic stochasticity and age structure. From this model we obtain estimates of population size, as well as age-specific survival and fecundity over time. The model provides estimates of age structure at a finer scale than that found in the census data, and enables us to estimate a survival parameter for which there is no information in the mark-recapture data. We use data from independent censuses of the same population to evaluate population estimates obtained from the model, and show that it is successful at correcting for different types of observation error. Our work demonstrates how integrated Bayesian population Oikos F o r R e v i e w O n l y modeling can be used to increase the amount of information extracted from collections of data. This includes estimating age structure from nonage-structured census data and combining it with estimates of age-specific life history parameters, while accounting for different sources of variability. This represents an important step towards increasing the predictive ability of population growth models for long-lived species.

We examined whether differences in life-history characteristics can explain interspecific variation in stochastic population dynamics in nine marine fish species living in the Barents Sea system. After observation errors in population estimates were accounted for, temporal variability in natural mortality rate, annual recruitment, and population growth rate was negatively related to generation time. Mean natural mortality rate, annual recruitment, and population growth rate were lower in long-lived species than in short-lived species. Thus, important species-specific characteristics of the population dynamics were related to the species position along the slow-fast continuum of life-history variation. These relationships were further associated with interspecific differences in ecology: species at the fast end were mainly pelagic, with short generation times and high natural mortality, annual recruitment, and population growth rates, and also showed high temporal variability in those demographic traits. In contrast, species at the slow end were long-lived, deepwater species with low rates and reduced temporal variability in the same demographic traits. These interspecific relationships show that the life-history characteristics of a species can predict basic features of interspecific variation in population dynamical characteristics of marine fish, which should have implications for the choice of harvest strategy to facilitate sustainable yields.

Classical approaches for the analyses of density dependence assume that all the individuals in a population equally respond and equally contribute to density dependence. However, in age-structured populations, individuals of different ages may differ in their responses to changes in population size and how they contribute to density dependence affecting the growth rate of the whole population. Here we apply the concept of critical age classes, i.e., a specific scalar function that describes how one or a combination of several age classes affect the demographic rates negatively, in order to examine how total density dependence acting on the population growth rate depends on the age-specific population sizes. In a 38-yr dataset of an age-structured great tit (Parus major) population, we find that the age classes, including the youngest breeding females, were the critical age classes for density regulation. These age classes correspond to new breeders that attempt to take a territory and that have the strongest competitive effect on other breeding females. They strongly affected population growth rate and reduced recruitment and survival rates of all breeding females. We also show that depending on their age class, females may differently respond to varying density. In particular, the negative effect of the number of breeding females was stronger on recruitment rate of the youngest breeding females. These findings question the classical assumptions that all the individuals of a population can be treated as having an equal contribution to density regulation and that the effect of the number of individuals is age independent. Our results improve our understanding of density regulation in natural populations.

.Life-history theory predicts that the vital rates that infl uence population growth the most should be buffered against environmental fl uctuations due to selection for reduced variation. However, it remains unclear whether populations actually are infl uenced by such "demographic buffering," because variation in vital rates can be compared on different measurement scales, and there has been little attempt to investigate whether the choice of scale infl uences the chance of detecting demographic buffering. We compared two statistical approaches to examine whether demographic buffering has infl uenced vital rates in wild Svalbard reindeer ( Rangifer tarandus platyrhynchus ). To account for statistical variance constraints on vital rates limited between 0 and 1 in analyses of demographic buffering, one approach is to scale observed variation by the maximum possible variation on the arithmetic scale. When applying this approach, the results suggested that demographic buffering was occurring. However, when we applied an alternative approach that identifi ed statistical variance constraints on the logit scale, there was no evidence for demographic buffering. Thus, the choice of measurement scale must be carefully considered before one can fully understand whether demographic buffering infl uences life histories. Defi ning the appropriate scale may require an understanding of the mechanisms through which demographic buffering may have evolved.