Contrasting effects of summer and winter warming onbody mass explain population dynamics in a food-limitedArctic herbivore

-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. Keywords: climate change, density dependence, extreme events, icing, nutrition, primary production, Rangifer, reindeer, Svalbard, weathe

Genetic variability and structure of the water vole Arvicola amphibius across four metapopulations in northern Norway

Water vole Arvicola amphibius populations have recently experienced severe decline in several European countries as a consequence of both reduction in suitable habitat and the establishment of the alien predator American mink Neovison vison. We used DNA microsatellite markers to describe the genetic structure of 14 island populations of water vole off the coast of northern Norway. We looked at intra- and inter-population levels of genetic variation and examined the effect of distance among pairs of populations on genetic differentiation (isolation by distance). We found a high level of genetic differentiation (measured by FST) among populations overall as well as between all pairs of populations. The genetic differentiation between populations was positively correlated with geographic distance between them. A clustering analysis grouped individuals into 7 distinct clusters and showed the presence of 3 immigrants among them. Our results suggest a small geographic scale for evolutionary and population dynamic processes in our water vole populations

Demographic buffering of life histories? Implications of the choice of measurement scale

Life-history theory predicts that the vital rates that influence population growth the most should be buffered against environmental fluctuations due to selection for reduced variation. However, it remains unclear whether populations actually are influenced 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 influences the chance of detecting demographic buffering. We compared two statistical approaches to examine whether demographic buffering has influenced vital rates in wild Svalbard reindeer. 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 identified 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 influences life histories. Defining the appropriate scale may require an understanding of the mechanisms through which demographic buffering may have evolved.acceptedVersio

An integrated population model for a long-lived ungulate: more efficient data use with Bayesian methods

We develop an integrated population model for Svalbard reindeer Rangifer tarandus platyrhynchus, and demonstrate how this type of model can be used to extract more information from the data and separate different sources of variability in population estimates. Our 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 annual estimates of age-specific population size, survival and fecundity. The model provides estimates of age structure at a finer scale than that found in the census data, and enables us to estimate survival for the period before calves are first caught and marked, i.e. before they enter the individual mark–recapture data. The modeling framework provides an improved approach to studying age-structured populations that are imperfectly censused and where the demography of only a sample of individuals is known. 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. Based on our model results, we suggest that allocating resources to the collection of supplementary mark–recapture data could improve the reliability of population projections more than making regular population censuses as exhaustive as possible. Our work demonstrates how integrated Bayesian population modeling can be used to increase the amount of information extracted from collections of data, identifying and disentangling sources of variation in individual performance and population size. This represents an important step towards increasing the predictive ability of population growth models for long-lived species experiencing changes in environmental conditions and harvesting regimes.submittedVersio

An integrated population model for a long-lived ungulate: more efficient data use with Bayesian methods

We develop an integrated population model for Svalbard reindeer Rangifer tarandus platyrhynchus, and demonstrate how this type of model can be used to extract more information from the data and separate different sources of variability in population estimates. Our 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 annual estimates of age-specific population size, survival and fecundity. The model provides estimates of age structure at a finer scale than that found in the census data, and enables us to estimate survival for the period before calves are first caught and marked, i.e. before they enter the individual mark–recapture data. The modeling framework provides an improved approach to studying age-structured populations that are imperfectly censused and where the demography of only a sample of individuals is known. 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. Based on our model results, we suggest that allocating resources to the collection of supplementary mark–recapture data could improve the reliability of population projections more than making regular population censuses as exhaustive as possible. Our work demonstrates how integrated Bayesian population modeling can be used to increase the amount of information extracted from collections of data, identifying and disentangling sources of variation in individual performance and population size. This represents an important step towards increasing the predictive ability of population growth models for long-lived species experiencing changes in environmental conditions and harvesting regimes