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Population dynamics are the result of an interplay between extrinsic and intrinsic environmental drivers. Predicting the effects of environmental change on wildlife populations therefore requires a thorough understanding of the mechanisms through which different environmental drivers interact to generate changes in population size and structure. In this study, we disentangled the roles of temperature, food availability and population density in shaping short- and long-term population dynamics of the African striped mouse, a small rodent inhabiting a semidesert with high intra- and interannual variation in environmental conditions. We parameterized a female-only stage-structured matrix population model with vital rates depending on temperature, food availability and population density, using monthly mark-recapture data from 1609 mice trapped over 9 years (2005-2014). We then applied perturbation analyses to determine relative strengths and demographic pathways of these drivers in affecting population dynamics. Furthermore, we used stochastic population projections to gain insights into how three different climate change scenarios might affect size, structure and persistence of this population. We identified food availability, acting through reproduction, as the main driver of changes in both short- and long-term population dynamics. This mechanism was mediated by strong density feedbacks, which stabilized the population after high peaks and allowed it to recover from detrimental crashes. Density dependence thus buffered the population against environmental change, and even adverse climate change scenarios were predicted to have little effect on population persistence (extinction risk over 100 years <5%) despite leading to overall lower abundances. Explicitly linking environment-demography relationships to population dynamics allowed us to accurately capture past population dynamics. It further enabled establishing the roles and relative importances of extrinsic and intrinsic environmental drivers, and we conclude that doing this is essential when investigating impacts of climate change on wildlife populations., (© 2018 The Authors. Journal of Animal Ecology © 2018 British Ecological Society.)

The integration and synthesis of the data in different areas of science is drastically slowed and hindered by a lack of standards and networking programmes. Long‐term studies of individually marked animals are not an exception. These studies are especially important as instrumental for understanding evolutionary and ecological processes in the wild. Furthermore, their number and global distribution provides a unique opportunity to assess the generality of patterns and to address broad‐scale global issues (e.g. climate change).To solve data integration issues and enable a new scale of ecological and evolutionary research based on long‐term studies of birds, we have created the SPI‐Birds Network and Database (www.spibirds.org)—a large‐scale initiative that connects data from, and researchers working on, studies of wild populations of individually recognizable (usually ringed) birds. Within year and a half since the establishment, SPI‐Birds has recruited over 120 members, and currently hosts data on almost 1.5 million individual birds collected in 80 populations over 2,000 cumulative years, and counting.SPI‐Birds acts as a data hub and a catalogue of studied populations. It prevents data loss, secures easy data finding, use and integration and thus facilitates collaboration and synthesis. We provide community‐derived data and meta‐data standards and improve data integrity guided by the principles of Findable, Accessible, Interoperable and Reusable (FAIR), and aligned with the existing metadata languages (e.g. ecological meta‐data language).The encouraging community involvement stems from SPI‐Bird's decentralized approach: research groups retain full control over data use and their way of data management, while SPI‐Birds creates tailored pipelines to convert each unique data format into a standard format. We outline the lessons learned, so that other communities (e.g. those working on other taxa) can adapt our successful model. Creating community‐specific hubs (such as ours, COMADRE for animal demography, etc.) will aid much‐needed large‐scale ecological data integration. [ABSTRACT FROM AUTHOR]

Climate change has different and sometimes divergent effects on terrestrial and marine food webs, and in coastal ecosystems, these effects are tightly interlinked. Responses of opportunistic coastal predators and scavengers to climate change may thus be complex and potentially highly flexible, and can simultaneously serve as indicators of, and have profound impacts on, lower trophic levels. Gaining mechanistic understanding of these responses is therefore important, but often not feasible due to lack of long‐term data from marked individuals. Here, we used a Bayesian integrated population model (IPM) to elucidate the effects of arctic warming and concurrent changes in terrestrial and marine resource availability on population dynamics of the opportunistic arctic fox (Vulpes lagopus) in Svalbard. Joint analysis of four types of data (den survey, age‐at‐harvest, placental scars, mark‐recovery) revealed relatively stable population size and age structure over the last 22 yr (1997–2019) despite rapid environmental change linked to climate warming. This was related to the fact that terrestrial resources (reindeer carcasses, geese) became more abundant while the availability of marine resources (seal pups/carrion) decreased, and was driven by divergent trends in different vital rates (e.g., increased pregnancy rate but decreased pup survival). Balanced contributions of survival vs. reproduction and of immigration vs. local demography further stabilized population size. Our study thus sheds light on the mechanisms underlying population dynamics of opportunistic carnivores exploiting terrestrial and marine resources and suggests that exploitation of resources across different ecosystems can buffer predators against climate change. Additionally, it highlights the large potential of IPMs as tools to understand and predict the effects of environmental change on wildlife populations, even when data on marked individuals are sparse. [ABSTRACT FROM AUTHOR]