Sea ice loss may have dramatic consequences for population connectivity, extinction–colonization dynamics, and even the persistence of Arctic species subject to climate change. This is of particular concern in face of additional anthropogenic stressors, such as overexploitation. In this study, we assess the population‐genetic implications of diminishing sea ice cover in the endemic, high Arctic Svalbard reindeer (Rangifer tarandus platyrhynchus) by analyzing the interactive effects of landscape barriers and reintroductions (following harvest‐induced extirpations) on their metapopulation genetic structure. We genotyped 411 wild reindeer from 25 sampling sites throughout the entire subspecies' range at 19 microsatellite loci. Bayesian clustering analysis showed a genetic structure composed of eight populations, of which two were admixed. Overall population genetic differentiation was high (mean FST = 0.21). Genetic diversity was low (allelic richness [AR] = 2.07–2.58; observed heterozygosity = 0.23–0.43) and declined toward the outer distribution range, where populations showed significant levels of inbreeding. Coalescent estimates of effective population sizes and migration rates revealed strong evolutionary source–sink dynamics with the central population as the main source. The population genetic structure was best explained by a landscape genetics model combining strong isolation by glaciers and open water, and high connectivity by dispersal across winter sea ice. However, the observed patterns of natural isolation were strongly modified by the signature of past harvest‐induced extirpations, subsequent reintroductions, and recent lack of sea ice. These results suggest that past and current anthropogenic drivers of metapopulation dynamics may have interactive effects on large‐scale ecological and evolutionary processes. Continued loss of sea ice as a dispersal corridor within and between island systems is expected to increase the genetic isolation of populations, and thus threaten the evolutionary potential and persistence of Arctic wildlife.

The synchrony of population dynamics in space has important implications for ecological processes, for example affecting the spread of diseases, spatial distributions and risk of extinction. Here, we studied the relationship between spatial scaling in population dynamics and species position along the slow‐fast continuum of life history variation. Specifically, we explored how generation time, growth rate and mortality rate predicted the spatial scaling of abundance and yearly changes in abundance of eight marine fish species. Our results show that population dynamics of species' with ‘slow’ life histories are synchronised over greater distances than those of species with ‘fast’ life histories. These findings provide evidence for a relationship between the position of the species along the life history continuum and population dynamics in space, showing that the spatial distribution of abundance may be related to life history characteristics.

The degree of spatial autocorrelation in population fluctuations increases with dispersal and geographical covariation in the environment, and decreases with strength of density dependence. Because the effects of these processes can vary throughout an individual’s lifespan, we studied how spatial autocorrelation in abundance changed with age in three marine fish species in the Barents Sea. We found large interspecific differences in age‐dependent patterns of spatial autocorrelation in density. Spatial autocorrelation increased with age in cod, the reverse trend was found in beaked redfish, while it remained constant among age classes in haddock. We also accounted for the average effect of local cohort dynamics, i.e. the expected local density of an age class given last year’s local density of the cohort, with the goal of disentangling spatial autocorrelation patterns acting on an age class from those formed during younger age classes and being carried over. We found that the spatial autocorrelation pattern of older age classes became increasingly determined by the distribution of the cohort during the previous year. Lastly, we found high degrees of autocorrelation over long distances for the three species, suggesting the presence of far‐reaching autocorrelating processes on these populations. We discuss how differences in the species’ life history strategies could cause the observed differences in age‐specific variation in spatial autocorrelation. As spatial autocorrelation can differ among age classes, our study indicates that fluctuations in age structure can influence the spatio‐temporal variation in abundance of marine fish populations.

Environmental variation in time and space generates complex patterns in the spatial structure of temporally covarying populations. Accounting for spatial population structure is important for sustainable management and harvest, but there is a need for a better understanding of the many mechanisms affecting the spatial structure of populations. In the large-scale research project SUSTAIN, detailed long-term data from several taxa within the boreal and Arctic ecosystems were used to address key research questions about spatial population structure. Here, we synthesise the main findings from these studies. Because nearby populations experience similar environmental variation, populations close to each other show more correlated dynamics than those at greater distances. However, several mechanisms can affect the extent of such spatial population synchrony, and we point to some similarities across systems that can explain the observed discrepancy between the spatial structure of the environment and that of population dynamics. We discuss the consequences of these findings for the practical management of species in a changing environment and the need for further research on how populations and ecosystems are affected by the spatial structure of the environment.

Understanding how population dynamics are influenced by species interactions and the surrounding community is crucial for addressing many ecological questions, but requires modelling of complex systems involving direct, indirect and often asymmetric species interactions. Progress in developing multispecies models that can tackle this task is being made in multiple subfields of ecology, often with varying approaches and end goals but also facing shared challenges. We review some of the main challenges and the ways in which they are being addressed, highlighting a wide variety of methods that can support the development of multispecies models for understanding population dynamics. The main challenges that we examine are estimation of species interactions from limited data, the necessity of simplifications, and handling uncertainty in complex, multispecies models. In addition to reviewing a wide variety of approaches and methods for dealing with these challenges, we discuss future directions and make suggestions for how we believe the development of multispecies models for understanding population dynamics can move forward more efficiently.