• Energy Research

SummaryUnderstanding how climate change will affect animal population dynamics remains a major challenge, especially in long‐distant migrants exposed to different climatic regimes throughout their annual cycle.We evaluated the effect of temperature throughout the annual cycle on demographic parameters (age‐specific survival and recruitment, breeding propensity and fecundity) of the greater snow goose (Chen caerulescens atlanticaL.), an arctic‐nesting species. As this is a hunted species, we used the theory of exploited populations to estimate hunting mortality separately from natural mortality in order to evaluate climatic effects only on the latter form of mortality.Our analysis was based on a 22‐year marking study (n = 27 150 females) and included live recaptures at the breeding colony and dead recoveries from hunters. We tested the effect of climatic covariates by applying a procedure that accounts for unexplained environmental variation in the demographic parameter to a multistate capture–mark–recapture recruitment model.Breeding propensity, clutch size and hatching probability all increased with high temperatures on the breeding grounds. First‐year survival to natural causes of mortality increased when temperature was high at the end of the summer, whereas adult survival was not affected by temperature. On the contrary, accession to reproduction decreased with warmer climatic conditions during the non‐breeding season.Survival was strongly negatively related to hunting mortality in adults, as expected, but not in first‐year birds, which suggests the possibility of compensation between natural and hunting mortality in the latter group.We show that events occurring both at and away from the breeding ground can affect the demography of migratory birds, either directly or through carryover effects, and sometimes in opposite ways. This highlights the need to account for the whole life cycle of an animal when attempting to project the response of populations to future climatic changes.

This dataset provides estimates of live, oven-dried aboveground biomass of all plants (tree, shrub, graminoid, forb, bryophyte) and all woody plants (tree, shrub) at 30-meter resolution across the Arctic tundra biome. Estimates of woody plant dominance are also provided as: (woody plant biomass / plant biomass) * 100. Plant biomass and woody plant biomass were estimated for each pixel (grams per square meter [g / m2]) using field harvest data for calibration/validation along with modeled seasonal surface reflectance data derived using Landsat satellite imagery and the Continuous Change Detection and Classification algorithm, and other supplementary predictors related to topography, region (e.g. bioclimate zone, ecosystem type), land cover, and derivative spectral products. Modeling was performed in a two-stage process using random forest models. First, biomass presence/absence was predicted using probability forests. Then, biomass quantity was predicted using regression forests. The model outputs were combined to produce final biomass estimates. Pixel uncertainty was assessed using Monte Carlo iterations. Field and remote sensing data were permuted during each iteration and the median (50th percentile, p500) predictions for each pixel were considered best estimates. In addition, this dataset provides the lower (2.5th percentile, p025) and upper (97.5th percentile, p975) bounds of a 95% uncertainty interval. Estimates of woody plant dominance are not modeled directly, but rather derived from plant biomass and woody plant biomass best estimates. The Pan Arctic domain includes both the Polar Arctic, defined using bioclimate zone data from the Circumpolar Arctic Vegetation Mapping Project (CAVM; Walker et al., 2005), and the Oro Arctic (treeless alpine tundra at high latitudes outside the Polar Arctic), defined using tundra ecoregions from the RESOLVE ecoregions dataset (Dinerstein et al., 2017) and treeline data from CAVM (CAVM Team, 2003). The mapped products focus on Arctic tundra vegetation biomass, but the coarse delineation of this biome meant some forested areas were included within the study domain. Therefore, this dataset also provides a tree mask product that can be used to mask out areas with canopy height ≥ 5 meters. This mask helps reduce, but does not eliminate entirely, areas of dense tree cover within the domain. Users should be cautious of predictions in forested areas as the models used to predict biomass were not well constrained in these areas. This dataset includes 132 files: 128 cloud-optimized GeoTIFFs, 2 tables in comma-separated values (CSV) format, 1 vector polygon in Shapefile format, and one figure in JPEG format. Raster data is provided in the WGS 84 / North Pole LAEA Bering Sea projection (EPSG:3571) at 30 meter (m) resolution. Raster data are tiled with letters representing rows and numbers representing columns, but note that some tiles do not contain unmasked pixels. We included all tiles nonetheless to maintain consistency. Tiling information can be found in the ‘metadata’ directory as a figure (JPEG) or shapefile.

AbstractMany avian migrants have not adjusted breeding phenology to climate warming resulting in negative consequences for their offspring. We studied seasonal changes in reproductive success of the greater snow goose (Anser caerulescens atlantica), a long-distance migrant. As the climate warms and plant phenology advances, the mismatch between the timing of gosling hatch and peak nutritive quality of plants will increase. We predicted that optimal laying date yielding highest reproductive success occurred earlier over time and that the seasonal decline in reproductive success increased. Over 25 years, reproductive success of early breeders increased by 42%, producing a steeper seasonal decline in reproductive success. The difference between the laying date producing highest reproductive success and the median laying date of the population increased, which suggests an increase in the selection pressure for that trait. Observed clutch size was lower than clutch size yielding the highest reproductive success for most laying dates. However, at the individual level, clutch size could still be optimal if the additional time required to acquire nutrients to lay extra eggs is compensated by a reduction in reproductive success due to a delayed laying date. Nonetheless, breeding phenology may not respond sufficiently to meet future environmental changes induced by warming temperatures.

Invertebrate herbivores depend on external temperature for growth and metabolism. Continued warming in tundra ecosystems is proposed to result in increased invertebrate herbivory. However, empirica ...

The science of complex systems is increasingly asked to forecast the consequences of climate change. As a result, scientists are now engaged in making predictions about an uncertain future, which entails the efficient communication of this uncertainty. Here we show the benefits of hierarchically decomposing the uncertainty in predicted changes in animal population size into its components due to structural uncertainty in climate scenarios (greenhouse gas emissions and global circulation models), structural uncertainty in the demographic model, climatic stochasticity, environmental stochasticity unexplained by climate–demographic trait relationships, and sampling variance in demographic parameter estimates. We quantify components of uncertainty surrounding the future abundance of a migratory bird, the greater snow goose ( Chen caeruslescens atlantica ), using a process-based demographic model covering their full annual cycle. Our model predicts a slow population increase but with a large prediction uncertainty. As expected from theoretical variance decomposition rules, the contribution of sampling variance to prediction uncertainty rapidly overcomes that of process variance and dominates. Among the sources of process variance, uncertainty in the climate scenarios contributed less than 3% of the total prediction variance over a 40-year period, much less than environmental stochasticity. Our study exemplifies opportunities to improve the forecasting of complex systems using long-term studies and the challenges inherent to predicting the future of stochastic systems.

During the cold arctic winter, small mammals like lemmings seek refuge inside the snowpack to keep warm and they dig tunnels in the basal snow layer, usually formed of a soft depth hoar, to find vegetation on which they feed. The snowpack, however, is a heterogenous medium and lemmings should use habitats where snow properties favor their survival and winter reproduction. We determined the impact of snow physical properties on lemming habitat use and reproduction in winter by sampling their winter nests for 13 years and snow properties for 6 years across 4 different habitats (mesic, riparian, shrubland, and wetland) on Bylot Island in the Canadian High Arctic. We found that lemmings use riparian habitat most intensively because snow accumulates more rapidly, the snowpack is the deepest and temperature of the basal snow layer is the highest in this habitat. However, in the deepest snowpacks, the basal depth hoar layer was denser and less developed than in habitats with shallower snowpacks, and those conditions were negatively related to lemming reproduction in winter. Shrubland appeared a habitat of moderate quality for lemmings as it favored a soft basal snow layer and a deep snowpack compared with mesic and wetland, but snow conditions in this habitat critically depend on weather conditions at the beginning of the winter. With climate change, a hardening of the basal layer of the snowpack and a delay in snow accumulation are expected, which could negatively affect the winter habitat of lemmings and be detrimental to their populations.

AbstractArctic warming can influence tundra ecosystem function with consequences for climate feedbacks, wildlife and human communities. Yet ecological change across the Arctic tundra biome remains poorly quantified due to field measurement limitations and reliance on coarse-resolution satellite data. Here, we assess decadal changes in Arctic tundra greenness using time series from the 30 m resolution Landsat satellites. From 1985 to 2016 tundra greenness increased (greening) at ~37.3% of sampling sites and decreased (browning) at ~4.7% of sampling sites. Greening occurred most often at warm sampling sites with increased summer air temperature, soil temperature, and soil moisture, while browning occurred most often at cold sampling sites that cooled and dried. Tundra greenness was positively correlated with graminoid, shrub, and ecosystem productivity measured at field sites. Our results support the hypothesis that summer warming stimulated plant productivity across much, but not all, of the Arctic tundra biome during recent decades.