Loss of connectivity among island-dwelling Peary caribou following sea ice decline

Predicting areas important for ecological connectivity throughout Canada

Governments around the world have acknowledged that urgent action is needed to conserve and restore ecological connectivity to help reverse the decline of biodiversity. In this study we tested the hypothesis that functional connectivity for multiple species can be estimated across Canada using a single, upstream connectivity model. We developed a movement cost layer with cost values assigned using expert opinion to anthropogenic land cover features and natural features based on their known and assumed effects on the movement of terrestrial, non-volant fauna. We used Circuitscape to conduct an omnidirectional connectivity analysis for terrestrial landscapes, in which the potential contribution of all landscape elements to connectivity were considered and where source and destination nodes were independent of land tenure. Our resulting map of mean current density provided a seamless estimate of movement probability at a 300 m resolution across Canada. We tested predictions in our map using a variety of independently collected wildlife data. We found that GPS data for individual caribou, wolves, moose, and elk that traveled longer distances in western Canada were all significantly correlated with areas of high current densities. The frequency of moose roadkill in New Brunswick was also positively associated with current density, but our map was not able to predict areas of high road mortality for herpetofauna in southern Ontario. The results demonstrate that an upstream modelling approach can be used to characterize functional connectivity for multiple species across a large study area. Our national connectivity map can help governments in Canada prioritize land management decisions to conserve and restore connectivity at both national and regional scales.

Wolverines (Gulo gulo) in the Arctic: Revisiting distribution and identifying research and conservation priorities amid rapid environmental change

Wolverines (Gulo gulo) occupy most of the globe’s Arctic tundra. Given the rapidly warming climate and expanding human activity in this biome, understanding wolverine ecology, and therefore the species’ vulnerability to such changes, is increasingly important for developing research priorities and effective management strategies. Here, we review and synthesize knowledge of wolverines in the Arctic using both Western science sources and available Indigenous Knowledge (IK) to improve our understanding of wolverine ecology in the Arctic and better predict the species’ susceptibility to change. To accomplish this, we update the pan-Arctic distribution map of wolverines to account for recent observations and then discuss resulting inference and uncertainties. We use these patterns to contextualize and discuss potential underlying drivers of distribution and population dynamics, drawing upon knowledge of food habits, habitat associations, and harvest, as well as studies of wolverine ecology elsewhere. We then identify four broad areas to prioritize conservation and research efforts: (1) Monitoring trends in population abundance, demographics, and distribution and the drivers thereof, (2) Evaluating and predicting wolverines’ responses to ongoing climate change, particularly the consequences of reduced snow and sea ice, and shifts in prey availability, (3) Understanding wolverines’ response to human development, including the possible impact of wintertime over-snow travel and seismic testing to reproductive denning, as well as vulnerability to hunting and trapping associated with increased human access, and (4) Ensuring that current and future harvest are sustainable.

Caribou and reindeer migrations in the changing Arctic

Caribou and reindeer, Rangifer tarandus, are the most numerous and socio-ecologically important terrestrial species in the Arctic. Their migrations are directly and indirectly affected by the seasonal nature of the northernmost regions, human development and population size; all of which are impacted by climate change. We review the most critical drivers of Rangifer migration and how a rapidly changing Arctic may affect them. In order to conserve large Rangifer populations, they must be allowed free passage along their migratory routes to reach seasonal ranges. We also provide some pragmatic ideas to help conserve Rangifer migrations into the future.

Harnessing paleo-environmental modeling and genetic data to predict intraspecific genetic structure

Spatially explicit simulations of gene flow within complex landscapes could help forecast the responses of populations to global and anthropological changes. Simulating how past climate change shaped intraspecific genetic variation can provide a validation of models in anticipation of their use to predict future changes. We review simulation models that provide inferences on population genetic structure. Existing simulation models generally integrate complex demographic and genetic processes but are less focused on the landscape dynamics. In contrast to previous approaches integrating detailed demographic and genetic processes and only secondarily landscape dynamics, we present a model based on parsimonious biological mechanisms combining habitat suitability and cellular processes, applicable to complex landscapes. The simulation model takes as input (a) the species dispersal capacities as the main biological parameter, (b) the species habitat suitability, and (c) the landscape structure, modulating dispersal. Our model emphasizes the role of landscape features and their temporal dynamics in generating genetic differentiation among populations within species. We illustrate our model on caribou/reindeer populations sampled across the entire species distribution range in the Northern Hemisphere. We show that simulations over the past 21 kyr predict a population genetic structure that matches empirical data. This approach looking at the impact of historical landscape dynamics on intraspecific structure can be used to forecast population structure under climate change scenarios and evaluate how species range shifts might induce erosion of genetic variation within species.

Sea ice loss increases genetic isolation in a high Arctic ungulate metapopulation

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 F_ST  = 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.

Effects of sea ice on Arctic biota: an emerging crisis discipline

The rapid decline in Arctic sea ice (ASI) extent, area and volume during recent decades is occurring before we can understand many of the mechanisms through which ASI interacts with biological processes both at sea and on land. As a consequence, our ability to predict and manage the effects of this enormous environmental change is limited, making this a crisis discipline Here, we propose a framework to study these effects, defining direct effects as those acting on life-history events of Arctic biota, and indirect effects, where ASI acts upon biological systems through chains of events, normally involving other components of the physical system and/or biotic interactions. Given the breadth and complexity of ASI's effects on Arctic biota, Arctic research requires a truly multidisciplinary approach to address this issue. In the absence of effective global efforts to tackle anthropogenic global warming, ASI will likely continue to decrease, compromising the conservation of many ASI-related taxonomic groups and ecosystems. Mitigation actions will rely heavily on the knowledge acquired on the mechanisms and components involved with the biological effects of ASI.

Prioritization of landscape connectivity for the conservation of Peary caribou

Adequate connectivity between discontinuous habitat patches is crucial for the persistence of metapopulations across space and time. Loss of landscape connectivity is often a direct result of fragmentation caused by human activities but also can be caused indirectly through anthropogenic climate change. Peary caribou (Rangifer tarandus pearyi) are widely dispersed across the islands of the Canadian Arctic Archipelago and rely on sea ice to move seasonally between island habitats throughout their range. Seasonal connectivity provided by sea ice is necessary to maintain genetic diversity and to facilitate dispersal and recolonization of areas from which caribou have been extirpated. We used least-cost path analysis and circuit theory to model connectivity across Peary caribou range, and future climate projections to investigate how this connectivity might be affected by a warming climate. Further, we used measures of current flow centrality to estimate the role of High Arctic islands in maintaining connectivity between Peary caribou populations and to identify and prioritize those islands and linkages most important for conservation. Our results suggest that the Bathurst Island complex plays a critical role in facilitating connectivity between Peary caribou populations. Large islands, including Banks, Victoria, and Ellesmere have limited roles in connecting Peary caribou. Without rigorous greenhouse gas emission reductions our projections indicate that by 2100 all connectivity between the more southern Peary caribou populations will be lost for important spring and early-winter movement periods. Continued connectivity across the Canadian Arctic Archipelago, and possibly Peary caribou persistence, ultimately hinges on global commitments to limit climate change. Our research highlights priority areas where, in addition to emission reductions, conservation efforts to maintain connectivity would be most effective.

Using seasonal landscape models to predict space use and migratory patterns of an arctic ungulate

BACKGROUND

Caribou in the Western Arctic Herd undertake one of the longest, remaining intact migrations of terrestrial mammals in the world. They are also the most important subsistence resource for many northern rural residents, who rely on the caribou's migratory movements to bring them near for harvest. Migratory geography has never been static, but subsistence harvesters have reported recent shifts in migration away from areas where they traditionally occurred. The reasons behind these changes are not well-understood, but may be related to rapid climate change and anthropogenic disturbances.

METHODS

To predict changes in distribution and shifting migratory areas over the past decade, we used GPS telemetry data from adult females to develop predictive ecological niche models of caribou across northwestern Alaska. We employed the machine-learning algorithm, TreeNet, to analyze interactive, multivariate relationships between telemetry locations and 37 spatial environmental layers and to predict the distributions of caribou during spring, calving season, insect-harassment season, late summer, fall, and winter from 2009 to 2017. Model results were analyzed to identify regions of repeated predicted use, quantify mean longitude, predict land cover selection, and track migratory changes over time.

RESULTS

Distribution models accurately predicted caribou at a spatially-explicit, 500-m scale. Model analyses identified migratory areas that shifted annually across the region, but which predicted 4 main areas of repeated use. Niche models were defined largely by non-linear relationships with coastally-influenced, climatic variables, especially snow-free date, potential evapo-transpiration, growing season length, proximity to sea ice, winter precipitation and fall temperature. Proximity to roads and communities were also important and we predicted caribou to generally occur more than 20-100 km from these features.

CONCLUSIONS

Western Arctic Herd caribou were predicted to occur in warmer, snow-free and treeless areas that may provide conditions conducive for efficient travel and foraging. Rapidly changing seasonal climates and coastal influences that determine forage availability, and human impediments that slow or divert movements are related to geographically and phenologically dynamic migration patterns that may periodically shift caribou away from traditional harvest areas. An enhanced understanding of the geographic behavior of caribou over time could inform traditional harvests and help conserve important Western Arctic caribou migratory areas.

Loss of connectivity among island-dwelling Peary caribou following sea ice decline

Global warming threatens to reduce population connectivity for terrestrial wildlife through significant and rapid changes to sea ice. Using genetic fingerprinting, we contrasted extant connectivity in island-dwelling Peary caribou in northern Canada with continental-migratory caribou. We next examined if sea-ice contractions in the last decades modulated population connectivity and explored the possible impact of future climate change on long-term connectivity among island caribou. We found a strong correlation between genetic and geodesic distances for both continental and Peary caribou, even after accounting for the possible effect of sea surface. Sea ice has thus been an effective corridor for Peary caribou, promoting inter-island connectivity and population mixing. Using a time series of remote sensing sea-ice data, we show that landscape resistance in the Canadian Arctic Archipelago has increased by approximately 15% since 1979 and may further increase by 20-77% by 2086 under a high-emission scenario (RCP8.5). Under the persistent increase in greenhouse gas concentrations, reduced connectivity may isolate island-dwelling caribou with potentially significant consequences for population viability.

Model selection with multiple regression on distance matrices leads to incorrect inferences

In landscape genetics, model selection procedures based on Information Theoretic and Bayesian principles have been used with multiple regression on distance matrices (MRM) to test the relationship between multiple vectors of pairwise genetic, geographic, and environmental distance. Using Monte Carlo simulations, we examined the ability of model selection criteria based on Akaike's information criterion (AIC), its small-sample correction (AICc), and the Bayesian information criterion (BIC) to reliably rank candidate models when applied with MRM while varying the sample size. The results showed a serious problem: all three criteria exhibit a systematic bias toward selecting unnecessarily complex models containing spurious random variables and erroneously suggest a high level of support for the incorrectly ranked best model. These problems effectively increased with increasing sample size. The failure of AIC, AICc, and BIC was likely driven by the inflated sample size and different sum-of-squares partitioned by MRM, and the resulting effect on delta values. Based on these findings, we strongly discourage the continued application of AIC, AICc, and BIC for model selection with MRM.