Protecting boreal caribou habitat can help conserve biodiversity and safeguard large quantities of soil carbon in Canada.

Boreal caribou require large areas of undisturbed habitat for persistence. They are listed as threatened with the risk of extinction in Canada because of landscape changes induced by human activities and resource extraction. Here we ask: Can the protection of habitat for boreal caribou help Canada meet its commitments under the United Nations Convention on Biological Diversity and United Nations Framework Convention on Climate Change? We identified hotspots of high conservation value within the distribution of boreal caribou based on: (1) three measures of biodiversity for at risk species (species richness, unique species and taxonomic diversity); (2) climate refugia or areas forecasted to remain unchanged under climate change; and, (3) areas of high soil carbon that could add to Canada's greenhouse gas emissions if released into the atmosphere. We evaluated the overlap among hotspot types and how well hotspots were represented in Canada's protected and conserved areas network. While hotspots are widely distributed across the boreal caribou distribution, with nearly 80% of the area falling within at least one hotspot type, only 3% of the distribution overlaps three or more hotspots. Moreover, the protected and conserved areas network only captures about 10% of all hotspots within the boreal caribou distribution. While the protected and conserved areas network adequately represents hotspots with high numbers of at risk species, areas occupied by unique species, as well as the full spectrum of areas occupied by different taxa, are underrepresented. Climate refugia and soil carbon hotspots also occur at lower percentages than expected. These findings illustrate the potential co-benefits of habitat protection for caribou to biodiversity and ecosystem services and suggest caribou may be a good proxy for future protected areas planning and for developing effective conservation strategies in regional assessments.

Multi-objective optimization can balance trade-offs among boreal caribou, biodiversity, and climate change objectives when conservation hotspots do not overlap.

The biodiversity and climate change crises have led countries-including Canada-to commit to protect more land and inland waters and to stabilize greenhouse gas concentrations. Canada is also obligated to recover populations of at-risk species, including boreal caribou. Canada has the opportunity to expand its protected areas network to protect hotspots of high value for biodiversity and climate mitigation. However, co-occurrence of hotspots is rare. Here we ask: is it possible to expand the network to simultaneously protect areas important for boreal caribou, other species at risk, climate refugia, and carbon stores? We used linear programming to prioritize areas for protection based on these conservation objectives, and assessed how prioritization for multiple, competing objectives affected the outcome for each individual objective. Our multi-objective approach produced reasonably strong representation of value across objectives. Although trade-offs were required, the multi-objective outcome was almost always better than when we ignored one objective to maximize value for another, highlighting the risk of assuming that a plan based on one objective will also result in strong outcomes for others. Multi-objective optimization approaches could be used to plan for protected areas networks that address biodiversity and climate change objectives, even when hotspots do not co-occur.

Natural climate solutions for Canada.

Alongside the steep reductions needed in fossil fuel emissions, natural climate solutions (NCS) represent readily deployable options that can contribute to Canada's goals for emission reductions. We estimate the mitigation potential of 24 NCS related to the protection, management, and restoration of natural systems that can also deliver numerous co-benefits, such as enhanced soil productivity, clean air and water, and biodiversity conservation. NCS can provide up to 78.2 (41.0 to 115.1) Tg CO2e/year (95% CI) of mitigation annually in 2030 and 394.4 (173.2 to 612.4) Tg CO2e cumulatively between 2021 and 2030, with 34% available at ≤CAD 50/Mg CO2e. Avoided conversion of grassland, avoided peatland disturbance, cover crops, and improved forest management offer the largest mitigation opportunities. The mitigation identified here represents an important potential contribution to the Paris Agreement, such that NCS combined with existing mitigation plans could help Canada to meet or exceed its climate goals.

Cross-scale integration of knowledge for predicting species ranges: a metamodeling framework.

AIM: Current interest in forecasting changes to species ranges have resulted in a multitude of approaches to species distribution models (SDMs). However, most approaches include only a small subset of the available information, and many ignore smaller-scale processes such as growth, fecundity, and dispersal. Furthermore, different approaches often produce divergent predictions with no simple method to reconcile them. Here, we present a flexible framework for integrating models at multiple scales using hierarchical Bayesian methods. LOCATION: Eastern North America (as an example). METHODS: Our framework builds a metamodel that is constrained by the results of multiple sub-models and provides probabilistic estimates of species presence. We applied our approach to a simulated dataset to demonstrate the integration of a correlative SDM with a theoretical model. In a second example, we built an integrated model combining the results of a physiological model with presence-absence data for sugar maple (Acer saccharum), an abundant tree native to eastern North America. RESULTS: For both examples, the integrated models successfully included information from all data sources and substantially improved the characterization of uncertainty. For the second example, the integrated model outperformed the source models with respect to uncertainty when modelling the present range of the species. When projecting into the future, the model provided a consensus view of two models that differed substantially in their predictions. Uncertainty was reduced where the models agreed and was greater where they diverged, providing a more realistic view of the state of knowledge than either source model. MAIN CONCLUSIONS: We conclude by discussing the potential applications of our method and its accessibility to applied ecologists. In ideal cases, our framework can be easily implemented using off-the-shelf software. The framework has wide potential for use in species distribution modelling and can drive better integration of multi-source and multi-scale data into ecological decision-making.