Abstract (from ESA Journals): Climate change is a well-documented driver and threat multiplier of infectious disease in wildlife populations. However, wildlife disease management and climate-change adaptation have largely operated in isolation. To improve conservation outcomes, we consider the role of climate adaptation in initiating or exacerbating the transmission and spread of wildlife disease and the deleterious effects thereof, as illustrated through several case studies. We offer insights into best practices for disease-smart adaptation, including a checklist of key factors for assessing disease risks early in the climate adaptation process. By assessing risk, incorporating uncertainty, planning for change, and monitoring outcomes, natural resource managers and conservation practitioners can better prepare for and respond to wildlife disease threats in a changing climate.

Under climate change, ecosystems are experiencing novel drought regimes, often in combination with stressors that reduce resilience and amplify drought’s impacts. Consequently, drought appears increasingly likely to push systems beyond important physiological and ecological thresholds, resulting in substantial changes in ecosystem characteristics persisting long after drought ends (i.e., ecological transformation). In the present article, we clarify how drought can lead to transformation across a wide variety of ecosystems including forests, woodlands, and grasslands. Specifically, we describe how climate change alters drought regimes and how this translates to impacts on plant population growth, either directly or through drought's interactions with factors such as land management, biotic interactions, and other disturbances. We emphasize how interactions among mechanisms can inhibit postdrought recovery and can shift trajectories toward alternate states. Providing a holistic picture of how drought initiates long-term change supports the development of risk assessments, predictive models, and management strategies, enhancing preparedness for a complex and growing challenge.

Grassland birds in North America have declined sharply over the last 60 years, driven by the widespread loss and degradation of grassland habitats. Climate change is occurring more rapidly in grasslands relative to some other ecosystems, and exposure to extreme and novel climate conditions may affect grassland bird ecology and demographics. To determine the potential effects of weather and climate variability on grassland birds, we conducted a systematic review of relationships between temperature and precipitation and demographic responses in grassland bird species of North America. Based on 124 independent studies, we used a vote-counting approach to quantify the frequency and direction of significant effects of weather and climate variability on grassland birds. Grassland birds tended to experience positive and negative effects of higher temperatures and altered precipitation. Moderate, sustained increases in mean temperature and precipitation benefitted some species, but extreme heat, drought, and heavy rainfall often reduced abundance and nest success. These patterns varied among climate regions, temporal scales of temperature and precipitation (<1 or ≥1 month), and taxa. The sensitivity of grassland bird populations to extreme weather and altered climate variability will likely be mediated by regional climates, interaction with other stressors, life-history strategies of various species, and species’ tolerances for novel climate conditions.

This dataset represents a climate-informed management alternative for maintaining whitebark pine (Pinus albicaulis) in the Greater Yellowstone Ecosystem. This data was developed for use in a landscape simulation modeling study aimed at evaluating how well alternative management strategies maintain whitebark pine populations under historical climate and future climate conditions. For the study, we developed three spatial management alternatives for whitebark pine in the Greater Yellowstone Ecosystem representing no active management, current management, and climate-informed management. These management alternatives were implemented in the simulaton model FireBGCv2 under historical climate and three future climate change scenarios - the HadGEM-ES, CESM1-CAM5, and CNRM-CM5 Global Circulation Models under the RCP 8.5 emissions scenario. We worked with the Greater Yellowstone Coordinating Committee's (GYCC) Whitebark Pine Subcommittee to develop this spatial representation of their current management strategy. The treatments mapped represent a set of the treatments recommended in the GYCC Whitebark Pine 2011 Strategy document and include planting blister-rust resistant whitebark pine seedlings, competition removal thinning, wildland fire use and prescribed fire, and protection from mountain pine beetles using verbenone and carbaryl. We used historical and future projections of climate suitability based on species distribution models for whitebark pine (Chang et al. 2014) to map zones of core, deteriorating, and future whitebark pine habitat. Core zones were those areas that are currently suitable for whitebark and remain suitable in the future. Deteriorating zones were where the climatic conditions for whitebark pine are expected to decline. Future zones were areas that are projected to become newly suitable for whitebark pine. We then overlaid our climate zones for whitebark pine with similar projections of future climate suitability for all of whitebark pine’s competitors - Engelmann spruce, subalpine fir, lodgepole pine, and Douglas-fir (Piekielek et al. 2015. We discussed the different combinations of climate suitability zones (core, deteriorating, future) and potential future level of competition (low or high) from other species with the GYCC Whitebark Pine Subcommittee to determine which management activities should be prioritized within each management zone. The result is a map of management zones where different activities are prioritized to meet the goal of maintaining whitebark pine populations. This was used to determine which treatments would be implemented spatially during the simulation modeling, dependent upon additional criteria related to simulated stand-level conditions.