Establishing connections among natural landscapes is the most frequently recommended strategy for adapting management of natural resources in response to climate change. The U.S. Northern Rockies still support a full suite of native wildlife, and survival of these populations depends on connected landscapes. Connected landscapes support current migration and dispersal as well as future shifts in species ranges that will be necessary for species to adapt to our changing climate. Working in partnership with state and federal resource managers and private land trusts, we sought to: understand how future climate change may alter habitat composition of landscapes expected to serve as important connections for wildlife, estimate how wildlife species of concern are expected to respond to these changes, develop climate-smart strategies to help stakeholders manage public and private lands in ways that allow wildlife to continue to move in response to changing conditions, and explore how well existing management plans and conservation efforts are expected to support crucial connections for wildlife under climate change. We assessed vulnerability of eight wildlife species and four biomes to climate change, with a focus on potential impacts to connectivity. Our assessment provides some insights about where these species and biomes may be most vulnerable or most resilient to loss of connectivity and how this information could support climate-smart management action. We also encountered high levels of uncertainty in how climate change is expected to alter vegetation and how wildlife are expected to respond to these changes. This uncertainty limits the value of our assessment for informing proactive management of climate change impacts on both species-specific and biome-level connectivity (although biome-level assessments were subject to fewer sources of uncertainty). We offer suggestions for improving the management relevance of future studies based on our own insights and those of managers and biologists who participated in this assessment and provided critical review of this report. 

This is a spatially-explicit state-and-transition simulation model of rangeland vegetation dynamics in southwest South Dakota. It was co-designed with resource management partners to support scenario planning for climate change adaptation. The study site encompasses part of multiple jurisdictions, including Badlands National Park, Buffalo Gap National Grasslands, and Pine Ridge Indian Reservation. The model represents key vegetation types, grazing, exotic plants, fire, and the effects of climate and management on rangeland productivity and composition (i.e., distribution of ecological community phases). See Miller et al. (2017) for further details. The model was built using the ST-Sim software platform (www.apexrms.com/stsm). ST-Sim allows users to develop and run spatially-explicit, stochastic state-and-transition simulation models (STSMs) of vegetation change, and is designed to simulate and compare possible vegetation conditions across a landscape over time by considering the interaction between succession, disturbances and management. ST-Sim is the latest in a 20-year lineage of STSM development tools that includes the Vegetation Dynamics Development Tool (VDDT), the Tool for Exploratory Landscape Scenario Analysis (TELSA), and the Path Landscape Model (Path). ST-Sim is intended as an upgrade to Path: in addition to all of the previous Path features, ST-Sim also provides a new option to run raster-based, spatially-explicit simulations.

The viability of the whitebark pine (Pinus albicaulis) species is under threat due to precipitously declining populations.  This study investigates the sources of differing levels of concern about climate-driven effects on whitebark pine trees.  It also investigates support for different Whitebark Pine (WBP) management strategies on federal public lands. 

Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive – such as means or extremes – can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the ‘model space’ approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling.

Abstract (from http://journals.ametsoc.org/doi/10.1175/WCAS-D-16-0121.1): Much of the academic literature and policy discussions about sustainable development and climate change adaptation focus on poor and developing nations, yet many tribal communities inside the United States include marginalized peoples and developing nations who face structural barriers to effectively adapt to climate change. There is a need to critically examine diverse climate change risks for indigenous peoples in the United States and the many structural barriers that limit their ability to adapt to climate change. This paper uses a sustainable climate adaptation framework to outline the context and the relationships of power and authority, along with different ways of knowing and meaning, to illustrate the underpinnings of some tribes’ barriers to sustainable climate adaptation. The background of those structural barriers for tribes is traced, and then the case of water rights and management at the Wind River Reservation in Wyoming is used to illustrate the interplay of policy, culture, climate, justice, and limits to adaptation. Included is a discussion about how the rulings of the Big Horn general stream adjudication have hindered tribal climate change adaptation by limiting the quantity of tribal reserved water rights, tying those rights to the sole purposes of agriculture, which undermines social and cultural connections to the land and water, and failing to recognizing tribal rights to groundwater. Future climate projections suggest increasing temperatures, and changes in the amount and timing of snowpack, along with receding glaciers, all of which impact water availability downstream. Therefore, building capacity to take control of land and water resources and preparing for climate change and drought at Wind River Reservation is of critical importance.

Pan evaporation is a measure of atmospheric evaporative demand (E0) for which long term and spatially distributed observations are available from the NOAA Cooperative Observer (COOP) Network. However, this data requires extensive quality control and homogenization due to documented and undocumented station moves and other factors including human errors in recording or digitization. Station-based Pan Evaporation measurements (in mm) from 247 stations across the continental United States were compiled and quality controlled for the analysis shown in Dewes et al., 2017. This dataset reports warm season (May-October; for 21 stations the data is only available for May-September) pan evaporation with at least 20 years of data between 1950 and 2001. Both monthly values and long-term monthly averages are made available, including the climatological measure for standard deviation and coefficient of variation. Dewes et al. (2017) used this dataset to evaluate the ability of different E0 formulations – Hargreaves-Samani, Priestly-Taylor, and Penman-Monteith – to reproduce the spatial patterns of observed warm-season E0 and its interannual variability. This data is an extension of the dataset described in Hobbins (2004) and Hobbins et al. (2004) with 21 additional stations north of 41oN latitude. The extension was needed in order to include data in the North Central Climate Science Center region. For these added stations, the procedure described in Hobbins (2004) for quality control was applied, including an adjustment in the mean when documented station moves occurred, and the removal of obvious outliers. The quality control procedure for the extended dataset did not automate tests for undocumented inhomogeneities for these stations. For all stations, a visual inspection of the timeseries was used to add additional breakpoints in the data for homogenization (only two were added in the extended set), and to eliminate two stations from consideration.

Abstract (from http://www.sciencedirect.com/science/article/pii/S2212096317300153): In recent years, federal land management agencies in the United States have been tasked to consider climate change vulnerability and adaptation in their planning. Ecological vulnerability approaches have been the dominant framework, but these approaches have significant limitations for fully understanding vulnerability in complex social-ecological systems in and around multiple-use public lands. In this paper, we describe the context of United States federal public lands management with an emphasis on the Bureau of Land Management to highlight this unique decision-making context. We then assess the strengths and weaknesses of an ecological vulnerability approach for informing decision-making. Next, we review social vulnerability methods in the context of public lands to demonstrate what these approaches can contribute to our understanding of vulnerability, as well as their strengths and weaknesses. Finally, we suggest some key design principles for integrated social-ecological vulnerability assessments considering the context of public lands management, the limits of ecological vulnerability assessment, and existing approaches to social vulnerability assessment. We argue for the necessity of including social vulnerability in a more integrated social-ecological approach in order to better inform climate change adaptation.

In southwestern Colorado, land managers anticipate the impacts of climate change to include higher temperatures, more frequent and prolonged drought, accelerated snowmelt, larger and more intense fires, more extreme storms, and the spread of invasive species. These changes put livelihoods, ecosystems, and species at risk. Focusing on communities in southwestern Colorado’s San Juan and Gunnison river basins, this project will expand opportunities for scientists, land managers, and affected residents to identify actions that can support resilience and adaptation in the face of changing climate conditions.   This project builds on the project “Building Social and Ecological Resilience to Climate Change in southwestern Colorado: Phase 1”. Phase 1 focused on developing integrated social-ecological science and adaptation strategies for four target landscapes: spruce-fir forests, pinyon-juniper woodlands, sagebrush scrublands, and seeps, springs and wetlands.   Phase 2 will further advance adaptation strategy development in the region and share the results with other communities, land managers, and decision-makers. Specifically, researchers will identify concrete actions that can be taken to carry out each adaptation strategy, and will develop solutions to address barriers identified by stakeholders in Phase 1 that could impede implementation. Ultimately, this project will result in landscape-scale conservation goals and actions that conserve key species, ecosystems, and resources, address the economic and social systems of local communities, and provide science resources for natural resource managers in the face of a changing climate.

The goal of this project was to identify climate-related scientific information needs in the North Central region that will support the management of key species and help avoid species declines. Researchers worked closely with state fish and wildlife agencies, the U.S. Fish and Wildlife Service, tribes, and other relevant natural resource management and conservation agencies to identify priority information needs and to design and implement studies that will address these needs.   Researchers identified stakeholders, including those engaged by the North Central Climate Science Center USGS Liaisons project. Researchers worked with stakeholders to identify priority conservation targets. Selected targets were those that are of high priority to managers, are the subject of a pending or planned decision or action, and for which the decision would benefit from information on climate change exposure, impacts, or adaptation. The outcome was the identification of key climate science needs that can help advance near-term conservation decision-making. As a final component of the project, researchers initiated working groups to spearhead the development of research plans that can address these priority, stakeholder-defined climate science needs in the region. These working groups were comprised of management representatives and researchers affiliated with the North Central Climate Science Center.   By working closely with resource managers to identify information gaps and initiate plans to address these gaps, this project was designed to support the development of usable, relevant, and timely science that directly addresses on-the-ground needs.