Projected suitable habitat models were constructed using a set of presence points for the species derived from element occurrence and herbarium records, together with temperature, precipitation, and soil variables. The current distribution used modeled historic period (1970-2000) climate variables from the appropriate matching GCM model run. These model parameters were then used with projected climate data to get future (2020-2050) modeled suitable habitat for each scenario. Modeled past suitable habitat and modeled future suitable habitat are combined to show areas of change, using various thresholds to distinguish change categories, as well as comparison to current mapped habitats from SWReGAP landcover (USGS 2004) or LANDFIRE existing vegetation (version 1.3.0). The change categories are (raster values in parentheses): (1) Lost = will not remain in place (2) Threatened = unlikely to remain in place, especially after a disturbance (3) Persistent = conditions remain within historical range (4) Emergent = new areas where climate will become suitable

Projected suitable habitat models were constructed using a set of presence points for the species derived from element occurrence and herbarium records, together with temperature, precipitation, and soil variables. The current distribution used modeled historic period (1970-2000) climate variables from the appropriate matching GCM model run. These model parameters were then used with projected climate data to get future (2020-2050) modeled suitable habitat for each scenario. Modeled past suitable habitat and modeled future suitable habitat are combined to show areas of change, using various thresholds to distinguish change categories, as well as comparison to current mapped habitats from SWReGAP landcover (USGS 2004) or LANDFIRE existing vegetation (version 1.3.0). The change categories are (raster values in parentheses): (1) Lost = will not remain in place (2) Threatened = unlikely to remain in place, especially after a disturbance (3) Persistent = conditions remain within historical range (4) Emergent = new areas where climate will become suitable

America’s remaining grassland in the Prairie Pothole Region (PPR) is at risk of being lost to crop production. When crop prices are high, like the historically high corn prices that the U.S. experienced between 2008 and 2014, the risk of grassland conversion is even higher. Changing climate will add uncertainties to any efforts toward conservation of grassland in the PPR. Grassland conversion to cropland in the region would imperil nesting waterfowl among other species and further impair water quality in the Mississippi watershed. In this project, we sought to contribute to the understanding of land conversion in the PPR with the aim to better target the use of public and private funds allocated toward incentivizing grassland preservation on private lands in the Dakotas. We assembled data on historical land switching in the area and on land conversion costs. We analyzed crop vulnerabilities to weather and climate change. We examined practical analytical tools to assess the likelihood of grassland conversion to cropping. With our weather-yield-land use modeling framework we evaluated the likely outcomes of land use changes in the region. Among other land use patterns, our research indicated a possible increase of grassland acres as grasses could be less adversely impacted by changing climate. Working with farmers and conservation partners, our project assessed drivers of land use changes. In particular, while economics and climate factors were admittedly obvious important motivations for land use changes, our findings suggested that landowners’ decisions were significantly affected by non-pecuniary factors, like lifestyle choices, or some behavioral biases (e.g., recency bias, and anticipated regret factor) whose roles in economic decision making have been increasingly recognized. As landowners and conservation managers gradually adapt to changing climates, it is not only important that we understand the impacts of the changing climates, but also imperative that we are aware how landowners perceive how climate is changing and how well they are willing to embrace sound adaptation strategies.

On the Western Slope of Colorado, variable climate and precipitation conditions are typical. Periods of drought—which may be defined by lack of water, high temperatures, low soil moisture, or other indicators—cause a range of impacts across sectors, including water, land, and fire management.The Western Slope’s Upper Colorado River Basin (UCRB) was one of the first pilot areas in which the National Integrated Drought Information System (NIDIS) implemented a drought early warning system (DEWS) in 2009. NIDIS presently supports eight regional DEWS; as of 2016, the UCRB DEWS has been incorporated into an expanded Intermountain West (IMW) DEWS. The selection of the UCRB for an initial DEWS reflects the regional importance of drought information for managing water supply for agriculture and other uses, and the need for effective decision support related to drought. Additionally, new drought information products were developed specifically for the UCRB DEWS, and a number of others have been created since 2009, adding to the preexisting toolkit for drought decision making.The various elements of the UCRB drought early warning system can be expected to be more or less suitable for the needs of different decision makers. As a result, the UCRB makes an ideal case study to examine the use of scientific information products and tools in which the broad decision context (managing drought) is defined, but information needs of current and prospective users vary. Thus decision makers will make varied choices about which of the available tools to use or not use, depending on the particular management and institutional context in which they work. This report investigates the factors that affect the choices of decision makers about whether and how to use particular information sources, products, and tools. The investigation focused on the following research questions:What decisions do managers make related to drought in the Upper Colorado region and particularly the Western Slope of Colorado? About which impacts of drought are they most concerned?What indicators and information products do decision makers rely on to manage for the impacts of drought in this region?How do decision makers find out about and choose between available drought information sources, products, and tools?What gaps (if any) do they perceive in currently available drought information and tools?Studies of decision support tools or information sources often concentrate on the known users of a given tool(s). Such an approach can yield useful information; it provides rich insight into the experiences of users and can suggest design modifications to make existing tools more effective. Yet it is not an effective approach to capture the perspectives and needs of prospective tool users or to investigate the factors that affect whether or not someone chooses to use tools in the first place. To overcome this challenge, in this study the author instead used a geographically based sampling strategy in which a range of natural resource managers from preidentified Federal management units and selected State agencies on the Western Slope were considered prospective users of tools. Prospective users were then asked to describe in an open-ended fashion what information and tools they do or do not use and why. This approach allowed for respondents to report both use and nonuse of tools, and thus the ability to identify factors that influence information and tool use choices by managers.

In the previous first phase of the Impacts and Vulnerability project, we made substantial progress in assessing climate and land use change impacts across the NCCASC domain. These include: quantifying the rates of land use change in greater wildland ecosystems (GWEs), determining the extent of fragmentation in major ecosystems across GWEs, assessing climate change impacts on public, private, and tribal lands within GWEs, evaluating evaporative demands across hydroclimatic gradients of eight ecoregions across north central U.S., and predicting forest ecosystem responses to climate change. We found that rates of climate and land use change varied across the Great Plains and Rocky Mountains, as did the responses of ecosystems to these changes. We also identified the major locations highly impacted by these changes that call for crafting locally relevant adaptation strategies to cope with these changes. This second phase of the project (FY’17) aimed to generate coproduction of knowledge with a wide range of stakeholders to support decision making for the management and conservation of affected areas. During this FY’17 phase of the project, we worked with various user groups to evaluate potential land use and climate impacts and adaptation strategies for the most affected areas and ecosystem types identified by our previous work. Specifically, we focused on forest and shrubland vegetation and habitat of a selected wildlife species (Gulo gulo) in the Rocky Mountains and Washington Cascade regions. We also designed and produced resource briefs on land use and climate change assessments of selected areas and ecosystem types to provide information to coordinated management. Thirdly, we conducted series of webinars and workshops with federal, private, and NGO stakeholders to draw on all of the science results (e.g., from species distribution models, state and transition models, and mechanistic models) to identify and evaluate vegetation climate adaptation strategies for the Custer Gallatin National Forest Plan Revision that are robust under climate uncertainty.     

Land use change ranges in each panel are in acres per thousand county acres. The white colored counties represent missing yields for at least one crop in all years.

2006 Land Use in the Dakotas (Cropland Data Layer, USDA NASS). The color legend represents various land use types in the region.

Historical (1981-2005) vs. Projected (2031-’55) Yields. Each year’s crop yields are calculated as an average of all counties in North and South Dakota. Hashed representations of projected yields are from RCP 4.5 emissions scenario from seven GCMs, namely CESM (Community Earth System Model), CNRM (Center National de Recherches Météorologiques (France)), GFDL (Geophysical Fluid Dynamics Laboratory), GISS (Goddard Institute of Space Studies), HADGEM (Hadley Global Environment Model), IPSL (Institut Pierre-Simon Laplace (France)) and MIROC (Model for Interdisciplinary Research on Climate). Median projection in a given year is calculated by taking the median yield value of the yield projections from each of seven climate model outputs in each county and then taking the average across counties. We restrict spring wheat and alfalfa yield forecasts to zero for years in which these are projected to be negative values.