Covering 120 million acres across 14 western states and 3 Canadian provinces, sagebrush provides critical habitat for species such as pronghorn, mule deer, and sage-grouse – a species of conservation concern. The future of these and other species is closely tied to the future of sagebrush. Yet this important ecosystem has already been affected by fire, invasive species, land use conversion, and now, climate change.   In the western U.S., temperatures are rising and precipitation patterns are changing. However, there is currently a limited ability to anticipate the impacts of climate change on sagebrush. Current methods suffer from a range of weakness that limits the reliability of results. In fact, the current uncertainty about future changes in sagebrush has been identified as a critical constraint on climate change adaptation planning in the West. To address this need, researchers forecasted the effects of climate change on the distribution and abundance of sagebrush, and integrated several modeling approaches that take into account historical data, disturbances such as fire, and changes in temperature and precipitation. This integrated method is expected to produce more accurate estimates of future sagebrush distribution and abundance.   The results of this research will be effectively communicated to land managers so that they can inform conservation planning, and sage-grouse management in particular, across the Intermountain West. Improved sagebrush forecasting will increase the capacity of land managers to prioritize future investments in sagebrush conservation and management by identifying areas where sagebrush are most and least vulnerable to climate change.

Land managers in the Pacific Northwest have reported a need for updated scientific information on the ecology and management of mixed-conifer forests east of the Cascade Range in Oregon and Washington. Of particular concern are the moist mixed-conifer forests, which have become drought-stressed and vulnerable to high-severity fire after decades of human disturbances and climate warming. This synthesis responds to this need. We present a compilation of existing research across multiple natural resource issues, including disturbance regimes, the legacy effects of past management actions, wildlife habitat, watershed health, restoration concepts from a landscape perspective, and social and policy concerns. We provide considerations for management, while also emphasizing the importance of local knowledge when applying this information at the local and regional level.

Abstract (from http://www.aimspress.com/article/10.3934/environsci.2015.2.400): State-and-transition simulation models (STSMs) are known for their ability to explore the combined effects of multiple disturbances, ecological dynamics, and management actions on vegetation. However, integrating the additional impacts of climate change into STSMs remains a challenge. We address this challenge by combining an STSM with species distribution modeling (SDM). SDMs estimate the probability of occurrence of a given species based on observed presence and absence locations as well as environmental and climatic covariates. Thus, in order to account for changes in habitat suitability due to climate change, we used SDM to generate continuous surfaces of species occurrence probabilities. These data were imported into ST-Sim, an STSM platform, where they dictated the probability of each cell transitioning between alternate potential vegetation types at each time step. The STSM was parameterized to capture additional processes of vegetation growth and disturbance that are relevant to a keystone species in the Greater Yellowstone Ecosystem—whitebark pine ( Pinus albicaulis). We compared historical model runs against historical observations of whitebark pine and a key disturbance agent (mountain pine beetle,  Dendroctonus ponderosae), and then projected the simulation into the future. Using this combination of correlative and stochastic simulation models, we were able to reproduce historical observations and identify key data gaps. Results indicated that SDMs and STSMs are complementary tools, and combining them is an effective way to account for the anticipated impacts of climate change, biotic interactions, and disturbances, while also allowing for the exploration of management options.

Abstract (from http://www.sciencedirect.com/science/article/pii/S1574954115001466): Anticipating the ecological effects of climate change to inform natural resource climate adaptation planning represents one of the primary challenges of contemporary conservation science. Species distribution models have become a widely used tool to generate first-pass estimates of climate change impacts to species probabilities of occurrence. There are a number of technical challenges to constructing species distribution models that can be alleviated by the use of scientific workflow software. These challenges include data integration, visualization of modeled predictor–response relationships, and ensuring that models are reproducible and transferable in an adaptive natural resource management framework. We used freely available software called VisTrails Software for Assisted Habitat Modeling ( VisTrails:SAHM) along with a novel ecohydrological predictor dataset and the latest Coupled Model Intercomparison Project 5 future climate projections to construct species distribution models for eight forest and shrub species in the Greater Yellowstone Ecosystem in the Northern Rocky Mountains USA. The species considered included multiple species of sagebrush and juniper,  Pinus flexilis,  Pinus contorta,  Pseudotsuga menziesii,  Populus tremuloides,  Abies lasciocarpa, Picea engelmannii, and  Pinus albicaulis. Current and future species probabilities of occurrence were mapped in a GIS by land ownership category to assess the feasibility of undertaking present and future management action. Results suggested that decreasing spring snowpack and increasing late-season soil moisture deficit will lead to deteriorating habitat area for mountain forest species and expansion of habitat area for sagebrush and juniper communities. Results were consistent across nine global climate models and two representative concentration pathway scenarios. For most forest species their projected future distributions moved up in elevation from general federal to federally restricted lands where active management is currently prohibited by agency policy. Though not yet fully mature, custom scientific workflow software shows considerable promise to ease many of the technical challenges inherent in modeling the potential ecological impacts of climate change to support climate adaptation planning.

Abstract (from http://www.islandpress.org/book/climate-change-in-wildlands): Scientists have been warning for years that human activity is heating up the planet and climate change is under way. In the past century, global temperatures have risen an average of 1.3 degrees Fahrenheit, a trend that is expected to only accelerate. But public sentiment has taken a long time to catch up, and we are only just beginning to acknowledge the serious effects this will have on all life on Earth. The federal government is crafting broad-scale strategies to protect wildland ecosystems from the worst effects of climate change. The challenge now is to get the latest science into the hands of resource managers entrusted with protecting water, plants, fish and wildlife, tribal lands, and cultural heritage sites in wildlands. Teaming with NASA and the Department of the Interior, ecologist Andrew Hansen, along with his team of scientists and managers, set out to understand how climate and land use changes affect montane landscapes of the Rockies and the Appalachians, and how these findings can be applied to wildlands elsewhere. They examine changes over the past century as well as expected future change, assess the vulnerability of species and ecosystems to these changes, and provide new, collaborative management approaches to mitigate expected impacts. A series of case studies showcases how managers might tackle such wide-ranging problems as the effects of warming streams on cold-water fish in Great Smoky Mountain National Park and dying white-bark pine stands in the Greater Yellowstone area. A surprising finding is that species and ecosystems vary dramatically in vulnerability to climate change. While many will suffer severe effects, others may actually benefit from projected changes. Climate Change in Wildlands is a collaboration between scientists and managers, providing a science-derived framework and common-sense approaches for keeping parks and protected areas healthy on a rapidly changing planet. - See more at: http://www.islandpress.org/book/climate-change-in-wildlands#sthash.ZdEUAf26.dpuf

This data set contains output from the dynamic vegetation model MC1, as modified to simulate future woody encroachment in the northern Great Plains. Simulations were done for the historical period (1895-2005) and the future period (2006-2100). Separate simulations were done for eastern and western portions of the region, with the eastern simulations using model parameters appropriate for Juniperus virginiana as the major evergreen needle-leaf life form, and the western simulations using model parameters appropriate for Pinus ponderosa as the major evergreen needle-leaf life form. Simulations in each portion were run for two A2 emissions scenario climate projections (CSIRO, representing moderate temperature increases and wetter conditions, and MIROC, representing very hot and dry conditions) crossed with 8 (eastern portion) or 6 (western portion) fire x grazing x tree regeneration capacity (eastern only) scenarios. Output variables provided on a yearly basis are potential evapotranspiration, live aboveground tree carbon and aboveground grass net primary production. Output variables provided as decadal averages are live aboveground tree carbon, tree leaf area index, soil available water for plant survival, surface runoff, potential evapotranspiration, streamflow, and actual evapotranspiration. Child records contain command files for running the model, model parameters, model input, and output from model runs for the equilibrium and spinup stages of model runs (precursors to running historical and future simulations).

An increase in land conversion from grassland to cropland in the United States has attracted attention in recent years. According to Claassen et al. (2011a), grassland to cropland conversion is concentrated in the Northern Plains, including Kansas, Nebraska, North Dakota and South Dakota, which encompasses only 18% of U.S. rangeland but accounted for 57 percent of U.S. rangeland to cropland conversion during the study period of 1997 to 2007. Focusing on land cover data in the Western Corn Belt, Wright and Wimberly (2013) also pointed out that grassland conversion was mostly concentrated in the Dakotas, east of the Missouri River and between 2006 and 2011.

Abstract (from http://econpapers.repec.org/paper/agsaaea16/235895.htm): We evaluate the regional-level agricultural impacts of climate change in the Northern Great Plains. We first estimate a non-linear yield-weather relationship for all major commodities in the area: corn, soybeans, spring wheat and alfalfa. We separately identify benevolent and harmful temperature thresholds for each commodity, and control for severe-to-extreme dry/wet conditions in our yield models. Analyzing all major commodities in a region extends the existing literature beyond just one crop, most typically corn yields. Alfalfa is particularly interesting since it is a legume-crop that is substitutable with grasses as animal feed and rotated with other row-crops for nitrogen-fixation of soils. Our model includes trend-weather and soil-weather interaction terms that extend the existing yield-weather models in the literature. Results suggest that temporal adaptations have not mitigated the negative impacts of weather stressors in the past, and that the spatial soil profile only weakly influences weather impacts on crop yields. We estimate yield-weather elasticities and find that historical weather patterns in the region have benefited corn and soybeans (spring wheat) the most (least). We expand our analysis to formally evaluate the role of short-run weather fluctuations in determining land-use decisions. We utilize decomposed crop yield estimates due to trend and weather in order to model crop acreage shares. Our preliminary results suggest that short-run weather fluctuations are an important factor for decisions on soybeans and spring wheat shares, however only yield trends drive corn shares.