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.

Abstract (from IOP Science): Global agriculture is challenged to increase soil carbon sequestration and reduce greenhouse gas emissions while providing products for an increasing population. Growing crop production could be achieved through higher yield per hectare (i.e. intensive farming) or more hectares (extensive farming), which however, have different ecological and environmental consequences. Multiple lines of evidence indicate that expanding cropland for additional production may lead to loss of vegetation and soil carbon, and threaten the survival of wildlife. New concerns about the impacts of extensive farming have been raised for the US Corn Belt, one of the world's most productive regions, as cropland has rapidly expanded northwestward unto grasslands and wetlands in recent years. Here we used a process-based ecosystem model to distinguish and quantify how natural drivers as well as intensive and extensive farming practices have altered grain production, soil carbon storage, and agricultural carbon footprint in the US Western Corn Belt since 1980. Compared to the period 1980–2005, we found that cropland expansion more than tripled in the most recent decade (2006–2016), becoming a significant factor contributing to growing grain production. Land use change in this period led to a soil carbon loss of 90.8 ± 14.7 Tg (1 Tg = 1012 g). As a result, grain production in this region shifted from carbon neutral to a carbon loss of 2.3 kg C kg−1 grain produced. The enlarging negative carbon footprint (ΔC/ΔP) indicates the major role that cropland expansion has had on the carbon cost of grain production in this region. Therefore, we should be more cautious to pursue high crop production through agricultural cropland conversion, particularly in those carbon-rich soils.

Historical and projected suitable habitat of 14 tree and shrub species a under CCSM4 GCMs from 2000 to 2099 was predicted to assess projected climate change impacts in forest communities of North Central U.S. We obtained presence/absence record of each species from Forest Inventory and Analysis (FIA) data. required ata. Historical tme period ranges from 1971 to 2000, and projected time period ranges from 2071 to 2100. Random Forest was used to project historical and future suitable habitat of all species across West U.S. using the Biomod2 software programmed in R environment. We adopted a climate change scenarios generated from the experiments conducted under fifth assessment of Coupled Model Intercomparison Project (CMIP5) for the Intergovernmental Panel on Climate Change. Selected climate change scenarios include high representative concentrative pathway (RCP8.5).

The National Park Service (NPS) is responsible for managing livestock grazing in 94 units, and several park grazing management planning efforts are currently underway. However, there is a recognized need to update grazing management practices to address potential future effects of management practices and climate change. The goal of this project is to outline the steps required for developing NPS grazing management plans, to identify information needs and availability for these planning processes, and to initiate a scenario-based pilot project for meeting these needs at a given park unit. This will serve as an important step toward developing a transferable process to help parks ensure that grazing management practices are responsive and adaptive to future climate change.   In the first phase of the project, the team will engage resource managers from three NPS units in western Colorado: Dinosaur National Monument (DINO), Curecanti National Recreation Area (CURE), and Black Canyon of the Gunnison National Park (BLCA). Working with resource managers and subject-matter experts, the team will articulate and describe the planning processes and available information with regard to NPS grazing management. The team will then convene researchers, managers, subject-matter experts, and climate change adaptation specialists at a participatory climate change scenario planning workshop to develop a small set (3-5) of challenging, plausible, relevant, and divergent future scenarios that qualitatively assess how grazing resources and management at DINO may be affected under climate change. Concurrently, the process will identify common key characteristics that may be regionally applicable to BLCA and CURE, and will consider caveats for broader use at parks managing grazing in other regional biomes. Workshop participants will provide input on the project and process, identify quantitative information needs, and offer recommendations for streamlining the effort into a scalable, transferable approach that could be used to guide other park units seeking to update their own grazing management plans.    A potential second phase of this project would entail the development of a modeling approach to provide quantitative information to NPS units that allow livestock grazing. This task would leverage recent advances in our ability to model grazing activities to evaluate the effects of climate change and management actions on vegetation within a specific park unit. Management actions that could be evaluated include stocking rates, prescribed fire, and invasive plant management practices.

Grasslands in the northern Great Plains are important ecosystems that support local economies, tribal communities, livestock grazing, diverse plant and animal communities, and large-scale migrations of big game ungulates, grassland birds, and waterfowl. Climate change and variability impact how people and animals live on and interact with grasslands, and can bring more frequent droughts, fires, or new plant species that make managing these landscapes challenging. Understanding how climate change and variability will impact grassland ecosystems and their management in the 21st century first requires a synthesis of what is known across all of these scales and a gap analysis to identify key areas of focus for future research. Researchers will address this need by conducting a series of synthesis efforts to (1) identify and describe known management questions and information needs of stakeholders related to grasslands; (2) assess the state-of-the-science on climate change and variability in the northern Great Plains region; and (3) describe ecological responses to climate variability and change across the grasslands, including tipping points, changing fire patterns, spreading invasive species, changing species distributions, habitat fragmentation, and other changes in ecological communities. This project supports resource managers by providing them with the scientific information needed to make best-practice management decisions about northern Great Plains grasslands and will foster relationships with the conservation and management organizations that will utilize this science to make decisions about public lands.

Forests in the western U.S. are increasingly impacted by climate change. Warmer and drier conditions both increase fire activity in western forests and make it more difficult for forests to recover after wildfires. If forests fail to recover, they may shift to non-forest ecosystems like grasslands or shrublands. It is important to understand where fires may result in the loss of forests because forests provide a variety of ecosystem services that human communities rely on, including carbon storage, water regulation and supply, and biodiversity. Western forests are also integral for the timber industry and valued for their recreation opportunities. Anticipating future changes to forest ecosystems, particularly at local scales relevant to land and resource managers, requires an understanding of the vulnerability of forests to fire-catalyzed change. The main goal of this work is to create a vulnerability assessment that highlights geographic areas and forest types most vulnerable to fire-catalyzed ecosystem change under current and future climate change scenarios. Researchers will assess the different parts of forest vulnerability, including exposure to varying elements of climate change (e.g. temperature and moisture balance), exposure to varying types of fires (e.g. high vs. low severity fire), and sensitivity of post-fire seedlings to climate-related mortality (e.g. through water stress).  Previous research findings on this topic, funded by the Joint Fire Science Program, the National Science Foundation, and NASA, are directly relevant to land managers, but require “translation” into practical and usable tools and resources. This project will rely on and strengthen communications and collaborations between researchers and federal land managers from the U.S. Forest Service and U.S. Department of the Interior bureaus through face-to-face interactions to ensure that managers have access to the science in a form that is useful. The proposed vulnerability assessment will help managers anticipate when and where wildfires will impact ecosystems in new ways, potentially causing ecosystem shifts from forested to non-forested areas, or to fundamentally different forest types.

Abstract (from ScienceDirect): Dryland ecosystems play an important role in determining how precipitation anomalies affect terrestrial carbon fluxes at regional to global scales. Thus, to understand how climate change may affect the global carbon cycle, we must also be able to understand and model its effects on dryland vegetation. Dynamic Global Vegetation Models (DGVMs) are an important tool for modeling ecosystem dynamics, but they often struggle to reproduce seasonal patterns of plant productivity. Because the phenological niche of many plant species is linked to both total productivity and competitive interactions with other plants, errors in how process-based models represent phenology hinder our ability to predict climate change impacts. This may be particularly problematic in dryland ecosystems where many species have developed a complex phenology in response to seasonal variability in both moisture and temperature. Here, we examine how uncertainty in key parameters as well as the structure of existing phenology routines affect the ability of a DGVM to match seasonal patterns of leaf area index (LAI) and gross primary productivity (GPP) across a temperature and precipitation gradient. First, we optimized model parameters using a combination of site-level eddy covariance data and remotely-sensed LAI data. Second, we modified the model to include a semi-deciduous phenology type and added flexibility to the representation of grass phenology. While optimizing parameters reduced model bias, the largest gains in model performance were associated with the development of our new representation of phenology. This modified model was able to better capture seasonal patterns of both leaf area index (R2 = 0.75) and gross primary productivity (R2 = 0.84), though its ability to estimate total annual GPP depended on using eddy covariance data for optimization. The new model also resulted in a more realistic outcome of modeled competition between grass and shrubs. These findings demonstrate the importance of improving how DGVMs represent phenology in order to accurately forecast climate change impacts in dryland ecosystems.

Globally, spring phenology and abiotic processes are shifting earlier with warming. Differences in the magnitudes of these shifts may decouple the timing of plant resource requirements from resource availability. In riparian forests across the northern hemisphere, warming could decouple seed release from snowmelt peak streamflow, thus reducing moisture and safe sites for dominant tree recruitment. We combined field observations with climate, hydrology, and phenology models to simulate future change in synchrony of seed release and snowmelt peaks in the South Platte River Basin, Colorado, for three Salicaceae species that dominate western USA riparian forests. Chilling requirements for overcoming winter endodormancy were strongest in Salix exigua, moderately supported for Populus deltoides, and indiscernible in Salix amygdaloides. Ensemble mean projected warming of 3.5°C shifted snowmelt peaks 10–19 d earlier relative to S. exigua and P. deltoides seed release, because decreased winter chilling combined with increased spring forcing limited change in their phenology. By contrast, warming shifted both snowmelt peaks and S. amygdaloides seed release 21 d earlier, maintaining their synchrony. Decoupling of snowmelt from seed release for Salicaceae with strong chilling requirements is likely to reduce resources critical for recruitment of these foundational riparian forests, although the magnitude of future decoupling remains uncertain.

Abstract From: (The growth and distribution of plant species in water limited environments is often limited by the atmospheric evaporative demands which us measured in terms of potential evaporation (PET). While PET estimated by different methods have been widely used to assess vegetation response to climate change, species distribution models offer unique opportunity to compare their efficiency in predicting habitat suitability of plant species. In this study, we perform the first multi-species comparison of two widely used metrics of PET i.e., Penman-Monteith and Thornthwaite, and show how they result in similar or different on projected distribution of water limited species and potential consequences on their conservation strategies across North Central U.S. To build species distribution models of eight species, we used two sets of environmental predictors which were identical except for the metric of PET (Penman-Monthith vs Thornthwaite) and projected habitat suitability for historical (2005) and future (0399) periods. We found an excellent model performance with no difference under two sets of predictors (AUC + ~0.93). The relative influence of Thornthwaite PET on habitat prediction was higher than Penman PET for most of the species. We observered that the area of the projected suitable habitat was always higher under Thornthwaite set of predictors which were than Penman set of predictors (ranges from 25% to 941%), with the exception of Pinus contorta for which the reverse was true. In most cases, these differences were non-trivial, indicating that the choice of the PET metric, although both of them are commonly used, can have dramatic consequences on the conservation management decisions. Therefore, the conservation management decisions can be markedly different depending on the choice of the PET metric used for species distribution modeling of water limited species.)