Climate change is expected to alter the distribution and abundance of tree species, impacting ecosystem structure and function. Yet, anticipating where this will occur is often hampered by a lack of understanding of how demographic rates, most notably recruitment, vary in response to climate and competition across a species range. Using large-scale monitoring data on two dry woodland tree species (Pinus edulis and Juniperus osteosperma), we develop an approach to infer recruitment, survival, and growth of both species across their range. In doing so, we account for ecological and statistical dependencies inherent in large-scale monitoring data. We find that warming and drying conditions generally lead to declines in recruitment and survival, but there were some idiosyncrasy in the strength of responses across species. Climate conditions lead to vulnerable regions, such as Pinus edulis in N. Arizona, where both survival and recruitment are low. Our approach provides a path forward for leveraging emerging large-scale monitoring and remotely sensed data to anticipate the impacts of global change on species distributions.

Forested areas in the Western U.S. that are impacted by disturbances such as fire and drought have increased in recent decades. This trend is likely to continue, with the increase in frequency and extent of wildfire activity being especially concerning. Resource managers need reliable scientific information to better understand wildfire occurrence, which can vary substantially across landscapes and throughout time. However, few scientific models capture this variability, and projections of future potential changes in fire occurrence can include some uncertainty. This uncertainty can limit our ability to anticipate potential wildfire impacts on society and ecological systems. Another method to help managers prepare for the future is to examine post-fire conditions and asses how and if forests might transition to different landscape types after wildfires (e.g. a change from conifer to deciduous forest). Some studies show that post-fire tree regeneration has been limited in many of the areas burned, especially in large high-severity patches, changing the composition of the landcover. However, it is also unclear how common this post-fire state transition is and what thresholds (e.g., fire severity, burn patch size, post-fire weather conditions) predict such transitions. This research will investigate the impacts that fire disturbances and drought have on the structure and composition of forest ecosystems across the Western U.S. There will be three main areas of focus: 1) simulating interactions among climate, drought, vegetation, and disturbances, like fire; 2) monitoring and predicting post-fire forest vegetation recovery using remote sensing and simulation models, and 3) modeling wildfire occurrence and risk using historical data. This project builds off work previously done under the former USGS LandCarbon program.   Products from this project will be used to assess past patterns of wildfire risk to homes and project future potential changes in fire occurrence and risk across the conterminous U.S. Outputs from this project will also inform fire management decision-making and can also be used to advance existing predictive technology, including landscape simulation models such as LANDIS-II, to help resource manager better prepare for future conditions.

Vegetation phenology and productivity play a crucial role in surface energy balance, plant and animal distribution, and animal movement and habitat use and can be measured with remote sensing metrics including start of season (SOS), peak instantaneous rate of green-up date (PIRGd), peak of season (POS), end of season (EOS), and integrated vegetation indices. However, for most metrics, we do not yet understand the agreement of remotely sensed data products with near-surface observations. We also need summaries of changes over time, spatial distribution, variability, and consistency in remote sensing dataset metrics for vegetation timing and quality. We compare metrics from 10 leading remote sensing datasets against a network of PhenoCam near-surface cameras throughout the western United States from 2002 to 2014. Most phenology metrics representing a date (SOS, PIRGd, POS, and EOS), rather than a duration (length of spring, length of growing season), better agreed with near-surface metrics but results varied by dataset, metric, and land cover, with absolute value of mean bias ranging from 0.38 (PIRGd) to 37.92 days (EOS). Datasets had higher agreement with PhenoCam metrics in shrublands, grasslands, and deciduous forests than in evergreen forests. Phenology metrics had higher agreement than productivity metrics, aside from a few datasets in deciduous forests. Using two datasets covering the period 1982–2016 that best agreed with PhenoCam metrics, we analyzed changes over time to growing seasons. Both datasets exhibited substantial spatial heterogeneity in the direction of phenology trends. Variability of metrics increased over time in some areas, particularly in the Southwest. Approximately 60% of pixels had consistent trend direction between datasets for SOS, POS, and EOS, with the direction varying by location. In all ecoregions except Mediterranean California, EOS has become later. This study comprehensively compares remote sensing datasets across multiple growing season metrics and discusses considerations for applied users to inform their data choices.

Managers are increasingly being asked to integrate climate change adaptation into public land management. The literature discusses a range of adaptation approaches, including managing for resistance, resilience, and transformation; but many strategies have not yet been widely tested. This study employed in-depth interviews and scenario-based focus groups in the Upper Gunnison Basin in Colorado to learn how public land managers envision future ecosystem change, and how they plan to utilize different management approaches in the context of climate adaptation. While many managers evoked the past in thinking about projected climate impacts and potential responses, most managers in this study acknowledged and even embraced (if reluctantly) that many ecosystems will experience regime shifts in the face of climate change. However, accepting that future ecosystems will be different from past ecosystems led managers in different directions regarding how to respond and the appropriate role of management intervention. Some felt management actions should assist and even guide ecosystems toward future conditions. Others were less confident in projections and argued against transformation. Finally, some suggested that resilience could provide a middle path, allowing managers to help ecosystems adapt to change without predicting future ecosystem states. Scalar challenges and institutional constraints also influenced how managers thought about adaptation. Lack of institutional capacity was believed to constrain adaptation at larger scales. Resistance, in particular, was considered impractical at almost any scale due to institutional constraints. Managers negotiated scalar challenges and institutional constraints by nesting different approaches both spatially and temporally.

In recent decades, Rocky Mountain accumulated snowpack levels have experienced rapid declines, yet long-term records of snowpack prior to the installation of snowpack observation stations in the early and mid 20th century are limited. To date, a small number of tree-ring based reconstructions of April 1 Snow Water Equivalent (SWE) in the northern Rocky Mountains have extended modern records of snowpack variability to ∼1200 C.E. Carbonate isotope lake sediment records, provide an opportunity to further extend tree-ring based reconstructions through the Holocene, providing a millennial-scale temporal record that allows for an evaluation of multi-scale drivers of snowpack variability, from internal climate dynamics to orbital-scale forcings. Here we present a ∼2200 year preliminary reconstruction of northern Rockies snowpack based on δ18O measurements of sediment carbonates collected from Foy Lake, Montana. We explore the statistical calibration of lake sediment δ18O to an annually resolved snowpack reconstruction from tree rings, and develop an approach to assess and quantify potential sources of error in this reconstruction approach. The sediment-based snowpack reconstruction shows strong low-frequency variability in snowpack over the last two millennia with few snow droughts approaching the magnitude of recent snowpack declines. Given the growing availability of high-resolution, carbonate-rich lake sediment records, such reconstructions could help improve our understanding of how snowpack conditions varied under previous climatic events (mid-Holocene climate optimum ca. 9−6 ka), providing critical insights for anticipating future snowpack conditions.

Conversion of grassland to cropland in the US Prairie Pothole Region is of longstanding concern. The region's grasslands are carbon (C) sinks and provide important breeding grounds for many migratory bird species. Crop production requires more input use, potentially increasing pollution in the greater Mississippi watershed. Previous analyses of land conversion in the Prairie Pothole Region generally invoke neoclassical economic models and typically use secondary data to assess conversion decisions. To more deeply investigate farmers' land use choices, we use data from focus group meetings to learn about their conversion decisions, conversion costs, and motives. Farmers mentioned profit-related factors frequently as a factor in their land use decisions. However, our respondents who converted to cropland report conversion costs to be well below estimated increases in land value. This suggests that those who choose not to convert land forego such gains, and thus financial motivations may be far from complete in explaining land conversion decisions. We found several quantitative indications that other factors might be crucial in preventing more grassland losses: (1) for those who converted from grass to crop, the gain in returns is so large that conversion costs could be recovered in one year; (2) for those who converted from crop to grass, the gain was negative; and (3) lifestyle choices and stewardship opinions were found to be statistically significant in land use decisions. Thus, nonmarket factors, including lifestyle choice and stewardship perspectives, may be important determinants of land use decisions and act to slow the rate of conversion to cropping.

Accurate estimation of cropping intensity (CI), an indicator of food production, is well aligned with the ongoing efforts to achieve sustainable development goals (SDGs) under diminishing natural resources. The advancement in satellite remote sensing provides unprecedented opportunities for capturing CI information in a spatially continuous manner. However, challenges remain due to the lack of generalizable algorithms for accurately and efficiently mapping global CI with a fine spatial resolution. In this study, we developed a 30-m planetary-scale CI mapping framework with the reconstructed time series of Normalized Difference Vegetation Index (NDVI) from multiple satellite images. Using a binary crop phenophase profile indicating growing and non-growing periods, we estimated pixel-by-pixel CI by enumerating the total number of valid cropping cycles during the study years. Based on the Google Earth Engine cloud computing platform, we implemented the framework to estimate CI during 2016–2018 in eight geographic regions across continents that are representative of global cropping system diversity. Comparison with PhenoCam network data in four cropland sites suggests that the proposed framework is capable of capturing the seasonal dynamics of cropping practices. Spatially, overall accuracies based on validation samples range from 80.0% to 98.9% across different regions worldwide. Regarding the CI classes, single cropping systems are associated with more robust and less biased estimations than multiple cropping systems. Finally, our CI estimates reveal high agreement with two widely used land surface phenology products, including Vegetation Index and Phenology V004 (VIP4) and Moderate Resolution Imaging Spectroradiometer Land Cover Dynamics (MCD12Q2), meanwhile providing much more spatial details. Due to its robustness, the developed CI framework can be potentially generalized to produce global fine resolution CI products for food security and other applications.

We conducted a workshop for tribes in the north central region who are in some stage of climate adaptation planning or implementation. This was a partnership between Colorado State University, the Confederated Salish and Kootenai Tribes, and ITEP (Institute for Tribal Environmental Professionals) Climate Change Program. We built upon ITEPs climate adaptation training and tailored it to the specific needs of tribes in the region. We originally planned to develop the next iteration (“2.0”) of ITEP training to help tribes who need support with their plans and implementing their plans. However, upon surveying the tribes in the region, we quickly learned that there was a wide range of development along a spectrum from tribes who did not even know where to start all the way to tribes who were far into their climate adaptation and/or implementation programs. We had a core planning team of experts who developed the training materials and planned and conducted the workshop. We invited tribal professionals from the EPA Region 8 Regional Tribal Operations Committee (RTOC) 2 with whom the PIs have experience working on climate issues. The workshop was held in Pickstown, South Dakota at the Fort Randall Casino on the Yankton Sioux Reservation. We had 30 or so participants throughout the workshop. Workshop participants reported that they got a lot of useful and usable information to take home and use for their respective tribes’ climate change programs.

Natural and cultural resource managers across the country have begun to use a tool known as "scenario planning" to help prepare for climate change effects that may unfold in the future. In this process, scientific projections are used to identify different plausible, relevant, and divergent climate conditions for a particular area, and then through a participatory process, scientists and resource managers develop "scenarios" which describe the implications of these different conditions for resources and management. The North Central CASC has been working with the National Park Service (NPS) Climate Change Response Program (CCRP) to encourage and support national parks in incorporating climate science and scenario planning into their park management and planning processes. These efforts have helped resource managers to better prepare for the uncertainty of how climate will affect their resources and wild lands. To enhance the capacity and scope of these engagements, the North Central CASC brought on two new individuals with expertise and technical skills related to ecological responses to climate. Over the next two years, these individuals will serve as liaisons between the North Central CASC and the NPS CCRP, and as resources in the development and application of climate change scenarios with NPS and other resource management partners. They will help to plan and facilitate scenario planning workshops, synthesize scientific literature on the climate sensitivities of priority resources, develop and apply ecological response models to enhance scenario planning applications, co-produce reports and literature on the outcomes of scenario planning engagements, and evaluate the efficacy of scaling up scenarios from the individual park level to the regional level to broaden their use and applicability for resource managers.