We provide a collection of data reflecting estimates of soil-climate properties (moisture, temperature, and regimes) based on climate normals (1981-2010). Specifically, we provide estimates for soil moisture (monthly, seasonal, and annual), trends of spring and growing season soil moisture (Theil-Sen estimates), soil temperature and moisture regimes (STMRs; discrete classes defined by United States Department of Agriculture [USDA] Natural Resources Conservation Service [NRCS]), seasonal Thornthwaite moisture index (TMI; precipitation minus PET), and seasonality of TMI and soil moisture (30-meter rasters). Moisture values were estimated using our spatial implementation of the Newhall simulation model that relies on the Thornthwaite-Matter-Sellers potential evapotranspiration (PET) index. Among many enhancements, our application is the first known soil-climate model to include the effects of snow (for example, sublimation, snowmelt, attenuated evaporation, and insulation from air temperatures). Notably, we developed procedures that facilitate data substitution using spatial_nsm, supporting many use cases and flexibility, such as assessing projected climate scenarios. Our results provide evidence of the utility of spatially explicit soil-climate products, which could support subsequent use for modeling and managing ecosystem, habitat, and species distributions. For example, we demonstrated soil-climate properties had significant correlations with vegetation patterns: soil moisture variables predicted sagebrush (R^2 = 0.51), annual herbaceous plant cover (R^2 = 0.687), exposed soil (R^2 = 0.656), and fire occurrence (R^2 = 0.343). These statistical results suggested the data captured distributions of soil moisture and STMRs that can explain landscape and vegetation patterns. Refer to the Cross Reference section for all citations referenced in metadata supporting methods. This section also references our software used for developing these data products (nsm_spatial). Refer to the Larger Citation describing this project in full. Normal (1981 – 2010): Describes climate conditions averaged (temperature) or summed (precipitation) across 30-year climate period.

Over time, the idea of the public value of federally funded science has slowly transitioned from basic science that helps fight disease and maintain national security (Bush 1945) to use-inspired science that directly informs decisions about the most urgent issues facing society, such as climate change (Lubchenco 1998, 2017). Natural and cultural resources across the world are already experiencing demonstrable impacts due to changes in our climate system, and stewards of these resources are turning to the scientific community for actionable science – information and tools that can be directly applied to decisions about how best to adapt to these future conditions (IPCC 2022). Such societal impact is more likely to be met when decision makers are engaged in the process of knowledge production (Ferguson et al. 2022). In response, public funders of climate science are changing how they design solicitations, review proposals, and make other programmatic decisions to encourage research to meet decision making needs (Arnott et al. 2020a). However, when the knowledge created does not fit decision contexts or is not used appropriately, vulnerability or contributions to climate change can instead increase and maladaptation can occur (Barnett and O’Neill 2010). Ensuring that engagement of decision makers in actionable science is carried out in a thoughtful and reflexive way is critical to achieving the desired societal impact. In this dissertation, I examine three key questions regarding the engagement of stakeholders in the production of actionable climate adaptation science. First, how should researchers align their stakeholder engagement processes with their desired goals for societal impact and actionability? In Chapter 2, I propose a framework of engagement approaches that describes the wide variety of tools that are available for including stakeholders in the creation of actionable science, and I provide guidance on how researchers might consider tradeoffs among those approaches and tools. Second, how should researchers conduct engagement for societal impact in a way that maximizes benefits and minimizes harms to stakeholders? In Chapter 3, I analyze interviews (n=15) with stakeholders who were highly engaged in actionable climate adaptation science projects to examine their perspectives on the benefits and harms that they experienced, and I argue that researchers must proactively consider the ethical implications of engagement when developing their project idea. Third, how should researchers define successful societal impact and evaluate against such standards? In Chapter 4, I draw on the discipline of evaluation to develop a survey tool to examine the process, outputs, and outcomes of actionable science based on the perspectives of stakeholders engaged in those projects, and I analyze survey responses (n=49) in a case-study deployment of the tool. In Chapter 5, I synthesize the findings from Chapters 2-4 and summarize key takeaways. Overall, I recommend that researchers thoughtfully consider stakeholder engagement goals and benefits as early as possible to best meet expectations of societal impact and actionability, ideally at or prior to the proposal development stage. Doing so in a robust manner often involves skills in which many biological and physical researchers may not be trained, requiring additional resources and expertise to be included in project plans.

This file provides a table of all the of Species of Greatest Conservation Need listed in the North Central states' (MT, WY, CO, ND, SD, NE, and KS) State Wildlife Action Plans as of summer 2020. Species are organized by the number of states which listed them as Species of Greatest Conservation Need, and then by scientific name. Federal status is also provided for each species. This table is adapted from an unpublished species list compiled by the North Central Climate Adaptation Science Center.

The broadly shared information needs for grassland managers in the North Central region to meet conservation goals in a changing climate are presented and ranked as highly relevant, somewhat relevant, or not relevant for federal, state, tribal, and non-governmental grassland-managing entities.

Increased wildfire activity combined with warm and dry post-fire conditions may undermine the mechanisms maintaining forest resilience to wildfires, potentially causing ecosystem transitions, or fire-catalyzed vegetation shifts. Stand-replacing fire is especially likely to catalyze vegetation shifts expected from climate change, by killing mature trees that are less sensitive to climate than juveniles. To understand the vulnerability of forests to fire-catalyzed vegetation shifts it is critical to identify both where fires will burn with stand-replacing severity and where climate conditions limit seedling recruitment. We used an extensive dendrochronological dataset to model the influence of seasonal climate on post-fire recruitment probability for ponderosa pine and Douglas-fir. We applied this model to project annual recruitment probability in the US intermountain west under contemporary and future climate conditions, which we compared to modeled probability of stand-replacing fire. We categorized areas as ‘vulnerable to fire-catalyzed vegetation shifts,’ if they were likely to burn at stand-replacing severity, if a fire were to occur, and had post-fire climate conditions unsuitable for tree recruitment. Climate suitability for recruitment declined over time in all ecoregions: 21% and 15% of the range of ponderosa pine and Douglas-fir, respectively, had climate conditions unsuitable for recruitment in the 1980s, whereas these values increased to 61% (ponderosa pine) and 34% (Douglas-fir) for the future climate scenario. Less area was vulnerable to fire-catalyzed vegetation shifts, but these values also increased over time, from 6% and 4% of the range of ponderosa pine and Douglas-fir in the 1980s, to 16% (ponderosa pine) and 10% (Douglas-fir) under the future climate scenario. Southern ecoregions had considerably higher vulnerability to fire-catalyzed vegetation shifts than northern ecoregions. Overall, our results suggest that the combination of climate warming and an increase in wildfire activity may substantially impact species distributions through fire-catalyzed vegetation shifts.

Climate change is expected to disproportionately affect species occupying ecosystems with relatively hard boundaries, such as alpine ecosystems. Wildlife managers must identify actions to conserve and manage alpine species into the future, while considering other issues and uncertainties. Climate change and respiratory pathogens associated with widespread pneumonia epidemics in bighorn sheep (Ovis canadensis) may negatively affect mountain goat (Oreamnos americanus) populations. Mountain goat demographic and population data are challenging to collect and sparsely available, making population management decisions difficult. We developed predictive models incorporating these uncertainties and analyzed results within a structured decision making framework to make management recommendations and identify priority information needs in Montana, USA. We built resource selection models to forecast occupied mountain goat habitat and account for uncertainty in effects of climate change, and a Leslie matrix projection model to predict population trends while accounting for uncertainty in population demographics and dynamics. We predicted disease risks while accounting for uncertainty about presence of pneumonia pathogens and risk tolerance for mixing populations during translocations. Our analysis predicted that new introductions would produce more area occupied by mountain goats at mid-century, regardless of the effects of climate change. Population augmentations, carnivore management, and harvest management may improve population trends, although this was associated with considerable uncertainty. Tolerance for risk of disease transmission affected optimal management choices because translocations are expected to increase disease risks for mountain goats and sympatric bighorn sheep. Expected value of information analyses revealed that reducing uncertainty related to population dynamics would affect the optimal choice among management strategies to improve mountain goat trends. Reducing uncertainty related to the presence of pneumonia-associated pathogens and consequences of mixing microbial communities should reduce disease risks if translocations are included in future management strategies. We recommend managers determine tolerance for disease risks associated with translocations that they and constituents are willing to accept. From this, an adaptive management program can be constructed wherein a portfolio of management actions are chosen based on risk tolerance in each population range, combined with the amount that uncertainty is reduced when paired with monitoring, to ultimately improve achievement of fundamental objectives.  

Vegetation phenology is one of the most sensitive indicators to environmental and climate changes. In order to characterize the seasonal variation in relatively pure or homogenous vegetation types, fine spatial resolution satellite data (≤ 30 m), such as Landsat, Sentinel-2, PlanetScope, or Harmonized Landsat and Sentinel-2 (HLS), have been increasingly applied to detect land surface phenology (LSP). However, the most critical challenge in LSP detections is the gaps in temporal satellite observations caused by noise and persistent cloud/snow cover. Therefore, this study presented a novel algorithm for generating synthetic gap-free time series at the field scale (30 m) for LSP detections. Specifically, we first developed a framework to establish a large collection of temporal shapes of vegetation growth with as many as 100 grid-based Green Chromatic Coordinate (GCC) time series in a single PhenoCam site. For a given HLS pixel, the two-band Enhanced Vegetation Index (EVI2) time series was matched and fused with the most comparable temporal GCC shape selected from the collection of PhenoCam GCC time series to generate a synthetic gap-free HLS-PhenoCam EVI2 time series, which was used to detect the 30 m phenometrics. The detected phenometrics were evaluated using manually selected and spatially matched GCC observations as well as phenology detections from HLS alone. The result indicates that the HLS-PhenoCam phenometrics are very close to the observations from PhenoCam network with a correlation coefficient (R) of 0.82–0.97, a mean absolute difference (MAD) of 2.8–3.5 days, a root mean squared error (RMSE) of 3.5–4.0 days, and a mean systematic bias (MSB) of 0.1–2.2 days. The HLS-PhenoCam detections are significantly improved relative to the HLS phenometrics that have a statistic accuracy of R = 0.57–0.78, MAD = 6.4–9.3 days, RMSE = 8.8–13.9 days, MSB = -5.2–5.9 days. The difference between HLS-PhenoCam and HLS alone LSP detections over a HLS tile could be on average larger than two weeks if high-quality observation (HQO) proportion in the annual HLS time series is <10%, which exponentially reduces with the increase of HQO in HLS observations. The analyses in this study suggest that the gap-free HLS-PhenoCam time series is able to be generated for producing high-quality phenology datasets across a local and regional scale, to bridge near-surface PhenoCam observations with satellite observations data at various scales, and to be used as a scalable phenology dataset for the validation of global MODIS and VIIRS LSP products.

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 drying and warming conditions generally lead to declines in recruitment and survival, but the strength of responses varied between species. These climate conditions point to geographic regions of high vulnerability for particular species, such as Pinus edulis in northern 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.      

This dataset includes spatial projections of the post-fire recruitment index for ponderosa pine (Pinus ponderosa) and Douglas-fir (Pseudotsuga menziesii) using climate data from different time periods (1980-1989, 1990-1999, 2000-2009, 2010-2014) and a future climate scenario of a global mean increase in temperature of two degrees Celsius. The post-fire recruitment index varies from 0 to 1 and represents the proportion of the first five years following wildfire that had climate suitable for regeneration of the given species. We chose a five-year window because the majority (69%) of recruitment across all sites in the dataset used to build our recruitment models occurred within the first five post-fire years. In the projections, climate and time since fire varies by year but other predictors stay constant at fixed values. Distance to seed source was set at 50 m and fire severity, measured as the differenced normalized burn ratio (dNBR), was set at 400 for all projections. Because we hold distance to seed source and fire severity constant, the post-fire recruitment index is interpreted as the climate suitability for post-fire recruitment, under the given scenario. We recognize that post-fire recruitment is also influenced by other local factors that are unaccounted for in our models, including biotic interactions, such as herbivory and competition, and abiotic factors, such as substrate, topography and soil moisture.

The sagebrush ecosystem is home to diverse wildlife, including charismatic species such as pronghorn, pygmy rabbits, mule deer and Greater Sage-Grouse. Historic and contemporary land-uses, large wildfires, non-native (introduced) plant invasions, climate cycles with droughts, and long-term climate trends characterize the list of widespread threats to this heavily used landscape. Semi-arid habitats, such as sagebrush ecosystems, present challenges for management due to natural limitations and variability in growing conditions across the landscape and over time. Differences in environmental conditions lead to differences in plant composition, fuel accumulation, resilience to stress and disturbance and restoration/management outcomes. Much variability is created by the combination of soil and climate conditions that create a water availability gradient. Therefore, climate-soil-plant relations describe a fundamental ecosystem function with direct implications for management. Until now, lack of accurate representation of the spatial and temporal heterogeneity in soil moisture and temperature have restricted use of soil-climate in ecological applications. We created a simulation model that accounts for soil water content over the course of a year using precipitation, temperature, soil water capacity, evapotranspiration, and related environmental characteristics (such as topography) across large landscapes. We modeled soil-climate using averaged climate conditions (30-years) across 313 million hectares (774 million acres) of the western U.S., encompassing most of the sagebrush biome (approximately 17 million hectares were excluded along the southern extent, but sagebrush cover is very low there). The model produced spatially independent cell-by-cell soil-climate classification and estimates of soil moisture across the entire region. Models were tested using climate station data, qualitative comparison with soil data, and correlation with ecological patterns using shrub and annual grass cover, exposed soil, and fire frequency. Analyses indicated strong connections between our results and landscape patterns: 66% of variation (deviance explained) in exposed bare ground (inverse of plant cover) was explained by spring soil moisture alone, and 51% of the variability in sagebrush cover was explained by combination of spring and winter soil moisture. Importantly, soil moisture combined with burn frequency explained 69% of the deviance in annual herbaceous grasses across the region (this is very strong). These results improve understanding of ecosystem patterns (demonstrated by plant cover here) and management risks (such as, fire and invasion) and provide useful data for management and research applications.