Grasslands, and the depressional wetlands that exist throughout them, are endangered ecosystems that face both climate and land-use change pressures. Tens of millions of dollars are invested annually to manage the existing fragments of these ecosystems to serve as critical breeding habitat for migratory birds. The North American Prairie Pothole Region (PPR) contains millions of depressional wetlands that produce between 50% and 80% of the continent’s waterfowl population. Previous modeling efforts suggested that climate change would result in a shift of suitable waterfowl breeding habitat from the central to the southeast portion of the PPR, an area where over half of the depressional wetlands have been drained. The implications of these projections suggest a massive investment in wetland restoration in the southeastern PPR would be needed to sustain waterfowl populations at harvestable levels. We revisited these modeled results indicating how future climate may impact the distribution of waterfowl-breeding habitat using up-to-date climate model projections and a newly developed model for simulating prairie-pothole wetland hydrology. We also presented changes to the number of “May ponds,” a metric used by the U.S. Fish and Wildlife Service to estimate waterfowl breeding populations and establish harvest regulations. Based on the output of 32 climate models and two emission scenarios, we found no evidence that the distribution of May ponds would shift in the future. However, our results projected a 12% decrease to 1% increase in May pond numbers when comparing the most recent climate period (1989–2018) to the end of the 21st century (2070–2099). When combined, our results suggest areas in the PPR that currently support the highest densities of intact wetland basins, and thus support the largest numbers of breeding-duck pairs, will likely also be the places most critical to maintaining continental waterfowl populations in an uncertain future.

Regeneration is an essential demographic step that affects plant population persistence, recovery after disturbances, and potential migration to track suitable climate conditions. Challenges of restoring big sagebrush (Artemisia tridentata) after disturbances including fire-invasive annual grass interactions exemplify the need to understand the complex regeneration processes of this long-lived, woody species that is widespread across the semiarid western U.S. Projected 21st century climate change is expected to increase drought risks and intensify restoration challenges. A detailed understanding of regeneration will be crucial for developing management frameworks for the big sagebrush region in the 21st century. Here, we used two complementary models to explore spatial and temporal relationships in the potential of big sagebrush regeneration representing (1) range-wide big sagebrush regeneration responses in natural vegetation (process-based model) and (2) big sagebrush restoration seeding outcomes following fire in the Great Basin and the Snake River Plains (regression-based model). The process-based model suggested substantial geographic variation in long-term regeneration trajectories with central and northern areas of the big sagebrush region remaining climatically suitable, whereas marginal and southern areas are becoming less suitable. The regression-based model suggested, however, that restoration seeding may become increasingly more difficult, illustrating the particularly difficult challenge of promoting sagebrush establishment after wildfire in invaded landscapes. These results suggest that sustaining big sagebrush on the landscape throughout the 21st century may climatically be feasible for many areas and that uncertainty about the long-term sustainability of big sagebrush may be driven more by dynamics of biological invasions and wildfire than by uncertainty in climate change projections. Divergent projections of the two models under 21st century climate conditions encourage further study to evaluate potential benefits of re-creating conditions of uninvaded, unburned natural big sagebrush vegetation for post-fire restoration seeding, such as seeding in multiple years and, for at least much of the northern Great Basin and Snake River Plains, the control of the fire-invasive annual grass cycle.

Phenology camera (PhenoCam) data and the derived green chromatic coordinate (GCC) time series are commonly used to track seasonal changes in canopy greenness. However, the GCC time series is noisy because color distortion commonly exists in the captured photographs owing to the varying illumination conditions and nonlinear response of the consumer-grade camera to the incoming light. Hence, we proposed an optimal color composition (OCC) method to generate high-quality daily photographic time series by compositing multiple photographs captured in a single day. First, the optimal acquisition time with good illumination conditions and correct exposure settings is determined for each pixel throughout the day based on a comprehensive color index, combining the brightness and saturation. A virtual photograph consisting of the selected digital numbers acquired at the optimal time is then composited for each day. Finally, the daily GCC time series is calculated based on the virtual photographs. By testing the photographs of six forest sites, the proposed method was compared with the commonly used 90th percentile (Per90) filter. The results show that the daily photographs composited using the OCC method were more homogeneous with less shaded areas compared to those selected by the Per90 filter, and the corresponding GCC time series derived from the OCC method is more stable and less influenced by varying atmospheric conditions and solar angles than the Per90 filter. These results indicate that the OCC method can generate high-quality daily photographic time series with the potential to better indicate seasonal color changes in the forest canopy.

Plant community response to climate change will be influenced by individual plant responses that emerge from competition for limiting resources that fluctuate through time and vary across space. Projecting these responses requires an approach that integrates environmental conditions and species interactions that result from future climatic variability. Dryland plant communities are being substantially affected by climate change because their structure and function are closely tied to precipitation and temperature, yet impacts vary substantially due to environmental heterogeneity, especially in topographically complex regions. Here, we quantified the effects of climate change on big sagebrush (Artemisia tridentata Nutt.) plant communities that span 76 million ha in the western United States. We used an individual-based plant simulation model that represents intra- and inter-specific competition for water availability, which is represented by a process-based soil water balance model. For dominant plant functional types, we quantified changes in biomass and characterized agreement among 52 future climate scenarios. We then used a multivariate matching algorithm to generate fine-scale interpolated surfaces of functional type biomass for our study area. Results suggest geographically divergent responses of big sagebrush to climate change (changes in biomass of −20% to +27%), declines in perennial C3 grass and perennial forb biomass in most sites, and widespread, consistent, and sometimes large increases in perennial C4 grasses. The largest declines in big sagebrush, perennial C3 grass and perennial forb biomass were simulated in warm, dry sites. In contrast, we simulated no change or increases in functional type biomass in cold, moist sites. There was high agreement among climate scenarios on climate change impacts to functional type biomass, except for big sagebrush. Collectively, these results suggest divergent responses to warming in moisture-limited versus temperature-limited sites and potential shifts in the relative importance of some of the dominant functional types that result from competition for limiting resources.

A robust method for characterizing the biophysical environment of terrestrial vegetation uses the relationship between Actual Evapotranspiration (AET) and Climatic Water Deficit (CWD). These variables are usually estimated from a water balance model rather than measured directly and are often more representative of ecologically-significant changes than temperature or precipitation. We evaluate trends and spatial patterns in AET and CWD in the Continental United States (CONUS) during 1980–2019 using a gridded water balance model. The western US had linear regression slopes indicating increasing CWD and decreasing AET (drying), while the eastern US had generally opposite trends. When limits to plant performance characterized by AET and CWD are exceeded, vegetation assemblages change. Widespread increases in aridity throughout the west portends shifts in the distribution of plants limited by available moisture. A detailed look at Sequoia National Park illustrates the high degree of fine-scale spatial variability that exists across elevation and topographical gradients. Where such topographical and climatic diversity exists, appropriate use of our gridded data will require sub-setting to an appropriate area and analyzing according to categories of interest such as vegetation communities or across obvious physical gradients. Recent studies have successfully applied similar water balance models to fire risk and forest structure in both western and eastern U.S. forests, arid-land spring discharge, amphibian colonization and persistence in wetlands, whitebark pine mortality and establishment, and the distribution of arid-land grass species and landscape scale vegetation condition. Our gridded dataset is available free for public use. Our findings illustrate how a simple water balance model can identify important trends and patterns at site to regional scales. However, at finer scales, environmental heterogeneity is driving a range of responses that may not be simply characterized by a single trend.

Land cover change plays a critical role in influencing hydrological responses. Change in land cover has impacted runoff across basins with substantial human interference; however, the impacts in basins with minimal human interference have been studied less. In this study, we investigated the impacts of directional land cover changes (forest to/from combined grassland and shrubland) in runoff coefficient (RC; ratio of runoff to precipitation) and runoff volume across 603 low human interference reference basins in the conterminous United States (CONUS). The results indicate basins with significant (p<0.05) increasing trends in runoff and RC were across the northeast and northwest regions of CONUS, and basins with decreasing trends were in the southern CONUS region. A unit percent increase in basin area from grassland and shrubland to forest was associated with a ∼4% decrease in RC across basins with decreasing RC trends. Similarly, a unit percent increase in basin area from forest to a combined grassland and shrubland was associated with a ∼1% increase in RC across increasing RC trend basins. Runoff volume was decreased (increased) by ∼25 × 106 m3 yr−1 (∼9 × 106 m3 yr−1) across basins with decreasing (increasing) trends in runoff and RC. When relating runoff volume with the area of directional land cover changes, each 1 km2 increase in area from grassland and shrubland to forest resulted in a decrease of ∼530,000 m3 runoff volume across basins with decreasing trends. In contrast, each 1 km2 increase in area from forest to grassland and shrubland increased runoff volume by ∼200,000 m3 across increasing trend basins. Basins in the southern region of CONUS were more impacted by runoff parameters (RC and runoff volume) from directional land cover changes than basins in the northern region. The findings of this study are useful for planning and managing water availability for sustainable and adaptive water resources management at regional scales.