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Aridland breeding bird communities of the United States are among the most vulnerable to drought, with many species showing significant population declines associated with decreasing precipitation and increasing temperature. Individual breeding bird species have varied responses to drought which suggests complex responses to changes in water availability. Here, we evaluated the influence of water deficit, an integrative metric of drought stress, on breeding bird communities within 3 distinct aridland habitat types: riparian, pinyon-juniper, and sagebrush shrubland. We used 12 years of breeding bird survey data from 11 national parks and monuments in the Northern Colorado Plateau Inventory and Monitoring Network (NCPN). We used a multivariate community-level approach to test for the effect of annual water deficit on the bird communities in the 3 habitats. We found that bird communities responded to water deficit in all 3 habitat types, and 70% of the 30 species–habitat combinations show significant relationships between density and variation in water deficit. Our analyses revealed that the direction and magnitude of species responses to water deficit were habitat-dependent. The habitat-specific responses we observed suggest that the adaptive capacity of some species depends on the habitat in which they occur, a pattern only elucidated with our habitat-based approach. The direction and magnitude of the relationships between predicted densities and annual water deficit can be used to predict the relative sensitivity of species within habitat climate changes. These results provide the first attempt to determine how the indirect effect of changes in water availability might affect aridland breeding birds in distinct habitat types. Linking breeding bird density to annual water deficit may be valuable for predicting changes in species persistence and distribution in response to climate change.

Researchers often use gridded datasets to develop statistical relationships between climate and natural resources at locations that are distant from weather stations. These gridded datasets may provide inaccurate estimates of temperature at sites with elevations that differ significantly from the elevation assigned to the corresponding grid cell. We assess the accuracy of three gridded climate datasets commonly used in ecological studies (800‐m resolution PRISM, 4‐km GRIDMET, and 1‐km Daymet) compared with a network of 153 temperature dataloggers arranged along elevation transects in Yellowstone National Park (Wyoming, Montana, Idaho). Measured lapse rates for monthly average daytime high temperatures were generally steeper (more cooling per unit of elevation increase) than lapse rates in gridded datasets and steeper than those in similar mountainous regions. Measured lapse rates for monthly average nighttime lows were similar to gridded data lapse rates. Our measured lapse rates are most useful for the adjustment of daytime highs from weather stations or gridded data during warmer months. Temperatures during cooler months sometimes are strongly affected by other factors. Ecologically relevant metrics calculated from temperatures adjusted with constant lapse rates can increase multiplicatively and by varying amounts from year to year. For example, high‐elevation estimates of climatic water deficit (CWD) calculated from gridded data that were not adjusted with our measured lapse rates were 2.5–4 times greater (depending on the year) than calculations from temperatures that were adjusted. Our results emphasize the importance of correcting grid‐based climate estimates for elevation in complex terrain when accurate, site‐specific data are required. When the elevation assigned to grid cells differs significantly from the elevation of points within the cell, the lapse rate obtained for data extracted from grids can be shallower than the true rate. We illustrate the implications of these findings with case studies in Yellowstone.

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.

Research Highlights: The efficacy of planting for restoration is important for ecosystem managers. Planting efforts represent an opportunity for conserving and managing species during a population crisis. Background and Objectives: Federal agencies have been planting whitebark pine (WBP), an important subalpine species that is late to mature and long-lived, for three decades in the Greater Yellowstone Ecosystem (GYE). These efforts have been met with varying success, and they have not been evaluated beyond the first five years post-planting. Ecosystem managers will continue to plant WBP in the GYE for years to come, and this research helps to inform and identify higher quality habitat during a period of changing climate and high GYE WBP mortality rates. Materials and Methods: We use a combination of field sampling and a water balance model to investigate local biophysical gradients as explanatory variables for WBP performance at twenty-nine GYE planting sites. Results: We found that the WBP growth rate was positively correlated with actual evapotranspiration (AET) and was greatest when cumulative growing season AET was above 350 mm. Growth rate was not strongly affected by competition at the levels found in this study. However, site density change over time was negatively affected by mean growing season temperature and when more than five competitors were present within 3.59 m radius. Conclusions: If they make it to maturity, trees that are planted this season will not begin to produce cones until the latter half of this century. We recommend planting efforts that optimize AET for growth rate objectives, minimize water deficit (WD) that cause stress and mortality, and removing competitors if they exceed five within a short distance of seedlings.

Research and management issues related to pine forests, snowpack and refugia are relevant to mountainous ecosystems globally. For this study, we investigated local snowpack longevity as an explanatory variable for whitebark pine performance (survival rate, growth rate and condition). We used Sentinel-2 imagery to monitor local snowpack longevity. This new imagery is spatially and temporally more appropriate than other publicly available satellite imagery, and early results indicate that Sentinel-2 imagery can be successfully used for this purpose. Sites were selected based on a multi-decade management effort by federal agencies to plant whitebark pine in the Greater Yellowstone Ecosystem. Relative to initial planting records, present-day field sampling affords an opportunity to evaluate whitebark pine performance over time. Featured photo from Figure 3 in report.

Whitebark pine, a foundation species at tree line in the Western U.S. and Canada, has declined due to native mountain pine beetle epidemics, wildfire, and white pine blister rust. These declines are concerning for the multitude of ecosystem and human benefits provided by this species. An understanding of the climatic correlates associated with spread is needed to successfully manage impacts from forest pathogens. Since 2000 mountain pine beetles have killed 75% of the mature cone-bearing trees in the Greater Yellowstone Ecosystem, and 40.9% of monitored trees have been infected with white pine blister rust. We identified models of white pine blister rust infection which indicated that an August and September interaction between relative humidity and temperature are better predictors of white pine blister rust infection in whitebark pine than location and site characteristics in the Greater Yellowstone Ecosystem. The climate conditions conducive to white pine blister rust occur throughout the ecosystem, but larger trees in relatively warm and humid conditions were more likely to be infected between 2000 and 2018. We mapped the infection probability over the past two decades to identify coarse-scale patterns of climate conditions associated with white pine blister rust infection in whitebark pine.

Managing climate impacts to natural resources in protected areas can be hampered by lack of monitoring data, poor understanding of natural resource responses to climate, or lack of timely condition assessments that can inform management actions. Here we demonstrate the utility of water balance as a tool for understanding natural resource responses to climate by developing case studies focused on stream flow, vegetation production, and wildfire ignition at Great Sand Dunes National Park and Preserve (GSDNP), U.S.A. The efficacy of water balance to predict these responses stems from the explicit integration of climate with site conditions that modify the effects of climate. This in turn results in estimates of water availability, water use, and water need that are proximal drivers of aquatic and terrestrial natural resource conditions. The water balance model successfully forecasted stream flow (r2 = 0.69, P

Whitebark pine (Pinus albicaulis) forests in the western United States have been adversely affected by an exotic pathogen (Cronartium ribicola, causal agent of white pine blister rust), insect outbreaks (Dendroctonus ponderosae, mountain pine beetle), and drought. We monitored individual trees from 2004 to 2013 and characterized stand‐level biophysical conditions through a mountain pine beetle epidemic in the Greater Yellowstone Ecosystem. Specifically, we investigated associations between tree‐level variables (duration and location of white pine blister rust infection, presence of mountain pine beetle, tree size, and potential interactions) with observations of individual whitebark pine tree mortality. Climate summaries indicated that cumulative growing degree days in years 2006–2008 likely contributed to a regionwide outbreak of mountain pine beetle prior to the observed peak in whitebark mortality in 2009. We show that larger whitebark pine trees were preferentially attacked and killed by mountain pine beetle and resulted in a regionwide shift to smaller size class trees. In addition, we found evidence that smaller size class trees with white pine blister rust infection experienced higher mortality than larger trees. This latter finding suggests that in the coming decades white pine blister rust may become the most probable cause of whitebark pine mortality. Our findings offered no evidence of an interactive effect of mountain pine beetle and white pine blister rust infection on whitebark pine mortality in the Greater Yellowstone Ecosystem. Interestingly, the probability of mortality was lower for larger trees attacked by mountain pine beetle in stands with higher evapotranspiration. Because evapotranspiration varies with climate and topoedaphic conditions across the region, we discuss the potential to use this improved understanding of biophysical influences on mortality to identify microrefugia that might contribute to successful whitebark pine conservation efforts. Using tree‐level observations, the National Park Service‐led Greater Yellowstone Interagency Whitebark Pine Long‐term Monitoring Program provided important ecological insight on the size‐dependent effects of white pine blister rust, mountain pine beetle, and water availability on whitebark pine mortality. This ongoing monitoring campaign will continue to offer observations that advance conservation in the Greater Yellowstone Ecosystem.

Groundwater-fed springs are essential habitat for many dryland species. Climate projections forecast an increasingly arid climate for the southwestern United States. Therefore, an understanding of the relationships between climate and spring discharge is increasingly important. Monthly discharge measurements were recorded from 2001 to 2014 at three jointed bedrock springs in and near Arches National Park, Utah, United States. Discharge was compared with the potential evapotranspiration (PET) and precipitation derived from Daymet gridded climate data. Despite the similarities in location, aquifer type, and climate exposure, all three springs showed different responses to local climate. Two springs emerging from the western aquifer had decreases in discharge during differing portions of their record, while the eastern aquifer spring had stable discharge. At the monthly scale, there was a strong inverse relationship between measured discharge and PET at all three springs, likely due to vegetation accessing the water prior to its surface expression. Annual average winter discharge from both western aquifer springs responded to reductions in 10-year cumulative winter precipitation, while discharge from the eastern aquifer spring did not correspond well to precipitation within the period of record. Uncertainty in climate projections for aquifer recharge remains high, but increasing air temperatures will likely lead to increased PET and reduced spring surface flow. Better characterization of climate and spring discharge relationships will help managers protect contributing areas that may be more susceptible to groundwater withdrawal and better understand the available habitat for groundwater-dependent ecosystems and species.

Freshwater wetlands are particularly vulnerable to climate change. Specifically, changes in temperature, precipitation, and evapotranspiration (i.e., climate drivers) are likely to alter flooding regimes of wetlands and affect the vital rates, abundance, and distributions of wetland‐dependent species. Amphibians may be among the most climate‐sensitive wetland‐dependent groups, as many species rely on shallow or intermittently flooded wetland habitats for breeding. Here, we integrated multiple years of high‐resolution gridded climate and amphibian monitoring data from Grand Teton and Yellowstone National Parks to explicitly model how variations in climate drivers and habitat conditions affect the occurrence and breeding dynamics (i.e., annual extinction and colonization rates) of amphibians. Our results showed that models incorporating climate drivers outperformed models of amphibian breeding dynamics that were exclusively habitat based. Moreover, climate‐driven variation in extinction rates, but not colonization rates, disproportionately influenced amphibian occupancy in monitored wetlands. Long‐term monitoring from national parks coupled with high‐resolution climate data sets will be crucial to describing population dynamics and characterizing the sensitivity of amphibians and other wetland‐dependent species to climate change. Further, long‐term monitoring of wetlands in national parks will help reduce uncertainty surrounding wetland resources and strengthen opportunities to make informed, science‐based decisions that have far‐reaching benefits.

Measurement sensitivity and bias quality control metrics are commonly reported for water-quality parameters measured in the laboratory. Less commonly recognized is that they should also be reported for field-measured parameters. Periodic evaluation helps document data quality and can help serve as early warning if there are problems with methods or techniques that could negatively affect ability to interpret threshold values and trends over time. This study focuses on traditional assessment of bias and introduces a new method for estimating measurement sensitivity of water-quality parameters measured monthly in the field. Alternative measurement sensitivity is a new data quality indicator used to demonstrate how quantifying sensitivity at the measurement level can improve understanding the uncertainty affecting each reported data value. That, in turn, can help interpret the meaning of results from many separate data points measured in the field. In this 30-month study, pH and specific conductance consistently met, and dissolved oxygen did not always meet NPS and USGS quality control standards for bias. Evaluation of dissolved oxygen bias and sensitivity during the study provided impetus to improve calibration techniques that resulted in data that later met quality goals.

Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soils from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed. In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soils information using simple parameter estimation with few but appropriately timed remote sensing retrievals.

A GIS framework, the Army Remote Moisture System (ARMS), has been developed to link the Land Information System (LIS), a high performance land surface modeling and data assimilation system, with remotely sensed measurements of soil moisture to provide a high resolution estimation of soil moisture in the near surface. ARMS uses available soil (soil texture, porosity, K"s"a"t), land cover (vegetation type, LAI, Fraction of Greenness), and atmospheric data (Albedo) in standardized vector and raster GIS data formats at multiple scales, in addition to climatological forcing data and precipitation. PEST (Parameter EStimation Tool) was integrated into the process to optimize soil porosity and saturated hydraulic conductivity (K"s"a"t), using the remotely sensed measurements, in order to provide a more accurate estimate of the soil moisture. The modeling process is controlled by the user through a graphical interface developed as part of the ArcMap component of ESRI ArcGIS.

Knowledge of climatic variability at small spatial extents (< 50 km) is needed to assess vulnerabilities of biological reserves to climate change. We used empirical and modeled weather station data to test if climate change has increased the synchrony of surface air temperatures among 50 sites within the Greater Yellowstone Area (GYA) of the interior western United States. This important biological reserve is the largest protected area in the Lower 48 states and provides critical habitat for some of the world's most iconic wildlife. We focused our analyses on temporal shifts and shape changes in the annual distributions of seasonal minimum and maximum air temperatures among valley-bottom and higher elevation sites from 1948-2012. We documented consistent patterns of warming since 1948 at all 50 sites, with the most pronounced changes occurring during the Winter and Summer when minimum and maximum temperature distributions increased. These shifts indicate more hot temperatures and less cold temperatures would be expected across the GYA. Though the shifting statistical distributions indicate warming, little change in the shape of the temperature distributions across sites since 1948 suggest the GYA has maintained a diverse portfolio of temperatures within a year. Spatial heterogeneity in temperatures is likely maintained by the GYA's physiographic complexity and its large size, which encompasses multiple climate zones that respond differently to synoptic drivers. Having a diverse portfolio of temperatures may help biological reserves spread the extinction risk posed by climate change.

A study was conducted to determine the potential of using Advanced Very High Resolution Radiometer (AVHRR) imagery to monitor short-term changes in rangeland forage conditions on a regional scale. Forage biomass and nitrogen concentration were estimated at 6 study sites throughout a typical grazing season (April to October). Study sites were located in northern and southern Montana in areas classified as foothills grassland and shortgrass prairie. Normalized Difference Vegetation Index (NDVI) values from AVHRR imagery (1 km pixels) were used to predict live biomass, dead standing biomass, total biomass, nitrogen (N) concentration and standing N. Values of the NDVI were correlated (r > 0.4, P 0.05) for all 6 study sites, which indicates that NDVI could be used to predict forage abundance at multiple locations and at variable dates. Using simple linear regression, NDVI accounted for 63% of the variation in live and total biomass, 18% of the variation in dead biomass, 66% of the variation in standing N, but < 1% of the variation in N concentration. The NDVI obtained from AVHRR imagery was a good predictor of forage abundance as measured by live, dead and total biomass as well as standing N, but it was not related to forage quality as measured by N or crude protein concentration. On a regional basis, land managers could use AVHRR-NDVI values to identify areas with high or low levels of forage abundance that may result from factors such as drought, variable precipitation patterns, or uneven grazing. DOI:10.2458/azu_jrm_v55i4_thoma