These data were compiled to evaluate pinyon-juniper regeneration dynamics following stand-replacing wildfire and thinning treatments. Objectives of our study were to investigate vegetation community composition and tree recruitment in post-fire and post-thinning environments. These data represent plant and biological soil crust community composition and climatological records among intact, thinned, and burned pinyon–juniper woodlands. These data were collected in Mesa Verde National Park and Ute Mountain Ute Tribal Park from 6/1/2021 to 6/10/2021 and from 03/1/2022 to 11/30/2022 at two burned and two intact pinyon-juniper ecosystems in Mesa Verde National Park only. These data were collected by the U.S. Geological Survey, National Park Service, and Northern Arizona University through field observation and sensor arrays. These data can be used to interpret community composition and climatological differences among intact, thinned, and burned pinyon–juniper woodlands.

With the Surface-Water Index of Permanence (SWIPe) we provide a standardized metric for describing one- to five-year anomalies of the annual minimum surface-water extent of streams and wetlands for multiple spatial scales including basin (4-digit hydrologic unit codes [HUCs]) to subwatersheds (12-digit HUCs). Drier conditions are represented by negative SWIPE values that range from 0 to -3 standard deviations from zero, or the normal condition. SWIPe is calculated for the upper Missouri River basin using streamflow permanence probability estimates from the Probability of Streamflow Permanence for the upper Missouri River basin (PROSPERum) model and surface-water inundation observations from the Dynamic Surface Water Extent (DSWE) dataset for years 1989-2021. The upper Missouri River basin consists of four-digit HUCs 1002-1013. Intrinsic mode functions that describe overall and interannual trends in the underlying SWIPe timeseries, and the significance, are provided as part of this data release. SWIPe is calculated using several different cumulative distribution functions (CDFs) including generalized normal, generalized extreme value, generalized logistic, Pearson-3, Weibull, and generalized Pareto. The CDF with p-values < 0.05 based on Kolmogorov-Smirnov (K-S) test and the lowest the lowest Akaike Information Criterion was used to model probabilities on which SWIPe is based. An empirical CDF was applied when all of the theoretical CDFs resulted in p-values > 0.05. The probabilities were standardized to have a mean around zero and standard deviation of one.

Climate change is a primary threat to biodiversity, but for most species, we still lack information required to assess their potential vulnerability to changes. Climate change vulnerability assessment (CCVA) is a widely-used technique to rank relative vulnerability to climate change based on species distributions, habitat associations, environmental tolerances, and life-history traits. For species that we expect are vulnerable to climate change yet are data deficient, like many amphibians, we often lack information required to construct traditional CCVAs. We extended the CCVA framework by constructing models based on life history theory, using empirical evidence of traits and distributions that reflected sensitivity of data-deficient species to environmental perturbation. These csv data files were assembled to perform climate change vulnerability assessments of the 31 amphibian species, both across the north central region and within individual US states. We incorporated information from species' life history traits and other characteristics along with climate projections of evapotranspiration deficit change, to score relative vulnerability of the 31 amphibians. Associated R code is for scoring relative vulnerability, where overall score is a product of exposure to climate change times sensitivity to that change, minus adaptive capacity of each species. All species are listed as Species of Greatest Conservation Need in at least one of 7 states in the North Central United States: Montana, Wyoming, Colorado, North Dakota, South Dakota, Nebraska, and Kansas.

The North Central Regional Invasive Species and Climate Change (NC RISCC) network includes ~150 members working at the nexus of climate change and invasive species. In late 2021, the NC RISCC leadership team surveyed regional practitioners working on issues related to invasive species management to understand their priorities and practices. Survey participants represented a variety of entities, with the most representation from: county government, academia/universities, federal government, non-governmental organizations (NGOs), and state government. Survey participants volunteered to complete the survey that contained 19-20 questions depending on if they self identified as a researcher or manager. The NC RISCC Survey Results dataset contains the de-identified responses from the 69 survey respondents. This data release also includes the analysis code used to clean and summarize the data. 

Climate change is leading to global increases in extreme events, such as drought, that threaten the persistence of freshwater biodiversity. Identification and management of drought refuges, areas that promote resistance and resilience to drought, will be critical for preserving and recovering aquatic biodiversity in the face of climate change and increasing human water use. Although several reviews have addressed the effects of droughts and highlighted the role of refuges, a need remains on how to identify functional refuges that can be used in a drought management framework to support fish assemblages. We synthesize literature on drought refuges and propose a framework to identify and manage functional refuges that incorporate species physiological tolerances, behaviours and life-history strategies. Stream pools, perennial reaches and off-channel habitat were identified as important drought refuges for fish. The ability of refuges to improve species resistance and resilience to drought requires careful consideration of the biology of the target species and targeted management to promote persistence, quality and connectivity of refuges. Case studies illustrate that management of drought refuges can be challenging because of competing demands for water, incomplete knowledge of ecological requirements for target species and the increasing occurrence of multi-year droughts. Climate adaptation is increasingly important, and drought refuges can increase fish resistance and resilience to climate-related drought across the riverscape.    

Responding to climate impacts and expanding adaptation efforts necessitates getting the right knowledge and tools in the hands of land managers and decision-makers. In 2022–2023, several regional US Geological Survey Climate Adaptation Science Centers partnered with the US Fish and Wildlife Service (FWS) Science Applications Program on the first targeted climate training series designed for the FWS Grassland Ecosystem Team. This training spanned multiple months and formats with self-paced virtual lessons, webinars, and an in-person workshop. As the FWS Grassland Ecosystem Team is tasked with conservation planning for grassland birds and other species, the focus of the workshop was an interactive collaborative activity incorporating species adaptive capacity assessments, future climate projections, and adaptation menus into the decision-making process. Herein, we describe the methods used to design and deliver the training series, as well as lessons learned for future climate literacy programs aimed at natural resource managers.

Terrestrial evapotranspiration is the second-largest component of the land water cycle, linking the water, energy, and carbon cycles and influencing the productivity and health of ecosystems. The dynamics of ET across a spectrum of spatiotemporal scales and their controls remain an active focus of research across different science disciplines. Here, we provide an overview of the current state of ET science across in situ measurements, partitioning of ET, and remote sensing, and discuss how different approaches complement one another based on their advantages and shortcomings. We aim to facilitate collaboration among a cross-disciplinary group of ET scientists to overcome the challenges identified in this paper and ultimately advance our integrated understanding of ET.  

These data accompany Hobart et al. “Annual grass invasion is transforming the sagebrush biome’s songbird communities” (in review). The primary data include (i) results from IMBCR (Integrated Monitoring in Bird Conservation Regions) survey efforts and accompanying site-level covariate values; (ii) regional rasters of relevant covariate raster values for regional predictions.

The dataset consists of projections of 1-12 months Standardized Precipitation Evapotranspiration Index (SPEI) between 1950-2099 for the contiguous United States from 20 climate models and 2 emission scenarios at a 4km spatial resolution. The SPEI dataset was developed using the SPEI package in R (Beguería & Vicente-Serrano, 2023). SPEI quantifies standardized departures in the balance between precipitation and potential evapotranspiration (PET) across varying timescales, making it highly suitable for assessing drought and water availability (Vicente-Serrano et al., 2010). Monthly precipitation and PET data were sourced from the MACAv2-METDATA dataset for climate projections between 1950-2099 based on 20 global climate models under RCP 4.5 and RCP 8.5 emission scenarios (Abatzoglou, 2013). Projected SPEI values were calculated relative to the 1981-2020 reference period, with SPEI computed using a log-logistic distribution fitted to the difference between precipitation and PET values. This methodology standardizes SPEI values as z-scores, allowing for comparative evaluations of drought and wetness across different regions and timescales (1 to 12 months).

VegDischarge v1, which covers over 64,000 river segments in Africa, is a natural river discharge dataset produced by coupled modeling; the agro-hydrologic VegET model and the mizuRoute routing model for the period 2001-2021. Using remote sensing data and hydrological modeling system, the 1-km runoff field simulated by VegET, was routed with mizuRoute. Performance metrics show strong model reliability, with R² of 0.5–0.9, NSE of 0.6–0.9, and KGE of 0.5–0.8 at the continental scale. The total average annual discharge for Africa is quantified at 3271.4 km³·year−1, with contributions to oceanic basins: 1000.0 km³·year−1 to the North Atlantic, primarily from the Senegal, Gambia, Volta, and Niger Rivers; 1327.2 km³·year−1 to the South Atlantic, largely from the Congo River; 214.7 km³·year−1 to the Mediterranean Sea, predominantly from the Nile River; and 729.4 km³·year−1 to the Indian Ocean, with inputs from rivers such as the Zambezi. The dataset is valuable for stakeholders and researchers to understand water availability, its temporal and spatial variations that affect water-related infrastructure planning, sustainable resource allocation, and the development of climate resilience strategies.