This archive includes field data, post-fire recruitment models, and spatial projections of post-fire recruitment probability under different climate and fire severity scenarios produced for Davis et al. (2023). Field plot data ("Davis_et_al_regen_plot_data.csv") includes post-fire regeneration density for eight conifer species from the western US that were surveyed in plots 2-30 years following fire. The final generalized linear mixed models for recruitment probability for each species and for all species combined used to make spatial projections of post-fire recruitment probability in Davis et al. 2023 are inlcuded as .rds files. Spatial projections of recruitment probability made with the final models for 10-years post-fire under four climate scenarios (1981-2000, 2001-2020, 2031-2050 RCP 4.5, 2031-2050 RCP 8.5) and two fire severity scenarios (low severity: 10 m to a seed source, 30% surrounding live tree cover within 300-m radius of plot, relativized burn ratio (RBR) of 100; high severity: 150 m to a seed source, 10% surrounding live tree cover within 300-m radius of plot, RBR of 400). Spatial projections are made for all study species combined ("all") and for each individual species. Please see the metadata file for each dataset in the dryad repository ("Davis_et_al_postfire_recruitment_plot_data_2022.xml and "Davis_et_al_postfire_recruitment_projections_2022.xml") for detailed descriptions of the datasets and their components.

Climate change is expected to influence aquatic habitats and associated fish populations, yet we know little about the impact on recreational anglers. Our goal was to explore whether interannual fluctuations in waterbody surface area and other explanatory variables could be used as indicators of changes in angler fishing effort. Our approach leveraged a combination of remotely sensed waterbody surface area, environmental and fish population data, and onsite angler survey monitoring data for Devils Lake, North Dakota, USA during the open-water fishing period (May 1st to August 31st) for 9 years (1992–2021). The information was used to develop a dynamic waterbody size-angler effort model. Changes in waterbody surface area reliably predicted changes in angler effort (r2 = 0.60). Increases in waterbody surface area led to increases in angler effort, and decreases in waterbody surface area led to decreases in angler effort. Our findings show promise that remotely sensed fluctuations in waterbody surface area could be used as an indicator of interannual angler effort dynamics. Dynamic waterbody size-angler effort models could provide managers the ability to predict changes in angler effort via climate-related hydrological cycles that affect the size and distribution of waterbodies on the landscape.

From a resource management perspective, climate change is considered to be one of the main threats to high-elevation ecosystems. However, these valuable ecosystems present unique challenges to climate change adaptation (actions in response to environmental change and its effects in a way that seeks to reduce harm) due to their rugged and remote characteristics. Within this context, we summarized literature on climate change impacts and adaptation actions across U.S. Rocky Mountain high-elevation ecosystems to address the important question: What are the knowledge gaps for climate change responses within this ecosystem that limit the ability of natural resource managers to perform successful climate change adaptation? In addressing this question, we focus specifically on the U.S. Rocky Mountains but also place regional conclusions for climate change adaptation in high-elevation ecosystems into a broader context. Overall, we found that the complex topography and temporally variable climate of mountains promote potential refugia that may buffer alpine obligate species in the near-term but also challenge resource managers to consider biological lags within this ecosystem.

This dataset contains the accumulated stream survey data collected to identify climate impacts to fish communities and assess stream restoration as a potential climate-change mitigation action across the Great Plains and High Desert of Wyoming and Montana (2021-2024). We also provide data of incidental observations of amphibians, reptiles, crayfishes, and mussels seen while conducting fisheries work, as well as structured surveys for amphibians and crayfishes. Habitat data for many sites is also provided.

Soil organic carbon ("SOC") in drylands comprises nearly a third of the global SOC pool and has relatively rapid turnover and thus is a key driver of variability in the global carbon cycle. SOC is also a sensitive indicator of longer-term directional change and disturbance-responses of ecosystem C storage. Biome-scale disruption of the dryland carbon cycle by exotic annual grass invasions (mainly Bromus tectorum, "Cheatgrass") threatens carbon storage and corresponding benefits to soil hydrology and nutrient retention. Past studies on cheatgrass impacts mainly focused on total C, and of the few that evaluated SOC, none compared the very different fractions of SOC, such as relatively unstable particulate organic carbon (POC) or relatively stable, mineral-associated organic carbon (MAOC). We measured SOC and its POC and MAOC constituents in the surface soils of sites that had sagebrush canopies but differed in whether their understories had been invaded by cheatgrass or not, in both warm and relatively colder ecoregions of the western USA. MAOC stocks were 36.1% less in the 0-10 cm depth and 46.1% less in the 10-20 cm depth in the cheatgrass-invaded stands compared to the uninvaded stands of the warmer Colorado Plateau, but not in the cooler and more carbon-rich Wyoming Basin ecoregion. In plots where cheatgrass increased SOC, it was via unstable POC. These findings indicate that cheatgrass effects on the distribution of soil carbon among POC and MAOC fractions may vary among ecoregions, and that cheatgrass can reduce forms of carbon that are otherwise considered stable and 'secure', i.e. sequestered.  

The Low Flow Data and Model Discovery Table is a concise summary of data and model products that provide information on low-flow conditions in Montana, and which can be used for a range of water and land management decisions. The table was developed through a workshop series funded by the USGS North Central Climate Adaptation Science Center that included Federal, State, and Tribal participation. Descriptions of assumptions, uncertainties, and limitations of the products as well as use examples are included for each product to provide context for how these data can be used for a variety of applications.

Scientific data concerning climate change are critical for designing mitigation and adaptation strategies. Equally important is how stakeholders perceive climate change because perceptions influence decision-making. In this paper, we employ spatially-delineated primary surveys to evaluate weather perception biases among corn and soybean farmers located on western frontier of the U.S. Corn Belt where substantial loss of grassland has been documented. We characterize farmers’ perception biases by measuring the gap between survey-based perception reports for three distinct weather indicators (i.e., temperature, precipitation and drought) and corresponding meteorological evidence. About 70% farmers in our sample misperceive past weather changes. Three-fourths of these misperceiving farmers over-estimate local temperatures and drought frequency and 40% of them under-estimate precipitation trends relative to past records. We further find evidence that farmers’ weather change perceptions are systematically biased in a manner that would justify past land use decisions. Particularly, higher cropping incidence on previously protected grasslands effected more farmers to under-perceive drier conditions and over-perceive wetter conditions. Our investigation of perception biases across distinct weather indicators with a reference to past economic decisions enriches the understanding of climate change perceptions and related policies.

Climate change is a primary threat to biodiversity, but for many species, we still lack information required to assess their relative vulnerability to changes. Climate change vulnerability assessment (CCVA) is a widely used technique to rank relative vulnerability to climate change based on species characteristics, such as their distributions, habitat associations, environmental tolerances, and life-history traits. However, for species that we expect are vulnerable to climate change yet are understudied, like many amphibians, we often lack information required to construct CCVAs using existing methods. We used the CCVA framework to construct trait-based models based on life history theory, using empirical evidence of traits and distributions that reflected sensitivity of amphibians to environmental perturbation. We performed CCVAs for amphibians in 7 states in the north-central USA, focusing on 31 aquatic-breeding species listed as species of greatest conservation need by at least 1 state. Because detailed information on habitat requirements is unavailable for most amphibian species, we used species distributions and information on traits expected to influence vulnerability to a drying climate (e.g., clutch size and habitat breadth). We scored species vulnerability based on changes projected for mid-century (2040−2069) from 2 climate models representing “least-dry” and “most-dry” scenarios for the region. Species characteristics useful for discriminating vulnerability in our models included small range size, small clutch size, inflexible diel activity patterns, and smaller habitat breadth. When projected climate scenarios included a mix of drier and wetter conditions in the future, the exposure of a species to drying conditions was most important to relative rankings. When the scenario was universally drier, species characteristics were more important to relative rankings. Using information typically available even for understudied species and a range of climate projections, our results highlight the potential of using life history traits as indicators of relative climate vulnerability. The commonalities we identified provide a framework that can be used to assess other understudied species threatened by climate change.  

These data consist of three primary types of products for managers of boreal toads in the Southern Rocky Mountains: 1) Re-constructed hydroperiods for historical breeding sites from LANDSAT imagery from 1985-2022 (SMA_hydroperiod_reconstruction.csv). This dataset was developed using a Spectral Mixture Analysis (SMA). 2) Current (1985-2022) and future (2040-2069) predictions of probability of drying and surface area estimates for historical breeding sites (Current_hydrology_predictions.csv, Future_hydrology_predictions.csv). These datasets were developed using a Bayesian hurdle model with the surface water area estimate from the SMA as the response variable. 3) Current (1985-2020) and future (1955-1969) predictions of occupancy for boreal toads (Anaxyrus boreas boreas) and the amphibian chytrid fungus (Batrachochytrium dendrobatidis) at three spatial scales; breeding site (Current_future_occ_prob_ind_site.csv), mountain range (Current_future_occ_prob_mtn_range.csv) and Southern Rocky Mountain Region (Current_future_occ_prob_SRM.csv). These datasets were developed from a Bayesian dynamic state-space community model.

Tribal resource managers in the southwest U.S. are facing a host of challenges related to environmental change, including increasing temperatures, longer periods of drought, and invasive species. These threats are exacerbating the existing challenges of managing complex ecosystems. In a rapidly changing environment, resource managers need powerful tools and the most complete information to make the most effective decisions possible. Located in southwest Colorado, the Ute Mountain Ute Tribal managers are experiencing these impacts firsthand which is why in 2019 they developed a climate adaptation plan. This project builds off the climate adaptation plan to connect managers at Ute Mountain Ute’s Environmental Department with data, tools, and information for making decisions about natural and cultural resources in the context of climate change. This project focused on 3 main objectives: 1) a workshop to plan for potential scenarios of climate change and the impact climate may have on their natural and cultural resource; 2) an invasive species mapping project to understand how invasive species are impacting a culturally and ecologically important area on the reservation; and 3) connecting art, students, and elders to the broader effort by the UMU Environmental Department to incorporate climate change into their decision making. The project team was successful in completing all three objectives, which empower managers at UMU to implement their climate adaptation plan and become more familiar with the suite of resources available to continue to navigate the impacts of climate change across their lands. The art component of the project is notable as it provides an exemplary process for producing culturally relevant products for a tribal community in a transparent and inclusive process.