Project Overview Migratory big game species, like mule deer, are at risk due to human development and more frequent drought events that can limit access to food resources during migration. To address this, researchers supported by this North Central CASC project will collaborate with State, Tribal, and Federal agencies to examine the effectiveness of corridor conservation as a strategy to improve drought resilience for over 40 mule deer herds across Western states. Ultimately, results from this project will benefit ongoing conservation efforts by identifying what levels of development impacts the species’ ability to deal with drought. Project Summary Every year, migratory big game move across landscapes to seek out important food resources and to avoid harsh weather. Yet, the landscapes animals move through are experiencing rapid changes from human development and shifting climatic conditions, which put these ecologically and culturally important migrations at risk. Mule deer, for example, are negatively impacted by drought, which changes when and where key food resources will be available along their migration route. To conserve big game migrations, State, Tribal, and Federal agencies are working together to map and protect migration corridors. Although it is often assumed that corridor conservation should enhance the resilience of migrants to climate change, the idea remains poorly tested. This project will examine the effectiveness of corridor conservation as a drought resiliency strategy for mule deer across the West. As climate change leads to more frequent and longer drought events, it will likely become even more important for deer to freely move and access critical and limited food resources during migrations. At the same time, mule deer movements are altered by human disturbances, which can cause deer to miss out on foraging opportunities. This project will bring together data and partnerships to investigate these two threats on more than 40 mule deer herds across the West, with the aim of understanding the importance of freedom of movement in the survival and resiliency of mule deer in a changing world. Results from this project will identify the amount of human development that constrains the movements of mule deer and the impacts of diminished mobility on drought resiliency. This research links two USGS priorities – conserving big game migrations and enhancing climate resiliency – while filling important knowledge gaps needed to strategically target ongoing conservation efforts.
Project Overview Prairie dog colonies in North America’s Central Grasslands undergo cycles of collapse and recovery caused by the non-native sylvatic plague, and each phase of the cycle negatively affects wildlife or livestock. Researchers supported by this North Central-CASC project will develop a decision-support web tool for users to predict prairie dog colony dynamics under changing climatic conditions to help optimize management strategies of wildlife and cattle. Project Summary Prairie dogs are crucial to North America’s Central Grasslands, creating habitat for other wildlife by digging burrows and clipping vegetation, and serving as a key food source for many predators. However, the sylvatic plague, a non-native disease with over 99% mortality in prairie dogs leads to sudden population die-offs followed by several years of recovery. While wildlife that depend on prairie dogs for food and habitat are negatively affected when populations collapse, the recovery period can cause conflicts with livestock producers, as prairie dogs decrease the available vegetation for grazing cattle. Researchers supported by this project will create an interactive web-based tool that can be used by managers and stakeholders for decision support, as the tool predicts where and when prairie dog colony growth and collapses are likely to occur under changing climatic conditions. This should allow managers to strategically reduce the likelihood of undesirable outcomes for wildlife and livestock. Additionally, the interactive tool will help users determine the best management approaches to achieve their specific goals and will evaluate different management strategies with a cost analysis assessment. This project will involve a diverse group of stakeholders from state and federal agencies, non-governmental organizations, and Tribal Nations to co-produce the tool. A decision support tool is much needed to facilitate co-existence of the prairie dog ecosystem with local communities and livestock producers, especially under an increasingly uncertain and changing climate.
Simulation models are valuable tools for estimating ecosystem response to environmental conditions and are particularly relevant for investigating climate change impacts. However, because of high computational requirements, models are often applied over a coarse grid of points or for representative locations. Spatial interpolation of model output can be necessary to guide decision-making, yet interpolation is not straightforward because the interpolated values must maintain the covariance structure among variables. We present methods for two key steps for utilizing limited simulations to generate detailed maps of multivariate and time series output. First, we present a method to select an optimal set of simulation sites that maximize the area represented for a given number of sites. Then, we introduce a multivariate matching approach to interpolate simulation results to detailed maps for the represented area. This approach links simulation output to environmentally analogous matched sites according to user-defined criteria. We demonstrate the methods with case studies using output from (1) an individual-based plant simulation model to illustrate site selection, and (2) an ecosystem water balance simulation model to illustrate interpolation. For the site selection case study, we identified 200 simulation sites that represented 96% of a large study area (1.12 × 106 km2) at a ~1-km resolution. For the interpolation case study, we generated ~1-km resolution maps across 4.38 × 106 km2 of drylands in North America from a 10 × 10 km grid of simulated sites. Estimates of interpolation errors using cross validation were low (<10% of the range of each variable). Our point selection and interpolation methods, which are available as an easy-to-use R package, provide a means of cost-effectively generating detailed maps of expensive, complex simulation output (e.g., multivariate and time series) at scales relevant for local conservation planning. Our methods are flexible and allow the user to identify relevant matching criteria to balance interpolation uncertainty with areal coverage to enhance inference and decision-making at management-relevant scales across large areas.
Fire suppression is the primary management response to wildfires in many areas globally. By removing less-extreme wildfires, this approach ensures that remaining wildfires burn under more extreme conditions. Here, we term this the “suppression bias” and use a simulation model to highlight how this bias fundamentally impacts wildfire activity, independent of fuel accumulation and climate change. We illustrate how attempting to suppress all wildfires necessarily means that fires will burn with more severe and less diverse ecological impacts, with burned area increasing at faster rates than expected from fuel accumulation or climate change. Over a human lifespan, the modeled impacts of the suppression bias exceed those from fuel accumulation or climate change alone, suggesting that suppression may exert a significant and underappreciated influence on patterns of fire globally. Managing wildfires to safely burn under low and moderate conditions is thus a critical tool to address the growing wildfire crisis.
Phenology detection from remotely sensed data remains challenging in semi-arid ecosystems due to the unique spatial heterogeneity and irregular temporal growth in plants. PlanetScope imagery, with fine spatial and temporal resolutions, is revolutionizing the earth observation sector. It has demonstrated its effectiveness in monitoring phenology dynamics across various terrestrial ecosystems. However, the quality and accuracy of PlanetScope data for depicting plant growth development and detecting phenological metrics (phenometrics) in semi-arid environments have not been systematically examined. In this study, we evaluated the capability of PlanetScope for monitoring plant-specific phenology across the semi-arid western United States, by comparing phenometrics (onsets of greenup, maturity, senescence, and dormancy) retrieved from time series of two PlanetScope vegetation indices (VI), which are EVI2 (two-band Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index), with a set of PhenoCam observations at 15 sites. To conduct a comprehensive comparison, PhenoCam time series and phenometrics were extracted from infrared-enabled PhenoCam EVI2 and NDVI, as well as commonly used PhenoCam GCC (Green Chromatic Coordinate). Our results show that (1) time series of PlanetScope VI were consistent with PhenoCam GCC and VI time series during greenup phase but moderately comparable during senescence phase, with an average R2 of 0.67 and 0.57 for greenup and senescence phases, respectively; (2) phenometrics derived from PlanetScope VI exhibited better agreement with those from PhenoCam GCC and VI in greenup phase (greenup and maturity onsets) than in senescence phase (senescence and dormancy onsets), with an average R2 of 0.81, 0.84, 0.72, and 0.53 for greenup, maturity, senescence, and dormancy onsets, respectively; (3) PlanetScope-detected senescence onset and dormancy onset were systematically later than PhenoCam-based retrievals with a mean systematic bias of 12.6 days and 18.2 days, respectively; (4) phenometrics derived from PlanetScope VI were more comparable with PhenoCam VI retrievals than with PhenoCam GCC retrievals, which are reflected in better correlations and smaller bias between phenometrics, especially during senescence phase; and (5) PlanetScope and PhenoCam EVI2 time series produced the most comparable phenometrics, suggesting that EVI2 is an optimal index for vegetation phenology detections from different sensors. In summary, this study suggests PlanetScope has the ability to detect plant-specific phenology and to improve our understanding of phenology dynamics in heterogeneous semi-arid ecosystems at fine scales.
The operational Simplified Surface Energy Balance (SSEBop) model has been utilized to generate gridded evapotranspiration data from Landsat images. These estimates are primarily driven by two sources of information: reference evapotranspiration and Landsat land surface temperature (LST) values. Hence, SSEBop is limited by the availability of Landsat data. Here, in this proof-of-concept paper, we utilize the Continuous Change Detection and Classification (CCDC) algorithm to generate synthetic Landsat data, which are then used as input for SSEBop to generate evapotranspiration estimates for six target areas in the continental United States, representing forests, shrublands, and irrigated agriculture. These synthetic land cover data are then used to generate the LST data required for SSEBop evapotranspiration estimates. The synthetic LST, evaporative fractions, and evapotranspiration data from CCDC closely mirror the phenological cycles in the observed Landsat data. Across the six sites, the median correlation in seasonal LST was 0.79, and the median correlation in seasonal evapotranspiration was 0.8. The median root mean squared error (RMSE) values were 2.82 °C for LST and 0.50 mm/day for actual evapotranspiration. CCDC predictions typically underestimate the average evapotranspiration by less than 1 mm/day. The average performance of the CCDC evaporative fractions, and corresponding evapotranspiration estimates, were much better than the initial LST estimates and, therefore, promising. Future work could include bias correction to improve CCDC’s ability to accurately reproduce synthetic Landsat data during the summer, allowing for more accurate evapotranspiration estimates, and determining the ability of SSEBop to predict regional evapotranspiration at seasonal timescales based on projected land cover change from CCDC.
Soil moisture is crucial for agriculture and hydrology, but its accurate prediction is challenging due to inadequate representation of various complex land surface processes and meteorological influences. In this research, we employ the Long Short-Term Memory (LSTM) framework, a specific architecture of deep learning networks that is effective in processing time series data, for predicting soil moisture. We have developed the Next Generation Interactive Soil Moisture Forecasting System to advance skillful soil moisture predictions at sub-seasonal timescales by leveraging advanced analytics and deep learning, with LSTM at its core. We combined the state-of-the-art climate model's (Community Earth System Model Version 2) forecast that incorporates the effects of the large-scale climatic drivers, including sea-surface temperature and atmosphere circulation features into soil water forecast with the LSTM-based Deep Learning model. Our Deep Learning model understands the local forecast biases using the weekly hindcast data from 1999 to 2016. We used this trained LSTM model to test its performance from 2017 to 2021 and enhanced the forecast proficiency and aid in analyzing future soil moisture anomalies, i.e., departure from climatology using data fusion and spatial downscaling. For performance assessment, optimal metrics include Mean Absolute Error (MAE) values near 0 (0-0.6), Root Mean Square Error (RMSE) around 0.5, and Anomaly Correlation Coefficient (ACC) nearing 1. These breakthroughs in system design and modeling facilitate improved soil moisture prediction, benefiting water management and our understanding of land-atmosphere interactions.
Climate change is altering fire regimes and post-fire conditions, contributing to relatively rapid transformation of landscapes across the western US. Studies are increasingly documenting post-fire vegetation transitions, particularly from forest to non-forest conditions or from sagebrush to invasive annual grasses. The prevalence of climate-driven, post-fire vegetation transitions is likely to increase in the future with major impacts on social–ecological systems. However, research and management communities have only recently focused attention on this emerging climate risk, and many knowledge gaps remain. We identify three key needs for advancing the management of post-fire vegetation transitions, including centering Indigenous communities in collaborative management of fire-prone ecosystems, developing decision-relevant science to inform pre- and post-fire management, and supporting adaptive management through improved monitoring and information-sharing across geographic and organizational boundaries. We highlight promising examples that are helping to transform the perception and management of post-fire vegetation transitions.
Human fossil fuel use and agricultural practices have increased atmospheric nitrogen deposits (e.g., through snow and rain) to mountain ecosystems. This, along with increasing measurable climate warming is affecting soil and water acidity and altering nutrient balances. In this project, North Central CASC-supported researchers will analyze decades of unexplored data, including surface water chemistry from the Loch Vale watershed in Rocky Mountain National Park and other long-term data from Colorado and Wyoming, to understand climate change and atmospheric nitrogen deposition impacts on high-elevation ecosystems. Synthesis workshops with resource management partners will be held to apply the data products and new knowledge to frame future conditions and management options for these mountain ecosystems. Climate change and atmospheric nitrogen deposition are rapidly altering the ecology and biogeochemistry of mountain ecosystems worldwide. In the US, nearly all high elevation ecosystems are on public lands that are managed federally (e.g., National Park Service, USDA Forest Service, and Bureau of Land Management) or by states and tribes. Changes to ecological processes and species’ assemblages that began in the mid-20th century are continuing at accelerated rates, especially in high-elevation lakes, forests, and the alpine. This work will augment and extend research supported by the USGS Climate Research & Development program for the project “Interpreting the impact of global change on alpine and subalpine ecosystems – synthesizing legacy data to provide scientific and management insight for Rocky Mountain National Park and beyond.” Long-term research by this project team in Loch Vale watershed in Rocky Mountain National Park has been foundational for guiding public policy in Colorado and informing resource management in the park. While many products (a book, more than 120 papers and 22 graduate projects) have shared knowledge and insight on ecosystem processes related to climate and nutrient impacts in the area, a vast amount of data are still unpublished and unexplored. This project will evaluate past patterns of surface water chemistry and ecosystem processes using a legacy of long-term data in Loch Value watershed (from 1983) and Green Lakes Valley (from 1968). The project team will also initiate discussions and host a synthesis workshop with the North Central CASC and natural resource management partners to apply the knowledge gained from the legacy data to help frame potential future conditions and management options for alpine and subalpine ecosystems.
The goals of this workshop series were to 1) identify areas of transformation due towildfire and grass invasion and 2) evaluate management practices that enhancecarbon storage, native biodiversity, and improve resilience, including tradeoffsamong priorities.