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At least 37 records · Page 2Linked to original sources
USGS2019

Developing and optimizing shrub parameters representing sagebrush (Artemisia spp.) ecosystems in the Northern Great Basin using the Ecosystem Demography (EDv2.2) model

Ecosystem dynamic models are useful for understanding ecosystem characteristics over time and space because of their efficiency over direct field measurements and applicability to broad spatial extents. Their application, however, is challenging due to internal model uncertainties and complexities arising from distinct qualities of the ecosystems being analyzed. The sagebrush-steppe in western North America, for example, has substantial spatial and temporal heterogeneity as well as variability due to anthropogenic disturbance, invasive species, climate change, and altered fire regimes, which collectively make modelling dynamic ecosystem processes difficult. Ecosystem Demography (EDv2.2) is a robust ecosystem dynamic model, initially developed for tropical forests, that simulates energy, water, and carbon fluxes at fine scales. Although EDv2.2 has since been tested on different ecosystems via development of different Plant Function Types (PFT), it still lacks a shrub PFT. In this study, we developed and parameterized a shrub PFT representative of sagebrush (Artemisia spp.) ecosystems in order to initialize and test it within EDv2.2, and to promote future broad-scale analysis of restoration activities, climate change, and fire regimes in the sagebrush-steppe. Specifically, we parameterized the sagebrush PFT within EDv2.2 to estimate gross primary production (GPP), using data from two sagebrush study sites in the northern Great Basin. To accomplish this, we employed a three-tier approach: 1) To initially parameterize the sagebrush PFT, we fitted allometric relationships for sagebrush using field-collected data, information from existing sagebrush literature, and parameters from other land models. 2) To determine influential parameters in GPP prediction, we used a sensitivity analysis to identify the five most sensitive parameters. 3) To improve model performance and validate results, we optimized these five parameters using an exhaustive search method to estimate GPP, and compared results with observations from two Eddy Covariance (EC) sites in the study area. Our modeled results were encouraging, with reasonable fidelity to observed values, although some negative biases (i.e., seasonal underestimates of GPP) were apparent.

Great Basin↗
USGS2023

Future direction of fuels management in sagebrush rangelands

Sagebrush ecosystems in the United States have been declining since EuroAmerican settlement, largely due to agricultural and urban development, invasive species, and altered fire regimes, resulting in loss of biodiversity and wildlife habitat. To combat continued conversion to undesirable ecological states and loss of habitat to invasive species fueled by frequent fire, a variety of fuel treatments, including networks of fuel breaks, are being implemented or proposed in sagebrush ecosystems, particularly in and around the Great Basin. In this forum paper we briefly review current knowledge of common fuel treatment approaches, their intended benefits, potential risks, and limitations. We additionally discuss challenges for fuel treatment strategies in the context of changes in climate, invasive species, wildlife habitat, and human population, and we explore how advances in geospatial technologies, monitoring, and fire behavior modeling, as well as accounting for social context, can improve the efficacy of fuels management in sagebrush ecosystems. Finally, given continued potential for ecosystem transformation, we describe approaches to future fuels management by considering the applicability of the Resist-Accept-Direct (RAD) framework. The intent of the paper is to provide scientists and land managers with key information and a forward-thinking framework for fuels science and adaptive management that can respond to both expected and unexpected changes in sagebrush rangelands.

Rangeland Ecology and Management↗
USGS1999

Assessing the effects of fire disturbances on ecosystems: A scientific agenda for research and management

A team of fire scientists and resource managers convened 17-19 April 1996 in Seattle, Washington, to assess the effects of fire disturbance on ecosystems. Objectives of this workshop were to develop scientific recommendations for future fire research and management activities. These recommendations included a series of numerically ranked scientific and managerial questions and responses focusing on (1) links among fire effects, fuels, and climate; (2) fire as a large-scale disturbance; (3) fire-effects modeling structures; and (4) managerial concerns, applications, and decision support. At the present time, understanding of fire effects and the ability to extrapolate fire effects knowledge to large spatial scales are limited, because most data have been collected at small spatial scales for specific applications. Although we clearly need more large-scale fire-effects data, it will be more expedient to concentrate efforts on improving and linking existing models that simulate fire effects in a georeferenced format while integrating empirical data as they become available. A significant component of this effort should be improved communication between modelers and managers to develop modeling tools to use in a planning context. Another component of this modeling effort should improve our ability to predict the interactions of fire and potential climatic change at very large spatial scales. The priority issues and approaches described here provide a template for fire science and fire management programs in the next decade and beyond.

General Technical Report↗
USGS2022

A new indicator approach to reconstruct agricultural land use in Europe from sedimentary pollen assemblages

The reconstruction of human impact is pivotal in palaeoecological studies, as humans are among the most important drivers of Holocene vegetation and ecosystem change. Nevertheless, separating the anthropogenic footprint on vegetation dynamics from the impact of climate and other environmental factors (disturbances such as fire, erosion, floods, landslides, avalanches, volcanic eruptions) is a challenging and still largely open issue. For this purpose, palynologists mostly rely on cultural indicator pollen types and related indices that consist of sums or ratios of these pollen types. However, the high environmental and biogeographical specificity of cultural indicator plants hinders the application of the currently available indices to wide geographical settings. Furthermore, the achievable taxonomic resolution of cultural indicator pollen types may hamper their indicative capacity. In this study, we propose the agricultural land use probability (LUP) index, a novel approach to quantify human impact intensity on European ecosystems based on cultural indicator pollen types. From the ‘classic’ cultural indicators, we construct the LUP index by selecting those with the best indicator capacity based on bioindication criteria. We first train the LUP index using twenty palynological sequences along a broad environmental gradient, spanning from treeless alpine to subtropical mediterranean evergreen plant communities. We then validate the LUP index using independent pollen datasets and archaeological proxies. Finally, we discuss the suitability of the selected pollen types and the potential of the LUP index for quantifying Holocene human impact in Europe, concluding that careful application of the LUP index may significantly contribute to refining pollen-based land-use reconstructions.

Palaeogeography, Palaeoclimatology, Palaeoecology↗
USGS2018

STEPWAT2: An individual‐based model for exploring the impact of climate and disturbance on dryland plant communities

The combination of climate change and altered disturbance regimes is directly and indirectly affecting plant communities by mediating competitive interactions, resulting in shifts in species composition and abundance. Dryland plant communities, defined by low soil water availability and highly variable climatic regimes, are particularly vulnerable to climatic changes that exceed their historical range of variability. Individual‐based simulation models can be important tools to quantify the impacts of climate change, altered disturbance regimes, and their interaction on demographic and community‐level responses because they represent competitive interactions between individuals and individual responses to fluctuating environmental conditions. Here, we introduce STEPWAT2, an individual plant‐based simulation model for exploring the joint influence of climate change and disturbance regimes on dryland ecohydrology and plant community composition. STEPWAT2 utilizes a process‐based soil water model (SOILWAT2) to simulate available soil water in multiple soil layers, which plant individuals compete for based on the temporal matching of water and active root distributions with depth. This representation of resource utilization makes STEPWAT2 particularly useful for understanding how changes in soil moisture and altered disturbance regimes will concurrently impact demographic and community‐level responses in drylands. Our goals are threefold: (1) to describe the core modules and functions within STEPWAT2 (model description), (2) to validate STEPWAT2 model output using field data from big sagebrush plant communities (model validation), and (3) to highlight the usefulness of STEPWAT2 as a modeling framework for examining the impacts of climate change and disturbance regimes on dryland plant communities under future conditions (model application). To address goals 2 and 3, we focus on 15 sites that span the spatial extent of big sagebrush plant communities in the western United States. For goal 3, we quantify how climate change, fire, and grazing can interact to influence plant functional type biomass and composition. We use big sagebrush‐dominated plant communities to demonstrate the functionality of STEPWAT2, as these communities are among the most widespread dryland ecosystems in North America.

Ecosphere↗
USGS2024

Multi-decadal vegetation transformations of a New Mexico ponderosa pine landscape after severe fires and aerial seeding

Wildfires and climate change are having transformative effects on vegetation composition and structure, and post-fire management may have long-lasting impacts on ecosystem reorganization. Post-fire aerial seeding treatments are commonly used to reduce runoff and soil erosion, but little is known about how seeding treatments affect native vegetation recovery over long periods of time, particularly in type-converted forests which have been dramatically transformed by the effects of repeated, high-severity fire. In this study, we analyze and report on a rare long-term (23-year) dataset that documents vegetation dynamics following a 1996 post-fire aerial seed treatment and subsequent 2011 high-severity reburn in a dry conifer forest of northern New Mexico in the southwestern United States. Repeated surveys between 1997 – 2019 of 49 permanent transects were used to test for differences in vegetation cover, richness, and diversity between seeded and unseeded areas, and to characterize the development of seeded and unseeded vegetation communities through time and across gradients of burn severity, elevation, and soil-available water capacity. Post-fire seeding led to a clear and sustained divergence in herbaceous community composition. Seeded plots had much higher cover of non-native graminoids, primarily Bromus inermis , a likely contaminant in the seed mix. High-severity reburning in all plots in 2011 reduced native graminoid cover by half at seeded plots compared to both pre-fire levels and to plots that were unseeded following the initial 1996 fire. In addition, increased fire severity was associated with increased non-native graminoid cover and reduced native graminoid cover, native species richness, and species diversity. This study documents a fire-driven ecosystem transformation from a former conifer forest into a shrub-grass system, reinforced by aerial seeding of grasses and high-severity reburning. This unique long-term dataset illustrates that post-fire seeding carries significant risk of unwanted non-native species invasions that persist through subsequent fires – indicating that alternative post-fire management actions merit consideration to better support native ecosystem resilience in the face of emergent climate change and increasing disturbance. Lastly, this study highlights the importance of long-term monitoring of post-fire vegetation dynamics, as short-term assessments will miss key elements of the full complexity of ecosystem responses to fire and post-fire management actions.

New Mexico↗
USGS2026

Post-wildfire water quality and aquatic ecosystem response in the U.S. Pacific Northwest: science and monitoring gaps

An increase in the occurrence of large, high severity wildfires in the western Pacific Northwest (PNW), USA, has created an urgent need for science to better inform forest management and policy decisions to maintain source water quality in the region. The western PNW faces similar challenges to other regions with shifting wildfire regimes and large population centers reliant on surface water from forested catchments. However, the uniquely wet and highly seasonal climate of the western PNW suggests that findings from other, more frequently burned regions may not be directly applicable. To identify science, monitoring, and management gaps and opportunities in the western PNW, this review was collaboratively undertaken by academics, non-government and industry representatives, and local, state, and federal government entities who have been working together since the 2020 Labor Day fires in Oregon. Focusing on Oregon and Washington, we found that monitoring networks for continuous water quantity and quality cover much of the state with greater representation in western U.S. ecoregions, but few studies have analyzed and published these data to capture and communicate the post-wildfire response. Approximately half of the streamgages in Oregon and Washington record major water quality parameters, and hundreds of sites in the area have discrete sampling for a wide range of water quality constituents. Still, numerous gaps exist in understanding the short- and long-term impacts of wildfire on hydrology, water chemistry, including pH and dissolved oxygen, mobilization of metals, aquatic ecosystems, and downstream drinking water treatment. Collective action to further collect, analyze, interpret, and publish the key data could help improve our understanding of post-wildfire water quality impacts in this and other increasingly wildfire-affected regions.

Oregon, Washington↗
USGS2012

Assessment of boreal forest historical C dynamics in the Yukon River Basin: relative roles of warming and fire regime change

Carbon (C) dynamics of boreal forest ecosystems have substantial implications for efforts to mitigate the rise of atmospheric CO 2 and may be substantially influenced by warming and changing wildfire regimes. In this study we applied a large-scale ecosystem model that included dynamics of organic soil horizons and soil organic matter characteristics of multiple pools to assess forest C stock changes of the Yukon River Basin (YRB) in Alaska, USA, and Canada from 1960 through 2006, a period characterized by substantial climate warming and increases in wildfire. The model was calibrated for major forests with data from long-term research sites and evaluated using a forest inventory database. The regional assessment indicates that forest vegetation C storage increased by 46 Tg C, but that total soil C storage did not change appreciably during this period. However, further analysis suggests that C has been continuously lost from the mineral soil horizon since warming began in the 1970s, but has increased in the amorphous organic soil horizon. Based on a factorial experiment, soil C stocks would have increased by 158 Tg C if the YRB had not undergone warming and changes in fire regime. The analysis also identified that warming and changes in fire regime were approximately equivalent in their effects on soil C storage, and interactions between these two suggests that the loss of organic horizon thickness associated with increases in wildfire made deeper soil C stocks more vulnerable to loss via decomposition. Subbasin analyses indicate that C stock changes were primarily sensitive to the fraction of burned forest area within each subbasin and that boreal forest ecosystems in the YRB are currently transitioning from being sinks to sources at ∼0.7% annual area burned. We conclude that it is important for international mitigation efforts focused on controlling atmospheric CO 2 to consider how climate warming and changes in fire regime may concurrently affect the CO 2 sink strength of boreal forests. It is also important for large-scale biogeochemical and earth system models to include organic soil dynamics in applications to assess regional C dynamics of boreal forests responding to warming and changes in fire regime.

Ecological Applications↗
USGS2024

Wildfire probability estimated from recent climate and fine fuels across the big sagebrush region

Background Wildfire is a major proximate cause of historical and ongoing losses of intact big sagebrush ( Artemisia tridentata Nutt.) plant communities and declines in sagebrush obligate wildlife species. In recent decades, fire return intervals have shortened and area burned has increased in some areas, and habitat degradation is occurring where post-fire re-establishment of sagebrush is hindered by invasive annual grasses. In coming decades, the changing climate may accelerate these wildfire and invasive feedbacks, although projecting future wildfire dynamics requires a better understanding of long-term wildfire drivers across the big sagebrush region. Here, we integrated wildfire observations with climate and vegetation data to derive a statistical model for the entire big sagebrush region that represents how annual wildfire probability is influenced by climate and fine fuel characteristics. Results Wildfire frequency varied significantly across the sagebrush region, and our statistical model represented much of that variation. Biomass of annual and perennial grasses and forbs, which we used as proxies for fine fuels, influenced wildfire probability. Wildfire probability was highest in areas with high annual forb and grass biomass, which is consistent with the well-documented phenomenon of increased wildfire following annual grass invasion. The effects of annuals on wildfire probability were strongest in places with dry summers. Wildfire probability varied with the biomass of perennial grasses and forbs and was highest at intermediate biomass levels. Climate, which varies substantially across the sagebrush region, was also predictive of wildfire probability, and predictions were highest in areas with a low proportion of precipitation received in summer, intermediate precipitation, and high temperature. Conclusions We developed a carefully validated model that contains relatively simple and biologically plausible relationships, with the goal of adequate performance under novel conditions so that useful projections of average annual wildfire probability can be made given general changes in conditions. Previous studies on the impacts of vegetation and climate on wildfire probability in sagebrush ecosystems have generally used more complex machine learning approaches and have usually been applicable to only portions of the sagebrush region. Therefore, our model complements existing work and forms an additional tool for understanding future wildfire and ecological dynamics across the sagebrush region.

Fire Ecology↗
USGS2024

A spatiotemporal optimization engine for prescribed burning in the Southeast US

Many ecosystems in the Southeast US are dependent upon frequent low-intensity surface fires to sustain native biodiversity, ecosystem services, and endangered species populations. Today, landscape-scale prescribed fire is required to manage these systems for conservation objectives and to mitigate wildland fire risk. Successful application of prescribed fire in this region requires careful planning and assessment of the risks and tradeoffs involved when deciding whether or not to conduct a burn. Many of these risks are closely tied to ambient environmental conditions and are reflected in sets of ‘prescription’ parameters that define safe and effective operating conditions to meet objectives or regulatory requirements. To facilitate effective decision making and acknowledge growing uncertainties related to climate change effects on wildland fire operations, we developed a spatiotemporal optimization engine to identify near-term optimal burning opportunities for prescribed fire implementation. By mining historical 3-day numerical weather forecasts and observation-based weather data for 2015–2021, we have developed a Bayesian hierarchical model for forecast verification that provides calibrated daily weather forecasts and joint uncertainty estimates on meteorological variables of interest, with the latter serving as a measure of risk associated with prescribed fire activities. Burn allocation decisions are then optimized by considering this risk jointly with the utility of burning a particular habitat parcel. The initial iteration of the optimization engine is demonstrated through a case study of short-term meteorological conditions for the Eglin Air Force Base, located in Florida, USA. Results indicate agreement between the optimization engine and the observed past decision-making, with the largest divergences likely arising primarily from differences between utility functions presumed important and used to develop the optimization engine versus the true utility functions driving management behavior in practice.

Florida↗
USGS2003

Predicting coastal retreat in the Florida Big Bend region of the Gulf Coast under climate change induced sea-level rise

Many wildlife preserves and refuges in coastal areas of our nation are slowly being inundated by rising sea-level. Land elevation and tidal flooding are key factors controlling the extent and zonation of coastal habitats. Warming of our global environment threatens to speed the rate of sea-level rise and perhaps further amplify the detrimental effects of tropical storms, droughts, and lightning fires. A field and modeling study was conducted to determine the current status of emergent vegetation and surficial hydrology and to predict marsh transgression under rising sea-level. Field surveys were conducted to relate vegetation cover and ecotones to surface elevation and tidal inundation. A regional site application of a GIS-based simulation model, WETLANDS, was developed to predict ecosystem response to changing sea-level conditions on a coastal reach of the Big Bend region in northwest Florida. The WETLANDS model contains functional probabilities of community tolerance to flooding conditions that dictate the rate and process of ecological succession and coastal retreat. Map information of hypsography and bathymetry of the study area were digitized and interpolated to construct a digital elevation model. Classified thematic mapper imagery of aquatic and terrestrial habitat at a community level was used to initialize model simulation by vegetative type. Model simulations were generated to predict a likelihood index of habitat change and conversion under different scenarios of sea-level rise. The WETLANDS model was applied to track the process and pattern of coastal inundation over space and time for low, mid, and high sea-level rise projections of 15, 50, and 95 cm over the next century. Model results indicated that major portions of this coastal zone will be permanently inundated by 2100, bringing about a combined migration of marsh habitat and displacement of forest habitat. Results show that lowland pine forests will undergo retreat on the order of thousands of hectares over the 21st century. Coastal marsh extent may actually increase slightly as a function of the low lying topography. Socioeconomic implications may be nominal for this area given its remote and fairly undeveloped and protected coast-202 line. The model offers a technological tool for research and policy purposes that allows for effective land and water management, risk assessment, and cumulative impact analysis of wetland systems and landscapes.

Florida↗
USGS2024

North Central Climate Adaptation Science Center regional science plan—2023–28

The U.S. Geological Survey North Central Climate Adaptation Science Center (NC CASC), established in 2012, is part of a national network supporting climate-informed decisions that benefit wildlife and habitats. The NC CASC provides climate science for the U.S. Department of the Interior, State agencies, and Tribal nations to support effective resource management. Collaborating with the National and Regional Climate Adaptation Science Center network, the NC CASC addresses multiregion challenges through a partnership involving the U.S. Geological Survey and additional regional partners. Input is collected from an Advisory Committee of State, Federal, and Tribal Government representatives. The center hosts Tribal Climate Resilience Liaisons, which are funded by the Bureau of Indian Affairs Branch of Tribal Climate Resilience and aligned with a Tribal Engagement Strategy. Positioned at the meeting point of scientists and managers and guided by core values, the NC CASC studies diverse ecosystems affected by climate change and empowers resource managers with actionable data and innovative science. This report outlines the 5-year Science Plan (hereafter “the Plan”) for the NC CASC. The center uses stakeholder engagement and coproduction of knowledge for effective collaboration, including engagement of Advisory Committee members and other management partners in the development of the Plan. The Plan provides a high-level overview of the NC CASC, covering the center’s geographical scope, key collaborators, stakeholders, and the strategic framework guiding the application of climate science to make informed management decisions. The Plan also summarizes management challenges and scientific objectives related to the impact of climate change on habitat, ecological drought, wildlife disease, invasives and encroachment, fire, and phenology.

Colorado, Kansas, Montana, North Dakota, Nebraska,↗
USGS2013

LANDFIRE 2010 - updated data to support wildfire and ecological management

Wildfire is a global phenomenon that affects human populations and ecosystems. Wildfire effects occur at local to global scales impacting many people in different ways (Figure 1). Ecological concerns due to land use, fragmentation, and climate change impact natural resource use, allocation, and conservation. Access to consistent and current environmental data is a constant challenge, yet necessary for understanding the complexities of wildfire and ecological management. Data products and tools from the LANDFIRE Program help decision-makers to clarify problems and identify possible solutions when managing fires and natural resources. LANDFIRE supports the reduction of risk from wildfire to human lives and property, monitoring of fire danger, prediction of fire behavior on active incidents, and assessment of fire severity and impacts on natural systems [1] [2] [3]. LANDFIRE products are unique in that they are nationally consistent and provide the only complete geospatial dataset describing vegetation and wildland fuel information for the entire U.S. As such, LANDFIRE data are useful for many ecological applications [3]. For example, LANDFIRE data were recently integrated into a decision-support system for resource management and conservation decision-making along the Appalachian Trail. LANDFIRE is a joint effort between the U.S. Department of the Interior Office of Wildland Fire, U.S. Department of Agriculture Forest Service Fire & Aviation Management, and The Nature Conservancy. To date, seven versions of LANDFIRE data have been released, with each successive version improving the quality of the data, adding additional features, and/or updating the time period represented by the data. The latest version, LANDFIRE 2010 (LF 2010), released mid-2013, represents circa 2010 landscape conditions and succeeds LANDFIRE 2008 (LF 2008), which represented circa 2008 landscape conditions. LF 2010 used many of the same processes developed for the LF 2008 effort [3]. Ongoing refinement of the LANDFIRE vegetation and fuel data is necessary to improve the quality and usability of the data and to capture landscape disturbance. LANDFIRE relies on Landsat multi-spectral imagery to produce and update vegetation and fuel data. The deep Landsat archive provides data needed for vegetation classification, change analysis, and historical disturbance characterization, for which LANDFIRE has used more than 24,000 image scenes since the program’s inception. In addition, LF 2010 used airborne and spaceborne lidar, and spaceborne synthetic aperture radar (SAR) to map vegetation structure in areas where ground-based field information was lacking, including Alaska and U.S.-affiliated islands in the Caribbean and the Pacific. The mapping of insular areas is new for the 2010 data release; previous versions of LANDFIRE were limited to the conterminous U.S., Alaska, and Hawaii.

Earthzine↗
USGS2017

Historical and projected trends in landscape drivers affecting carbon dynamics in Alaska

Modern climate change in Alaska has resulted in widespread thawing of permafrost, increased fire activity, and extensive changes in vegetation characteristics that have significant consequences for socioecological systems. Despite observations of the heightened sensitivity of these systems to change, there has not been a comprehensive assessment of factors that drive ecosystem changes throughout Alaska. Here we present research that improves our understanding of the main drivers of the spatiotemporal patterns of carbon dynamics using in situ observations, remote sensing data, and an array of modeling techniques. In the last 60 yr, Alaska has seen a large increase in mean annual air temperature (1.7°C), with the greatest warming occurring over winter and spring. Warming trends are projected to continue throughout the 21st century and will likely result in landscape-level changes to ecosystem structure and function. Wetlands, mainly bogs and fens, which are currently estimated to cover 12.5% of the landscape, strongly influence exchange of methane between Alaska's ecosystems and the atmosphere and are expected to be affected by thawing permafrost and shifts in hydrology. Simulations suggest the current proportion of near-surface (within 1 m) and deep (within 5 m) permafrost extent will be reduced by 9–74% and 33–55% by the end of the 21st century, respectively. Since 2000, an average of 678 595 ha/yr was burned, more than twice the annual average during 1950–1999. The largest increase in fire activity is projected for the boreal forest, which could result in a reduction in late-successional spruce forest (8–44%) and an increase in early-successional deciduous forest (25–113%) that would mediate future fire activity and weaken permafrost stability in the region. Climate warming will also affect vegetation communities across arctic regions, where the coverage of deciduous forest could increase (223–620%), shrub tundra may increase (4–21%), and graminoid tundra might decrease (10–24%). This study sheds light on the sensitivity of Alaska's ecosystems to change that has the potential to significantly affect local and regional carbon balance, but more research is needed to improve estimates of land-surface and subsurface properties, and to better account for ecosystem dynamics affected by a myriad of biophysical factors and interactions.

Alaska↗
USGS2024

Nontarget effects of pre-emergent herbicides and a bioherbicide on soil resources, processes, and communities

Community-type conversions, such as replacement of perennials by exotic annual grasses in semiarid desert communities, are occurring due to plant invasions that often create positive plant–soil feedbacks, which favor invaders and make restoration of native perennials difficult. Exotic annual grass control measures, such as pre-emergent herbicides, can also alter soil ecosystems directly or indirectly (i.e. via the plant community), yet there are few studies on the topic in natural, non-cropped landscapes. We asked how spray treatments applied to soil post-fire with the intention of inhibiting invasive annual grasses (such as Bromus tectorum L.) and releasing existing native perennial grasses affected soil resources, a microbial process, and invertebrates in three climatically varied sagebrush steppe sites. Spray treatments included chemical herbicides (imazapic and rimsulfuron) that strongly affected plant communities and a bioherbicide ( Pseudomonas fluorescens strain D7) that did not. Chemical herbicides increased soil mineral nitrogen in proportion to their negative effects on plant cover for 2 years after treatments in all sites and increased soil water and net N mineralization (measured at one site) but did not affect total carbon, nitrogen, or organic matter. Invertebrate responses to herbicides varied by site, and invertebrates increased with chemical herbicides at the highest, wettest site. We show that herbicide treatments can exacerbate pulses of mineral nutrients, which previous studies have shown can weaken ecosystem resistance to invasion. Thus, restoration strategies that increase the likelihood that desired plants can capture mineralized nutrients after herbicide application will likely be more successful.

Restoration Ecology↗
USGS2025

Post-fire recovery of sagebrush-steppe communities is better explained by elevation than climate-derived indicators of resistance and resilience

More landscapes require restoration than can feasibly be treated, and so decision-support tools to prioritize areas for treatment are needed. Moreover, restoration is complicated by the threat of biological invasion in disturbed areas, and so indicators of ecosystem resistance to invasion and resilience to disturbance (hereafter R&R) are important candidate criteria for prioritizing sites for restoration. We asked how climate-based R&R indicators that differed in being either categorical or continuous compared in their ability to explain plant-community recovery after six wildfires that collectively encompassed >750,000 ha and 7803 plot-year observations in sagebrush steppe of the western USA. Unique associations of species that most frequently co-occurred were identified using structural topic modelling. Mixed effect random forests were used to identify the relative importance of various R&R indicators in explaining post-fire plant associations compared with weather, landscape characteristics and treatment history. Simple metrics (elevation, latitude, longitude and year of monitoring) were more informative predictors of post-fire recovery than climate-based R&R indicators. However, small differences in the abundances of perennial grass and especially annual grass associations were predicted by the spring modified Thornthwaite Moisture Index (difference between precipitation and potential evapotranspiration). Synthesis and applications : The convenience of categorical resistance and resilience indicators has led to their widespread adoption for large-scale planning of restoration. Our results reveal that none of the resistance and resilience indicators assessed effectively explained post-fire restoration better than elevation, although a simple continuous resistance and resilience indicator describing water balance performed better than categorical indicators for explaining small but critical differences in cheatgrass association abundances.

California, Idaho, Nevada, Oregon↗
USGS2016

Climate change and the eco-hydrology of fire: Will area burned increase in a warming western USA?

Wildfire area is predicted to increase with global warming. Empirical statistical models and process-based simulations agree almost universally. The key relationship for this unanimity, observed at multiple spatial and temporal scales, is between drought and fire. Predictive models often focus on ecosystems in which this relationship appears to be particularly strong, such as mesic and arid forests and shrublands with substantial biomass such as chaparral. We examine the drought–fire relationship, specifically the correlations between water-balance deficit and annual area burned, across the full gradient of deficit in the western USA, from temperate rainforest to desert. In the middle of this gradient, conditional on vegetation (fuels), correlations are strong, but outside this range the equivalence hotter and drier equals more fire either breaks down or is contingent on other factors such as previous-year climate. This suggests that the regional drought–fire dynamic will not be stationary in future climate, nor will other more complex contingencies associated with the variation in fire extent. Predictions of future wildfire area therefore need to consider not only vegetation changes, as some dynamic vegetation models now do, but also potential changes in the drought–fire dynamic that will ensue in a warming climate.

Western United States↗
USGS2013

Monitoring the status of forests and rangelands in the Western United States using ecosystem performance anomalies

The effects of land management and disturbance on ecosystem performance (i.e. biomass production) are often confounded by those of weather and site potential. The current study overcomes this issue by calculating the difference between actual and expected ecosystem performance (EEP) to generate ecosystem performance anomalies (EPA). This study aims to delineate and quantify average EPA from 2000–2009 within the Greater Platte and Upper Colorado River Basins, USA. Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) images averaged over the growing season (GSN) served as a proxy of actual ecosystem performance. Yearly EEP was determined with rule-based piecewise regression tree models of abiotic data (climate, soils, elevation, etc.), independently created for each land cover. EPA were calculated as the residuals of the EEP to GSN relationship, and characterized as normal performing, underperforming, and overperforming at the 90% confidence level. Validation revealed that EPA values were related to biomass production (R 2 = 0.56, P = 0.02) and likely to the proportion of biomass removed by livestock in the Nebraska Sandhills. Overall, 60.6% of the study area was (normal) performing near its EEP, 3.0% was severely underperforming, 5.0% was highly overperforming, and the remainder was slightly underperforming or overperforming. Generally, disturbances such as fires, floods, and insect damage, in addition to high grazing intensity, result in a negative EPA. Conversely, mature stands and appropriate management often result in positive EPA values. This method provides information critical to land managers to evaluate the appropriateness of previous management practices and restoration efforts and quantify disturbance impacts. Results are at a scale sufficient for many of the large management units of the region and for locating areas needing further investigation. Applications of EPA data to monitoring invasive species, grazing impacts, and vulnerability to plant community shifts have been suggested by land management professionals.

Arizona;Colorado;Kansas;Nebraska;New Mexico;South ↗