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At least 253 records · Page 14Linked to original sources
USGS2018

A social–ecological perspective for riverscape management in the Columbia River Basin

Riverscapes are complex, landscape-scale mosaics of connected river and stream habitats embedded in diverse ecological and socioeconomic settings. Social–ecological interactions among stakeholders often complicate natural-resource conservation and management of riverscapes. The management challenges posed by the conservation and restoration of wild salmonid populations in the Columbia River Basin (CRB) of western North America are one such example. Because of their ecological, cultural, and socioeconomic importance, salmonids present a complex management landscape due to interacting environmental factors (eg climate change, invasive species) as well as socioeconomic and political factors (eg dams, hatcheries, land-use change, transboundary agreements). Many of the problems in the CRB can be linked to social–ecological interactions occurring within integrated ecological, human–social, and regional–climatic spheres. Future management and conservation of salmonid populations therefore depends on how well the issues are understood and whether they can be resolved through effective communication and collaboration among ecologists, social scientists, stakeholders, and policy makers.

Columbia River Basin↗
USGS2025

Apparent annual survival of adult Golden-winged Warblers (Vermivora chrysoptera) may not differ by sex or region

Understanding range-wide demographic, spatial, and temporal variation in annual survival is essential for managing species of conservation concern. Multi-population models are useful tools for integrating diverse datasets, reducing biases, and deriving survival estimates across differing spatial scales. We conducted a range-wide, multi-population apparent annual survival analysis for a declining songbird, Vermivora chrysoptera (Golden-winged Warbler), using data from 18 sites across its breeding and nonbreeding grounds. This Nearctic-Neotropical migrant breeds in 2 disjunct regional populations, the Great Lakes and Appalachian Mountains, which are experiencing different rates of decline. We aimed to quantify regional-, site-, and sex-specific apparent annual survival estimates to identify geographic patterns or demographic factors influencing population declines. We used simulations to assess the precision of our estimates. Our models did not reveal a substantial difference in apparent annual survival between the Great Lakes (0.41, 95% credible interval (CrI):0.31–0.50) and the Appalachian regions (0.49, 95% CrI: 0.36–0.60), as CrIs overlapped. Site-specific estimates also showed no clear differences in apparent annual survival among sites representing both regional populations. Male apparent annual survival tended to be greater than female apparent annual survival in both regions, though CrI’s overlapped. Our study suggests demographic factors other than adult annual survival likely play a larger role in recent regional and range-wide population declines, such as productivity, juvenile/immature survival, or recruitment. Simulations indicate that improving recapture probability and study duration of datasets could lead to more precise apparent annual survival estimates. However, our model produced CrI ranges comparable to the most ideal data collection scenario, suggesting the lack of trends we found was not due to variability in our estimates. We stress the importance of addressing inherent biases in survival datasets and the need for standardized collaborative efforts to inform species conservation on a range-wide scale.

Ornithology↗
USGS2022

Adaptive problem maps (APM): Connecting data dots to build increasingly informed and defensible environmental conservation decisions

Connecting individual datasets from different projects to each other and to decisions can help manager-researcher-administrator teams build on what is known and adapt their environmental decision-making process as new information becomes available. Throughout their careers, environmental professionals often collect data on many individual projects that address similar sets of natural resource conservation problems. Consequently, the institutions, agencies, and organizations that employ these environmental professionals accumulate a large reservoir of project-specific information. However, bigger-picture opportunities to advance broader natural resource conservation goals are lost if individual projects and datasets are not integrated. Here we illustrate how our adaptive problem mapping (APM) process provides a framing and internal structure that charts relationships among pertinent information types, germane data sets, applicable concepts, and relevant decisions. In the APM process, appropriately defined problem statements and coordinated bridging questions connect data and concepts to build a network of increasingly informed and defensible decisions. Although our APM process can be applied to many environmental problems, here we focus on examples from aquatic systems in which fish are conservation priorities. Prioritizing an initial evaluation and regular modification of the relationships among datasets and decisions using the APM process helps manager-research-administrator teams envision, track, and update what is known, unknown, learned, and needed. The resulting broader point of view advances strategic planning, evaluations of progress, assessments of opportunity costs, identification of options, and justifications of decision-related actions.

Journal of Environmental Management↗
USGS2009

The needs of salmon and steelhead in balancing their conservation and use

Over the past 100 years, Pacific salmon Oncorhynchus spp. and steelhead trout O. mykiss populations in the Pacific Northwest have experienced dramatic declines as a result of human population growth and associated development of the region's natural resources. Any strategy to reverse those declines will depend on achieving consensus among a diverse group of stakeholders willing to (1) restore damaged habitats and watersheds; (2) consider the biological needs of salmonid fishes; and (3) understand, conserve, and manage existing levels of biodiversity within an ecosystem context. The objective of this chapter is to review and summarize the needs of Pacific salmon and steelhead trout from perspectives that promote their sustainability and perpetuity, and, within that context, provide a framework, for the development of a sustainable fisheries strategy. Although volumes of information are available on the life histories and habitat requirements of Pacific salmonids, new concepts have begun to address the importance of habitat complexity, genetic diversity among locally adapted populations, and the need for appropriate units of conservation. Habitat variability and complexity are the templates that produce diverse, locally adapted populations of Pacific salmon and steelhead trout. Molded by conditions in the environments they colonized, salmon and steelhead have unique adaptations they may have taken hundred i?? perhaps thousands i?? of years to evolve. Any strategy to reverse the declines of salmon and steelhead must focus on conserving both current levels of genetic diversity as well as restoration and maintenance of habitats compatible with the specific needs of individual populations. An argument can be made that we know enough already about the needs of our salmon and steelhead to allow us to make appropriate decisions. There is strong need to integrate what we know about local salmonid adaptations and habitat needs with conservation and management strategies that address the problems we have created. It may be possible to have healthy, sustainable salmonid populations coastwide if we do not wait too long to make up our minds.

Book chapter↗
USGS2019

Incorporating citizen science data in spatially explicit integrated population models

Information about population abundance, distribution, and demographic rates is critical for understanding a species’ ecology and for effective conservation and management. To collect data over large spatial and temporal extents for such inferences, especially for species with low densities or wide distributions, citizen science can be an efficient approach. Integrated models have also emerged as an important methodology to estimate population parameters by combining multiple types of data, including citizen science data. We developed a spatially explicit integrated model that combines opportunistically collected presence–absence (PA) data, commonly collected in citizen science efforts, with systematically collected spatial capture–recapture (SCR) data, which are often limited to small spatial and temporal extents. We conducted single and multi‐season simulations with parameters informed by North American black bear ( Ursus americanus ) populations, to evaluate the influence of varying amounts of opportunistic PA data collected at larger spatial and temporal extents on the estimation of population‐level parameters. Integrating opportunistic PA data increased the precision and accuracy of posterior estimates of abundance, and survival and recruitment rates. In some cases, adding PA locations improved abundance estimates more than increasing PA detection probability. Posterior estimates were as precise and unbiased as when higher quality, but sparse, SCR data were available. We also applied the integrated model to SCR and citizen science PA data collected on black bears in New York, with results consistent with our simulations. Our findings indicate that citizen science in integrated models can be a cost‐efficient way to improve estimates of population parameters and increase the spatiotemporal extent of inference. Continued developments with integrated models and citizen science data will offer additional ways to improve our understanding of population structure and demographics.

Ecology↗
USGS2020

Robust age estimation of southern sea otters from multiple morphometrics

Reliable age estimation is an essential tool to assess the status of wildlife populations and inform successful management. Aging methods, however, are often limited by too few data, skewed demographic representation, and by single or uncertain morphometric relationships. In this study, we synthesize age estimates in southern sea otters Enhydra lutris nereis from 761 individuals across 34 years of study, using multiple noninvasive techniques and capturing all life stages from 0 to 17 years of age. From wild, stranded, and captive individuals, we describe tooth eruptions, tooth wear, body length, nose scarring, and pelage coloration across ontogeny and fit sex‐based growth functions to the data. Dental eruption schedules provided reliable and identifiable metrics spanning 0.3–9 months. Tooth wear was the most reliable predictor of age of individuals aged 1–15 years, which when combined with total length, explained >93% of observed age. Beyond age estimation, dental attrition also indicated the maximum lifespan of adult teeth is 13‒17 years, corresponding with previous estimates of life expectancy. Von Bertalanffy growth function model simulations of length at age gave consistent estimates of asymptotic lengths (male L oo = 126.0‒126.8 cm, female L oo = 115.3‒115.7 cm), biologically realistic gestation periods ( t 0 = 115 days, SD = 10.2), and somatic growth (male k = 1.8, SD = 0.1; female k = 2.1, SD = 0.1). Though exploratory, we describe how field radiographic imaging of epiphyseal plate development or fusions may improve aging of immature sea otters. Together, our results highlight the value of integrating information from multiple and diverse datasets to help resolve conservation problems.

Ecology and Evolution↗
USGS2024

The interplay of future solar energy, land cover change, and their projected impacts on natural lands and croplands in the US

Projections for deep decarbonization require large amounts of solar energy, which may compete with other land uses such as agriculture, urbanization, and conservation of natural lands. Existing capacity expansion models do not integrate land use land cover change (LULC) dynamics into projections. We explored the interaction between projected LULC, solar photovoltaic (PV) deployment, and solar impacts on natural lands and croplands by integrating projections of LULC with a model that can project future deployment of solar PV with high spatial resolution for the conterminous United States. We used scenarios of LULC projections from the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios from 2010 to 2050 and two electricity grid scenarios to model future PV deployment and compared those results against a baseline that held 2010 land cover constant through 2050. Though solar PV's overall technical potential was minimally impacted by LULC scenarios, deployed PV varied by −16.5 to 11.6 % in 2050 from the baseline scenario. Total land requirements for projected PV were similar to other studies, but measures of PV impacts on natural systems depended on the underlying land change dynamics occurring in a scenario. The solar PV deployed through 2050 resulted in 1.1 %–2.4 % of croplands and 0.3 %–0.7 % of natural lands being converted to PV. However, the deepest understanding of PV impacts and interactions with land cover emerged when the complete net gains and losses from all land cover change dynamics, including PV, were integrated. For example, one of the four LULC projections allows for high solar development and a net gain in natural lands, even though PV drives a larger percentage of natural land conversion. This paper shows that integrating land cover change dynamics with energy expansion models generates new insights into trade offs between decarbonization, impacts of renewables, and ongoing land cover change.

conterminous United States↗
USGS2022

The global environmental agenda urgently needs a semantic web of knowledge

Progress in key social-ecological challenges of the global environmental agenda (e.g., climate change, biodiversity conservation, Sustainable Development Goals) is hampered by a lack of integration and synthesis of existing scientific evidence. Facing a fast-increasing volume of data, information remains compartmentalized to pre-defined scales and fields, rarely building its way up to collective knowledge. Today's distributed corpus of human intelligence, including the scientific publication system, cannot be exploited with the efficiency needed to meet current evidence synthesis challenges; computer-based intelligence could assist this task. Artificial Intelligence (AI)-based approaches underlain by semantics and machine reasoning offer a constructive way forward, but depend on greater understanding of these technologies by the science and policy communities and coordination of their use. By labelling web-based scientific information to become readable by both humans and computers, machines can search, organize, reuse, combine and synthesize information quickly and in novel ways. Modern open science infrastructure—i.e., public data and model repositories—is a useful starting point, but without shared semantics and common standards for machine actionable data and models, our collective ability to build, grow, and share a collective knowledge base will remain limited. The application of semantic and machine reasoning technologies by a broad community of scientists and decision makers will favour open synthesis to contribute and reuse knowledge and apply it toward decision making.

Environmental Evidence↗
USGS2022

Integrating climate-informed planning into State Wildlife Action Plans in the north central United States

State fish and wildlife agencies are required to submit a State Wildlife Action Plan (SWAP) every 10 years to be eligible for grants through the State Wildlife Grant Program. With the next round of revisions due in 2025, the U.S. Geological Survey North Central Climate Adaptation Science Center is evaluating how to best support states with further integrating climate-informed planning in their SWAPs. Here, we summarize how states in the North Central region, which includes Colorado, Kansas, Montana, Nebraska, North Dakota, South Dakota, and Wyoming, addressed adaptation planning in their 2015 SWAPs. We review climate adaptation considerations in required SWAP elements one through five, including species, habitats, threats, conservation actions, and conservation monitoring, respectively. We also include a list of potential strategies that the North Central Climate Adaptation Science Center could use to support states in the North Central region with further advancing the integration of climate informed planning in ongoing and future SWAP revisions. These strategies were primarily identified during meetings in 2021 with six of seven state fish and wildlife agencies in the North Central region where we discussed their priority needs relating to climate-informed planning. Our hope is that these strategies will enhance existing efforts in the region to advance the integration of climate change in conservation planning.

Colorado, Kansas, Montana, Nebraska, North Dakota,↗
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↗
USGS2025

Linking environmental variability to long-term demographic change of an endangered species using integrated population models

Understanding how species populations change with environmental conditions is important for implementing effective habitat management and conservation strategies. Challenges to evaluating population-level responses to environmental conditions arise when data are sparse or not spatiotemporally aligned, especially for at-risk species with small, declining numbers. We synthesized 30 years (1992–2021) of three partially aligned data sets to build a Bayesian integrated population model (IPM) and evaluate demographic and environmental drivers of growth rates for six separately managed ‘subpopulations’ (A–F) of the federally endangered Cape Sable seaside sparrow endemic to the Florida Everglades. We found that juvenile survival peaked at inundation periods (hydroperiods) around 100–220 days and dropped sharply outside those values, while adult survival increased with longer periods of water depth <20 cm, but not with longer periods of water depth >20 cm. Fecundity increased when water depths were more stable, more area was dry, intervals between fires were longer and less area was burned. Changes in population growth rates tended to occur in years that juvenile and adult survival were associated with hydroperiod, especially in the two largest subpopulations B and E. Population growth rates were also associated with hydrologic conditions during the breeding season and fire dynamics through changes in fecundity, most notably in the smaller subpopulations A, C/F and D. Synthesis and applications . Our IPM represents the first long-term population analysis of the Cape Sable seaside sparrow connecting demographic processes to environmental factors. Our results suggest that sustaining periods of shallow water year-round may enhance Cape Sable seaside sparrow survival and population growth. Also, limiting water depth variability and maintaining dry conditions during the breeding season and inhibiting fires in consecutive years may increase fecundity and population growth. Identifying the mechanistic links between environmental and population dynamics could inform how species are expected to respond to management decisions and anticipated ecosystem changes.

Florida↗
USGS2024

Evaluating the sagebrush conservation design through the lens of a sagebrush indicator species

Sagebrush ecosystems support a suite of unique species such as the emblematic greater sage-grouse ( Centrocercus urophasianus ; sage-grouse) but are under increasing pressure from anthropogenic stressors such as annual grass invasion, conifer encroachment, altered wildfire regimes, and land use change. We examined the ability of an ecosystem-based framework for sagebrush conservation, the sagebrush conservation design (SCD) strategy, and the associated model of sagebrush ecological integrity (SEI), to identify and rank priority habitats for sage-grouse, a sagebrush indicator species. We compared sage-grouse population trends from 1996–2021 across the three ranked SEI categories. We then modeled those trends directly as a function of the same landcover predictors underlying SEI, used the median trend estimates to recategorize the sage-grouse’s range, and used spatial correlation methods to compare our sage-grouse performance categories with those of SEI. Finally, we compared the sage-grouse condition categories, predicted by our landcover-based model, to empirical trends derived from population count data. We found that the SCD and SEI were effective tools for identifying and ranking priority habitats for sage-grouse. Population trends were stable in the core areas identified by SEI but declining in the lower (i.e., growth and other) condition categories. As a result, core areas encompassed an increasingly larger share of the total sage-grouse population in a disproportionately smaller area. Our model supports the general functional relationships between landcover and sage-grouse performance suggested by SEI. We found strong spatial congruence between our categories of predicted sage-grouse population performance, the condition categories of SEI, and empirical trends derived from population count data. Our analysis demonstrates that proactive ecosystem-based approaches to the conservation of the sagebrush biome can help optimize the return on limited conservation resources and benefits for sagebrush obligate species and help reduce some of the real and perceived conflicts inherent in single-species management.

Arizona, California, Colorado, Idaho, New Mexico, ↗
USGS2021

Using landscape metrics to characterize towns along an urban-rural gradient

Context Urban-rural gradients are useful tools when examining the influence of human disturbances on ecological, social and coupled systems, yet the most commonly used gradient definitions are based on single broad measures such as housing density or percent forest cover that fail to capture landscape patterns important for conservation. Objectives We present an approach to defining urban–rural gradients that integrates multiple landscape pattern metrics related to ecosystem processes important for natural resources and wildlife sustainability. Methods We develop a set of land cover composition and configuration metrics and then use them as inputs to a cluster analysis process that, in addition to grouping towns with similar attributes, identifies exemplar towns for each group. We compare the outcome of the cluster-based urban-rural gradient typology to outcomes for four commonly-used rule-based typologies and discuss implications for resource management and conservation. Results The resulting cluster-based typology defines five town types (urban, suburban, exurban, rural, and agricultural) and notably identifies a bifurcation along the gradient distinguishing among rural forested and agricultural towns. Landscape patterns (e.g., core and islet forests) influence where individual towns fall along the gradient. Designations of town type differ substantially among the five different typologies, particularly along the middle of the gradient. Conclusions Understanding where a town occurs along the urban-rural gradient could aid local decision-makers in prioritizing and balancing between development and conservation scenarios. Variations in outcomes among the different urban-rural gradient typologies raise concerns that broad-measure classifications do not adequately account for important landscape patterns. We suggest future urban-rural gradient studies utilize more robust classification approaches.

Connecticut. Maine, Massachusetts, New Hampshire, ↗
USGS2017

Beyond exposure, sensitivity and adaptive capacity: A response based ecological framework to assess species climate change vulnerability

As the impacts of global climate change on species are increasingly evident, there is a clear need to adapt conservation efforts worldwide. Species vulnerability assessments (VAs) are increasingly used to summarize all relevant information to determine a species’ potential vulnerability to climate change and are frequently the first step in informing climate adaptation efforts. VAs commonly integrate multiple sources of information by utilizing a framework that distinguishes factors relevant to species exposure, sensitivity, and adaptive capacity. However, this framework was originally developed for human systems, and its use to evaluate species vulnerability has serious practical and theoretical limitations. By instead defining vulnerability as the degree to which a species is unable to exhibit any of the responses necessary for persistence under climate change (i.e., toleration of projected changes, migration to new climate-compatible areas, enduring in microrefugia, and evolutionary adaptation), we can bring VAs into the realm of ecological science without applying borrowed abstract concepts that have consistently challenged species-centric research and management. This response-based framework to assess species vulnerability to climate change allows better integration of relevant ecological data and past research, yielding results with much clearer implications for conservation and research prioritization.

Climate Change Responses↗
USGS2004

Assessment of current and proposed nature reserves of Mexico based on their capacity to protect geophysical features and biodiversity

Mexico currently has 144 nature reserves covering approximately 9.1% of its land area. These reserves were established for a variety of reasons - often unrelated to the protection of biodiversity. In 2000 in response to a growing concern about the lack of organized conservation reserve planning to protect the important threatened biological and physical features of Mexico, the Mexican Commission for Knowledge and Use of Biodiversity (CONABIO) proposed the establishment of 151 new reserves for Mexico covering 51,429,500 ha. We compiled a GIS analysis using digital thematic maps of physical and biological features to examine how the existing and proposed reserves serve to protect the biodiversity and physical features of the country. Using a conservation target of placing a minimum of 12% of the land area of each important biophysical feature in nature reserves, we found that the 144 existing nature reserves covering 18 million ha (9% of the country) only meet that target for elevation ranges >3000 m and areas with poor soils. These mountainous areas represent less than 1% of the country. The gaps in the existing nature reserves network occur mainly at lower and intermediate elevations (<3000 m) areas with xeric, tropical, and temperate ecosystems, and high productivity soils. The areas proposed by CONABIO increase the proportion of protected lands in the country to over 27% and most of the conservation targets for geophysical features, and land cover, categories are met. Whether this area would be sufficient to maintain viable populations and ecological integrity of species and ecosystems is unknown. Even with the new reserves, low elevation coastal lands would be below the conservation target in the nature reserves. To include a representative sample of these lands would be difficult as these are the same areas where the majority of people live. ?? 2003 Elsevier Ltd. All rights reserved.

Biological Conservation↗
USGS2022

Development of an online reporting format to facilitate the inclusion of ecosystem services into Conservation Reserve Enhancement Program reports

The Conservation Reserve Enhancement Program is a program administered by the U.S. Department of Agriculture’s Farm Service Agency. The Secretary of Agriculture is required to submit an annual report to Congress on Conservation Reserve Enhancement Program agreements that, among other things, reports on the progress made towards fulfilling commitments outlined in the agreements. The U.S. Geological Survey developed an online reporting form designed to ensure that consistent information is submitted to the Farm Service Agency from Conservation Reserve Enhancement Program State partners. Combined with the automated importation of text from partner-provided forms to word-processing documents, individual State reports and annual reports to Congress can now be produced efficiently and in a standardized format. Use of a standardized reporting format will also assist the Farm Service Agency in collecting information needed to support ecosystem service quantifications that go beyond the quantifications required from partners to document progress towards meeting the specific purposes and objectives identified in each agreement. Addition of these overarching conservation effect quantifications builds upon past ecosystem services modeling efforts based on the Integrated Valuation of Ecosystem Services and Tradeoffs suite of open-source software models; these offer a spatially explicit means to quantify additional ecosystem services across diverse partners in a consistent manner. Data sources are currently available to provide much of the information needed to run these models and complete simulations that would facilitate the quantification and reporting of the societal values of conservation actions taken under the Conservation Reserve Enhancement Program. It is the aim of this report to provide the information needed to move towards widescale monitoring of the Nation’s ecosystem services in a natural accounting framework, similar to the framework used to value financial and human capital.

Open-File Report↗
USGS1995

Water, energy, and biogeochemical budget research at Sleepers River Research Watershed, Vermont

The U.S. Geological Survey has selected the Sleepers River Research Watershed (Sleepers River) near Danville, Vt., as one of five sites for the investigation of Water, Energy, and Biogeochemical Budgets (WEBB). Sleepers River was chosen because it is a well-designed outdoor laboratory with a long history of hydrologic data collection and research, and also because it provides an ideal opportunity for collaboration among the U.S. Geological Survey, other Federal agencies, and universities at the site. The multiple subwatersheds at Sleepers River present a unique opportunity to investigate hydrologic, energy, and biogeochemical processes over a variety of spatial scales. This WEBB study builds on fundamental research on process mechanisms and rates at the plot scale (in this case, a hillslope). Results then are scaled up to interpret the hydrochemical response of first- and higher- order basins. Five research elements make up the Sleepers River WEBB project. Individually, each of the five elements is designed to investigate specific WEBB processes (such as CO2 efflux through a snowpack), address specific WEBB issues (such as scaling and flowpaths), or apply specific WEBB approaches (such as integrated chemical and physical study of a hillslope). The research elements overlap so that many of the processes investigated will be assessed in more than one way, thus allowing independent verification of research results. For example, flowpath information will be derived separately by use of isotopic tracers, conservative chemical solutes, and soil-moisture fluxes. Collectively, the five elements constitute an integrated approach to a comprehensive understanding of WEBB processes needed for the prediction of the effects of global change.

Open-File Report↗
USGS2022

A sagebrush conservation design to proactively restore America’s sagebrush biome

A working group of experts with diverse professional backgrounds and disciplinary expertise was assembled to conceptualize a spatially explicit conservation design to support and inform the Sagebrush Conservation Strategy Part 2. The goal was to leverage recent advancements in remotely sensed landcover products to develop spatially and temporally explicit maps of sagebrush rangeland condition and landscape threats. In addition, the group sought to provide a common basis for understanding the state of sagebrush rangelands through time. First, the study team developed a spatially explicit model to assess geographic patterns in sagebrush ecological integrity and used this model to identify core sagebrush areas (CSAs), growth opportunity areas (GOAs), and other rangeland areas (ORAs) across the biome. Among the identified rangelands, 33.4 million acres were classified as CSAs; 84.3 million acres as GOAs; and 127.2 million acres as ORAs as of 2020. Second, the team sought to demonstrate the ecological relevance of the identified CSAs and GOAs by comparing these data with independent datasets for sagebrush obligate species of conservation concern. Geographical patterns in sagebrush ecological integrity were strongly associated with the occurrence of high-priority species and also displayed clear links to population performance for greater sage-grouse. Third, the team parsed out the type, location, and acres of primary threats within the different categories (CSAs, GOAs, and ORAs) to help focus active management by identifying places where multiagency and organization efforts can protect CSAs and GOAs that have higher levels of integrity with lower cumulative threats. The assessment of the condition of the sagebrush biome (that is, the location, amount, and conservation status) indicated that complex ecosystem function problems are driving ~73 percent of the demonstrated threats within the CSAs and GOAs (rather than point-source problems, such as human development). Fourth, the team developed trend estimates for the identified CSAs and GOAs and three selected primary threats (invasive annual grasses, conifer encroachment, and human modification) to the sagebrush biome from 2001 to 2020. Results showed that an average of 1.3 million acres per year have transitioned to ORAs at an annual rate of −1.34 percent. Fifth, the team developed an approach to integrate climate change effects into the threat-based landscape conservation design and conducted an initial assessment on the magnitude of near-term climate effects in the context of observed historical trends. The team’s analysis suggests that climate change alone is unlikely to be the dominant threat to sagebrush ecological integrity in the next few decades, although interactions of climate with wildfire and invasive annual grasses may be an important threat, especially in the longer term. A spatial overlap analysis was performed and highlighted 45.8 million acres of shared priorities among existing conservation frameworks to help anchor and guide collaborative landscape-scale conservation of areas that still have no to low threats. This information is critical to provide context for decisions about the volume and nature of conservation actions and funding requirements.

western United States↗