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At least 775 records · Page 43Linked to original sources
USGS2025

Scoping decision-maker needs and science availability to support regional natural capital accounting in the U.S. Colorado River Basin

Natural capital accounting has the potential to yield important policy insights at multiple scales, but there remains a disconnect between regional-scale natural capital accounts and their use for informing policy. In this paper, we propose a roadmap that could lead to the creation of policy-relevant regional accounts, with steps split across an initial scoping phase and a subsequent development phase. We demonstrate the scoping steps in action with an application to the Colorado River Basin (“Basin”), a large watershed in the southwestern United States (U.S.) that has faced aridification and substantial high-profile tradeoffs around the use of its water and other natural resources. Drawing on prior U.S. Geological Survey science co-production efforts, we conducted a series of eight discussion sessions with 41 scientists and science representatives whose work is relevant to Basin water, riparian and riverine ecosystems, upland ecosystems and energy and minerals. We summarise participants' thoughts on key topics and economic linkages, their insights and questions of interest and their recommendations on existing scientific data sources and gaps. We evaluate the suitability of the available data for construction of System of Environmental-Economic Accounting (SEEA) Central Framework and SEEA Ecosystem Accounting accounts, including those for land, water, forests, energy and minerals and ecosystems (covering extent, condition and ecosystem services). We present a series of lessons learned during the scoping phase, as well as lessons that could be relevant for future practitioners engaging in the development phase. The information can help guide the development of timely and relevant regional-scale environmental-economic accounts in the U.S. and beyond.

Arizona, California, Colorado, Nevada, New Mexico,↗
USGS2006

Spatial modeling of biological soil crusts to support rangeland assessment and monitoring

Biological soil crusts are a diverse soil surface community, prevalent in semiarid regions, which function as ecosystem engineers and perform numerous important ecosystem services. Loss of crusts has been implicated as a factor leading to accelerated soil erosion and other forms of land degradation. To support assessment and monitoring efforts aimed at ensuring the sustainability of rangeland ecosystems, managers require spatially explicit information concerning potential cover and composition of biological soil crusts. We sampled low disturbance sites in Grand Staircase-Escalante National Monument (Utah, USA) to determine the feasibility of modeling the potential cover and composition of biological soil crusts in a large area. We used classification and regression trees to model cover of four crust types (light cyanobacterial, dark cyanobacterial, moss, lichen) and 1 cyanobacterial biomass proxy (chlorophyll a), based upon a parsimonious set of GIS (Geographic Information Systems) data layers (soil types, precipitation, and elevation). Soil type was consistently the best predictor, although elevation and precipitation were both invoked in the various models. Predicted and observed values for the dark cyanobacterial, moss, and lichen models corresponded moderately well (R 2 = 0.49, 0.64, 0.55, respectively). Cover of late successional crust elements (moss + lichen + dark cyanobacterial) was also successfully modeled (R2 = 0.64). We were less successful with models of light cyanobacterial cover (R2 = 0.22) and chlorophyll a (R2 = 0.09). We believe that our difficulty modeling chlorophyll a concentration is related to a severe drought and subsequent cyanobacterial mortality during the course of the study. These models provide the necessary reference conditions to facilitate the comparison between the actual cover and composition of biological soil crusts at a given site and their potential cover and composition condition so that sites in poor condition can be identified and management actions can be taken.

Rangeland Ecology and Management↗
USGS2010

Importance of coastal change variables in determining vulnerability to sea- and lake-level change

In 2001, the U.S. Geological Survey began conducting scientific assessments of coastal vulnerability to potential future sea- and lake-level changes in 22 National Park Service sea- and lakeshore units. Coastal park units chosen for the assessment included a variety of geological and physical settings along the U.S. Atlantic, Pacific, Gulf of Mexico, Gulf of Alaska, Caribbean, and Great Lakes shorelines. This research is motivated by the need to understand and anticipate coastal changes caused by accelerating sea-level rise, as well as lake-level changes caused by climate change, over the next century. The goal of these assessments is to provide information that can be used to make long-term (decade to century) management decisions. Here we analyze the results of coastal vulnerability assessments for several coastal national park units. Index-based assessments quantify the likelihood that physical changes may occur based on analysis of the following variables: tidal range, ice cover, wave height, coastal slope, historical shoreline change rate, geomorphology, and historical rate of relative sea- or lake-level change. This approach seeks to combine a coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, and it provides a measure of the system's potential vulnerability to the effects of sea- or lake-level change. Assessments for 22 park units are combined to evaluate relationships among the variables used to derive the index. Results indicate that Atlantic and Gulf of Mexico parks have the highest vulnerability rankings relative to other park regions. A principal component analysis reveals that 99% of the index variability can be explained by four variables: geomorphology, regional coastal slope, water-level change rate, and mean significant wave height. Tidal range, ice cover, and historical shoreline change are not as important when the index is evaluated at large spatial scales (thousands of kilometers). ?? 2010 Coastal Education and Research Foundation.

Journal of Coastal Research↗
USGS1976

Hydrologic Unit Map - 1974, state of Montana

This map and accompanying table show Hydrologic Units that are basically hydrographic in nature. The Cataloging Units shown will supplant the Cataloging Units previously used by the U.S. Geological Survey in its Catalog of Informaiton on Water Data (1966-72). The previous U.S. Geological Survey Catalog-Indexing System was by map number and letter, such as 49M. The boundaries as shown have been adapted from "The Catalog of Information on Water Data" (1972), "Water Resources Regions and Subregions for the National Assessment of Water and Related Land Resources" by the U.S. Water Reseources Council (1970), "River Basins of the United States" by the U.S. Soil Conservation Service ((1963, 1970), "River Basin Maps Showing Hydrologic Stations" by the Inter-Agency Committee on Water Resources, Subcommittee on Hydrology (1961), and State planning maps.

Montana↗
USGS2010

Trait-based approaches in the analysis of stream fish communities

Species traits are used to study the functional organization of fish communities for a range of reasons, from simply reducing data dimensionality to providing mechanistic explanations for observed variation in communities. Ecological and life history traits have been used to understand the basic ecology of fishes and predict (1) species and community responses to habitat and climate alteration, and (2) species extinction, species invasion, and community homogenization. Many approaches in this arena have been developed during the past three decades, but they often have not been integrated with related ecological concepts or subdisciplines, which has led to confusion in terminology. We review 102 studies of species traits and then summarize patterns in traits being used and questions being addressed with trait-based approaches. Overall, studies of fish–habitat relationships that apply habitat templates and hierarchical filters dominate our sample; the most frequently used traits are related to feeding. We define and show the relationships among key terms such as fundamental and realized niches; functional traits, performance, and fitness; tactic, trait-state, syndromes, and strategies; and guilds and functional groups. We propose accelerating research to (1) quantify trait plasticity, (2) identify traits useful for testing ecological hypotheses, (3) model habitat and biotic interactions in communities while explicitly accounting for phylogenetic relationships, (4) explore how traits control community assembly, and (5) document the importance of traits in fish– community responses to anthropogenic change and in delivering ecosystem services. Further synthesis of these topics is still needed to develop concepts, models, and principles that can unify the disparate approaches taken in trait-based analysis of fish communities, link fish community ecology to general community ecology, and inform sustainable management of ecosystems.

Conference Paper↗
USGS2020

Linking monitoring and data analysis to predictions and decisions for the range-wide eastern black rail status assessment

The US Fish and Wildlife Service has initiated a re-envisioned approach for providing decision makers with the best available science and synthesis of that information, called the Species Status Assessment (SSA), for endangered species decision making. The SSA report is a descriptive document that provides decision makers with an assessment of a species’ current status and predicted future status. These analyses support all manner of decisions under the US Endangered Species Act, such as listing, reclassification, recovery planning, etc. Novel scientific analysis and predictive modeling in SSAs could be an important part of rooting species conservation decisions in current data and cutting edge analytical and modeling techniques. Here we describe a novel analysis of available data to assess current condition of eastern black rail across its range in a dynamic occupancy analysis. We used the results of the analysis to develop a site occupancy projection model where the model parameters (initial occupancy, site persistence, colonization) were linked to environmental covariates, such as land management and land cover change (sea-level rise, development, etc.). We used the projection model to predict future conditions under multiple sea-level rise and habitat management scenarios. Occupancy probability and site colonization were low in all analysis units and site persistence was also low, suggesting low resiliency and redundancy currently. Extinction probability was high for all analysis units in all simulated scenarios except one with significant effort to preserve existing habitat, suggesting low future resiliency and redundancy. With results of these data analyses and predictive modeling, the US Fish and Wildlife Service concluded that protections of the Endangered Species Act were warranted for this subspecies.

Endangered Species Research↗
USGS2001

A metadata initiative for global information discovery

The Global Information Locator Service (GILS) encompasses a global vision framed by the fundamental values of open societies. Societal values such as a free flow of information impose certain requirements on the society's information infrastructure. These requirements in turn shape the various laws, policies, standards, and technologies that determine the infrastructure design. A particular focus of GILS is the requirement to provide the means for people to discover sources of data and information. Information discovery in the GILS vision is designed to be decentralized yet coherent, and globally comprehensive yet useful for detailed data. This article introduces basic concepts and design issues, with emphasis on the techniques by which GILS supports interoperability. It explains the practical implications of GILS for the common roles of organizations involved in handling information, from content provider through system engineer and intermediary to searcher. The article provides examples of GILS initiatives in various types of communities: bibliographic, geographic, environmental, and government. ?? 2001 Elsevier Science Inc.

Government Information Quarterly↗
USGS2020

Abundance and productivity of marbled murrelets ( Brachyramphus marmoratus ) off central California during the 2019 breeding season

Marbled murrelets ( Brachyramphus marmoratus ) have been listed as “endangered” by the State of California and “threatened” by the U.S. Fish and Wildlife Service since 1992 in California, Oregon, and Washington. Information regarding marbled murrelet abundance, distribution, population trends, and habitat associations is critical for risk assessment, effective management, evaluation of conservation efficacy, and ultimately, to meet Federal and State recovery efforts for this species. During June–August 2019, the U.S. Geological Survey Western Ecological Research Center continued previously established, long-term (1996–2019), at-sea surveys to estimate abundance and productivity of marbled murrelets in U.S. Fish and Wildlife Service Conservation Zone 6 (San Francisco Bay to Point Sur in central California). Using conventional distance sampling methods, we estimated marbled murrelet abundance using 125 detections of 216 murrelets (mean group size, 1.72) observed on 8 surveys. The abundance estimated for the entire study area using all surveys in 2019 was 404 birds (95-percent confidence interval, 272–601 birds). Estimated abundance from 2019 is comparable to most prior years of study. In 2019, we estimated reproductive productivity (calculated as the hatch-year [HY] to after-hatch-year [AHY] ratio) using three detections of three HY murrelets observed on six surveys. After date-correcting HY and AHY counts to account for birds expected to be absent from the water while inland at nests, the date-corrected juvenile ratio was 0.025±0.020 standard error. We discuss changes in methodologies during 1996–2019 that could be addressed in re-analysis of this long-term dataset. We updated a synthesized database of all Zone 6 marbled murrelet survey data since 1999 with 2019 data to allow scientists and managers to evaluate established survey methods and assess trends in abundance and productivity estimates.

California↗
USGS2015

Towards automating measurements and predictions of Escherichia coli concentrations in the Cuyahoga River, Cuyahoga Valley National Park, Ohio, 2012–14

Nowcasts are systems that can provide estimates of the current bacterial water-quality conditions based on predictive models using easily-measured, explanatory variables; nowcasts can provide the public with the information to make informed decisions on the risk associated with recreational activities in natural water bodies. Previous studies on the Cuyahoga River within Cuyahoga Valley National Park (CVNP) have found that predictive models can be used to provide accurate assessments of the recreational water quality. However, in order to run the previously developed nowcasts for CVNP, manual collection and processing of samples is required on a daily basis to acquire the required explanatory variable data (laboratory-measured turbidity). The U.S. Geological Survey and the National Park Service collaborated to develop a more automated approach to provide more timely results to park visitors regarding the recreational water quality of the river. In May 2012, an in-stream water-quality sensor was installed by the U.S. Geological Survey at Jaite, Ohio (a site centrally located in CVNP on the Cuyahoga River), to provide near-real-time measurements of turbidity and water temperature. To transition from methods used during previous studies at CVNP, a relation between laboratory- and in-stream measured turbidity was developed after the recreational season of 2012. During the recreational seasons of 2012 through 2014, discrete water samples were collected and processed to determine Escherichia coli ( E. coli ) concentrations at Jaite and one site upstream of Jaite (Lock 29) within CVNP. Predictive models, using in-stream turbidity measurements, were developed for the recreational seasons of 2013 and 2014 to estimate recreational water quality in regards to Ohio’s single-sample water-quality standard for primary-contact recreation. A computer program was developed to manage the nowcasts by running the predictive models and posting the results to a publicly accessible Web site daily by 9 a.m. The nowcasts were able to correctly predict E. coli concentrations above or below the water-quality standard at Jaite for 79 percent of the samples compared with the measured concentrations. In comparison, the persistence model (using the previous day’s sample concentration) correctly predicted concentrations above or below the water-quality standard in only 68 percent of the samples. To determine if the Jaite nowcast could be used for the stretch of the river between Lock 29 and Jaite, the model predictions for Jaite were compared with the measured concentrations at Lock 29. The Jaite nowcast provided correct responses for 77 percent of the Lock 29 samples, which was a greater percentage than the percentage of correct responses (58 percent) from the persistence model at Lock 29.

Ohio↗
USGS2026

Morphometric properties of the CP-21 landing site on the Moon at Mons Gruithuisen Gamma

Characterizing terrain surface properties is an essential step in assessing the feasibility of landing successfully at a location on a planetary surface. Slopes and terrain ruggedness index (TRI) values derived from high-resolution (2 m pixel −1 ) digital terrain models provided important constraints in selecting the landing site for the upcoming Payloads and Research Investigations on the Surface of the Moon program as part of the Commercial Lunar Payload Services task order CP-21 mission. The selected landing site needed to balance safety requirements with the ability to achieve the science and exploration goals of the Lunar Vulkan Imaging and Spectroscopy Explorer payload. In this study, we compare several morphometric parameters in the context of the CP-21 landing site on Mons Gruithuisen Gamma, or the Gamma dome, and quantify the information they convey about lunar surface properties to assess their utility for future landing site evaluation. TRI was found to be a useful metric for assessing landing site safety. Metrics that better decouple slope and surface roughness, the vector ruggedness measure and the standard deviation of slope, provided additional information about surface characteristics and textures such as the degree to which roughness is isotropic.

Planetary Science Journal↗
USGS2013

Assessing the risk of nitrogen deposition to natural resources in the Four Corners area

Nitrogen (N) deposition in the western U.S. is on the rise and is already dramatically affecting terrestrial ecosystems. For example, N deposition has repeatedly been shown to lower air and water quality, increase greenhouse gas emissions, alter plant community composition, and significantly modify fire regimes. Accordingly, the effects of N deposition represent one of our largest environmental challenges and make difficult the National Park Service’s (NPS) important mission to “preserve the scenery and the natural and historic objects and the wildlife… unimpaired for the enjoyment of future generations”. Due to increased population growth and energy development (e.g., natural gas wells), the Four Corners region has become a notable ‘hotspot’ for N deposition. However, our understanding of how increased N deposition will affect these unique ecosystems, as well as how much deposition is actually occurring, remains notably poor. Here we used a multi-disciplinary approach to gathering information in an effort to help NPS safeguard the Four Corners national parks, both now and into the future. We applied modeling, field, and laboratory techniques to clarify current N deposition gradients and to help elucidate the ecosystem consequences of N deposition to the national parks of the Four Corners area. Our results suggest that NOx deposition does indeed represent a significant source of N to Mesa Verde National Park and, as expected, N deposition significantly affects coupled biogeochemical cycling (N, carbon, and phosphorus) of these landscapes. We also found some surprising results. For example, perhaps due to the low nutrient availability in these (and other) dryland ecosystems, although most other research suggests that adding N reduces N fixation rates, N additions did not consistently reduce natural N inputs via biological N2 fixation at our dryland sites. While the timeline of this pilot project is too brief to elucidate all the potential insight from the approach utilized here (e.g., we have fertilization plots to explore how N deposition affects Bromus tectorum invasion that will surely yield provoking results), we plan to continue this exciting line of questioning and expect further insight to be forthcoming.

Arches National Park, Canyonland National Park, Me↗
USGS2015

Economic assessment of the use value of geospatial information

Geospatial data inform decision makers. An economic model that involves application of spatial and temporal scientific, technical, and economic data in decision making is described. The value of information (VOI) contained in geospatial data is the difference between the net benefits (in present value terms) of a decision with and without the information. A range of technologies is used to collect and distribute geospatial data. These technical activities are linked to examples that show how the data can be applied in decision making, which is a cultural activity. The economic model for assessing the VOI in geospatial data for decision making is applied to three examples: (1) a retrospective model about environmental regulation of agrochemicals; (2) a prospective model about the impact and mitigation of earthquakes in urban areas; and (3) a prospective model about developing private–public geospatial information for an ecosystem services market. Each example demonstrates the potential value of geospatial information in a decision with uncertain information.

ISPRS International Journal of Geo-Information↗
USGS2002

Distribution of grizzly bears in the Greater Yellowstone Ecosystem, 1990-2000

The Yellowstone grizzly bear ( Ursus arctos horribilis ) has been expanding its range during the past 2 decades and now occupies historic habitats that had been vacant. A current understanding of the distribution of grizzly bears within the ecosystem is useful in the recovery process and to help guide the state and federal land management agencies and state wildlife agencies of Idaho, Wyoming, and Montana as they prepare management plans. We used kernel estimators to develop distribution maps of occupied habitats based on initial sightings of unduplicated females (n = 300) with cubs-of-the-year, information from radiomarked bears (n = 105), and locations of conflicts, confrontations, and mortalities (n = 1,235). Although each data set was constrained by potential sampling bias, together they provided insight into areas within the Greater Yellowstone Ecosystem (GYE) currently occupied by grizzly bears. The current distribution (1990-2000) extends beyond the recovery zone identified in the U.S. Fish and Wildlife Service (USFWS) Recovery Plan. Range expansion is particularly evident in the southern portion of the ecosystem in Wyoming. A comparison of our results from the 1990s to previously published distribution maps show an approximate increase in occupied habitat of 48% and 34% from the 1970s and 1980s, respectively. We discuss data biases and problems implicit to the analysis.

Wyoming↗
USGS2014

Fitting statistical distributions to sea duck count data: implications for survey design and abundance estimation

Determining appropriate statistical distributions for modeling animal count data is important for accurate estimation of abundance, distribution, and trends. In the case of sea ducks along the U.S. Atlantic coast, managers want to estimate local and regional abundance to detect and track population declines, to define areas of high and low use, and to predict the impact of future habitat change on populations. In this paper, we used a modified marked point process to model survey data that recorded flock sizes of Common eiders, Long-tailed ducks, and Black, Surf, and White-winged scoters. The data come from an experimental aerial survey, conducted by the United States Fish & Wildlife Service (USFWS) Division of Migratory Bird Management, during which east-west transects were flown along the Atlantic Coast from Maine to Florida during the winters of 2009–2011. To model the number of flocks per transect (the points), we compared the fit of four statistical distributions (zero-inflated Poisson, zero-inflated geometric, zero-inflated negative binomial and negative binomial) to data on the number of species-specific sea duck flocks that were recorded for each transect flown. To model the flock sizes (the marks), we compared the fit of flock size data for each species to seven statistical distributions: positive Poisson, positive negative binomial, positive geometric, logarithmic, discretized lognormal, zeta and Yule–Simon. Akaike’s Information Criterion and Vuong’s closeness tests indicated that the negative binomial and discretized lognormal were the best distributions for all species for the points and marks, respectively. These findings have important implications for estimating sea duck abundances as the discretized lognormal is a more skewed distribution than the Poisson and negative binomial, which are frequently used to model avian counts; the lognormal is also less heavy-tailed than the power law distributions (e.g., zeta and Yule–Simon), which are becoming increasingly popular for group size modeling. Choosing appropriate statistical distributions for modeling flock size data is fundamental to accurately estimating population summaries, determining required survey effort, and assessing and propagating uncertainty through decision-making processes.

Statistical Methodology↗
USGS2019

National earthquake information center strategic plan, 2019–23

Executive Summary Damaging earthquakes occur regularly around the world; since the turn of the 20th century, hundreds of earthquakes have caused significant loss of life and (or) millions of dollars or more in economic losses. While most of these did not directly affect the United States and its Territories, by studying worldwide seismicity we can better understand how to mitigate the effects of earthquakes when they do occur within U.S. borders. Within the U.S. Government, this mandate falls on the U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC), which has the statutory responsibility for monitoring and reporting on earthquakes domestically and globally. The NEIC has been operating since 1966, and throughout its history has been recognized as a world leader for earthquake information. For much of this time, NEIC has been cooperating with a number of regional seismic networks (RSNs) which operate in areas of heightened seismicity in the United States. In 2000, the Advanced National Seismic System (ANSS) was founded as a cooperative umbrella for earthquake-related data collection, analysis, and dissemination in the United States, thereby promoting advanced interoperability between the NEIC and RSN partners. The NEIC also cooperates and coordinates with dozens of global seismic networks. At present (2019), NEIC acquires real-time waveform data from more than 2,000 seismic stations worldwide, contributed from more than 130 seismic networks. Since 2006, the NEIC has operated on a 24-hour, 7-days per week (24/7) basis, and reports on about 30,000 earthquakes per year. Soon after the occurrence of a significant global earthquake, notifications are issued to government representatives, aid agencies, the press, and members of the general public by the Earthquake Notification Service (ENS), electronic feeds, and through the USGS Earthquake Hazards Program (EHP) website. Event-specific web pages provide detailed source parameter information outlining the location and magnitude of the earthquake, including more detailed source characteristics like moment magnitude and focal mechanisms and finite fault solutions. Further, NEIC produces a suite of real-time situational awareness products, including ShakeMap, ShakeCast, Did-You-Feel-It? (DYFI?), and Prompt Assessment of Global Earthquakes for Response (PAGER), to characterize the shaking resulting from the earthquake and the impact it is likely to have on nearby populations and infrastructure. All of these products are ultimately archived in the ANSS Comprehensive Catalog (ComCat), hosted and served by the NEIC. The NEIC also pursues an active research program to improve its ability to characterize earthquakes and understand their hazards. These efforts are all aimed at mitigating the risks of earthquakes to humankind. To maintain its prominent position in earthquake monitoring, the NEIC must continue to evolve, concurrently improving its operations and 24/7 robustness, streamlining services and infrastructure, and keeping pace with research and innovation in the field of seismology. This document outlines how the NEIC might best achieve such goals, by describing specific avenues and opportunities for development in the next five years (2019–23). Several key areas of operational and research focus are identified in this plan as being of the highest importance. First, NEIC must finalize improvements to its regional monitoring capabilities, including the implementation of a variety of improved earthquake detection and association algorithms. One of the most exciting avenues of recent research expansion in earthquake monitoring has involved the use of machine learning; NEIC must explore the benefits of machine learning for improved earthquake detection and source characterization. NEIC also needs to address issues related to the timeliness of earthquake information, exploring the benefits of distributing information as it becomes available, rather than when certain quality criteria are met. To that end, the incorporation of real-time Global Positioning System (GPS) data into the NEIC operational workflow will help improve the speed and accuracy of information for moderate-to-large earthquakes. Finally, NEIC should explore how to further expand and improve the quality and content of the products served during earthquake response efforts, including the generation of new earthquake sequence-specific products, adding an evolutionary component to earthquake information, and continued improvements to earthquake impact products.

Circular↗
USGS2018

Abundance and productivity of marbled murrelets (Brachyramphus marmoratus) off central California during the 2017 breeding season

Marbled murrelets ( Brachyramphus marmoratus ) have been listed as “Endangered” by the State of California and “Threatened” by the U.S. Fish and Wildlife Service since 1992 in California, Oregon, and Washington. Information regarding marbled murrelet abundance, distribution, population trends, and habitat associations is critical for risk assessment, effective management and evaluation of conservation efficacy, and ultimately to meet Federal- and State-mandated recovery efforts for this species. During June–August 2017, the U.S. Geological Survey Western Ecological Research Center continued previously established, long-term (1999–2016), at-sea surveys to estimate abundance and productivity of marbled murrelets in U.S. Fish and Wildlife Service Conservation Zone 6 (central California—San Francisco Bay to Monterey Bay). Using conventional distance sampling methods, we estimated marbled murrelet abundance using 189 detections of 321 individuals observed on nine unique surveys. The abundance estimated for the entire study area using all surveys in 2017 was 530 birds (95-percent confidence interval, 384–732 birds). Estimated abundance from 2017 is comparable to most prior years of study, except for 2008 and 2015, which had anomalously low abundances. We estimated productivity (calculated as the hatch-year [HY] to after-hatch-year [AHY] ratio) in 2017 using three detections of three individuals observed in six surveys. After date-correcting HY and AHY counts to account for birds expected to be absent from the water while inland at nests, the date-corrected juvenile ratio was 0.022 ± 0.014 standard error. We created a synthesized database of all marbled murrelet survey data from 1999 to 2017 to allow scientists and managers to evaluate established survey methods and assess trends in abundance and productivity estimates. Future modifications of survey design could help reduce variance in abundance estimation.

California↗
USGS2019

Abundance and productivity of marbled murrelets (Brachyramphus marmoratus) off central California during the 2018 breeding season

Executive Summary Marbled murrelets ( Brachyramphus marmoratus ) have been listed as “endangered” by the State of California and “threatened” by the U.S. Fish and Wildlife Service since 1992 in California, Oregon, and Washington. Information regarding marbled murrelet abundance, distribution, population trends, and habitat associations is critical for risk assessment, effective management, evaluation of conservation efficacy, and ultimately, to meet Federal- and State-mandated recovery efforts for this species. During June–August 2018, the U.S. Geological Survey Western Ecological Research Center continued previously established, long-term (1999–2018), at-sea surveys to estimate abundance and productivity of marbled murrelets in U.S. Fish and Wildlife Service Conservation Zone 6 (San Francisco Bay to Point Sur in central California). Using conventional distance sampling methods, we estimated marbled murrelet abundance using 137 detections of 227 individuals observed on 9 surveys. The abundance estimated for the entire study area using all surveys in 2018 was 370 birds (95-percent confidence interval, 250–546 birds). Estimated abundance from 2018 is comparable to most prior years of study, except for 2001–03, when greater abundances were estimated. In 2018, we estimated reproductive productivity (calculated as the hatch-year [HY] to after-hatch-year [AHY] ratio) using four detections of four HY individuals observed on six surveys. After date-correcting HY and AHY counts to account for birds expected to be absent from the water while inland at nests, the date-corrected juvenile ratio was 0.047 ± 0.024 standard error. We updated a synthesized database of all Zone 6 marbled murrelet survey data since 1999 with 2018 data to allow scientists and managers to evaluate established survey methods and assess trends in abundance and productivity estimates.

California↗
USGS2020

Social Values for Ecosystem Services, version 4.0 (SolVES 4.0)—Documentation and user manual

The geographic information system tool, Social Values for Ecosystem Services (SolVES), was developed to incorporate quantified and spatially explicit measures of social values into ecosystem service assessments. SolVES 4.0 provides an open-source version of SolVES, which was designed to assess, map, and quantify the social values of ecosystem services. Social values—the perceived, nonmarket values the public ascribes to ecosystem services, particularly cultural services, such as aesthetics and recreation—can be evaluated for various stakeholder groups. These groups are distinguishable by factors such as their attitudes and preferences regarding public uses (for example, motorized recreation and logging). As with previous versions, SolVES 4.0 derives a quantitative 10-point, social-values metric—the value index—from a combination of spatial and nonspatial responses to public value and preference surveys. The tool also calculates metrics characterizing the underlying environment, such as average distance to water and dominant landcover. SolVES 4.0 has been developed with Python using a QGIS user interface and a PostgreSQL database for required data. SolVES is integrated with Maxent maximum entropy modeling software to generate more complete social-value maps and offer robust statistical models describing the relation between the value index and explanatory environmental variables. A model’s goodness of fit to a primary study area and its potential performance in transferring social values to similar areas using value-transfer methods can be evaluated. SolVES 4.0 provides an improved open-source, public-domain tool for decision makers and researchers to evaluate the social values of ecosystem services and to facilitate discussions among diverse stakeholders regarding the tradeoffs among ecosystem services in a variety of biophysical and social contexts including mountain, forest, coastal, riparian, agricultural, and urban environments around the globe.

Techniques and Methods↗