USGS Science⌕ Search

SEARCH · USGS Science

Results for “Land Use and Land Cover”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,675 records · Page 93Linked to original sources
USGS2023

Changes in habitat suitability for wintering dabbling ducks during dry conditions in the Central Valley of California

In arid and Mediterranean regions, landscape-scale wetland conservation requires understanding how wildlife responds to dynamic freshwater availability and conservation actions to enhance wetland habitat. Taking advantage of Landsat satellite data and structured and community science bird survey data, we built species distribution models to describe how three duck species, the Northern Pintail ( Anas acuta ), Green-winged Teal ( Anas crecca ), and Northern Shoveler ( Anas clypeata ), respond to freshwater supply and food resources on different flooded land cover types in the Central Valley of California. Specifically, our models compared duck habitat suitability between the wettest and driest conditions in each month from September through April. Using abundance-weighted boosted regression trees, we created three sets of species occurrence models based on different covariates: (1) near real-time (hereafter “real-time”) covariates in which duck observations were matched to the water availability within the 16-day window of a Landsat observation, (2) a combination of real-time covariates and waterfowl food resource covariates describing annual corn and rice biomass and managed wetland moist soil seed yield estimates derived from Landsat data, and (3) long-term average covariates—the most common approach to species distribution modeling—in which long-term average surface water availability was used. We modeled the monthly occurrence of three duck species as a function of surface water availability, land cover type, road density, temperature, and bird data source. We found that dry conditions result in reduced habitat suitability, with the biggest reductions in November through January and in agricultural fields; in contrast, suitability of flooded wetland habitat was relatively robust to surface water availability. When models of habitat suitability based on long-term average climate conditions were compared to models based on real-time conditions, the highest long-term suitability values occurred in areas where suitability was high regardless of whether it was a wet or a dry year. While all models performed well, the inclusion of crop and wetland plant yield covariates resulted in slightly higher model performance. Overall, species distribution models created using data on the environmental conditions present at the time of bird observations can aid conservation efforts under extreme conditions over large spatial scales.

California↗
USGS2015

Spatially explicit modeling of blackbird abundance in the Prairie Pothole Region

Knowledge of factors influencing animal abundance is important to wildlife biologists developing management plans. This is especially true for economically important species such as blackbirds (Icteridae), which cause more than $100 million in crop damages annually in the United States. Using data from the North American Breeding Bird Survey, the National Land Cover Dataset, and the National Climatic Data Center, we modeled effects of regional environmental variables on relative abundance of 3 blackbird species (red-winged blackbird, Agelaius phoeniceus ; yellow-headed blackbird, Xanthocephalus xanthocephalus ; common grackle, Quiscalus quiscula ) in the Prairie Pothole Region of the central United States. We evaluated landscape covariates at 3 logarithmically related spatial scales (1,000 ha, 10,000 ha, and 100,000 ha) and modeled weather variables at the 100,000-ha scale. We constructed models a priori using information from published habitat associations. We fit models with WinBUGS using Markov chain Monte Carlo techniques. Both landscape and weather variables contributed strongly to predicting blackbird relative abundance (95% credibility interval did not overlap 0). Variables with the strongest associations with blackbird relative abundance were the percentage of wetland area and precipitation amount from the year before bird surveys were conducted. The influence of spatial scale appeared small—models with the same variables expressed at different scales were often in the best model subset. This large-scale study elucidated regional effects of weather and landscape variables, suggesting that management strategies aimed at reducing damages caused by these species should consider the broader landscape, including weather effects, because such factors may outweigh the influence of localized conditions or site-specific management actions. The regional species distributional models we developed for blackbirds provide a tool for understanding these broader landscape effects and guiding wildlife management practices to areas that are optimally beneficial. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Iowa, Minnesotta, Montana, Nebraska, North Dakota↗
USGS2019

Wetlands and development influence fish diversity in a species-rich small river

We identified in-stream and off-stream characteristics that influenced various species diversity metrics in reaches of the Duck River Basin, Tennessee, USA. This relatively small basin is home to one of the most diverse freshwater fish faunas in North America. In all, over 325,000 native fish representing 136 native fish species were electrofished in 207 collections across 86 stations. Diversity of native species and of seven taxa and functional guilds, including imperiled species, increased with size of the catchment above a station, an effect that was mediated by altitude. After removing the effects of catchment and altitude, diversity was influenced by in-stream factors and mostly by off-stream land composition such as wetlands within 0.1 km from the channel and urban/suburban development within 2.5 km. Pastures next to streams unexpectedly increased diversity. Our analyses suggest that there are detectable hotspots associated with off-stream landscape characteristics where conservation efforts may be focused. Our results may encourage conservation planners to apply geospatial analyses to identify diversity hotspots based on land cover distributions and develop recommendations for management of specific stream reaches. Alternatively, geospatial analysis may be used by planners to estimate the impacts of alternative land-use scenarios, thus preemptively conserving species richness.

Tennessee↗
USGS1981

The role of remotely sensed and other spatial data for predictive modeling: the Umatilla, Oregon example

The U. S. Geological Survey's Earth Resources Observations Systems Data Center, in cooperation with the U.S. Army Corps of Engineers, Portland District, developed and tested techniques that used remotely sensed and other spatial data in predictive models to evaluate irrigation agriculture in the Umatilla River Basin of north-central Oregon. Landsat data and 1:24,000-scale aerial photographs were initially used to map he expansion of irrigate from 1973 to 1979 and to identify crops under irrigation in 1979. The crop data were then used with historical water requirement figures and digital topographic and hydrographic data to estimate water and power use for the 1979 irrigation season. The final project task involved production of a composite map of land suitability for irrigation development based on land cover (from Landsat), land-ownership, soil irrigability, slope gradient, and potential energy costs. The methods and data used in the study demonstrated the flexibility of remotely sensed and other spatial data as input for predictive models. When combined, they provided useful answers to complex questions facing resource managers.

Oregon↗
USGS2020

Changes in climate and land cover affect seasonal streamflow forecasts in the Rio Grande headwaters

Seasonal streamflow forecast bias, changes in climate, snowpack, and land cover, and the effects of these changes on relations between basin‐wide snowpack, SNOw TELemetry (SNOTEL) station snowpack, and seasonal streamflow were evaluated in the headwaters of the Rio Grande, Colorado. Results indicate that shifts in the seasonality of precipitation and changing climatology are consistent with periods of overprediction and underprediction in streamflow forecasts. Multiple linear regression of SNOTEL data, postcedent precipitation, and land‐cover changes explained 2%–18% more variability in streamflow prediction than using SNOTEL station data alone. Simulated basin‐wide snowpack from a physically based model had significant negative trends in snow water equivalent (−4.33 mm/yr) and snow‐covered area (−0.05%/yr) during the melt period April–June. Simulated streamflow from a precipitation‐runoff model increased an average 5% when the effects of bark beetle‐induced tree mortality were compared to a baseline simulation with static vegetation. The effects of a 2013 wildfire increased simulated seasonal streamflow an average 35% for 1–4 years postfire. The combined effects of climate and land‐cover changes on snowpack‐streamflow relations highlight the difficulty in seasonal streamflow forecasting, which has important implications for water‐resource management.

Colorado↗
USGS1995

The strategy for improving water-quality monitoring in the United States; final report of the Intergovernmental Task Force on Monitoring Water Quality; technical appendices

The Intergovernmental Task Force on Monitoring Water Quality (ITFM) prepared this report in collaboration with representatives of all levels of government and the private sector. The report recommends a strategy for nationwide water-quality monitoring and technical monitoring improvements to support sound water-quality decisionmaking. The strategy is intended to achieve a better return on public and private investments in monitoring, environmental protection, and natural resources management. It is also designed to expand the base of information useful to a variety of users at multiple geographic scales. Institutional and technical changes are needed to improve water-quality monitoring and to meet the full range of monitoring requirements. Monitoring must be incorporated as a critical element of program planning, implementation, and evaluation. The strategy includes recommendations in many key elements, such as the development of goal-oriented monitoring and indicators, institutional collaboration, and methods comparability. Initial actions have been taken to implement the strategy. Several Federal agencies have jointly purchased and shared remotely sensed land-cover information needed for water assessment. Major agency data systems are using common data-element names and reference tables that will ensure easy sharing of data. A number of States have held meetings with collectors of water information to initiate statewide monitoring strategies. New monitoring guidance has been developed for Federal water-quality grants to States. Many State offices have changed monitoring programs to place emphasis on priority watersheds and to improve assessment of water quality. As the competition increases for adequate supplies of clean water, concerns about public health and the environment escalate, and more demands are placed on the water information infrastructure. To meet these demands, the collaborative approach has already produced benefits, which will continue to grow as the recommendations are implemented

Open-File Report↗
USGS2013

Land-cover effects on the fate and transport of surface-applied antibiotics and 17-beta-estradiol on a sandy outwash plain, Anoka County, Minnesota, 2008–09

A plot-scale field experiment on a sandy outwash plain in Anoka County in east-central Minnesota was used to investigate the fate and transport of two antibiotics, sulfamethazine (SMZ) and sulfamethoxazole (SMX), and a hormone, 17-beta-estradiol (17BE), in four land-cover types: bare soil, corn, hay, and prairie. The SMZ, SMX, and 17BE were applied to the surface of five plots of each land-cover type in May 2008 and again in April 2009. The cumulative application rate was 16.8 milligrams per square meter (mg/m 2 ) for each antibiotic and 0.6 mg/m2 for 17BE. Concentrations of each chemical in plant-tissue, soil, soil-water, and groundwater samples were determined by using enzyme-linked immunosorbent assay (ELISA) kits. Soil-water and groundwater sampling events were scheduled to capture the transport of SMZ, SMX, and 17BE during two growing seasons. Soil and plant-tissue sampling events were scheduled to identify the fate of the parent chemicals of SMZ, SMX, and 17BE in these matrices after two chemical applications. Areal concentrations (mg/m 2 ) of SMZ and SMX in soil tended to decrease in prairie plots in the 8 weeks after the second chemical application, from April 2009 to June 2009, but not in other land-cover types. During these same 8 weeks, prairie plots produced more aboveground biomass and had extracted more water from the upper 125 centimeters of the soil profile compared to all other land-cover types. Areal concentrations of SMZ and SMX in prairie plant tissue did not explain the temporal changes in areal concentrations of these chemicals in soil. The areal concentrations of SMZ and SMX in the aboveground plant tissues in June 2009 and August 2009 were much lower, generally two to three orders of magnitude, than the areal concentrations of these chemicals in soil. Pooling all treatment plot data, the median areal concentration of SMZ and SMX in plant tissues was 0.01 and 0.10 percent of the applied chemical mass compared to 22 and 12 percent in soil, respectively. Furthermore, areal concentrations of SMZ and SMX in plant-tissue samples were variable, and did not differ significantly between control and treatment plots within each land-cover type. SMZ was detected in 23 percent of soil-water samples and in 16 percent of groundwater samples collected between October 2008 and October 2009 in treatment plots, indicating that surface-applied SMZ leached below the rooting zone and reached groundwater. SMX was detected in only 1 percent of soil-water and groundwater samples during this same time period. In contrast to the antibiotics, 17BE was not reliably detected in soil samples. Additionally, ELISA-determined 17BE concentrations in plant-tissue, soil-water, and groundwater samples indicated the presence of chemicals that were not applied as part of this experiment [17BE from an external source or other chemical(s) that interfered with the 17BE ELISA kits].

Minnesota↗
USGS2013

A regional classification of the effectiveness of depressional wetlands at mitigating nitrogen transport to surface waters in the Northern Atlantic Coastal Plain

Nitrogen from nonpoint sources contributes to eutrophication, hypoxia, and related ecological degradation in Atlantic Coastal Plain streams and adjacent coastal estuaries such as Chesapeake Bay and Pamlico Sound. Although denitrification in depressional (non-riparian) wetlands common to the Coastal Plain can be a significant landscape sink for nitrogen, the effectiveness of individual wetlands at removing nitrogen varies substantially due to varying hydrogeologic, geochemical, and other landscape conditions, which are often poorly or inconsistently mapped over large areas. A geographic model describing the spatial variability in the likely effectiveness of depressional wetlands in watershed uplands at mitigating nitrogen transport from nonpoint sources to surface waters was constructed for the Northern Atlantic Coastal Plain (NACP), from North Carolina through New Jersey. Geographic and statistical techniques were used to develop the model. Available medium-resolution (1:100,000-scale) stream hydrography was used to define 33,799 individual watershed catchments in the study area. Sixteen landscape metrics relevant to the occurrence of depressional wetlands and their effectiveness as nitrogen sinks were defined for each catchment, based primarily on available topographic and soils data. Cluster analysis was used to aggregate the 33,799 catchments into eight wetland landscape regions (WLRs) based on the value of three principal components computed for the 16 original landscape metrics. Significant differences in topography, soil, and land cover among the eight WLRs demonstrate the effectiveness of the clustering technique. Results were used to interpret the relative likelihood of depressional wetlands in each WLR and their likely effectiveness at mitigating nitrogen transport from upland source areas to surface waters. The potential effectiveness of depressional wetlands at mitigating nitrogen transport varies substantially over different parts of the NACP. Depressional wetlands are common in three WLRs covering 32 percent of the area, and have a relatively high potential to mitigate nitrogen transport from nonpoint sources. Conversely, 37 percent of the study area includes rolling hills with relatively high slope and relief, and little likelihood of depressional wetlands. The remainder of the Coastal Plain includes relatively flat watersheds with moderate to low relative likelihood of nitrogen mitigation. The delineation of WLRs in this model should be useful for targeting wetland conservation or restoration efforts, and for estimating the effects of depressional wetlands on the regional nitrogen budget, but should be considered in light of limitations and assumptions inherent in the model.

Scientific Investigations Report↗
USGS2010

Quantifying human disturbance in watersheds: Variable selection and performance of a GIS-based disturbance index for predicting the biological condition of perennial streams

Characterizing the relative severity of human disturbance in watersheds is often part of stream assessments and is frequently done with the aid of Geographic Information System (GIS)-derived data. However, the choice of variables and how they are used to quantify disturbance are often subjective. In this study, we developed a number of disturbance indices by testing sets of variables, scoring methods, and weightings of 33 potential disturbance factors derived from readily available GIS data. The indices were calibrated using 770 watersheds located in the western United States for which the severity of disturbance had previously been classified from detailed local data by the United States Environmental Protection Agency (USEPA) Environmental Monitoring and Assessment Program (EMAP). The indices were calibrated by determining which variable or variable combinations and aggregation method best differentiated between least- and most-disturbed sites. Indices composed of several variables performed better than any individual variable, and best results came from a threshold method of scoring using six uncorrelated variables: housing unit density, road density, pesticide application, dam storage, land cover along a mainstem buffer, and distance to nearest canal/pipeline. The final index was validated with 192 withheld watersheds and correctly classified about two-thirds (68%) of least- and most-disturbed sites. These results provide information about the potential for using a disturbance index as a screening tool for a priori ranking of watersheds at a regional/national scale, and which landscape variables and methods of combination may be most helpful in doing so.

Ecological Indicators↗
USGS2014

Land cover and topography affect the land transformation caused by wind facilities

Land transformation (ha of surface disturbance/MW) associated with wind facilities shows wide variation in its reported values. In addition, no studies have attempted to explain the variation across facilities. We digitized land transformation at 39 wind facilities using high resolution aerial imagery. We then modeled the effects of turbine size, configuration, land cover, and topography on the levels of land transformation at three spatial scales. The scales included strings (turbines with intervening roads only), sites (strings with roads connecting them, buried cables and other infrastructure), and entire facilities (sites and the roads or transmission lines connecting them to existing infrastructure). An information theoretic modeling approach indicated land cover and topography were well-supported variables affecting land transformation, but not turbine size or configuration. Tilled landscapes, despite larger distances between turbines, had lower average land transformation, while facilities in forested landscapes generally had the highest land transformation. At site and string scales, flat topographies had the lowest land transformation, while facilities on mesas had the largest. The results indicate the landscape in which the facilities are placed affects the levels of land transformation associated with wind energy. This creates opportunities for optimizing wind energy production while minimizing land cover change. In addition, the results indicate forecasting the impacts of wind energy on land transformation should include the geographic variables affecting land transformation reported here.

PLoS ONE↗
USGS2016

One thousand years of fires: Integrating proxy and model data

The current fires raging across Indonesia are emitting more carbon than the annual fossil fuel emissions of Germany or Japan, and the fires are still consuming vast tracts of rainforest and peatlands. The National Interagency Fire Center (www.nifc.gov) notes that 2015 is one worst fire years on record in the U.S., where more than 9 million acres burned -- equivalent to the combined size of Massachusetts and New Jersey. The U.S. and Indonesian fires have already displaced tens of thousands of people, and their impacts on ecosystems are still unclear. In the case of Indonesia, the burning peat is destroying much of the existing soil, with unknown implications for the type of vegetation regrowth. Such large fires result from a combination of fire management practices, increasing anthropogenic land use, and a changing climate. The expected increase in fire activity in the upcoming decades has led to a surge in research trying to understand their causes, the factors that may have influenced similar times of fire activity in the past, and the implications of such fire activity in the future. Multiple types of complementary data provide information on the impacts of current fires and the extent of past fires. The wide array of data encompasses different spatial and temporal resolutions (Figure 1) and includes fire proxy information such as charcoal and tree ring fire scars, observational records, satellite products, modern emissions data, fire models within global land cover and vegetation models, and sociodemographic data for modeling past human land use and ignition frequency. Any single data type is more powerful when combined with another source of information. Merging model and proxy data enables analyses of how fire activity modifies vegetation distribution, air and water quality, and proximity to cities; these analyses in turn support land management decisions relating to conservation and development.

Frontiers of Biogeography↗
USGS2014

Effects of distributed and centralized stormwater best management practices and land cover on urban stream hydrology at the catchment scale

Urban stormwater runoff remains an important issue that causes local and regional-scale water quantity and quality issues. Stormwater best management practices (BMPs) have been widely used to mitigate runoff issues, traditionally in a centralized manner; however, problems associated with urban hydrology have remained. An emerging trend is implementation of BMPs in a distributed manner (multi-BMP treatment trains located on the landscape and integrated with urban design), but little catchment-scale performance of these systems have been reported to date. Here, stream hydrologic data (March, 2011–September, 2012) are evaluated in four catchments located in the Chesapeake Bay watershed: one utilizing distributed stormwater BMPs, two utilizing centralized stormwater BMPs, and a forested catchment serving as a reference. Among urban catchments with similar land cover, geology and BMP design standards (i.e. 100-year event), but contrasting placement of stormwater BMPs, distributed BMPs resulted in: significantly greater estimated baseflow, a higher minimum precipitation threshold for stream response and maximum discharge increases, better maximum discharge control for small precipitation events, and reduced runoff volume during an extreme (1000-year) precipitation event compared to centralized BMPs. For all catchments, greater forest land cover and less impervious cover appeared to be more important drivers than stormwater BMP spatial pattern, and caused lower total, stormflow, and baseflow runoff volume; lower maximum discharge during typical precipitation events; and lower runoff volume during an extreme precipitation event. Analysis of hydrologic field data in this study suggests that both the spatial distribution of stormwater BMPs and land cover are important for management of urban stormwater runoff. In particular, catchment-wide application of distributed BMPs improved stream hydrology compared to centralized BMPs, but not enough to fully replicate forested catchment stream hydrology. Integrated planning of stormwater management, protected riparian buffers and forest land cover with suburban development in the distributed-BMP catchment enabled multi-purpose use of land that provided esthetic value and green-space, community gathering points, and wildlife habitat in addition to hydrologic stormwater treatment.

Journal of Hydrology↗
USGS2011

Anisotropic path modeling to assess pedestrian-evacuation potential from Cascadia-related tsunamis in the US Pacific Northwest

Recent disasters highlight the threat that tsunamis pose to coastal communities. When developing tsunami-education efforts and vertical-evacuation strategies, emergency managers need to understand how much time it could take for a coastal population to reach higher ground before tsunami waves arrive. To improve efforts to model pedestrian evacuations from tsunamis, we examine the sensitivity of least-cost-distance models to variations in modeling approaches, data resolutions, and travel-rate assumptions. We base our observations on the assumption that an anisotropic approach that uses path-distance algorithms and accounts for variations in land cover and directionality in slope is the most realistic of an actual evacuation landscape. We focus our efforts on the Long Beach Peninsula in Washington (USA), where a substantial residential and tourist population is threatened by near-field tsunamis related to a potential Cascadia subduction zone earthquake. Results indicate thousands of people are located in areas where evacuations to higher ground will be difficult before arrival of the first tsunami wave. Deviations from anisotropic modeling assumptions substantially influence the amount of time likely needed to reach higher ground. Across the entire study, changes in resolution of elevation data has a greater impact on calculated travel times than changes in land-cover resolution. In particular areas, land-cover resolution had a substantial impact when travel-inhibiting waterways were not reflected in small-scale data. Changes in travel-speed parameters had a substantial impact also, suggesting the importance of public-health campaigns as a tsunami risk-reduction strategy.

Washington↗
USGS2006

Optimal interpolation analysis of leaf area index using MODIS data

A simple data analysis technique for vegetation leaf area index (LAI) using Moderate Resolution Imaging Spectroradiometer (MODIS) data is presented. The objective is to generate LAI data that is appropriate for numerical weather prediction. A series of techniques and procedures which includes data quality control, time-series data smoothing, and simple data analysis is applied. The LAI analysis is an optimal combination of the MODIS observations and derived climatology, depending on their associated errors σ o and σ c . The “best estimate” LAI is derived from a simple three-point smoothing technique combined with a selection of maximum LAI (after data quality control) values to ensure a higher quality. The LAI climatology is a time smoothed mean value of the “best estimate” LAI during the years of 2002–2004. The observation error is obtained by comparing the MODIS observed LAI with the “best estimate” of the LAI, and the climatological error is obtained by comparing the “best estimate” of LAI with the climatological LAI value. The LAI analysis is the result of a weighting between these two errors. Demonstration of the method described in this paper is presented for the 15-km grid of Meteorological Service of Canada (MSC)'s regional version of the numerical weather prediction model. The final LAI analyses have a relatively smooth temporal evolution, which makes them more appropriate for environmental prediction than the original MODIS LAI observation data. They are also more realistic than the LAI data currently used operationally at the MSC which is based on land-cover databases.

Remote Sensing of Environment↗
USGS2019

Drivers and uncertainties of forecasted range shifts for warm-water fishes under climate and land cover change

Land cover is an important determinant of aquatic habitat and is projected to shift with climate changes, yet climate-driven land cover changes are rarely factored into climate assessments. To quantify impacts and uncertainty of coupled climate and land cover change on warm-water fish species’ distributions, we used an ensemble model approach to project distributions of 14 species. For each species, current range projections were compared to 27 scenario-based projections and aggregated to visualize uncertainty. Multiple regression and model selection techniques were used to identify drivers of range change. Novel, or no-analogue, climates were assessed to evaluate transferability of models. Changes in total probability of occurrence ranged widely across species, from a 63% increase to a 65% decrease. Distributional gains and losses were largely driven by temperature and flow variables and underscore the importance of habitat heterogeneity and connectivity to facilitate adaptation to changing conditions. Finally, novel climate conditions were driven by mean annual maximum temperature, which stresses the importance of understanding the role of temperature on fish physiology and the role of temperature-mitigating management practices.

Canadian Journal of Fisheries and Aquatic Sciences↗
USGS2019

Characteristics of feeding sites of California Condors (Gymnogyps californianus) in the human-dominated landscape of Southern California

Wildlife conservation is often improved by understanding the movement ecology of species and adapting management strategies to dynamic conditions associated with movement. Despite a remarkable recovery over the past 30 year, the establishment of self-sustaining populations of California Condors (Gymnogyps californianus) has been challenging in the human-dominated landscapes of southern California. Among these challenges are those imposed by condor ground-foraging behavior that exposes them to environmental contamination. These include lead poisoning from the ingestion of spent ammunition and micro-trash ingestion and, during takeoff and landing, collisions with human structures. We tracked 28 California Condors for 24 months with patagially mounted GPS telemetry units to investigate the characteristics of ground sites condors visited and to identify spatiotemporal trends that might aid in conservation of this critically endangered species. Ground sites occurred on a wide variety of land cover types, primarily on steep slopes, and those more frequently used were associated with open cover. Condors concentrated their visits to ground sites around a 3 h period near midday, and usage increased from winter to late summer. Our study is the first to use remotely sensed telemetry data to describe fine-scale ecological correlates of condor ground-foraging ecology and therefore has important relevance for ongoing conservation and management strategies for this species. The descriptions of ground sites we provide can be used to target conservation or management actions.

California↗
USGS2019

Roost- and perch-site selection by Golden Eagles (Aquila chrysaetos) in eastern North America

Birds select critical resources to meet needs that vary in response to spatial, temporal, and individual variation. As an example, perch or roost sites may be at locations that provide protection from predators, mobbing, or inclement weather. Applied to large, soaring predators, this theory suggests that they may select perch and roost sites near food resources or at sites where environmental updrafts develop. To test these theories, we characterized selection of nonflight locations throughout the annual cycle for Golden Eagles ( Aquila chrysaetos ) in eastern North America. We determined factors associated with selection of perching (daytime) and roosting (nighttime) sites by eagles by comparing land cover and topographic characteristics of GPS telemetry locations for eagles (used) with random (available) locations. We separately assessed selection for perch and roost sites during each of 4 seasons (winter, summer, and spring and fall migration). Golden Eagles showed different selection patterns for perching by season and age. Throughout the year, eagles selected perch sites on steep slopes. The direction these slopes faced differed among seasons, with eagles selecting south-facing slopes in summer and east-facing slopes during migration. Adults showed greater preferences for broadleaf forests in summer and for ridges in fall. Patterns of perch-site use were consistent with selection for sites that provide thermal protection and access to thermal updrafts. We found few patterns of selection for roosting sites. Our analysis provides insight into decision-making by a longdistance migrant across its annual cycle and throughout its geographic range, and thus into how resource selection changes seasonally.

Wilson Journal of Ornithology↗
USGS2002

SAGEMAP: A web-based spatial dataset for sage grouse and sagebrush steppe management in the Intermountain West

The Snake River Field Station of the Forest and Rangeland Ecosystem Science Center has developed and now maintains a database of the spatial information needed to address management of sage grouse and sagebrush steppe habitats in the western United States. The SAGEMAP project identifies and collects infor-mation for the region encompassing the historical extent of sage grouse distribution. State and federal agencies, the primary entities responsible for managing sage grouse and their habitats, need the information to develop an objective assessment of the current status of sage grouse populations and their habitats, or to provide responses and recommendations for recovery if sage grouse are listed as a Threatened or Endangered Species. The spatial data on the SAGEMAP website (http://SAGEMAP.wr.usgs.gov) are an important component in documenting current habitat and other environmental conditions. In addition, the data can be used to identify areas that have undergone significant changes in land cover and to determine underlying causes. As such, the database permits an analysis for large-scale and range-wide factors that may be causing declines of sage grouse populations. The spatial data contained on this site also will be a critical component guiding the decision processes for restoration of habitats in the Great Basin. Therefore, development of this database and the capability to disseminate the information carries multiple benefits for land and wildlife management.

Fact Sheet↗