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At least 1,135 records · Page 63Linked to original sources
USGS1998

Systematic studies of Oryzomyine rodents (Muridae, Sigmodontinae): diagnoses and distributions of species formerly assigned to Oryzomys 'capito'

We describe the morphological species-boundaries and geographic distributions of ten Neotropical Oryzomys based on analyses of museum specimens (skins and skulls, examples preserved in fluid, chromosomal spreads, and information about collection sites from skin tags, field catalogs, and other sources). These species have been regarded as members of an Oryzomys capito complex and for a long time were consolidated into a single entity identified as O. capito. Our study documents the following: 1. Defining the limits of species within the O. capito complex first requires a comprehensive review and rigorous definition of O. capito itself. We consider Fischer's (1814) Mus megacephalus to be valid and available, designate a neotype to bear the name, and reinstate it as a senior synonym of capito Olfers (1818). We then provide a working definition of O. megacephalus and its close relative, O. laticeps, derived from analyses of morphometric variation, estimates of geographic distributions, and evaluations of synonyms. In our view, O. megacephalus occurs in Amazonia but also extends into eastern Paraguay; its synonyms are capito Olfers (1818), cephalotes Desmarest (1819), velutinus Allen and Chapman (1893), goeldi Thomas (1897), modestus Allen (1899), and perenensis Allen (1901). Oryzomys laticeps Lund (1840) occurs in the Atlantic Forest region of eastern Brazil. We designate a lectotype for laticeps and allocate the names saltator Winge (1887) and oniscus Thomas (1904) as synonyms. 2. We provide the first comprehensive taxonomic revision of Oryzomys yunganus Thomas (1902). Its range covers tropical evergreen rainforest formations in the Guiana region and the Amazon Basin where, as documented by voucher specimens, it has been collected at the same localities as O. megacephalus, O. nitidus, and O. tern of carotid arterial circulation, occlusal patterns of second upper and lower molars, cranial proportions, and chromosomal features. Appreciable intraspecific geographic variation occurs in diploid number of chromosomes and frequency of occurrence of the hypothenar plantar pad, but sampling inadequacies obscure the significance of this variation. Large body size is characteristic of populations in the western Amazon Basin and in the tepui region of eastern Venezuela; smaller size characterizes populations in the Guianas and along the eastern margin of the Amazon Basin. No other scientific name has been correctly associated with the species. Samples from Mirador, Palmera, and Mera in the western Andean foothills of central Ecuador possess a combination of pelage, cranial, and dental traits that distinguish them from all samples of O. yunganus. These specimens are the basis for a new species we describe here, one that is more closely related to O. yunganus than to any other member of the former O. 'capito' complex. 3. We redescribe Oryzomys bolivaris (reviewed by Pine, 1971, under the name O. bombycinus), amplify its geographic range, and contrast it with O. talamancae and O. alfaroi, two sympatric congeners often confused with it. A distinctive set of morphological traits allows unambiguous identification of specimens belonging to O. bolivaris. It is a trans-Andean species recorded from very wet tropical evergreen rainforests extending from eastern Honduras and Nicaragua through Costa Rica and Panama to western Colombia and Ecuador. Allen's (1901) bolivaris is the oldest name for this species; castaneus Allen (1901), rivularis Allen (1901), bombycinus Goldman (1912), alleni Goldman (1915), and orinus Pearson (1939) are synonyms. 4. We revise the definition of Oryzomys talamancae Allen (1891) provided by Musser and Williams (1985), document additional specimens, describe karyotypes from Ecuadoran and Venezuelan samples, and contrast its morphology, chromosomes, and distribution with those of O. alfaroi and O. megacephalus. The geographic distribution of O. talamancae is also trans-Andean, but it inh

Bulletin of the American Museum of Natural History↗
USGS2021

Roadside rights-of-way as pollinator habitat: A literature review

Pollination of crops and naturally - occurring flowering plants is a critical ecosystem service provided by managed and unmanaged animal pollinators. Insects are the most studied pollinators, particularly managed honey bees, unmanaged wild bees, and butterflies. Bees and butterflies thrive in early - successional habitat featuring grasses, exposed soil, wildfl owers, and shrubs, which is consistently found within transportation and utility rights - of - way (ROW). However, intensive management of ROW can reduce the amount of high - quality pollinator habitat; such practices include frequent mowing, broadcast herbicide use, and planting non - native cool season grasses. Here, we review peer - reviewed academic and non - peer reviewed gray literature describing ROW management practices and their effects on pollinator populations , focusing on applications of th e se practi ces in landscapes similar to t hose found in Main e and the nor theast Unit ed States ; that is, lan dscapes that are h e avily forested and inter spersed with agriculture, develop ed areas, and wetlands . T he li terature consistently recommend s these management practices to provide pollinator habitat in ROW and promote plant and pollinator diversity and abundance : 1) Reduce mowing frequency and time mowing to pollinator activity. 2) Target herbicide applications to undesirable plant sp ecies using backpack sprayers. 3) Plant native seeds, seedlings, or shrubs, leaving some exposed soil for nesting. We considered threats to plants and pollinators associated with ROW, including traffic volume and mortality, noise, light, and air pollution, and habitat fragmentation. The literature suggests that these threats vary widely across road sizes, types, and landscape context, and the overall negative impacts do not outweigh the potential benefits of promoting pollinator habitat in ROW. Landscape co ntext also influences the composition of ROW plant and pollinator communities. In Maine, agriculture and grassland in the surrounding generally reduced bumble bee and butterfly abundance in Priority 1 ROW sites. Many state Departments of Transportation ha ve incorporated integrative vegetation management (IVM) principles into ROW management, and we summarize a number of case studies here. Restoration projects in high - visibility areas are common; further, these can lead to public support for additional polli nator habitat enhancement. Implementing new management practices can be difficult, therefore we discuss strategies to aid in successful adoption, including gathering public support, collaborations between public and private agencies, and innovative funding opportunities. While assessing vegetation management impacts on bee and butterfly communities in ROW is a rapidly expanding area of research, there are still many gaps in current knowledge. We conclude this report by addressing these gaps and provide sugg estions for further study.

Maine↗
USGS2009

Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30??m to 1??km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600??ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400??m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine-resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire landscape. Failure to account for wetlands had little impact on landscape-scale estimates, because vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Remote Sensing of Environment↗
USGS2018

Monitoring global tree mortality patterns and trends. Report from the VW symposium ‘Crossing scales and disciplines to identify global trends of tree mortality as indicators of forest health’

From the 21stto the 23rdJune 2017, the Herrenhausen castle inHannover/Germany hosted a diverse and large crowd with morethan 70 tree physiologists, forest ecologists, forest inventoryexperts, remote-sensing scientists, and vegetation modele rs. Par-ticipants from six continent s and from more than 20 countriesgathered to discuss how to improve the scientific determination ofglobal-scale patterns, drivers, and trends of a threatening phe-nomenon: the apparent emergence of recent widespread treemortality events in diverse forests around the world.Continuin g the theme of a workshop held at the Max-PlanckInstitute for Biogeochemistry in Jena (Germany) in 2014 (Hart-mann et al., 2015), the Hanover meeting intended to developapproaches, tools and collaborative actions to accelerate progress inaddressing regional patterns and trends of tree mortality (Williamset al., 2013). Over the last decade climate change related treemortality events have been increasingly reported around the globe(van Mantgem et al., 2009; Carnicer et al., 2011; Peng et al., 2011;Brienen et al., 2015), but to what degree this is a global trend,amplifying under increasing climate change, remains uncertain.

New Phytologist↗
USGS2010

Two-species occupancy models: A new parameterization applied to co-occurrence of secretive rails

Two-species occupancy models that account for false absences provide a robust method for testing for evidence of competitive exclusion, but previous model parameteriza-tions were inadequate for incorporating covariates. We present a new parameterization that is stable when covariates are included: the conditional two-species occupancy model, which can be used to examine alternative hypotheses for species' distribution patterns. This new model estimates the probability of occupancy for a subordinate species conditional upon the presence of a dominant species. It can also be used to test if the detection of either species differs when one or both species are present, and if detection of the subordinate species depends on the detection of the dominant species when both are present. We apply the model to test if the presence of the larger Virginia Rail (Rallus limicola) affects probabilities of detection or occupancy of the smaller California Black Rail (Laterallus jamaicensis coturniculus) in small freshwater marshes that range in size from 0.013 to 13.99 ha. We hypothesized that Black Rail occupancy should be lower in small marshes when Virginia Rails are present than when they are absent, because resources are presumably more limited and interference competition should increase. We found that Black Rail detection probability was unaffected by the detection of Virginia Rails, while, surprisingly, Black and Virginia Rail occupancy were positively associated even in small marshes. The average probability of Black Rail occupancy was higher when Virginia Rails were present (0.74 ?? 0.053, mean ?? SE) than when they were absent (0.36 ?? 0.069), and for both species occupancy increased with marsh size. Our results contrast with recent findings from patchy forest systems, where small birds were presumed to be excluded from small habitat patches by larger competitors. ?? 2010 by the Ecological Society of America.

Ecological Applications↗
USGS2018

Executive summary. In Second State of the Carbon Cycle Report (SOCCR2): A Sustained Assessment Report

Central to life on Earth, carbon is essential to the molecular makeup of all living things and plays a key role in regulating global climate. To understand carbon’s role in these processes, researchers measure and evaluate carbon stocks and fluxes. A stock is the quantity of carbon contained in a pool or reservoir in the Earth system (e.g., carbon in forest trees), and a flux is the direction and rate of carbon’s transfer between pools (e.g., the movement of carbon from the atmosphere into forest trees during photosynthesis). This document, the Second State of the Carbon Cycle Report (SOCCR2), examines the patterns of carbon stocks and fluxes—collectively called the “carbon cycle.” Emphasis is given to these patterns in specific sectors (e.g., agriculture and energy) and ecosystems (e.g., forests and coastal waters) and to the response of the carbon cycle to human activity. The purpose of SOCCR2 is to assess the current state of the North American carbon cycle and to present recent advances in understanding the factors that influence it. Concentrating on North America—Canada, the United States, and Mexico—the report describes carbon cycling for air, land, inland waters (streams, rivers, lakes, and reservoirs), and coastal waters (see Figure ES.1, p. 23). The questions framing the publication A U.S. Carbon Cycle Science Plan (Michalak et al., 2011) inspired development of three slightly modified questions that guide SOCCR2’s content and focus on North America in a global context: How have natural processes and human actions affected the global carbon cycle on land, in the atmosphere, in the ocean and other aquatic systems, and at ecosystem interfaces (e.g., coastal, wetland, and urban-rural)? How have socioeconomic trends affected atmospheric levels of the primary carbon-containing gases, carbon dioxide (CO2) and methane (CH4)? How have species, ecosystems, natural resources, and human systems been impacted by increasing greenhouse gas (GHG) concentrations, associated changes in climate, and carbon management decisions and practices? SOCCR2 synthesizes the most recent understanding of carbon cycling in North America, assessing new carbon cycle findings and information, the state of knowledge regarding core methods used to study the carbon cycle, and future research needed to best inform carbon management and policy options. Focusing on scientific developments in the decade since the First State of the Carbon Cycle Report (SOCCR1; CCSP 2007), SOCCR2 summarizes the past, current, and projected state of carbon sources, sinks, and natural processes, as well as contributions by human activities. In addition to CO2 and CH4, the report sometimes discusses nitrous oxide (N2O), a GHG associated with activities and processes that affect fluxes of carbon gases.1 SOCCR2 also describes improvements in analysis tools; developments in decision support; and new insights into ecosystem carbon cycling, human causes of changes in the carbon cycle, and social science perspectives on carbon. Since publication of SOCCR1, coordinated research from agencies in the three North American countries has enabled innovative observational, analytical, and modeling capabilities to further advance understanding of the North American carbon cycle (see Appendix D: Carbon Measurement Approaches and Accounting Frameworks, p. 834). Some of the report’s main conclusions, based on the Key Findings of each chapter, are highlighted in Box ES.1, Main Findings of SOCCR2, p. 24.

Report↗
USGS2015

A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA

We used a statistical learning framework to evaluate the ability of three machine-learning methods to predict nitrate concentration in shallow groundwater of the Central Valley, California: boosted regression trees (BRT), artificial neural networks (ANN), and Bayesian networks (BN). Machine learning methods can learn complex patterns in the data but because of overfitting may not generalize well to new data. The statistical learning framework involves cross-validation (CV) training and testing data and a separate hold-out data set for model evaluation, with the goal of optimizing predictive performance by controlling for model overfit. The order of prediction performance according to both CV testing R 2 and that for the hold-out data set was BRT > BN > ANN. For each method we identified two models based on CV testing results: that with maximum testing R 2 and a version with R 2 within one standard error of the maximum (the 1SE model). The former yielded CV training R 2 values of 0.94–1.0. Cross-validation testing R 2 values indicate predictive performance, and these were 0.22–0.39 for the maximum R 2 models and 0.19–0.36 for the 1SE models. Evaluation with hold-out data suggested that the 1SE BRT and ANN models predicted better for an independent data set compared with the maximum R 2 versions, which is relevant to extrapolation by mapping. Scatterplots of predicted vs. observed hold-out data obtained for final models helped identify prediction bias, which was fairly pronounced for ANN and BN. Lastly, the models were compared with multiple linear regression (MLR) and a previous random forest regression (RFR) model. Whereas BRT results were comparable to RFR, MLR had low hold-out R 2 (0.07) and explained less than half the variation in the training data. Spatial patterns of predictions by the final, 1SE BRT model agreed reasonably well with previously observed patterns of nitrate occurrence in groundwater of the Central Valley.

California↗
USGS2004

Land cover mapping of North and Central America—Global Land Cover 2000

The Land Cover Map of North and Central America for the year 2000 (GLC 2000-NCA), prepared by NRCan/CCRS and USGS/EROS Data Centre (EDC) as a regional component of the Global Land Cover 2000 project, is the subject of this paper. A new mapping approach for transforming satellite observations acquired by the SPOT4/VGTETATION (VGT) sensor into land cover information is outlined. The procedure includes: (1) conversion of daily data into 10-day composite; (2) post-seasonal correction and refinement of apparent surface reflectance in 10-day composite images; and (3) extraction of land cover information from the composite images. The pre-processing and mosaicking techniques developed and used in this study proved to be very effective in removing cloud contamination, BRDF effects, and noise in Short Wave Infra-Red (SWIR). The GLC 2000-NCA land cover map is provided as a regional product with 28 land cover classes based on modified Federal Geographic Data Committee/Vegetation Classification Standard (FGDC NVCS) classification system, and as part of a global product with 22 land cover classes based on Land Cover Classification System (LCCS) of the Food and Agriculture Organisation. The map was compared on both areal and per-pixel bases over North and Central America to the International Geosphere–Biosphere Programme (IGBP) global land cover classification, the University of Maryland global land cover classification (UMd) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Global land cover classification produced by Boston University (BU). There was good agreement (79%) on the spatial distribution and areal extent of forest between GLC 2000-NCA and the other maps, however, GLC 2000-NCA provides additional information on the spatial distribution of forest types. The GLC 2000-NCA map was produced at the continental level incorporating specific needs of the region.

Remote Sensing of Environment↗
USGS2012

Groundwater simulation and management models for the upper Klamath Basin, Oregon and California

The upper Klamath Basin encompasses about 8,000 square miles, extending from the Cascade Range east to the Basin and Range geologic province in south-central Oregon and northern California. The geography of the basin is dominated by forested volcanic uplands separated by broad interior basins. Most of the interior basins once held broad shallow lakes and extensive wetlands, but most of these areas have been drained or otherwise modified and are now cultivated. Major parts of the interior basins are managed as wildlife refuges, primarily for migratory waterfowl. The permeable volcanic bedrock of the upper Klamath Basin hosts a substantial regional groundwater system that provides much of the flow to major streams and lakes that, in turn, provide water for wildlife habitat and are the principal source of irrigation water for the basin's agricultural economy. Increased allocation of surface water for endangered species in the past decade has resulted in increased groundwater pumping and growing interest in the use of groundwater for irrigation. The potential effects of increased groundwater pumping on groundwater levels and discharge to springs and streams has caused concern among groundwater users, wildlife and Tribal interests, and State and Federal resource managers. To provide information on the potential impacts of increased groundwater development and to aid in the development of a groundwater management strategy, the U.S. Geological Survey, in collaboration with the Oregon Water Resources Department and the Bureau of Reclamation, has developed a groundwater model that can simulate the response of the hydrologic system to these new stresses. The groundwater model was developed using the U.S. Geological Survey MODFLOW finite-difference modeling code and calibrated using inverse methods to transient conditions from 1989 through 2004 with quarterly stress periods. Groundwater recharge and agricultural and municipal pumping are specified for each stress period. All major streams and most major tributaries for which a substantial part of the flow comes from groundwater discharge are included in the model. Groundwater discharge to agricultural drains, evapotranspiration from aquifers in areas of shallow groundwater, and groundwater flow to and from adjacent basins also are simulated in key areas. The model has the capability to calculate the effects of pumping and other external stresses on groundwater levels, discharge to streams, and other boundary fluxes, such as discharge to drains. Historical data indicate that the groundwater system in the upper Klamath Basin fluctuates in response to decadal climate cycles, with groundwater levels and spring flows rising and declining in response to wet and dry periods. Data also show that groundwater levels fluctuate seasonally and interannually in response to groundwater pumping. The most prominent response is to the marked increase in groundwater pumping starting in 2001. The calibrated model is able to simulate observed decadal-scale climate-driven fluctuations in the groundwater system as well as observed shorter-term pumping-related fluctuations. Example model simulations show that the timing and location of the effects of groundwater pumping vary markedly depending on the pumping location. Pumping from wells close (within a few miles) to groundwater discharge features, such as springs, drains, and certain streams, can affect those features within weeks or months of the onset of pumping, and the impacts can be essentially fully manifested in several years. Simulations indicate that seasonal variations in pumping rates are buffered by the groundwater system, and peak impacts are closer to mean annual pumping rates than to instantaneous rates. Thus, pumping effects are, to a large degree, spread out over the entire year. When pumping locations are distant (more than several miles) from discharge features, the effects take many years or decades to fully impact those features, and much of the pumped water comes from groundwater storage over a broad geographic area even after two decades. Moreover, because the effects are spread out over a broad area, the impacts to individual features are much smaller than in the case of nearby pumping. Simulations show that the discharge features most affected by pumping in the area of the Bureau of Reclamation's Klamath Irrigation Project are agricultural drains, and impacts to other surface-water features are small in comparison. A groundwater management model was developed that uses techniques of constrained optimization along with the groundwater flow model to identify the optimal strategy to meet water user needs while not violating defined constraints on impacts to groundwater levels and streamflows. The coupled groundwater simulation-optimization models were formulated to help identify strategies to meet water demand in the upper Klamath Basin. The models maximize groundwater pumping while simultaneously keeping the detrimental impacts of pumping on groundwater levels and groundwater discharge within prescribed limits. Total groundwater withdrawals were calculated under alternative constraints for drawdown, reductions in groundwater discharge to surface water, and water demand to understand the potential benefits and limitations for groundwater development in the upper Klamath Basin. The simulation-optimization model for the upper Klamath Basin provides an improved understanding of how the groundwater and surface-water system responds to sustained groundwater pumping within the Bureau of Reclamation's Klamath Project. Optimization model results demonstrate that a certain amount of supplemental groundwater pumping can occur without exceeding defined limits on drawdown and stream capture. The results of the different applications of the model demonstrate the importance of identifying constraint limits in order to better define the amount and distribution of groundwater withdrawal that is sustainable.

Oregon;California↗
USGS2024

Environmental drivers and spatial patterns of antibiotic-resistant, enteric coliforms across a forest–urban riverscape

Antibiotic resistant bacteria are prevalent environmental contaminants in freshwaters, and antibiotic resistance genes circulate throughout the urban water cycle. The increase of antibiotic resistant pathogens threatens public health through direct and indirect exposure, and natural resource managers need information on the spatial patterns of antibiotic resistant bacteria and environmental factors associated with their distribution to improve water quality monitoring and to better assess human, animal, and environmental health risks. We collected water and epilithic biofilm samples and measured physicochemical environmental variables at 29 sites distributed longitudinally in the Green-Duwamish River basin, Washington, USA. We characterized catchment-wide patterns of gram-negative fecal indicator bacteria and hypothesized that the presence of antibiotic resistance would be associated with environmental heterogeneity, bacterial primary ecology, stream compartment, and stream type. Antibiotic resistance was determined by microbial growth on selective media supplemented with 3 different antibiotics (ampicillin, chloramphenicol, or tetracycline). Phenotypic antibiotic resistance was positively associated with disturbance, but resistance to at least 1 antibiotic was also detected in undeveloped river segments, with an 83% overall detection rate (i.e., 24 out of 29 sites, 17 in the mainstem and 7 in tributaries). The most probable number of Escherichia coli was associated with higher levels of antibiotic resistance of non- E. coli coliforms across the basin (ρ = 0.38, p < 0.01) but was not associated with antibiotic resistance of E. coli . Phenotypic resistance was highest among non- E. coli coliforms in the water column of tributaries draining moderately to extensively developed subcatchments. Generalized linear mixed-effects model results showed that 18% of the variance in presence of antibiotic resistance was explained by the fixed effects (summed CV across environmental variables, stream type, primary ecology, and stream compartment), and when a spatial random effect was included, the model explained 27% of the variance. Our study provides new evidence that environmental factors and bacterial primary ecology are important underlying factors associated with spatial patterns of antibiotic resistant enteric coliforms. We used macroecological concepts and a riverscape approach to characterize the distribution of antibiotic resistance with methods applicable to municipalities.

Freshwater Science↗
USGS2008

A regression model for computing index flows describing the median flow for the summer month of lowest flow in Michigan

In 2006, Michigan enacted laws to prevent new large capacity withdrawals from decreasing flows to the extent that they would functionally impair a stream's ability to support characteristic fish populations. The median streamflow for the summer month of lowest flow was specified by state decision makers as the index flow on which likely impacts of withdrawals would be assessed. At sites near long-term streamflow-gaging stations, analysis of streamflow records during July, August, and September was used to determine the index flow. At ungaged sites, an alternate method for computing the index flow was needed. This report documents the development of a method for computing index flows at ungaged stream sites in Michigan. The method is based on a regression model that computes the index water yield, which is the index flow divided by the drainage area. To develop the regression model, index flows were determined on the basis of daily flows measured during July, August, and September at 147 streamflow-gaging stations having 10 or more years of record (considered long-term stations) in Michigan. The corresponding index water yields were statistically related to climatic and basin characteristics upstream from the stations in the regression model. Climatic and basin characteristics selected as explanatory variables in the regression model include two aquifer-transmissivity and hydrologic-soil groups, forest land cover, and normal annual precipitation. Regression model estimates of water yield explain about 70.8 percent of the variability in index water yields indicated by streamflow-gaging station records. Index flows computed on the basis of regression-model estimates of water yield and corresponding drainage areas explain about 94.0 percent of the variability in index flows indicated by streamflow-gaging station records. No regional bias was detected in the regression-based estimates of water yield within seven hydrologic subregions spanning Michigan. Thus, the single regression model developed in this report can be used to produce unbiased estimates of index water yield and flow statewide. In addition, a technique is presented for computing prediction intervals about the index flow estimates.

Michigan↗
USGS2011

Occupancy and abundance of wintering birds in a dynamic agricultural landscape

Assessing wildlife management action requires monitoring populations, and abundance often is the parameter monitored. Recent methodological advances have enabled estimation of mean abundance within a habitat using presence–absence or count data obtained via repeated visits to a sample of sites. These methods assume populations are closed and intuitively assume habitats within sites change little during a field season. However, many habitats are highly variable over short periods. We developed a variation of existing occupancy and abundance models that allows for extreme spatio‐temporal differences in habitat, and resulting changes in wildlife abundance, among sites and among visits to a site within a field season. We conducted our study in sugarcane habitat within the Everglades Agricultural Area southeast of Lake Okeechobee in south Florida. We counted wintering birds, primarily passerines, within 245 sites usually 5 times at each site during December 2006–March 2007. We estimated occupancy and mean abundance of birds in 6 vegetation states during the sugarcane harvest and allowed these parameters to vary temporally or spatially within a vegetation state. Occupancy and mean abundance of the common yellowthroat ( Geothlypis trichas ) was affected by structure of sugarcane and uncultivated edge vegetation (occupancy = 1.00 [ = 0.96–1.00] and mean abundance = 7.9 [ = 3.2–19.5] in tall sugarcane with tall edge vegetation versus 0.20 [ = 0.04–0.71] and 0.22 [ = 0.04–1.2], respectively, in short sugarcane with short edge vegetation in one half of the study area). Occupancy and mean abundance of palm warblers ( Dendroica palmarum ) were constant (occupancy = 1.00, = 0.69–1.00; mean abundance = 18, = 1–270). Our model may enable wildlife managers to assess rigorously effects of future edge habitat management on avian distribution and abundance within agricultural landscapes during winter or the breeding season. The model may also help wildlife managers make similar management decisions involving other dynamic habitats such as wetlands, prairies, and even forested areas if forest management or fires occur during the field season.

Florida↗
USGS2019

Iterative models for early detection of invasive species across spread pathways

Species distribution models can be used to direct early detection of invasive species, if they include proxies for invasion pathways. Due to the dynamic nature of invasion, these models violate assumptions of stationarity across space and time. To compensate for issues of stationarity, we iteratively update regionalized species distribution models annually for European gypsy moth ( Lymantria dispar dispar ) to target early detection surveys for the USDA APHIS gypsy moth program. We defined regions based on the distances from the invasion spread front where shifts in variable importance occurred and included models for the non-quarantine portion of the state of Maine, a short-range region, an intermediate region, and a long-range region. We considered variables that represented potential gypsy moth movement pathways within each region, including transportation networks, recreational activities, urban characteristics, and household movement data originating from gypsy moth infested areas (U.S. Postal Service address forwarding data). We updated the models annually, linked the models to an early detection survey design, and validated the models for the following year using predicted risk at new positive detection locations. Human-assisted pathways data, such as address forwarding, became increasingly important predictors of gypsy moth detection in the intermediate-range geographic model as more predictor data accumulated over time (relative importance = 5.9%, 17.36%, and 35.76% for 2015, 2016, and 2018, respectively). Receiver operating curves showed increasing performance for iterative annual models (area under the curve (AUC) = 0.63, 0.76, and 0.84 for 2014, 2015, and 2016 models, respectively), and boxplots of predicted risk each year showed increasing accuracy and precision of following year positive detection locations. The inclusion of human-assisted pathway predictors combined with the strategy of iterative modeling brings significant advantages to targeting early detection of invasive species. We present the first published example of iterative species distribution modeling for invasive species in an operational context.

Minnesota, Wisconsin, Illinois, Indiana, Ohio, Wes↗
USGS2017

Multi-scale 46-year remote sensing change detection of diamond mining and land cover in a conflict and post-conflict setting

The town of Tortiya was created in the rural northern region of Côte d′Ivoire in the late 1940s to house workers for a new diamond mine. Nearly three decades later, the closure of the industrial-scale diamond mine in 1975 did not diminish the importance of diamond profits to the region's economy, and resulted in the growth of artisanal and small-scale diamond mining (ASM) within the abandoned industrial-scale mining concession. In the early 2000s, the violent conflict that arose in Côte d′Ivoire highlighted the importance of ASM land use to the local economy, but also brought about international concerns that diamond profits were being used to fund the rebellion. In recent years, cashew plantations have expanded exponentially in the region, diversifying economic activity, but also creating the potential for conflict between diamond mining and agricultural land uses. As the government looks to address the future of Tortiya and this potential conflict, a detailed spatio-temporal understanding of the changes in these two land uses over time may assist in informing policymaking. Remotely sensed imagery presents an objective and detailed spatial record of land use/land cover (LULC), and change detection methods can provide quantitative insight regarding regional land cover trends. However, the vastly different scales of ASM and cashew orchards present a unique challenge to comprehensive understanding of land use change in the region. In this study, moderate-scale categories of LULC, including cashew orchards, uncultivated forest, urban space, mining/ bare, and mixed vegetation, were produced through supervised classification of Landsat multispectral imagery from 1984, 1991, 2000, 2007, and 2014. The fine-scale ASM land use was identified through manual interpretation of annually acquired high resolution satellite imagery. Corona imagery was also integrated into the study to extend the temporal duration of the remote sensing record back to the period of industrial-scale mining. These different-scale analyses were then integrated to create a record of 46 years of mining activity and land cover change in Tortiya. While similar in spatial extent, the mining/ bare class in the integrated analysis exhibits a substantially different spatial distribution than in the original classifications. This additional information regarding the locations of ASM activity in the Tortiya area is important from a policy and planning perspective. The results of this study also suggest that LULC classifications of Landsat imagery do not consistently capture areas of ASM in the Côte d′Ivoire landscape.

Remote Sensing Applications: Society and Environme↗
USGS2018

Greater sage-grouse science (2015–17)—Synthesis and potential management implications

Executive Summary The greater sage-grouse ( Centrocercus urophasianus ; hereafter called “sage-grouse”), a species that requires sagebrush (Artemisia spp.), has experienced range-wide declines in its distribution and abundance. These declines have prompted substantial research and management investments to improve the understanding of sage-grouse and its habitats and reverse declines in distribution and population numbers. Over the past two decades, the U.S. Fish and Wildlife Service (USFWS) has responded to eight petitions to list the sage-grouse under the Endangered Species Act of 1973, with the completion of the most recent listing determination in September 2015. At that time, the USFWS determined that the sage-grouse did not warrant a listing, primarily because of the large scale science-based conservation and planning efforts completed or started by Federal, State, local agencies, private landowners, and other entities across the range. The planning efforts culminated in the development of the 2015 Bureau of Land Management (BLM) and U.S. Forest Service Land Use Plan Amendments, which provided regulatory certainty and commitment from Federal land-management agencies to limit, mitigate, and track anthropogenic disturbance and implement other sage-grouse conservation measures. After these policy decisions, the scientific community has continued to refine and expand the knowledge available to inform implementation of management actions, increase the efficiency and effectiveness of those actions, and continue developing an overall understanding of sage-grouse populations, habitat requirements, and their response to human activity and other habitat changes. The development of science has been driven by multiple prioritization documents including the “Greater Sage-Grouse National Research Strategy” (Hanser and Manier, 2013) and, most recently, the “Integrated Rangeland Fire Management Strategy Actionable Science Plan” (Integrated Rangeland Fire Management Strategy Actionable Science Plan Team, 2016). In October 2017, after a review of the 2015 Federal plans relative to State sage-grouse plans, in accordance with Secretarial Order 3353, the BLM issued a notice of intent to consider whether to amend some, all, or none of the 2015 land use plans. At that time, the BLM requested the U.S. Geological Survey (USGS) to inform this effort through the development of an annotated bibliography of sage-grouse science published since January 2015 and a report that synthesized and outlined the potential management implications of this new science. Development of the annotated bibliography resulted in the identification and summarization of 169 peer-reviewed scientific publications and reports. The USGS then convened an interagency team (hereafter referred to as the “team”) to develop this report that focuses on the primary topics of importance to the ongoing management of sage-grouse and their habitats. The team developed this report in a three-step process. First, the team identified six primary topic areas for discussion based on the members’ collective knowledge regarding sage-grouse, their habitats, and threats to either or both. Second, the team reviewed all the material in the “Annotated Bibliography of Scientific Research on Greater Sage-Grouse Published since January 2015” to identify the science that addressed the topics. Third, team members discussed the science related to each topic, evaluated the consistency of the science with existing knowledge before 2015, and summarized the potential management implications of this science. The six primary topics identified by the team were: Multiscale habitat suitability and mapping tools Discrete anthropogenic activities Diffuse activities Fire and invasive species Restoration effectiveness Population estimation and genetics

Open-File Report↗
USGS2026

High-resolution transboundary vegetation community maps of the Sonoran and Mojave Desert ecoregion to support critical landscape conservation planning and habitat management needs

We produced a 30-m resolution binational land cover map of Bird Conservation Region 33 (BCR 33) for the U.S. North American Bird Conservation Initiative. The region covers large portions of the Sonoran and Mojave Deserts. The map can support the U.S. Fish and Wildlife Service (FWS) Migratory Bird Program’s recovery planning efforts and constitutes the first known binational land cover dataset spanning sections of the United States–Mexico border and using a consistent classification system for both countries. The mapped region includes 152 distinct land cover classes, covering a total area of 38,421,453 ha (148,345 mi 2 ), of which 13,148,345 ha (52,706 mi 2 ) are located in Mexico and 24,770,640 ha (95,639 mi 2 ) in the United States. We primarily used Landsat 8 (OLI) imagery, supplemented by limited ground surveys from two field campaigns, drone-based aerial data, and existing vegetation classification frameworks from both countries. The classification applied a data-fusion approach integrating 30-m Landsat 8 imagery, decadal phenology metrics from vegetation indices, and a random forest model trained mainly with datasets from a comprehensive national mapping project from the U.S. Geological Survey (USGS) GAP Analysis Project (GAP) and federal wildland fire agencies’ Landscape Fire and Resource Management Planning Tools (LANDFIRE) (GAP/LANDFIRE) [United States side] and the National Institute of Statistics and Geography (INEGI) [Mexico side] as well as land cover maps and opportunistic open-access and field observations. Mapping of the full BCR 33 region was carried out in two phases: 1) Phase I, the prototype map, covered a smaller portion of the transboundary area and identified 31 land cover classes, and 2) Phase II, the full BCR 33 map (refer to Figure 1), which resulted in 152 land cover classes. Using a Random Forest classifier, we achieved an overall prediction accuracy of 92% for the Phase I map and 87% for the Phase II full region map. This slight decrease can be attributed to working on a larger, more complex area with a greater number of land cover classes. No formal validation was conducted, aside from using a subset of the collected field observations and training data to assess model performance during and after training. The training sites were further verified using Google Earth (Google, 2026) imagery. Two undergraduate students who worked for over a year visually inspected imagery and open access public images to confirm each training site during model training using in-house developed, online, visual tools. A portion of this field training data was reserved for model validation, and the corresponding results are to be presented in later sections. The project developed an end-to-end, medium- and fine-resolution remote sensing–based data fusion mapping approach. This effort produced a map (Nagler et al., 2025) and the online tools to support a dynamic, live, online map for visualizing the transboundary vegetation communities in BCR 33. The toolset is currently hosted by the University of Arizona (UofA) Vegetation Index and Phenology (VIP) Lab to support FWS partners (https://vip.arizona.edu/viplab_data_explorer?LCM_BCR33). The online map is designed to allow rapid updates using new training, validation, or correction data, making it dynamic and maintainable. The approach we took established a framework for rapid updating and correction of land cover maps, as the model can be quickly retrained with new field observations, updated training data, or other sources. This enables dynamic mapping and change detection of the region’s vegetation. This framework is an advance in data fusion and crowdsourced mapping of complex, vulnerable regions, providing support to regional stakeholders and the wider user community. This transboundary map can inform the protection, conservation, and restoration of vegetation, habitat, and ecosystems, particularly for threatened and endangered species across the two nations using consistent and harmonized binational mapping systems. Beyond supporting land management decisions and stakeholders in the transboundary desert ecoregions, this BCR 33 mapping effort establishes a foundation for future rapid, low-cost, cross-border land cover mapping that can benefit and advance ecosystem management.

Arizona, Baja California, California, Nevada, Sina↗
USGS1977

Abundance coefficients, a new method for measuring microorganism relative abundance

A new method of measuring the relative abundance of microorganisms by using a set of interrelated coefficients, termed 'abundance coefficients' or 'AC', is proposed. These coefficients provide a means of recording abundance for geometric density categories, and each density measurement represents an approximation of the Poisson parameter ??t. The AC is the natural logarithm of a 'characteristic value,' which is a particular number for each geometric density category. The 'characteristic values' are based upon a probabilistic error statement derived from the Poisson formula, and they present evidence for separation of the geometric category boundaries by e = 2.71828. The proposed AC provide a means for recording species abundance in a manner suitable for arithmetic manipulation, for population structure studies, and for the determination of practical limits for defining the presence or absence of a species. Further, these coefficients provide for both intrasample and intersample abundance comparisons. ?? 1977 Plenum Publishing Corporation.

Mathematical Geology↗
USGS2008

Use of an integrated flow model to estimate ecologically relevant hydrologic characteristics at stream biomonitoring sites

We developed an integrated hydroecological model to provide a comprehensive set of hydrologic variables representing five major components of the flow regime at 856 aquatic-invertebrate monitoring sites in New Jersey. The hydroecological model simulates streamflow by routing water that moves overland and through the subsurface from atmospheric delivery to the watershed outlet. Snow accumulation and melt, evapotranspiration, precipitation, withdrawals, discharges, pervious- and impervious-area runoff, and lake storage were accounted for in the water balance. We generated more than 78 flow variables, which describe the frequency, magnitude, duration, rate of change, and timing of flow events. Highly correlated variables were filtered by principal component analysis to obtain a non-redundant subset of variables that explain the majority of the variation in the complete set. This subset of variables was used to evaluate the effect of changes in the flow regime on aquatic-invertebrate assemblage structure at 856 biomonitoring sites. We used non-metric multidimensional scaling (NMS) to evaluate variation in aquatic-invertebrate assemblage structure across a disturbance gradient. We employed multiple linear regression (MLR) analysis to build a series of MLR models that identify the most important environmental and hydrologic variables driving the differences in the aquatic-invertebrate assemblages across the disturbance gradient. The first axis of NMS ordination was significantly related to many hydrologic, habitat, and land-use/land-cover variables, including the average number of annual storms producing runoff, ratio of 25-75% exceedance flow (flashiness), diversity of natural stream substrate, and the percentage of forested land near the stream channel (forest buffer). Modifications in the hydrologic regime as the result of changes in watershed land use appear to promote the retention of highly tolerant aquatic species; in contrast, species that are sensitive to hydrologic instability and other anthropogenic disturbance become much less prevalent. We also found strong relations between an index of invertebrate-assemblage impairment, its component metrics, and the primary disturbance gradient. The process-oriented watershed modeling approach used in this study provides a means to evaluate how natural landscape features interact with anthropogenic factors and assess their effects on flow characteristics and stream ecology. By combining watershed modeling and indirect ordination techniques, we were able to identify components of the hydrologic regime that have a considerable effect on aquatic-assemblage structure and help in developing short- and long-term management measures that mitigate the effects of anthropogenic disturbance in stream systems.

New Jersey↗