The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, collects data pertaining to the surface-water resources of Missouri. Established in 1964, the Ambient Water-Quality Monitoring Network (AWQMN) consisted of 69 sites in 2017. Two additional sites from the National Water-Quality Program are included with the AWQMN sites for the analyses in this report. The sites are sampled typically from 2 to 12 times per year for physical properties, total suspended solids, nutrients, fecal indicator bacteria, and trace elements.
The period of analysis for this study was from 1993 through 2017 and data analysis included 71 sites and 15 water-quality constituents plus discharge. Data analysis involved retrieving the data, conditioning the data for analysis, analyzing the data for trends, and analyzing the monitoring network to determine if potential data gaps or data redundancies exist in the network. Results from these analyses can be used to help manage the monitoring network into the future.
Water-quality data were analyzed using several software packages to provide graphical and statistical information for interpretation of trends in the data at selected sites. Discharge data at selected sites were analyzed to determine the general trends during the analysis period and how the water-quality samples represented the range of daily mean discharges at each site. Water-quality data also were analyzed at selected sites to determine the relative sensitivity of selected sites and constituents to changes in data collection frequency. Trend analysis at selected sites using a simulated reduction in sampling frequency was completed to compare to trends obtained using monthly data to determine the potential degradation in the ability of determining trends from a reduced sampling frequency. The viability of using estimated discharge to evaluate long-term trends for sites with no continuous discharge was investigated. Data from sites were statistically compared in groups to determine the relative similarity (or difference) between sites for each water-quality constituent to identify potentially redundant sites in the monitoring network.
Discharge-weighted long-term trends during 1993 through 2017 were analyzed for 15 water-quality constituents at 58 sites and results indicated there were significant single- or two-period trends in about 17 percent of the analyses. Some trends indicated improvement and some trends indicated deterioration of the general water quality at some sites in the AWQMN. No trend was indicated in about 31 percent of the analyses. The constituents pH, specific conductance, and total phosphorus showed the most frequent significant trends, and each of the 15 constituents examined had a significant trend at one or more sites. A total of 42 sites indicated at least 1 constituent with a significant single- or two-period trend, and 10 sites indicated 6 or more significant trends.
Potential data gaps identified for computing discharge-weighted long-term trends in the monitoring network included the lack of collection of continuous discharge at 23 sites, insufficient sampling frequency for some constituents (dissolved chloride and total and dissolved lead and zinc) at some sites, insufficient temporal sample distribution (lack of at least one sample in each season per year) at some sites, and insufficient sampling frequency for some highly censored constituents (nutrients and total and dissolved lead and zinc) at some sites. Potential data gaps based on site spatial distribution were identified in 7 basins greater than 800 square miles.
Potential site redundancies were identified in 4 basins that had an area greater than 500 square miles with a site density greater than 2 sites per 1,000 square miles. Potential site redundancies also were identified for nine site pairs by observing statistical similarities in the constituent data distributions. Sampling frequency was investigated to determine if reducing the sampling frequency of select constituents could provide a statistically similar data distribution. At 28 of 71 sites, 11 constituents had sufficient data collection frequency (approximately monthly) to allow for the creation of simulated datasets of various reduced data collection frequency. For the selected monitoring network sites analyzed, the data distribution of a simulated sampling frequency of four times per year or greater, roughly evenly distributed over the year, was not significantly different than the data distribution of the original monthly sampling frequency. Sites analyzed using varying simulated sampling frequencies tended to be more sensitive to sampling frequency changes if they were in basins classified as large or very large size and tended to be least sensitive in basins classified as small and medium size in the Ozark Plateaus Province. Simulated reduced frequency sampling analysis indicated that the constituents and measurements most sensitive to changes in sampling frequencies were water temperature, dissolved oxygen, discharge, and dissolved nitrate, and least sensitive were pH, total suspended solids, dissolved phosphorus, and total phosphorus. Discharge-weighted long-term trend analysis was repeated at 22 sites for 11 constituents using a simulated quarterly sampling frequency, and matched about 46 percent of the significant single-period trends identified using monthly data and about 65 percent of the analyses that indicated no trend using the monthly data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215079","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Richards, J.M., and Barr, M.N., 2021, General water-quality conditions, long-term trends, and network analysis at selected sites within the Ambient Water-Quality Monitoring Network in Missouri, water years 1993–2017: U.S. Geological Survey Scientific Investigations Report 2021–5079, 75 p., https://doi.org/10.3133/sir20215079.","productDescription":"Report: xi, 75 p.; Data Release; Dataset; 11 Tables","numberOfPages":"91","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-123674","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science 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U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Winters are changing rapidly across the globe but the implications for aquatic productivity and food webs are not well understood. In addition, the degree to which winter dynamics in aquatic systems respond to large-scale climate versus ecosystem-level factors is unclear but important for understanding and managing potential changes. We used a unique winter data set from the Upper Mississippi River System to explore spatial and temporal patterns in phytoplankton biomass (chlorophyll a, CHL) and associated environmental covariates across 25 years and ∼1,500 river km. To assess the role of regional climate versus site-specific drivers of winter CHL, we evaluated whether there were coherent long-term CHL dynamics from north to south and across lotic-lentic areas. We then estimated the degree to which these patterns were associated with climate variability (i.e., the Multivariate El Nino-Southern Oscillation Index), winter severity (freezing degree days), river discharge, or site-specific environmental variables (ice depth, snow depth, and nutrient concentrations). We found that winter CHL was typically highest in ice-free reaches and backwater lakes, occasionally exceeding summer values. We did not find highly synchronous CHL dynamics across the basin, but instead show that temporal trends were independent among river reaches and lotic-lentic areas of the river. Moreover, after accounting for these spatial dynamics, we found that CHL was most responsive to winter air temperature, being consistently higher in years with warmer winters across the basin. These results indicate that although productivity dynamics are highly dynamic within large river ecosystems, changes in the duration and severity of winter may uniformly increase wintertime productivity.
Southern sea otters (Enhydra lutris nereis) have recovered slowly from their near extinction a century ago, and their continued recovery has been challenged by multiple natural and anthropogenic factors. Development of an integrated population model (IPM) for southern sea otters has been identified as a management priority, to help in evaluating the relative impacts of known threats and guide best management options for species recovery. An IPM represents an analytical modeling framework where various types of data relevant to animal health, population trends, and survival can be evaluated collectively to project future population dynamics under different resource management scenarios. Here, we describe the development of a spatially explicit IPM for southern sea otters that is fit by using Bayesian methods to multiple datasets including a time series of range-wide survey counts, estimated survival rates of tagged animals from telemetry-based population studies, and cause-of-death data from comprehensive necropsies of beach-cast carcasses. The core of the model is a stage-structured matrix, in which survival rates for a given life history stage, year, and location are computed as the outcome of multiple ‘competing risks,’ or hazards, allowing for spatiotemporal variation in each hazard, density-dependence, and stochasticity. The parameterized IPM was used to (1) examine how age and sex-specific hazards vary over space and time, (2) gain insights into density-dependent variation in specific hazards, (3) assess population-level effects of known mortality hazards in the past and in future projections, and (4) evaluate the relative benefits of various potential management actions to address these hazards.
Our results indicated that different types of hazards have variable impacts at different life history stages of sea otters; for example, shark-bite mortality had a strong impact on mortality of subadult females but relatively low impacts on aged adult female survival, whereas End Lactation Syndrome showed just the opposite age-based pattern. There also was spatial and temporal variation in exposure to different hazards; for example, shark-bite mortality generally was highest at the north and south ends of the sea otter range, End Lactation Syndrome and cardiac disease were highest in the center part of the range, and harmful algal bloom intoxication and protozoal infection mortalities were highest around Morro Bay. The relative impacts of hazards depended on population density; for example, shark-bite mortality had the greatest effect on male survival when population abundance was low, but as densities increased the impacts of cardiac disease (for aged adults) and acanthocephalan peritonitis (for subadults) exceeded the effects of shark-bite mortality. Sensitivity analyses showed that modifying certain hazard rates can have substantial impacts on future population growth; for example, if the shark-bite hazard rate were to decrease by 20 percent, projected abundance after 50 years is predicted to be 18-percent higher, on average, than under baseline conditions. We used the IPM to evaluate the possible impacts of a potential management action: the reintroduction of sea otters to currently unoccupied parts of their historical range. We found that there were large increases in expected growth potential associated with reintroduction programs to various locations to the north and south of the currently occupied range, although a reintroduction to San Francisco Bay was projected to have the greatest potential impacts on future population growth.
The IPM for southern sea otters presented here provides resource managers with a useful tool for evaluating the impacts of specific hazards, forecasting future population dynamics and range expansion, and evaluating alternative management scenarios.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211076","programNote":"Wildlife Program","usgsCitation":"Tinker, M.T., Carswell, L.P., Tomoleoni, J.A., Hatfield, B.B., Harris, M.D., Miller, M.A., Moriarty, M.E., Johnson, C.K., Young, C., Henkel, L.A., Staedler, M.M., Miles, A.K., and Yee, J.L., 2021, An integrated population model for southern sea otters: U.S. Geological Survey Open-File Report 2021–1076, 50 p., https://doi.org/10.3133/ofr20211076.","productDescription":"vii, 50 p.","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-126237","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":387937,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1076/images"},{"id":387936,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1076/ofr20211076.xml"},{"id":387935,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1076/ofr20211076.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":387934,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1076/covrthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.23388671874999,\n 37.125286284966805\n ],\n [\n -121.59667968749999,\n 37.37015718405753\n ],\n [\n -121.55273437499999,\n 37.666429212090605\n ],\n [\n -122.1240234375,\n 38.61687046392973\n ],\n [\n -122.84912109375,\n 39.30029918615029\n ],\n [\n -123.37646484374999,\n 40.329795743702064\n ],\n [\n -123.37646484374999,\n 40.84706035607122\n ],\n [\n -123.3544921875,\n 41.705728515237524\n ],\n [\n -123.22265625000001,\n 42.00032514831621\n ],\n [\n -124.49707031249999,\n 42.01665183556825\n ],\n [\n -124.98046874999999,\n 40.94671366508002\n ],\n [\n -124.67285156250001,\n 39.90973623453719\n ],\n [\n -124.18945312500001,\n 38.92522904714054\n ],\n [\n -123.3544921875,\n 37.579412513438385\n ],\n [\n -122.9150390625,\n 37.23032838760387\n ],\n [\n -122.23388671874999,\n 37.125286284966805\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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In semi-arid environments, aperiodic rainfall pulses determine plant production and resource availability for higher trophic levels, creating strong bottom-up regulation. The influence of climatic factors on population vital rates often shapes the dynamics of small mammal populations in such resource-restricted environments. Using a 21-year biannual capture–recapture dataset (1993 to 2014), we examined the impacts of climatic factors on the population dynamics of the brush mouse (Peromyscus boylii) in semi-arid oak woodland of coastal-central California. We applied Pradel's temporal symmetry model to estimate capture probability (p), apparent survival (φ), recruitment (f), and realized population growth rate (λ) of the brush mouse and examined the effects of temperature, rainfall, and El Niño on these demographic parameters. The population was stable during the study period with a monthly realized population growth rate of 0.993 ± SE 0.032, but growth varied over time from 0.680 ± 0.054 to 1.450 ± 0.083. Monthly survival estimates averaged 0.789 ± 0.005 and monthly recruitment estimates averaged 0.175 ± 0.038. Survival probability and realized population growth rate were positively correlated with rainfall and negatively correlated with temperature. In contrast, recruitment was negatively correlated with rainfall and positively correlated with temperature. Brush mice maintained their population through multiple coping strategies, with high recruitment during warmer and drier periods and higher survival during cooler and wetter conditions. Although climatic change in coastal-central California will likely favor recruitment over survival, varying strategies may serve as a mechanism by which brush mice maintain resilience in the face of climate change. Our results indicate that rainfall and temperature are both important drivers of brush mouse population dynamics and will play a significant role in predicting the future viability of brush mice under a changing climate.
Phenology is the study of recurring plant and animal life-cycle stages which can be observed across spatial and temporal scales that span orders of magnitude (e.g., organisms to landscapes). The variety of scales at which phenological processes operate is reflected in the range of methods for collecting phenologically relevant data, and the programs focused on these collections. Consideration of the scale at which phenological observations are made, and the platform used for observation, is critical for the interpretation of phenological data and the application of these data to both research questions and land management objectives. However, there is currently little capacity to facilitate access, integration and analysis of cross-scale, multi-platform phenological data. This paper reports on a new suite of software and analysis tools – the “Pheno-Synthesis Software Suite,” or PS3 – to facilitate integration and analysis of phenological and ancillary data, enabling investigation and interpretation of phenological processes at scales ranging from organisms to landscapes and from days to decades. We use PS3 to investigate phenological processes in a semi-aride, mixed shrub-grass ecosystem, and find that the apparent importance of seasonal precipitation to vegetation activity (i.e., “greenness”) is affected by the scale and platform of observation. We end by describing potential applications of PS3 to phenological modeling and forecasting, understanding patterns and drivers of phenological activity in real-world ecosystems, and supporting agricultural and natural resource management and decision-making.
Aside from construction aggregate materials, the value of nonfuel mineral commodities that have been produced in North Dakota is small, although there is potential for the existence of several mineral resource deposit types which are not economically viable at this time. In this report, we present a mineral resource inventory of the State of North Dakota, developed by the U.S. Geological Survey at the request the Bureau of Land Management. To set the stage for that inventory, we briefly outline the long and complex geologic history of North Dakota that extends back more than 3 billion years. Using several existing databases, we summarize the distribution of known mineral commodities and the results of commodity exploration over time. Using all available data, we discuss the potential for economic occurrences of 13 commodities in North Dakota, including some listed as Critical Minerals.
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Dakota\",\"nation\":\"USA \"}}]}","contact":"Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Kansas is one of the most productive agricultural states in the United States, where agricultural irrigation is a primary user of underground and surface water. Because of low precipitation and declining groundwater levels in western and central Kansas, sustainable management of irrigation water resources is a critical issue in the agricultural productivity of the state. The objective of this study is to analyze and characterize the water use and water balance in the croplands of Kansas using satellite observations, meteorological data, and in situ irrigation water use records. We used actual evapotranspiration (ETa), precipitation, soil moisture, and irrigation water use to calculate water balance for Kansas in 2015 at scales of counties, climatic divisions, and groundwater management districts (GMD). The Operational Simplified Surface Energy Balance model was implemented to estimate 30-m resolution ETa. Results showed that the seasonal (May – September) precipitation, soil water storage change, and ETa are 528 mm, 80 mm, and 555 mm, respectively, on average of all croplands in the state. The annual net irrigation water consumption was 293 mm for irrigated croplands, indicating that irrigation water constitutes an substantial portion of the water supply in the state. The total volumetric irrigation water use was 3.24 km3 for all croplands within five GMDs in western and south-central Kansas, while only 0.38 km3 was outside of GMDs. The multiple regression models of ETa against precipitation and irrigation water use were statistically significant with R2 values of 0.71 and 0.87, respectively, at county and climate division scales. Regression models also indicated a higher rate of ETa response to irrigation water use than that to precipitation. Our study demonstrated the spatial patterns of crop water use and water balance in Kansas, which could provide useful information for management of irrigation agriculture and water resources for the state.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agwat.2021.107106","usgsCitation":"Ji, L., Senay, G.B., Friedrichs, M., Schauer, M., and Boiko, O., 2021, Characterization of water use and water balance for the croplands of Kansas using satellite, climate, and irrigation data: Agricultural Water Management, v. 256, 107106, 16 p., https://doi.org/10.1016/j.agwat.2021.107106.","productDescription":"107106, 16 p.","ipdsId":"IP-126709","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":451184,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agwat.2021.107106","text":"Publisher Index Page"},{"id":394759,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"256","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ji, Lei 0000-0002-6133-1036","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":272078,"corporation":false,"usgs":false,"family":"Ji","given":"Lei","affiliations":[{"id":56342,"text":"ASRC Federal Data Solutions, Contractor to USGS Earth Resources Observation and Science Center","active":true,"usgs":false}],"preferred":false,"id":831480,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":831481,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedrichs, MacKenzie 0000-0002-9602-321X","orcid":"https://orcid.org/0000-0002-9602-321X","contributorId":199093,"corporation":false,"usgs":false,"family":"Friedrichs","given":"MacKenzie","affiliations":[],"preferred":false,"id":831482,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schauer, Matthew 0000-0002-4198-3379","orcid":"https://orcid.org/0000-0002-4198-3379","contributorId":181608,"corporation":false,"usgs":false,"family":"Schauer","given":"Matthew","affiliations":[],"preferred":false,"id":831483,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boiko, Olena 0000-0002-2007-7852","orcid":"https://orcid.org/0000-0002-2007-7852","contributorId":272079,"corporation":false,"usgs":false,"family":"Boiko","given":"Olena","email":"","affiliations":[{"id":56343,"text":"KBR, Contractor to USGS Earth Resources Observation and Science Center","active":true,"usgs":false}],"preferred":false,"id":831484,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227097,"text":"70227097 - 2021 - Predicted spatial distribution of the Eastern Spotted Skunk (Spilogale putorius) in Virginia using detection and non-detection records","interactions":[],"lastModifiedDate":"2021-12-29T14:33:21.49436","indexId":"70227097","displayToPublicDate":"2021-08-13T08:29:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3444,"text":"Southeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Predicted spatial distribution of the Eastern Spotted Skunk (Spilogale putorius) in Virginia using detection and non-detection records","docAbstract":"The geographic distribution of a species is a fundamental component in understanding its ecology and is necessary for forming effective conservation plans. For rare and elusive species of conservation concern, accurate maps of predicted occurrence are particularly problematic and often highly subjective. Spilogale putorius (Eastern Spotted Skunk) populations have experienced large declines since the 1940s. Their elusive behavior and perceived rarity result in low detection probability when using conventional methods for sampling small mammals. Low detection probability often causes uncertainty as to where Eastern Spotted Skunks could be a management concern. We modeled the distribution of predicted occurrence of Eastern Spotted Skunks using verifiable occurrence and non-detection records obtained throughout Virginia from 2010 to 2020. Occurrence data consisted of trapping records reported to the Virginia Department of Wildlife Resources, incidental photo-verified reports of sightings and road-killed animals, and remote-camera detections. Non-detections were presumed at baited remote-camera locations following intense survey efforts. We fit predicted occurrence models using generalized linear modeling in an information-theoretic framework using the package ‘stats’ in Program R. Our results incidated a greater probability of presence from the Blue Ridge westward, increasing with slope steepness along northeastern- to southeastern-facing slopes and decreasing with slope steepness along southeastern- to southwestern-facing slopes. Emergent rock outcrops prominent along northeastern slopes offer ample protective rocky cover, whereas mixed Quercus spp. (oak), Kalmia latifolia (Mountain Laurel), and Rhododendron maximum (Rosebay Rhododendron) forest communities along southern-facing slopes provide suitable areas of cover, both of which are critical for spotted skunk survival and reproductive success. Our analysis provides insight into the relationships between landscape features and Eastern Spotted Skunk distributions across Virginia. Understanding these relationships is critical for the effective management and conservation of this vulnerable species.
Visual encounter distance models are important tools for predicting how light and water clarity mediate visual predator-prey interactions that affect the structure and function of aquatic ecosystems at multiple spatial, temporal, and organizational scales. The two main varieties of visual encounter distance models, mechanistic and empirical, are used for similar purposes but take fundamentally different approaches to model development and have different strengths and weaknesses in terms of predictive accuracy, physical and biological interpretability of parameters, ability to incorporate outside information, and utility for knowledge transfer. To overcome weaknesses of existing mechanistic and empirical models and bridge the gap between approaches, we developed a generalized visual reaction distance model that relaxes assumptions of a widely-used mechanistic model that are violated in real predator-prey interactions. We compared the performance of the generalized visual reaction distance model to a widely used mechanistic model and an empirical visual encounter distance model by fitting models to data from four predator-prey experiments. The generalized visual reaction distance model substantially outperformed the other models in all cases based on fit to reaction distance data and presents an attractive alternative to prior models based on comparatively high predictive accuracy, use of interpretable parameters, and ability to incorporate outside information—characteristics that facilitate knowledge transfer.
Residual pit lakes from mining are often dangerous to sample for water quality. Thus, pit lakes may be rarely (or never) sampled. This study developed new technology in which water-sampling devices, mounted on an unmanned aerial vehicle (UAV), were used to sample three pit lakes in Nevada, USA, during 1 week in 2017. Water-quality datasets from two of the three pit lakes on public lands, Dexter and Clipper, are presented here. The current conditions of the Dexter pit lake were assessed by examining cation and anion concentration changes that have occurred over a 17-year period since the pit lake was last sampled in 2000. Data gathered during this sampling campaign assessed 2017 conditions of the Dexter and Clipper pit lakes by comparing constituent concentrations to the Nevada Division of Environmental Protection (NDEP) pit lake water-quality requirements, indicating that selenium concentrations exceeded regulatory standards. We compared our sampling data for Dexter lake to prior water-quality data from the Dexter pit lake collected in 1999 and 2000. This comparison for the Dexter pit lake indicates that evapoconcentration may have caused increasing cation and anion concentrations. This UAV sampling approach can potentially incorporate the use of additional multiparameter probes: pH, oxygen concentration, turbidity, or chlorophyll. Some limitations of this UAV water-sampling methodology are battery duration, weather conditions, and payload capacity.
The US Environmental Protection Agency's short-term freshwater effluent test methods include a fish (Pimephales promelas), a cladoceran (Ceriodaphnia dubia), and a green alga (Raphidocelis subcapitata). There is a recognized need for additional taxa to accompany the three standard species for effluent testing. An appropriate additional taxon is unionid mussels because mussels are widely distributed, live burrowed in sediment and filter particles from the water column for food, and exhibit high sensitivity to a variety of contaminants. Multiple studies were conducted to develop a relevant and robust short-term test method for mussels. We first evaluated the comparative sensitivity of two mussel species (Villosa constricta and Lampsilis siliquoidea) and two standard species (P. promelas and C. dubia) using two mock effluents prepared by mixing ammonia and five metals (cadmium, copper, nickel, lead, and zinc) or a field-collected effluent in 7-day exposures. Both mussel species were equally or more sensitive (more than two-fold) to effluents compared with the standard species. Next, we refined the mussel test method by first determining the best feeding rate of a commercial algal mixture for three age groups (1, 2, and 3 weeks old) of L. siliquoidea in a 7-day feeding experiment, and then used the derived optimal feeding rates to assess the sensitivity of the three ages of juveniles in a 7-day reference toxicant (sodium chloride [NaCl]) test. Juvenile mussels grew substantially (30%–52% length increase) when the 1- or 2-week-old mussels were fed 2 ml twice daily and the 3-week-old mussels were fed 3 ml twice daily. The 25% inhibition concentrations (IC25s) for NaCl were similar (314–520 mg Cl/L) among the three age groups, indicating that an age range of 1- to 3-week-old mussels can be used for a 7-day test. Finally, using the refined test method, we conducted an interlaboratory study among 13 laboratories to evaluate the performance of a 7-day NaCl test with L. siliquoidea. Eleven laboratories successfully completed the test, with more than 80% control survival and reliable growth data. The IC25s ranged from 296 to 1076 mg Cl/L, with a low (34%) coefficient of variation, indicating that the proposed method for L. siliquoidea has acceptable precision. Environ Toxicol Chem 2021;40:3392–3409. © 2021 SETAC
The U.S. Geological Survey worked collaboratively with the New York State Department of Environmental Conservation to assess the potential sources of fecal contamination entering Hempstead Harbor, an embayment on the northern shore of Nassau County, Long Island, New York. Water samples are routinely collected by the New York State Department of Environmental Conservation in the harbor and analyzed for fecal coliform bacteria, an indicator of fecal contamination, to determine the need for closure of shellfish beds for harvest and consumption. Fecal coliform and other bacteria are an indicator of the potential presence of pathogenic (disease-causing) bacteria. However, indicator bacteria alone cannot determine the biological or geographical sources of contamination; therefore, microbial source tracking was implemented to determine various biological sources of contamination. In addition, information such as the location, weather and season, surrounding land use, and additional water-quality data (including nutrient and stable isotopes of nitrate analyses) for the location where a sample was collected help determine the geographical source and conveyance of land-based water to the embayment.
Our analysis revealed an abundance of human and canine fecal contamination throughout the Hempstead Harbor landscape and that water from municipal separate storm sewer system conveyances was the most likely transport mechanism of this fecal contamination. Resuspension of bed sediment may contribute to fecal contamination in the harbor, but more targeted analyses are needed to support this finding. There was little evidence of groundwater-contributing fecal bacteria by direct discharge from the subsurface. A classification scheme was developed to convey the degree of fecal contamination to stakeholders and resource managers. Based on this classification scheme, the culvert at Glenwood Road and the outfall and the spillway at Skillman Street were identified as locations that contribute substantial fecal contamination to Hempstead Harbor.
","language":"English","publisher":"U.S Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215042","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Tagliaferri, T.N., Fisher, S.C., Kephart, C.M., Cheung, N., Reed, A.P., and Welk, R.J., 2021, Using microbial source tracking to identify fecal contamination sources in an embayment in Hempstead Harbor on Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2021–5042, 19 p., https://doi.org/10.3133/sir20215042.","productDescription":"Report: vii, 19 p.; Data Release","numberOfPages":"19","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-116390","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":387786,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215042/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5042"},{"id":387710,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5042/sir20215042.XML"},{"id":387709,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5042/images"},{"id":387707,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20215033","text":"Scientific Investigations Report 2021–5033","linkHelpText":"- Overview and methodology for a study to identify fecal contamination sources using microbial source tracking in seven embayments on Long Island, New York"},{"id":387706,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"USGS water data for the nation"},{"id":387704,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5042/coverthb.jpg"},{"id":387705,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5042/sir20215042.pdf","text":"Report","size":"2.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5042"}],"country":"United States","state":"New York","otherGeospatial":"Hempstead Harbor, Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.6197280883789,\n 40.80653332421558\n ],\n [\n -73.63929748535156,\n 40.89353200999427\n ],\n [\n -73.71345520019531,\n 40.87692019266084\n ],\n [\n -73.73680114746094,\n 40.8725069777884\n ],\n [\n -73.69972229003906,\n 40.79769722250925\n ],\n [\n -73.6197280883789,\n 40.80653332421558\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing tidal marsh management decisions at the Long Island National Wildlife Refuge Complex in New York. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of five marsh management units within the refuge complex and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that could be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that could maximize total management benefits at different cost constraints at the refuge-complex scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to about $24,000, but that further expenditures may yield diminishing return on investment. Potential management actions in optimal portfolios at total costs less than $24,000 consistently included approaches for increasing drainage from the marsh surface within the marsh management units. The potential management benefits were derived from expected improvements in surface-water drainage and capacity for marsh elevation to keep pace with sea-level rise, and presumed increases in numbers of spiders (as an indicator of trophic health) and tidal marsh obligate birds. The prototype presented here does not resolve management decisions; rather, it provides a framework for decision making at the Long Island National Wildlife Refuge Complex that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211070","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., and Williams, M.R., 2021, Optimization of salt marsh management at the Long Island National Wildlife Refuge Complex, New York, through use of structured decision making (ver. 1.1, August 2021): U.S. Geological Survey Open-File Report 2021–1070, 34 p., https://doi.org/10.3133/ofr20211070.","productDescription":"Report: vi, 34 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126538","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":387845,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1070/versionHist.txt","size":"640 B","linkFileType":{"id":2,"text":"txt"}},{"id":387151,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1070/ofr20211070.pdf","text":"Report","size":"3.49 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1070"},{"id":387150,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1070/coverthb2.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0478515625,\n 40.576412521044425\n ],\n [\n -73.6138916015625,\n 40.54720023441049\n ],\n [\n -73.1854248046875,\n 40.60978237983301\n ],\n [\n -72.66357421875,\n 40.77638178482896\n ],\n [\n -72.015380859375,\n 40.96330795307353\n ],\n [\n -71.795654296875,\n 41.091772220976644\n ],\n [\n -72.2625732421875,\n 41.18278832811288\n ],\n [\n -72.7294921875,\n 41.02964338716638\n ],\n [\n -73.245849609375,\n 40.94256444133327\n ],\n [\n -73.4820556640625,\n 40.967455873296714\n ],\n [\n -73.707275390625,\n 40.8595252289932\n ],\n [\n -73.8775634765625,\n 40.79301881008675\n ],\n [\n -74.0203857421875,\n 40.693134153308065\n ],\n [\n -74.0478515625,\n 40.576412521044425\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: July 13, 2021; Version 1.1: August 11, 2021","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
A previous report by the authors described sediment sampling and drilling by the U.S. Geological Survey (USGS) beside the American and Sacramento Rivers near Sacramento, California, in support of a U.S. Army Corps of Engineers project focused on regional flood control. The drilling was performed to define lithology, extract samples for laboratory testing, and perform borehole erosion tests (BETs). The U.S. Department of Agriculture (USDA) performed jet erodibility tests (JETs) near each drilling site, and a team from Texas A&M University performed laboratory tests with an erosion function apparatus (EFA). Collectively, the effort was intended to reveal spatial variations in sediment erodibility and provide data for use in a model to simulate morphological response to a major flood. The data collected by the USGS are available in a public data release.
This report, developed in cooperation with the U.S. Army Corps of Engineers, provides comparisons of the three types of measurements of the erodibility of riverbed sediments. The BET is performed in the field and reveals erodibility of sediments below the bed surface. The JET is likewise performed in the field but reveals only erodibility of exposed sediments. The EFA test is done in the laboratory and was performed on soils extracted from different depths beneath the bed surface, in many cases reconstituted for laboratory testing. Tests were performed at nominally similar locations but differed by meters to tens of meters in horizontal locations.
The comparison was undertaken to investigate differences among results obtained by the individual measurement approaches and to elucidate pros and cons of each method. The critical shear stress to initiate erosion and the rate of change of erosion rate per unit increase of excess shear stress, sometimes referred to as the erosion coefficient, served as the primary basis for comparison. The three test methods in some cases resulted in order of magnitude differences in estimates of these parameters. Some differences could be attributed to variances in site location or result from testing surface sediment versus a deeper layer, but systematic differences are also evident in the results. The tests performed in the laboratory using the EFA resulted in much lower values of critical shear stress and much higher values of the erosion coefficient compared to the JET tests performed by the USDA team on surface sediments. Critical shear stress was poorly resolved in the BET results because of the limited number of results per site, but the erosion coefficients derived from BET results were systematically lower than those obtained using the EFA.
A new, simplified approach is also proposed to estimate the increase in channel cross-sectional area during a large flood, given data describing the initial river cross section, riverbed erodibility parameters, and peak flood discharge and duration. The model runs until the cross section erodes to an equilibrium condition or the flood ends. Output describes the area of the cross section at the end of the simulation and the time required to reach equilibrium if it was reached within the simulated period. The model assumes unique, constant values for both the critical shear stress and the erosion coefficient and represents the fluid mechanics in a simplified way, making it of limited value for quantitative predictions. It does, however, provide an indication of which cross sections are most likely to undergo the greatest change in the design event and can be used to investigate sensitivity of erosion predictions to variability in sediment erodibility measurements.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215052","collaboration":"Prepared in cooperation with U.S. Army Corps of Engineers","programNote":"Cooperative Research Units","usgsCitation":"Work, P., and Livsey, D., 2021, American and Sacramento Rivers, California, erodibility measurements and model: U.S. Geological Survey Scientific Investigations Report 2021–5052, 30 p., https://doi.org/10.3133/sir20215052.","productDescription":"Report: vii, 30 p.; Data Release","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-122004","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":387634,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5052/covrthb.jpg"},{"id":387637,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5052/images"},{"id":387635,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5052/sir20215052.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":387636,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5052/sir20215052.xml"},{"id":387638,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96MCT2Q","linkHelpText":"Borehole Erosion Test data, Lower American and Sacramento Rivers, California, 2019 (ver. 4.0, July 2021)"}],"country":"United States","state":"California","otherGeospatial":"American River, Sacramento River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.58981323242188,\n 38.41378642476067\n ],\n [\n -121.34124755859375,\n 38.41378642476067\n ],\n [\n -121.34124755859375,\n 38.60292007223949\n ],\n [\n -121.58981323242188,\n 38.60292007223949\n ],\n [\n -121.58981323242188,\n 38.41378642476067\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The U.S. Geological Survey (USGS) archive of Earth images acquired by Landsat 1-8 sensors is organized in collections of consistently calibrated, geolocated, and processed data products. Such an organization ensures consistent quality of the archived data within a collection over time and across all instruments within the Landsat mission. In December 2020, the USGS completed reprocessing of the archived Landsat data and released a new collection, Collection 2, which introduced surface reflectance and surface temperature Level-2 products, implemented improved ground control and elevation datasets, and brought several geometric and radiometric calibration enhancements. Radiometric enhancements include absolute and relative gain updates for both imaging sensors, Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), aboard Landsat 8 and a change in calculation of bias for OLI. In this paper we present the analysis of Collection 2 Landsat 8 products demonstrating stability over the mission lifetime, the improvement in OLI signal to noise ratio and along-track striping performance resulting from enhanced bias correction, as well as reduction of cross-track striping in TIRS data as a result of relative gain updates. In addition, we discuss effects of two safehold events that Landsat 8 experienced in November 2020 on the radiometric calibration of both sensors and product performance.
By merging our specialization silos, fisheries professionals can expand the options that are available to them to address difficult fisheries and aquatic conservation problems, which require an understanding of spatial patterns in geographically large systems. Our purpose is to start a profession-wide conversation about additional ways to think about and use spatial data. We use case studies to illustrate how identifying and merging multiple specialized approaches (e.g., fish tracking, fish surveys, geomorphology, social science, jurisdictional viewpoints) can create an ensemble that has advantages over the use of any single approach alone. Thus, our perspective is not about solving a specific technical problem with a new tool, but about the benefits of identifying gaps in data from one specialized approach, and filling those gaps with data from other specialized approaches. If multiple approaches are coordinated through a larger, problem-specific planning strategy, the result can be better outcomes for difficult problems through creative integration. We encourage others to add constructive ideas to the views initiated here.
American black bears (Ursus americanus) are an iconic wildlife species in the southern Appalachian highlands of the eastern United States and have increased in number and range since the early 1980s. Given an increasing number of human-bear conflicts in the region, many management agencies have liberalized harvest regulations to reduce bear populations to socially acceptable levels. Wildlife managers need reliable population data for assessing the effects of management actions for this high-profile species. Our goal was to use DNA extracted from hair collected at barbed-wire enclosures (i.e., hair traps) to identify individual bears and then use spatially explicit capture-recapture methods to estimate female black bear density, abundance, and harvest rate. We established 888 hair traps across 66,678 km2 of the southern Appalachian highlands in Georgia, North Carolina, South Carolina, and Tennessee, USA, in 2017 and 2018, arranged in 174 clusters of 2–9 traps/cluster. We collected 9,113 hair samples from those sites over 6 weeks of sampling, of which 1,954 were successfully genotyped to 462 individual female bears. Our spatially explicit estimator included a percent forest covariate to explain inhomogeneous bear density across the region. Densities ranged up to 0.410 female bears/km2 and regional abundance was 5,950 (95% CI = 4,988–7,098) female bears. Based on hunter kill data from 2016 to 2018, mean annual harvest rates for females were 12.7% in Georgia, 17.6% in North Carolina, 17.6% in South Carolina, and 22.8% in Tennessee. Our estimated harvest rates for most states approached or exceeded theoretical maximum sustainable levels, and population trend data (i.e., bait-station indices) indicated decreasing growth rates since about 2009. These data suggest that the increased harvest goals and poor hard mast production over a series of prior years reduced bear population abundance in many states. We were able to obtain reasonable population abundance and density estimates because of spatially explicit capture-recapture methods, cluster sampling, and a large spatial extent. Continued monitoring of bear populations (e.g., annual bait-station surveys and periodic population estimation using spatially explicit methods) by state jurisdictions would help to ensure that population trajectories are consistent with management goals. © 2021 The Wildlife Society.
We developed a new technique, utilizing species-specific counts of individuals from historical fish community samples, to examine landscape-level, spatio-temporal trends in relative abundance distributions. Abundance-based historical distribution analyses are often plagued by data comparability issues, but provide critical information about community composition trends inaccessible to those using analyses based only on species presence–absence. We established trends in native and non-native fish abundance and community homogenization, uniqueness and diversity to help local conservation managers prioritize targets and motivate similar studies globally to support fish conservation.
Upper and middle New River (UMNR) basin, Appalachian Mountains, USA.
We compiled catch data from 61 years of fish community surveys (1958–2019) and tested for community homogenization by comparing data from repeatedly sampled sites (1900s versus 2000s samples) using dispersion analyses. We measured community uniqueness (site contributions to beta diversity) and species diversity (Shannon index) at sampled streams to identify potential conservation hotspots. We then used regression analyses and Wilcoxon signed-rank tests to examine species-specific basin-wide and local abundance trends and identify species of potential conservation concern.
Dispersion of sites in species abundance space was significantly greater in the 1900s compared with the 2000s, indicating homogenization had occurred. Of 36 native species analysed, 44.4% (16) showed basin-wide declines. Non-native species exhibited mixed patterns; site-level abundance increased in 2 of 15 species analysed (13%).
Our results indicate basin-wide community homogenization has occurred within the UMNR, but many unique and diverse communities persist. If conserved, these could help maintain regional fish diversity. We found basin-wide declines in four endemic species, as well as spread patterns of non-native and native species that were not detected by a presence–absence analysis applied within the same study area. This finding illustrates the importance of considering both species’ abundance and occurrence patterns as separate dimensions of biodiversity to inform conservation planning.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13393","usgsCitation":"Sleezer, L., Angermeier, P., Frimpong, E., and Brown, B., 2021, A new composite abundance metric detects stream fish declines and community homogenization during six decades of invasions: Diversity and Distributions, v. 27, no. 11, p. 2136-2156, https://doi.org/10.1111/ddi.13393.","productDescription":"21 p.","startPage":"2136","endPage":"2156","ipdsId":"IP-127926","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481102,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/ddi.13393","text":"External Repository"},{"id":480938,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Virginia, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.15910582240531,\n 38.72335424270793\n ],\n [\n -81.8153590308615,\n 36.0378116437692\n ],\n [\n -81.15910582240531,\n 36.04395501786985\n ],\n [\n -80.45378441771783,\n 36.778939995013474\n ],\n [\n -79.88242907709224,\n 38.72335424270793\n ],\n [\n -81.15910582240531,\n 38.72335424270793\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"27","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-08-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Sleezer, Logan J.","contributorId":349198,"corporation":false,"usgs":false,"family":"Sleezer","given":"Logan J.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":924138,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Angermeier, Paul L. 0000-0003-2864-170X","orcid":"https://orcid.org/0000-0003-2864-170X","contributorId":204519,"corporation":false,"usgs":true,"family":"Angermeier","given":"Paul L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924137,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frimpong, Emmanuel A.","contributorId":349199,"corporation":false,"usgs":false,"family":"Frimpong","given":"Emmanuel A.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":924139,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Bryan L.","contributorId":349201,"corporation":false,"usgs":false,"family":"Brown","given":"Bryan L.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":924140,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70222679,"text":"ofr20211030D - 2021 - System characterization report on Planet’s Dove-R","interactions":[{"subject":{"id":70222679,"text":"ofr20211030D - 2021 - System characterization report on Planet’s Dove-R","indexId":"ofr20211030D","publicationYear":"2021","noYear":false,"chapter":"D","displayTitle":"System Characterization Report on Planet’s Dove-R","title":"System characterization report on Planet’s Dove-R"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2021-08-25T20:34:50.342041","indexId":"ofr20211030D","displayToPublicDate":"2021-08-09T14:39:33","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1030","chapter":"D","displayTitle":"System Characterization Report on Planet’s Dove-R","title":"System characterization report on Planet’s Dove-R","docAbstract":"This report addresses system characterization of Planet’s Dove-R and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Since 2013, Planet has launched more than 360 Dove 3U CubeSats, where U stands for 10-centimeter (cm) x 10-cm x 10-cm stowed dimensions, each weighing about 5 kilograms. Since 2015, all Dove satellites have had four-band imagers with about a 4-meter (m) pixel ground sample distance. Since 2016, all Doves have been launched into Sun-synchronous orbits varying from 474 to 524 kilometers, with inclinations between 97 and 98 degrees. The Dove series satellites do not have orbit maintenance capabilities; thus, their orbits decay slowly over time, contributing to shorter lifetimes of about 3 years. More information on Planet satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.planet.com/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that Dove-R has an interior geometric performance in the range of −0.306 (−0.102 pixel) to 0.286 m (0.095 pixel) in easting and 0.090 (0.030 pixel) to 1.084 m (0.361 pixel) in northing in band-to-band registration, an exterior geometric performance of −5.10 m (−0.51 pixel) in easting and 3.30 m (0.33 pixel) in northing offset in comparison to Sentinel-2, a radiometric performance in the range of −0.023 to −0.008 in offset and 0.948 to 1.077 in slope, and a spatial performance in the range of 2.96 to 3.15 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.001 to 0.003.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030D","usgsCitation":"Kim, M., Park, S., Anderson, C., and Stensaas, G.L., 2021, System characterization report on Planet’s Dove-R, chap. D of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 34 p., https://doi.org/10.3133/ofr20211030D.","productDescription":"v, 34 p.","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-126678","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":387784,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/d/ofr20211030d.pdf","text":"Report","size":"3.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030D"},{"id":387783,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/d/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Accurate population estimates are essential for monitoring and managing wildlife populations. Mark–recapture sampling methods have regularly been used to estimate population parameters for rare and cryptic species, including the federally listed Mojave desert tortoise (Gopherus agassizii); however, the methods employed are often plagued by violations of statistical assumptions, which have the potential to bias density estimates. By incorporating spatial information into conventional density estimation models, spatial capture–recapture (SCR) models can account for common assumption violations such as spatially heterogeneous detection probabilities and temporary emigration when animals leave plots during a survey. We conducted mark–recapture surveys at 10 1-km2 plots in and adjacent to the Ivanpah Valley of California and Nevada from 2015 to 2019. Locality data were collected concurrently using radio-telemetry and GPS data loggers. GPS data demonstrated that desert tortoises frequently exhibited temporary emigration outside a plot during the survey periods, thereby complicating standard approaches for closed-model density estimation. We integrated mark–recapture survey data for subadults and adults at each plot with corresponding spatial capture locations and supplementary spatial data using a modified SCR model fitted in a Bayesian framework. We compared density estimates modeled with conventional non-spatial methods, as well as three SCR models based on symmetrical usage areas described by various levels and types of supplementary spatial data. The conventional model consistently resulted in inflated estimates of density while the SCR models allowed us to generate spatially corrected estimates for a species where detectability and densities are low. However, we found that if not properly specified, the temporal scale of supplementary data may result in an unintended source of bias in parameter estimates. Integrating spatial data over a larger temporal scale than mark–recapture surveys were conducted resulted in higher detection probabilities and lower density estimates, due to an overestimation of space use. Our results not only demonstrate the importance of accounting for spatial information but also the value of understanding the potential for bias when integrating multiple data sets at different temporal resolutions. The methods presented can be used to enhance monitoring efforts for the Mojave desert tortoise and other species where mark–recapture methods are used.
Carnivore conservation and management are global research priorities focused on reversing population declines of imperiled species and identifying more effective and humane management of generalist carnivores with thriving populations. Nonlethal methods to mitigate conflict are increasingly used to advance conservation objectives; however, there is limited knowledge about the effectiveness of many nonlethal methods. We tested a nonlethal tool (fladry), that serves as a barrier to deter wolves Canis lupus and coyotes Canis latrans, for its efficacy at preventing coyotes from using prairie dog Cynomys ludovicianus colonies, the primary prey for critically endangered black-footed ferrets Mustela nigripes. We used camera trap data and an occupancy approach to evaluate the tool’s efficacy. We measured coyote response to fladry at both a coarse monthly scale (via use, attraction and avoidance probabilities) and a fine scale (via daily activity). Overall, use of areas inside exclosures declined by 60% after 60 days of fladry application and coyotes avoided some previously used areas both within and outside exclosures. Interestingly, coyotes were attracted to previously unused areas surrounding exclosures and increased activity around the periphery of fladry exclosures by 170% immediately after fladry installation, suggesting coyotes actively explored these areas and may have responded to fladry in a way that is counterintuitive to management expectations. Occupancy models provided more robust evaluation of fladry and revealed important behavioral responses relative to other common evaluation techniques (i.e. time until first detected crossing). Our results have implications for future development and evaluation of nonlethal tools for carnivore conservation and management globally.
","language":"English","publisher":"Zoological Society of London","doi":"10.1111/acv.12726","usgsCitation":"Windell, R., Bailey, L., Young, J.K., Livieri, T.M., Eads, D.A., and Breck, S., 2021, Improving evaluation of nonlethal tools for carnivore management and conservation: Evaluating fladry to protect an endangered species from a generalist mesocarnivore: Animal Conservation, v. 25, no. 1, p. 125-136, https://doi.org/10.1111/acv.12726.","productDescription":"12 p.","startPage":"125","endPage":"136","ipdsId":"IP-117480","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":396347,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","otherGeospatial":"Badlands National Park, Buffalo Gap National Grasslands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n 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]\n}","volume":"25","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-08-07","publicationStatus":"PW","contributors":{"editors":[{"text":"Penteriani, Vincenzo","contributorId":280007,"corporation":false,"usgs":false,"family":"Penteriani","given":"Vincenzo","email":"","affiliations":[],"preferred":false,"id":835841,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Ordiz, Andrés","contributorId":280008,"corporation":false,"usgs":false,"family":"Ordiz","given":"Andrés","affiliations":[],"preferred":false,"id":835842,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Windell, Rebecca","contributorId":279885,"corporation":false,"usgs":false,"family":"Windell","given":"Rebecca","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":835692,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bailey, Larissa L.","contributorId":229353,"corporation":false,"usgs":false,"family":"Bailey","given":"Larissa L.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":835693,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Julie K.","contributorId":196299,"corporation":false,"usgs":false,"family":"Young","given":"Julie","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":835694,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Livieri, Travis M.","contributorId":198977,"corporation":false,"usgs":false,"family":"Livieri","given":"Travis","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":835695,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eads, David A. 0000-0002-4247-017X deads@usgs.gov","orcid":"https://orcid.org/0000-0002-4247-017X","contributorId":173639,"corporation":false,"usgs":true,"family":"Eads","given":"David","email":"deads@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":835696,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Breck, Stewart","contributorId":199403,"corporation":false,"usgs":false,"family":"Breck","given":"Stewart","affiliations":[],"preferred":false,"id":835697,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223880,"text":"70223880 - 2021 - Evaluating the migration mortality hypothesis using monarch tagging data","interactions":[],"lastModifiedDate":"2021-09-14T11:35:01.272469","indexId":"70223880","displayToPublicDate":"2021-08-07T08:39:45","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the migration mortality hypothesis using monarch tagging data","docAbstract":"The decline in the eastern North American population of the monarch butterfly population since the late 1990s has been attributed to the loss of milkweed during the summer breeding season and the consequent reduction in the size of the summer population that migrates to central Mexico to overwinter (milkweed limitation hypothesis). However, in some studies the size of the summer population was not found to decline and was not correlated with the size of the overwintering population. The authors of these studies concluded that milkweed limitation could not explain the overwintering population decline. They hypothesized that increased mortality during fall migration was responsible (migration mortality hypothesis). We used data from the long-term monarch tagging program, managed by Monarch Watch, to examine three predictions of the migration mortality hypothesis: (1) that the summer population size is not correlated with the overwintering population size, (2) that migration success is the main determinant of overwintering population size, and (3) that migration success has declined over the last two decades. As an index of the summer population size, we used the number of wild-caught migrating individuals tagged in the U.S. Midwest from 1998 to 2015. As an index of migration success we used the recovery rate of Midwest tagged individuals in Mexico. With regard to the three predictions: (1) the number of tagged individuals in the Midwest, explained 74% of the variation in the size of the overwintering population. Other measures of summer population size were also correlated with overwintering population size. Thus, there is no disconnection between late summer and winter population sizes. (2) Migration success was not significantly correlated with overwintering population size, and (3) migration success did not decrease during this period. Migration success was correlated with the level of greenness of the area in the southern U.S. used for nectar by migrating butterflies. Thus, the main determinant of yearly variation in overwintering population size is summer population size with migration success being a minor determinant. Consequently, increasing milkweed habitat, which has the potential of increasing the summer monarch population, is the conservation measure that will have the greatest impact.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2020.00264","usgsCitation":"Taylor, O.R., Pleasants, J., Grundel, R., Pecoraro, S., Lovett, J.P., and Ryan, A., 2021, Evaluating the migration mortality hypothesis using monarch tagging data: Frontiers in Ecology and Evolution, v. 8, 264, 13 p., https://doi.org/10.3389/fevo.2020.00264.","productDescription":"264, 13 p.","ipdsId":"IP-106646","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":451254,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2020.00264","text":"Publisher Index Page"},{"id":389143,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.0,\n 40\n ],\n [\n -60.0,\n 40\n ],\n [\n -60.00,\n 50.0\n ],\n [\n -100.0,\n 50.0\n ],\n [\n -100.0,\n 40\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Orley R.","contributorId":168617,"corporation":false,"usgs":false,"family":"Taylor","given":"Orley","email":"","middleInitial":"R.","affiliations":[{"id":25342,"text":"Department of Ecology and Evolutionary Biology, University of Kansas","active":true,"usgs":false}],"preferred":false,"id":823073,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pleasants, John M.","contributorId":168616,"corporation":false,"usgs":false,"family":"Pleasants","given":"John M.","affiliations":[{"id":25341,"text":"Department of Ecology, Evolution, and Organismal Biology, Iowa State University","active":true,"usgs":false}],"preferred":false,"id":823074,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grundel, Ralph 0000-0002-2949-7087 rgrundel@usgs.gov","orcid":"https://orcid.org/0000-0002-2949-7087","contributorId":2444,"corporation":false,"usgs":true,"family":"Grundel","given":"Ralph","email":"rgrundel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":823075,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pecoraro, Samuel 0000-0002-3435-649X","orcid":"https://orcid.org/0000-0002-3435-649X","contributorId":221137,"corporation":false,"usgs":true,"family":"Pecoraro","given":"Samuel","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":823076,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lovett, James P.","contributorId":265598,"corporation":false,"usgs":false,"family":"Lovett","given":"James","email":"","middleInitial":"P.","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":823077,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ryan, Ann","contributorId":265599,"corporation":false,"usgs":false,"family":"Ryan","given":"Ann","email":"","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":823078,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230796,"text":"70230796 - 2021 - Comparing geometric differences between Landsat Collection 1 to Collection 2 level-1 products","interactions":[],"lastModifiedDate":"2022-04-26T15:52:18.27322","indexId":"70230796","displayToPublicDate":"2021-08-06T10:47:56","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Comparing geometric differences between Landsat Collection 1 to Collection 2 level-1 products","docAbstract":"In late 2020 the U.S. Geological Survey (USGS) began the distribution of Landsat products associated with their collection 2 reprocessing of the archive. Several changes were implemented within the Landsat Product Generation System (LPGS) and the calibration parameters applied to the Landsat imagery for the collection 2 processing. When comparing between collection 1 and collection 2 products, radiometric and geometric differences will be present. One of the most substantial changes between the two collections was an adjustment to the ground control which made the control more accurate from an absolute and relative perspective. Some of the other changes associated with collection 2 processing were to the Digital Elevation Model (DEM) used in terrain correction, the Thermal Infrared Sensor (TIRS) relative gains, TIRS absolute calibration, Operational Land Imagery (OLI) absolute gain model, OLI relative gain, and OLI bias calculation to name a few. Although these changes also have an effect on the differences between the product associated with these two collections, the change in the ground control, although typically less than one 30-meter multispectral pixel in magnitude, will have the largest effect on the differences between the products. This change in ground control is also a spatially dynamic change that although is low in spatial frequency, is nonlinear and is not a change that can be modeled on a global or even on a local Worldwide Reference System-2 (WRS-2) path and row scale. The effects of these ground control changes will be discussed and demonstrated within this paper along with examples showing their effect on specific datasets. This paper demonstrates some of these geometric differences associated with the ground control through both the registration statistics created during product generation and through an example comparison of a set of collection 1 and collection 2 products.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings volume 11829, Earth Observing Systems XXVI","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SPIE Optical Engineering + Applications","conferenceDate":"Aug 1-5, 2021","conferenceLocation":"San Diego, CA","language":"English","doi":"10.1117/12.2596204","usgsCitation":"Choate, M.J., Rengarajan, R., Micijevic, E., and Lubke, M., 2021, Comparing geometric differences between Landsat Collection 1 to Collection 2 level-1 products, in Proceedings volume 11829, Earth Observing Systems XXVI, v. 11829, San Diego, CA, Aug 1-5, 2021, 118290H, 11 p., https://doi.org/10.1117/12.2596204.","productDescription":"118290H, 11 p.","ipdsId":"IP-131285","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":399676,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11829","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Choate, Michael J. 0000-0002-8101-4994","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":216866,"corporation":false,"usgs":true,"family":"Choate","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":841371,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengarajan, Rajagopalan 0000-0003-1860-7110","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":242014,"corporation":false,"usgs":false,"family":"Rengarajan","given":"Rajagopalan","affiliations":[{"id":48475,"text":"KBR, Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":841372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Micijevic, Esad 0000-0002-3828-9239","orcid":"https://orcid.org/0000-0002-3828-9239","contributorId":290334,"corporation":false,"usgs":false,"family":"Micijevic","given":"Esad","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":841374,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lubke, Mark 0000-0002-7257-2337","orcid":"https://orcid.org/0000-0002-7257-2337","contributorId":261911,"corporation":false,"usgs":false,"family":"Lubke","given":"Mark","email":"","affiliations":[{"id":53079,"text":"KBR, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":841373,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}