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USGS2016

Archive of ground penetrating radar data collected during USGS field activity 13BIM01—Dauphin Island, Alabama, April 2013

From April 13 to 20, 2013, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) conducted geophysical and sediment sampling surveys on Dauphin Island, Alabama, as part of Field Activity 13BIM01 . The objectives of the study were to quantify inorganic and organic accretion rates in back-barrier and mainland marsh and estuarine environments. Various field and laboratory methods were used to achieve these objectives, including subsurface imaging using Ground Penetrating Radar (GPR), sediment sampling, lithologic and microfossil analyses, and geochronology techniques to produce barrier island stratigraphic cross sections to help interpret the recent (last 2000 years) geologic evolution of the island. This data series report is an archive of GPR and associated Global Positioning System (GPS) data collected in April 2013 from Dauphin Island and adjacent barrier-island environments. In addition to GPR data, marsh core and vibracore data were also collected collected but are not reported (or included) in the current report. Data products, including elevation-corrected subsurface profile images of the processed GPR data, unprocessed digital GPR trace data, post-processed GPS data, Geographic Information System (GIS) files and accompanying Federal Geographic Data Committee (FGDC) metadata, can be downloaded from the Data Downloads page.

Alabama↗

USGS2001

Water resources data-Maine, water year 2001

The Water Resources Division of the U.S. Geological Survey, in cooperation with State, Federal, and other local governmental agencies, obtains a large amount of data pertaining to the water resources of Maine each year. These data, accumulated during the many water years, constitute a valuable data base for developing an improved understanding of the water resources of the State. Water-resources data for the 2001 water year for Maine consists of records of stage, discharge, ground water levels, water quality of streams and ground-water wells, precipitation quantity, and snow quantity,. This report contains discharge records for: 5 gage-height stations, 58 discharge gaging stations, stream water-quality data for 3 stations, water levels for 17 ground-water wells, water-quality data for 1 ground-water well, precipitation quantity data for 12 stations, and snow quantity data for 82 stations. Additional water data were collected at other sites, not part of the systematic data-collection program, and are published as special study and miscellaneous record sections.

Water Data Report↗

USGS2003

Water resources data-Maine, water year 2002

The Water Resources Division of the U.S. Geological Survey, in cooperation with State, Federal, and other local governmental agencies, obtains a large amount of data pertaining to the water resources of Maine each year. These data, accumulated during the many water years, constitute a valuable data base for developing an improved understanding of the water resources of the State. Water-resources data for the 2002 water year for Maine consists of records of stage, discharge, ground water levels,water quality of streams and ground-water wells, precipitation quantity, and snow quantity,. This report contains discharge records for: 5 gage-height stations, 61 discharge gaging stations, stream water-quality data for 2 stations, water levels for 19 ground-water wells, water-quality data for 2 ground-water well, precipitation quantity data for 12 stations, and snow quantity data for 82 stations. Additional water data were collected at other sites, not part of the systematic data-collection program, and are published as special study and miscellaneous record.

Water Data Report↗

USGS2005

Water resources data-Maine water year 2004

The Water Resources Dicipline of the U.S. Geological Survey, in cooperation with State, Federal,and other local governmental agencies, obtains a large amount of data pertaining to the water resources of Maine each year. These data, accumulated during the many water years, constitute a valuable data base for developing an improved understanding of the water resources of the State. Water-resources data for the 2004 water year for Maine consists of records of stage, discharge, ground water levels, water quality of streams and ground-water wells, precipitation quantity, and snow quantity. This report contains discharge records for: 6 gage-height stations, 62 discharge gaging stations, stream water-quality data for 6 stations, water level for 23 ground-water wells, water-quality data for 24 ground-water wells, precipitation quantity data for 15 stations, and snow quantity data for 80 stations, Additional water data were collected at other sites, not part of the systematic data-collection program, and are published as special study and miscellaneous record sections.

Water Data Report↗

USGS1999

Water Resources Data Minnesota Water Year 1998

Water Resources Division of the U.S. Geological Survey (USGS), in cooperation with State agencies, obtains a large amount of data pertaining to the water resources of Minnesota each water year. These data, accumulated during many years, constitute a valuable data base for developing an improved understanding of the water resources of the State. To make these data readily available to interested parties outside the USGS, the data are published annually in this report series entitled 'Water Resources Data - Minnesota.' Water-resources data for the 1998 water year (hereinafter 1998) for Minnesota consist of records of stage, discharge, and water quality of streams; and stage of lakes and reservoirs; and water quality of ground water. This volume contains discharge records for 102 stream-gaging stations; stage for 9 lakes and reservoirs; water quality for 22 stream-gaging stations; and peak flow data for 87 high-flow partial-record stations. These data represent a part of the National Water Data System collected by the U.S. Geological Survey and cooperating State and Federal agencies in Minnesota.

Water Data Report↗

USGS2000

Water Resources Data Minnesota Water Year 1999

The Water Resources Division of the U.S. Geological Survey (USGS), in cooperation with State agencies, obtains a large amount of data pertaining to the water resources of Minnesota each water year. These data, accumulated during many years, constitute a valuable data base for developing an improved understanding of the water resources of the State. To make these data readily available to interested parties outside the USGS, the data are published annually in this report series entitled 'Water Resources Data - Minnesota.' Water-resources data for the 1999 water year (hereinafter 1999) for Minnesota consist of records of stage, discharge, and water quality of streams; and stage of lakes and reservoirs; and water quality of ground water. This volume contains discharge records for 103 stream-gaging stations; stage for 11 lakes and reservoirs; water quality for 13 stream-gaging stations; peak flow data for 87 high-flow partial-record stations; and 15 ground-water observation wells. These data represent a part of the National Water Data System collected by the USGS and cooperating State and Federal agencies in Minnesota.

Water Data Report↗

USGS1972

Water resources data for Wyoming, water year 1971; part 2, water quality records

Water-resources data for the 1971 water year for Wyoming include records of data for the chemical and physical characteristics of surface and ground water. Data on the quality of surface water (chemical, temperature, and sediment) were collected from designated sampling sites at predetermined intervals such as once daily, weekly, monthly or less frequently. At some sites water-temperature data were recorded on charts by recorders. Data are given for 190 sampling stations of which 107 are continuous-record stations and 83 are miscellaneous sites. Chemical analyses also are given for 118 ground-water sites. Locations of surface water-quality stations are shown in figure 1 and locations of ground-water sites are shown in figure 2. Data on a few pertinent stations in bordering States are also included. The data were collected by the Water Resources Division of the U.S. Geological Survey under the direction of Robert L. Cushman, district chief. These data represent that portion of the National Water Data System collected by the U.S. Geological Survey and cooperating State and Federal agencies in Wyoming. The Geological Survey has published records of chemical quality, water temperatures, and sediment since 1941 in an annual series of water-supply papers entitled, "Quality of Surface Water of the United States." Beginning with the 1964 water year, water-quality records have been released by the Geological Survey in annual reports on a State-boundary basis. These reports are for limited distribution and are designed primarily for rapid release of data shortly after the end of the water year. These records will be published later in Geological Survey water-supply papers.

Wyoming↗

USGS2014

An integrated modeling approach to estimating Gunnison Sage-Grouse population dynamics: Combining index and demographic data

Evaluation of population dynamics for rare and declining species is often limited to data that are sparse and/or of poor quality. Frequently, the best data available for rare bird species are based on large‐scale, population count data. These data are commonly based on sampling methods that lack consistent sampling effort, do not account for detectability, and are complicated by observer bias. For some species, short‐term studies of demographic rates have been conducted as well, but the data from such studies are typically analyzed separately. To utilize the strengths and minimize the weaknesses of these two data types, we developed a novel Bayesian integrated model that links population count data and population demographic data through population growth rate ( λ ) for Gunnison sage‐grouse ( Centrocercus minimus ). The long‐term population index data available for Gunnison sage‐grouse are annual (years 1953–2012) male lek counts. An intensive demographic study was also conducted from years 2005 to 2010. We were able to reduce the variability in expected population growth rates across time, while correcting for potential small sample size bias in the demographic data. We found the population of Gunnison sage‐grouse to be variable and slightly declining over the past 16 years.

Colorado, Utah↗

USGS2022

Classifying behavior from short-interval biologging data: An example with GPS tracking of birds

Recent advances in digital data collection have spurred accumulation of immense quantities of data that have potential to lead to remarkable ecological insight, but that also present analytic challenges. In the case of biologging data from birds, common analytical approaches to classifying movement behaviors are largely inappropriate for these massive data sets. We apply a framework for using K -means clustering to classify bird behavior using points from short time interval GPS tracks. K -means clustering is a well-known and computationally efficient statistical tool that has been used in animal movement studies primarily for clustering segments of consecutive points. To illustrate the utility of our approach, we apply K -means clustering to six focal variables derived from GPS data collected at 1–11 s intervals from free-flying bald eagles ( Haliaeetus leucocephalus ) throughout the state of Iowa, USA. We illustrate how these data can be used to identify behaviors and life-stage- and age-related variation in behavior. After filtering for data quality, the K -means algorithm identified four clusters in >2 million GPS telemetry data points. These four clusters corresponded to three movement states: ascending, flapping, and gliding flight; and one non-moving state: perching. Mapping these states illustrated how they corresponded tightly to expectations derived from natural history observations; for example, long periods of ascending flight were often followed by long gliding descents, birds alternated between flapping and gliding flight. The K -means clustering approach we applied is both an efficient and effective mechanism to classify and interpret short-interval biologging data to understand movement behaviors. Furthermore, because it can apply to an abundance of very short, irregular, and high-dimensional movement data, it provides insight into small-scale variation in behavior that would not be possible with many other analytical approaches.

Ecology and Evolution↗

USGS2022

Errors in aerial survey count data: Identifying pitfalls and solutions

Accurate estimates of animal abundance are essential for guiding effective management, and poor survey data can produce misleading inferences. Aerial surveys are an efficient survey platform, capable of collecting wildlife data across large spatial extents in short timeframes. However, these surveys can yield unreliable data if not carefully executed. Despite a long history of aerial survey use in ecological research, problems common to aerial surveys have not yet been adequately resolved. Through an extensive review of the aerial survey literature over the last 50 years, we evaluated how common problems encountered in the data (including nondetection, counting error, and species misidentification) can manifest, the potential difficulties conferred, and the history of how these challenges have been addressed. Additionally, we used a double-observer case study focused on waterbird data collected via aerial surveys and an online group (flock) counting quiz to explore the potential extent of each challenge and possible resolutions. We found that nearly three quarters of the aerial survey methodology literature focused on accounting for nondetection errors, while issues of counting error and misidentification were less commonly addressed. Through our case study, we demonstrated how these challenges can prove problematic by detailing the extent and magnitude of potential errors. Using our online quiz, we showed that aerial observers typically undercount group size and that the magnitude of counting errors increases with group size. Our results illustrate how each issue can act to bias inferences, highlighting the importance of considering individual methods for mitigating potential problems separately during survey design and analysis. We synthesized the information gained from our analyses to evaluate strategies for overcoming the challenges of using aerial survey data to estimate wildlife abundance, such as digital data collection methods, pooling species records by family, and ordinal modeling using binned data. Recognizing conditions that can lead to data collection errors and having reasonable solutions for addressing errors can allow researchers to allocate resources effectively to mitigate the most significant challenges for obtaining reliable aerial survey data.

Alabama, Florida, Louisiana, Mississippi, Texas↗

USGS2025

Missing data in ecology: Syntheses, clarifications, and considerations

In ecology and related sciences, missing data are common and occur in a variety of different contexts. When missing data are not handled properly, subsequent statistical estimates tend to be biased, inefficient, and lack proper confidence interval coverage. Missing data are often grouped into three categories: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). We review each category and compare their benefits and drawbacks. We review several approaches to handling missing data including complete case analysis, imputation, inverse probability weighting, and data augmentation. We clarify what types of variables should accompany imputation methods and how those variables are influenced by the analysis methods. Additionally, we discuss missing data that lack a formal basis for measurement and hence are fundamentally different from MCAR, MAR, and MNAR missing data. Throughout, we introduce concepts and numeric examples using both simulated data and data from the United States Environmental Protection Agency's 2016 National Wetland Condition Assessment. We conclude by providing five considerations for ecologists and other scientists handling missing data.

Ecological Monographs↗

USGS2019

Incorporating citizen science data in spatially explicit integrated population models

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

Ecology↗

USGS2025

A metadata checklist and data formatting guidelines to make eDNA FAIR (Findable, Accessible, Interoperable and Reusable)

The success of environmental DNA (eDNA) approaches for species detection has revolutionized biodiversity monitoring and distribution mapping. Targeted eDNA amplification approaches, such as quantitative PCR, have improved our understanding of species distribution, and metabarcoding-based approaches have enabled biodiversity assessment at unprecedented scales and taxonomic resolution. eDNA datasets, however, are often scattered across repositories with inconsistent formats, varying access restrictions, and inadequate metadata; this limits their interoperation, reuse, and overall impact. Adopting FAIR (Findable, Accessible, Interoperable, and Reusable) data practices with eDNA data can transform the monitoring of biodiversity and individual species and support data-driven biodiversity management across broad scales. FAIR practices remain underdeveloped in the eDNA community, partly due to gaps in adapting existing vocabularies, such as Darwin Core (DwC) and Minimum Information about any (x) Sequence (MIxS), to eDNA-specific needs and workflows. To address these challenges, we propose a comprehensive FAIR eDNA (FAIRe) Metadata Checklist, which integrates existing data standards and introduces new terms tailored to eDNA workflows. Metadata are systematically linked to both raw data (e.g., metabarcoding sequences, Ct/Cq values of targeted qPCR assays) and derived biological observations (e.g., Amplicon Sequence Variant (ASV)/Operational Taxonomic Unit (OTU) tables, species presence/absence). Along with formatting guidelines, tools, templates, and example datasets, we introduce a standardized, ready-to-use approach for FAIR eDNA practices. Through broad collaboration, we seek to integrate these guidelines into established biodiversity and molecular data standards, promote journal data policies, and foster user-driven improvements and uptake of FAIR practices among eDNA data producers. In proposing this standardized approach and developing a long-term plan with key databases and data standard organizations, the goal is to enhance accessibility, maximize reuse, and elevate the scientific impact of these valuable biodiversity data resources.

Environmental DNA↗

USGS2014

Data worth and prediction uncertainty for pesticide transport and fate models in Nebraska and Maryland, United States

BACKGROUND Complex environmental models are frequently extrapolated to overcome data limitations in space and time, but quantifying data worth to such models is rarely attempted. The authors determined which field observations most informed the parameters of agricultural system models applied to field sites in Nebraska (NE) and Maryland (MD), and identified parameters and observations that most influenced prediction uncertainty. RESULTS The standard error of regression of the calibrated models was about the same at both NE (0.59) and MD (0.58), and overall reductions in prediction uncertainties of metolachlor and metolachlor ethane sulfonic acid concentrations were 98.0 and 98.6% respectively. Observation data groups reduced the prediction uncertainty by 55–90% at NE and by 28–96% at MD. Soil hydraulic parameters were well informed by the observed data at both sites, but pesticide and macropore properties had comparatively larger contributions after model calibration. CONCLUSIONS Although the observed data were sparse, they substantially reduced prediction uncertainty in unsampled regions of pesticide breakthrough curves. Nitrate evidently functioned as a surrogate for soil hydraulic data in well-drained loam soils conducive to conservative transport of nitrogen. Pesticide properties and macropore parameters could most benefit from improved characterization further to reduce model misfit and prediction uncertainty. RESULTS: The standard error of regression of the calibrated models was about the same at both NE (0.59) and MD (0.58), and overall reductions in prediction uncertainties of metolachlor and metolachlor ethane sulfonic acid concentrations were 98.0 and 98.6% respectively. Observation data groups reduced the prediction uncertainty by 55–90% at NE and by 28–96% at MD. Soil hydraulic parameters were well informed by the observed data at both sites, but pesticide and macropore properties had comparatively larger contributions after model calibration. CONCLUSIONS: Although the observed data were sparse, they substantially reduced prediction uncertainty in unsampled regions of pesticide breakthrough curves. Nitrate evidently functioned as a surrogate for soil hydraulic data in well-drained loam soils conducive to conservative transport of nitrogen. Pesticide properties and macropore parameters could most benefit from improved characterization further to reduce model misfit and prediction uncertainty.

Maryl;Nebraska↗

USGS2016

Integrated groundwater data management

The goal of a data manager is to ensure that data is safely stored, adequately described, discoverable and easily accessible. However, to keep pace with the evolution of groundwater studies in the last decade, the associated data and data management requirements have changed significantly. In particular, there is a growing recognition that management questions cannot be adequately answered by single discipline studies. This has led a push towards the paradigm of integrated modeling, where diverse parts of the hydrological cycle and its human connections are included. This chapter describes groundwater data management practices, and reviews the current state of the art with enterprise groundwater database management systems. It also includes discussion on commonly used data management models, detailing typical data management lifecycles. We discuss the growing use of web services and open standards such as GWML and WaterML2.0 to exchange groundwater information and knowledge, and the need for national data networks. We also discuss cross-jurisdictional interoperability issues, based on our experience sharing groundwater data across the US/Canadian border. Lastly, we present some future trends relating to groundwater data management.

Book chapter↗

USGS1999

Comparison of temporal trends in ambient and compliance trace element and PCB data in pool 2 of the Mississippi River, USA, 1985-1995

The Intergovernmental Task Force on Monitoring has suggested studies on ambient (in-stream) and compliance (wastewater) data to determine if monitoring can be reduced locally or nationally. The similarity in temporal trends between retrospective ambient and compliance water-quality data collected from Pool 2 of the Mississippi River, USA, was determined for 1985–1995. Constituents studied included the following trace elements: arsenic (As), cadmium (Cd), chromium (Cr), hexavalent chromium (Cr61), copper (Cu), lead (Pb), mercury (Hg), nickel (Ni), selenium (Se), zinc (Zn), and polychlorinated biphenyls (PCBs). Water-column, bed-sediment, and fish-tissue (fillets) data collected by five government agencies comprised the ambient data set; effluent data from five registered facilities comprised the compliance data set. The nonparametric MannKendall trend test indicated that 33% of temporal trends in all data were statistically significant (P , 0.05). Possible reasons for this were low sample sizes, and a high percentage of samples below the analytical detection limit. Trends in compliance data were more distinct; most trace elements decreased significantly, probably due to improvements in wastewater treatment. Seven trace elements (Cr, Cd, Cu, Pb, Hg, Ni, and Zn) had statistically significant decreases in wastewater and portions of either or both ambient water and bed sediment. No trends were found in fish tissue. Inconsistency in trends between ambient and compliance data were often found for individual constituents, making overall similarity between the data sets difficult to determine. Logistical differences in monitoring programs, such as varying field and laboratory methods among agencies, made it difficult to assess ambient temporal trends.

Minnesota↗

USGS1995

Recent data and information system initiatives for remotely sensed measurements of the land surface

As part of the International Satellite Land Satellite Climatology Program (ISLSCP) Workshop on Remote Sensing of the Land Surface for Studies of Global Change, five invited presentations were given on recent data initiatives relevant to the ISLSCP community. The presentations are summarized in this paper along with some observations by the authors on data systems for the land sciences community. The invited presentations are by no means all inclusive but were selected as examples of current data activities, representing a range of topics associated with data for land sciences including: the generation of global and local scale data sets, the reworking of historical data sets, new data initiatives and some programmatic aspects of land data base development. This paper serves to provide information on these data initiatives and to air some of the issues concerning land science data systems that were raised at the meeting.

Remote Sensing of Environment↗

USGS2018

Small values in big data: The continuing need for appropriate metadata

Compiling data from disparate sources to address pressing ecological issues is increasingly common. Many ecological datasets contain left-censored data – observations below an analytical detection limit. Studies from single and typically small datasets show that common approaches for handling censored data — e.g., deletion or substituting fixed values — result in systematic biases. However, no studies have explored the degree to which the documentation and presence of censored data influence outcomes from large, multi-sourced datasets. We describe left-censored data in a lake water quality database assembled from 74 sources and illustrate the challenges of dealing with small values in big data, including detection limits that are absent, range widely, and show trends over time. We show that substitutions of censored data can also bias analyses using ‘big data’ datasets, that censored data can be effectively handled with modern quantitative approaches, but that such approaches rely on accurate metadata that describe treatment of censored data from each source.

Ecological Informatics↗