Search USGS⌕ Search

SEARCH · Search USGS

Results for “Data Science in Science”

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

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

At least 37 records · Page 2Linked to original sources
USGS2026

Birds, breakpoints, and baselines: How citizen science data can reveal ecological boundaries in Kenya’s Upper Tana watershed

Tropical watersheds are increasingly threatened by climate change, land-use conversion, and resource extraction, yet conventional biodiversity monitoring in these systems is often spatially and temporally limited. Citizen science offers a complementary approach, enabling biodiversity data collection over large areas that can supplement professional scientific surveys. We analyzed 10 years (2012–2022) of Upper Tana Watershed bird data from the Kenya Bird Map project, covering 114 pentads (9 × 9 km) within a 17,000 km 2 watershed to assess patterns of bird community composition and distribution across this watershed and to also evaluate the effects of environmental variables, seasonality, and sampling effort to help inform improvements in future citizen science projects. Citizen (or community) scientists recorded 575 species (>50% of Kenya’s total avifauna) in 74 families. Asymptotic species accumulation indicates that most probable species present in the watershed were detected. Threshold indicator taxa analysis revealed distinct ecological boundaries along elevation (∼1,500 m), precipitation (∼1,100 mm), and mean temperature (∼19°C) gradients, corresponding to a turnover from xeric savanna to mesic montane forest assemblages. Notably, bird communities showed little seasonal differentiation between wet and dry periods, consistent with dominance by resident year-round species. Data limitations including uneven survey distribution and frequency, absence of abundance metrics, and coarse representation of local environmental conditions that likely reduced our ability to detect fine-scale species–habitat relationships. Addressing these gaps through spatially balanced sampling at greater resolution, greater survey frequency in underrepresented areas, and improved capture of habitat metrics could strengthen the use of citizen-science bird data for watershed bioassessment. Our findings demonstrate that structured citizen-science initiatives can identify ecological boundaries and inform adaptive management of tropical socio-ecological systems under rapid environmental change.

Upper Tana watershed↗
USGS2017

Applying citizen-science data and mark-recapture models to estimate numbers of migrant golden eagles in an important bird area in eastern North America

Estimates of population abundance are important to wildlife management and conservation. However, it can be difficult to characterize the numbers of broadly distributed, low-density, and elusive bird species. Although Golden Eagles (Aquila chrysaetos) are rare, difficult to detect, and broadly distributed, they are concentrated during their autumn migration at monitoring sites in eastern North America. We used hawk-count data collected by citizen scientists in a virtual mark–recapture modeling analysis to estimate the numbers of Golden Eagles that migrate in autumn along Kittatinny Ridge, an Important Bird Area in Pennsylvania, USA. In order to evaluate the sensitivity of our abundance estimates to variation in eagle capture histories, we applied candidate models to 8 different sets of capture histories, constructed with or without age-class information and using known mean flight speeds 6 1, 2, 4, or 6 SE for eagles to travel between hawk-count sites. Although some abundance estimates were produced by models that poorly fitted the data ( ĉ > 3.0), 2 sets of population estimates were produced by acceptably performing models (cˆ less than or equal to 3.0). Application of these models to count data from November, 2002–2011, suggested a mean population abundance of 1,354 6 117 SE (range: 873–1,938). We found that Golden Eagles left the ridgeline at different rates and in different places along the route, and that typically ,50% of individuals were detected at the hawk-count sites. Our study demonstrates a useful technique for estimating population abundance that may be applicable to other migrant species that are repeatedly detected at multiple monitoring sites along a topographic diversion or leading line.

Pennslyvania↗
USGS2004

Science data in support of environmental health studies in the U.S.-Mexico border region

The border region of the United States and Mexico encompasses a vast and diverse array of physical settings and habitats that include wetlands, deserts, rangeland, mountains, and forests, which are unique in terms of the diversity of their water, mineral, and biological resources. The region is interconnected economically, politically, and socially owing to its binational heritage. In 1995, nearly 11 million people lived immediately adjacent to the border. By one account, that population could more than double by 2020. This rapid population growth and consequent economic development and land-use changes are pushing the limits of environmental sustainability and quality. Infrastructure development has lagged behind the rapid growth of the region, resulting in a shortage of water for municipal, agricultural, and industrial uses. These stressors threaten the quality of life in the region and raise concerns about the interdependence of environmental quality and human health. To allow for continued economic growth while protecting the area’s natural resources and fostering a high quality of life, the United States and Mexico need an improved understanding of the threats posed by these anthropogenic changes. Issues of particular concern include (1) contaminants in ground water, surface water, and biota from agricultural, municipal, and industrial activities; (2) airborne pollutants from fossil-fuel combustion and other activities; (3) contaminants from past and present mining activities and mineral deposits; and (4) pathogens, pharmaceuticals, hormones, and other contaminants released in treated and untreated human and animal wastewaters.

Fact Sheet↗
USGS2024

U.S. Geological Survey Mississippi River Science Forum—Summary of data and science needs and next steps

The U.S. Geological Survey hosted a Mississippi River Science Forum with Federal agencies; Tribal, State, and local governments located in States that border the Mississippi River; academia; and other interested stakeholders. The purpose of the forum was to share current (2023) science; identify data gaps and areas of concern; and to prioritize next steps needed to advance the goals of improving water quality, restoring habitat and natural systems, improving navigation, eliminating aquatic invasive species, and building local resilience to natural disasters along the Mississippi River. The forum was a directive for the U.S. Geological Survey in the Consolidated Appropriations Act of 2022 (Public Law 117—103, 136 Stat. 49). Participants and stakeholders that attended the Mississippi River Science Forum indicated the following. A Mississippi River Science Committee could bring together the voices of all stakeholders, including Federal agencies; State, local, and Tribal governments; academia; nongovernmental organizations; business and industry; and other interested parties. This committee would also work with (but not replace) Mississippi River Basin entities already in place and establish effective communication to identify and address Basin-wide management needs and to develop holistic solutions for those needs. A Mississippi River science plan could expand on the science and data gaps identified by the Forum. This plan could guide data collection and help resource managers develop and implement restoration initiatives within the Basin, recognizing that addressing these needs will require substantial resources. The science committee, working with other entities in the basin, should develop data standards for the Mississippi River Basin that allow stakeholders to utilize comparable data to address their needs. These standards would include data findability, accessibility, interoperability, and reusability. This report highlights data gaps and areas of concern discussed during the forum, and it identifies needs to advance the goals of improving water quality, restoring habitat and natural systems, improving navigation, eliminating aquatic invasive species, and building local resilience to natural disasters with specific emphasis on data collection and measurement, and scientific investigation. The report also summarizes stakeholder input and feedback and outlines next steps identified by forum participants.

Open-File Report↗
USGS2021

U.S. Geological Survey Great Lakes Science Forum—Summary of remaining data and science needs and next steps

A U.S. Geological Survey-led assessment of data gaps and science needs across the Great Lakes ecosystem indicated the following: • Expanded data collection or monitoring would provide basic ecosystem, social, and public health data to manage the Great Lakes system and to develop and test models and decision support tools. • New science and advanced technologies (for example, sensors and high-performance computing capability) would improve the understanding of critical threats, such as harmful algae blooms and high-water levels. Although there is significant scientific knowledge in specific areas or for specific topics, managers could use improved models and decision support tools, strengthened by extensive data collection and developed at multiple scales, to better inform decision making in the future. Enhanced coordination of agency efforts and associated data collection across data types (for example, prey fish populations and water levels) is needed to effectively manage the Great Lakes. This report highlights the data gaps; benefits of better, more structured coordination; and areas of concern specifically related to data collection/measurement and science efforts. It summarizes and analyzes stakeholder feedback and information from review of scientific literature. Finally, the report outlines steps necessary to create an integrated Great Lakes science plan.

Open-File Report↗
USGS2015

Building a multi-scaled geospatial temporal ecology database from disparate data sources: Fostering open science through data reuse

Although there are considerable site-based data for individual or groups of ecosystems, these datasets are widely scattered, have different data formats and conventions, and often have limited accessibility. At the broader scale, national datasets exist for a large number of geospatial features of land, water, and air that are needed to fully understand variation among these ecosystems. However, such datasets originate from different sources and have different spatial and temporal resolutions. By taking an open-science perspective and by combining site-based ecosystem datasets and national geospatial datasets, science gains the ability to ask important research questions related to grand environmental challenges that operate at broad scales. Documentation of such complicated database integration efforts, through peer-reviewed papers, is recommended to foster reproducibility and future use of the integrated database. Here, we describe the major steps, challenges, and considerations in building an integrated database of lake ecosystems, called LAGOS (LAke multi-scaled GeOSpatial and temporal database), that was developed at the sub-continental study extent of 17 US states (1,800,000 km 2 ). LAGOS includes two modules: LAGOS GEO , with geospatial data on every lake with surface area larger than 4 ha in the study extent (~50,000 lakes), including climate, atmospheric deposition, land use/cover, hydrology, geology, and topography measured across a range of spatial and temporal extents; and LAGOS LIMNO , with lake water quality data compiled from ~100 individual datasets for a subset of lakes in the study extent (~10,000 lakes). Procedures for the integration of datasets included: creating a flexible database design; authoring and integrating metadata; documenting data provenance; quantifying spatial measures of geographic data; quality-controlling integrated and derived data; and extensively documenting the database. Our procedures make a large, complex, and integrated database reproducible and extensible, allowing users to ask new research questions with the existing database or through the addition of new data. The largest challenge of this task was the heterogeneity of the data, formats, and metadata. Many steps of data integration need manual input from experts in diverse fields, requiring close collaboration.

Connecticut, Delaware, Illinois, Indiana, Iowa, Ma↗
USGS2021

Science storms the cloud

The core tools of science (data, software, and computers) are undergoing a rapid and historic evolution, changing what questions scientists ask and how they find answers. Earth science data are being transformed into new formats optimized for cloud storage that enable rapid analysis of multi-petabyte data sets. Data sets are moving from archive centers to vast cloud data storage, adjacent to massive server farms. Open source cloud-based data science platforms, accessed through a web-browser window, are enabling advanced, collaborative, interdisciplinary science to be performed wherever scientists can connect to the internet. Specialized software and hardware for machine learning and artificial intelligence are being integrated into data science platforms, making them more accessible to average scientists. Increasing amounts of data and computational power in the cloud are unlocking new approaches for data-driven discovery. For the first time, it is truly feasible for scientists to bring their analysis to data in the cloud without specialized cloud computing knowledge. This shift in paradigm has the potential to lower the threshold for entry, expand the science community, and increase opportunities for collaboration while promoting scientific innovation, transparency, and reproducibility. Yet, we have all witnessed promising new tools which seem harmless and beneficial at the outset become damaging or limiting. What do we need to consider as this new way of doing science is evolving?

AGU Advances↗
USGS2020

Bayesian modeling of non-stationary, univariate, spatial data for the Earth sciences

Some Earth science data, such as geochemical measurements of element concentrations, are non-stationary—the mean and the standard deviation vary spatially. It is important to estimate the spatial variations in both statistics because such information is indicative of geological and other Earth processes. To this end, an estimation method is formulated as a Bayesian hierarchical model. The method represents the spatially varying mean and the spatially varying standard deviation with basis functions; this formulation implicitly accounts for a spatially varying covariance function. A unique advantage of this method is that it can map the mean, the standard deviation, quantiles, and exceedance probabilities. The method is demonstrated by mapping titanium concentrations, which are measured in the coastal plain of the southeastern United States. Various checks demonstrate that the model fits the data and that the estimated statistics are geologically plausible.

Techniques and Methods↗
USGS2025

Exploring the science and data foundation for Federal public lands decisions

Public lands provide diverse resources, values, and services worldwide. Laws and policies typically require consideration of science in public lands decisions, and resource managers are committed to science-informed decision-making. However, it can be challenging for managers to use, and document the use of, science and data in their decisions. To better understand science and data use in Federal public lands decisions in the United States, we assessed the number, type, and age of documents cited in 70 Environmental Assessments (EAs) completed by the Bureau of Land Management (BLM) in Colorado from 2015–2019. We focused on the BLM, as they manage the largest area of public lands in the United States. We selected Colorado as our study area, as actions proposed on BLM lands in Colorado are representative of those across the nation. Fifty percent of citations were categorized as science and 23% as data. EAs contained an average of 17 citations (range 0–111), with documents analyzing effects of oil and gas development and recreation actions including the highest and lowest mean number of citations (41 and 6, respectively). Of individual resource analysis sections within EAs, 24% contained ≥1 science citation and 21% contained ≥1 data citation. Journal articles were the most cited type of document (26% of citations) followed by non-BLM inventories (13%). Forty-seven percent of citations were relatively recent (2010 or later); the oldest citation was from 1927. Commonly analyzed resources with the highest mean number of citations were socioeconomics, mineral resources, and noise. Fourteen of 33 commonly analyzed resources included <1 citation on average. Actions and resources with no or few citations represent opportunities for strengthening the transparent use of science and data in public lands decision-making.

Colorado↗
USGS2015

Collections management plan for the U.S. Geological Survey Woods Hole Coastal and Marine Science Center Data Library

The U.S. Geological Survey Woods Hole Coastal and Marine Science Center has created a Data Library to organize, preserve, and make available the field, laboratory, and modeling data collected and processed by Woods Hole Coastal and Marine Science Center staff. This Data Library supports current research efforts by providing unique, historic datasets with accompanying metadata. The Woods Hole Coastal and Marine Science Center&rsquo;s Data Library has custody of historic data and records that are still useful for research, and assists with preservation and distribution of marine science records and data in the course of scientific investigation and experimentation by researchers and staff at the science center. The data accession and retention policies employed by the Woods Hole Coastal and Marine Science Center Data Library are based on scientific need and the National Archives and Records Administration standards for Federal records retention. Criteria for inclusion of data and records into the Data Library, the scope of the Data Library holdings, and operating procedures for the management and running of the library are designed to support the research operations of the U.S. Geological Survey. This report explains the roles and detailed responsibilities of library and scientific staff, and provides step-by-step instructions for managing the collections of the Woods Hole Coastal and Marine Science Center Data Library.

Open-File Report↗
USGS2026

Population trends of dabbling ducks wintering in the alluvial valleys of Arkansas and Mississippi

Population abundances, distributions, and compositions across a diversity of taxa are changing, partly as a consequence of human-induced global modifications. Although linking population fluctuations to anthropogenic-induced alterations can be challenging, it is increasingly clear that long-term monitoring is critical to understanding changing populations. For waterfowl, concerns over recent declines in breeding populations or possible redistribution from using traditional wintering grounds for some species support continued monitoring of regional populations. To further understand population trends on traditional wintering grounds, we examined abundance trends over time within the Mississippi and Arkansas portions of the Mississippi Alluvial Valley, using two sources of data: systematic aerial surveys and citizen science data. We found great annual variability in rates of population change ( r ranging from −1.04 to 0.71) in five species of dabbling ducks. Despite this annual variation, green-winged teal Anas carolinensis , mallards Anas platyrhynchos , northern pintail Anas acuta , and northern shoveler Spatula clypeata showed no significant trends in population size over approximately 15 years in Mississippi and Arkansas, but gadwall Mareca strepera showed a 4% annual increase in the citizen science data for Mississippi. Despite substantial variation in annual abundance, the timing of peak abundance was remarkably consistent from year to year, with peaks typically occurring in January. Our results demonstrate that autumn–winter population numbers of dabbling ducks can be quite variable interannually, but they lack a consistent trend in the Mississippi Alluvial Valley during the past two decades. Going forward, investigation into how citizen science data could be leveraged to increase the precision of trends within this landscape might be of benefit, and sampling efforts could perhaps be added towards the latter portion of the wintering period. As populations continue to adjust in a changing world, maintaining monitoring surveys in regions important to waterfowl is an effective and important means of evaluating trends.

Arkansas, Illinois, Kentucky, Louisiana, Mississip↗
USGS2024

Standard data management practices

Accomplishing data management in a standardized and practical way begins with an understanding of what data management is. The Data Management Association defines data management as “the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles” (Earley 2017). In this chapter, we present the tenets of data management in the context of fisheries for our target audience: fisheries biologists who may have limited formal training in data science. Experienced data managers may also benefit from the contents of this chapter, but the authors’ goal is to enhance and improve those activities that produce data, from project planning to acquiring data through field sampling, to cataloging and analyzing data, to sharing and archiving data.

Book chapter↗
USGS2023

Leveraging the strengths of citizen science and structured surveys to achieve scalable inference on population size

Population size is a key metric for management and policy decisions, yet wildlife monitoring programmes are often limited by the spatial and temporal scope of surveys. In these cases, citizen science data may provide complementary information at higher resolution and greater extent. We present a case study demonstrating how data from the eBird citizen science programme can be combined with regional monitoring efforts by the US Fish and Wildlife Service to produce high-resolution estimates of golden eagle abundance. We developed a model that uses aerial survey data from the western United States to calibrate high-resolution annual estimates of relative abundance from eBird. Using this model, we compared regional population size estimates based on the calibrated eBird information with those based on aerial survey data alone. Population size estimates based on the calibrated eBird information had strong correspondence to estimates from aerial survey data in two out of four regions, and population trajectories based on the two approaches showed high correlations. We demonstrate how the combination of citizen science data and targeted surveys can be used to (a) increase the spatial resolution of population size estimates, (b) extend the spatial extent of inference and (c) predict population size beyond the temporal period of surveys. Findings based on this case study can be used to refine policy metrics used by the US Fish and Wildlife Service and inform permitting regulations (e.g. mortality/harm associated with wind energy development). Policy implications : Our results demonstrate the ability of citizen science data to complement targeted monitoring programmes and improve the efficacy of decision frameworks that require information on population size or trajectory. After validating citizen science data against survey-based benchmarks, agencies can harness strengths of citizen science data to supplement information needs and increase the resolution and extent of population size predictions.

Journal of Applied Ecology↗