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Results for “Frontiers in Big Data”

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11 recordsLinked to original sources
USGS2015

Smartphones reveal angler behavior: A case study of a popular mobile fishing application in Alberta, Canada

Successfully managing fisheries and controlling the spread of invasive species depends on the ability to describe and predict angler behavior. However, finite resources restrict conventional survey approaches and tend to produce retrospective data that are limited in time or space and rely on intentions or attitudes rather than actual behavior. In this study, we used three years of angler data from a popular mobile fishing application in Alberta, Canada, to determine province-wide, seasonal patterns of (1) lake popularity that were consistent with conventional data and (2) anthropogenic lake connectivity that has not been widely described in North America. Our proof-of-concept analyses showed that mobile apps can be an inexpensive source of high-resolution, real-time data for managing fisheries and invasive species. We also identified key challenges that underscore the need for further research and development in this new frontier that combines big data with increased stakeholder interaction and cooperation.

Fisheries↗
USGS2021

Testing a generalizable machine learning workflow for aquatic invasive species on Rainbow Trout (Oncorhynchus mykiss) in northwest Montana

Biological invasions are accelerating worldwide, causing major ecological and economic impacts in aquatic ecosystems. The urgent decision-making needs of invasive species managers can be better met by the integration of biodiversity big data with large-domain models and data-driven products. Remotely sensed data products can be combined with existing invasive species occurrence data via machine learning models to provide the proactive spatial risk analysis necessary for implementing coordinated and agile management paradigms across large scales. We present a workflow that generates rapid spatial risk assessments on aquatic invasive species using occurrence data, spatially explicit environmental data, and an ensemble approach to species distribution modeling using five machine learning algorithms. For proof of concept and validation, we tested this workflow using extensive spatial and temporal hybridization and occurrence data from a well-studied, ongoing, and climate-driven species invasion in the upper Flathead River system in northwestern Montana, USA. Rainbow Trout (RBT; Oncorhynchus mykiss), an introduced species in the Flathead River basin, compete and readily hybridize with native Westslope Cutthroat Trout (WCT; O. clarkii lewisii), and the spread of RBT individuals and their alleles has been tracked for decades. We used remotely sensed and other geospatial data as key environmental predictors for projecting resultant habitat suitability to geographic space. The ensemble modeling technique yielded high accuracy predictions relative to 30-fold cross-validated datasets (87% 30-fold cross-validated accuracy score). Both top predictors and model performance relative to these predictors matched current understanding of the drivers of RBT invasion and habitat suitability, indicating that temperature is a major factor influencing the spread of invasive RBT and hybridization with native WCT. The congruence between more time-consuming modeling approaches and our rapid machine-learning approach suggest that this workflow could be applied more broadly to provide data-driven management information for early detection of potential invaders.

Alberta, British Columbia, Montana↗
USGS2019

Overhauling ocean spatial planning to improve marine megafauna conservation

Tracking data have led to evidence-based conservation of marine megafauna, but a disconnect remains between the many thousands of individual animals that have been tracked and the use of these data in conservation and management actions. Furthermore, the focus of most conservation efforts is within Exclusive Economic Zones despite the ability of these species to move thousands of kilometres across multiple national jurisdictions. To assist the goal of the United Nations General Assembly’s recent effort to negotiate a global treaty to conserve biodiversity on the high seas, we propose the development of a new frontier in dynamic marine spatial management. We argue that a global approach combining tracked movements of marine megafauna and human activities at-sea, and using existing and emerging technologies (e.g., through new tracking devices and big data approaches) can be applied to deliver near real-time diagnostics on existing risks and threats to mitigate global risks for marine megafauna. With technology developments over the next decade expected to catalyse the potential to survey marine animals and human activities in ever more detail and at global scales, the development of dynamic predictive tools based on near real-time tracking and environmental data will become crucial to address increasing risks. Such global tools for dynamic spatial and temporal management will, however, require extensive synoptic data updates and will be dependent on a shift to a culture of data sharing and open access. We propose a global mechanism to store and make such data available in near real-time, enabling a holistic view of space use by marine megafauna and humans that would significantly accelerate efforts to mitigate impacts and improve conservation and management of marine megafauna.

Frontiers in Marine Science↗
USGS2022

Landsat data ecosystem case study: Actor perceptions of the use and value of landsat

It is well-known that Earth observation (EO) data plays a critical role in scientific understanding about the global environment. There is also growing support for the use of EO data to provide context-specific insights, with significant implications for their use in decision support systems. Technological development over recent years, including cloud computing infrastructure, machine learning techniques, and rapid expansion of the velocity, volume, and variety of space-borne data sources, offer huge potential to provide solutions to the myriad environmental problems facing society and the planet. The USGS/NASA Landsat Program, the longest continuously gathered source of land surface data, has played a central role in our understanding of environmental change, particularly for its contribution of longitudinal products that offer greater context for present research and decision support activities. The challenge facing the Landsat and EO data community, however, now lies in moving beyond context-specific knowledge generation to translating such knowledge into tangible value for society. Drawing from an open data ecosystem framework and qualitative social science methods, we map the Landsat data ecosystem (LDE) and the relationships linking multiple actors responsible for processing, indexing, analyzing, synthesizing, and translating raw Landsat data into information that is useful, useable, and used by end users in particular social-environmental contexts. Both the role of Big Data and associated technologies are discussed as they relate to the ultimate use of Landsat-derived information products to guide decision-making, and key data ecosystem characteristics that shape the likelihood of these products’ use are highlighted.

Frontiers in Environmental Science↗
USGS1951

Ground-water resources of the Paintrock irrigation project, Wyoming, with a section on the quality of the water

The ground-water conditions of the area covered by the Paintrock irrigation project, in north-central Wyoming, were investigated during the summer of 1947. The purpose of the study was to obtain a general evaluation of ground-water recharge, discharge, and storage in the area now irrigated and in the adjacent areas where additional lands are to be irrigated. Much of the area covered by this report consists of flat to gently sloping stream terraces and alluvial-bottoms along Nowood, Paintrock, and Medicine Lodge Creeks. The stream-terrace materials consist of fluviatile sand, clay, and gravel. The alluvium is very fine grained and in general has low permeability. The materials underlying the stream terraces and the bottomlands became progressively finer grained and less permeable downstream. The bedrock formations underlying the area studied range from the Madison limestone of Mississippian age to the Fort Union formation of Paleocene age. Beds have been folded into several prominent structures which trend northwest-southeast across the area. Several of the formations exposed in the area serve as aquifers and yield water to domestic and stock wells. The most important bedrock aquifers are the Fort Union, Lance, Meeteetee, Mesaverde, Frontier, Cloverly and Morrison formations , the Tensleep sandstone, the Amsden formation, and the Madison limestone. More than 7,000 feet of strata are exposed in the area, the older beds being exposed on the western flank of the Big Horn Range near the eastern end of the area. The quality of the water in the project ranges within wide limits. The concentration of dissolved solids in seven samples of ground water ranges from 279 parts per million for a water in the Tensleep sandstone to 4,590 parts per million for a water in the Morrison formation. The hardness as calcium carbonate (CaCO 3 ) ranges from 13 to 1,680 parts per million. Limited data on the quality of water in Nowood and Paintrock Creeks indicate that these waters are suitable for irrigation. The water in Paintrock Creek near Tensleep is higher in mineral content and hardness than the water upstream at Hyattville as a result of return flow of the irrigation water that is applied to farm lands above Tensleep.

Wyoming↗
USGS2020

Post-fire vegetation response in a repeatedly burned low-elevation sagebrush steppe protected area provides insights about resilience and invasion resistance

Sagebrush steppe ecosystems are threatened by human land-use legacies, biological invasions, and altered fire and climate dynamics. Steppe protected areas are therefore of heightened conservation importance but are few and vulnerable to the same impacts broadly affecting sagebrush steppe. To address this problem, sagebrush steppe conservation science is increasingly emphasizing a focus on resilience to fire and resistance to non-native annual grass invasion as a decision framework. It is well-established that the positive feedback loop between fire and annual grass invasion is the driving process of most contemporary steppe degradation. We use a newly developed ordinal zero-augmented beta regression model fit to large-sample vegetation monitoring data from John Day Fossil Beds National Monument, USA, spanning 7 years to evaluate fire responses of two native perennial foundation bunchgrasses and two non-native invasive annual grasses in a repeatedly burned, historically grazed, and inherently low-resilient protected area. We structured our model hierarchically to support inferences about variation among ecological site types and over time after also accounting for growing-season water deficit, fine-scale topographic variation, and burn severity. We use a state-and-transition conceptual diagram and abundances of plants listed in ecological site reference conditions to formalize our hypothesis of fire-accelerated transition to ecologically novel annual grassland. Notably, big sagebrush ( Artemisia tridentata ) and other woody species were entirely removed by fire. The two perennial grasses, bluebunch wheatgrass ( Pseudoroegneria spicata ) and Thurber's needlegrass ( Achnatherum thurberianum ) exhibited fire resiliency, with no apparent trend after fire. The two annual grasses, cheatgrass ( Bromus tectorum ) and medusahead ( Taeniatherum caput-medusae ), increased in response to burn severity, most notably medusahead. Surprisingly, we found no variation in grass cover among ecological sites, suggesting fire-driven homogenization as shrubs were removed and annual grasses became dominant. We found contrasting responses among all four grass species along gradients of topography and water deficit, informative to protected-area conservation strategies. The fine-grained influence of topography was particularly important to variation in cover among species and provides a foothold for conservation in low-resilient, aridic steppe. Broadly, our study demonstrates how to operationalize resilience and resistance concepts for protected areas by integrating empirical data with conceptual and statistical models.

Oregon↗
USGS2022

On the documentation, independence, and stability of widely used seismological data products

Earthquake scientists have traditionally relied on relatively small data sets recorded on small numbers of instruments. With advances in both instrumentation and computational resources, the big-data era, including an established norm of open data-sharing, allows seismologists to explore important issues using data volumes that would have been unimaginable in earlier decades. Alongside with these developments, the community has moved towards routine production of interpreted data products such as seismic moment tensor catalogs that have provided an additional boon to earthquake science. As these products have become increasingly familiar and useful, it is important to bear in mind that they are not data, but rather interpreted data products. As such, they differ from data in ways that can be important, but not always appreciated. Important - and sometimes surprising - issues can arise if methodology is not fully described, data from multiple sources are included, or data products are not versioned (time-stamped). The line between data and data products is sometimes blurred, leading to an underappreciation of issues that affect data products. This note illustrates examples from two widely used data products: moment tensor catalogs and Did You Feel It? (DYFI) macroseismic intensity values. These examples show that increasing a data product’s documentation, independence, and stability can make it even more useful. To ensure the reproducibility of studies using data products, time-stamped products should be preserved, for example as electronic supplements to published papers, or, ideally, a more permanent repository.

Frontiers in Earth Science↗
USGS2023

United States Gulf of Mexico waters provide important nursery habitat for Mexico’s green turtle nesting populations

Resolving natal populations for juvenile green turtles is challenging given their potential for extensive dispersal during the oceanic stage and ontogenetic shifts among nursery habitats. Mitochondrial DNA markers have elucidated patterns of connectivity between green turtle nesting populations (rookeries) and juvenile foraging aggregations. However, missing rookery baseline data and haplotype sharing among populations have often impeded inferences, including estimating origins of Gulf of Mexico juveniles. Here, we assessed genetic structure among seven foraging aggregations spanning southern Texas (TX) to southwestern Florida (SWFL), including Port Fourchon, Louisiana (LA); a surface-pelagic aggregation (SP) offshore of Louisiana and Florida; Santa Rosa Island, Florida (SRI); St. Joseph Bay, Florida (SJB); and the Big Bend region, Florida (BB). We estimated source contributions to aggregations with novel genetic data (excluding SP and BB) using a Bayesian many-to-one mixed stock analysis (MSA) approach. Haplotype frequencies for western (TX, LA, SP, SRI) and eastern (SJB, BB, SWFL) aggregations were significantly differentiated. The largest shift in haplotype frequencies between proximal nursery sites occurred between SRI and SJB, separated by only 150 km, highlighting the lack of a geographic yardstick for predicting genetic structure. In contrast to previous MSA results, there was no signal of Florida juveniles at any foraging site. Mexican contributions dominated in all aggregations, with strong connectivity between western Bay of Campeche (Tamaulipas/Veracruz) rookeries and western foraging aggregations. MSA indicated more diverse Mexican origins for eastern aggregations, with larger inputs from the eastern Bay of Campeche (Campeche/Yucatán), Campeche Bank, and Quintana Roo rookeries. These results demonstrate the significance of the Gulf of Mexico coast and offshore waters of the United States as important nursery habitat for green turtles of Mexican origin and highlight the need for international coordination for management of these populations.

Alabama, Florida, Louisiana, Mississippi, Texas↗
USGS2021

Rapid-response unsaturated zone hydrology: Small-scale data, small-scale theory, big problems

The unsaturated zone (UZ) extends across the Earth’s terrestrial surface and is central to many problems related to land and water resource management. Flow of water through the UZ is typically thought to be slow and diffusive, such that it could attenuate fluxes and dampen variability between atmospheric inputs and underlying aquifer systems. This would reduce water resource vulnerability to contaminants and water-related hazards. Reducing or negating that effect, however, spatially concentrated and rapid flow and transport through the unsaturated zone is surprisingly common and becoming more so with the increasing frequency and magnitude of extreme hydroclimatic events. Arising from the wide range in the rates and complex modes of nonlinear flow processes, these effects are among the most poorly characterized hydrologic phenomena. Issues of scale present additional difficulties. Equations representing unsaturated processes have been developed and tested on the basis of field and laboratory measurements typically made at scales from pore size to plot size. In contrast, related problems of significant interest to society, including floods, aquifer recharge, landslides, and groundwater contamination, range from watershed to regional scales. The disparity between the scale of our understanding and the scale of interest for societal problems has spurred application of these model equations at increasingly coarse resolutions over larger areas than can be justified by existing measurements or theory. This mismatch in scales requires an assumption that spatially averaging slow diffusive flow and rapid preferential flow can effectively represent the influence of both processes across vast areas. Given the currently inadequate recognition and quantitative characterization of focused and rapid processes in unsaturated flow, these phenomena are critically in need of expanded attention and effort.

Frontiers in Earth Science↗
USGS2020

Components and predictors of biological soil crusts vary at the regional vs. plant community scales

Although biological soil crusts (biocrusts) occur globally in arid and semi-arid environments, most of our knowledge of biocrust cover and ecology is from a relatively small number of locations worldwide. Some plant communities are known to have high cover of biocrusts, but the abundance of biocrusts is largely undocumented in most plant communities. Using a data driven approach, we identified 16 plant communities based on plant cover from the Assessment, Inventory, and Monitoring Strategy data from the Bureau of Land Management (AIM, 5,200 plots). We found that abundance of lichens and mosses varies among communities, but that both components of biocrusts are present in all plant communities. Biocrusts are indicators of two of these communities: one that is defined by high cover of mosses and basin big sagebrush and one that is defined by high cover of lichens and shadscale saltbush. Using non-parametric multiplicative regression, we evaluated a suite of abiotic and disturbance variables to assess the degree to which climate and soils are associated with the abundance of lichens and mosses at the regional scale. At the regional scale, soil depth and maximum vapor pressure deficit were found to be strongly associated with the abundance of lichens and January minimum temperature dictated the abundance of mosses. At the scale of plant communities, community specific metrics of soils and climate were better able to explain the abundance of biocrusts. Our demonstration of the presence of biocrusts across the western US suggests that studies on ecosystem function could include these organisms because they are present in all plant communities, maintain arguably stronger associations with climatic variation, are directly associated with soils, and contribute to ecosystem functions that are not solely maintained by vascular plants.

Frontiers in Ecology and Evolution↗
USGS2022

Modflow-setup: Robust automation of groundwater model construction

In an age of both big data and increasing strain on water resources, sound management decisions often rely on numerical models. Numerical models provide a physics-based framework for assimilating and making sense of information that by itself only provides a limited description of the hydrologic system. Often, numerical models are the best option for quantifying even intuitively obvious connections between human activities and water resource impacts. However, despite many recent advances in model data assimilation and uncertainty quantification, the process of constructing numerical models remains laborious, expensive, and opaque, often precluding their use in decision making. Modflow-setup aims to provide rapid and consistent construction of MODFLOW groundwater models through robust and repeatable automation. Common model construction tasks are distilled in an open-source, online code base that is tested and extensible through collaborative version control. Input to Modflow-setup consists of a single configuration file that summarizes the workflow for building a model, including source data, construction options, and output packages. Source data providing model structure and parameter information including shapefiles, rasters, NetCDF files, tables, and other (geolocated) sources to MODFLOW models are read in and mapped to the model discretization, using Flopy and other general open-source scientific Python libraries. In a few minutes, an external array-based MODFLOW model amenable to parameter estimation and uncertainty quantification is produced. This paper describes the core functionality of Modflow-setup, including a worked example of a MODFLOW 6 model for evaluating pumping impacts to a lake in central Wisconsin, United States.

Wisconsin↗