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29 records · Page 2Linked to original sources
USGS2024

A spatiotemporal optimization engine for prescribed burning in the Southeast US

Many ecosystems in the Southeast US are dependent upon frequent low-intensity surface fires to sustain native biodiversity, ecosystem services, and endangered species populations. Today, landscape-scale prescribed fire is required to manage these systems for conservation objectives and to mitigate wildland fire risk. Successful application of prescribed fire in this region requires careful planning and assessment of the risks and tradeoffs involved when deciding whether or not to conduct a burn. Many of these risks are closely tied to ambient environmental conditions and are reflected in sets of ‘prescription’ parameters that define safe and effective operating conditions to meet objectives or regulatory requirements. To facilitate effective decision making and acknowledge growing uncertainties related to climate change effects on wildland fire operations, we developed a spatiotemporal optimization engine to identify near-term optimal burning opportunities for prescribed fire implementation. By mining historical 3-day numerical weather forecasts and observation-based weather data for 2015–2021, we have developed a Bayesian hierarchical model for forecast verification that provides calibrated daily weather forecasts and joint uncertainty estimates on meteorological variables of interest, with the latter serving as a measure of risk associated with prescribed fire activities. Burn allocation decisions are then optimized by considering this risk jointly with the utility of burning a particular habitat parcel. The initial iteration of the optimization engine is demonstrated through a case study of short-term meteorological conditions for the Eglin Air Force Base, located in Florida, USA. Results indicate agreement between the optimization engine and the observed past decision-making, with the largest divergences likely arising primarily from differences between utility functions presumed important and used to develop the optimization engine versus the true utility functions driving management behavior in practice.

Florida↗
USGS2025

Two-stage models improve machine learning classifiers in wildlife research: A case study in identifying false positive detections of Ruffed Grouse

Autonomous recording units are increasingly being used to monitor wildlife on large geographic and temporal scales, paired with machine learning (ML) to automate detection of wildlife. However, false positive detections from ML classifiers can result in erroneous ecological models that can lead to misguided management and conservation actions. We used a two-stage general approach to understand and reduce false positive detections, a technique in which outputs of the primary classification model are passed to a secondary classification model to yield the probability that a detection from the primary model is a true positive detection. This approach is demonstrated on two open-source models that detect Ruffed Grouse ( Bonasa umbellus ). We analyzed over 9500 h of acoustic data collected in 2022–2023 from the Green Mountain National Forest in Vermont, USA, and found the two models detected different types of acoustic signals associated with differing life history traits. The first model yielded 4106 detections (71.5 % true positives) while the second model yielded 524 detections (17.0 % true positives). Secondary logistic regression models separated true positives and false positives with high accuracy (84.5 % and 89.8 % respectively). Our findings go beyond improving Ruffed Grouse monitoring and conservation efforts to, more broadly, illustrate how two-stage ML approaches can improve the use of model-derived detections in wildlife research.

Vermont↗
USGS2025

A compilation pipeline for wildlife tracking datasets collected from ground-based and satellite-based telemetry transmission devices

Wildlife conservation planning increasingly requires collaboration and integration of research from discrete studies spanning large geographic areas. Tracking datasets are essential for analyzing animal movements and species distributions in relation to environmental conditions and combining them can enable powerful analyses to further aid planning efforts. However, combining datasets necessitates addressing variation in study designs, tracking methodologies, location uncertainty, and data attributes. We outline a compilation pipeline to integrate ground-based and satellite-based telemetry tracking datasets, motivated from our work with greater sage-grouse ( Centrocercus urophasianus ), a highly imperiled species of western North America. Our objective was to create a database with a standardized set of attributes to facilitate filtering locations for spatial analyses. Our pipeline phases are: (1) dataset pre-processing, (2) formatting individual datasets to a common template, (3) dataset binding, (4) error checking, and (5) filtering. Our pipeline includes additional functionality to identify coordinates from recurrently visited locations (e.g., nest sites), which may be of special interest. The final compiled sage-grouse database included nearly 5 million locations collected from 53 datasets and over 19,000 birds tracked from 1980 to 2022, including over 11,000 nest locations. Our error checks flagged 3.9 % of locations as likely errors, predominantly collected from satellite-based telemetry transmissions. We demonstrate the ability of our pipeline to identify nest locations and flag erroneous locations by applying it to simulated tracking datasets. Overall, our workflow offers a transferable approach for researchers aiming to standardize wildlife telemetry datasets and conduct ecological analyses for both individual studies and large-scale collaborations.

California, Colorado, Idaho, Montana, Nevada, Nort↗
USGS2025

Integrating multiple indices of habitat quality to inform habitat management for a sagebrush indicator species

Robust science is needed to inform national resource management and policy decisions. Predictive species habitat maps are frequently employed in conservation decision-making but are often based on a single metric representing habitat quality. We outlined a framework that combines multiple spatially explicit indices of potential habitat quality that could be used to identify and prioritize habitat management areas, using the greater sage-grouse ( Centrocercus urophasianus ; hereafter sage-grouse) as an example species. Due to large-scale changes in sagebrush ecosystems, sage-grouse have suffered significant population declines in recent decades and have become key to land management plans throughout the western United States, where comprehensive habitat maps are crucial to effective conservation efforts. We evaluated habitat selection and survival patterns of sage-grouse across six distinct annual life stages and seasons to generate predictive habitat map surfaces, mapped the distribution of current occupancy, and combined maps of potential selection and survival patterns with space use and occupancy indices to delineate example habitat management categories. Our approach facilitates identification of priority areas to target for habitat preservation and areas where anthropogenic impacts could occur with likely minimal impact to the species. Overall, by combining indices representing selection, survival, and current occupancy, we provide a framework to allow for a flexible and targeted management approach that could be adapted to a wide variety of species.

California, Nevada↗
USGS2026

Hierarchical mixture models and high-resolution monitoring data can inform siting and operational strategies to mitigate bat fatalities at wind turbines

Bats provide critical ecosystem services, but bat fatalities due to wind energy development may imperil some bat populations. Statistical models are used to estimate the total fatalities that occur based on carcasses observed during monitoring surveys. Current models often estimate fatalities aggregated across species, time, and/or turbines, but fall short of reliably informing siting and operational collision mitigation strategies that account for species-specific fatality patterns on a fine spatiotemporal scale. We developed a hierarchical mixture model for estimating species-specific covariate effects and total fatalities per species at each turbine on weekly intervals. We applied the model to a high-resolution dataset of bat carcasses found during turbine searches across nineteen wind facilities in Iowa over two years. Our model explains species-specific variation in bat fatalities at individual wind turbines according to turbine proximity to bat habitat, turbine design specifications, seasonal trends, and weather conditions such as nightly air temperature, air pressure, and wind speed. Turbines located on the edge of wind facilities had higher fatalities, and proximity to roosting and foraging habitat accounted for variation in species-specific fatality estimates. These insights into turbine placement effects can inform siting strategies. We also discovered species-specific relationships with average nightly wind speed and air temperature, among other weather conditions, that could inform operational mitigation strategies such as smart curtailment. Our model can transform observations of carcasses found during turbine searches across multiple facilities, years, and variable search efforts into estimates of total fatalities per species associated with species-specific spatial, temporal, and environmental covariate effects.

Ecological Informatics↗
USGS2026

Two-stage approach to automatic detection with machine learning for improved surveillance of the invasive Cuban treefrog

The Cuban treefrog ( Osteopilus septentrionalis ), as an invasive species in the southern United States, presents a need for effective surveillance. Automated detection expedites processing of audio data for large-scale surveillance and monitoring programs. However, current available methods commonly used for anuran species have not been sufficient to detect Cuban treefrogs. Here, we present results from a two-stage method for automated detection that employs both cross-correlation template matching and secondary supervised learning classifiers. In the first stage, audio data are screened for initial detections using template matching, in which the detections contain both true and false positives. In the second stage, the false positives are screened out using classifier algorithms. We used this method to process 139,985 audio recordings, consisting of 596,046 total minutes, collected at 13 locations in Louisiana and Florida from 2014 to 2022. From the stage 1 template matching, we detected 83,191 Cuban treefrog signals across recordings. The stage 2 machine learning model was able to identify stage 1 false positive detections with a testing accuracy of 98.46% and a testing false positive rate of 1.116%. After pruning false positive detections, a total of 20,271 individual Cuban treefrog detections remained, distributed mainly across 3 sites in an area with known presence. Locations with presumed absence had an easily verifiable number of false positive detections ( n = 109 across all other sites). The two-stage methodology utilizing both template matching and machine learning algorithms can be integrated into wildlife surveillance or monitoring programs for species with distinctive, conserved calls as an effective way to achieve sensitive species detection with a low incidence of false positives.

Florida, Louisiana↗
USGS2026

A Bayesian hierarchical modeling approach for species diversity in ecology

Species diversity is the foundation of many ecological disciplines. This metric is often approximated using species richness and evenness, even though actual richness likely exceeds observations due to imperfect sampling methods. Estimating the “true” species richness, which includes identifying the number of missing species, has intrigued ecologists for decades. We adopted a parametric model that appeared in Fisher et al. (1943), which models the numbers of individuals from different species as random samples from a negative binomial distribution, and developed a Bayesian computational approach to directly estimate the distribution model parameters. The model parameters represent species abundance and evenness, and can be used to derive species richness. We evaluated our parametric approach using (1) a simulation study and (2) three historical data sets. Furthermore, we illustrated the hierarchical modeling approach to combine data from multiple parallel studies using a biannual fishery survey data set. Our parametric model formulation is computationally efficient, and the hierarchical structure facilitates embedding diversity estimation into broader application, such as assessing spatial and temporal trends in species diversity associated with environmental stressors. Additionally, because the two parameters of the negative binomial distribution model represent species abundance and evenness of a community, this parametric approach facilitates a deeper understanding of the ecological systems under study. The negative binomial distribution model works with a wide range of species frequency distribution types. As a result, our emphasis on a parametric model can help us characterize the structure of an ecosystem and provide a greater depth of ecologically meaningful information.

Ecological Informatics↗
USGS2026

A site prioritization tool for invasive species management: Integrating diverse spatial data to improve decision making

Resource managers are tasked with protecting natural areas from invasive species with limited resources. Further, invasive management goals can vary greatly based on different management priorities specific to management agencies or taxa of interest. The site prioritization tool for invasive species management addresses these challenges by creating a platform to view and combine diverse spatial data layers to estimate cumulative invasion risk based on user-specific needs. For this tool, we developed a human transport risk layer, estimating invasion risk based on proximity to human population centers and transportation corridors, and created maps of non-native species richness across the conterminous United States. The tool also includes spatial layers showing projected changes in key climate variables through the end of the century to identify areas where invasion risk may shift. Users can explore these layers to prioritize sites based on the invasive taxa of interest, likely invasion pathways, and disturbances that may elevate invasion risk. This interactive tool will allow managers to make the spatial comparisons needed to focus efforts on areas that are highly susceptible to invasion and efficiently target monitoring and suppression efforts.

Ecological Informatics↗
USGS2018

Iterative near-term ecological forecasting: Needs, opportunities, and challenges

Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Proceedings of the National Academy of Sciences of↗
USGS1998

Geospatial Technology Applications and Infrastructure in the Biological Resources Division

Executive Summary -- Automated spatial processing technology such as geographic information systems (GIS), telemetry, and satellite-based remote sensing are some of the more recent developments in the long history of geographic inquiry. For millennia, humankind has endeavored to map the Earth's surface and identify spatial relationships. But the precision with which we can locate geographic features has increased exponentially with satellite positioning systems. Remote sensing, GIS, thematic mapping, telemetry, and satellite positioning systems such as the Global Positioning System (GPS) are tools that greatly enhance the quality and rapidity of analysis of biological resources. These technologies allow researchers, planners, and managers to more quickly and accurately determine appropriate strategies and actions. Researchers and managers can view information from new and varying perspectives using GIS and remote sensing, and GPS receivers allow the researcher or manager to identify the exact location of interest. These geospatial technologies support the mission of the U.S. Geological Survey (USGS) Biological Resources Division (BRD) and the Strategic Science Plan (BRD 1996) by providing a cost-effective and efficient method for collection, analysis, and display of information. The BRD mission is 'to work with others to provide the scientific understanding and technologies needed to support the sound management and conservation of our Nation's biological resources.' A major responsibility of the BRD is to develop and employ advanced technologies needed to synthesize, analyze, and disseminate biological and ecological information. As the Strategic Science Plan (BRD 1996) states, 'fulfilling this mission depends on effectively balancing the immediate need for information to guide management of biological resources with the need for technical assistance and long-range, strategic information to understand and predict emerging patterns and trends in ecological systems.' Information sharing plays a key role in nearly everything BRD does. The Strategic Science Plan discusses the need to (1) develop tools and standards for information transfer, (2) disseminate information, and (3) facilitate effective use of information. This effort centers around the National Biological Information Infrastructure (NBII) and the National Spatial Data Infrastructure (NSDI), components of the National Information Infrastructure. The NBII and NSDI are distributed electronic networks of biological and geographical data and information, as well as tools to help users around the world easily find and retrieve the biological and geographical data and information they need. The BRD is responsible for developing scientifically and statistically reliable methods and protocols to assess the status and trends of the Nation's biological resources. Scientists also conduct important inventory and monitoring studies to maintain baseline information on these same resources. Research on those species for which the Department of the Interior (DOI) has trust responsibilities (including endangered species and migratory species) involves laboratory and field studies of individual animals and the environments in which they live. Researchboth tactical and strategicis conducted at the BRD's 17 science centers and 81 field stations, 54 Cooperative Fish and Wildlife Research Units in 40 states, and at 11 former Cooperative Park Study Units. Studies encompass fish, birds, mammals, and plants, as well as their ecosystems and the surrounding landscape. Biological Resources Division researchers use a variety of scientific tools in their endeavors to understand the causes of biological and ecological trends. Research results are used by managers to predict environmental changes and to help them take appropriate measures to manage resources effectively. The BRD Geospatial Technology Program facilitates the collection, analysis, and dissemination of data and informat

Information and Technology Report↗
USGS2018

Integrated observations and informatics improve understanding of changing marine ecosystems

Marine ecosystems have numerous benefits for human societies around the world and many policy initiatives now seek to maintain the health of these ecosystems. To enable wise decisions, up to date and accurate information on marine species and the state of the environment they live in is required. Moreover, this information needs to be openly accessible to build indicators and conduct timely assessments that decision makers can use. The questions and problems being addressed demand global-scale investigations, transdisciplinary science, and mechanisms to integrate and distribute data that otherwise would appear to be disparate. Essential Ocean Variables (EOVs) and marine Essential Biodiversity Variables (EBVs), conceptualized by the Global Ocean Observing System (GOOS) and the Marine Biodiversity Observation Network (MBON), respectively, guide observation of the ocean. Additionally, significant progress has been made to coordinate efforts between existing programs, such as the GOOS, MBON, and Ocean Biogeographic Information System collaboration agreement. Globally and nationally relevant indicators and assessments require increased sharing of data and analytical methods, sustained long-term and large-scale observations, and resources to dedicated to these tasks. We propose a vision and key tenets as a guiding framework for building a global integrated system for understanding marine biological diversity and processes to address policy and resource management needs. This framework includes: using EOVs and EBVs and implementing the guiding principles of Findable, Accessible, Interoperable, Reusable (FAIR) data and action ecology. In doing so, we can encourage relevant, rapid, and integrative scientific advancement that can be implemented by decision makers to maintain marine ecosystem health.

Frontiers in Marine Science↗