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At least 19 recordsLinked to original sources
USGS2016

Advancing environmental flow science: Developing frameworks for altered landscapes and integrating efforts across disciplines.

Environmental flows represent a legal mechanism to balance existing and future water uses and sustain non-use values. Here, we identify current challenges, provide examples where they are important, and suggest research advances that would benefit environmental flow science. Specifically, environmental flow science would benefit by (1) developing approaches to address streamflow needs in highly modified landscapes where historic flows do not provide reasonable comparisons, (2) integrating water quality needs where interactions are apparent with quantity but not necessarily the proximate factor of the ecological degradation, especially as frequency and magnitudes of inflows to bays and estuaries, (3) providing a better understanding of the ecological needs of native species to offset the often unintended consequences of benefiting non-native species or their impact on flows, (4) improving our understanding of the non-use economic value to balance consumptive economic values, and (5) increasing our understanding of the stakeholder socioeconomic spatial distribution of attitudes and perceptions across the landscape. Environmental flow science is still an emerging interdisciplinary field and by integrating socioeconomic disciplines and developing new frameworks to accommodate our altered landscapes, we should help advance environmental flow science and likely increase successful implementation of flow standards.

Environmental Management↗
USGS1998

Estimation of octanol/water partition coefficients using LSER parameters

The logarithms of octanol/water partition coefficients, log Kow , were regressed against the linear solvation energy relationship (LSER) parameters for a training set of 981 diverse organic chemicals. The standard deviation for log Kow was 0.49. The regression equation was then used to estimate log Kow for a test of 146 chemicals which included pesticides and other diverse polyfunctional compounds. Thus the octanol/water partition coefficient may be estimated by LSER parameters without elaborate software but only moderate accuracy should be expected.

Advances in Environmental Research↗
USGS2003

Preparing for climate change: The potential consequences of climate variability and change

Over the past decades, scientific research has greatly advanced the knowledge and understanding of global environmental change. Research supported by the U. S. Global Change Research Programme (USGCRP) and research and assessment results by international organizations such as the Intergovernmental Panel on Climate Change (IPCC), the World Climate Research Program (WCRP), and the International Geosphere and Biosphere Programme (IGBP) have demonstrated that human activities exert powerful environmental influences on global, regional, and local scales. Recent findings by the Intergovernmental Panel on Climate Change (IPCC, 1997) indicate that human activities are increasing the atmospheric concentrations of carbon dioxide (CO 2 ) and other greenhouse gases such as nitrous oxide (NO x ), methane (CH 4 ), chlorofluorocarbons (CFCS), partially halogenated fluorocarbons, and ozone (O 3 ), which alter radiative balances, and tend to warm the Earth’s surface. These changes in greenhouse gases and aerosols constitute key factors in global and regional changes in temperature, precipitation, and other climate variables, resulting in local and regional changes in soil moisture, an increase in global mean sea level, and prospects for more severe extreme high temperature events, floods, and droughts in some places. In the United States and elsewhere in the industrialized world, energy use contributes to global warming more than any other human activity. This is because most of our energy comes from carbon-based fossil fuels (coal, oil, and natural gas). Fossil fuels provide energy for a variety of purposes, including transporting goods and people, manufacturing products, heating and cooling buildings, lighting spaces, and cooking foods. Each year U.S. energy use releases more than 5.5 billion tons of carbon dioxide into the atmosphere. Present global CO2 concentrations in the atmosphere are 130% of pre-industrial levels (Figure 1). The global surface temperature last century is warmer than any other century in the past millennium. The global average temperature has increased by about 1 o F over the last century and is projected to rise another 2-6.5 o F by year 2100 (Figure 2). The last two decades have been the warmest last century. Average global sea level has risen about 4 to 10 inches in the last hundred years, and is projected to rise another 6-38 inches by year 2100. Mid- and low- latitude mountain glaciers have retreated world-wide last century. As greenhouse gases continue to accumulate in the atmosphere, it is expected that an increase in rainfall amount and consequent increase in river flooding will occur. Recent floods in the Gulf Coast areas (1993, 1997) are examples of such events, and perhaps indicate the high sensitivity of flood occurrence to changing climate. Because of its unique location adjacent to the Gulf of Mexico, the Gulf Coast region of the United States is particularly vulnerable to various environmental alterations resulting from climate change.

Alabama, Florida, Georgia, Louisiana, Mississippi,↗
USGS2021

Techniques to improve ecological interpretability of black box machine learning models

Statistical modeling of ecological data is often faced with a large number of variables as well as possible nonlinear relationships and higher-order interaction effects. Gradient boosted trees (GBT) have been successful in addressing these issues and have shown a good predictive performance in modeling nonlinear relationships, in particular in classification settings with a categorical response variable. They also tend to be robust against outliers. However, their black-box nature makes it difficult to interpret these models. We introduce several recently developed statistical tools to the environmental research community in order to advance interpretation of these black-box models. To analyze the properties of the tools, we applied gradient boosted trees to investigate biological health of streams within the contiguous USA, as measured by a benthic macroinvertebrate biotic index. Based on these data and a simulation study, we demonstrate the advantages and limitations of partial dependence plots (PDP), individual conditional expectation (ICE) curves and accumulated local effects (ALE) in their ability to identify covariate–response relationships. Additionally, interaction effects were quantified according to interaction strength (IAS) and Friedman’s H 2 "> H 2 statistic. Interpretable machine learning techniques are useful tools to open the black-box of gradient boosted trees in the environmental sciences. This finding is supported by our case study on the effect of impervious surface on the benthic condition, which agrees with previous results in the literature. Overall, the most important variables were ecoregion, bed stability, watershed area, riparian vegetation and catchment slope. These variables were also present in most identified interaction effects. In conclusion, graphical tools (PDP, ICE, ALE) enable visualization and easier interpretation of GBT but should be supported by analytical statistical measures. Future methodological research is needed to investigate the properties of interaction tests. Supplementary materials accompanying this paper appear on-line.

Journal of Agricultural, Biological, and Environme↗
USGS2019

Landsat: The cornerstone of global land imaging

Since 1972, the joint NASA/ U.S. Geological Survey Landsat series of Earth Observation satellites have provided an uninterrupted space-based data record of the Earth’s land surface to help advance scientific research towards the understanding of our planet and the environmental impact of its inhabitants. Early Landsat satellites offered a wealth of new data that improved mapping of remote areas and geologic features along with digital analysis of vegetation. The utility of Landsat’s spatial and spectral resolution has advanced its use for applications that benefit society such as global crop forecasting, forest monitoring, water use, carbon assessments, and the base for Google Maps. Landsat’s long-term data record provides an unrivaled resource for observing land cover and land-use change over a timescale of decades. The free and open Landsat data policy in 2008 was a paradigm shift for the world. Today, due to improved analytical and computing capabilities, the Landsat archive is poised to shift into a more real-time monitoring and understanding of the Earth.

GIM International↗
USGS2018

Evaluating and managing environmental water regimes in a water-scarce and uncertain future

While the number of environmental flows and water science programmes continues to grow across the globe, there remains a critical need to better balance water availability in support of human and ecological needs and to recognise the environment as a legitimate user of water. In water‐stressed areas, this recognition has resulted in friction between water users in the public and private sectors. An opportunity exists for practitioners to be on the forefront of the science determining best practices for supporting environmental water regimes. This Special Issue brings together a collection of environmental flows science and water management papers organised around three major themes: (1) method development and testing; (2) application case studies; and (3) efficacy evaluation. Contents of this Special Issue are intended to foster collaboration and broaden transferability of the information, technical tools, models and methods needed to support environmental water management programmes. The technical sophistication of methods and modelling tools, while important to the advancement of environmental water science, may come at the expense of easily interpretable outcomes that positively influence management decisions. Researchers need to be more proactive in translating the results of advanced modelling methodologies into user‐friendly tools and methods. This will allow stakeholders and water managers to proactively test alternative water allocation scenarios to help address growing human water demands in the face of droughts and changes in climatic patterns. The application of environmental flows science and water management strategies cannot be done in isolation. Implementation involves a complex decision‐making process that integrates ecological, hydrologic and social science across diverse multifaceted governance systems and requires active stakeholder involvement. Scientists and managers must strengthen partnerships at multiple scales to develop sensible science investment strategies so that collective knowledge can be translated into wise environmental water management decisions.

Freshwater Biology↗
USGS2022

Global genetic diversity status and trends: Towards a suite of Essential Biodiversity Variables (EBVs) for genetic composition

Biodiversity underlies ecosystem resilience, ecosystem function, sustainable economies, and human well-being. Understanding how biodiversity sustains ecosystems under anthropogenic stressors and global environmental change will require new ways of deriving and applying biodiversity data. A major challenge is that biodiversity data and knowledge are scattered, biased, collected with numerous methods, and stored in inconsistent ways. The Group on Earth Observations Biodiversity Observation Network (GEO BON) has developed the Essential Biodiversity Variables (EBVs) as fundamental metrics to help aggregate, harmonize, and interpret biodiversity observation data from diverse sources. Mapping and analyzing EBVs can help to evaluate how aspects of biodiversity are distributed geographically and how they change over time. EBVs are also intended to serve as inputs and validation to forecast the status and trends of biodiversity, and to support policy and decision making. Here, we assess the feasibility of implementing Genetic Composition EBVs (Genetic EBVs), which are metrics of within-species genetic variation. We review and bring together numerous areas of the field of genetics and evaluate how each contributes to global and regional genetic biodiversity monitoring with respect to theory, sampling logistics, metadata, archiving, data aggregation, modeling, and technological advances. We propose four Genetic EBVs: ( i ) Genetic Diversity; ( ii ) Genetic Differentiation; ( iii ) Inbreeding; and ( iv ) Effective Population Size ( N e ). We rank Genetic EBVs according to their relevance, sensitivity to change, generalizability, scalability, feasibility and data availability. We outline the workflow for generating genetic data underlying the Genetic EBVs, and review advances and needs in archiving genetic composition data and metadata. We discuss how Genetic EBVs can be operationalized by visualizing EBVs in space and time across species and by forecasting Genetic EBVs beyond current observations using various modeling approaches. Our review then explores challenges of aggregation, standardization, and costs of operationalizing the Genetic EBVs, as well as future directions and opportunities to maximize their uptake globally in research and policy. The collection, annotation, and availability of genetic data has made major advances in the past decade, each of which contributes to the practical and standardized framework for large-scale genetic observation reporting. Rapid advances in DNA sequencing technology present new opportunities, but also challenges for operationalizing Genetic EBVs for biodiversity monitoring regionally and globally. With these advances, genetic composition monitoring is starting to be integrated into global conservation policy, which can help support the foundation of all biodiversity and species' long-term persistence in the face of environmental change. We conclude with a summary of concrete steps for researchers and policy makers for advancing operationalization of Genetic EBVs. The technical and analytical foundations of Genetic EBVs are well developed, and conservation practitioners should anticipate their increasing application as efforts emerge to scale up genetic biodiversity monitoring regionally and globally.

Biological Reviews↗
USGS2019

Toward sustainable environmental quality: Priority research questions for North America

Anticipating, identifying, and prioritizing strategic needs represent essential activities by research organizations. Decided benefits emerge when these pursuits engage globally important environment and health goals, including the United Nations Sustainable Development Goals. To this end, horizon scanning efforts can facilitate identification of specific research needs to address grand challenges. We report and discuss 40 priority research questions following engagement of scientists and engineers in North America. These timely questions identify the importance of stimulating innovation and developing new methods, tools, and concepts in environmental chemistry and toxicology to improve assessment and management of chemical contaminants and other diverse environmental stressors. Grand challenges to achieving sustainable management of the environment are becoming increasingly complex and structured by global megatrends, which collectively challenge existing sustainable environmental quality efforts. Transdisciplinary, systems‐based approaches will be required to define and avoid adverse biological effects across temporal and spatial gradients. Similarly, coordinated research activities among organizations within and among countries are necessary to address the priority research needs reported here. Acquiring answers to these 40 research questions will not be trivial, but doing so promises to advance sustainable environmental quality in the 21st century.

Environmental Toxicology and Chemistry↗
USGS2022

The future of fungi: Threats and opportunities

The fungal kingdom represents an extraordinary diversity of organisms with profound impacts across animal, plant, and ecosystem health. Fungi simultaneously support life, by forming beneficial symbioses with plants and producing life-saving medicines, and bring death, by causing devastating diseases in humans, plants, and animals. With climate change, increased antimicrobial resistance, global trade, environmental degradation, and novel viruses altering the impact of fungi on health and disease, developing new approaches is now more crucial than ever to combat the threats posed by fungi and to harness their extraordinary potential for applications in human health, food supply, and environmental remediation. To address this aim, the Canadian Institute for Advanced Research (CIFAR) and the Burroughs Wellcome Fund convened a workshop to unite leading experts on fungal biology from academia and industry to strategize innovative solutions to global challenges and fungal threats. This report provides recommendations to accelerate fungal research and highlights the major research advances and ideas discussed at the meeting pertaining to 5 major topics: (1) Connections between fungi and climate change and ways to avert climate catastrophe; (2) Fungal threats to humans and ways to mitigate them; (3) Fungal threats to agriculture and food security and approaches to ensure a robust global food supply; (4) Fungal threats to animals and approaches to avoid species collapse and extinction; and (5) Opportunities presented by the fungal kingdom, including novel medicines and enzymes.

G3 Genes, Genomes, Genetics↗
USGS2014

USGS investigations of water produced during hydrocarbon reservoir development

Significant quantities of water are present in hydrocarbon reservoirs. When brought to the land surface during oil, gas, and coalbed methane production, the water—either naturally occurring or injected as a method to enhance production—is termed produced water. Produced water is currently managed through processes such as recycling, treatment and discharge, spreading on roads, evaporation or infiltration, and deep well injection. U.S. Geological Survey (USGS) scientists conduct research and publish data related to produced water, thus providing information and insight to scientists, decisionmakers, the energy industry, and the public. The information advances scientific knowledge, informs resource management decisions, and facilitates environmental protection. This fact sheet discusses integrated research being conducted by USGS scientists supported by programs in the Energy and Minerals and Environmental Health Mission Areas. The research products help inform decisions pertaining to understanding the nature and management of produced water in the United States.

Fact Sheet↗
USGS2011

Silver nanoparticles: Behaviour and effects in the aquatic environment

This review summarises and evaluates the present knowledge on the behaviour, the biological effects and the routes of uptake of silver nanoparticles (Ag NPs) to organisms, with considerations on the nanoparticle physicochemistry in the ecotoxicity testing systems used. Different types of Ag NP syntheses, characterisation techniques and predicted current and future concentrations in the environment are also outlined. Rapid progress in this area has been made over the last few years, but there is still a critical lack of understanding of the need for characterisation and synthesis in environmental and ecotoxicological studies. Concentration and form of nanomaterials in the environment are difficult to quantify and methodological progress is needed, although sophisticated exposure models show that predicted environmental concentrations (PECs) for Ag NPs in different environmental compartments are at the range of ng L − 1 to mg kg − 1 . The ecotoxicological literature shows that concentrations of Ag NPs below the current and future PECs, as low as just a few ng L − 1 , can affect prokaryotes, invertebrates and fish indicating a significant potential, though poorly characterised, risk to the environment. Mechanisms of toxicity are still poorly understood although it seems clear that in some cases nanoscale specific properties may cause biouptake and toxicity over and above that caused by the dissolved Ag ion. This review concludes with a set of recommendations for the advancement of understanding of the role of nanoscale silver in environmental and ecotoxicological research.

Environment International↗
USGS2015

Aquatics Systems Branch: transdisciplinary research to address water-related environmental problems

The Aquatic Systems Branch at the Fort Collins Science Center is a group of scientists dedicated to advancing interdisciplinary science and providing science support to solve water-related environmental issues. Natural resource managers have an increasing need for scientific information and stakeholders face enormous challenges of increasing and competing demands for water. Our scientists are leaders in ecological flows, riparian ecology, hydroscape ecology, ecosystem management, and contaminant biology. The Aquatic Systems Branch employs and develops state-of-the-science approaches in field investigations, laboratory experiments, remote sensing, simulation and predictive modeling, and decision support tools. We use the aquatic experimental laboratory, the greenhouse, the botanical garden and other advanced facilities to conduct unique research. Our scientists pursue research on the ground, in the rivers, and in the skies, generating and testing hypotheses and collecting quantitative information to support planning and design in natural resource management and aquatic restoration.

Fact Sheet↗
USGS2018

Understanding how microbiomes influence the systems they inhabit

Translating the ever-increasing wealth of information on microbiomes (environment, host, or built environment) to advance the understanding of system-level processes is proving to be an exceptional research challenge. One reason for this challenge is that relationships between characteristics of microbiomes and the system-level processes they influence are often evaluated in the absence of a robust conceptual framework and reported without elucidating the underlying causal mechanisms. The reliance on correlative approaches limits the potential to expand the inference of a single relationship to additional systems and advance the field. We propose that research focused on how microbiomes influence the systems they inhabit should work within a common framework and target known microbial processes that contribute to the system-level processes of interest. Here we identify three distinct categories of microbiome characteristics (microbial processes, microbial community properties, and microbial membership) and propose a framework to empirically link each of these categories to each other and the broader system level processes they affect. We posit that it is particularly important to distinguish microbial community properties that can be predicted from constituent taxa (community aggregated traits) from and those properties that are currently unable to be predicted from constituent taxa (emergent properties). Existing methods in microbial ecology can be applied to more explicitly elucidate properties within each of these categories and connect these three categories of microbial characteristics with each other. We view this proposed framework, gleaned from a breadth of research on environmental microbiomes and ecosystem processes, as a promising pathway with the potential to advance discovery and understanding across a broad range of microbiome science.

Nature Microbiology↗
USGS2011

The role of remote sensing observations and models in hydrology: The science of evapotranspiration

Over 15 years ago, Morton (1994) summarized the state of evapotranspiration (ET) research pessimistically: ‘There have been few significant advances in our knowledge of evaporation on an environmental scale over the past four decades, a state of affairs linked to the current sterility of hydrology and related environmental sciences. Furthermore, almost none of the advances have been used successfully in practice.’ He did not foresee the rapid progress in the ensuing years. These advances can be attributed largely to three convergent themes: 1) technical innovation; 2) synergy between disciplines; and 3) expressed need. The papers in this special issue address all of these three themes on remote sensing methods for ET estimation.

Hydrological Processes↗
USGS2026

Stacked machine learning for timber identification with laser-induced breakdown spectroscopy (LIBS)

This study presents a new approach to wood species identification using laser-induced breakdown spectroscopy (LIBS) combined with stacked machine learning techniques. The research analyzed 700 samples comprising nine Dalbergia species and nine additional tropical timber species, utilizing a handheld LIBS analyzer. A stacking methodology was developed by integrating three support vector machine (SVM) models with different kernel functions (linear, polynomial, and radial) in a one-versus-all (OVA) configuration. These SVM outputs were then combined using a partial least squares discriminant analysis (PLS-DA) meta-learner. Through PCA-based variable selection, the dimensionality was reduced from 23 401 to wavelengths while maintaining classification accuracy. The stacking approach achieved a Cohen's kappa value of 0.8671 in the validation set, significantly outperforming traditional flat classifiers. Variable importance analysis revealed calcium, magnesium, and barium as crucial elements for species differentiation, with their concentrations reflecting environmental conditions and geographical origins. This research demonstrates the potential of combining LIBS spectroscopy with advanced machine learning techniques for rapid, non-invasive timber identification, which can support efforts against illegal logging and enforcement of international trade regulations.

Applied Spectroscopy↗
USGS2018

Continuing progress toward a national assessment of water availability and use

Executive Summary The Omnibus Public Land Management Act of 2009 (Public Law 111—11) was passed into law on March 30, 2009. Subtitle F, also known as the SECURE Water Act, calls for the establishment of a “national water availability and use assessment program” within the U.S. Geological Survey (USGS). The USGS issued the first report on the program in 2013. Program progress over the period 2013–17 is reported herein to fulfill the requirement to inform Congress on implementation of the national water availability and use assessment program, also referred to as the USGS National Water Census (the Water Census). Much work has been accomplished during 2013–17 on producing water budgets for the nation, a goal USGS outlined in its first report on program progress to Congress. The USGS has completed three geographic focus area studies and has begun three others. Work has advanced on nationwide efforts in streamflow analysis, groundwater assessment and research, evapotranspiration studies, water use, environmental water science, and drought science. The USGS works with Federal and non-Federal agencies, universities, and other organizations to ensure that the information can be aggregated with other types of water-availability and socioeconomic information, such as data on food and energy production. The USGS has also made great strides in measures for delivering data and information on the Water Census to stakeholders and the public. Much work remains to be accomplished for the Nation to have a comprehensive, ongoing Water Census. In this report, the USGS lays out activities to be accomplished in the next 5 years (2017–22), based upon current funding levels. These include selecting new focus area studies, conducting hydrologic modeling to complete water budgets for the conterminous United States, expanding groundwater modeling efforts, mapping a national classification system for environmental water science, and developing an inventory of interbasin water transfers. All of these steps are necessary in order for the Water Census to achieve the goals outlined by Congress in the SECURE Water Act.

Circular↗
USGS2005

A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets

Accurate and up-to-date global land cover data sets are necessary for various global change research studies including climate change, biodiversity conservation, ecosystem assessment, and environmental modeling. In recent years, substantial advancement has been achieved in generating such data products. Yet, we are far from producing geospatially consistent high-quality data at an operational level. We compared the recently available Global Land Cover 2000 (GLC-2000) and MODerate resolution Imaging Spectrometer (MODIS) global land cover data to evaluate the similarities and differences in methodologies and results, and to identify areas of spatial agreement and disagreement. These two global land cover data sets were prepared using different data sources, classification systems, and methodologies, but using the same spatial resolution (i.e., 1 km) satellite data. Our analysis shows a general agreement at the class aggregate level except for savannas/shrublands, and wetlands. The disagreement, however, increases when comparing detailed land cover classes. Similarly, percent agreement between the two data sets was found to be highly variable among biomes. The identified areas of spatial agreement and disagreement will be useful for both data producers and users. Data producers may use the areas of spatial agreement for training area selection and pay special attention to areas of disagreement for further improvement in future land cover characterization and mapping. Users can conveniently use the findings in the areas of agreement, whereas users might need to verify the informaiton in the areas of disagreement with the help of secondary information. Learning from past experience and building on the existing infrastructure (e.g., regional networks), further research is necessary to (1) reduce ambiguity in land cover definitions, (2) increase availability of improved spatial, spectral, radiometric, and geometric resolution satellite data, and (3) develop advanced classification algorithms.

Remote Sensing of Environment↗
USGS2025

Leveraging artificial intelligence and machine learning to advance Chesapeake Bay research and management: A review of status, challenges, and opportunities

The Chesapeake Bay and its watershed (hereafter “Chesapeake Bay region”) have been the focus of extensive restoration efforts for several decades. These restoration efforts are guided by the Chesapeake Bay Watershed Agreement (Chesapeake Executive Council 2014) which outlines 10 goals and 31 measurable outcomes. The Chesapeake Bay is globally recognized as a model for coastal restoration due to long-term investments in monitoring, modeling, implementation and research by the Chesapeake Bay Program (CBP) partnership. These monitoring network spans tidal and non-tidal regions and provides data across multiple scales. Artificial intelligence (AI), particularly machine-learning (ML) and deep learning (DL), has emerged as a powerful tool for analyzing large, complex datasets. These techniques have gained widespread adoption across various disciplines, including ecology, hydrology, and environmental science. In the Bay context, AI/ML is increasingly being used to explore drivers of environmental change, analyze system dynamics, and predict conditions in areas with limited monitoring. The CBP partnership, particularly its Scientific and Technical Advisory Committee (STAC), has increasingly recognized the growing role of AI/ML in watershed and estuarine management. Recent Chesapeake Community Research Symposium sessions and initiatives such as the Chesapeake Global Collaboratory highlight increasing regional momentum to apply big data and AI/ML for environmental solutions. Together, these developments underscore the timely need to explore how AI/ML can help advance Chesapeake Bay restoration and management. This STAC workshop, titled “Leveraging Artificial Intelligence and Machine learning to Advance Chesapeake Bay Research and Management: A review of status, challenges, and opportunities,” was held from February 24-25, 2025, in Edgewater, Maryland to bring together over 50 federal, state, and academic scientists and partners to synthesize the current state of AI/ML applications and identify research gaps in Chesapeake Bay research and management. The workshop focused on three main objectives: 1. Summarize recent AI/ML applications and lessons learned in both tidal and nontidal areas of the Chesapeake Bay region. 2. Identify challenges and gaps in applying AI/ML approaches to Chesapeake Bay data. Such challenges and gaps may include data limitations, harmonization issues, ineffective communication of AI/ML insights, and a lack of coordination among research and management institutions. 3. Develop recommendations and identify opportunities for leveraging AI/ML to address issues across the Chesapeake Bay region. Key areas of focus may include generating new information to support watershed management, delivering AI/MLgenerated insights to managers in a clear and actionable way, and fostering greater collaboration among stakeholders within the CBP Partnership. Workshop participants engaged in science presentations and breakout sessions to develop recommendations for advancing the integration of AI/ML techniques into research and management across the Chesapeake Bay region. By synthesizing current applications, identifying challenges, and exploring new opportunities, the workshop has provided valuable insights and recommendations for better leveraging AI/ML approaches to support the success of Bay restoration efforts. Together, these recommendations provide a roadmap for enhancing data-driven, science-based decision making aligned with the goals and outcomes of the Chesapeake Bay Watershed Agreement.

Delaware, Maryland, Virginia↗