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At least 739 records · Page 41Linked to original sources
USGS2024

Stable isotopes reveal intertidal fish and crabs use bivalve farms as foraging habitat in Puget Sound, Washington

Bivalves such as oysters and clams have been farmed in intertidal zones across the Puget Sound region of the Salish Sea for thousands of years. The variety of gear types used on bivalve farms creates complex vertical structure and attachment points for aquatic epiphytes and invertebrates which increases habitat structural complexity, but may alter eelgrass cover in areas where bivalve farms and eelgrass meadows overlap. Eelgrass meadows are highly productive and ecologically foundational nearshore habitats that provide valuable ecosystem services including the provision of nursery, refuge, and foraging habitat. Aquaculture has been a key feature of the environment in the Puget Sound for millennia, however, little is known about how well aquaculture practices are integrated into the system, and what services they provide to mobile species assemblages relative to unfarmed eelgrass meadows. We used stable isotope mixing models to estimate, for several species of nearshore fish and crab in two areas of North Puget Sound, Washington, the percent diet originating from either a natural bottom habitat (eelgrass meadows), farm habitat (oyster farms), or pelagic planktonic sources. Our results indicate that several species of nearshore fish such as surf perch and staghorn sculpin derive a significant proportion of their diets from farm areas, while crabs derive most of their diets from eelgrass habitat, and stickleback derive a significant proportion of their diets from planktonic sources. The results indicate that foraging habitat uses are species specific, and that several species that spatially overlap bivalve farms obtained a large percentage of their diets from adjacent bivalve farm habitat.

Washington↗
USGS2025

Assessing the effect of coral reef restoration location on coastal flood hazard along the San Juan Coastline, Puerto Rico

Coastal resilience has become a pressing global issue due to the growing vulnerability of coastlines to the effects of climate change. Nature-based solutions have emerged as a promising approach to coastal protection to not only enhance coastal resilience, but also restore critical ecosystems. Coral reef restoration has the potential to provide ecosystem services benefits; however, there are still key uncertainties in linking restoration design to reductions in coastal flood hazard under current and future climate conditions. In this study, we applied one-dimensional and two-dimensional numerical coastal engineering models, calibrated and validated using field data, to evaluate the effectiveness of coral restoration scenarios on coastal waves, water levels, and flooding along the coast of San Juan, Puerto Rico, U.S.A. Model results indicate a small reduction in maximum water levels under the proposed restoration scenarios. This underscores the importance of these endeavors, not only for ecological preservation but also for preventing further reef deterioration. Such preservation is essential for mitigating the increased coastal risks anticipated in the future. Results from this study provide information to guide policymakers and coastal managers in making informed decisions on viable restoration project design options. By systematically evaluating how restoration location impacts coastal flood hazards, communities can develop and implement proactive strategies to mitigate flood-related risk. In addition, by restoring coral reefs, communities can contribute to environmental preservation while ensuring sustainable development and protection of coastal environments.

Puerto Rico, San Juan coastline↗
USGS2025

Submarine groundwater discharge creates cold‐water refugia that can mitigate exposure of heat stress in nearshore corals

Coral reef mortality around the world is accelerating due to human activities and rising sea temperatures that cause bleaching, which is expected to become more frequent. Our ability to predict which corals will be most resilient, however, remains limited due to insufficient information characterizing nearshore temperature and habitat conditions. In this study, we examine how submarine groundwater discharge (SGD) reduces nearshore water temperatures and exposure of corals to heat stress, complementing the understanding that SGD can adversely affect coral when it contains elevated nutrient concentrations. Data from fixed nearshore sensors and vertical depth profiles along ~100 km of the western shoreline of the Island of Hawai’i from 2003 to 2014 demonstrate that submarine groundwater discharge (SGD) can reduce nearshore water temperatures by 1 °C–5°C and create estuarine-like conditions with salinities as low as 20 PSU, where the prevalent coral species, Pocillopora meandrina, Porites lobata , and Montipora capitata , thrive. Time-series temperature records reveal that exposure to high ambient ocean temperatures, which are known to initiate bleaching events, are reduced up to 5%–46% of the time. Coral health surveys indicated coral bleaching in response to moderately high annual temperatures in 2010 and 2011, with more colonies affected farther from cold, SGD-fed waters. Synthesis of these results, along with coral response data following the more extreme marine heat wave of 2014–2015, demonstrates lower coral loss and greater coral recovery near groundwater seeps, particularly those with higher flux and influence on reducing nearshore water temperatures. Our results demonstrate that SGD may therefore provide a beneficial ecosystem service and enhance coral reef resilience, particularly where human-related nutrient additions to groundwater can be mitigated. The implications of our findings are relevant across tropical coasts where groundwater inputs can be substantial, such as the Caribbean and Indo-Pacific, and contribute to improving our understanding of coral sensitivity to gradients in temperature and nutrient stress. Improved management of groundwater resources could thus be vital to local–regional strategies for mitigating future heat stress.

Hawaii↗
USGS2022

Medium spatial resolution mapping of global land cover and land cover change across multiple decades from Landsat

Land cover maps are essential for characterizing the biophysical properties of the Earth’s land areas. Because land cover information synthesizes a rich array of information related to both the ecological condition of land areas and their exploitation by humans, they are widely used for basic and applied research that requires information related to land surface properties (e.g., terrestrial carbon models, water balance models, weather, and climate models) and are core inputs to models and analyses used by natural resource scientists and land managers. As the Earth’s global population has grown over the last several decades rates of land cover change have increased dramatically, with enormous impacts on ecosystem services (e.g., biodiversity, water supply, carbon sequestration, etc.). Hence, accurate information related to land cover is essential for both managing natural resources and for understanding society’s ecological, biophysical, and resource management footprint. To address the need for high-quality land cover information we are using the global record of Landsat observations to compile annual maps of global land cover from 2001 to 2020 at 30 m spatial resolution. To create these maps we use features derived from time series of Landsat imagery in combination with ancillary geospatial data and a large database of training sites to classify land cover at annual time step. The algorithm that we apply uses temporal segmentation to identify periods with stable land cover that are separated by breakpoints in the time series. Here we provide an overview of the methods and data sets we are using to create global maps of land cover. We describe the algorithms used to create these maps and the core land cover data sets that we are creating through this effort, and we summarize our approach to accuracy assessment. We also present a synthesis of early results and discuss the strengths and weaknesses of our early map products and the challenges that we have encountered in creating global land cover data sets from Landsat. Initial accuracy assessment for North America shows good overall accuracy (77.0 ± 2.0% correctly classified) and 79.8% agreement with the European Space Agency (ESA) WorldCover product. The land cover mapping results we report provide the foundation for robust, repeatable, and accurate mapping of global land cover and land cover change across multiple decades at 30 m spatial resolution from Landsat.

Frontiers in Remote Sensing↗
USGS2022

Integrating urban planning and water management through green infrastructure in the United States-Mexico border

Creating sustainable, resilient, and livable cities calls for integrative approaches and collaborative practices across temporal and spatial scales. However, practicability is challenged by institutional, social, and technical complexities and the need to build collective understanding of integrated approaches. Rapid urbanization along the United States-Mexico border, fueled by industrialization, trade, and migration, has resulted in cities confronted with recurrent flooding risk, extended drought, water pollution, habitat destruction and systemic vulnerabilities. The international border, which separates natural and built ecosystems, is both a challenge and an opportunity, making a unique social and institutional setting ideal for testing the integration of urban planning and water management. Our research focuses on fusing multi-functional and multi-scalar green infrastructure to restore ecosystem services through a strategic binational planning process. This paper describes this planning process, including the development and application of both a land suitability analysis and a hydrological model to optimally site green infrastructure in the Nogales, Arizona, United States—Nogales, Sonora, Mexico, cross border region. We draw lessons from this process and stakeholder feedback focused on the potential for urban green infrastructure, to allow for adaptation and even transformation in the face of current and future challenges such as limited resources, underdeveloped governance, bordering, and climate change. In sum, a cross border network of green infrastructure can provide a backbone to connect this transboundary watershed while providing both hydrological and social benefits.

Frontiers in Water↗
USGS2018

What is the value of wild bee pollination for wild blueberries and cranberries, and who values it?

Pollinator conservation efforts and growing interest in wild bee pollination have increased markedly in the last decade, making it increasingly important to have clear and practical estimates of the value of pollinators to agriculture. We used agricultural statistics, socio-economic producer surveys, and agronomic field research data to estimate traditional pollination value metrics and create novel approaches to the valuation of the ecosystem services provided by wild pollinators. Using two regionally important United States (USA) crops—Maine wild blueberry and Massachusetts cranberry—as models, we present the perceived values of wild bee pollinators from the perspectives of both consumers and producers. The net income attributable to wild bees was similar for wild blueberry ($613/ha) and cranberry ($689/ha). Marginal profit from incrementally adding more hives per ha was greater from stocking a third/fourth hive for cranberry ($6206/ha) than stocking a ninth/10th hive for wild blueberry ($556/ha), given the greater initial responsiveness of yield, revenue, and profit using rented honey bee hives in cranberry compared with wild blueberry. Both crops’ producers were willing to annually invest only $140–188/ha in wild pollination enhancements on their farms, justifying government financial support. Consumers are willing to pay ≈6.7 times more to support wild bees than producers, which indicates a potential source for market-based subsidies for invertebrate conservation.

Environments↗
USGS2021

Applying biodiversity metrics as surrogates to a habitat conservation plan

Unabated urbanization has led to environmental degradation and subsequent biodiversity loss across the globe. As an outcome of unmitigated land use, multi-jurisdictional agencies have developed land use plans that attempt to protect threatened or endangered species across selected areas by which some trade-offs between harm to species and additional conservation approaches are allowed among the partnering organizations. Typical conservation plans can be created to focus on single or multiple species, and although they may protect a species or groups of species, they may not account for biodiversity or its protection across the given area. We applied an approach that clustered deductive habitat models for terrestrial vertebrates into metrics that serve as surrogates for biodiversity and relate to ecosystem services. In order to evaluate this process, we collaborated with the partnering agencies who are creating a Multi-Species Habitat Conservation Plan in southern California and compared it to the entire Mojave Desert Ecoregion. We focused on total terrestrial vertebrate species richness and taxon groupings representing amphibians, birds, mammals, and reptiles, and two special status species using the Normalized Index of Biodiversity (NIB). The conservation planning area had a lower NIB and was less species rich than the Mojave Desert Ecoregion, but the Mojave River riparian corridor had a higher NIB and was more species-rich, and while taxon analysis varied across the geographies, this pattern generally held. Additionally, we analyzed desert tortoise ( Gopherus agassizii ) and desert kit fox ( Vulpes macrotis arsipus ) as umbrella species and determined that both species are associated with increased NIB and large numbers of species for the conservation area. Our process provided the ability to incorporate value-added surrogate information into a formal land use planning process and used a metric, NIB, which allowed comparison of the various planning areas and geographic units. Although this process has been applied to Apple Valley, CA, and other geographies within the U.S., the approach has practical application for other global biodiversity initiatives.

California↗
USGS2022

Are existing modeling tools useful to evaluate outcomes in mangrove restoration and rehabilitation projects? A minireview

Ecosystem modeling is a critical process for understanding complex systems at spatiotemporal scales needed to conserve, manage, and restore ecosystem services (ESs). Although mangrove wetlands are sources of ESs worth billions of dollars, there is a lack of modeling tools. This is reflected in our lack of understanding of mangroves’ functional and structural attributes. Here, we discuss the “state of the art” of mangrove models used in the planning and monitoring of R/R projects during the last 30 years. The main objectives were to characterize the most frequent modeling approach, their spatiotemporal resolution, and their current utility/application in management decisions. We identified 281 studies in six broad model categories: conceptual, agent-based (ABM), process-based (PBM), spatial, statistical, and socioeconomic/management (ScoEco). The most widely used models are spatial and statistical, followed by PBM, ScoEco, and conceptual categories, while the ABMs were the least frequently used. Yet, the application of mangrove models in R/R projects since the early 1990s has been extremely limited, especially in the mechanistic model category. We discuss several approaches to help advance model development and applications, including the targeted allocation of potential revenue from global carbon markets to R/R projects using a multi-model and integrated approach.

Forests↗
USGS2016

Regional differences in upland forest to developed (urban) land cover conversions in the conterminous U.S., 1973–2011

In this U.S. Geological Survey study of forest land cover across the conterminous U.S. (CONUS), specific proportions and rates of forest conversion to developed (urban) land were assessed on an ecoregional basis. The study period was divided into six time intervals between 1973 and 2011. Forest land cover was the source of 40% or more of the new urban land in 35 of the 84 ecoregions located within the CONUS. In 11 of these ecoregions this threshold exceeded in every time interval. When the percent of change, forest to urban, was compared to the percent of forest in each ecoregion, 58 ecoregions had a greater percent of change and, in six of those, change occurred in every time interval. Annual rates of forest to urban land cover change of 0.2% or higher occurred in 12 ecoregions at least once and in one ecoregion in all intervals. There were three ecoregions where the above conditions were met for nearly every time interval. Even though only a small number of the ecoregions were heavily impacted by forest loss to urban development within the CONUS, the ecosystem services provided by undeveloped forest land cover need to be quantified more completely to better inform future regional land management.

Conterminous United States↗
USGS2015

Economic assessment of the use value of geospatial information

Geospatial data inform decision makers. An economic model that involves application of spatial and temporal scientific, technical, and economic data in decision making is described. The value of information (VOI) contained in geospatial data is the difference between the net benefits (in present value terms) of a decision with and without the information. A range of technologies is used to collect and distribute geospatial data. These technical activities are linked to examples that show how the data can be applied in decision making, which is a cultural activity. The economic model for assessing the VOI in geospatial data for decision making is applied to three examples: (1) a retrospective model about environmental regulation of agrochemicals; (2) a prospective model about the impact and mitigation of earthquakes in urban areas; and (3) a prospective model about developing private–public geospatial information for an ecosystem services market. Each example demonstrates the potential value of geospatial information in a decision with uncertain information.

ISPRS International Journal of Geo-Information↗
USGS2016

Mineral resources: Reserves, peak production and the future

The adequacy of mineral resources in light of population growth and rising standards of living has been a concern since the time of Malthus (1798), but many studies erroneously forecast impending peak production or exhaustion because they confuse reserves with “all there is”. Reserves are formally defined as a subset of resources, and even current and potential resources are only a small subset of “all there is”. Peak production or exhaustion cannot be modeled accurately from reserves. Using copper as an example, identified resources are twice as large as the amount projected to be needed through 2050. Estimates of yet-to-be discovered copper resources are up to 40-times more than currently-identified resources, amounts that could last for many centuries. Thus, forecasts of imminent peak production due to resource exhaustion in the next 20–30 years are not valid. Short-term supply problems may arise, however, and supply-chain disruptions are possible at any time due to natural disasters (earthquakes, tsunamis, hurricanes) or political complications. Needed to resolve these problems are education and exploration technology development, access to prospective terrain, better recycling and better accounting of externalities associated with production (pollution, loss of ecosystem services and water and energy use).

Resources↗
USGS2020

Mapping forested wetland inundation in the Delmarva Peninsula, USA: Use of deep learning model

The Delmarva Peninsula in the eastern United States is dominated by thousands of small, forested depressional wetlands that are highly sensitive to climate change and climate variability but provide critical ecosystem services. Due to the relatively small size of these depressional wetlands and occurrence under forest canopy cover, it is very challenging to map their inundation status based on existing remote sensing data and traditional classification approaches. In this study, we applied a state-of-the-art deep semantic segmentation network to map forested wetland inundation in the Delmarva region by integrating leaf-off Worldview-3 (WV3) multispectral data with fine resolution light detection and ranging (lidar) intensity and topographic data, including digital elevation model (DEM) and topographic wetness index (TWI). Wetland inundation maps generated from lidar intensity were used for model calibration and validation. The wetland inundation map results were also validated by field polygons and compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and a random forest output from a previous study. Our results demonstrate that our deep learning model can accurately determine inundation status with an overall accuracy of 95% against field data and high overlap with lidar mapped inundation. The integration of topographic metrics in deep learning model can improve classification accuracy in depressional wetlands. This study highlights the great potential of deep learning models to map wetland inundation through use of high resolution optical and lidar remote sensing datasets.

Maryland↗
USGS2021

Riparian area changes in greenness and water use on the Lower Colorado River in the USA from 2000-2020

Declines in riparian ecosystem greenness and water use have been observed in the delta of the Lower Colorado River (LCR) since 2000. The purpose of our case study was to measure these metrics on the U.S. side of the border between Hoover and Morelos Dams to see if declining greenness was unique to the portion of the river in Mexico. In this case study, five riparian reaches of the LCR from Hoover to Morelos Dam since 2000 were studied to evaluate trends in riparian ecosystem health. We measure these riparian woodlands using remotely sensed measurements of the two-band Enhanced Vegetation Index (EVI2; a proxy for greenness); daily evapotranspiration (ET; mmd −1 ) using EVI2 (ET(EVI2)); and an annualized ET based on EVI2, the Phenology Assessment Metric (PAM ET), an annualized ET using Landsat time-series. A key finding is that riparian health and its water use has been in decline since 2000 on the U.S. portion of the LCR, depicting a loss of green vegetation over the last two decades. EVI2 results show a decline of −13.83%, while average daily ET(EVI2) between the first and last decade had a decrease of over 1 mmd −1 (−27.30%) and the respective average PAM ET losses were 170.91 mmyr −1 (−17.95%). The difference between the first and last five-year periods, 2000–2005 and 2016–2020, showed the largest decrease in daily ET(EVI) of 1.24 mmd −1 (−32.61%). These declines come from a loss in healthy, green, riparian plant-cover, not a change in plant water use efficiency nor efficient use of managed water resources. Our results suggest further deterioration of biodiversity, wildlife habitat and other key ecosystem services on the U.S. portion of the LCR.

Arizona, California↗
USGS2022

Integration of satellite-based optical and synthetic aperture radar imagery to estimate winter cover crop performance in cereal grasses

The magnitude of ecosystem services provided by winter cover crops is linked to their performance (i.e., biomass and associated nitrogen content, forage quality, and fractional ground cover), although few studies quantify these characteristics across the landscape. Remote sensing can produce landscape-level assessments of cover crop performance. However, commonly employed optical vegetation indices (VI) saturate, limiting their ability to measure high-biomass cover crops. Contemporary VIs that employ red-edge bands have been shown to be more robust to saturation issues. Additionally, synthetic aperture radar (SAR) data have been effective at estimating crop biophysical characteristics, although this has not been demonstrated on winter cover crops. We assessed the integration of optical (Sentinel-2) and SAR (Sentinel-1) imagery to estimate winter cover crops biomass across 27 fields over three winter–spring seasons (2018–2021) in Maryland. We used log-linear models to predict cover crop biomass as a function of 27 VIs and eight SAR metrics. Our results suggest that the integration of the normalized difference red-edge vegetation index (NDVI_RE1; employing Sentinel-2 bands 5 and 8A), combined with SAR interferometric (InSAR) coherence, best estimated the biomass of cereal grass cover crops. However, these results were season- and species-specific (R 2 = 0.74, 0.81, and 0.34; RMSE = 1227, 793, and 776 kg ha −1 , for wheat ( Triticum aestivum L.), triticale ( Triticale hexaploide L.), and cereal rye ( Secale cereale ), respectively, in spring (March–May)). Compared to the optical-only model, InSAR coherence improved biomass estimations by 4% in wheat, 5% in triticale, and by 11% in cereal rye. Both optical-only and optical-SAR biomass prediction models exhibited saturation occurring at ~1900 kg ha −1 ; thus, more work is needed to enable accurate biomass estimations past the point of saturation. To address this continued concern, future work could consider the use of weather and climate variables, machine learning models, the integration of proximal sensing and satellite observations, and/or the integration of process-based crop-soil simulation models and remote sensing observations.

Maryland↗
USGS2016

Comparing three approaches of evapotranspiration estimation in mixed urban vegetation; field-based, remote sensing-based and observational-based methods

Despite being the driest inhabited continent, Australia has one of the highest per capita water consumptions in the world. In addition, instead of having fit-for-purpose water supplies (using different qualities of water for different applications), highly treated drinking water is used for nearly all of Australia’s urban water supply needs, including landscape irrigation. The water requirement of urban landscapes, and particularly urban parklands, is of growing concern. The estimation of ET and subsequently plant water requirements in urban vegetation needs to consider the heterogeneity of plants, soils, water and climate characteristics. Accurate estimation of evapotranspiration (ET), which is the main component of a plant’s water requirement, in urban parks is highly desirable because this water maintains the health of green infrastructure and this in turn provides essential ecosystem services. This research contributes to a broader effort to establish sustainable irrigation practices within the Adelaide Parklands in Adelaide, South Australia.

Remote Sensing↗
USGS2016

Differential heating in the Indian Ocean differentially modulates precipitation in the Ganges and Brahmaputra basins

Indo-Pacific sea surface temperature dynamics play a prominent role in Asian summer monsoon variability. Two interactive climate modes of the Indo-Pacific—the El Niño/Southern Oscillation (ENSO) and the Indian Ocean dipole mode—modulate the amount of precipitation over India, in addition to precipitation over Africa, Indonesia, and Australia. However, this modulation is not spatially uniform. The precipitation in southern India is strongly forced by the Indian Ocean dipole mode and ENSO. In contrast, across northern India, encompassing the Ganges and Brahmaputra basins, the climate mode influence on precipitation is much less. Understanding the forcing of precipitation in these river basins is vital for food security and ecosystem services for over half a billion people. Using 28 years of remote sensing observations, we demonstrate that (i) the tropical west-east differential heating in the Indian Ocean influences the Ganges precipitation and (ii) the north-south differential heating in the Indian Ocean influences the Brahmaputra precipitation. The El Niño phase induces warming in the warm pool of the Indian Ocean and exerts more influence on Ganges precipitation than Brahmaputra precipitation. The analyses indicate that both the magnitude and position of the sea surface temperature anomalies in the Indian Ocean are important drivers for precipitation dynamics that can be effectively summarized using two new indices, one tuned for each basin. These new indices have the potential to aid forecasting of drought and flooding, to contextualize land cover and land use change, and to assess the regional impacts of climate change.

Remote Sensing↗
USGS2016

Avoiding decline: Fostering resilience and sustainability in midsize cities

Eighty-five percent of United States citizens live in urban areas. However, research surrounding the resilience and sustainability of complex urban systems focuses largely on coastal megacities (>1 million people). Midsize cities differ from their larger counterparts due to tight urban-rural feedbacks with their immediate natural environments that result from heavy reliance and close management of local ecosystem services. They also may be less path-dependent than larger cities due to shorter average connection length among system components, contributing to higher responsiveness among social, infrastructural, and ecological feedbacks. These distinct midsize city features call for a framework that organizes information and concepts concerning the sustainability of midsize cities specifically. We argue that an integrative approach is necessary to capture properties emergent from the complex interactions of the social, infrastructural, and ecological subsystems that comprise a city system. We suggest approaches to estimate the relative resilience of midsize cities, and include an example assessment to illustrate one such estimation approach. Resilience assessments of a midsize city can be used to examine why some cities end up on sustainable paths while others diverge to unsustainable paths, and which feedbacks may be partially responsible. They also provide insight into how city planners and decision makers can use information about the resilience of midsize cities undergoing growth or shrinkage relative to their larger and smaller counterparts, to transform them into long-term, sustainable social-ecological systems.

Sustainability↗
USGS2019

Wetland biomass and productivity in Coastal Louisiana: Base line data (1976-2015) and knowledge gaps for the development of spatially explicit models for ecosystem restoration and rehabilitation initiatives

Coastal Louisiana host 37% of the coastal wetland area in the conterminous US, including one of the deltaic coastal regions more susceptible to the synergy of human and natural impacts causing wetland loss. As a result of the construction of flood protection infrastructure, dredging of channels across wetlands for oil/gas exploration and maritime transport activities, coastal Coastal Louisiana hosts 37% of the coastal wetland area in the conterminous US, including one of the deltaic coastal regions more susceptible to the synergy of human and natural impacts causing wetland loss. As a result of the construction of flood protection infrastructure, dredging of channels across wetlands for oil/gas exploration and maritime transport activities, coastal Louisiana has lost approximately 4900 km2 of wetland area since the early 1930s. Despite the economic relevance of both wetland biomass and net primary productivity (NPP) as ecosystem services, there is a lack of vegetation simulation models to forecast the trends of those functional attributes at the landscape level as hydrological restoration projects are implemented. Here, we review the availability of peer-reviewed biomass and NPP wetland data (below and aboveground) published during the period 1976–2015 for use in the development, calibration and validation of high spatial resolution (<200 m × 200 m) vegetation process-based ecological models. We discuss and list the knowledge gaps for those species that represent vegetation community associations of ecological importance, including the long-term research issues associated to limited number of paired belowground biomass and productivity studies across hydrological basins currently undergoing different freshwater diversions management regimes and hydrological restoration priorities.

Louisiana↗