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USGS2018

Use of imaging spectroscopy and LIDAR to characterize fuels for fire behavior prediction

To protect ecosystem services and the increasing wildland urban interface in a world with fire, comprehensive maps of wildland fuels are needed to predict fire behavior and effects. Traditionally, fuels have been categorized into a classification scheme whereby a single metric represents vegetation composition and structure, which can then be parameterized based on variable vegetation amount and condition. Remote sensing has been used to extrapolate between known field plots across the landscape, however until recently, those technologies have had limited ability to characterize fuels (e.g., composition, horizontal and vertical connectivity). Using new technologies (imaging spectroscopy and LIDAR), the objectives of this study are to assess: 1) how fuel characteristics observed from remote sensing affect categorical fuel classifications, and 2) how fuel characteristics affect landscape-scale fire behavior (spread rate, areal extent and perimeter). The analysis was conducted over the 2014 California King Fire that burned ~40,000 ha over lands with varying use and history and has unique remote sensing observations from before and after the fire. This analysis compares fuel classifications from a synergistic field, model, and Landsat approach (LANDFIRE) and products derived from the Airborne Visible/Infrared Imaging Spectrometer and LIDAR (MapFUELS). Each classification focuses on different fuel characteristics, which were then used to compare differences in a fire simulation model (CAWFE) and actual fire behavior. The results show that fuel characteristic inputs such as horizontal connectivity or fuel type and vertical structure affect fire spread rate and final fire extent (respectively). These results present the opportunity for future integration of fuel characteristics observed at coarser resolutions (900 m 2 ) into predictions of fire behavior a similar spatial resolutions (as opposed to the current standard based on empirical relationships between fuel and fire behavior at ~12 m 2 resolution).

Remote Sensing Applications: Society and Environme↗

USGS2011

Integration of Palmer Drought Severity Index and remote sensing data to simulate wetland water surface from 1910 to 2009 in Cottonwood Lake area, North Dakota

Spatiotemporal variations of wetland water in the Prairie Pothole Region are controlled by many factors; two of them are temperature and precipitation that form the basis of the Palmer Drought Severity Index (PDSI). Taking the 196 km 2 Cottonwood Lake area in North Dakota as our pilot study site, we integrated PDSI, Landsat images, and aerial photography records to simulate monthly water surface. First, we developed a new Wetland Water Area Index (WWAI) from PDSI to predict water surface area. Second, we developed a water allocation model to simulate the spatial distribution of water bodies at a resolution of 30 m. Third, we used an additional procedure to model the small wetlands (less than 0.8 ha) that could not be detected by Landsat. Our results showed that i) WWAI was highly correlated with water area with an R 2 of 0.90, resulting in a simple regression prediction of monthly water area to capture the intra- and inter-annual water change from 1910 to 2009; ii) the spatial distribution of water bodies modeled from our approach agreed well with the water locations visually identified from the aerial photography records; and iii) the R 2 between our modeled water bodies (including both large and small wetlands) and those from aerial photography records could be up to 0.83 with a mean average error of 0.64 km 2 within the study area where the modeled wetland water areas ranged from about 2 to 14 km 2 . These results indicate that our approach holds great potential to simulate major changes in wetland water surface for ecosystem service; however, our products could capture neither the short-term water change caused by intensive rainstorm events nor the wetland change caused by human activities.

North Dakota↗

USGS2016

Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014)

Remote sensing has proven a useful way of evaluating long-term trends in vegetation “greenness” through the use of vegetation indices like Normalized Differences Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In particular, analyses of greenness trends have been performed for large areas (continents, for example) in an attempt to understand vegetation response to climate. These studies have been most often used coarse resolution sensors like Moderate Resolution Image Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). However, trends in greenness are also important at more local scales, particularly in and around cities as vegetation offers a variety of valuable ecosystem services ranging from minimizing air pollution to mitigating urban heat island effects. To explore the ability to monitor greenness trends in and around cities, this paper presents a new way for analyzing greenness trends based on all available Landsat 5, 7, and 8 images and applies it to Guangzhou, China. This method is capable of including the effects of land cover change in the evaluation of greenness trends by separating the effects of abrupt and gradual changes, and providing information on the timing of greenness trends. An assessment of the consistency of surface reflectance from Landsat 8 with past Landsat sensors indicates biases in the visible bands of Landsat 8, especially the blue band. Landsat 8 NDVI values were found to have a larger bias than the EVI values; therefore, EVI was used in the analysis of greenness trends for Guangzhou. In spite of massive amounts of development in Guangzhou from 2000 to 2014, greenness was found to increase, mostly as a result of gradual change. Comparison of the greening magnitudes estimated from the approach presented here and a Simple Linear Trend (SLT) method indicated large differences for certain time intervals as the SLT method does not include consideration for abrupt land cover changes. Overall, this analysis demonstrates the importance of considering land cover change when analyzing trends in greenness from satellite time series in areas where land cover change is common.

Guangzhou↗

USGS2023

Elevation-based probabilistic mapping of irregularly flooded wetlands along the northern Gulf of Mexico coast

Irregularly flooded wetlands are found above the mean high water tidal datum and are exposed to tides and saltwater less frequently than daily. These wetlands provide important ecosystem services, such as providing habitat for fish and wildlife, enhancing water quality, ameliorating flooding impacts, supporting coastal food webs, and protecting upslope areas from erosion. Mapping irregularly flooded wetlands is challenging given their expansive coverage and dynamic nature. Furthermore, coastal wetlands are expected to change over the coming century due to sea-level rise and changes in the frequency and intensity of extreme storms. Consequently, coastal managers need baseline information on the spatial distribution of wetlands along with efficient and repeatable methods for observing changes. In this study, we used coastal wetlands from existing land use land cover data, best available lidar-derived digital elevation models, and Monte Carlo simulations to incorporate elevation uncertainty to create a probabilistic map of irregularly flooded wetlands along the northern Gulf of Mexico coast (USA). Our approach integrated findings from a review of coastal wetland elevation error in lidar datasets and an analysis of spatial autocorrelations of wetland elevation. We found a positive correlation ( r = 0.563, p < 0.0001) when comparing the probability estimated from a digital elevation model and in situ elevation observations. The differences in probability had a mean bias error of −0.04 (i.e., digital elevation model-based probability tends to be slightly lower), a mean absolute error of 0.20, and a root mean square error of 0.26. Beyond this overall validation, we explored error metrics for land cover classes and lidar collection details. To quantify areal coverage of the probabilistic output, we classified the probability values into equal bins using an interval of 0.33. The areal coverage of the lowest probability bin (“unlikely”; probability ≤0.33) was separated into the upper and lower portions of the irregularly flooded wetland zone. Of the coastal wetlands along the northern Gulf of Mexico coast about 38% were classified as unlikely and low with the greatest coverage in south Louisiana and the Everglades and around 33% were classified as unlikely and high with the greatest coverage in the Everglades and Texas. The relative coverage within the highest probability bin (“likely”; probability >0.66) covered around 13%, with the greatest coverage in south Florida, south Louisiana, and Texas. The framework developed in this study can be transferred to other coastal wetland areas and updated to observe changes with sea-level rise.

Gulf of Mexico coastline↗

USGS2024

Need and vision for global medium-resolution Landsat and Sentinel-2 data products

Global changes in climate and land use are threatening natural ecosystems, biodiversity, and the ecosystem services people rely on. This is why it is necessary to track and monitor spatiotemporal change at a level of detail that can inform science, management, and policy development. The current constellation of multiple Landsat and Sentinel-2 satellites collecting imagery at predominantly ≤ "> ≤ 30-m spatial resolution affords an opportunity for the generation of global medium- resolution products every few days. Our goal is to both identify the information needs and provide direction towards the generation of a suite of global, high-level, systematically-generated, medium-resolution products designed for both management and science. Our vision builds on the success of the NASA MODIS/VIIRS product suite, while recognizing the unique strengths of medium-resolution satellite data given their higher spatial resolution and longer time series. We propose a suite of 13 essential products that enable the characterization of the current state and changes in the biosphere, cryosphere, and hydrosphere, and would fill information needs identified by the Committee on Earth Observation Satellites for the Global Climate Observing System and the Global Terrestrial Observing System, by the National Research Council of the US National Academies in the decadal survey, and by others. These products are: land cover, land cover change, burned area, forest loss, vegetation indices, phenology, dynamic habitat indices, albedo, land surface temperature, snow cover, ice extent, surface water extent, and evapotranspiration. Furthermore, we provide a list of desirable products poised for addition to the essential products (e.g., crop type, emissivity, and ice sheet velocity). Lastly, we suggest aspirational products requiring further algorithm development (e.g., forest structure and crop yield). For the identified essential products, algorithms are in place, making it feasible to begin generating products systematically. These products should be accompanied by quality and accuracy assessments undertaken following consensus protocols. Five decades after the first Landsat satellite, and two decades after the MODIS products were first produced, it is time now for readily available, standardized, and consistent high-level products built upon medium-resolution imagery, thereby fulfilling the promise and the vision that inspired the Landsat program since its inception.

Remote Sensing of Environment↗

USGS2024

Predicting the effects of solar energy development on plants and wildlife in the Desert Southwest, United States

Utility-scale solar energy (USSE) is rapidly expanding and expected to compose the largest source of renewable-generated electricity in the United States and globally over the coming decades. Lands in the hot Desert Southwest (Chihuahuan, Mojave, Sonoran, and San Joaquin Deserts) are increasingly selected for USSE development because of their high solar irradiance. The Desert Southwest supports high biodiversity and provides many ecosystem services but is vulnerable to USSE disturbance and simultaneous stress from aridification and other growing land-use pressures. In this review, a framework is presented for predicting the effects of USSE development on plants and wildlife by linking disturbance types associated with USSE construction and operation to the traits and response strategies of species and guilds. Case studies from representative Desert Southwest species and guilds of conservation concern are used to: review known effects of USSE, predict unknown effects with the trait-based framework, and discuss mitigation strategies. This framework predicts that species with trait plasticity and broad ecological niches will be capable of exploiting USSE development, while species with specific habitat requirements and narrow niches will be more vulnerable. Opportunities for mitigation during development and operation that may lessen these effects are identified. This work is intended to inform USSE management decision-making and long-term planning, as well as encourage new research to test predicted effects and responses.

Renewable & Sustainable Energy Reviews (RSER)↗

USGS2016

Land use effects on pesticides in sediments of prairie pothole wetlands in North and South Dakota

Prairie potholes are the dominant wetland type in the intensively cultivated northern Great Plains of North America, and thus have the potential to receive pesticide runoff and drift. We examined the presence of pesticides in sediments of 151 wetlands split among the three dominant land use types, Conservation Reserve Program (CRP), cropland, and native prairie, in North and South Dakota in 2011. Herbicides (glyphosate and atrazine) and fungicides were detected regularly, with no insecticide detections. Glyphosate was the most detected pesticide, occurring in 61% of all wetlands, with atrazine in only 8% of wetlands. Pyraclostrobin was one of five fungicides detected, but the only one of significance, being detected in 31% of wetlands. Glyphosate was the only pesticide that differed by land use, with concentrations in cropland over four-times that in either native prairie or CRP, which were equal in concentration and frequency of detection. Despite examining several landscape variables, such as wetland proximity to specific crop types, watershed size, and others, land use was the best variable explaining pesticide concentrations in potholes. CRP ameliorated glyphosate in wetlands at concentrations comparable to native prairie and thereby provides another ecosystem service from this expansive program.

North Dakota, South Dakota↗

USGS2020

Estuarine submerged aquatic vegetation habitat provides organic carbon storage across a shifting landscape

Submerged aquatic vegetation (SAV) thrives across the estuarine salinity gradient providing valuable ecosystem services. Within the saline portion of estuaries, seagrass areas are frequently cited as hotspots for their role in capturing and retaining organic carbon (C org ). Non-seagrass SAV, located in the fresh to brackish estuarine areas, may also retain significant soil C org , yet their role remains unquantified. Given rapidly occurring landscape and salinity changes due to human and natural disturbances, landscape level carbon pool estimates from estuarine SAV habitat blue carbon estimates are needed. We assessed C org stocks in SAV habitat soils from estuarine freshwater to saline habitats (interior deltaic) to saline barrier islands (Chandeleur Island) within the Mississippi River Delta Plain (MRDP), Louisiana, USA. SAV habitats contain C org stocks equivalent to those reported for other estuarine vegetation types (seagrass, salt marsh, mangrove). Interior deltaic SAV C org stocks (231.6 ± 19.5 Mg C org ha −1 ) were similar across the salinity gradient, and significantly higher than at barrier island sites (56.6 ± 10.4 Mg C org ha −1 ). Within the MRDP, shallow water SAV habitat covers up to an estimated 28,000 ha, indicating that soil C org storage is potentially 6.4 ± 0.1 Tg representing an unaccounted C org pool. Extrapolated across Louisiana, and the Gulf of Mexico, this represents a major unaccounted pool of soil C org . As marshes continue to erode, the ability of coastal SAV habitat to offset some of the lost carbon sequestration may be valuable. Our estimates of C org sequestration rates indicated that conversion of eroding marsh to potential SAV habitat may help to offset the reduction of C org sequestration rates. Across Louisiana, we estimated SAV to offset this loss by as much as 79,000 Mg C yr −1 between the 1960s and 2000s.

Louisiana↗

USGS2024

Planning hydrological restoration of coastal wetlands: Key model considerations and solutions

The hydrological restoration of coastal wetlands is an emerging approach for mitigating and adapting to climate change and enhancing ecosystem services such as improved water quality and biodiversity. This paper synthesises current knowledge on selecting appropriate modelling approaches for hydrological restoration projects. The selection of a modelling approach is based on project-specific factors, such as costs, risks, and uncertainties, and aligns with the overall project objectives. We provide guidance on model selection, emphasising the use of simpler and less expensive modelling approaches when appropriate, and identifying situations when models may not be required for project managers to make informed decisions. This paper recognises and supports the widespread use of hydrological restoration in coastal wetlands by bridging the gap between hydrological science and restoration practices. It underscores the significance of project objectives, budget, and available data and offers decision-making frameworks, such as decision trees, to aid in matching modelling methods with specific project outcomes.

Science of the Total Environment↗

USGS2013

Biodiversity and disease: a synthesis of ecological perspectives on Lyme disease transmission.

Recent reviews have argued that disease control is among the ecosystem services yielded by biodiversity. Lyme disease (LD) is commonly cited as the best example of the ‘diluting’ effect of biodiversity on disease transmission, but many studies document the opposite relationship, showing that human LD risk can increase with forestation. Here, we unify these divergent perspectives and find strong evidence for a positive link between biodiversity and LD at broad spatial scales (urban to suburban to rural) and equivocal evidence for a negative link between biodiversity and LD at varying levels of biodiversity within forests. This finding suggests that, across zoonotic disease agents, the biodiversity–disease relationship is scale dependent and complex.

Trends in Ecology and Evolution↗

USGS2020

The IPBES global assessment: Pathways to action

The first Global Assessment of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services found widespread, accelerating declines in Earth’s biodiversity and associated benefits to people from nature. Addressing these trends will require science-based policy responses to reduce impacts, especially at national to local scales. Effective scaling of science-policy efforts driven by global and national assessments is a major challenge for turning assessment into action, and will require unprecedented commitments on the part of scientists to engage with communities of policy and practice. Fulfillment of science’s social contract with society, and with nature, will require strong institutional support for scientists’ participation in activities that transcend conventional research and publication.

Trends in Ecology and Evolution↗

USGS2022

Bright spots for inland fish and fisheries to guide future hydropower development

Hydropower production is one of the greatest threats to fluvial ecosystems and freshwater biodiversity. Now that we have entered the Anthropocene , there is an opportunity to reflect on what might constitute a ‘sustainable’ Anthropocene in the context of hydropower and riverine fish populations. Considering elements of existing practices that promote favorable social-ecological outcomes (i.e., ‘bright spots’) is timely given that there are plans to expand hydropower capacity in previously undammed rivers, intensify dam development in some of the world's largest river systems, and re-license existing facilities. We approach this from a pragmatic perspective: for the foreseeable future, hydropower will likely remain an important source of renewable electricity. To offer support for moving toward a more ‘sustainable’ Anthropocene, we provide syntheses of best practices during the siting, design, construction, operation, and compensation phases of hydropower development to minimize impacts on inland fish. For each phase, we offer positive examples (or what might be considered ‘bright spots’) pertaining to some of the approaches described within our syntheses, acknowledging that these projects may not be viewed as without ecological and (or) societal detriment by all stakeholders. Our findings underscore the importance of protecting critical habitat and free-flowing river reaches through careful site selection and basin-scale planning, infrastructure designs that minimize reservoir effects and facilitate safe passage of fish, construction of hydropower plants using best practices that minimize long-term damage, operating guidelines that mimic natural flow conditions, and compensation that is lasting, effective, inclusive, and locally relevant. Learning from these ‘bright spots’ may require engagement of diverse stakeholders, professionals, and governments at scales that extend well beyond a given site, river, or even basin. Indeed, environmental planning that integrates hydropower development into broader discussions of conserving regional biodiversity and ecosystem services will be of utmost importance.

Water Biology and Security↗

USGS2020

Critical shifts in trace metal transport and remediation performance under future low river flows

Exceptionally low river flows are predicted to become more frequent and more severe across many global regions as a consequence of climate change. Investigations of trace metal transport dynamics across streamflows reveal stark changes in water chemistry, metal transformation processes, and remediation effectiveness under exceptionally low-flow conditions. High spatial resolution hydrological and water quality datasets indicate that metal-rich groundwater will exert a greater control on stream water chemistry and metal concentrations because of climate change. This is because the proportion of stream water sourced from mined areas and mineralized strata will increase under predicted future low-flow scenarios (from 25% under Q45 flow to 66% under Q99 flow in this study). However, mineral speciation modelling indicates that changes in stream pH and hydraulic conditions at low flow will decrease aqueous metal transport and increase sediment metal concentrations by enhancing metal sorption directly to streambed sediments. Solute transport modelling further demonstrates how increases in the importance of metal-rich diffuse groundwater sources at low flow could minimize the benefits of point source metal contamination treatment. Understanding metal transport dynamics under exceptionally low flows, as well as under high flows, is crucial to evaluate ecosystem service provision and remediation effectiveness in watersheds under future climate change scenarios.

Environmental Science & Technology↗

USGS2025

Hydraulic connectivity and hydrochemistry influence microbial community structure in agriculturally-affected alluvial aquifers in the Midwestern United States

Alluvial aquifers can provide ecosystem services and drinking water, but much remains unknown about human effects on aquifer microbiomes. Therefore, we used amplicon sequencing and hydrochemical characterization to pair microbial communities with environmental conditions across 37 alluvial aquifer wells. The study region spanned eastern Iowa and southern Minnesota (USA) and contained a combination of drinking water and monitoring wells. In terms of microbial ecology, dominant phyla across the wells included Proteobacteria, Bacteroidota, Patescibacteria, Planctomycetota, and Nitrospirota. Tritium, an indicator of infiltration and surface water influence, was the highest correlated variable with the Shannon index (α-diversity) by the Spearman rank sum (ρ = 0.60) and one of only four significant environmental variables in the constrained correspondence analysis. We built random forest regression models to predict tritium concentrations from microbial family relative abundance (held-out testing coefficient of determination ( R 2 ) = 0.77 and mean absolute percentage error = 7%) and interpreted the models with Shapley additive explanation values. The most important families for predicting tritium concentrations were Nitrosopumilaceae and Methylomirabilaceae . Upwelling methane could contribute to the unusual coupling of ammonia oxidation by Nitrosopumilaceae with simultaneous nitrite-dependent methane oxidation by Methylomirabilaceae . Taken together, we illuminate the relationship among hydrochemistry, hydraulic connectivity, and alluvial aquifer microbiomes.

Iowa, Minnesota↗

USGS2010

Agricultural wetlands as potential hotspots for mercury bioaccumulation: Experimental evidence using caged fish

Wetlands provide numerous ecosystem services, but also can be sources of methylmercury (MeHg) production and export. Rice agricultural wetlands in particular may be important sites for MeHg bioaccumulation due to their worldwide ubiquity, periodic flooding schedules, and high use by wildlife. We assessed MeHg bioaccumulation within agricultural and perennial wetlands common to California’s Central Valley during summer, when the majority of wetland habitats are shallowly flooded rice fields. We introduced caged western mosquitofish ( Gambusia affinis ) within white rice ( Oryza sativa ), wild rice ( Zizania palustris ), and permanent wetlands at water inlets, centers, and outlets. Total mercury (THg) concentrations and body burdens in caged mosquitofish increased rapidly, exceeding baseline values at introduction by 135% to 1197% and 29% to 1566% among sites, respectively, after only 60 days. Mercury bioaccumulation in caged mosquitofish was greater in rice fields than in permanent wetlands, with THg concentrations at wetland outlets increasing by 12.1, 5.8, and 2.9 times over initial concentrations in white rice, wild rice, and permanent wetlands, respectively. In fact, mosquitofish caged at white rice outlets accumulated 721 ng Hg/fish in just 60 days. Mercury in wild mosquitofish and Mississippi silversides ( Menidia audens ) concurrently sampled at wetland outlets also were greater in white rice and wild rice than permanent wetlands. Within wetlands, THg concentrations and body burdens of both caged and wild fish increased from water inlets to outlets in white rice fields, and tended to not vary among sites in permanent wetlands. Fish THg concentrations in agricultural wetlands were high, exceeding 0.2 μg/g ww in 82% of caged fish and 59% of wild fish. Our results indicate that shallowly flooded rice fields are potential hotspots for MeHg bioaccumulation and, due to their global prevalence, suggest that agricultural wetlands may be important contributors to MeHg contamination.

California↗

USGS2018

Salt marsh loss affects tides and sediment budget in shallow bays

The current paradigm is that salt marshes and their important ecosystem services are threatened by global climate change; indeed, large marsh losses have been documented worldwide. Morphological changes associated with salt marsh erosion are expected to influence the hydrodynamics and sediment dynamics of coastal systems. Here the influence of salt marsh erosion on the tidal hydrodynamics and sediment storage capability of shallow bays is investigated. Hydrodynamics, sediment transport, and vegetation dynamics are simulated using the numerical framework Coupled Ocean‐Atmosphere‐Wave‐Sediment Transport in the Barnegat Bay‐Little Egg Harbor system, USA. We show that salt marsh erosion influences the propagation of tides into back‐barrier basins, reducing the periodic inundation and sediment delivery to marsh platforms. As salt marshes erode, the sediment trapping potential of marsh platforms decreases exponentially. In this test case, up to 50% of the sediment mass trapped by vegetation is lost once a quarter of the marsh area is eroded. Similarly, without salt marshes the sediment budget of the entire bay significantly declines. Therefore, a positive feedback might be triggered such that as the salt marsh retreats the sediment storage capacity of the system declines, which could in turn further exacerbate marsh degradation.

New Jersey↗

USGS2020

Modeling soil porewater salinity response to drought in tidal freshwater forested wetlands

There is a growing concern about the adverse effects of saltwater intrusion via tidal rivers, streams and creeks into tidal freshwater forested wetlands (TFFW) due to sea‐level rise (SLR) and intense and extended drought events. However, the magnitude and duration of porewater salinity in exceedance of plant salinity stress threshold (2 practical salinity units, psu) and the controlling factors remain unclear. In this study, we developed a TFFW soil porewater salinity model, in which the feedback mechanisms between soil salinity and evapotranspiration and hydraulic conductivity were incorporated. We selected sites (upper, middle, lower tidal freshwater forest sites and oligohaline marsh site) along the coastal floodplains of two rivers, the Waccamaw River (SC, USA) and the Savannah River (GA and SC, USA), that represent landscape salinity gradients from tidal influence of the Atlantic Ocean. The model results agreed well with field measurements and revealed that with drought‐induced saltwater intrusion, the mean annual soil porewater salinity and duration of elevated soil porewater salinity (> 2 psu) increased significantly compared to the normal (non‐drought) condition, posing a threat to the health and ecosystem services of TFFW even in the absence of SLR. Model results also showed more severe salinity stress under drought for the lower forest sites along the two rivers, where soil salinity values have already been at or in exceedance of the 2 psu threshold.

Georgia, South Carolina↗

USGS2020

Impacts of seagrass dynamics on the coupled long-term evolution of barrier-marsh-bay systems

Seagrass provides a wide range of economically and ecologically valuable ecosystem services, with shoreline erosion control often listed as a key service, but can also alter the sediment dynamics and waves within back-barrier bays. Here we incorporate seagrass dynamics into an existing barrier-marsh exploratory model, GEOMBEST++, to examine the coupled interactions of the back-barrier bay with both adjacent (marsh) and non-adjacent (barrier island) subsystems. While seagrass reduces marsh edge erosion rates and increases progradation rates in many of our 288 model simulations, seagrass surprisingly increases marsh edge erosion rates when sediment export from the back-barrier basin is negligible because the ability of seagrass to reduce the volume of marsh sediment eroded matters little for back-barrier basins in which all sediment is conserved. Our model simulations also suggest that adding seagrass to the bay subsystem leads to increased deposition in the bay, reduced sediment available to the marsh, and enhanced marsh edge erosion until the bay reaches a new, shallower equilibrium depth. In contrast, removing seagrass liberates previously-sequestered sediment that is then delivered to the marsh, leading to enhanced marsh progradation. Lastly, we find that seagrass reduces barrier island migration rates in the absence of back-barrier marsh by filling accommodation space in the bay. These model observations suggest that seagrass meadows operate as dynamic sources and sinks of sediment that can influence the evolution of coupled marsh and barrier island landforms in unanticipated ways.

Virginia↗