Increased water temperature from global climate change may exacerbate existing stresses to eelgrass (Zostera marina) meadows throughout the northeastern United States, possibly leading to declines in populations. In this study, we developed a data-driven model to estimate how seagrass distribution and abundance will likely change with expected temperature increases under climate-change scenarios and applied the model to Pleasant Bay, Massachusetts. Long-term seagrass and water quality monitoring data along with satellite temperature data were used to generate the spatial distribution of environmental drivers across the Bay. These data were then used in a 0-D point-model that incorporated both empirical and mechanistic relationships to predict future spatial seagrass distribution and abundance assuming increases of 1.2°C and 1.95°C by the year 2050. The model demonstrated decline in distribution and abundance with increasing temperature alongside estimated 49 to 93% reductions in biomass. There was a complete loss of regions able to sustain seagrass under the highest temperature scenario. Most of the predicted loss occurred along the shallow and deep edges of the meadows effectively squeezing eelgrass into a narrow depth range where both light and temperature conditions remain favorable for eelgrass growth. These results are intended to help inform the development of targeted conservation and management actions to address the region-wide downward trajectory and facilitate recovery.

Increased water temperature from global climate change may exacerbate existing stresses to eelgrass (Zostera marina) meadows throughout the northeastern United States, possibly leading to declines in populations. In this study, we developed a data-driven model to estimate how seagrass distribution and abundance will likely change with expected temperature increases under climate-change scenarios and applied the model to Pleasant Bay, Massachusetts. Long-term seagrass and water quality monitoring data along with satellite temperature data were used to generate the spatial distribution of environmental drivers across the Bay. These data were then used in a 0-D point-model that incorporated both empirical and mechanistic relationships to predict future spatial seagrass distribution and abundance assuming increases of 1.2°C and 1.95°C by the year 2050. The model demonstrated decline in distribution and abundance with increasing temperature alongside estimated 49 to 93% reductions in biomass. There was a complete loss of regions able to sustain seagrass under the highest temperature scenario. Most of the predicted loss occurred along the shallow and deep edges of the meadows effectively squeezing eelgrass into a narrow depth range where both light and temperature conditions remain favorable for eelgrass growth. These results are intended to help inform the development of targeted conservation and management actions to address the region-wide downward trajectory and facilitate recovery.

This dataset includes metrics of seagrass productivity and resilience collected from field sites along the Atlantic coast of Nova Scotia, Canada. Field sites were located across a gradient of temperature and light conditions. Sampling was conducted monthly from May 2018 to July 2019. Seagrass density and plants were sampled at 10 haphazardly distributed sampling stations within each seagrass bed at approximately the same depth. Stations were ~10m apart and at least 2m from any seagrass-bare interface. Quadrats were used to determine vegetative and reproductive shoot density, and hand corers to collect seagrass above and belowground biomass. Three plants from each sampling station were also collected and processed in the laboratory for length and width leaf 3, number leaves per shoot, rhizome width, and rhizome water soluble carbohydrates. Also included in this data set are time-series records of bottom temperature at each site measured in 15-mins intervals using HOBO TidbiTv2 loggers. Cite this data as: Wong, Melisa C., and Michael Dowd. 2023. “The Role of Short-Term Temperature Variability and Light in Shaping the Phenology and Characteristics of Seagrass Beds.” Ecosphere 14(11): e4698. https://doi.org/10.1002/ecs2.4698

Salinity regimes in coastal ecosystems are highly dynamic and driven by complex geomorphic and hydrological processes. Estuarine biota are generally adapted to salinity fluctuation, but are vulnerable to salinity extremes. Characterizing coastal salinity regime for ecological studies therefore requires representing extremes of salinity ranges at various time scales relevant to ecology (e.g., daily, monthly, seasonally). This data release provides supporting data for the journal article titled, "Quantifying uncertainty in coastal salinity regime for biological application using quantile regression," by Yurek et al. (2022). A spatially-resolved model was developed that derives quantile distributions of salinity related to various landscape variables, such as tidal forcing, wind velocity and direction, and freshwater discharge into the Suwannee Sound estuary. The model also considers various time scales of freshwater streamflow, from daily to bi-weekly scales, which represent terrestrial watershed dynamics such as time-of-travel of overland flow from headwaters to the coast. This data release provides programming routines and supporting data for the model, including: (1) scripts used to run the model written in R programming language, (2) input data used to fit the model, and (3) model output predictions across the spatial extent of the Suwannee Sound estuary. The predictions of the model represent a method of quantifying uncertainty in predictions, and represent approximate ranges of salinity conditions. These predictions are intended for use in future ecological modeling studies and analyses of impacts of salinity uncertainty on estuarine biota. They are limited by the data set used here and are not intended to indicate exact levels for any given location or time.

Salinity regimes in coastal ecosystems are highly dynamic and driven by complex geomorphic and hydrological processes. Estuarine biota are generally adapted to salinity fluctuation, but are vulnerable to salinity extremes. Characterizing coastal salinity regime for ecological studies therefore requires representing extremes of salinity ranges at various time scales relevant to ecology (e.g., daily, monthly, seasonally). This data release provides supporting data for the journal article titled, "Quantifying uncertainty in coastal salinity regime for biological application using quantile regression," by Yurek et al. (2022). A spatially-resolved model was developed that derives quantile distributions of salinity related to various landscape variables, such as tidal forcing, wind velocity and direction, and freshwater discharge into the Suwannee Sound estuary. The model also considers various time scales of freshwater streamflow, from daily to bi-weekly scales, which represent terrestrial watershed dynamics such as time-of-travel of overland flow from headwaters to the coast. This data release provides programming routines and supporting data for the model, including: (1) scripts used to run the model written in R programming language, (2) input data used to fit the model, and (3) model output predictions across the spatial extent of the Suwannee Sound estuary. The predictions of the model represent a method of quantifying uncertainty in predictions, and represent approximate ranges of salinity conditions. These predictions are intended for use in future ecological modeling studies and analyses of impacts of salinity uncertainty on estuarine biota. They are limited by the data set used here and are not intended to indicate exact levels for any given location or time.

Marine carbon dioxide removal (mCDR) approaches are under development to mitigate the effects of climate change by sequestering carbon in stable reservoirs, with potential co-benefits of local reduction of coastal acidification impacts. One such method is ocean alkalinity enhancement (OAE). A specific OAE method is the generation of aqueous alkalinity via electrochemistry to enhance the alkalinity of the receiving water by the extraction of acid from seawater, thereby avoiding issues of solid dissolution kinetics and the release of impurities into the ocean from alkaline minerals. While electrochemical acid extraction is a promising method for increasing the carbon dioxide sequestration potential of the ocean, the biological effects of increasing seawater alkalinity and pH within an OAE project site are relatively unknown. This study aims to address this knowledge gap by testing the effects of increased pH and alkalinity, delivered in the form of aqueous NaOH, on two eelgrass epifauna in the U.S. Pacific Northwest, Taylor’s sea hare (Phyllaplysia taylori) and eelgrass isopod (Idotea resecata), chosen for their ecological importance as salmon prey and for their roles in eelgrass ecosystems. Four-day experiments were conducted in closed bottles to allow measurements of the evolution of carbonate species throughout the experiment with water refreshed twice daily to maintain elevated pH, across pHNBS treatments ranging from 7.8 to 9.3. Sea hares experienced mortality in all pH treatments, ranging from 37% mortality at pHNBS 7.8 to 100% mortality at pHNBS 9.3. Isopods experienced lower mortality rates in all treatment groups, ranging from 13% at pHNBS 7.8 to 21% at pHNBS 9.3, which did not significantly increase with higher pH treatments. These experiments represent an extreme of constant exposure to elevated pH and alkalinity, which should be considered in the context of both the natural variation and the dilution of alkalinity experienced by marine communities across an OAE project site. Different invertebrate species will likely have different responses to increased pH and alkalinity, depending on their physiological vulnerabilities. Investigation of the potential vulnerabilities of local marine species will help inform the decision-making process regarding mCDR planning and permitting.

The Common Eelgrass (Zostera marina) monitoring program started in 2024 and follows a modified protocol based on the SeagrassNet protocol (https://www.scribd.com/document/39219811/Sea-Grass-Net-Manual-2006-Worldwide). It intends to track changes at 4 permanent sites composed of 3 transects/site and 12 quadrats/transect. The parameters recorded include percentage cover in a 50 X 50 cm quadrat, density (number of shoots) in a 25 X 25 cm quadrat and above + below ground biomass collected with a core sampler with a diameter of 7 cm.

A biogeochemistry model was developed to examine plant gross primary productivity (GPP), net primary productivity (NPP), plant respiration, soil respiration, soil organic carbon sequestration rate and storage under scenarios of drought and normal conditions at Tidal Freshwater Forested Wetlands (TFFW) sites along the Waccamaw River and Savannah River in the Southeastern United States.

A biogeochemistry model was developed to examine plant gross primary productivity (GPP), net primary productivity (NPP), plant respiration, soil respiration, soil organic carbon sequestration rate and storage under scenarios of drought and normal conditions at Tidal Freshwater Forested Wetlands (TFFW) sites along the Waccamaw River and Savannah River in the Southeastern United States.