The U.S. Northeast Shelf Large Marine Ecosystem, supports some of the nation’s most economically valuable coastal fisheries, yet most of this revenue comes from shellfish that are sensitive to ocean acidification (OA). Furthermore, the weakly buffered northern region of this area is expected to have greater susceptibility to OA. Existing OA observations in the NES do not sample at the time, space, and depth scales needed to capture the physical, biological, and chemical processes occurring in this dynamic coastal shelf region. Specific to inorganic carbon and OA, the data available in the region has not been leveraged to conduct a comprehensive regional-scale analysis that would increase the ability to understand and model seasonal-scale, spatial-scale, and subsurface carbonate chemistry dynamics, variability, and drivers in the NES. This project optimizes the NES OA observation network encompassing the Mid-Atlantic and Gulf of Maine regions by adding seasonal deployments of underwater gliders equipped with transformative, newly developed and tested deep ISFET-based pH sensors and additional sensors (measuring temperature, salinity for total alkalinity and aragonite saturation [ΩArag] estimation, oxygen, and chlorophyll), optimizing existing regional sampling to enhance carbonate chemistry measurements in several key locations, and compiling and integrating existing OA assets. The researchers will apply these data to an existing NES ocean ecosystem/biogeochemical (BGC) model that resolves carbonate chemistry and its variability. 

Over the past 15 years, waters in the Gulf of Maine have taken up
CO2at a rate significantly slower than that observed in the open oceans due to a combination of
the extreme warming experienced in the region and an increased presence of well-buffered Gulf
Stream water. The reduced uptake of CO2 by the shelves could
also alter local acidification rate, which differ from the global rates. The intrusion of
anthropogenic CO2is not the only mechanism that can reduce Ωarag within coastal surface waters.
Local processes like freshwater delivery, eutrophication, water column metabolism, and
sediment interactions that drive variability on regional scales can also modify spatial variability
in Ωarag. Global projections cannot resolve these local processes with resolution of a degree
or more. Some high-resolution global projections have been developed which perform well in
some coastal settings . However, these simulations do not include regional
biogeochemical processes described above which can amplify or dampen these global changes,
particularly in coastal shelf regions. Our hypothesis is that a regionally downscaled projection
for the east coast of the US can be used to evaluate the ability of the existing observational
network to detect changes in ocean acidification relevant stressors for scallops and propose a
process-based strategy for the network moving forward.

The wild oyster industry has suffered repeated collapses in the Chesapeake Bay due to overharvesting, disease, and declining environmental conditions. How future conditions will affect the Eastern oyster remain uncertain, not only because these conditions such as increased freshwater are difficult to predict , but also because the interactions between stressors such as ocean acidification, temperature, nutrient runoff and sea level rise could lead to unexpected chemical, biological, and economic change. The changes in stressors and their impacts do not always proceed in a straight line.The potential responses of various life stages of the Eastern oyster to stressors like acidification and eutrophication has received little attention. This project will study the impact of different stressors to Chesapeake Bay, a large estuarine system, and the Eastern oyster. The study will bring together different models to understand the relationship between biogeochemical cycling of carbon, oxygen, and nutrients, oyster growth and survival, and oyster economic profitability in the Chesapeake Bay ecosystem. The project will provide insights into future conditions and habitats where aquaculture and wild oyster populations may be most vulnerable to the climate and ocean changes.

How will nearshore and coastal ecosystems respond to ocean and coastal acidification in the Northeast? How will these changes affect human communities? An absence of actionable information and understanding of the dynamic nature of coastal acidification is a major challenge to Northeast seafood industry, resource managers, and coastal policymakers. This project will expand the existing Northeast Coastal Ocean Forecast System to develop actionable guidance for coastal water quality and marine resource managers through workshops and direct engagement. Workshops and focus groups will be held to determine information needs, decision scenarios, modeling priorities, and options for delivering actionable information for three specific users: (1) water quality managers and monitoring systems, (2) oyster growers, and (3) the wild harvest shellfishing industry. The research will focus on advancing ocean acidification detection and warning systems that take into account other environmental stressors in Northeast coastal waters.

Submerged Aquatic Vegetation (SAV), such as eelgrass, could mitigate the harmful impacts of ocean acidification on Eastern oysters by reducing the acidity of waters where oysters grow. These underwater grasses take up carbon dioxide and release oxygen into coastal waters, reducing the exposure of marine organisms to increases in acidity conditions that slow or stop oyster growth and reproduction. Oysters, in turn, improve water clarity forseagrasses to thrive by filtering particles out of the water and allowing more sunlight to penetrate. This modeling project will identify the threshold of acidification beyond which the economically important Eastern oyster is negatively impacted and will evaluate the potential benefit of seagrasses in protecting oysters and the ecosystem services they provide. The modeling tool will also identify the acidification conditions in which seagrass restoration is most helpful and when the economic benefits of this restoration to Easter oyster production outweigh the costs. At the end of this project, the final model will be freely available as an online tool and will help scientists, managers and oyster growers assess the potential for both seagrass and oyster restoration.

Alaska is expected to experience ocean acidification faster than any other United States coastal waters, primarily due to its colder water which absorbs more carbon dioxide than warmer waters. With seafood industry job incomes over $1.5 billion annually and a communities that rely on healthy oceans for subsistence, nutrition, and culture, increased ocean acidification is expected to have significant implications. Research on the potential impact to salmon has emerged as one of the top priorities, identified during a 2016 statewide workshop and stakeholder survey. Despite the economic importance of salmon, little research has been done on the effects of ocean acidification on salmon and the fishing industry and communities that depends on salmon. Acidification has been shown to impair coho salmon’s ability to smell and detect their prey. It has also been shown to reduce pink salmon growth rates. In addition, future ocean acidification is expected to affect salmon prey species, which is expected to affect Pacific salmon survival, abundance and productivity. This project will investigate the implication of ocean acidification thresholds and major ecosystem shifts in the Gulf of Alaska on salmon. Integrated human-ecological models will be developed to simulate management scenarios to assess the benefits of pre-emptive adaptation planning and policy making. The information from modeling these scenarios will help create decision tools for salmon managers.

The objective of this project is to make significant strides in bridging the gap between scientific knowledge and current management needs by integrating existing biogeochemical model frameworks, field measurements, and experimental work toward the goals of (1) delineating atmospheric and eutrophication drivers of Chesapeake Bay acidification and improve our understanding of estuarine carbonate chemistry, (2) developing a spatially explicit framework to identify shellfish restoration areas most and least prone to acidification impacts, and (3) better understanding feedbacks associated with future environmental conditions and shellfish restoration goals estuary-wide and within a model tributary. This effort includes (1) a field campaign to make the first comprehensive study of the spatial and temporal variability in the carbonate system in Chesapeake Bay, (2) experiments to quantify both carbonate and nutrient exchange between intact oyster reefs and the surrounding water while measuring response of these fluxes to reef structure and acidification, and (3) an advancement in numerical modeling tools to simultaneously simulate the dynamics of eutrophication, hypoxia, carbonate chemistry, and oyster reef growth and interaction with the water-column under present and future conditions.

The California Current System (CCS) is one of the most biologically productive regions of the world ocean, but seasonal upwelling of low oxygen and low-pH waters makes it particularly vulnerable to even small additional reductions in O₂ and/or pH, which have both been observed in recent decades. Three prominent coastal phenomena have been implicated in precisely these changes: 1) large scale acidification and deoxygenation of the ocean associated with climate warming, 2) natural climate variability, and 3) anthropogenic pollution of coastal waters, especially from nutrient discharge and deposition.  The relative importance of these drivers has not been systematically evaluated, and yet is critical information in any cost-effective strategy to manage coastal resources at local scales.  Disentangling the magnitude and interaction of these different ecosystem stresses requites an integrated systems modeling approach that is carefully validated against available datasets.

The goals of this project are three-fold: 1) develop an ocean hypoxia and acidifcation (OHA) model of the CCS (Baja California to British Columbia), comprising the circulation, biogeochemical cycles, and lower-trophic ecosystem of the CCS, with regional downscaling in the Southern California Bight, Central Coast, and the Oregon Coast; 2) use the model to understand the relative contributions of natural climate variability, anthropogenically induced climate change, and anthropogenic inputs on the status and trends of OHA in the CCS; and 3) transmit these findings to coastal zone mangers and help them explore the implications for marine resource management and pollution control.

Why we care

Winter flounder are a commercially harvested finfish that occur within the Mid-Atlantic Bight and support fisheries in several U.S. states. Understanding the potential or realized effects on ocean acidification (OA) on this fish and the implications on fished populations is essential for building resilience for this fish and the people who depend on them. This project makes the link between experimental results on the effects on winter flounder and populations using a modeling approach.

What we're doing

We are using data from experimental studies of the effects of ocean acidification on winter flounder to construct realistic population-process models of marine finfish. 

The models are of an individual‐ based model (IBM) category that use detailed biological responses of individuals to OA.  This tool synthesizes OA data in two different ways.  First, it accumulates and connects data through mechanistic relationships between the environment and fish life‐history.  Second, it allows exploration of the population‐level consequences of CO2 effects (the source of OA) which explicitly include population effects carried over from the highly sensitive early life‐stages (ELS). This information is fundamental to understanding the community and ecosystem effects of OA on living marine resources.  

The project directs efforts at two different, complimentary levels.  At the more detailed, specific level, winter flounder – an economically important, well‐studied fish of Mid‐Atlantic to New England waters – will be used as a model subject. Past work provides estimates of CO₂ effects on key life‐history and ecological parameters (e.g., fertilization, larval growth, development, and survival) that will enhance and update the model to include these parameters. We will evaluate the winter flounder OA‐IBM under multiple scenarios:  high average levels of CO₂ representing future oceans in shelf habitats; high and variable CO2 depicting future inshore, estuarine habitats; and covariances of CO₂ with other environmental stressors (e.g., warmer waters, hypoxia).  

Benefits of our work

The models help resource managers and others assess and predict the potential impacts of ocean acidification on winter flounder. The project will produce a web‐based tool that allows users to add details from other marine finfish of the northeaster USA and OA‐affected processes as relevant OA data on those species become available. 

 

The aim of this project was to forecast effects of ocean acidification on the commercially important Alaska crab stocks including the Bristol Bay red king crab (BBRKC) fishery, which is part of a modern fisheries management program, the Bering Sea and Aleutian Islands (BSAI) crab rationalization program. To investigate the biological and economic impacts of OA, a linked bioeconomic model was developed that a) integrates predictions regarding trends over time in ocean pH, b) separates life-history stages for growth and mortality of juveniles and adults, and c) includes fishery impacts by analyzing catch and effort in both biological and economic terms. By coupling a pre-recruitment component with post-recruitment dynamics, the BBRKC bioeconomic model incorporates effects of OA on vulnerable juvenile crabs in combination with effects of fishing on the BBRKC population as a whole. Many types of projections under management strategies can be made using linked bioeconomic models.