Ocean Acidification in the Gulf of Mexico: Drivers, Impacts, and Unknowns (Poster ID #162)

Emily B. Osborne¹, Xinping Hu² , Emily R. Hall³, Kimberly Yates⁴, Jennifer Vreeland-Dawson⁵, Kathryn Shamberger⁵, Leticia Barbero^1,6, José M Hernández-Ayón⁷, Fabian Gomez^1,8, Tacey Hicks⁵, Yuanyuan Xu^1,6, Melissa R. McCutcheon², Michael Acquafredda⁹, Cecilia Chapa-Balcorta¹⁰, Orion Norzagaray⁷, Denis Pierrot¹, Alain Munoz-Caravaca¹¹, Kerri L. Dobson⁹, Nancy Williams¹², Nancy N. Rabalais¹³, Padmanava Dash⁸, Hauke Kite-Powell¹⁴ and Casey Galvin¹⁴ 

(1) NOAA/AOML, Miami, FL, USA (2) Harte Research Institute for Gulf of Mexico Studies, Corpus Christi, TX, USA (3) Mote Marine Laboratory, Sarasota, FL, USA (4) United States Geological Survey, St. Petersburg, FL, USA (5) Texas A&M University, College Station, TX, USA (6) University of Miami, Miami, FL, USA (7) Universidad Autonoma de Baja California, Ensenada, BJ, Mexico (8) Mississippi State University, Starkville, MS, USA (9) NOAA, Silver Spring, MD, USA (10) Universidad del Mar, Puerto Escondido, Oaxaca, Mexico (11) Center for Environmental Studies in Cienfuegos, Cuba (12) University of South Florida, St. Petersburg, FL, USA (13) Louisiana State University, Baton Rouge, LA, USA (14) Woods Hole Oceanographic Institution, Woods Hole, MA, USA

I. Geographical extent of the Gulf of Mexico

Figure. 1 Map of the GOM identifying major regions examined in this synthesis. The solid gray line represents the 200 m isobath, defined as the boundary between the coastal continental shelf region and open GOM regions. The continental shelves are broken down geographically and are described in detail within Section 2.2. Selected major rivers, bays, and estuaries are identified as follows: MR-Mississippi River, AR-Atchafalaya River, BR-Brazos River, RGR-Rio Grande River, GUR-Grijalva-Usumacinta River; Estuaries: 1-Florida Bay 2-Charlotte Harbor, 3-Tampa Bay, 4-Apalachicola Bay, 5-Pensacola Bay, 6-Mobile Bay, 7-Mississippi Sound, 8-Barataria Bay, 9-Terrebonne/Timbalier Bays, 10-Atchafalaya/Vermilion Bays, 11-Sabine Lake, 12-Trinity-San Jacinto Estuary (Galveston Bay), 13-Colorado-Lavaca Estuary (Matagorda Bay), 14-Mission-Aransas Estuary, 15-Nueces Estuary (Corpus Christi Bay), 16-Laguna Madre (including both the upper and lower portions and Baffin Bay), 17-Laguna de Tamiahua, 18-Laguna de Tuxpan, 19-Laguna de Alvarado 20-Laguna de Terminos. The extents of the two US national marine sanctuaries (NMS), Flower Garden Banks (dark blue dots) and Florida Keys (dark blue box and inset map), are also shown. The maps on this page (expcept Fig. 2) were created using the MatLab routine M_Map.

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2. Modeled results

Figure 2. Modelled mean (2014-2020) GOM surface values of (a) HYCOM salinity, (b) total alkalinity (µmol/kg), (c) partial pressure of CO2 (pCO2, µatm), (d) pH, (e) calcite saturation state, and (f) aragonite saturation state. Carbonate system parameters presented were produced by calculating the average of the datasets created by the NOAA/AOML ACCRETE (Acidification, Climate, and Coral Reef Ecosystems Team; https://www.coral.noaa.gov/accrete/oaps.html; downloaded the dataset on June 12th 2021). The western Gulf coast is excluded from this analysis because ACCRETE does not calculate values for the region. The black lines indicate 200 m isobath. Map colorbars were created following Thyng et al. (2016).

3. GOM ocean acidification data

4. GOM habitats

Figure 4. Distribution of salt marsh (yellow), seagrass (green), mangroves (black), shallow coral reefs (cyan) and cold water corals (blue) in the Gulf of Mexico. Habitat data are from the UN Environment Programme World Conservation Monitoring Centre (https://data.unep-wcmc.org/, (UNEP-WCMC and Short 2020, Freiwald et al. 2017, UNEP-WCMC et al. 2010, Spalding et al. 2010, McCowen et al. 2017).The dark gray bathymetry contour represents the 200 m isobath.  Light grey bathymetry lines represent 1000 2000, and 3000 m depths, respectively.

5. Biological responses

Species	Response	References
Oysters (Crassostrea virginica)		1, 2, 3, 4, 5, 6, 7, 8
Bay scallop (Argopecten irradians)		1, 2
Hard clam (Mercenaria spp)		1, 2
Queen conch (Strombus gigas)		1
Gulf shrimp		1, 2, 3
Stone crab		1, 2, 3
Blue Crab		1
Spiny lobser		1
Finfish		1, 2
Corals		1, 2, 3, 4
Deep-sea corals		1, 2, 3, 4, 5, 6, 7
Sponges		1, 2, 3, 4, 5, 6
Sea urchins		1, 2, 3, 4
Habitats
Seagrass beds		1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18
Mangroves		1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
Salt marshes		1, 2, 3
Coral reefs		1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14
Oyster reefs		1, 2

 

Response	Sign
Negative
Unknown/Negative
Neutral/Positive
Neutral/Negative

6. GCAN GOM OA monitoring and research gaps

GOM OA Monitoring Gaps

The GOM lacks sustained observing assets in the open ocean, coastal zones, and estuaries needed to fill major data gaps that are critical to tracking the progression and understanding the dynamics of acidification. Observations need to include, at minimum, two of the four carbonate system variables (pH, pCO2, DIC, TA).

Open ocean observing

• Use of existing and new platform and buoy infrastructure for sustained time-series moorings
• Time-series monitoring at key GOM deep water coral locations
• Autonomous observing systems such as biogeochemical-Argo floats and gliders
• Leverage ship-based measurements (including shelf-wide cruises for hypoxia studies and NOAA surveys)

Coastal zone observing

• Coastal OA time-series data for characterizing sub-regional variability
• Monitoring locations need to focus on areas representative of broader GOM environmental conditions

Estuarine observing 

• Sustained long-term observing for characterizing variability, identifying acidification drivers and environmental influences, and determining time of emergence for OA signals
• Working with national, state, and regional level collaborators for expansion of existing monitoring programs include
• Consider river flow, submarine-groundwater discharge

GOM OA Research Gaps 

There are considerable knowledge gaps with respect to the dynamics, regional, and sub-regional impacts of OA in the GOM. Regionally-focused, hypothesis-driven research on GOM marine ecosystems, physical-chemical dynamics, and multi-stressor interactions is central to building a more robust scientific understanding of OA within the GOM.

Synthesizing existing OA-relevant data

• Ensuring that all relevant chemical, physical, biological, and biogeochemical data collected in the GOM are submitted to appropriate data centers, formatted, and documented using FAIR standards and appropriate quality flagging will maximize the utility of existing OA-related GOM datasets.
• Examining these data using climatological approaches for ‘time of emergence’ and ‘time of detection’ analysis
• Collaboration among the GOM states and nations is needed to enhance data sharing and collaborative modeling activities.
• Coastal and estuarine historical water quality data sets need to be identified and examined for their utility in modeling past changes in OA-related variables to aid in projecting future changes.
• A repository of consistently formatted biological data is needed for understanding OA on the GOM ecosystem. 

Improving near- and long-term regional and sub-regional projections

Modeling studies are required to improve the representation of carbon system dynamics and gain further insights on the carbonate system variability. Improvement in future modeling efforts are

• Increasing horizontal resolution to better resolve coastal circulation and biogeochemical processes at sub-regional or local scales (e.g. estuaries, bays)
• Realistic representation of land-ocean fluxes of nutrients, DIC, and alkalinity, including time evolving river chemistry, to evaluate the potential role of river runoff as modulator of coastal acidification patterns
• Eenhanced representations of remineralization processes to simulate realistic bottom acidification and carbon export patterns
• Higher-resolution models (at spatial and temporal scales appropriate for location-specific resource management guidance) are needed for high-priority sub-regions and estuaries
• Coupling existing physical models (hydrodynamics, seafloor elevation change, sea level rise, etc.) with water column and seafloor biogeochemistry models are needed to improve  characterization of current and prediction of future acidification impacts

Advancing ocean observing technologies

• Coordination among technology development research groups is needed to expedite sensor development, integration into observing platforms, field testing, and establishment of best practices for use in diverse GOM environments
• Efficient, cost-effective expansion and improvement of GOM OA spatial and time-series observations will benefit from development of new surface and profiling sensor capabilities for OA observing and build-out of an integrated network of gliders, floats, moorings, autonomous surface vehicles (ASVs), and other autonomous platforms (Bushinsky et al. 2019)
• Inclusion of sensors on observing platforms for measurement of additional OA-related variables (such as TA, DIC, nutrients, [CO₃^2-], chlorophyll, and others).

Generating paleo-records to extend the observational record

• Fossil foraminiferal shell geochemistry and physical characteristics can be used to estimate surface ocean pH of the past (e.g., Honish et al. 2012; Osborne et al. 2019).
• Coral cores have also been used to study past environmental conditions and rates of carbonate production; and existing coral core archives could be utilized to expand studies of past carbonate chemistry and calcification conditions (Wall et al. 2019; Tarique et al. 2021).

Understanding drivers and environmental influences

• Interactions among acidification, HABs, and hypoxia
• Impact of surface and deep water circulation
• Sea level rise and coastal inundation
• Freshwater inflow and episodic storm events
• Oil seeps and spills

Response of ecologically and economically important marine species

• Focused OA exposure studies in both the laboratory and field setting using GOM species
• Focused studies using GOM species of crustaceans (shrimp, lobster, crabs), bivalves (clams, oysters, scallops), conch, urchins, coral, finfish, and pelagic calcifiers (coccolithophores, pteropods, foraminifera) and for life stages of these species.

Impacts to ecosystems and ecosystem services

• Focused long-term studies on salt marsh, seagrass, mangrove, oyster reef, coral reef, and other carbonate ecosystems for understanding OA impacts on ecosystem resources and services
• Focused field and laboratory studies as well as model studies for range shifts of salt marshes, seagrass, mangroves, and changes in seafloor structure and habitats under future elevated pCO₂ and sea level rise
• Study the impacts of carbonate dissolution on seafloor chemical erosion, elevation loss, and associated coastal hazards
• Identification of resilient ecosystems; research to inform potential mitigation strategies for acidification
• Consequences of multi-stressor species impacts to ecosystem functions

Socioeconomic impacts

• Synthesis of socioeconomic data on potentially impacted species, ecosystems, industries, resources, dependencies, and adaptability for developing risk and vulnerability assessments at the regional, sub-regional scales, and associated with specific socioeconomic sectors that can be improved as understanding of OA in the GOM advances.

7. Next steps

Results from this synthesis will be used by GCAN to guide outreach and communication activities for informing the broader community on the state of OA knowledge in the GOM, and to initiate synthesis and examination of relevant socioeconomic data and information for identifying potential human and ecosystem risks and vulnerabilities.

• Engage regional stakeholders from the GOM’s neighboring nations in co-development of research and monitoring priorities to address knowledge gaps based on community needs.
• Facilitate communication and collaboration among stakeholders, scientists, resource managers, and industry partners though multidisciplinary, international working groups to encourage a transdisciplinary approach for integrating observational, experimental, and modeling research across the wide range of temporal and spatial scales required to advance OA knowledge in the GOM Region.
• Collaborate with GOA-ON and other scientific capacity building groups to help coordinate the multidisciplinary expertise and outcome-driven research initiatives needed to study acidification with shared community priorities across international borders.

Acknowledgements

The Gulf of Mexico Coastal Acidification Network (GCAN) initiated this synthesis, facilitated the collaborative process that led to this manuscript, and provided funding for its publication. GCAN is supported by NOAA’s Integrated Ocean Observing Program (IOOS), Gulf of Mexico Coastal Ocean Observing System (GCOOS), and NOAA Ocean Acidification Program. This research was carried out in part under the auspices of the Cooperative Institute for Marine and Atmospheric Studies (CIMAS) (EMO, LB, and YX), a Cooperative Institute of the University of Miami and the National Oceanic and Atmospheric Administration, cooperative agreement # NA20OAR4320472. XH was supported by NOAA’s Ocean Acidification Program (grant #. NA19OAR0170354) and NSF Chemical Oceanography Program (grant #. OCE-1654232). ERH was supported by State of Florida, Florida Fish and Wildlife Conservation Commission (cooperative agreement #20034) and SECOORA Agreement #IOOS.16(028)MOTE.EH.OA.5 under NOAA IOOS (award #NA16NOS0120028). KEFS was supported by NSF Division of Ocean Sciences (grants #1800913 and #1760381); EPA Gulf of Mexico Cooperative Agreement #12461398; and the Department of Oceanography, College of Geosciences, and T3: Texas A&M Triads for Transformation at Texas A&M University. TLH was supported by NSF S-STEM (grant #1355807); NOAA Texas Sea Grant Grants-In-Aid of Graduate Research Program; and the College of Geosciences Graduate Merit Fellowship and Department of Oceanography at Texas A&M University. JMHA was funded by the Mexican National Council for Science and Technology - Mexican Ministry of Energy - Hydrocarbon Fund [project 201441]. This is a contribution of the Gulf of Mexico Research Consortium (CIGoM). JMHA acknowledge PEMEX’s specific request to the Hydrocarbon Fund to address the environmental effects of oil spills in the Gulf of Mexico. AMC acknowledges support from Centro de Estudios Ambientales de Cienfuegos, Cuba. NLW was supported by the University of South Florida. FG was supported by the NOAA Ocean Acidification Program and the NOAA Climate Program Office Modeling, Analysis, Predictions, and Projections (MAPP) program.