Search

One of the world’s largest “blue carbon” ecosystems, Louisiana’s tidal wetlands on the US Gulf of Mexico coast, is rapidly being lost. Louisiana’s strong legal, regulatory, and monitoring framework, developed for one of the world’s largest tidal wetland systems, provides an opportunity for a programmatic approach to blue carbon accreditation to support restoration of these ecologically and economically important tidal wetlands. Louisiana’s coastal wetlands span ∼1.4 million ha and accumulate 5.5–7.3 Tg yr−1 of blue carbon (organic carbon), ∼6%–8% of tidal marsh blue carbon accumulation globally. Louisiana has a favorable governance framework to advance blue carbon accreditation, due to centralized restoration planning, long term coastal monitoring, and strong legal and regulatory frameworks around carbon. Additional restoration efforts, planned through Louisiana’s Coastal Master Plan, over 50 years are projected to create, or avoid loss of, up to 81,000 ha of wetland. Current restoration funding, primarily from Deepwater Horizon oil spill settlements, will be fully committed by the early 2030s and additional funding sources are required. Existing accreditation methodologies have not been successfully applied to coastal Louisiana’s ecosystem restoration approaches or herbaceous tidal wetland types. Achieving financial viability for accreditation of these restoration and wetland types will require expanded application of existing blue carbon crediting methodologies. It will also require expanded approaches for predicting the future landscape without restoration, such as numerical modeling, to be validated. Additional methodologies (and/or standards) would have many common elements with those currently available but may be beneficial, depending on the goals and needs of both the state of Louisiana and potential purchasers of Louisiana tidal wetland carbon credits. This study identified twenty targeted needs that will address data and knowledge gaps to maximize financial viability of blue carbon accreditation for Louisiana’s tidal wetlands. Knowledge needs were identified in five categories: legislative and policy, accreditation methodologies and standards, soil carbon flux, methane flux, and lateral carbon flux. Due to the large spatial scale and diversity of tidal wetlands, it is expected that progress in coastal Louisiana has high potential to be generalized to similar wetland ecosystems across the northern Gulf of Mexico and globally.

Submerged aquatic vegetation (SAV) are highly efficient at carbon sequestration and, despite their relatively small distribution globally, are recognized as a potentially valuable component of climate change mitigation. However, SAV mapping in tidal marshes presents a challenge due to optically complex constituents in the water. The emergence and advancement of deep learning-based techniques in the field of habitat mapping with remote sensing imagery provides an opportunity to address this challenge. In this study, an analytical framework was developed to quantify the carbon sequestration of SAV habitats in the Atchafalaya River Delta Estuary from field and remote sensing observations using deep convolutional neural network (DCNN) techniques. A U-Net-based model, Wetland-SAV Network, was trained to identify the SAV percent cover (high, medium, and low) as well as other estuarine habitat types from Landsat 8/9-OLI data. The areal extent of SAV was up to 8% of the total area (47,000 ha). The habitat areas and habitat-specific carbon fluxes were then used to quantify the net greenhouse gas (GHG) flux of the study area for with/without SAV scenarios in a carbon balance model. The total net GHG flux was in the range of −0.13 ± 0.06 to −0.86 ± 0.37 × 105 tonne CO2e y−1 and increased up to 40% (−0.23 ± 0.10 to −0.90 ± 0.39 × 105 tonne CO2e y−1) when SAV was accounted for within the calculation. At the hectare scale, the inclusion of SAV resulted in an increase of ~60% for the net GHG sink in shallow areas adjacent to the emergent marsh where SAV was abundant. This is the first attempt at remotely mapping SAV in coastal Louisiana as well as a first quantification of net GHG flux at the scale of hectares to thousands of hectares, accounting for SAV within these sub-tropical coastal delta marshes. Remote sensing and deep learning models have high potential for mapping and monitoring SAV in turbid sub-tropical coastal deltas as a component of the increasing accuracy of net GHG flux estimates at small (hectare) and large (coastal basin) scales.

Infaunal sedimentary communities underpin marine ecosystems worldwide. Understanding how disturbances such as oil spills influence infauna is therefore important, especially given that oil can be trapped in sediments for years or even decades. The 2010 Deepwater Horizon (DWH) event was the largest marine oil spill in United States history, impacting habitats throughout the Northern Gulf of Mexico. We investigated infaunal community structure at two shallow sites in the Chandeleur Islands, LA, United States, over a 2-year period from 2015 to 2016 (5–6 years post-spill). One site was moderately contaminated with oil from the DWH spill, while the other was only lightly contaminated. Both sites featured patchy Ruppia seagrass meadows, allowing us to compare infaunal communities between sites for seagrass versus unvegetated sediment. The moderately-oiled site featured a significantly different community than that of the lightly oiled site; these differences were driven by altered abundance of key taxa, with some taxa being less abundant at the moderately oiled site but others more abundant. During our second year of sampling, a crude oil slick moved transiently through the moderately-oiled site, allowing us to directly observe responses to an acute re-oiling event. Virtually every taxonomic and community-level metric declined during the re-oiling, with effects more pronounced in seagrass beds than in unvegetated sediment. The sole exception was the snail, Neritina usnea, which we found exclusively at the more-oiled site. Our observations suggest that oil responses are driven more by key taxa than by entire guilds responding together. By identifying the families and genera that showed the largest signal at this pair of sites, we can begin laying groundwork for understanding which benthic taxa are most likely to be impacted by oil spills, both in the immediate aftermath of a spill and through longer-term contamination. While more studies will certainly be needed, this contribution is a step towards developing clear a priori hypotheses that can inform future oil-spill work. Such hypotheses would help to focus future sampling efforts, allowing resources to be directed towards those taxa that are most likely to be responding, and which are potential bio-indicators of oil exposure.

Infaunal organisms are susceptible to disturbances such as hypoxia and sediment contamination; changes in infaunal community structure are therefore often used as indicators of anthropogenic disturbance. Susceptibility to disturbance varies across taxa, either due to physiological factors or to behaviors or functional roles that increase exposure. Both sources of variability are likely to be heritable and shared among related taxa. Thus, we would expect oil disturbance to disproportionately affect related taxa and therefore decrease phylogenetic diversity (PD). We test this hypothesis for a shallow water marine infaunal community using a simulation approach that iteratively removes clades with shared vulnerability to oil exposure. Infauna were sampled at two sites in the Chandeleur Islands, LA, that reflect different exposures to crude oil after the Deepwater Horizon event. Seagrass and adjacent bare sediment habitats were sampled in 2015, 5 years after initial oil exposure, and again in 2016 after an acute re-oiling event. We found that strong correlation between PD and family richness masked any detectable PD patterns with oil exposure. For our full community tree, sensitivity analysis indicated that the removal of larger clades did not disproportionately reduce PD, against our prediction. For this pair of sites, PD did not provide a better metric for assessing the impacts of oil exposure than family richness alone. It is possible, however, that finer-scale taxonomic resolution of infaunal communities may better decouple PD from taxonomic richness. More work is needed to fully evaluate the impacts of disturbance on PD.

Long-term fisheries-independent sampling data inform population status and trends of species-specific biomass and are often used to drive biomass-based food web models such as the Comprehensive Aquatic Systems Model (CASM). Indicators such as total biomass and mean trophic level derived from these data and from CASM outputs inform management and facilitate assessments of on-going and predicted coastal change and restoration activities on fisheries, but rely on consistent sampling to enable comparisons across space and time. Changes in coastal estuarine gradients, combined with the availability of new sampling technologies, highlight a need to assess the potential consequences of changing sampling technologies on fisheries data and the cascading impact on model outputs. In Louisiana, USA, CASM models are used to inform coastal restoration projects, relying on 40 years of fisheries-independent data derived from 50′ seine sampling. However, alternative use of electrofishers as a sampling method has been proposed to replace the seine sampling. In this study, we examine data from concurrent seine and electrofisher sampling in Barataria Basin, Louisiana, and compare biomass, assemblage data and CASM outputs related to species biomass, food web structure and energy cycling. In a paired comparison of data in 2018–2019, the electrofisher captured higher total catch and diversity compared to the seine. The electrofisher samples were dominated by shrimp (grass, white, brown) and larger bodied fish, while seine samples were dominated by smaller-bodied fish (i.e., anchovy, menhaden). Ecosystem indicators derived from running the CASM using biomass data from seine and electrofisher sampling separately in two different simulation exercises provide contrasting results. In Simulation Exercise 1, the use of different datasets (long-term CASM calibration, 2018–2019 seine, 2018–2019 electrofisher) to initialize the CASM biomasses did not result in large or long-running changes in the simulated biomasses over time. In contrast, in Simulation Exercise 2, CASM model outputs using adjusted gear ratios indicated changes in biomass structure when using electrofisher data, with a doubling of total food web biomass due to the increased shrimp count, and a 13% increase in total energy flow through the food web. Conversions based on area and gear efficiency for overall catch may be useful in maintaining the continuity of historical data. However, differences in species-specific catch due to gear selectivity could have large consequences for constructing and calibrating fish and ecosystem models and remain difficult to reconcile. These differences in assemblages, and estimated biomasses for key food web species, suggest careful consideration in changing gears.