Harmful algal blooms (HABs) pose a significant threat to aquatic ecosystems and public health. In the Chowan River-Albemarle Sound system of North Carolina, recurrent HABs pose persistent ecological and management challenges. However, physical processes governing HAB movement in this region remain insufficiently characterized, limiting our understanding of bloom connectivity and effective monitoring strategies. Using a Lagrangian particle tracking framework coupled with hydrodynamic simulations and in situ observations, we investigated how physical processes shape HAB transport under contrasting river discharge conditions. Four experiments were initialized during a low-discharge period at Arrowhead and Leary Landing sampling stations during July 2019, along with two high-discharge scenarios in June 2019. Results demonstrate that bloom connectivity is jointly controlled by winds and river discharge, producing both upstream and downstream transport pathways across the estuarine network. During low-discharge periods, wind-driven circulation promoted upstream transport and cross-channel exchange, whereas high discharge enhanced downstream exports. In all scenarios, blooms showed limited downstream transport toward western Albemarle Sound. Blooms initialized at Arrowhead failed to reach the Sound within 10 days, while those initialized at Leary Landing reached Edenton Bay by day 10. Simulations also identified previously unsampled regions in northern western Albemarle Sound that are susceptible to transported blooms under specific hydrodynamic conditions. By revealing how physical processes regulate bloom connectivity, transport pathways and potential exposure locations, our framework provides insights that can inform the spatial and temporal design of HAB monitoring. Further model evaluations and explicit assessment of alternative monitoring strategies are needed before making specific monitoring recommendations.
Ecosystem modelling constitutes a fundamental approach to gain insights into the structure, function and dynamic processes of complex marine ecosystems. In this context, species aggregation introduces a way to reduce model complexity and evaluate the effects on model output. Increasing model complexity does not necessarily improve model performance. Regarding model structure and complexity, Models of Intermediate Complexity for Ecosystem assessments (MICE) are positioned between whole-of-ecosystem models, such as Ecopath with Ecosim (EwE), and single-species models. In this study, different Ecopath models were developed, based on an existing EwE model of the Aegean Sea, in which their functional groups (FGs) were gradually aggregated from 44 to 10. A sensitivity analysis was conducted, concerning this stepwise reduction in the number of FGs, aiming in determining the optimum level or “sweet spot” of model complexity. A simplified model of 20 FGs was identified as the optimal balance point between complexity and performance, resulting in an EwE-MICE approach which can be later investigated further with different management scenarios, including spatio-temporal dynamics. Aggregation altered the values of several indicators related to the structural and ecological properties of the ecosystem, while the type of aggregation approach also influenced model output. These findings confirmed that aggregation can be a powerful tool; nevertheless, the optimum complexity can be influenced by research objectives and further research might be required, regarding the methodology for identifying this “sweet spot” in ecosystem models.
Restoration of seaweed beds is expected to contribute to biodiversity conservation, biological productivity, and climate-change mitigation in coastal ecosystems. However, relatively few studies have evaluated these effects while explicitly considering benthic–pelagic coupling, carbonate chemistry, and physical carbon transport. In this study, the benthic–pelagic coupled ecosystem model EMAGIN-B.C. was applied to shallow coastal waters of southern Osaka Bay, Japan, including an OECM-recognized coastal area and blue-carbon-credit-certified seaweed habitats. A Control case and two restoration scenarios with 50% and 100% prescribed seaweed standing biomass were compared.Nutrient concentrations changed by <0.001 mg L⁻¹, while dissolved oxygen increased by approximately 0.01 and 0.02 mg L⁻¹ in the 50% and 100% scenarios, respectively. Phytoplankton production decreased by approximately 0.6% and 1.2%, whereas deposit-feeder production increased by approximately 2.8% and 5.7%, indicating a shift in carbon transfer from pelagic to benthic pathways. Atmospheric CO₂ absorption increased by only 0.02% and 0.04%, whereas net carbon fixation increased by approximately 78% and 154%. The sedimentary carbon-storage function, evaluated as the sum of separately quantified organic-carbon and CaCO₃ burial fluxes, increased by approximately 14% and 30%, respectively.Carbon-budget analysis showed that the large increase in fixation was balanced predominantly by increased DIC inflow across the system boundary rather than by a proportional increase in local atmospheric CO₂ uptake. Physical DIC replenishment therefore buffered the seaweed-induced decrease in local DIC and pCO₂. Carbon storage (③) was resolved into organic-carbon burial and CaCO₃ burial. Organic-carbon burial increased from 96.58 ton C km⁻² yr⁻¹ in the Control to 110.09 and 125.37 ton C km⁻² yr⁻¹ in the 50% and 100% scenarios, while CaCO₃ burial increased from 3.80 to 4.30 and 4.66 ton C km⁻² yr⁻¹. In EMAGIN-B.C., the effects of CaCO₃ formation and dissolution on DIC, TA, pCO₂, and air–sea CO₂ exchange are already represented through carbonate chemistry and are therefore reflected in the carbon-absorption function (①). CaCO₃ burial is reported separately as an inorganic component of sedimentary carbon storage and should not by itself be interpreted as equivalent net atmospheric CO₂ removal.These results demonstrate that seaweed-bed restoration affects coastal carbon cycling through interconnected biological, physical, and sedimentary processes, and that its ecosystem effects cannot be evaluated solely from local atmospheric CO₂ absorption.
