Implications of macroalgae blooms to the spatial structure of seagrass seascapes: The case of the Anadyomene spp. (Chlorophyta) bloom in Biscayne Bay, Florida

Integrating temporal decomposition and data-driven approaches for predicting coastal harmful algal blooms

Frequent coastal harmful algal blooms (HABs) threaten the ecological environment and human health. Biscayne Bay in southeastern Florida also faces algal bloom issues; however, the mechanisms driving these blooms are not fully understood, emphasizing the importance of HAB prediction for effective environmental management. The overarching goal of this study is to offer a robust HAB predictive framework and try to enhance the understanding of HAB dynamics. This study established three scenarios to predict chlorophyll-a concentrations, a recognized representative of HABs: Scenario 1 (S1) using single nonlinear machine learning (ML) algorithms, hybrid Scenario 2 (S2) combining linear models and nonlinear ML algorithms, and hybrid Scenario 3 (S3) combining temporal decomposition and ML (TD-ML) algorithms. The novel-developed S3 TD-ML hybrid models demonstrated superior predictive capabilities, achieving all R2 values above 0.9 and MAPE under 30% in tests, significantly outperforming the S1 with an average R2 of 0.16 and the S2 with an R2 of -0.06. S3 models effectively captured the algal dynamics, successfully predicting complex time series with extremes and noise. In addition, we unveiled the relationship between environmental variables and chlorophyll-a through correlation analysis and found that climate change might intensify the HABs in Biscayne Bay. This research developed a precise predictive framework for early warning and proactive management of HABs, offering potential global applicability and improved prediction accuracy to address HAB challenges.

Predicting coastal harmful algal blooms using integrated data-driven analysis of environmental factors

Coastal harmful algal blooms (HABs) have become one of the challenging environmental problems in the world's thriving coastal cities due to the interference of multiple stressors from human activities and climate change. Past HAB predictions primarily relied on single-source data, overlooked upstream land use, and typically used a single prediction algorithm. To address these limitations, this study aims to develop predictive models to establish the relationship between the HAB indicator - chlorophyll-a (Chl-a) and various environmental stressors, under appropriate lagging predictive scenarios. To achieve this, we first applied the partial autocorrelation function (PACF) to Chl-a to precisely identify two prediction scenarios. We then combined multi-source data and several machine learning algorithms to predict harmful algae, using SHapley Additive exPlanations (SHAP) to extract key features influencing output from the prediction models. Our findings reveal an apparent 1-month autoregressive characteristic in Chl-a, leading us to create two scenarios: 1-month lead prediction and current-month prediction. The Extra Tree Regressor (ETR), with an R2 of 0.92, excelled in 1-month lead predictions, while the Random Forest Regressor (RFR) was most effective for current-month predictions with an R2 of 0.69. Additionally, we identified current month Chl-a, developed land use, total phosphorus, and nitrogen oxides (NOx) as critical features for accurate predictions. Our predictive framework, which can be applied to coastal regions worldwide, provides decision-makers with crucial tools for effectively predicting and mitigating HAB threats in major coastal cities.

The ecological effect of large-scale coastal natural and cultivated seaweed litter decay processes: An overview and perspective

Seaweeds are important components of marine ecosystems and can form a large biomass in a few months. The decomposition of seaweed litter provides energy and material for primary producers and consumers and is an important link between material circulation and energy flow in the ecosystem. However, during the growth process, part of the seaweed is deposited on the sediment surface in the form of litter. Under the joint action of the environment and organisms, elements enriched in seaweed can be released back into the environment in a short time, causing pollution problems. The cultivation yield of seaweed worldwide reached 34.7 million tons in 2019, but the litter produced during the growth and harvest process has become a vital bottleneck that restricts the further improvement of production and sustainable development of the seaweed cultivation industry. Seaweed outbreaks worldwide occur frequently, producing a mass of litter and resulting in environmental pollution on coasts and economic losses, which have negative effects on coastal ecosystems. The objective of this review is to discuss the decomposition process and ecological environmental effects of seaweed litter from the aspects of the research progress on seaweed litter; the impact of seaweed litter on the environment; and its interaction with organisms. Understanding the decomposition process and environmental impact of seaweed litter can provide theoretical support for coastal environmental protection, seaweed resource conservation and sustainable development of the seaweed cultivation industry worldwide. This review suggests that in the process of large-scale seaweed cultivation and seaweed outbreaks, ageing or falling litter should be cleared in a timely manner, mature seaweed should be harvested in stages, and dried seaweed produced after harvest and washed up on shore should be handled properly to ensure the benefits of environmental protection provided by seaweed growth and sustainable seaweed resource development.

Cascading effects of climate change on recreational marine flats fishes and fisheries

Tropical and subtropical coastal flats are shallow regions of the marine environment at the intersection of land and sea. These regions provide myriad ecological goods and services, including recreational fisheries focused on flats-inhabiting fishes such as bonefish, tarpon, and permit. The cascading effects of climate change have the potential to negatively impact coastal flats around the globe and to reduce their ecological and economic value. In this paper, we consider how the combined effects of climate change, including extremes in temperature and precipitation regimes, sea level rise, and changes in nutrient dynamics, are causing rapid and potentially permanent changes to the structure and function of tropical and subtropical flats ecosystems. We then apply the available science on recreationally targeted fishes to reveal how these changes can cascade through layers of biological organization-from individuals, to populations, to communities-and ultimately impact the coastal systems that depend on them. We identify critical gaps in knowledge related to the extent and severity of these effects, and how such gaps influence the effectiveness of conservation, management, policy, and grassroots stewardship efforts.

The takeover of Thalassia testudinum by Anadyomene sp. at Biscayne Bay, USA, cannot be simply explained by competition for nitrogen and phosphorous

Apart from direct light effects, we tested whether the takeover of the seagrass Thalassia testudinum by the seaweed Anadyomene sp. in high nutrient areas of Biscayne Bay, Florida, USA, is related to a faster nutrient surge uptake capacity of the seaweed and/or a negative effect on the seagrass uptake rates. Anadyomene sp. and T. testudinum showed a similar ammonium surge uptake capacity, but the seagrass performed better than the seaweed in mixed incubations at high ammonium concentrations. T. testudinum was faster than Anadyomene sp. at taking up pulses of phosphate, but the uptake rates of the seagrass were significantly decreased in the presence of the seaweed. The takeover of T. testudinum by Anadyomene sp. at Biscayne Bay is likely dominated by light and cannot be simply explained by their single or mixed nutrient surge uptake rates, but the phosphate availability and the seagrass uptake inhibition by the seaweed may also play a key role in the process.

Hindcast and Near Real-Time Monitoring of Green Macroalgae Blooms in Shallow Coral Reef Lagoons Using Sentinel-2: A New-Caledonia Case Study

Despite the necessary trade-offs between spatial and temporal resolution, remote sensing is an effective approach to monitor macroalgae blooms, understand their origins and anticipate their developments. Monitoring of small tropical lagoons is challenging because they require high resolutions. Since 2017, the Sentinel-2 satellites has provided new perspectives, and the feasibility of monitoring green algae blooms was investigated in this study. In the Poé-Gouaro-Déva lagoon, New Caledonia, recent Ulva blooms are the cause of significant nuisances when beaching. Spectral indices using the blue and green spectral bands were confronted with field observations of algal abundances using images concurrent with fieldwork. Depending on seabed compositions and types of correction applied to reflectance data, the spectral indices explained between 1 and 64.9% of variance. The models providing the best statistical fit were used to revisit the algal dynamics using Sentinel-2 data from January 2017 to December 2019, through two image segmentation approaches: unsupervised and supervised. The latter accurately reproduced the two algal blooms that occurred in the area in 2018. This paper demonstrates that Sentinel-2 data can be an effective source to hindcast and monitor the dynamics of green algae in shallow lagoons.