Non-linear impacts of harmful algae blooms on the coastal tourism economy

How temperature and meteorological conditions trigger algal blooms on tropical reefs

Agglomerations of macroalgae have increased globally due to changing oceanic conditions, leading to a rise in studies of blooms driven by socio-economic and ecological impacts. Rhizoclonium-like macroalgae blooms have been reported in estuaries, but their occurrence on tropical reefs remains unexplored. This study, conducted between 2005 and 2018, documents the seasonal occurrence of Rhizoclonium-like blooms on reefs and identifies key environmental drivers. These blooms, first recorded in 2006 and becoming more frequent by 2010, occur annually between May and August. The algae form carpets near the substrate, interacting with organisms like reef-building corals, octocorals, ascidians, and sponges. Statistical analysis links the blooms to increased atmospheric pressure and a wider temperature range. The blooms are not influenced by upwelling or land-based nutrients, suggesting meteorological and temperature factors as key drivers. This is the first documented occurrence of large Rhizoclonium-like blooms on tropical reefs, highlighting the need for further study amid global environmental change.

Domoic Acid: A Review of Its Cytogenotoxicity Within the One Health Approach

In this review, we toxicologically assessed the naturally occurring toxin domoic acid. We used the One Health approach because the impact of domoic acid is potentiated by climate change and water pollution on one side, and reflected in animal health, food security, human diet, and human health on the other. In a changing environment, algal blooms are more frequent. For domoic acid production, the growth of Pseudo-nitzschia diatoms is of particular interest. They produce this toxin, whose capability of accumulation and biomagnification through the food web impacts other organisms in the ecosystem. Domoic acid targets nervous system receptors inducing amnestic shellfish poisoning, among other less severe health-related problems. However, the impact of domoic acid on non-target cells is rather unknown, so we reviewed the currently available literature on cytogenetic effects on human and animal cells. The results of different studies indicate that domoic acid has the potential to induce early molecular events, such as oxidative imbalance and DNA damage, thus posing an additional threat which needs to be thoroughly addressed and monitored in the future.

Enhanced nitrogen and phosphorus removal from mariculture water using immobilized bacteria and macroalgae

With the vigorous development of the mariculture industry, the untreated wastewater from mariculture has exerted significant pressure on the water environment. The untreated N and P in the wastewater from mariculture can deteriorate the quality of the mariculture. In this study, a composite in situ treatment system involving macroalgae (Caulerpa lentillifera/Caulerpa sertularoides f. Longipes) and immobilized degrading bacteria was established to handle wastewater from shrimp culture. The changes in nutrients in aquaculture wastewater were studied by chemical analysis, and the microbial community structure was analyzed using molecular biology technology and high-throughput sequencing technology. The removal efficacy of nutrients in aquaculture wastewater and the composition of microorganisms in the wastewater were examined, and the primary causes for the alteration of the microbial community were analyzed. The results demonstrated that when the macroalgae in the system were Caulerpa lentillifera (CL), the removal efficiencies of TN, PO43--P, and COD from shrimp culture were 59.04%, 34.26%, and 68.61% respectively. When the macroalgae was Caulerpa sertularoides f. Longipes (CSF), the removal efficiencies of TN, PO43--P, and COD generated by experimental shrimp culture were 51.50%, 33.69%, and 50.88% respectively. The biomass (wet weight) of both macroalgae species also increased, facilitating the removal of nutrients from the wastewater. Additionally, both Proteobacteria and Bacteroidetes were the dominant bacteria in the three samples, and the addition of the composite in-situ treatment system had no impact on the dominant bacteria in the water. The results of FAPROTAX analysis indicated that compared with the untreated samples, the abundances of methyl-functional bacteria and amino acid-functional bacteria in the samples increased due to organic matter such as COD produced during shrimp culture and the addition of feed, suggesting that shrimp culture can influence the abundances of functional bacteria in the water. In conclusion, the combined in situ treatment system can effectively eliminate nutrients from aquaculture wastewater, and the combined effect of macroalgae and immobilized degrading bacteria plays a vital role in this process.

Enhanced forecasting of chlorophyll-a concentration in coastal waters through integration of Fourier analysis and Transformer networks

The accurate prediction of chlorophyll-a (chl-a) concentration in coastal waters is essential to coastal economies and ecosystems as it serves as the key indicator of harmful algal blooms. Although powerful machine learning methods have made strides in forecasting chl-a concentrations, there remains a gap in effectively modeling the dynamic temporal patterns and dealing with data noise and unreliability. To wiggle out of quagmires, we introduce an innovative deep learning prediction model (termed ChloroFormer) by integrating Transformer networks with Fourier analysis within a decomposition architecture, utilizing coastal in-situ data from two distinct study areas. Our proposed model exhibits superior capabilities in capturing both short-term and middle-term dependency patterns in chl-a concentrations, surpassing the performance of six other deep learning models in multistep-ahead predictive accuracy. Particularly in scenarios involving extreme and frequent blooms, our proposed model shows exceptional predictive performance, especially in accurately forecasting peak chl-a concentrations. Further validation through Kolmogorov-Smirnov tests attests that our model not only replicates the actual dynamics of chl-a concentrations but also preserves the distribution of observation data, showcasing its robustness and reliability. The presented deep learning model addresses the critical need for accurate prediction on chl-a concentrations, facilitating the exploration of marine observations with complex dynamic temporal patterns, thereby supporting marine conservation and policy-making in coastal areas.

Identifying environmental impacts on planktonic algal proliferation and associated risks: a five-year observation study in Danjiangkou Reservoir, China

Understanding the risks of planktonic algal proliferation and its environmental causes is crucial for protecting water quality and controlling ecological risks. Reservoirs, due to the characteristics of slow flow rates and long hydraulic retention times, are more prone to eutrophication and algal proliferation. Chlorophyll-a (Chl-a) serves as an indicator of planktonic algal biomass. Exploring the intricate interactions and driving mechanisms between Chl-a and the water environment, and the potential risks of algal blooms, is crucial for ensuring the ecological safety of reservoirs and the health of water users. This study focused on the Danjiangkou Reservoir (DJKR), the core water source of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC). The multivariate statistical methods and structural equation modeling were used to explore the relationships between chlorophyll-a (Chl-a) contents and water quality factors and understand the driving mechanisms affecting Chl-a variations. The Copula function and Bayesian theory were combined to analyze the risk of changes in Chl-a concentrations at Taocha (TC) station, which is the core water source intake point of the MRSNWDPC. The results showed that the factors driving planktonic algal proliferation were spatially heterogeneous. The main factors affecting Chl-a concentrations in Dan Reservoir (DR) were water physicochemical factors (water temperature, dissolved oxygen, pH value, and turbidity) with a total contribution rate of 60.18%, whereas those in Han Reservoir (HR) were nutrient factors (total nitrogen, total phosphorus, and ammonia nitrogen) with a total contribution rate of 73.58%. In TC, the main factors were water physicochemical factors (turbidity, pH, and water temperature) and nutrient factors (total phosphorus) with total contribution rates of 39.76% and 45.78%, respectively. When Chl-a concentrations in other areas of the DJKR ranged from the minimum to the uppermost quartile, the probabilities that Chl-a concentrations at the TC station exceeded 3.4 μg/L (the benchmark value of Chl-a for lakes in the central-eastern lake area of China) owing to the influence of these areas were all less than 10%. Thus, the risk of planktonic algal proliferation at the MRSNWDPC intake point is low. This study developed an integrated framework to investigate spatiotemporal changes in algal proliferation and their driving factors in reservoirs, which can be used to support water quality management in mega hydro projects.

What's in a name? Political and economic concepts differ in social media references to harmful algae blooms

Policies and management decisions in the marine environment are driven in part by public sentiment which can grow more intense during hazard events like Harmful Algae Blooms (HABs). The public conversations on social media sites like Twitter (before X) reveal the polarized nature of HABs through nuanced language and sentiment. This article uses mixed methods of machine learned topic modeling and inductive qualitative coding to describe the ways the long-term 2017-2019 Karenia brevis "red tide" bloom were politicized across Florida's South West coast. It finds that there are topical differences in keywords related to place (e.g. beach, Florida, coast), agent (individual or organization), and epistemic values (reliance on scientific and/or media reports). These topical differences demonstrate different levels of politicization and partisanship in qualitative analysis. Conceptually, this research demonstrates the ways different dimensions of a long-duration marine hazard can be polarized. Regarding management, this research provides insights to political and organizational stakeholders and the gaps in the discourse shaping marine hazards which can be used to strategically guide future social media engagement to manage politicization. What if all the careful work that resource and environmental managers do can be undone by simple, seemingly uncontroversial words? In an era of increased environmental and marine distress-coupled with short format communication-the ways environmental managers choose their words is crucial, even between ostensibly inconsequential nouns like "red tide" or "algae bloom." Policies and management decisions in the marine environment are driven in part by public sentiment which can grow more intense during hazard events like Harmful Algae Blooms (HABs). The public conversations on social media sites like Twitter (before X) reveal the polarized nature of HABs through nuanced language and sentiment. This article relies on mining social media posts, and uses mixed methods of machine-learned topic modeling and human-driven inductive qualitative coding to describe the ways the long-term 2017-2019 Karenia brevis "red tide" blooms were politicized across Florida's South West coast. It finds that there are topical differences in keywords related to place (e.g. beach, Florida, coast), agent (individual or organization), and epistemic values (reliance on scientific and/or media reports). These topical differences demonstrate different levels of politicization and partisanship in qualitative analysis. Conceptually, this research demonstrates the ways different dimensions of a long-duration marine hazard can be polarized. Regarding management, this research provides insights to political and organizational stakeholders and the gaps in the discourse shaping marine hazards which can be used to strategically guide future social media engagement to manage politicization.