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• W4385776934 abstract "Algae are deemed to be the highest carbon drawdown contributors in the world. Almost half of the world’s organic carbon is fixed by marine phytoplankton, especially during algal blooms, despite accounting for less than one percent of the total photosynthetic biomass on Earth. Nonetheless, there are growing concerns about the rise of harmful algal blooms in terms of their persistence and distribution that is a fingerprint of both global climate and local anthropogenic pressure. A novel forecasting model – combining transfer entropy network inference and a forecasting convolution neural-network – is used to predict bloom and non-bloom epidemic regimes, and their environmental determinants to define bloom sources, causes and systemic risk. The model shows high forecasting skills by extracting salient ecosystem features even without spatial dependencies. We defined a 2D entropic ecosystem mandala where the ecological impact, manifested by the distribution’s randomness of Cyanobacteria-driven chlorophyll-a (CHL-a), is proportional to the systemic environmental pressure/stress determined by a set of erratic ocean/climate and coastal/nutrient factors. Originally, a spatial risk was defined based on CHL-a magnitude, persistence and shifts. Considering the temporal variability of CHL-a in Florida Bay (FL Bay) as a case-study, we show how shifts in algal blooms are becoming more persistent in shallow areas with higher dinoflagellate/diatom ratio. This emphasizes the likely key role of cyanobacteria disorganization into phytoplankton organization, and thus the role of land discharge into the marine microbiome balance. This presents major challenges considering the increasing potential causality of green–blue algal blooms (river-dominated) for harmful red-tides with cascading socio-ecological effects, including carbon cycling alteration and ingrained eutrophication in coastal ecosystems. A universal threshold on the top 20% Pareto CHL-a extremes clearly defines bloom and non-bloom regimes independently of being endemic or epidemic, underpinned by distinct eco-environmental interactions, where the largest biogeochemical stress is structurally scale-free with CHL-a as the hub. Forecasting algal bloom in short- and long-term is indispensable for quantifying ecosystem health considering coastal-marine habitats, species and humans, as well as impacts into environmental processes such as carbon sequestration capacity. However, the increasing disorganization of biogeochemical stress decreases our forecasting ability of algal blooms except during outbreaks when is too late for prevention. This has impacts for investigating and controlling eco-environmental determinants of undesired algal bloom emergence and spread." @default.
• W4385776934 created "2023-08-12" @default.
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• W4385776934 date "2023-10-01" @default.
• W4385776934 modified "2023-10-14" @default.
• W4385776934 title "Algal bloom ties: Systemic biogeochemical stress and Chlorophyll-a shift forecasting" @default.
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• W4385776934 doi "https://doi.org/10.1016/j.ecolind.2023.110760" @default.
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