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博士 （情報科学） >
Algal Blooms as Marine Ecosystem Risk: Forecasting Spread and Biogeochemical Stress
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| Title: | Algal Blooms as Marine Ecosystem Risk: Forecasting Spread and Biogeochemical Stress |
| Other Titles: | 海洋生態系リスクとしての藻類の爆発的増殖: 拡散と生物地球化学的ストレスの予測 |
| Authors: | 王, 浩炯 Browse this author |
| Issue Date: | 25-Dec-2023 |
| Publisher: | Hokkaido University |
| Abstract: | Algae, accounting for less than one percent of Earth’s total photosynthetic biomass, are remarkable carbon drawdown contributors, fixing nearly half of the world’s organic carbon, especially during algal blooms. However, escalating concerns surround algal blooms, their persistence, and distribution, serving as indicators of both global climate shifts and local anthropogenic pressures. These phenomena intertwine with coastal marine ecosystems worldwide, where tide disruptions and human-induced disturbances increasingly degrade water quality, fostering frequent algal blooms. The growing duration of these blooms poses a significant threat, impacting vital ecological processes and services, such as carbon cycling and sequestration. Yet, unraveling the complex interplay between ecological factors and environmental stressors, along with deciphering algal bloom patterns, remains a formidable challenge due to limited data and a lack of universally applicable analytical approaches. An innovative predictive model merges transfer entropy network inference with a forecasting graph neural network to anticipate both blooming and non-blooming epidemic scenarios, along with their underlying environmental factors that elucidate bloom sources, causes and systemic risk. This model exhibits strong predictive capabilities, extracting crucial ecosystem features even in the absence of spatial dependencies. A novel 2D entropic ecosystem mandala is introduced, wherein the ecological impact, manifested through the distribution’s Cyanobacteria-driven chlorophylla (CHL-a) randomness, correlates proportionally with systemic environmental stress, governed by erratic oceanic, climatic, and coastal nutrient factors. Originally, a spatial risk was defined based on CHL-a magnitude, persistence and shifts. Through a case study in Florida Bay (FL Bay), we unveil how algal bloom shifts endure in shallow regions with elevated dinoflagellate-to-diatom ratios, underscoring Cyanobacteria’s pivotal role in phytoplankton dynamics and the influence of terrestrial discharge on marine microbiome equilibrium. This unfolding scenario presents formidable challenges, notably the heightened potential for green-blue algal blooms (associated with river dominance) to trigger harmful red tides, with cascading socio-ecological impacts spanning carbon cycling disruptions and entrenched eutrophication in coastal ecosystems. A universal threshold on the top 20% Pareto extremes of CHL-a, distinctly defines bloom and non-bloom phases, independent of endemic or epidemic categorization, driven by distinct eco-environmental interactions, where the paramount biogeochemical stress follows a scale-free structure with CHL-a acting as the central hub. Predicting algal blooms in the short and long term is crucial for assessing the well-being of ecosystems, encompassing coastal-marine environments, species, and human populations. Furthermore, it offers insights into the effects on environmental processes like carbon sequestration. However, the escalating disruption in biogeochemical balance compromises our capacity to forecast algal blooms, barring during outbreaks when intervention becomes belated. This deficiency hampers the investigation and management of the underlying eco-environmental factors triggering undesirable algal bloom occurrences and propagation. And our ideas improved this difficulty to some extent, both in terms of causal inference and model prediction. |
| Conffering University: | 北海道大学 |
| Degree Report Number: | 甲第15694号 |
| Degree Level: | 博士 |
| Degree Discipline: | 情報科学 |
| Examination Committee Members: | (主査) 教授 大鐘 武雄, 教授 齊藤 晋聖, 教授 西村 寿彦, 准教授 Matteo Convertino （清華大学深セン国際大学院） |
| Degree Affiliation: | 情報科学院(情報科学専攻) |
| Type: | theses (doctoral) |
| URI: | http://hdl.handle.net/2115/91245 |
| Appears in Collections: | 課程博士 (Doctorate by way of Advanced Course) > 情報科学院(Graduate School of Information Science and Technology)
学位論文 (Theses) > 博士 （情報科学）
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