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相关概念视频

Red Algae01:23

Red Algae

Red algae, also known as rhodophytes, are primarily found in marine environments, though some species inhabit freshwater and terrestrial ecosystems. These organisms exist in both unicellular and multicellular forms, with some multicellular varieties reaching macroscopic sizes.As phototrophic organisms, red algae contain chlorophyll a; however, their chloroplasts lack chlorophyll b. Instead, they possess phycobiliproteins, which serve as major light-harvesting pigments, similar to those found in...
Marine Microbial Ecology01:30

Marine Microbial Ecology

Marine microbial ecosystems are shaped by distinct physicochemical limits, including high salinity, low nutrient availability, and fluctuating oxygen levels. These conditions favor smaller microbial cell sizes, which maximize their surface-to-volume ratio for efficient nutrient uptake.Microbial activity and community composition are closely linked to biogeochemical cycles, particularly in dynamic environments like estuaries, where halotolerant microbes thrive in response to variable salinity...
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Sources of Food Contamination

Contamination of food by microbial agents and natural toxins poses significant risks to public health. These hazards can be introduced at various points across the food supply chain, ranging from environmental sources to processing and storage stages. Understanding these contamination pathways is critical for developing strategies to ensure food safety.Seafood is particularly vulnerable to contamination through both environmental exposure and microbial colonization. Toxins from harmful algal...

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相关实验视频

Updated: Jun 24, 2026

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
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通过海洋数据正义镜头预测有害藻类.

Nicholas R Record1, Benjamin Tupper1, Kenneth Douyon1

  • 1Bigelow Laboratory for Ocean Sciences, East Boothbay, Maine 04544, United States.

Environmental science & technology
|June 10, 2025
PubMed
概括

有害藻类繁殖 (HAB) 预测系统可能会加剧全球不平等. 数据正义原则可以指导全球HAB监测和预测网络的公平发展.

关键词:
数据 正义 数据 正义预测 预测 预测 预测全球 全球 全球 全球 全球 全球有害的藻类繁荣海洋数字双胞胎数字海洋海洋预测 海洋预测

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科学领域:

  • 环境科学和数据科学环境科学和数据科学
  • 海洋学和生态学
  • 数据正义和算法问责制

背景情况:

  • 通过网络数据,有害藻类繁殖 (HAB) 的监测和预测正在全球范围内扩大.
  • 现有的海洋预测系统已经证明了意想不到的后果和超出范围的应用.
  • 数据正义领域为分析数据技术带来的危害提供了一个框架,数据技术越来越多地应用于环境问题.

研究的目的:

  • 将数据正义镜头应用于HAB预测系统的开发.
  • 在全球HAB数据库和预测工作中识别潜在的偏见和不公平.
  • 为HABs提供公平和可访问的全球数据和预测系统提供指导.

主要方法:

  • 调查了三个主要的全球数据库:海洋生物多样性信息系统,有害藻类事件数据库和AlgaeBase.
  • 分析了一套文献和更广泛的海洋预测文献.
  • 检查了数据收集,知识集中和分类学可见性的模式.

主要成果:

  • HAB的监测和知识集中在北大西洋北部的高GDP国家.
  • 数据模式反映并可能加强海洋健康现有的全球经济和政治不平等.
  • 在藻类类的可见性方面存在偏见,有利于与特定地区相关的藻类.

结论:

  • 当前的HAB数据系统可能会延续全球海洋压力和不平等.
  • 数据正义,设计正义和算法问责制的原则对于公平的HAB预测至关重要.
  • 集中权益和准入对于构建包容性全球数据和预测系统至关重要.