4.7 Article

Perceived global increase in algal blooms is attributable to intensified monitoring and emerging bloom impacts

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SPRINGERNATURE
DOI: 10.1038/s43247-021-00178-8

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  1. IOC (Intergovernmental Oceanographic Commission of UNESCO)
  2. ICES (International Council for the Exploration of the Sea)
  3. PICES (North Pacific Marine Science Organisation)
  4. IAEA (International Atomic Energy Agency)
  5. Government of Flanders (Belgium)

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According to an analysis of global harmful algal bloom events, it was found that there is no uniform increase in the global occurrence of these events over time. The recorded increase in events is primarily due to enhanced coastal monitoring and aquaculture practices.
Global trends in the occurrence, toxicity and risk posed by harmful algal blooms to natural systems, human health and coastal economies are poorly constrained, but are widely thought to be increasing due to climate change and nutrient pollution. Here, we conduct a statistical analysis on a global dataset extracted from the Harmful Algae Event Database and Ocean Biodiversity Information System for the period 1985-2018 to investigate temporal trends in the frequency and distribution of marine harmful algal blooms. We find no uniform global trend in the number of harmful algal events and their distribution over time, once data were adjusted for regional variations in monitoring effort. Varying and contrasting regional trends were driven by differences in bloom species, type and emergent impacts. Our findings suggest that intensified monitoring efforts associated with increased aquaculture production are responsible for the perceived increase in harmful algae events and that there is no empirical support for broad statements regarding increasing global trends. Instead, trends need to be considered regionally and at the species level. The global occurrence of harmful algal bloom events is not increasing uniformly over time and the increase in recorded events is due to enhanced coastal monitoring and aquaculture, according to an analysis of a 33-year global dataset.

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