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Citizen science in marine litter research: A review

期刊

MARINE POLLUTION BULLETIN
卷 182, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.marpolbul.2022.114011

关键词

Public participation in science; Assessment; Volunteering; Ethics in citizen science; Clean-up

资金

  1. Global Challenge Research Fund [88200/03]
  2. Coordena?a?o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES)
  3. Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?ogico (CNPq) [309697/2015-8, 310553/2019-9]

向作者/读者索取更多资源

Citizen science plays an important role in addressing the issue of marine litter, but there is a lack of research in developing regions. Current studies mainly focus on the science of marine litter on the shoreline, with limited information on citizen scientists, hindering analysis of good practices in this aspect. The lack of standardization in collecting types and sizes of items hampers data meta-analyses. Standardizing citizen science methods and providing detailed reports on citizen scientists are essential for advancing research on marine litter.
Citizen science (CS) can help to tackle the emerging and worldwide problem of marine litter (ML), from col-lecting data to engaging different stakeholders. We reviewed what and how the scientific literature is reporting CS on ML to identify possible gaps to be improved. The 92 search results (separate occasions when 48 different CS initiatives were discussed across 85 publication records) revealed an under-representation of studies in developing regions. Most search results focused on the science of ML, whilst information regarding citizen sci-entists was commonly vague or missing, preventing critical analysis of good practices on this aspect. The studies concentrated on the shoreline and did not harmonize types and sizes of items collected, thus precluding data meta-analyses. The standardisation of CS methods and approaches and the detailed report of aspects related to citizen scientists are essential to support the science we need for the advances in CS efforts to face ML.

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