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Zooplankton biodiversity monitoring in polluted freshwater ecosystems: A technical review

期刊

出版社

ELSEVIER
DOI: 10.1016/j.ese.2019.100008

关键词

Biodiversity; Micro-eukaryotes; Metabarcoding; Freshwater ecosystem; Water pollution

资金

  1. National Natural Science Foundation of China [31800307, 31572228]
  2. National Key R&D Program of China [2016YFC0500406]
  3. Chinese Academy of Science [ZDRW-ZS-2016-5-6]

向作者/读者索取更多资源

Freshwater ecosystems harbor a vast diversity of micro-eukaryotes (rotifers, crustaceans and protists), and such diverse taxonomic groups play important roles in ecosystem functioning and services. Unfortunately, freshwater ecosystems and biodiversity therein are threatened by many environmental stressors, particularly those derived from intensive human activities such as chemical pollution. In the past several decades, significant efforts have been devoted to halting biodiversity loss to recover services and functioning of freshwater ecosystems. Biodiversity monitoring is the first and a crucial step towards diagnosing pollution impacts on ecosystems and making conservation plans. Yet, bio-monitoring of ubiquitous micro-eukaryotes is extremely challenging, owing to many technical issues associated with micro-zooplankton such as microscopic size, fuzzy morphological features, and extremely high biodiversity. Here, we review current methods used for monitoring zooplankton biodiversity to advance management of impaired freshwater ecosystems. We discuss the development of traditional morphology-based identification methods such as scanning electron microscope (SEM) and ZOOSCAN and FlowCAM automatic systems, and DNA-based strategies such as metabarcoding and real-time quantitative PCR. In addition, we summarize advantages and disadvantages of these methods when applied for monitoring impacted ecosystems, and we propose practical DNA-based monitoring workflows for studying biological consequences of environmental pollution in freshwater ecosystems. Finally, we propose possible solutions for existing technical issues to improve accuracy and efficiency of DNA-based biodiversity monitoring.

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