Abstract：
:There has been increasing interest in diatom-based bio-assessment but we still lack a comprehensive understanding of how to capture diatoms' temporal dynamics with an appropriate sampling frequency (ASF). To cover this research gap, we collected and analyzed daily riverine diatom samples over a 1-year period (25 April 2013-30 April 2014) at the outlet of a German lowland river. The samples were classified into five clusters (1-5) by a Kohonen Self-Organizing Map (SOM) method based on similarity between species compositions over time. ASFs were determined to be 25 days at Cluster 2 (June-July 2013) and 13 days at Cluster 5 (February-April 2014), whereas no specific ASFs were found at Cluster 1 (April-May 2013), 3 (August-November 2013) (>30 days) and Cluster 4 (December 2013 - January 2014) (<1 day). ASFs showed dramatic seasonality and were negatively related to hydrological wetness conditions, suggesting that sampling interval should be reduced with increasing catchment wetness. A key implication of our findings for freshwater management is that long-term bio-monitoring protocols should be developed with the knowledge of tracking algal temporal dynamics with an appropriate sampling frequency.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Wu N,Faber C,Sun X,Qu Y,Wang C,Ivetic S,Riis T,Ulrich U,Fohrer Ndoi
10.1038/srep36950subject
Has Abstractpub_date
2016-11-14 00:00:00pages
36950issn
2045-2322pii
srep36950journal_volume
6pub_type
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