Response of phytoplankton traits to environmental variables in French lakes: New perspectives for bioindication. (January 2020)

• Record Type: Journal Article
• Authors: Derot, J.; Jamoneau, A.; Teichert, N.; Rosebery, J.; Morin, S.; Laplace-Treyture, C.
• Is Part Of: Ecological indicators. Volume 108(2020)
• Publication Date: 2020-01
• Subjects: French lakes -- Phytoplankton morpho-functional traits -- Water quality -- Indicator metrics -- Lake typology -- Random forest model -- Partial dependence plot -- K-means -- Null models; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2019.105659
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Response of phytoplankton traits to environment was investigated in French lakes. 21 traits were selected out of the 84 initially present in our database. Some interesting phytoplankton traits were related to nitrate concentrations. The response of phytoplanktonic traits was consistent across lake typologies. Identified traits could be used in future bioassessment programs. Abstract: The restoration and the preservation of aquatics ecosystems is a critical issue in our contemporary society. In lake ecosystems, phytoplankton taxonomic-based indicators have been developed to evaluate water quality, but suffer of limited ecosystem ecological value. The recent development of functional approaches may allow to evaluate other aspects of ecosystem quality, and to develop new trait-based indicators responding to different environmental conditions. Here, our aim was to analyze the response of phytoplankton traits to numerous environmental variables and to identify relevant traits for the development of future indicator metrics. We used a French national database of 469 lakes, consisting in phytoplankton biovolumes and physicochemical values. The response of 84 morpho-functional traits towards environmental variables was tested with Machine Learning models, taking into account lake typology. We identified 21 traits significantly related to environmental variables. Dissolved Organic Carbon, Nitrates and Total Suspend Solids were the physiochemical parameters which had the higher influence on our traits selection. However the response of phytoplanktonic traits to environmental variables did not change according to lake typology, advocating for a consistent response at the whole national scale contrary to the classical taxonomic approach. We finally identified several candidate traits that could be used for the development of new metrics for French lakes in a context of bio-assessment programs.