Response of phytoplankton traits to environmental variables in French lakes: New perspectives for bioindication

Derot, Jonathan; Jamoneau, Aurélien; Teichert, Nils; Rosebery, Juliette; Morin, Soizic; Laplace-Treyture, Christophe

doi:10.1016/j.ecolind.2019.105659

Cited by 23 publications

(16 citation statements)

References 60 publications

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“…A recent study, performed in another Swiss peri-alpine lake, obtained good results for the forecast of phytoplankton and cyanobacteria distribution using an RF model (Thomas et al, 2018). On a broader level, the RF model also yielded good prediction and forecast results in freshwater environments (Derot et al, 2020;Yajima and Derot, 2018). These studies indicated that the RF model is well adapted to our current problem.…”

Section: Machine Learning Modelsmentioning

confidence: 58%

“…For the minimum number of observations in each node of the RF model, also called the minimum leaf size (min-leaf), we used the default setup proposed by MATLAB. This implies that the value of min-leaf is equal 5 and 1 in the regression and classifications modes, respective (Derot et al, 2020;Yajima and Derot, 2018).…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…In lacustrine ecosystems, the pairing of longterm and or high-frequency databases and a random forest (RF) model, seems to be a good alternative for hydro-ecological models for predicting the phytoplankton biomass and bloom (Breiman, 2001;Thomas et al, 2018;Yajima and Derot, 2018). Moreover, some clustering algorithms based on machine learning models such as the Kmeans method, enable the creation of classes to address environmental problems (Derot et al, 2020;Hartigan and Wong, 1979;Rousseeuw et al, 2014;Solidoro et al, 2007). Furthermore, the predictive performance of an RF model can be improved by utilizing it in conjunction with a K-means model (Kwon and Park, 2016;Liu and Sun, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva

2020

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Machine Learning Modelsmentioning

confidence: 58%

Section: Machine Learning Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva

2020

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“…Viridiplantae and Alveolata had an intermediate number of ITS-SWARMS per V9-group (average number of 2.2–2.6) and a low to intermediate geographical restriction of ITS-SWARMS (42% to 52% respectively). In all taxonomic groups some taxa included certain geographically restricted ITS-SWARMS (Figure S5 42 ). Aside from indications for a geographical restriction of some subpopulations, the ITS-SWARMS also indicate an ecophysiological differentiation of clades below the resolution of V9-groups (Table 1 ).…”

Section: Resultsmentioning

confidence: 99%

Centers of endemism of freshwater protists deviate from pattern of taxon richness on a continental scale

Olefeld

Böck

Jensen

et al. 2020

Sci Rep

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Here, we analyzed patterns of taxon richness and endemism of freshwater protists in europe. even though the significance of physicochemical parameters but also of geographic constraints for protist distribution is documented, it remains unclear where regional areas of high protist diversity are located and whether areas of high taxon richness harbor a high proportion of endemics. further, patterns may be universal for protists or deviate between taxonomic groups. Based on amplicon sequencing campaigns targeting the SSU and itS region of the rDnA we address these patterns at two different levels of phylogenetic resolution. Our analyses demonstrate that protists have restricted geographical distribution areas. for many taxonomic groups the regions of high taxon richness deviate from those having a high proportion of putative endemics. in particular, the diversity of high mountain lakes as azonal habitats deviated from surrounding lowlands, i.e. many taxa were found exclusively in high mountain lakes and several putatively endemic taxa occurred in mountain regions like the Alps, the pyrenees or the Massif central. Beyond that, taxonomic groups showed a pronounced accumulation of putative endemics in distinct regions, e.g. Dinophyceae along the Baltic Sea coastline, and chrysophyceae in Scandinavia. Many other groups did not have pronounced areas of increased endemism but geographically restricted taxa were found across europe.

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“…Phytoplankton was collected at the deepest point of the lakes, in the euphotic part of the water column. Taxa were determined at the species level in the laboratory and their abundances were weighted by taxa biovolume [49] using standard cell values defined in the software Phytobs [50], or measured directly from the sample if the values were lacking. Additionally, chlorophyll-a was collected from the euphotic zone during each sampling event and measured using the standard methods NF-T 90-117 [49,51,52].…”

Section: Biological Datamentioning

confidence: 99%

How Do Eutrophication and Temperature Interact to Shape the Community Structures of Phytoplankton and Fish in Lakes?

Bouraï

Logez

Laplace-Treyture

et al. 2020

Water

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Freshwater ecosystems are among the systems most threatened and impacted by anthropogenic activities, but there is still a lack of knowledge on how this multi-pressure environment impacts aquatic communities in situ. In Europe, nutrient enrichment and temperature increase due to global change were identified as the two main pressures on lakes. Therefore, we investigated how the interaction of these two pressures impacts the community structure of the two extreme components of lake food webs: phytoplankton and fish. We modelled the relationship between community components (abundance, composition, size) and environmental conditions, including these two pressures. Different patterns of response were highlighted. Four metrics responded to only one pressure and one metric to the additive effect of the two pressures. Two fish metrics (average body-size and biomass ratio between perch and roach) were impacted by the interaction of temperature and eutrophication, revealing that the effect of one pressure was dependent on the magnitude of the second pressure. From a management point of view, it appears necessary to consider the type and strength of the interactions between pressures when assessing the sensitivity of communities, otherwise their vulnerability (especially to global change) could be poorly estimated.

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Response of phytoplankton traits to environmental variables in French lakes: New perspectives for bioindication

Cited by 23 publications

References 60 publications

Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva

Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva

Centers of endemism of freshwater protists deviate from pattern of taxon richness on a continental scale

How Do Eutrophication and Temperature Interact to Shape the Community Structures of Phytoplankton and Fish in Lakes?

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