Lakes are essential ecosystems that provide a large number of ecosystem services whose quality is strongly impacted by human pressures. Optimal uses of lakes require adapted management practices which in turn rely on physico-chemical and biological monitoring. Long-term ecological monitoring provides large sets of environmental data. When such data are available, they have to be associated to metadata and to be stored properly to be accessible and useable by the scientific community. We present a data informatics system accessible to anyone who requests it. Maintained online since 2014 (https://si-ola.inrae.fr), it is originated from the Observatory on LAkes (OLA). It contains long-term data from 4 peri-alpine lakes (Lakes Aiguebelette, Annecy, Bourget, Geneva/Léman) and 24 high-altitude lakes of the northern French Alps. We describe the generated long-term data series, the data type, the methodologies and quality control procedures, and the information system where data are made accessible. Data use is allowed under the condition of providing reference to the original source. We show here how such a platform clearly enhances data sharing and scientific collaboration. Various studies referring to these data are regularly published in peer-reviewed journals; providing in fine a better understanding of lakes’ ecosystems functioning under local and global pressures.
The effectiveness of environmental protection measures is based on the early identification and diagnosis of anthropogenic pressures. Similarly, restoration actions require precise monitoring of changes in the ecological quality of ecosystems, in order to highlight their effectiveness. Monitoring the ecological quality relies on bioindicators, which are organisms revealing the pressures exerted on the environment through the composition of their communities. Their implementation, based on the morphological identification of species, is expensive because it requires time and experts in taxonomy. Recent genomic tools should provide access to reliable and high-throughput environmental monitoring by directly inferring the composition of bioindicators' communities from their DNA (metabarcoding). The French-Swiss program SYNAQUA (INTERREG France-Switzerland 2017-2019) proposes to use and validate the tools of environmental genomic for biomonitoring and aims ultimately at their implementation in the regulatory bio-surveillance. SYNAQUA will test the metabarcoding approach focusing on two bioindicators, diatoms, and aquatic oligochaetes, which are used in freshwater biomonitoring in France and Switzerland. To go towards the renewal of current biomonitoring practices, SYNAQUA will (1) bring together different actors: scientists, environmental managers, consulting firms, and biotechnological companies, (2) apply this approach on a large scale to demonstrate its relevance, (3) propose robust and reliable tools, and (4) raise public awareness and train the various actors likely to use these new tools. Biomonitoring approaches based on such environmental genomic tools should address the European need for reliable, higher-throughput monitoring to improve the protection of aquatic environments under multiple pressures, guide their restoration, and follow their evolution.
This dataset complement a previously published dataset [1] and corresponds to the physico-chemical parameters data series produced during the MESOLAC experimental project [2] . The presented dataset is composed of: 1. In situ profiles (0–3m) of temperature, conductivity, pH, dissolved oxygen (concentration and saturation). 2. In situ measurements of light spectral UV/VIS/IR irradiance (300–950 nm wavelength range) taken at 0, 0.25, 0.5, 1, 1.5, 2 and 2.5m. 3. Laboratory chemical analysis of samples collected at 0 and 2 m (conductivity, pH, total alkalinity, NH 4 , NO 2 , NO 3 , total and particulate nitrogen (Ntot, Npart), PO 4 , total and particulate phosphorus (Ptot, Ppart), total, organic particulate and total particulate carbon (Ctot, Cpart-org, Cpart-tot), Cl, SO 4 , SiO 2 . 4. Laboratory analysis of pigments extracted from samples collected at 0 and 2 m (Chl a , Chl c , carotenoids, phaeopigments). The experimental design is the same as in Tran-Khac et al [1] . Briefly, it consisted of nine pelagic mesocosms (about 3000 L, 3m depth) deployed in July 2019 in Lake Geneva near the shore of Thonon les Bains (France) aiming to simulate predicted climate scenarios (i.e. extreme events) and assess the response of planktonic communities, ecosystem functioning and resilience. During the experiment, physical parameters were measured twice a week. At the same time, samples were collected at 0 and 2m of depth for subsequent chemical laboratory analyses. These data are presented in the dataset file, ordered by sampling event (numbered from S1 to S8), treatment (Control-C, High-H and Medium-M) and replicates (1 to 3). For each sampling point the measured parameters are listed in columns, missing data and values below the detection limit are marked as NA (not available). This data set aims to contribute to the understanding of the effect of environmental forcing on lake physico-chemical characteristics (such as temperature, oxygen and nutrient concentration) under simulated intense weather events. To a broader extent, the presented data can be used for a wide variety of applications, including monitoring of a large peri-alpine lake functioning under environmental stress and being included in further meta-analysis to generalise the effect of climate change on large lakes. The two complementary dataset differ in the acquired data and methods, temporal and spatial resolution. They complete each other in terms of physico-chemical characterization of the experimental treatments and together can allow comparison of the two different monitoring strategies (continuous vs punctual) during in situ experimental manipulations.
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