Microbialites as bioindicators of lake hydric dynamics: the evolution of microbial populations and mineralogy of Mexican lacustrine microbialites along an alkaline-saline gradient
Notice bibliographique
Résumé
Modern microbialites are frequently studied as analogues of ancient microbialites, the oldest of which date back to ~3.5 Ga. These organo-sedimentary structures are generated by complex microbial communities developing under specific physicochemical conditions, such that fossil microbialites attest for past microbial ecosystems. Lacustrine microbialites, in contrast with marine ones, show a large range of morphologies and mineralogical compositions, including various carbonate and non-carbonate (e.g. Mg-silicates) phases. Major dominant prokaryotic groups (e.g. Cyanobacteria, Planctomycetes or Alphaproteobacteria) and taxa‐associated functions (e.g. oxygenic and anoxygenic photosynthesis) appear conserved across microbialite ecosystems. However, the evolution of the microbial community and/or the chemical and mineralogical composition of lacustrine microbialites with the hydrogeochemistry of lakes remains undescribed. In the present work, we analysed the mineralogical and chemical composition, including major and trace element composition of microbialites as well as their microbial community using samples from ten crater lakes of the Trans‐Mexican volcanic belt along an alkalinity-salinity gradient. We also characterized lake hydrochemistry and planktonic communities to compare them with those of microbialites. We found a large diversity of microbialites in terms of mineralogical composition which was primarily controlled by orthosilicic acid (H4SiO4) concentrations and Mg/Ca ratios of the solutions. In addition, microbialite size correlated positively with salinity, (Mg/Ca)aq ratio and alkalinity. Our observations suggest that alkalinity values above 1.23 mM and salinity above 0.08 g.L-1 constitute potential chemical threshold above which lacustrine microbialites can occur. The composition of both prokaryotic and the eukaryotic microbialite-associated communities varied significantly across lakes, correlating with the alkalinity and salinity gradient. Moreover, microbialite-associated communities were clearly distinguishable from their surrounding planktonic communities, being more similar to those of microbialites from distant and chemically different lakes than to planktonic communities present in the same lake. In fact, we identified a microbial core of 247 operational taxonomic units shared by all lake microbialites. This core, mainly dominated by Cyanobacteria, Bacteroidetes, Planctomycetes, Chloroflexi, Alphaproteobacteria and Gammaproteobacteria, represented up to 40% of the relative abundance of the community in lakes displaying the highest alkalinity and most conspicuous microbialites (Alchichica and Atexcac). This suggests a prominent ecological role for those organisms in microbialite formation. We could also show that, in Lake Alchichica, microbialites formed very rapidly on inert surfaces (e.g. plastic) with rates of ~0.6 (and up to 1) mm/year and that nascent hydromagnesite and aragonite-rich microbialites harboured communities similar to the mature ones in native microbialites. Our study establishes a connection between the chemical, mineralogical and microbial composition of microbialites and the hydrogeochemical evolution of lakes. Alkalinity and salinity gradients reflect lake hydrological balance and status along an evaporation progress trend and/or weathering intensity of the surrounding bedrocks. In this context, microbial communities associated with modern lacustrine microbialites may possibly be used as indicators for management/prediction of limnologic states along alkalinity-salinity gradients.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».