Microbialites as bioindicators of lake hydric dynamics: the evolution of microbial populations and mineralogy of Mexican lacustrine microbialites along an alkaline-saline gradient
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".