Builders, tenants, and squatters: the origins of genetic material in modern stromatolites

Petryshyn, Victoria A.; Junkins, Emily N.; Stamps, Blake W.; Bailey, Jake V.; Stevenson, Bradley S.; Spear, John R.; Corsetti, Frank A.

doi:10.1111/gbi.12429

Cited by 9 publications

(7 citation statements)

References 69 publications

(142 reference statements)

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“…However, in this case study of SL with principal component regression, we found that the microbiome provides the sufficient complexity required to predict NH 4 + . This suggests that even abiogenic analyte pools such as NH 4 + after wildfire can be explained by microbial tenants [64] of an ecosystem —independent of whether or not those microbiota actually produce or consume the analyte. These tenants may be responding to other environmental changes post-fire (e.g., increases in pH and acid-neutralizing capacity (ANC)), but their aggregate responses to these changes still aid in the prediction of NH 4 + through a complex web of co-variances and inter-dependencies.…”

Section: Resultsmentioning

confidence: 99%

Statistical learning and uncommon soil microbiota explain biogeochemical responses after wildfire

Honeyman

Fegel

Peel

et al. 2022

Preprint

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Wildfires are a perennial event globally and the biogeochemical underpinnings of soil responses at relevant spatial and temporal scales are unclear. Soil biogeochemical processes regulate plant growth and nutrient losses that affect water quality, yet the response of soil after variable intensity fire is difficult to explain and predict. To address this issue, we examined two wildfires in Colorado, USA across the first and second post-fire years and leveraged Statistical Learning (SL) to predict and explain biogeochemical responses. We found that SL predicts biogeochemical responses in soil after wildfire with surprising accuracy. Of the 13 biogeochemical analytes analyzed in this study, 9 are best explained with a hybrid microbiome + biogeochemical SL model. Biogeochemical-only models best explain 3 features, and 1 feature is explained equally well with hybrid or biogeochemical-only models. In some cases, microbiome-only SL models are also effective (such as predicting NH4+). Whenever a microbiome component is employed, selected features always involve uncommon soil microbiota (i.e., the 'rare biosphere', existing at < 1% relative abundance). Here, we demonstrate that SL paired with DNA sequence and biogeochemical data predict environmental features in post-fire soils, though this approach could likely be applied to any biogeochemical system.

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Section: Resultsmentioning

confidence: 99%

Statistical learning and uncommon soil microbiota explain biogeochemical responses after wildfire

Honeyman

Fegel

Peel

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Further, elevated NH . This suggests that even abiogenic analyte pools such as NH 4 + after wildfire can be explained by microbial tenants [64] of an ecosystem -independent of whether or not those microbiota actually produce or consume the analyte. These tenants may be responding to other environmental changes post-fire (e.g., increases in pH and acid-neutralizing capacity (ANC)), but their aggregate responses to these changes still aid in the prediction of NH 4 + through a complex web of co-variances and inter-dependencies.…”

Section: Resultsmentioning

confidence: 99%

Statistical Learning and Uncommon Soil Microbiota Explain Biogeochemical Responses after Wildfire

Honeyman

Fegel

Peel

et al. 2022

Appl Environ Microbiol

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“…Although the microbial biofilms in tufas seem to affect the carbonate microfabric morphology, it is not entirely clear whether microbes also enhance carbonate precipitation and promote tufa growth. Demonstrating how microbes are involved in the formation of microbialites is challenging even in modern environments (e.g., Petryshyn et al, 2021). Finding in situ evidence of microbial metabolisms in active tufa mounds, however, may help us understand the potential microbial contributions of tufa formation.…”

Section: Discussionmentioning

confidence: 99%

Potential role for microbial ureolysis in the rapid formation of carbonate tufa mounds

et al. 2021

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Modern carbonate tufa towers in the alkaline (~pH 9.5) Big Soda Lake (BSL), Nevada, exhibit rapid precipitation rates (exceeding 3 cm/year) and host diverse microbial communities. Geochemical indicators reveal that carbonate precipitation is, in part, promoted by the mixing of calcium‐rich groundwater and carbonate‐rich lake water, such that a microbial role for carbonate precipitation is unknown. Here, we characterize the BSL microbial communities and evaluate their potential effects on carbonate precipitation that may influence fast carbonate precipitation rates of the active tufa mounds of BSL. Small subunit rRNA gene surveys indicate a diverse microbial community living endolithically, in interior voids, and on tufa surfaces. Metagenomic DNA sequencing shows that genes associated with metabolisms that are capable of increasing carbonate saturation (e.g., photosynthesis, ureolysis, and bicarbonate transport) are abundant. Enzyme activity assays revealed that urease and carbonic anhydrase, two microbial enzymes that promote carbonate precipitation, are active in situ in BSL tufa biofilms, and urease also increased calcium carbonate precipitation rates in laboratory incubation analyses. We propose that, although BSL tufas form partially as a result of water mixing, tufa‐inhabiting microbiota promote rapid carbonate authigenesis via ureolysis, and potentially via bicarbonate dehydration and CO2 outgassing by carbonic anhydrase. Microbially induced calcium carbonate precipitation in BSL tufas may generate signatures preserved in the carbonate microfabric, such as stromatolitic layers, which could serve as models for developing potential biosignatures on Earth and elsewhere.

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Builders, tenants, and squatters: the origins of genetic material in modern stromatolites

Cited by 9 publications

References 69 publications

Statistical learning and uncommon soil microbiota explain biogeochemical responses after wildfire

Statistical learning and uncommon soil microbiota explain biogeochemical responses after wildfire

Statistical Learning and Uncommon Soil Microbiota Explain Biogeochemical Responses after Wildfire

Potential role for microbial ureolysis in the rapid formation of carbonate tufa mounds

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