Microbial growth in a warming Arctic: Exploring controls and temperature responses in permafrost soils
Notice bibliographique
Résumé
Permafrost soils are particularly vulnerable to climate warming. With ~1,500 Gt Carbon (C), they store a significant proportion of global soil C. Organic matter that was frozen and thus unavailable for microbial decomposition for millennia, is now thawing. How much of this permafrost C is decomposed will be determined by microbial activities and the partitioning of assimilated C to microbial growth (potential C stabilization) or microbial respiration (C loss). Our current knowledge on the controls of microbial growth and respiration in permafrost soils is, however, limited.The objective of this study was to analyze microbial growth and respiration in permafrost soils and to explore soil organic matter composition, microbial community composition and various soil parameters as potential drivers. We collected 81 soil samples from four soil layers (organic, mineral, cryoturbated, permafrost) and three lowland tundra polygon types (low-center, flat-center, high-center) in Arctic Canada. We used pyrolysis-GC-MS fingerprinting to characterize soil organic matter composition and amplicon sequencing (16S, ITS1) to identify archaeal, bacterial, and fungal community composition. Temperature responses (Q10) were analyzed in an 8-week laboratory incubation experiment, subjecting soil aliquots to 4 °C and 14 °C. Microbial growth was determined by 18O-H2O-incorporation into DNA and microbial respiration by gas analysis.Soil organic matter composition differed between soil layers along a gradient of degradation and C content. Organic matter complexity and diversity decreased with the level of decomposition. We found distinct soil organic matter composition for each polygon type, including all soil layers, suggesting different decomposition pathways, induced by differences in vegetation and soil water regime. Anoxic conditions in low-center polygons resulted in more archaea and distinct fungal communities. Microbial community composition differed among all soil layers, with particularly more fungi in organic soils. Microbial mass-specific growth and respiration differed among polygons and soil layers, and both increased with warming. Overall, temperature responses (Q10) were higher for respiration than for growth, implying that microbes are less efficient in using C for growth. Linear mixed effect models revealed that soil organic matter composition and microbial community composition were good predictors for mass-specific growth at field and warmed conditions. Mass-specific respiration was best explained by microbial community composition. Our predictors, however, did not explain the temperature responses.Our results indicate that under warming, microbes allocated more C to respiration, leading to increased greenhouse gas emissions per unit of carbon taken up. We found these results while including all soil layers and polygon types, suggesting these responses to be representative for lowland Arctic ecosystems. Moreover, we could show that organic matter composition and microbial community composition are good predictors for microbial growth and respiration, thus deserving more attention in future studies.This study is part of the EU H2020 project “Nunataryuk”.
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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 | 0,001 | 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 ».