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Enregistrement W7070432592

Organic matter quality in cryosols : effect on soil nitrogen dynamics and greenhouse gas emissions

2011· article· en· W7070432592 sur OpenAlexfundno aff

Notice bibliographique

RevueUniversity Library - University of Saskatchewan (University of Saskatchewan) · 2011
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueMachine Learning in Bioinformatics
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaChurchill Northern Studies Centre
Mots-clésGreenhouse gasSoil organic matterNitrogenOrganic matterQuality (philosophy)Soil water
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Over the past millennia, complex terrestrial ecosystems have evolved in the Arctic. However, the stability of these unique ecosystems is in jeopardy because of climate changes. Due to the fact that Arctic soils store great amounts of carbon (C) in soil organic matter (SOM), any change that may occur in SOM with climate changes may substantially affect many aspects of Arctic ecosystems such as vegetation, animals, and humans. On a more global perspective, any change in Arctic SOM has the potential of modifying the overall world climate by affecting the global greenhouse gas (GHG) budget. A better understanding of the soil factors that affect soil N and C cycling at the landscape scale, such as moisture, temperature, and SOM characteristics, is necessary to produce better models. The overall objective of this study was to characterize the properties of SOM in Arctic soils and their influence on soil N and C cycling dynamics - including GHG emissions - at the landscape scale. This study was conducted in three distinct Arctic ecosystems: Sub-Arctic (Churchill, MB), Low-Arctic (Daring Lake, NWT), and High-Arctic (Truelove, NU). For each site, the sampling locations were evenly divided into five landform units: 1) upper slope (Up), 2) back slope (Back), and 3) lower slope (Low) for catena sites, and 4) hummock (Hum) and 5) wedges of hummock (W) for hummocky sites (i.e., hummock in Churchill and ice-wedge polygons in Truelove). All sites were sampled at the end of their growing season (from 2 to 3 weeks before plant senescence). The characteristics of SOM were assessed using three methods: 1) density fractionation to separate the uncomplexed light fraction (LF) from heavy fraction (HF) of SOM (LF < 1.55 g mL⁻¹ < HF), 2) solid-state CPMAS ¹³C nuclear magnetic resonance (NMR) spectroscopy that determined the relative proportions of carbonyl-C (CbyC), alkyl-C (AC), aromatic-C (AroC), o-alkyl-C (OAC), and carbohydrates-C (CC), and 3) water-extractable organic matter (WEOM) that estimated SOM diluted in soil solution. Soil gross N mineralization was measured in situ using ¹⁵N dilution technique. Soil GHG emissions [nitrous oxide (N₂O), methane (CH₄), and carbon dioxide (CO₂)] were measured in situ using a multicomponent Fourier transform infrared gas analyzer coupled with an automated dark chamber. The first study showed that organic surface soils, which had more than 17% soil organic C (SOC) by weight, contained relatively more labile SOM than mineral surface soils (< 17% SOC). For example, OAC:AroC ratios of the organic soils ranged from 25 to 75% greater compared to mineral soils. At Churchill, Daring Lake, and Truelove, 53, 73, and 20% of the C and N was included in the LF, respectively. All results show that the organic soils of Sub- and Low-Arctic ecosystems sampled for this study contain more fresh and un-decomposed plant residues than High-Arctic organic soils. The second study showed that both topography and ecosystems had a significant impact on gross N mineralization and CO₂ emission rates. For example, at Churchill, gross N mineralization increased about 6-fold from upper slope to lower slope areas. Similarly, at Daring Lake, CO₂ emissions increase about 5-fold from upper slope to lower slope areas. Topography and ecosystems had a very limited impact on soil N₂O and CH₄ emissions most likely because net emissions were extremely low. The third study showed that soil moisture, SOM quantity, and labile SOM parameters such as OAC:AroC and water-soluble organic carbon (WSOC) positively influenced gross N mineralization, N₂O, and CO₂ emissions, whereas the relative proportion of AroC negatively influenced gross N mineralization, N₂O, and CO₂ emissions. Relationships between SOM characteristics and CH₄ emissions were not significant. This study showed that Up and Back areas tended to store relatively more recalcitrant SOM (AroC) than Low, Hum, and W areas, suggesting less fresh plant input on these landform units. Assessing SOM qualities with the ability of the soils to mineralize N (i.e., gross N mineralization) and release GHG at the landscape scale and across the Arctic represents a great advance in the understanding of these complex and unique ecosystems. Lower proportion of fresh and labile SOM found on Up and some Back landform units compared to Low and hummocky sites suggest that plants have more difficulties establishing and growing on these landform units (e.g., Up and Back) that experience harsh climates. Therefore, generalizations of the climate change impacts on soil N and C cycling processes throughout Arctic landscapes and ecosystems are less certain if topography is not considered. These results are particularly important because they can be used to produce better models that evaluate SOM stocks and dynamics under several climate scenarios and across Arctic landscapes and ecosystems.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,130
Score d'incertitude au seuil0,259

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,005
Tête enseignante GPT0,176
Écart entre enseignants0,171 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2011
Routes d'admission1
Résumé présentnon

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