Notice bibliographique
Résumé
Our current understanding of the tropical terrestrial nitrogen (N) cycle has been shaped by decades of field-based research, predominately at a small subset of sites across the globe. These field data inform hypotheses on how N cycling is mediated by biotic and abiotic drivers, and inform the paradigm that tropical wet forests are characterized by high rates of N inputs and outputs compared with other systems, driven by high inorganic N availability. However, recent findings that do not seem to conform to this paradigm call into question how well the bulk of our underlying data represents the diversity of the tropics as a whole. We propose that there may be blind spots in our understanding of N cycling created by a paucity of sampling from areas where environmental factor combinations differ from those often studied. Identifying these blind spots may help to resolve which drivers can be generalized across the tropics as a whole, versus which sustain system heterogeneity. We conducted a pan-tropical synthesis of field sampling sites for N fixation and denitrification (two key processes that influence system N inputs and outputs, as a proxy for general understanding of N cycling) and sampling intensity between 1950 and 2022. As a metric of geographic biases in general understanding, we tallied citations counts for each study over time. We also collated globally gridded data for a range of factors hypothesized to control N cycling rates, including soil and climatic variables, productivity, topography, vegetation type, biogeographic region, and disturbance. With these data, we: 1) mapped major axes of variation in tropical environmental conditions using principal components, 2) determined the distribution of environmental variables within sampled sites versus the tropics as a whole, and 3) identified regions where unique combinations of conditions are under sampled.Preliminary results show a relative over-representation of evergreen broadleaf forests, and under-representation of grasslands and savannas. This corresponded to a proportional oversampling of sites with higher soil fertility (soil N and P), high net primary productivity, high rainfall, and low rainfall seasonality. To quantify system-level biases we also explored intra-biome sampling variability for factors such as fertility and elevation (e.g., tropical montane versus lowland forests). Denitrification and N fixation tended to follow similar patterns in site characteristics, suggesting that these metrics are a good proxy for overall N cycling understanding.Overall, our study identifies regions of the global tropics where environmental drivers are similar to those dictating existing knowledge, as well as understudied regions that should be targeted to explore system heterogeneity. Future work can leverage this information to design cross-system comparisons to explicitly test current hypothesized mechanisms to advance our understanding of the N cycle. Constraining nutrient availability and cycling in tropical ecosystems is especially important in the context of global change, where shifting environmental conditions may alter how forests cycle and retain N and, therefore, how nutrient limitation will constrain productivity responses to rising carbon dioxide.
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 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 | 0,007 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».