Different management practices impact on CO2 and H2O budget of wind disturbed forest sites- 5-year dataset
Notice bibliographique
Résumé
<p>There is evidence of increasing severity of extreme meteorological events, which due to climate warming are also more frequent than in the past few decades. Any disturbances (either natural or anthropogenic) exert a significant influence on the forest’s functioning. In Canada and the USA, fires and insect outbreaks cause the greatest damage while in Europe wind disturbances are the main threat. Since in Poland the majority of forests are managed by the State Forests, after such events disturbed areas are almost immediately designated for reforestation. While natural regeneration still contributes the least to forest restoration, the most common practices in our country include harvesting, soil preparation (ploughing) and manual seedlings introduction, which in this sense is similar to clear-cut’s management. </p><p>Once such an event happened in Poland two EC stations were set up in the area of an 80-year old pine forest, which had been wiped out by a tornado in July 2012, to asses the impact of forest management. To date, there have been more than 5 full years of continuous carbon and energy fluxes measurement, allowing insight into forest regeneration patterns due to manual reforestation, as well as differences in CO<sub>2</sub> losses connected to chosen treatments. The two sites (Tlen I and Tlen II) differ mostly in terms of soil preparation – at Tlen I site soil was ploughed before replanting and at Tlen II soil cover remained almost intact. Additionally, at the second location, only trunks and main branches were harvested, while all uprooted stumps were left to decompose. Both meteorological and soil conditions have been investigated, with most of them not being significantly different, which allowed drawing the conclusion that observed differences in GHGs balance <span>are most likely related to chosen forest management practices</span>. Thorough analysis of quality checked EC data revealed that in 5-year perspective the application of traditional method (Tlen I site), mainly due to soil ploughing, resulted in much less total CO<sub>2</sub> loss to the atmosphere, reaching C-neutrality point in only 6 years after the damage as well as better seedling growth in general in comparison to the technique, where the soil cover was not disrupted. Moreover, it seems that furrows created at the conventionally managed forest site (“double” organic layer) serve as crucial water reservoirs during water shortage periods, preventing from the pine plantation damage caused by prolonged droughts.</p><p>This work advances our understanding of how different forest management practices can help to sustain the least CO<sub>2</sub> losses on the example of wind-disturbed forests. Although, it has to be remembered that long-term studies are needed to point the best option from the perspective of climate change mitigation. </p>
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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 tête enseignante, 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 ».