Carbon Dynamics in Temperate Forests
Notice bibliographique
Résumé
Forests ecosystems cover about 30% of the Earth’s land surface, corresponding to an area of roughly 42 million km2 globally. Forests play an important role in the global carbon cycle by exchanging carbon dioxide (CO2) with the atmosphere. Annually, forests act to effectively sequester large amounts of anthropogenically-emitted CO2 from the atmosphere through photosynthetic processes. Through the unparalleled increase of CO2 emissions over the past century and the subsequent climatic inconsistencies due to global climate change, the carbon sink-capacity of the world’s forests remains uncertain. Furthermore, since increasing temperatures have been shown to extend the vegetative growing season in forests, phenological responses to this change are of particular interest. In an effort to effectively assess the future carbon sequestration potential of forests, a better understanding of the climatic controls on phenology, and its influence on carbon processes, is needed. The eddy covariance (EC) technique is a stand-level, in-situ, method used widely to assess the net CO2 exchange across the canopy-atmosphere interface. Together with meteorological data, the sequestration of CO2 and the subsequent ecosystem productivity can be quantified over various time scales (half-hours to decades). This dissertation reports results from field observations of EC measured fluxes that were used to study the climatic impacts on forest phenology and the resulting carbon dynamics in southern Ontario, Canada. The study sites, part of the Turkey Point Observatory, consisted of two similarly-aged, temperate, North American forests growing under similar climatic and edaphic conditions: the 80-year old (in 2019) white pine plantation (coniferous evergreen) and 90+ year-old, naturally-regenerated, white oak (deciduous broadleaf) forest. These forests were studied from 2012 to 2017, using the EC technique, digital phenological cameras, and remote-sensing measurements. At the deciduous broadleaf forest, mid-summer (July and August) meteorological conditions were the key period in determining the annual carbon sink-strength of the site, acting to regulate the interannual variability in carbon uptake. The forest experienced higher net ecosystem productivity (+NEP; carbon sink) when soil temperatures ranged from 15 to 20°C and vapor pressure deficit was 0.7 and 1.2 kPa. From 2012 to 2016, the forest remained a net annual sink, with mean NEP of 206 ± 92 g C m-2 yr-1, similar to that of other North American deciduous forests. Spring and autumn phenological transition dates were calculated for each year (2012 to 2017) from measured EC data and digital camera greenness indices. The timing of spring and autumn transition dates were impacted by seasonal changes in air temperature and other meteorological variables. Contrary to past studies, an earlier growing season start did not equate to increased annual carbon uptake. In autumn, a later end to the deciduous forest growing season negatively impacted the net carbon uptake of the forest, as ecosystem respiration (RE) outweighed the gains of photosynthesis. The digital camera indices failed to capture the peak dates of photosynthesis, but accurately measured the spring and autumn transition dates, which may be useful in future remote sensing applications. A comparison of the two forests from 2012 to 2017 found the coniferous forest to have higher but more variable annual NEP (218 ± 109 g C m-2 yr-1) compared to that of the deciduous broadleaf forest (200 ± 83 g C m-2 yr-1). Similarly, the mean annual evapotranspiration (ET) was higher (442 ± 33 mm yr-1) at the coniferous forest compared to that of the broadleaf forest (388 ± 34 mm yr-1). The greatest difference between years resulted from the response to heat and drought. During drought years, deciduous carbon and water fluxes were less sensitive to changes in temperature or water availability compared to the evergreen forest. Carotenoid sensitive vegetative indices and the red-edge chlorophyll index were shown to effectively capture seasonal changes in photosynthesis phenology within both forests via proximal remote sensing measurements during the 2016 growing season. Satellite vegetative indices were highly correlated to EC photosynthesis, but significant interannual variability resulted from either meteorological inputs or the heterogeneous landscapes of the agriculturally-dominated study area. This dissertation improved our understanding of the dynamics of carbon exchange within the northeastern North American deciduous forest ecozone, through the examination of climatic variability and its impact on carbon and phenology. This dissertation also contributed to efforts being made to better evaluate the impact of species composition on carbon dynamics in geographically similar forests. Moreover, the use of the digital phenological camera observations and remote sensing techniques to complement and better understand the fluxes observed with the EC method was innovative and may help other researchers in future studies.
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 | 0,000 | 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) | 0,002 | 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 ».