Component CO2 Emissions of Planted Pine Forests
Notice bibliographique
Résumé
The rapid increase of athropogenically-derived CO2 in the atmposphere, during the past century, has been linked to unprecendented global climate change. Forests and various forest management techniques have been proposed as a potential way to help sequester some ofthe atmpospheric CO2. In order to evaluate the CO2 sink potential offorests, a good understanding oftheir carbon dynamics is required over various stages oftheir development and growth. This dissertation reports results of a field study that focused on characterizing and quantifying CO2 emissions from various components of planted white pine (Pinus Strobus L.) forest ecosystems, growing in southern Ontario, Canada. The study site, called the Turkey Point Flux Station (TPFS), consisted offour stands, aged: 70-, 35-, 20-and 7 years-old, as of year 2009. Three major components of ecosystem respiration, Re, were studied using the chamber-method: soil respiration, Rs (both, autotrophic and heterotrophic ), foliar respiration, Rf, and live woody-tissue respiration, Rw. Chamber-based estimates of annual Re across the four different stands were: 1527 ± 137, 1313 ± 137, 2079 ± 293, and 769 ± 46 g C m^(-2) yr^(-1) for the 70-, 35-, 20-, and 7-year-old stands, respectively, and were generally higher compared to literature reported values. Annually Rf accounted for 48, 40, 58, and 31% of Re at the 70-, 35-, 20-, and 7-year-old stands, respectively, and dominated Re during the growing season at the three oldest stands. In contrast, Rs was the dominant Re component at the youngest stand and during the winter months at all four sites. Annually Rs accounted for 44, 40, 29, and 69 % ofRe across the respective TPFS sites. Rw was the smallest component of annual Re, accounting for only 9, 15, 13 and 0.1 % ofRe, respectively. Differences in leaf area indices among the stands were responsible for most ofthe intersite variability in Re, as well as for differences between Re values obtained in this study and those reported in the literature. Results from this study highlight the importance of considering site age and knowledge ofpast land-use history when assessing carbon budgets of afforested or planted ecosystems. They also suggest that Rf may be the more dominant and determinant component ofRe in young to mature afforested stands, which is in contrast to the widely reported Rs dominance of Re in forest ecosystems. Soil respiration was studied in detail across TPFS, as part ofthis dissertation, to determine the driving factors ofits temporal variability, considering seasonal, interannual (3 years ofmeasurements) and decadal (over the TPFS age-sequence) timescales. The range ofRs values across TPFS over the course of three study years was 539 ± 31 to 732 ± 31 g C m^(-2) yr^(-1). In general, annual soil emissions from the oldest stand were higher compared to those from the youngest two stands. However, emissions from the 35-year-old stand were comparable to those from the 20-and 7-year-old stands. Intersite differences in soil emissions were driven mostly by stand physiology, while interannual differences relfected interannual variability in climatic factors, as well as differences in stand physiology that modified the site's microclimates. In particular, results from this study suggest that soil moisture may have a larger effect on the heterotrophic rather than on the rhizospheric component of soil respiration in these forest ecosystems, supporting evidence from other literaturereported studies. Finally, the chamber-based Re values derived in this study were compared with Re values derived from congruent eddy covariance measurements at TPFS. Based on annual totals, Re calculated from chamber measurements overestimated Re calculated from eddy covariance measurements on average by: 18, 75, 24 and 39% at the 70-, 35-, 20-, and 7-year-old stands, respectively. These results highlight the continued need to resolve the discrepancy between the two methodologies used to estimate Re, before measurements from both methods can be used together to make conclusions about the composition of forest carbon budgets. As part of this dissertation, a statistical method of data analysis was used to implement temporal flexibility in the conventional Q10 model, widely used to simulate various Re components of forested ecosystems. The outcome of that analysis highlighted two things: a) for the case of soil respiration, the exponential relationship between Rs and Ts may be limited to the so called "ecologically optimum Ts range" for fine root growth; b) the functional form of the Q10 model is inadequate for simulating the Rs-Ts relationship across a wide range ofTs values, even after the implementation of temporal flexibility into the model, which allowed both of its model parameters to vary. The consequence of the latter result led to the development of a new empirical model -the Gamma model -for use in simulating respiration with temperature. The statistical method and the new emprical model were used to simulate CO2 emissions in this study and to identify additional environmental and physiological factors that explained some of the variability in the individual Re components across TPFS. Thus, temperature was found to be the dominant controlling factor of respiration at all four sites. However, occurrence of precipitation events, vapour pressure deficit, photosynthetically active radiation, the thickness ofthe LFH soil horizon (i.e. litter layer), and soil nutrients, were also shown to explain some ofthe variability ofthe various respiratory components. This dissertation fills some of the gaps in literature on studies of Re component fluxes ofplanted young to mature forests, especially of those growning in the temperate climate of eastern North America, where afforestation and plantations are most likely to occur. The study should be of interest to carbon cycle modellers, field ecologists, the eddy covariance community. It should also be of interest to those involved in forest carbon accounting, management, and policy development, by adding knowledge to our understanding of global carbon cycling and the potential for using afforested sites in global warming mitigation attempts.
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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 | 0,000 | 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 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 ».