Notice bibliographique
Résumé
The vegetation within the southwest Yukon consists of a complex mosaic of boreal forests dominated by white spruce (Picea glauca) and Festuca-Artemisia grasslands.In this study, these forest-grassland ecotones were used to study the effects of different vegetation types on a variety of soil properties including; percent moisture, fine earth bulk density, pH, total carbon and nitrogen, organic matter, total carbonate carbon and soil organic carbon.The effects of vegetation on these soil properties were able to be studied independently from other soilforming factors because the transition in vegetation occurs over relatively small spatial scales in which other soil-forming factors such as parent material, climate, topography and time are similar.Total carbonate carbon did not differ at any depth or position along the ecotone, and the only variation in soil pH across the ecotone occurred in the 5-10cm depth increment.Bulk density varied along the ecotone in all depth increments except the 10-20cm.All other soil properties varied significantly along the ecotone, but only in the organic horizon, if analyzed, and 0-5cm depth.Therefore, the only significant difference in soil properties occurred in surface horizons, which can be used to hypothesize that the forest-grassland mosaic in the southwest Yukon is not driven by differences in soil.However, because this study examined the relationship between soil and vegetation by assuming that the southwest Yukon was a steadystate system, future research may wish to examine all state factors to confirm this assumption before further analysis is completed.Additionally, since the patterns of ecotonal and vertical distribution of soil properties appear to be linked to the patterns of organic matter, future research may consider quantifying controls on organic matter such as above and belowground plant allocation in order to gain a better appreciation of total ecosystem carbon dynamics and potential effects of climate change on soil.I would first like to thank my supervisor, Dr. Ryan Danby, for all his support, encouragement and knowledge in helping me complete all aspects of this thesis.His assistance in creating the sampling design, interpreting results within SPSS and editing of this manuscript was extremely helpful and ensured that proper data and results were obtained for this thesis.Additionally, I would like to thank my co-supervisor, Dr. Neal Scott, for his extensive knowledge in soil science and carbon and nitrogen cycling.This thesis would not have been possible without his help in determining laboratory methods and procedures, analyzing results and editing.Furthermore, I would like to thank Alix Conway, Ashley Lowcock and Lucas Brehaut for their advice and help with both field and the laboratory procedures.Additionally, thanks to Anthony Bassutti for patiently teaching me all the detailed methods and procedures necessary to run samples on the LECO elemental analyzer and Lyn Garrah for emotional support while analyzing ANOVA results and editing my poster presentation.Thanks to Karen Depew and Dr. Brian Cumming for coordinating meetings and providing continual support throughout the year.Thank-you to the Kluane Lake Research Station (KLRS) for an incredible fieldwork season this past summer and to the Queen's Summer Work Experience Program (SWEP) and NSERC grant to Dr. Danby for funding my stay
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 | 0,000 | 0,001 |
| 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 ».