Effects of variable rate aerial application of Vision on moose (Alces alces) winter browsing and hardwood vegetation
Notice bibliographique
Résumé
Experimental aerial treatment of 7 mixedwood areas in late summer for conifer \nrelease with Vision? at 0.80, 1.06, and 1.60 kg a.e./ha, decreased living hardwood stem densities \nafter ten months by 42, 61 and 42% respectively on treated plots, while controls increased by 13%. \nTwenty two months after treatment stem densities were reduced (from pre-spray levels) by 48, 65 and \n61%; controls increased 19%. Greatest numbers of stems occurred on moderately deep, fresh soils. \nAfter treatment, winter browsing rates decreased in both six and 18 months post spray on all plots \nand were consistently higher on controls when compared with treated sub-blocks. Decline was \nprogressive over two years after treatment on sprayed areas but recovered in the second year on \ncontrols. The two highest application rates had the lowest browsing levels. Conversely, winter \ntrack data showed no differences in moose use between sprayed areas and controls, nor any \ndifference among treatments. This suggested moose still traveled through sprayed areas, but did not \nstop to browse. In addition to stem density counts, cover (%) for both herbs \nand hardwoods were estimated to evaluate the effectiveness of Vision? as a conifer release. \nHardwood cover was reduced significantly by all application rates; differences among treatments \nwere not significant. Herbaceous ground cover was reduced approximately 20% on all treated areas \none season after spray but by next year these sprayed areas had recovered to equivalent levels as \ncontrols. Neither crop tree diameter \nnor height growth was affected by Vision? application at this early stage of the experiment. Moose \ndensities within these study areas appear to be low enough that food is not a limiting factor. \nThe small amount of spraying in Ontario (relative to the productive forest land base) is not \nexpected to affect moose populations. However, in areas with high concentrations of sprayed \ncutovers there should be concern. Results of this short term study suggest that 0.80 kg a.e./ha \ncontrolled hardwood and herbaceous competition as well as 1.06 & 1.60 kg a.e./ha. However, the \nlowest application rate showed signs of increased moose use two years post spray compared with the \ntwo higher rates. Consequently, when spray programs are concentrated in one management unit, the \n0.80 kg a.e./ha rate is recommended.
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Comment cette classification a été obtenuedéplier
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 ».