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Enregistrement W2624874596

Limitations of forest overstory composition and age as proxies for habitat in a harvested boreal forest

2014· dissertation· en· W2624874596 sur OpenAlexfundaboutno aff
Julee J. Boan

Notice bibliographique

RevueKnowledge Commons (Lakehead University) · 2014
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueForest Management and Policy
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoTrent UniversityLakehead UniversityMinistry of Natural ResourcesWildlife Conservation Society
Mots-clésTaigaBorealHabitatEcologyForestryEnvironmental scienceGeographyComposition (language)Biology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Despite the importance of wildlife habitat protection in meeting land use management objectives, criteria for habitat identification are surprisingly amorphous. For example, while much current habitat modeling has tended to
\navoid the term "niche modeling," niche assumptions are implicit - the presence of predators and competitors is essential to whether or not a species uses, or will use, an area. Nonetheless, there are species for which important elements of niche are not generally associated with legal interpretations of their "habitat". Woodland caribou (Rangifer tarandus caribou) are one such example. The range of the
\nforest-dwelling ecotypes of woodland caribou has been declining in Canada since at least the late 1940s, and woodland caribou were assessed as Threatened by the Committee on the Status of Endangered Wildlife in Canada and were listed under the Federal Species at Risk Act in 2003. They are also protected under Ontario's Endangered Species Act (2007) and other provincial and territorial legislation. The consequences of management decisions, and the lens through which these decisions are assessed, have been intensified due to these legal implications.
\nMost current research supports the hypothesis that higher predation is the key factor in decline and that larger wolf (Canis lupus) populations are due to
\nincreased abundance of early seral stage, forage-rich hardwood and mixedwood
\nforests, created largely by logging, which support additional prey for wolves, including moose (Alces alces L.). While predators and apparent competitors appear to play a primary role in habitat selection by caribou, habitat modeling generally relies on forest overstory and age as a surrogate for predator avoidance. Yet, how well these models correspond to caribou, wolf, and moose use is largely unknown. Legal interpretations of protection rest primarily on interpretations of forest overstory and age, making explicit only the importance of forest disturbance.
\nHere, I tested the ability of forest resource inventories (FRI), a key tool in identifying and quantifying wildlife habitat in forest management, to assess 3 key elements associated with caribou winter habitat: lichen, regenerating understory and predator use. I assessed the presence of Cladonia lichen, an important winter forage species for woodland caribou, using stand characteristics provided in FRIs. Further, I used ground data collected from regenerating areas (2009-2010) of previously conifer-dominated forests in northwestern Ontario, Canada, 10 and 30 years after logging, and 10 and 30 years after fire, to test if understory development and moose forage abundance differed between the two disturbance types and artificial or natural regeneration approaches. In addition, I used winter aerial surveys (2010-2013) and logistic regression to compare the characteristics of a conventional habitat model (forest overstory composition and age) to other habitat characteristics (and/or their surrogates). I also applied a novel approach for Structural Equation Modeling (SEM) to explore causal and indirect caribou habitat relationships at broad and fine scales.
\nI found FRI was not capable of accurately predicting understory vegetation, specifically Cladonia lichen, in spite of the ability of field-based data using the same characteristics providing strong prediction. Further, I found understory composition varied significantly depending on post-harvest regeneration approach. Abundance of shrubs, as well as herbaceous plants (forage for apparent competitors for woodland caribou), was greater in naturally- regenerated post-harvest stands than similarly aged fire origin or post-harvest stands that used more intensive regeneration approaches. And lastly, I found that older, conifer forests alone, as depicted in FRI, did not provide good predictive capabilities of caribou use at broad scales.
\nWhile conventional models based on forest overstory composition and age may be useful as a coarse filter in interpreting caribou habitat, more attention should be given to their limitations in landscapes changed by industrial development, particularly where road networks are likely to facilitate predator access and the identification of such habitat has legal implications.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,948
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,032
Tête enseignante GPT0,242
Écart entre enseignants0,210 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2014
Routes d'admission2
Résumé présentoui

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