Limitations of forest overstory composition and age as proxies for habitat in a harvested boreal forest
Notice bibliographique
Résumé
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.
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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 | 0,001 | 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 ».