Landscape spatial patterns and forest fragmentation in managed forests in southeast British Columbia : perceptions, measurements, and scale
Notice bibliographique
Résumé
Forest spatial patterns are a central topic in contemporary landscape ecology, largely because of concerns about forest fragmentation. Forest fragmentation is thought to be a major threat to biodiversity because remnant forest patches, left from human disturbances such as logging, would support fewer species and be more prone to local extinctions because they are small and isolated from each other, as predicted from an extension of island biogeography theory. These and other theoretical predictions stemming from the forest fragmentation paradigm remain virtually unchallenged by empirical data. I investigated landscape spatial patterns in managed forests of the Slocan Valley in southeast British Columbia, and focused my investigations on theoretical predictions concerning forest fragmentation and the distinction between habitat amount and spatial configuration effects. I first investigated human perception of fragmentation to assess the usefulness of current methods in quantifying landscape spatial pattern and to investigate definitions and confusion about fragmentation. I then used traditional landscape indices to test predictions about fragmentation trends in the Slocan Valley by focusing on the effect of forest harvesting on old growth forest fragmentation. I then created a unique method of assessing landscape connectivity, the inverse of fragmentation, using a scale-dependent, organism-centered technique based on an organism's ability to move between habitat patches. Finally, I tested mule deer scale-dependant selection of forest edges, patch size, and logging roads relative to amount of forest, since these landscape elements are implicated in the fragmentation issue and are either untested or unresolved for mule deer. I found people associate fragmentation with high patch density, which was highly correlated with amount of harvesting, illustrating the confusion between habitat amount and spatial configuration. Landscape indices were of very limited use in deriving absolute values of fragmentation, and are likely best used to compare landscapes and pattern trends. I found little evidence of an old growth forest fragmentation trend in the Slocan Valley. Most predictions concerning a fragmentation trend were falsified. Using an organism-centered method to assess connectivity among old growth patches, I found the landscape to be accessible to all old growth associates at maximum dispersal distances, with the exception of the northern flying squirrel (Glaucomys sabrinus). At median dispersal distances however, only larger more vagile carnivorous birds could access all old growth patches in the landscape. Of particular concern are flying squirrels which had access to only 10% of the landscape at median dispersal distances. Mule deer displayed selection of landscape elements at the landscape scale only. The best predictors of mule deer winter use were mature forest patch size and amount of mature forest. Because of high correlation between these two variables, distinction between them was difficult and illustrates this persistent problem in empirical work. Empirical field studies are direly needed to test the existing fragmentation theoretical framework. Future work must distinguish between habitat loss effects and independent fragmentation effects.
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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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».