Activity Patterns of Wildlife at Crossing Structures as Measure of Adaptability and Performance
Notice bibliographique
Résumé
Wildlife in mountainous regions are affected by naturally and non-naturally fragmented habitats. Nonnatural habitat fragmentation is caused by human development and activities, which tend to be concentrated in biologically rich and easily accessible valley bottom habitats. Human activity can strongly influence wildlife behavior and activity patterns and can differentially alter large mammal distributions. Typically, national parks and other protected areas were created and are currently managed for preservation of natural heritage and conservation of biodiversity. However, recreation, tourism and human infrastructure within parks and protected areas may have demographic and genetic consequences on wildlife populations and alter wildlife behavior. The effects of transportation infrastructure on wildlife are well known. In addition to road-related mortality and habitat fragmentation, transportation infrastructure can also influence habitat selection and behavior. In response to the mortality and habitat fragmentation effects of roads wildlife managers have employed mitigation measures such as fencing and wildlife crossing structures. However, for these measures to be effective wildlife have to find them and eventually use them in a biologically significant way (e.g., they must maintain or improve levels of fitness). However, sensory disturbance from traffic noise may affect movements and habitat use of sensitive species in areas near or in transportation corridors. Wildlife behaviour may be used as an indicator of how well crossing structures restore movements and connect habitats. We argue that, if wildlife crossing structures are fully functional, then wildlife activity patterns at crossing structures should reflect baseline activity parameters in areas characterized by little or no human disturbance (i.e., away from transportation infrastructure). The purpose of our presentation is to describe diel (24-hour) activity patterns of a range of large mammal species at crossing structures as a measure of adaptation and performance, and contrast these patterns to baseline conditions. Specifically, we are interested in determining whether wildlife activity at crossing structures is different from control areas without effects of transportation corridors. We analyze a long-term dataset on large mammal activity patterns obtained from infrared-operated digital cameras (camera traps) at 40 wildlife crossing structures (n=48 cameras deployed) along the Trans-Canada Highway (TCH) between 2005 and 2012. These data were compared with data obtained from camera traps (n=42) located in the backcountry of Banff National Park. The mean distance of backcountry cameras from TCH was 29.2 km (SD=11.7, min=9.3km, max=49.6km). Our results will provide an understanding of the activity patterns of wildlife at crossing structures as a measure of adaptation and performance evaluation. This is the first attempt we are aware of to utilize camera trap metadata at wildlife crossing structures other than for passage detections. Our results should assist transportation and land managers with mitigation evaluations and help devise sound attenuation strategies to enhance wildlife use of crossing structures.
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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) | 0,004 | 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 ».