Effects of energy development on habitat use of an avian peatland specialist and generalist at multiple spatial scales
Notice bibliographique
Résumé
With the persistent demand for petroleum energy products, the energy development footprint in northern Alberta continues to expand, perforating habitat in the boreal forest. Modern in situ, or below ground, bitumen extraction techniques have broadened the extent of accessible reserves and are creating a different type of footprint than conventional surface mining. Development of exploratory vegetation clearings, including 3-dimensional seismic lines (hereafter “seismic”), winter roads, and well sites, as well as permanent infrastructure, such as roads, pipelines, extraction wells, and industrial facilities, creates an expansive network of small-scale disturbances that alter biological communities, including songbirds. Additional work is needed to understand how different development features influence species-specific habitat use and selection for songbirds, especially in peatland habitats that are less-well understood. Here I examined the influence of different in situ oil sands development feature types on habitat use and selection of a conifer generalist, Dark-eyed Junco (Junco hyemalis), and peatland specialist, Palm Warbler (Setophaga palmarum), songbird at multiple spatial scales within peatland habitats. Specifically, the objectives of this study were to: (1) identify the relative effects of different in situ development and habitat features on landscape-level avian occurrence and local-scale habitat selection for songbirds with different habitat niche breadths; and (2) understand potential behavioural mechanisms affecting species-specific responses to in situ developments on individual territory placement and within-territory habitat selection. I used point counts to sample Dark-eyed Junco and Palm Warbler occurrence at 157 peatland sites located across a range of development feature types and intensities. I also examined territory-level (2nd order) and within-territory (3rd order) habitat selection by conducting behavioural observations within 11, 25-hectare sites. I modelled occurrence (presence-absence) for each species using binomial logistic regression; and modelled territory and within-territory selection using generalized linear mixed effect models to compare used to available locations for 80-82 individuals of each species. Permanent polygonal and linear features influenced occurrence for both species, whereas exploratory features did not, suggesting that permanent development features have a greater impact on avian populations. Both species avoided permanent (e.g. polygonal or linear) or larger exploratory features (e.g. well sites) at the territory scale, but relative use of development features was greater for Dark-eyed Juncos than Palm Warblers. At the within-territory scale, Dark-eyed Juncos avoided permanent features and well sites for singing but were more likely to sing by wide linear features in areas with taller vegetation, relative to locations with shorter vegetation. Juncos were more likely to forage on or near all features except seismic, but relative intensity of use for some features was greater in areas near higher productivity vegetation. Palm Warblers avoided singing by well sites but selected singing locations near wide linear features in areas with taller vegetation. Palm Warblers avoided foraging on or near well features across all habitat productivity levels but selected seismic features only in low productivity habitats. Our results show that avian responses to different development features are complex, with species-specific responses that vary for different behaviours and local habitat characteristics. Use, selection, and avoidance of different development features varies with spatial scale, and even small, 2-3 m wide seismic features can influence avian behaviours. I recommend pairing fine-scale behavioural studies with coarse-scale avian monitoring to increase understanding of the mechanisms driving species-specific responses to different development feature types and inform landscape management decisions.
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 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 | 0,000 |
| 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) | 0,001 | 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 ».