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Enregistrement W4405446302 · doi:10.3897/natureconservation.57.116972

The potential of electrified barriers to keep black bears out of fenced road corridors at low volume access roads

2024· article· en· W4405446302 sur OpenAlexfundno aff
Marcel P. Huijser, S.C. Getty

Notice bibliographique

RevueNature Conservation · 2024
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueWildlife-Road Interactions and Conservation
Établissements canadiensnon disponible
Organismes subventionnairesU.S. Fish and Wildlife ServiceOregon Department of TransportationIowa Department of TransportationNevada Department of TransportationNew Mexico Department of TransportationFederal Highway AdministrationParks CanadaMontana Department of TransportationCalifornia Department of TransportationMinnesota Department of TransportationAlaska Department of Transportation and Public FacilitiesMichigan Department of TransportationU.S. Department of Transportation
Mots-clésFencingFence (mathematics)NettingTransport engineeringWildlifeChannel (broadcasting)Traffic volumeEnvironmental scienceComputer scienceEngineeringBusinessTelecommunicationsEcology

Résumé

récupéré en direct d'OpenAlex

Fences can reduce wildlife-vehicle collisions, but it is not always possible to fence over long distances, especially not in multi-functional landscapes. Side roads, driveways, and the need for access to agricultural fields all result in gaps in the fence. In some cases, wildlife guards or gates are installed at access points. However, gates usually require people to get in and out of their vehicle and they are often left open. Wildlife guards are typically only suited for low traffic speed, and while they can be a substantial barrier to ungulates, they are readily crossed by species with paws, including bears. Electrified barriers embedded in travel lanes can be a substantial barrier to both ungulates and bear species and while they can be suitable for higher traffic volume and speed, the costs are typically higher than for low volume and low speed roads. We explored the potential of low-cost electrified barriers to keep bears from accessing fenced road corridors at low traffic volume and low speed vehicle access points. As a first step, we conducted the study on private land at a melon patch that was a known attractant for black bears. We investigated the effectiveness of an electric fence and 5 different types of electrified barriers designed to keep black bears out of the melon patch. The electrified barriers included a swing gate, a standard bump-gate, a modified bump-gate with conductive netting, drive-over wires a few inches above the ground, and a drive-over mat. Trail cameras were installed at each access point to document approaching black bears and potential crossings into the melon patch. The swing gate, modified bump-gate, drive-over wires, and drive-over mat were an absolute (100%) or near absolute barrier (94.3%) for black bears while the standard bump-gate was a poor barrier (48.4%). Through a step-by-step process, the weak points of the electrified barriers at the vehicle access points and the electric fence around the melon patch were addressed. After addressing a weak point at a vehicle access point, the bears increasingly dug under the fence to enter the melon patch. However, eventually the melon patch became almost inaccessible to black bears. The number of black bears trying and succeeding to enter the melon patch at a particular location depended on how difficult it was to enter at other locations. This illustrates that fences and vehicle access points should be designed, operated, maintained, and monitored as a system rather than as individual features, regardless of whether the goal is to protect crops or to keep animals out of a fenced road corridor. The total number of black bear observations at the locations monitored with a trail camera, regardless of which side of the fence or electrified barriers the bears were on, was 95% lower in 2021 than in 2020. Combined with having no indication of a substantial drop in black bear population size from 2020 to 2021, this suggests that after the black bears were no longer able to enter the melon patch, they drastically reduced their presence in the immediate surroundings and reduced their effort to try and access the crop; the attraction of the melon patch and the habit of eating its melons was broken.

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 candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,597
Score d'incertitude au seuil0,869

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
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)0,0010,000

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 Opus0,006
Tête enseignante GPT0,254
Écart entre enseignants0,248 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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