Community‐informed and codesigned research to understand the impacts of railways on wildlife
Notice bibliographique
Résumé
Trains cause mortalities of several animal species, but the ecological impacts of railways are understudied. Most research on wildlife-train collisions has focused on large mammals, but understanding railway-specific risks for underrepresented taxa, especially vulnerable species, is important for developing effective mitigation strategies. Based on the concerns and interests of 2 Anishinaabek communities-Shawanaga First Nation (SFN) and Magnetawan First Nation (MFN)-we collaboratively aimed to fill knowledge gaps with a community-informed approach in which Indigenous knowledge (IK) and Western science were used to investigate wildlife interactions with railways bisecting the 2 Traditional Territories in Ontario, Canada. To inform study design, First Nation community members were invited to share knowledge concerning wildlife-railway impacts in semistructured interviews. Weekly walking surveys were conducted in 3 field seasons along two 3.6-km sections of railway in SFN and MFN during which locations of all wildlife observed alive or dead were recorded. We also deployed game cameras at 500-m intervals along the railway survey areas. On walking surveys, we recorded 462 observations of individuals from 42 species, of which 76% were found dead. These findings complemented the shared IK that railways affect a wide variety of wildlife, often causing mortalities. Reptiles and amphibians were the most severely affected taxa, accounting for 87% of observed mortalities. Seven species at risk of extinction interacted with the railway, including 3 turtle species and one snake species, all of which were found dead on the railway tracks. Cameras recorded 43 wildlife species; most detections were of large and medium mammals, suggesting they frequently interact with the railway but may face lower mortality risks than herpetofauna in our study area. Ultimately, our study highlights the holistic outcomes that are possible through collaborative research that embraces complementary knowledge systems and indicates that reptile and amphibian populations may be particularly susceptible to railway mortality.
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 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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».