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Enregistrement W2766602926 · doi:10.1111/acv.12374

Railways offer grain on a silver platter to wildlife, but at what cost?

2017· article· en· W2766602926 sur OpenAlexaffabout
Jesse N. Popp

Notice bibliographique

RevueAnimal Conservation · 2017
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueWildlife-Road Interactions and Conservation
Établissements canadiensLaurentian University
Organismes subventionnairesnon disponible
Mots-clésWildlifeGrizzly BearsUrsusEcologyGeographyHabitatWildlife conservationTrainBiologyPopulationArchaeology

Résumé

récupéré en direct d'OpenAlex

It is well known that transportation corridors affect wildlife in a variety of ways, both directly and indirectly. Road ecology is a well-developed field of transportation ecology, and over the past few decades, there has been substantial research focused on investigating the influence of roads on wildlife and ecological processes (Rytwinski & Fahrig, 2015). Railway ecology on the other hand is a highly neglected aspect of transportation ecology, with little known about the effects of railways on wildlife (Popp & Boyle, 2017). Like roads, railways are known to fragment habitat as well as to lead to wildlife mortality through vehicle collisions (van der Grift, 1999; Mateo-Sánchez, Cushman & Saura, 2014), however, wildlife-railway related investigations have been very sparse in the literature (Popp & Boyle, 2017). This issue's Feature paper, ‘Grain spilled from moving trains create a substantial wildlife attractant in protected areas’ (Gangadharan et al., 2017), presents an excellent example of why it is important to study the effects of railways on wildlife. Gangadharan et al. (2017) investigated grain spillage from trains travelling along 134 km of railway in Canada and highlight the implications to wildlife, specifically grizzly bears (Ursus arctos). The authors explain that, based on their calculations, enough grain is spilled from moving trains on the railway every year to feed 42–52 grizzly bears all of their caloric needs! But that is, of course, if these bears can get to all of the grain first. Many other animals presumably take advantage of this abundance of food that is essentially presented on a silver platter. In my own current research in progress, I have documented an abundance of wildlife species using an Ontario railway, including medium to large mammals and many species of birds. I myself have seen first hand grain trails left along the rail, and often observe birds taking full advantage of the easy food source. But at what cost does this optimal foraging opportunity come? Aside from the more well-known issues associated with feeding wildlife (e.g. unnatural food reliance, reduced intake of natural forage, increased risk of disease or parasites associated with the close proximity of conspecifics) (Putman & Staines, 2004), a plethora of cascading effects are likely to exist. One of the obvious repercussions of railway use by wildlife is train-induced mortality (Bertch & Gibeau, 2010), which is the leading cause of mortality for grizzly bears in the Gangadharan et al. (2017) study area. My current research in progress has shown that train collisions are also the leading cause of mortality for reintroduced elk (Cervus elaphus) in north-central Ontario. In addition to the mortality risk trains present, prey animals attracted to grain may become more spatially predictable to predators. Wolves have been found to select for areas within 25 m of railways (Whittington, St. Clair & Mercer, 2005). Birds have also been found to alter their use of space in response to railways, and their species richness and abundance have been shown to be greater closer to railways (Li et al., 2010). Although not yet documented, carrion-feeding species may also be attracted to railways, as scavenging opportunities may increase through the availability of carcasses from train collisions. But at what rate do trains kill wildlife? How often are animals using railways in comparison to how often mortality occurs? The risk of wildlife mortality on railways is relatively unknown, however, the implications are important, especially for species at risk like grizzly bears. Transportation corridors may be used by wildlife for reasons other than optimal foraging opportunities. For example, in accordance with the Law of Least Effort (Zipf, 1949), transportation corridors likely create easy travel path opportunities for wildlife, especially in winter when snow depths in surrounding environments may impede animal movement (Andreassan, Gunderson & Storaas, 2005). Roads and railways are both linear corridors but they may provide very different environments to animals, however, such comparisons have rarely been made. These differences may result in one type of transportation corridor being perceived by wildlife as less risky, which could result in different rates of use, and potentially, mortality. These are some of the many questions in transportation ecology we should address but currently do not have the answers to. Gangadharan et al. (2017) present one of the very few studies that assess the potential impacts of railways on wildlife. This study is a novel, important contribution to a very poorly understood topic in ecology and by example, provides invitation for curious ecologists to delve more deeply into this area.

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 candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,300
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,002
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,005

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 Opus0,033
Tête enseignante GPT0,272
Écart entre enseignants0,239 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

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

Citations6
Publié2017
Routes d'admission2
Résumé présentoui

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