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Enregistrement W2766279826 · doi:10.1111/acv.12373

Food waste is still an underappreciated threat to wildlife

2017· article· en· W2766279826 sur OpenAlexaboutno aff
Thomas M. Newsome, Lily M. van Eeden

Notice bibliographique

RevueAnimal Conservation · 2017
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueWildlife Ecology and Conservation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGrizzly BearsWildlifeUrsusLivestockPopulationWildlife managementGeographyFood securityEcologyEnvironmental scienceEnvironmental protectionAgricultureBiologyForestryEnvironmental healthArchaeology

Résumé

récupéré en direct d'OpenAlex

Large quantities of food produced for human or livestock consumption are lost during production, transportation and storage, or simply dumped and discarded (Oro et al., 2013; Gordon et al., 2016). If this food is subsequently eaten by wildlife it can alter their ecology and behavior (Newsome et al., 2015), in some instances affecting their health and exacerbating human-wildlife conflicts (Newsome & van Eeden, 2017). Despite such outcomes, and the potential to improve food security, there does not appear to be a major management shift to reduce food waste today. The study by Gangadharan et al. (2017) goes some way to addressing this issue for two reasons. First, it is one of few studies to quantify the amount of food that is wasted by humans, in this instance grain that has spilled from moving freight trains. Gangadharan et al. found that around 110 tons of grain may be deposited on average per year in Banff and Yoho National Parks. Second, Gangadharan et al. use their results to determine how many grizzly bears Ursus arctos horribilis could be supplemented by the spilt grain. They found that 42–54 grizzly bears could be supported, which is a high number given the total regional population is estimated at 50–73 animals. Without this estimate it would be very difficult to convince those who operate the trains to make substantial changes in grain management, because the amount of grain lost represents a tiny fraction of the total transported (millions of tons). An outstanding question, however, is what happens to grizzly bear populations when the grain is removed? Gangadharan et al. note that the removal of the grain needs to be carefully planned so as to minimize the impact on bears that may rely on the grain as a food source. But what is needed, in addition to careful planning, is an investigation into the population density and dynamics of grizzly bear populations before, during and after a concerted effort to remove or properly transport the grain. Such insights would aid in determining how best to deal with the fact the many animals around the world have become dependent on human-provided foods (Oro et al., 2013; Newsome et al., 2015). Gangadharan et al.'s focus was on the quantity of grain that may be available to grizzly bears. But bears represent a small part of the food chain and it is important to consider the broader consequences of this food source on ecosystems (Newsome & van Eeden, 2017). Grain deposition, for example, can disperse seeds (Bailleul et al., 2012) and alter soil nutrients, and provides resources for organisms from invertebrates to large vertebrates (Oro et al., 2013). Thus, there are likely to be ecosystem-wide effects when grain is available, rather than on single species. Indeed, Murray et al. (2017) analyzed scat content in the same area and found that not only were grizzly bear scats collected near the railway line more likely to contain grain, but they were also more likely to contain hair from ungulate carcasses and exoskeletons from ants, as the bears consumed other scavengers also using the grain resources, indicating that food waste can influence multi-level trophic systems in complex ways. More broadly, research on the impacts of food waste has mostly focused on large, charismatic species and birds. Research on the effects of railway lines on wildlife, in particular, has focused mostly on large-bodied animals like bears and moose (Dorsey, 2011), perhaps because these species are conspicuous, receive conservation attention, and as large-bodied animals can cause damage to trains (Dorsey, Olsson & Rew, 2015). A bias typical of scientific research in general (Clark & May, 2002), there appears to be limited research on the effect of food waste on non-marine invertebrates, and thus our knowledge of food waste impacts is limited to a small component of whole ecosystems from a mostly top-down perspective. Evidently, there remain several major gaps in our understanding of the consequences of food waste on ecosystems and wildlife. By quantifying grain spillage in these Canadian National Parks and the potential significance for bears, Gangadharan et al. has opened up opportunities for further research on consequences to the broader ecosystem. We need to quantify various examples of resource provision and expand our research to include broader trophic levels, in order to appreciate the full extent to which food waste impacts on wildlife.

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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,011
Score d'incertitude au seuil0,747

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 savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

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 Opus0,041
Tête enseignante GPT0,269
Écart entre enseignants0,228 · 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

Citations10
Publié2017
Routes d'admission1
Résumé présentoui

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