MétaCan
Menu
Back to cohort
Record W2766279826 · doi:10.1111/acv.12373

Food waste is still an underappreciated threat to wildlife

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

Bibliographic record

VenueAnimal Conservation · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsGrizzly BearsWildlifeUrsusLivestockPopulationWildlife managementGeographyFood securityEcologyEnvironmental scienceEnvironmental protectionAgricultureBiologyForestryEnvironmental healthArchaeology

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.269
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAnimal ConservationSame topicWildlife Ecology and ConservationFrench-language works237,207