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Record W2288672479 · doi:10.1111/brv.12258

Agriculture and herbivorous waterfowl: a review of the scientific basis for improved management

2016· review· en· W2288672479 on OpenAlexaboutno aff
Anthony David Fox, Johan Elmberg, Ingunn Tombre, Rebecca Hessel

Bibliographic record

VenueBiological reviews/Biological reviews of the Cambridge Philosophical Society · 2016
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Biological Studies
Canadian institutionsnot available
FundersNaturvårdsverket
KeywordsWaterfowlHerbivoreAgricultureFisheryEnvironmental resource managementGeographyEcologyEnvironmental scienceBiologyHabitat

Abstract

fetched live from OpenAlex

Swans, geese and some ducks (Anatidae) are obligate herbivores, many are important quarry species and all contribute to a variety of ecosystem services. Population growth and shifting ranges have led to increasing proximity to man and thus increasing conflicts. We review and synthesize the role of these birds as herbivores on agricultural land (cropland, rotational grassland and pasture) and other terrestrial habitats where conflict with human interests may occur. A bibliographic analysis of peer-reviewed papers (N = 359) shows that publication activity peaked in 1991-2000 in North America and 2000-2010 in Europe, and has decreased since. Taxonomic and geographical biases are obvious in research to date: Snow Goose Chen caerulescens was the most studied species (N = 98), and Canada Branta canadensis, Barnacle B. leucopsis and Brent geese B. bernicla all featured in more than 40 studies; most studies originated in northwest Europe or North America, very few have been carried out in Asia and European Russia. On the basis of nutrient/energy budgets of herbivorous waterfowl, it is evident that dense single-species crops (such as rotational grassland, early-growth cereals and root crops) and spilled grain in agricultural landscapes offer elevated energetic and nutritional intake rates of food of higher quality compared to natural or semi-natural vegetation. Hence, although affected by seasonal nutritional demands, proximity to roost, field size, disturbance levels, access to water, food depletion and snow cover, agricultural landscapes tend to offer superior foraging opportunities over natural habitats, creating potential conflict with agriculture. Herbivorous waterfowl select for high protein, soluble carbohydrate and water content, high digestibility as well as low fibre and phenolic compounds, but intake rates from grazing varied with goose body and bill morphology, creating species-specific loci for conflict. Crop damage by trampling and puddling has not been demonstrated convincingly, nor do waterfowl faeces deter grazing stock, but where consumption of crops evidently reduces yields this causes conflict with farmers. Studies show that it is difficult and expensive to assess the precise impacts of waterfowl feeding on yield loss because of other sources of variation. However, less damage has been documented from winter grazing compared to spring grazing and yield loss after spring grazing on grassland appears more pronounced than losses on cereal fields. Although yield losses at national scales are trivial, individual farmers in areas of greatest waterfowl feeding concentrations suffer disproportionately, necessitating improved solutions to conflict. Accordingly, we review the efficacy of population management, disturbance, provision of alternative feeding areas, compensation and large-scale stakeholder involvement and co-management as options for resolving conflict based on the existing literature and present a framework of management advice for the future. We conclude with an assessment of the research needs for the immediate future to inform policy development, improve management of waterfowl populations and reduce conflict with agriculture.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.007
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.108
GPT teacher head0.297
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations197
Published2016
Admission routes1
Has abstractyes

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