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Record W4248796394 · doi:10.7287/peerj.preprints.1940

Avian management at Vancouver International Airport: Painting a landscape of fear with trained raptors

2016· preprint· en· W4248796394 on OpenAlexaboutno aff
David Bradbeer, Kristine Kirkby, Gillian Radcliffe

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsPredationWaterfowlCalidrisHabitatGeographyPredatorForagingHavenEcologyFisheryBiology

Abstract

fetched live from OpenAlex

Vancouver International Airport (CYVR) is located in the Fraser Delta and provides an enticing haven for migratory and wintering birds seeking refuge, compromising aircraft safety and operations. To manage this risk, the Vancouver Airport Authority has invested in innovative and adaptive strategies to reduce bird strikes, including an active daily falconry program conducted during the winter months, when local shorebird and waterfowl populations swell. This presentation discusses the falconry program, initiated in 2011, and the results on one primary target species in particular, the Dunlin (Calidris alpina). The behavior and habitat use of prey species is strongly influenced by predation risk. The landscape at CYVR comprises a complex interplay of multiple species of both predator and prey. Wild raptors are diverse and relatively numerous, and they interact with one another and with a wide range of prey species. The dynamics of these interactions are strongly influenced by an ever-­‐changing palette of managed airside habitats, the vagaries of weather, airport operations (including bird control activities), and climate and landscape level changes. Manipulation of these dynamics – through, for example, effectively increasing the apparent density and hunting activities of a natural local predator, could facilitate the management of problem avian species in and around the airport. Dunlins are the most abundant wintering shorebirds locally, and a long-­‐time problem species at the airport. Traditional hazing with pyrotechnics and auditory harassment, adequate for many problem species, proved relatively ineffective in deterring Dunlin from seeking refuge on the airfield, especially during high tides and inclement weather. It was our hypothesis that we should be able to influence the behavior of winter resident Dunlins by increasing perceived predation risk. To effect this, trained falcons, primarily Peregrines (Falco peregrinus) -­‐ a natural predator on Dunlins -­‐ are actively flown on the airfield daily to paint a landscape of fear for the shorebirds. The relative success of the program to date is strongly supported by a decline in strikes involving Dunlins -­‐ quantified by mass -­‐ as well as by more anecdotal observations of related behavioural changes. Since implementation of the program, the cross-­‐wind runway, historically closed for much of the winter due to shorebird hazard, has remained open and ready for use during poor weather conditions when it is most needed. Active management using trained predators helps reduce the need for more lethal forms of avian control. Direct and indirect influences on the local predator-­‐prey systems, both within and beyond the boundaries of the airport, are poorly understood. Future exploration and quantification of behavioural adaptations and habitat management, in relation to dynamics of Dunlin populations, may assist in further reducing the occurrence of aircraft / shorebird collisions at Vancouver International Airport, to the benefit of both human and avian species.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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