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Enregistrement W2995883207 · doi:10.14264/uql.2019.896

Improving wildlife detection dog team selection and training

2019· dissertation· en· W2995883207 sur OpenAlex
La Toya Jamieson

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aboutLe titre ou le résumé porte un signal canadien du lexique géographique.
no affAucune affiliation canadienne : ce travail est invisible pour une base fondée sur la seule affiliation.
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Notice bibliographique

RevueThe University of Queensland · 2019
Typedissertation
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueHuman-Animal Interaction Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWildlifeSelection (genetic algorithm)BreedAnimal welfareWildlife managementGeographyEnvironmental resource managementComputer scienceBiologyEcologyArtificial intelligenceEnvironmental science

Résumé

récupéré en direct d'OpenAlex

Wildlife can only be properly managed when populations are accurately monitored. Commonly used monitoring methods, including camera-trapping and visual surveys, are often costly, labour intensive, with low detection rates. To address these issues wildlife detection dogs are increasingly being used in ecological research. These dogs non-invasively locate live individuals, their scats, carcasses, and denning/nesting sites. The success of this method is dependent most notably on the dog and handler, and their training. Whilst incorrectly selecting dogs and handlers is costly and a welfare concern, selection is often based on personal preference rather than scientific evidence. Working dog selection remains focused on breeding programs that are financially expensive with highly varied success. Certain breeds are therefore commonly excluded during selection. Selecting unsuitable individuals, or incorrectly managing these teams, will not only reduce team performance but may also tarnish wildlife detection dogs’ reputation. There is currently minimal research on the selection, training and management of wildlife detection dog teams, especially in Australia. Given wildlife detection dogs have unique working requirements research on other working dog fields is often not comparable. Thus, to investigate factors important to detection dog and handler selection and management, I trained 12 dogs from three breeds at detection work, experimentally assessing their training times and odour discrimination ability. After reviewing the literature three breeds were selected. The breed with the greatest number of suitable behavioural and physical characteristics for wildlife detection (Border Collies); the breed with the least number of suitable characteristics (Greyhounds); and the breed used most commonly for detection work (Labrador Retrievers). These dogs were trained to detect Bengal Tiger (Panthera tigris tigris) scat as it was a novel odour which they would not encounter outside training. Training sessions were filmed to determine the time required to achieve specific training competencies, and behaviour coded to record smelling times and behaviours related to the dogs’ true and false indications. Once the dogs achieved all training competencies their odour discrimination ability was assessed during single-blind trials, with both a familiar and unfamiliar handler.All Border Collies and Labrador Retrievers, and one Greyhound, completed training. Overall the Border Collies had the quickest training times and the highest accuracy scores. Individual variation was, however, significant within the breeds’ training times and accuracy. During training the dogs’ smelling times were significant factors influencing their indications, with specific behaviours (e.g. paw-lifting) being correlated more often with true, rather than false, positives. The only Greyhound to complete training had higher accuracy scores than half of the Labrador Retrievers during testing. There was therefore a weak correlation between the dogs’ training times and detection accuracy. During testing the dogs had significantly higher accuracy scores when handled by their familiar handler. With the unfamiliar handler the dogs performed significantly more stress-related behaviours and were distracted for a higher proportion of time, which was negatively correlated to detection accuracy.Important dog handler traits and skills were also determined through emailing questionnaires to Australian and New Zealand wildlife detection dog handlers. These questionnaires asked the handlers to complete personality assessments and rate handler skills based on importance for wildlife detection work. The handlers shared similar mean personality scores, however, these scores had large ranges. Handlers rated skills specific to their dog, such as understanding dog body language, as highly important for field success.Individual variation was prominent in all major findings. Due to the large range in the dog handlers’ personality scores, personality may not be as important as their training or dog–handler relationship. The large variation within the breeds training times and accuracy further suggests that a dog’s breed may not be the best predictor of their trainability or detection aptitude. These dogs’ accuracy was further impacted by changing handlers. Future research is required to determine if professional dogs are impacted similarly, and the best ways to manage dog-handler transitions. Lastly my research demonstrated that dogs’ smelling times and their associated behaviours can assist handlers discriminate between dogs’ true and false indications.My research challenges how working dogs are currently globally managed. Due to the level of individual variation among dogs suitable for working roles, dogs should not be excluded purely because of their breed. Individual team’s performances must also continue to be evaluated due to the highly site-specific nature of their effectiveness. Management strategies must also take into consideration how influential the dog-handler relationship is on team performance. Prior to my study no research had investigated how detrimental changing a dog’s handler is on their welfare and performance. It is therefore crucial to continue challenging and advancing best practises, not only for animal welfare but also for the success of the working dog industry. Continuing research on wildlife detection dogs, including best avenues to source dogs, is crucial for this emerging method and will ensure the greatest outcomes are achieved.

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.

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: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,636
Score d'incertitude au seuil0,412

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 technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,011
Tête enseignante GPT0,261
Écart entre enseignants0,250 · 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