Individual-based movement model of mule deer (Odocoileus hemionus) contacts and application to artificial attractants
Notice bibliographique
Résumé
Chronic wasting disease (CWD) is an emerging prion disease in Canada that infects mule deer, white-tailed deer, elk, and moose by direct and environmental transmission and is invariably fatal. CWD spread can be promoted at “hotspots” that attract deer, such as attractants that are created in fields by hay bales and grain bags, and attractants such as grain bins and agricultural storage at farm sites. An individual-based model was created to investigate the effects of different densities and arrangements of hotspots on contact rates between- and within-groups. The model tracks contacts (when two individuals come within five meters of one another), which are defined as between- or within-group depending on the group membership of the two individuals. Simulations are run in Netlogo on a heterogeneous landscape and include behaviours such as grouping and home ranges. Using a two-hour time step, deer are moved across the landscape based on both step-selection movement rules relative to resources and group behaviours. The integrated step-selection function utilizes GIS layers for environmental weights and GPS-collar movement data for calculating step-selection coefficients, and step length distributions. Sensitivity analysis was performed on the model and revealed a greater sensitivity of within-group contacts to changes in model parameters, particularly group cohesion. Following model analysis, simulations were run to assess the effect of artificial attractant (AA) density and configuration using two strategies for initial placement of attractants, random and clustered around farms, and two strategies for removing them, random and by proximity to woody cover. Simulations revealed that reducing the number of attractants on the increases between-group contacts as well as unique contacts between deer. Additionally, reducing AA density generally increased overall unique visits per site indicating potentially greater environmental contamination at remaining sites. Although having no attractants produced the lowest contact rates, management must take into consideration the feasibility of eliminating all attractants and the potentially negative impacts if sufficient reduction of AAs is not achieved. Additionally, the strategy used to eliminate attractants must be considered because although removal by proximity to woody cover and random removal showed similar patterns, removing by proximity to woody cover caused a greater increase in contacts for field attractants. For removal at clusters around farms, removing individual attractants versus all attractants in a cluster resulted in different trends as removing individually had a limited effect on contacts, whereas removing by cluster caused an increase in between-group contacts. If feasible, management should aim to eliminate attractants via mitigation strategies and enforcement; however, if insufficient resources are available for enforcement, then management strategies should be taken with caution because insufficient reduction of attractants could worsen contact rates.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».