Individual-based movement model of mule deer (Odocoileus hemionus) contacts and application to artificial attractants
Bibliographic record
Abstract
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.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".