Behavioral and demographic response of small Indian mongooses (<i>Urva auropunctata</i>) to experimental population reduction
Bibliographic record
Abstract
Abstract The small Indian mongoose (Urva auropunctata) is a non‐native invasive species throughout the Caribbean and the primary terrestrial wildlife rabies reservoir on 4 islands in the region. In the 1970s and 1980s, island‐wide attempts to control and eliminate mongoose rabies through culling or poisoning in Cuba and Grenada proved unsuccessful. On some islands, localized population reduction of mongooses is used to mitigate predation on endangered species or to reduce the nuisance and frequency of interactions with humans. However, the short‐ and medium‐term demographic responses of mongooses to local population reduction and the impacts for infectious disease transmission remain unexplored. We conducted an experimental removal of mongooses across a 0.42‐km2 area of dry forest in St. Kitts. Employing capture‐mark‐recapture techniques, we quantified the demographic and behavioral responses of mongooses within the study area. We collected individual‐level data using an automated radio‐telemetry system, monitoring the daily presence of 19 collared mongooses for 7 months before and up to 7 weeks after experimental removals. The mongoose population density rebounded to pre‐removal levels within 7 weeks of the removal, primarily because of the influx of reproductively active females. The proportion of juveniles increased from 1–3% before removals to 14% at 7 weeks after removals yet returned to baseline levels at 6 months after removals. The local immigration of mongooses to the site was evident through changes in capture per unit effort, observed as early as the first week after removals. Tagged mongooses that frequented the study area during the pre‐removal period increased their daily presence for 5–30 days after removals. Our results indicate that a localized and intensive mongoose removal program targeting a high‐density population has short‐term but not long‐term residual impacts to the population. Further investigation into contact rates among mongooses and space use among resident and immigrating individuals is essential to advance our understanding of the impacts of localized removals on short‐ and long‐term mongoose population disease dynamics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".