Population Density of the Small Indian Mongoose (Urva auropunctata) Across Multiple Habitat Types and Seasons in Puerto Rico
Bibliographic record
Abstract
The small Indian mongoose (Urva auropunctata) is a rabies reservoir on several Caribbean Islands including Puerto Rico. In the continental United States, oral rabies vaccination (ORV) has been used to control and locally eliminate rabies viruses targeting meso-carnivores including raccoons (Procyon lotor), grey foxes (Urocyon cinereoargenteus), and coyotes (Canis latrans), and has more recently been proposed to mitigate and control mongoose rabies in Puerto Rico. A fundamental understanding of the population density of the target species is an important factor in planning bait application rates prior to ORV operations. In Puerto Rico, most ecological studies on mongooses have been restricted to the rainforest region in the northeastern portion of the island. We calculated population density estimates for mongooses at seven sites representing four habitat types in Puerto Rico. We marked 445 unique mongooses across 593 capture events during 12,530 trap days during 2016–2021. Mean (SE, 95% CI) population densities were greater in closed to open broadleaved evergreen forest habitat (0.79 ±0.13, 0.67–0.92 mongooses/ha) compared to grasslands (0.43 ± 0.10; 0.35–0.55 mongooses/ha), rainfed croplands (0.26 ±0.10, 0.18–0.38 mongooses/ha), and shrub/herbaceous habitat (0.19 ±0.05, 0.15–0.25 mongooses/ha). We did not detect seasonal variation in mongoose population density (0.48 [0.06; 0.35–0.62] and 0.39 [0.06; 0.27–0.50] mongooses/ha measured in the wet (May–November) and dry (December–April) seasons, respectively. Multiple ORV applications may be needed annually for adequate population immunity, particularly in habitats with high mongoose population densities and rapid population turnover.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".