Ocelot home range, overlap and density: comparing radio telemetry with camera trapping
Bibliographic record
Abstract
Abstract Because ocelots Leopardus pardalis and other solitary carnivores are elusive and hard to study, little is known about their density and population status. In the past few years, camera trapping and mark–recapture statistics have been used to estimate the density of a number of felids. Although camera trapping is now providing baseline data for managers and conservationists alike, recent doubts have been raised concerning the accuracy of the standard camera trapping procedure. We used radio telemetry to gain new information on ocelot home‐range size and spatial organization in Central America, and compared the radius of our average ocelot home range with the standard camera trapping buffer. We compared the resulting density estimates to assess the current camera trapping methodology's ability to estimate animal density. Five adult ocelots (two male and three female) were tracked to determine an average ocelot home range of 26.09 km 2 (95% fixed kernel) and 18.91 km 2 (100% minimum convex polygon), with males demonstrating larger ranges than females. All ocelots had larger home ranges in the dry season. Male–male home‐range overlap averaged 9% while female–female overlap averaged 21%. Males shared 56% of their range with a primary female and 16% with a second and third female, while females shared 58% of their home range with a primary male and 3% with a secondary male. Density estimates based on the average home‐range radius (11.24–12.45 ocelots per 100 km 2 ) were less than those determined from standard camera trapping methods (25.88 ocelots per 100 km 2 ), but similar to those determined using twice the camera trapping buffer to estimate density (12.61 ocelots per 100 km 2 ). Our results suggest that a standard camera trapping protocol may overestimate ocelot density. Accurate representation of animal densities and standardization of density estimation techniques are paramount for comparative analyses across sites and are vital for felid conservation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".