Successful conservation translocation: Population dynamics of tiger recovery in Panna Tiger Reserve, Central India
Bibliographic record
Abstract
Abstract Tiger ( Panthera tigris ) is an indicator species of ecological health and conservation efforts. Due to poaching and other causes, the tiger became locally extinct in Panna Tiger Reserve, Central India. Subsequent reintroduction efforts have brought the species back from the extinction and have demonstrated the success of conservation translocations in response to such critical situations. We studied the demographic characteristics of the reintroduced tiger population based on an ensemble approach of different sampling techniques and direct observations from a long‐term dataset spanning more than 10 years. We evaluated different demographic indicators (population status, growth rate, mean litter size, inter‐birth interval and survival probability). Since reintroduction in 2009 and until the reporting period, 18 females have recruited 120 cubs from 45 litters. This led to 59 individuals in 2021 with a growth rate of ~26%. The mean litter size was 2.66 (SE 0.1), and the inter‐birth interval was 19.16 months (SE 0.5). The high survival rate of the reintroduced population (0.82 ± 0.2) helped to achieve the success of reintroduction. We observed non‐constant mortality trajectories for both sexes (higher survival probabilities for females) with a moderately higher risk of death in younger (<1 year) and older (>10 years) individuals. Our results showed the effectiveness of translocation and conservation efforts. The recovered population can be used as a founder for augmentation in other recovering tiger populations. A long‐term tiger‐centric management plan should be implemented in the area adjacent to Panna Tiger Reserve to conserve and secure the habitat of the entire landscape for the long‐term survival of the reintroduced population in a metapopulation framework.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".