Reduction of free-roaming cat population requires high-intensity neutering in spatial contiguity to mitigate compensatory effects
Bibliographic record
Abstract
Abstract When free-roaming in natural areas, the domestic cat ( Felis silvestris catus ) is ranked high among the most destructive alien species. Near human dwellings, it might risk humans, impair sanitation, and suffer from poor welfare. Cats’ popularity as companion animals complicates their population control. Thus, culling is often replaced by a fertility control method called “Trap-Neuter-Return/Release (TNR),” which is considered more humane. Despite the extensive application of TNR, a long-term controlled study was never performed to test its effectiveness. We present a uniquely designed controlled field experiment for examining TNR effectiveness. The study was performed over a twelve-year period, divided into pre-intervention, mixed- and full-intervention phases, and spanned a 20Km 2 urban area. Trends of cat, intact-female, and kitten counts, cat reproduction, and carcass reports were compared among study phases and areas with different neutering intensities. The cat population increased during the first two study phases and did not decline in highly neutered populations, presumably due to cat immigration. Expansion of high-intensity neutering to the entire city in the full-intervention phase (>70% neutering percentage) reversed cat population growth, reaching an annual ca. 7% reduction. This population reduction was limited by a rebound increase in cat reproduction and longevity. We conclude that cat population management by TNR should be performed in high-intensity, continuously, and in geographic contiguity to enable population reduction. To enhance management effectiveness and mitigate compensatory effects, we recommend further evaluating an integrated strategy that combines TNR with complementary methods (e.g., vital resource regulation, ill cat euthanasia, and adoption). Significance Statement Though popular companion animals, domestic cats pose numerous problems when free-roaming, including predation of wildlife, hazards to humans, impaired sanitation, and a decrease in their welfare. Thus, managing their populations is essential. The Trap-Neuter-Return method (TNR, capturing, sterilizing, returning/releasing) is widely employed for managing cat populations. However, there is a lack of long-term controlled evidence for its effectiveness. We examined the outcomes of high-intensity TNR by performing a twelve-year controlled field experiment, the largest to date. Neutering over 70% of the cats caused population decline when applied over contiguous areas. However, it was limited by a rebound increase in reproduction and survival. These findings provide a robust quantification of the limitations and the long-term effectiveness of TNR.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".