Does culling predatory gulls enhance the productivity of breeding common terns?
Bibliographic record
Abstract
Summary Large gulls Larus spp. are voracious predators of eggs and chicks of other colonial birds and may threaten rare or endangered species. In this study we tested the effectiveness of removing individual predatory gulls as a management technique for enhancing the productivity of common terns Sterna hirundo nesting in Carleton, Québec, Canada. The productivity and fate of common tern chicks were assessed by following ringed individuals from hatching to fledging during three breeding seasons (1993–95). Concurrently, predation and consumption rates of all predatory gulls were measured before and after the culling started. The culling programme was conducted serially in 1994 by removing the most important predator first until all predators were removed. The rate of chick disappearance was lower and the life span of tern broods was higher in 1994 when the culling was conducted, compared with 1993 and 1995. As a result, the productivity of the tern colony was zero in 1993 and 1995, but positive in 1994 (0·33 chicks pair −1 ). Measurements of chick mass in 1993 and 1994 showed that growth was normal, indicating that poor feeding conditions or disease were not the cause of chick disappearance. Average predation rates for 1993 (23·3 chicks day −1 ) and 1995 (14·8 chicks day −1 ) equated to 61% and 66% of available chicks being taken by gulls, respectively. The predation rate before the culling started in 1994 was similar to 1993 and 1995, with 15·9 chicks day −1 , but dropped to 5·1 chicks day −1 after the first gull was shot, and decreased to zero once all predatory gulls were removed. Only five individual predatory gulls were identified during the cull. Predation rates differed markedly amongst specialist predatory gulls, with one individual accounting for 85% of all successful attempts made during the baseline period. Once that gull was removed, the remaining predators increased their predation rate in a manner suggestive of a despotic system. Observations conducted in 1995 showed that the predation rate was almost zero at the beginning of the season but increased dramatically later in the summer, with two gulls together making about 60% of the captures. It is concluded that culling predatory gulls can be an effective management tool to enhance productivity in sensitive or endangered species. However, our data suggest that such culling would need to be repeated each year in order to protect a sensitive species over consecutive years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".