The case for lethal control of gulls on seabird colonies
Bibliographic record
Abstract
ABSTRACT Lethal control of wildlife represents an ethical concern for managers, exacerbated by a lack of replicated or controlled data for most taxa or regions. The Gulf of Maine (GOM) has a history of intensive lethal and nonlethal predator control to protect terns ( Sterna spp.) from inflated populations of predatory gulls, especially herring ( Larus argentatus ) and great black‐backed gulls ( L. marinus ; large gulls). We described management strategies in the GOM, reviewed methods of nonlethal and lethal types of control, and compared the effectiveness of 3 control regimes (lethal, nonlethal‐only, and no control) using weighted means of reproductive success metrics for 4 tern species. Nonlethal‐only control is the least effective method of predator control; lethal control is consistently the most effective. Arctic terns ( Sterna paradisaea ) were the most susceptible to predation, whereas common terns ( Sterna hirundo ) were the most resilient. We concluded that targeted lethal control is necessary in the GOM to protect tern colonies from depredation and nesting exclusion by large gulls, and cannot be substituted with nonlethal control. Cessation of lethal control leads to abandonment of tern colonies within 6–7 years, but resumption of appropriately timed lethal control can lead to recolonization the same year. A combination of nonlethal and lethal methods can minimize the number of gulls taken. We recommend that any application of lethal control carefully considers the local needs of any target species and recognizes the need for spatial and temporal commitment. © 2017 The Wildlife Society.
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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 | 0.001 | 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 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".