A Large‐Scale Experiment to Evaluate Control of Invasive Muskrats
Bibliographic record
Abstract
ABSTRACT The muskrat ( Ondatra zibethicus ) is an invasive species in Europe. The extensive waterways of the Netherlands provide ideal habitat for muskrats, and a large population established itself after arrival in 1941. A control program was put into effect immediately because muskrat burrowing can compromise the integrity of dikes and, hence, poses a significant public safety risk. The current (2015) annual catch of approximately 89,000 individuals is equivalent to approximately 0.30 muskrats/km of waterway, well above the national objective in spite of decades of effort. The control program is expensive (€35 M annually) and contested by animal rights groups. These factors created the need for a careful evaluation of the full range of control possibilities, from ‘no control’ to ‘extermination.’ As part of this, we experimentally evaluated the validity of a previously published correlation (based on historical data) between catch and effort. We raised or lowered removal effort (2013–2016) in a stratified random sample of 117 5‐km × 5‐km ‘atlas squares’ from the national grid. We found that catch‐per‐unit effort (CPUE) decreased after effort was increased, and rose after effort was decreased, by amounts slightly greater than expected based on the correlational data, though confidence intervals enclose zero. As anticipated, CPUE varied consistently and strongly between seasons. The biggest (and unanticipated) effects were those of the catch in the preceding 3 years (‘history’), and surrounding area (‘neighborhood’). Our experiment confirms estimates of intensity of control required to lower muskrat populations. These results will help with more effective allocation of control effort, and better‐informed evaluation of the economic costs of various control options. © 2020 The Authors. Wildlife Society Bulletin published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".