Interior fences can reduce cost and uncertainty when eradicating invasive species from large islands
Bibliographic record
Abstract
Summary The conservation of many threatened species can be advanced by the eradication of alien invasive animals from islands. However, island eradications are an expensive, difficult and uncertain undertaking. An increasingly common eradication strategy is the construction of ‘interior fences’ to partition islands into smaller, independent eradication regions that can be treated sequentially or concurrently. Proponents argue that, while interior fences incur substantial up front construction costs, they reduce overall eradication costs. However, this hypothesis lacks an explicit theoretical or empirical justification. We formulate a general theory that relates the number of interior fences to the magnitude and variation of the economic cost of island eradication. We use this theory to explore the conditions under which interior fences represent a defensible management strategy, under cost and risk minimisation objectives. We then specifically consider the forthcoming eradication of catsFelis catusfrom Dirk Hartog Island, Western Australia, by parameterising our general theory using published data on the cost and success of previous projects. Our results predict that under a wide range of reasonable conditions, interior fences can reduce the expected cost of a successful invasive alien animal eradication from large islands. On Dirk Hartog Island, interior fences will marginally reduce eradication costs, with two fences reducing expected costs by 3%. Interior fences have a much more substantial effect on the variability of eradication costs: two fences reduce the width of the 95% confidence bounds by more than one‐third and halve the size of the average project cost overrun/underrun. Our results reveal that the construction of interior fences is a defensible management strategy for eradicating alien invasive species from islands. However, the primary benefit of interior fences will be risk management, rather than a reduction in expected project costs.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".