Drivers of Seabird Population Recovery on New Zealand Islands after Predator Eradication
Bibliographic record
Abstract
Eradication of introduced mammalian predators from islands has become increasingly common, with over 800 successful projects around the world. Historically, introduced predators extirpated or reduced the size of many seabird populations, changing the dynamics of entire island ecosystems. Although the primary outcome of many eradication projects is the restoration of affected seabird populations, natural population responses are rarely documented and mechanisms are poorly understood. We used a generic model of seabird colony growth to identify key predictor variables relevant to recovery or recolonization. We used generalized linear mixed models to test the importance of these variables in driving seabird population responses after predator eradication on islands around New Zealand. The most influential variable affecting recolonization of seabirds around New Zealand was the distance to a source population, with few cases of recolonization without a source population ≤25 km away. Colony growth was most affected by metapopulation status; there was little colony growth in species with a declining status. These characteristics may facilitate the prioritization of newly predator-free islands for active management. Although we found some evidence documenting natural recovery, generally this topic was understudied. Our results suggest that in order to guide management strategies, more effort should be allocated to monitoring wildlife response after eradication.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".