Real-time invasion dynamics reveal the drivers of predator spread and prey extirpation on an island
Bibliographic record
Abstract
Abstract Non-native predators profoundly restructure island biological communities worldwide, yet their management is hindered by limited empirical understanding of the dynamics of predator spread and prey extirpation. Here, we integrate two decades of field surveys, citizen-science records, and standardized transects to reconstruct the invasion of the snake Hemorrhois hippocrepis on Ibiza and its impact on the endangered, endemic lizard Podarcis pityusensis. We found a marked acceleration in range expansion despite intensified culling, evidence for the transition from establishment to spread stages of this invasion. Spatially explicit invasion maps combined with citizen reports of lizard disappearance show a non-linear acceleration in the interval from predator arrival to local prey extirpation, shortening from over ten years in early-invaded areas to three years in recently invaded areas. The observed patterns are consistent with density-dependent predator pressure, which generates a wave-like, rapidly advancing invasion front evidenced by declining snake body condition and reduced efficiency of snake captures in long-invaded areas. Our study empirically confirms theoretical predictions of nonlinear spread and impact dynamics and highlights how citizen science can crucially help documenting early invasion stages. Effectively incorporating the non-linear dynamics of invasions into conservation strategies is urgent to mitigate invasion-driven transformations of fragile island ecosystems worldwide.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".