Invasion lags: The stories we tell ourselves and our inability to infer process from pattern
Bibliographic record
Abstract
Abstract Aim Many alien species experience a lag phase between arriving in a region and becoming invasive, which can provide a valuable window of opportunity for management. Our ability to predict which species are experiencing lags has major implications for management decisions that are worth billions of dollars and that may determine the survival of some native species. To date, timing and causes of lag and release have been identified post hoc, based on historical narratives. Location Global. Methods We use a simple but realistic simulation of population spread over a fragmented landscape. To break the invasion lag, we introduce a sudden, discrete change in dispersal. Results We show that the ability to predict invasion lags is minimal even under controlled circumstances. We also show a non‐negligible risk of falsely attributing lag breaks to mechanisms based on invasion trajectories and coincidences in timing. Main conclusions We suggest that post hoc narratives may lead us to erroneously believe we can predict lags and that a precautionary approach is the only sound management practice for most alien species.
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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 | 0.030 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.010 | 0.027 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".