A null model of temporal trends in biological invasion records
Bibliographic record
Abstract
Biological invasions are a growing aspect of global biodiversity change. In many regions, introduced species richness increases supralinearly over time. This does not, however, necessarily indicate increasing introduction rates or invasion success. We develop a simple null model to identify the expected trend in invasion records over time. For constant introduction rates and success, the expected trend is exponentially increasing. Model extensions with varying introduction rate and success can also generate exponential distributions. We then analyse temporal trends in aquatic, marine and terrestrial invasion records. Most data sets support an exponential distribution (15/16) and the null invasion model (12/16). Thus, our model shows that no change in introduction rate or success need be invoked to explain the majority of observed trends. Further, an exponential trend does not necessarily indicate increasing invasion success or 'invasional meltdown', and a saturating trend does not necessarily indicate decreasing success or biotic resistance.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".