A latitudinal gradient in Darwin’s naturalization conundrum at the global scale for flowering plants
Bibliographic record
Abstract
Darwin's naturalization conundrum describes two seemingly contradictory hypotheses regarding whether alien species closely or distantly related to native species should be more likely to naturalize in regional floras. Both expectations have accumulated empirical support, and whether such apparent inconsistency can be reconciled at the global scale is unclear. Here, using 219,520 native and 9,531 naturalized alien plant species across 487 globally distributed regions, we found a latitudinal gradient in Darwin's naturalization conundrum. Naturalized alien plant species are more closely related to native species at higher latitudes than they are at lower latitudes, indicating a greater influence of preadaptation in harsher climates. Human landscape modification resulted in even steeper latitudinal clines by selecting aliens distantly related to natives in warmer and drier regions. Our results demonstrate that joint consideration of climatic and anthropogenic conditions is critical to reconciling Darwin's naturalization conundrum.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".