Contributions of precipitation and temperature to the large scale geographic distribution of fleshy-fruited plant species: Growth form matters
Bibliographic record
Abstract
Fruit type, an important reproductive trait, is closely related to reproduction strategy, community dynamics and biotic interactions. However, limited research has explored the geographic distribution of fruit type and the underlying abiotic factors influencing this on a large scale. Here we aim to study large-scale distribution patterns of fleshy-fruited plant species and the most important environmental drivers for different growth forms in utilizing the fruit type and distribution data for over 27000 plant species in China. Results indicated that the proportion of fleshy-fruited species was higher in southeast China, and this pattern was consistent between different growth forms. Overall, the proportion of fleshy-fruited species was higher in wet, warm, and stable environments. Notably, mean annual precipitation had the greatest predictive contribution to woody fleshy-fruited species distributions, but mean annual temperature best predicted the herbaceous fleshy-fruited species distributions. We provide the first map of a large-scale distribution of fleshy-fruited plant species for different growth forms in the northern hemisphere and show that these geographic patterns are mainly determined by contrasting climatic gradients. Recognizing that climate factors have different relationships with different growth forms of fleshy-fruited species advances our knowledge about fruit type and environment. This work contributes to predictions of the global distribution of fleshy-fruited species under future climate change scenarios and provides a reference for continued research on the complex interactions between plants, frugivores and the environment.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".