Relative importance of water, energy, and heterogeneity in determining regional pteridophyte and seed plant richness in China
Bibliographic record
Abstract
Abstract Environmental variables, such as ambient energy, water availability, and environmental heterogeneity have been frequently proposed to account for species diversity gradients. How taxon‐specific functional traits define large‐scale richness gradients is a fundamental issue in understanding spatial patterns of species diversity, but has not been well documented. Using a large dataset on the regional flora from China, we examine the contrast spatial patterns and environmental determinants between pteridophytes and seed plants which differ in dispersal capacity and environmental requirements. Pteridophyte richness shows more pronounced spatial variation and stronger environmental associations than seed plant richness. Water availability generally accounts for more spatial variance in species richness of pteridophytes and seed plants than energy and heterogeneity do, especially for pteridophytes which have high dependence on moist and shady environments. Thus, pteridophyte richness is disproportionally affected by water‐related variables; this in turn results in a higher proportion of pteridophytes in regional vascular plant floras (pteridophyte proportion) in wet regions. Most of the variance in seed plant richness, pteridophyte richness, and pteridophyte proportion explained by energy is included in variation that water and heterogeneity account for, indicating the redundancy of energy in the study extent. However, heterogeneity is more important for determining seed plant distributions. Pteridophyte and seed plant richness is strongly correlated, even after the environmental effects have been removed, implying functional linkages between them. Our study highlights the importance of incorporating biological traits of different taxonomic groups into the studies of macroecology and global change biology.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".