The proportion of low abundance species is a key predictor of plant β‐diversity across the latitudinal gradient
Bibliographic record
Abstract
Abstract The diversity of life displays very strong patterns of disparity across the Earth. Beta (β)‐diversity (species compositional differences among sites) of woody plants, for instance, has usually been documented to decline with increasing latitude. Understanding these patterns, however, remains a grand challenge in ecology and evolution. We develop a mathematical model to explain patterns of β‐diversity across multiple landscapes. The model effectively predicts β‐diversity in simulated and natural communities, regardless of the types of species abundance distributions. Our model provides the novel insight that the proportion of species in the lowest abundance category ( P L ), which represents the share of relatively rare species in the regional species pool, is the key predictor of plant β‐diversity. By applying the model to global forest inventories sampled from 40.7° S to 60.7° N, we find that P L explains nearly 85% of the variation in plant β‐diversity along the global latitudinal gradient. Through a series of numerical simulations, we further show that the predictive power of P L on plant β‐diversity on a global scale is largely determined by the variation of intraspecific aggregation among different communities. Synthesis : We develop a new sampling model to predict patterns of β‐diversity and find that the P L explains the majority of the variation in plant β‐diversity along the latitudinal gradient. Our work provides a new tool in analysing β‐diversity and advances the theoretical understanding of large‐scale β‐diversity patterns across environmental gradients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 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".