Divergent patterns of richness and density in the global soil seed bank
Bibliographic record
Abstract
Abstract Soil seed banks are an important component of plant population and community dynamics, buffering temporal heterogeneity and allowing populations to recover following disturbance. At the same time, patterns in soil seed banks are likely to reflect scale-dependent patterns in above-ground vegetation, patterns that differ strongly across world regions. Here, we investigate components of diversity in the soil seed bank across global biomes and ecosystems. We found that patterns of species richness in the soil seed bank diverged from patterns of seed density, although there were high levels of uncertainty, especially at smaller spatial scales. Importantly, habitat degradation consistently led to lower richness, potentially limiting future ecosystem recovery. Among ecosystems, mediterranean and tropical regions had high species richness, but seed density m −2 in the soil was highest in wetlands and arable systems. Lower seed densities were found in both high-diversity tropical biomes that are characterised by short-lived seeds, and low-diversity boreal and tundra biomes with more stable established vegetation. Our findings of divergences between species richness and seed density across global biomes, and how they are impacted by habitat degradation, give valuable insights to the understanding of plant biodiversity worldwide.
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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 | 0.001 | 0.001 |
| 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".