Nonconstituent species in soil seed banks as indicators of anthropogenic disturbance in forest fragments
Bibliographic record
Abstract
Forest fragmentation is prevalent in regions with high human population density, but also with high biological diversity, such as southwestern China. Predicting potential changes in species composition in native forest ecosystems resulting from forest fragmentation is an important approach in assessing forest ecosystems and sustainable management of forests. The term nonconstituent species is proposed for plant species that occur in a natural landscape but are not native to it. We hypothesized that fragmentation of continuous forests significantly increases both the richness and the size of soil seed banks of nonconstituent species. To test our hypothesis we compared soil seed banks in three small fragments and one large tract of subtropical evergreen broad-leaved forest in Yunnan Province, southwestern China. In total, 249 species were identified in all soil samples from the four sites. Nonconstituent species represented 36.4%–61.5% of the total taxa and 33.8%–59.6% of total germinants from soil samples in the three forest fragments, but only 3.7%–7.9% of total taxa and 7.5%–9.4% of total germinants from soil samples in the continuous forest tract. Both richness and size of soil seed banks of nonconstituent species were greater under forest fragments than under continuous forest. A majority of nonconstituent species were native weeds and annual herbs with wind-dispersed seeds; most were present in soil seed banks but not in aboveground vegetation. We suggest that nonconstituent species in soil seed banks could be used as an indicator species group for assessing human disturbance. If so, the richness and size of soil seed banks of nonconstituent species could play an important role in quantifying disturbance in forests.
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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 | 0.001 |
| 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".