Seed Banks in Savanna, Forest Fragments, and Continuous Forest Edges Differ in a Tropical Dry Forest in Madagascar
Bibliographic record
Abstract
Abstract Rapid deforestation has fragmented habitat across the landscape of Madagascar. To determine the effect of fragmentation on seed banks and the potential for forest regeneration, we sampled seed viability, density and diversity in 40 plots of 1 m2 in three habitat types: forest fragments, the near edge of continuous forest, and deforested savanna in a highly fragmented dry deciduous forest landscape in northwestern Madagascar. While seed species diversity was not different between forest fragments and continuous forest edge, the number of animal‐dispersed seeds was significantly higher in forest fragments than in continuous forest edge, and this pattern was driven by a single, small‐seeded species. In the savanna, seeds were absent from all but three of the 40 plots, indicating that regeneration potential is low in these areas. Several pre‐ and post‐dispersal biotic and abiotic factors, including variation in the seed predator communities and edge effects could explain these findings. Understanding the extent to which seed dispersal and seed banks influence the regeneration potential of fragmented landscapes is critical as these fragments are the potential sources of forest expansion and re‐connectivity.
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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 | 0.000 |
| 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".