Replication Data for: Litterfall dynamics along a successional gradient in a Brazilian tropical dry forest
Bibliographic record
Abstract
ABSTRACT Background: This study aimed to determine the influence of soil traits, vegetation structure and climate on litterfall dynamics along a successional gradient in a tropical dry forest (TDF) in southeastern Brazil. We used a chronosequence design consisting of three successional stages (early, intermediate, and old-growth) defined based on forest age and vertical and horizontal structures. Methods: Litterfall was recorded monthly for three years in 12 plots of 50 × 20 m (four plots per stage) where vegetation parameters (species richness, basal area, density and height for trees with diameter at breast height ≥ 5 cm) and soil chemical and physical traits were previously obtained. We placed eight 0.5-m2 litter traps in each plot, totalling 96 traps. Samples were sorted into leaves, twigs, reproductive parts, and debris. Results: Litterfall mass was composed mainly of leaves and varied slightly among years (4 to 4.5 Mg∙ha− 1), within the range observed for other TDFs. Annual litterfall mass was higher at the old-growth forest than at the early and intermediate forest stages and this successional pattern was driven by vegetation characteristics (forest structural parameters and plant functional groups) and soil traits related to water-holding capacity. Litter amount in the intermediate stage was lower than expected for its forest structure (and similar to the early stage), possibly because its higher soil clay content increased the water holding capacity and leaf retention during the dry season. Seasonal variations in monthly litterfall were strongly driven by forest deciduousness and affected by climatic factors related to water availability. This pattern was consistent across the successional gradient, although differences for each litterfall component were observed. Conclusions: Our results suggest that litter production in the studied TDF is influenced by multiple factors along succession, such as above-ground biomass and the degree of leaf retention mediated by soil water-holding capacity. Further studies on community phenological patterns can allow a better understanding of successional changes on litterfall and how fast this fundamental function recovers in secondary 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".