Leaf litter decomposition rates: influence of successional age, topography and microenvironment on six dominant tree species in a tropical dry forest
Bibliographic record
Abstract
Litter decomposition is a central process in forest ecosystems because of its role in carbon and nutrient cycling and maintaining soil fertility. Decomposition is affected by plant traits, soil and microenvironmental conditions, topography, and vegetation structure, which varies with successional age. However, it is unclear how all these factors affect leaf decomposition of dominant tree species in tropical dry forests (TDFs). The objective of this study was to compare the decomposition rates of six dominant tree species: three legumes ( Caesalpinia gaumeri , Lysiloma latisiliquum , Piscidia piscipula ) and three non-legumes ( Bursera simaruba , Gymnopodium floribundum , Neomillspaughia emarginata ) in five successional age categories (8–10, 15–22, 23–30, 65–84, > 85 years-old) and two topographic conditions (flat and sloping sites) in a TDF, and to analyze the association with leaf traits (toughness, N, C and total phenols content) soil properties (bulk density, organic carbon, pH, clay), microenvironmental (litter and soil moisture, leaf area index), and vegetation variables (basal area, aboveground biomass, tree diameter, tree height). Litterbags were placed in 30–400 m 2 circular plots distributed in forests of the Yucatan, Mexico, and collected on six occasions spread over 230 days (540 samples per species). L. latisiliquum and C. gaumeri had the highest decomposition rates (as well as leaf nitrogen concentration and the lowest leaf toughness). Conversely, G. floribundum had the lowest decay rate. Decomposition rate reached high values at intermediate successional ages, suggesting that soil fertility recovers rapidly after disturbance, although only L. latisiliquum showed significant differences among stand age categories. Decomposition rate was consistently higher at flat sites than on slopes but the difference was significant only for L. latisiliquum . The soil, vegetation structure and microenvironmental variables that contributed most to explaining variation in decay rates varied among species. Decomposition tended to increase with soil moisture and clay content, and to decrease with soil organic carbon and pH suggesting susceptibility to climate change and soil erosion, particularly in sloping areas. Our results highlight the importance of analyzing species-specific responses, especially for dominant species, which likely contribute most to leaf litter decomposition, and to consider key ecological factors that influence this key process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".