Spatial and temporal heterogeneity of light and soil water along a terra firme transect in Amazonian Ecuador: effects on tree seedling survivorship, growth, and allocation
Bibliographic record
Abstract
To better understand the availability of plant resources on the forest floor in the Amazon and also to show the effect of their heterogeneity on tree seedlings, I described the spatial and temporal variation of light and soil water along a 100 m transect in a terra firme forest for 6 months and recorded responses of three tree species planted on that transect after 1 year’s growth. I found that (i) the spatial heterogeneity across the transect was greater than the temporal heterogeneity at any given microsite on the transect for both light and water and there was a positive correlation between them, (ii) Couepia obovata Ducke, the largest seeded and the only subcanopy tree, survived the best and showed both the largest relative height growth rate (RHGR) and the largest specific leaf area (SLA), while among the two early successional trees, Tapirira guianensis J.B. Aublet had the largest leaf area ratio (LAR) and the largest leaf mass ratio (LMR) and Duguethia spixiana Mart. had the largest root to shoot ratio (RTOS), (iii) for T. guianensis, SLA increased with increasing light and soil water potential predicted both increasing LMR and decreasing RTOS with increasing soil water, and (iv) soil water potential could also predict increasing LAR with increasing water for D. spixiana and, for C. obovata, soil water potential predicted more survivorship, LMR, and RHGR but less RTOS, all with increasing soil water. I conclude that some subcanopy trees may survive and grow more than open-canopy trees when presented with water stress in the forest understory and that within the ranges of light and soil water sampled here, plants responded more to spatial variation in water compared with light.
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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 | 0.000 | 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) | 0.000 | 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 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".