Hydraulic and structural traits of trees across light gradients in the Amazon secondary forest
Bibliographic record
Abstract
Amazonian species are generally unable to adapt to long drought periods, indicating a low capacity to adjust their hydraulic traits. Secondary forests account for 20% of forest cover in the Amazon, making natural regeneration species crucial under climate change scenarios. In this study, we compared the hydraulic traits of five species, including non-pioneers (Bertholletia excelsa Bonpl., Carapa guianensis Aubl., Hymenaea courbaril L.) and pioneers [Cedrela fissilis Vell., Tabebuia rosea (Bertol.) Bertero ex A.DC.], across light conditions (understory, intermediate, gap) in a 22-year-old secondary forest in Central Amazon, Brazil. Twenty-five saplings were planted and monitored in 3 plots × 5 blocks. Five years after the plantation, we assessed growth, wood density, leaf water potential at predawn and midday, xylem embolism resistance (P50), and hydraulic safety margins (HSM). The leaf water potential ranged from -2.9 to 0 MPa. The non-pioneer species C. guianensis and H. courbaril exhibited the lowest P50 (-4.06 MPa), indicating higher embolism resistance, whereas the pioneer T. rosea had the highest P50 (-1.25 MPa), indicating lower resistance. The HSM varied from -1.60 to 3.26 MPa, with lower values in gap conditions during the dry period (-1.60 MPa), especially affecting pioneer species. Wood density was influenced by both light and species type, with non-pioneers showing a generally higher density, with H. courbaril reaching 0.75 g cm-3 in the understory while the pioneer T. rosea showed the lowest density (0.27 g cm-3). These results highlight that light conditions affect hydraulic traits differently across species strategies, especially during early growth. Non-pioneer, slow-growing native species appear more resilient to light variation, making them suitable for future plantations aimed at climate adaptation in secondary forests.
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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".