Seeing the woods through the saplings: Using wood density to assess the recovery of human‐modified Amazonian forests
Bibliographic record
Abstract
Abstract Most of the world's remaining tropical forests have been affected by either selective logging, understorey fires, fragmentation or are regrowing in areas that were previously deforested. Despite the ubiquity of these human‐modified forests, we have a limited knowledge of their potential to recover key traits linked to ecosystem processes and consequent services. Here we present data from 31,095 trees and saplings distributed across 121 plots of undisturbed and disturbed primary forests as well as secondary forests in the eastern Amazon. We examined the post‐disturbance recovery trajectory of an important plant functional trait, wood density. We tested whether human‐modified Amazonian forests are experiencing a rapid or a slow, or even impeded, recovery of this trait, which is associated with the provision of a fundamental ecosystem service—carbon storage. As expected, we found that the plot‐level wood density of trees and saplings in disturbed primary and secondary forests was significantly lower than in undisturbed forests. However, there was no significant difference in the average wood density of saplings between disturbed primary and secondary forests, possibly indicating a process of secondarization. We also found evidence that the recovery of wood density in human‐modified forests is being severely disrupted due to edge effects (in the case of disturbed primary forests) and high liana densities (in the case of both disturbed primary and secondary forests). Surprisingly, these two factors were more important predictors of wood density recovery than the time elapsed since the disturbance event. Synthesis . Plant communities in human‐modified Amazonian forests appear to not be recovering a key functional property—wood density, which in turn may affect their ability to store carbon in the future. If the aim of conservation programs in tropical forests is to maintain existing rates of ecosystem functions, processes and services, then they must concentrate efforts on avoiding anthropogenic disturbance in areas of currently undisturbed forests. It is also vital to prevent further disturbance in human‐modified forests to avoid disrupting even more their recovery.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".