Tree aboveground biomass and species richness of the mature tropical forests of Darien, Panama, and their role in global climate change mitigation and biodiversity conservation
Bibliographic record
Abstract
Abstract The remote forests of the Darien region in eastern Panama are among the last remnants of relatively undisturbed forest habitat in the Central American isthmus. Despite decades of efforts by the government, nongovernmental organizations, and civil society, including Indigenous peoples, to protect the region's natural heritage, it remains under significant threat due to widespread illegal logging. Now, the Panamanian government is considering the mechanism, Reducing Emissions from Deforestation and Forest Degradation (REDD+), as another option to limit forest loss. Central to the proper functioning of REDD+ is the need to reduce uncertainties in estimates of aboveground biomass (AGB). These estimates are used to establish realistic reference levels against which additional contributions to reducing carbon dioxide emissions from the loss and degradation of forests can be financially compensated. Also, highly desirable to REDD+ is the achievement of biodiversity cobenefits. REDD+ investments will likely be directed primarily to areas where the potential to simultaneously mitigate climate change and conserve biodiversity is highest. Here, we present the results of a field‐based forest carbon inventorying method tested in Darien's mature forests with the participation of Embera and Wounaan Indigenous peoples. We also explore whether variations in field‐based estimates of AGB across mature forests, in both undisturbed and disturbed areas, are detectable through free and readily‐available remote sensing data sources. Furthermore, we examine and compare AGB and tree species richness in Darien with other well‐studied forest sites across the tropics. Our findings reveal that Darien's forests play a crucial role globally and regionally in storing carbon and housing biodiversity, and support the imperative need to protect these forests in a culturally appropriate manner with the region's Indigenous peoples.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".