Exploring liana-driven vertical complexity using GEDI simulator and Lorenz-entropy in a neotropical dry forest
Bibliographic record
Abstract
• Lianas significantly elevate Lorenz-entropy values in intermediate and late stages. • Combining LiDAR and GEDI simulation enhances liana detection at plot scale. • Early-stage non-infested plots exhibit greater entropy due to canopy gaps. Lianas play a crucial role in shaping the vertical structural complexity of tropical forests; however, their impact is not well understood. This study evaluates the influence of lianas on the Lorenz-entropy (LE) index, a measure of canopy heterogeneity, in a Neotropical tropical dry forest (TDF) in Santa Rosa National Park, Costa Rica. Using full-waveform LiDAR data and simulated Global Ecosystem Dynamic Investigation (GEDI) waveforms, we compared liana-infested and non-infested plots across early, intermediate, and late successional stages. Using Mann-Whitney U test with Benjamini-Hochberg (BH) correction (95 % confidence level) our findings indicate that liana-infested plots exhibit significantly higher Lorenz-entropy index values in intermediate (P< 0.05 , P < BH-threshold), and late (P < 3.33 × 10 - 2 , P < BH-threshold) successional stages, while early-stage in non-infested plots showed significantly higher entropy values (P< 1.67 × 10 - 2 , P < BH-threshold). Effect size analysis showed a moderate impact in the intermediate stage (Cliff’s δ = 0.35, 95 % CI: 0.07–0.61) and a moderate to large impact in the late stage (Cliff’s δ = 0.41, 95 % CI: 0.11–0.67). In early-stage plots, liana-infested stands had significantly lower LE index values than non-infested plots (Cliff’s δ = –0.50, 95 % CI: –0.78 to –0.19). These results demonstrate that the LE index effectively captures liana-driven increases in vertical canopy stratification and heterogeneity, particularly in more mature forest stages. The integration of airborne LiDAR and GEDI simulations offers an approach for assessing structural complexity at the plot level. These findings highlight the need for further research to understand the long-term ecological consequences of liana abundance, in the context of forest monitoring.
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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 | 0.001 |
| 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".