Landscape‐scale forest disturbance regimes in southern Peruvian Amazonia
Bibliographic record
Abstract
Landscape-scale gap-size frequency distributions in tropical forests are a poorly studied but key ecological variable. Currently, a scale gap currently exists between local-scale field-based studies and those employing regional-scale medium-resolution satellite data. Data at landscape scales but of fine resolution would, however, facilitate investigation into a range of ecological questions relating to gap dynamics. These include whether canopy disturbances captured in permanent sample plots (PSPs) are representative of those in their surrounding landscape, and whether disturbance regimes vary with forest type. Here, therefore, we employ airborne LiDAR data captured over 142.5 km2 of mature, swamp, and regenerating forests in southeast Peru to assess the landscape-scale disturbance at a sampling resolution of up to 2 m. We find that this landscape is characterized by large numbers of small gaps; large disturbance events are insignificant and infrequent. Of the total number of gaps that are 2 m2 or larger in area, just 0.45% were larger than 100 m2, with a power-law exponent (alpha) value of the gap-size frequency distribution of 2.22. However, differences in disturbance regimes are seen among different forest types, with a significant difference in the alpha value of the gap-size frequency distribution observed for the swamp/regenerating forests compared with the mature forests at higher elevations. Although a relatively small area of the total forest of this region was investigated here, this study presents an unprecedented assessment of this landscape with respect to its gap dynamics. This is particularly pertinent given the range of forest types present in the landscape and the differences observed. The coupling of detailed insights into forest properties and growth provided by PSPs with the broader statistics of disturbance events using remote sensing is recommended as a strong basis for scaling-up estimates of landscape and regional-scale carbon balance.
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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) | 0.003 | 0.013 |
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; both teacher heads agree on what is shown here.
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".