Limited influence from edges and topography on plant structural and taxonomic diversity in fragments of Atlantic Forest
Bibliographic record
Abstract
Abstract Although Atlantic Forest is very diverse and heavily fragmented, little is known about the impact of created edges on forest structure and plant diversity within its forest remnants. We aimed to determine the distance of edge influence on vegetation in forest fragments in the Atlantic Forest; to compare the effects of edge influence, topography and their interaction on vegetation structure; and to assess patterns of structural and taxonomic diversity. We collected data on forest structure, plant functional groups, plant families and vertical vegetation structure in 2 m x 2 m contiguous quadrats along 250 m transects across the edges of 24 fragments approx. 70 km west of São Paulo. We used randomization tests to estimate the magnitude and distance of edge influence, generalized linear mixed model to assess the effect of topography, and wavelet analysis to evaluate spatial patterns. We found evidence of edge degradation (lower diversity and cover of most plant groups compared to interior forest) and edge sealing (abrupt changes at the edge particularly for leafy vertical structural diversity), but edge influence did not extend very far into forest with a distance of edge influence or less than 20 m for most variables. Less extensive edge influence compared to other tropical forests was not explained by topography (slope) but could be due to more extensive fragmentation and land use history. The use of multiple approaches to studying forest edges provided complementary information to improve our understanding of the structure of anthropogenic edges in Atlantic Forest.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".