Trends in studies of edge influence on vegetation at human-created and natural forest edges across time and space
Bibliographic record
Abstract
Forest edges, or boundaries between adjacent ecosystems, play important ecological roles. Both anthropogenic and natural forest edges affect vegetation while contributing to landscape heterogeneity. The recent proliferation of studies on vegetation at edges suggests that a comprehensive review of global edge studies is timely. We reviewed the literature on forest edges to identify trends in edge studies over time, determine types and localities of studied edges, and compare findings on edge influence. We found 446 studies conducted in 55 different countries that considered edge influence on vegetation structure and (or) composition. Research on vegetation at anthropogenic edges has increased and expanded geographically, but studies are still scarce in some areas and at natural forest edges. Forest edges were generally characterized by greater species diversity and nonnative species abundance than interior forest. Distance of edge influence on vegetation extended furthest at tropical anthropogenic forest edges compared with other edge types and locations. Edge influence on responses caused by indirect effects of edges generally extended further into the forest than edge influence on responses related to forest structure. Our findings indicate that vegetation characteristics differ between edge and forest types and should be considered in the sustainable management of heterogeneous forested landscapes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".