Edge effects on components of diversity and above‐ground biomass in a tropical rainforest
Bibliographic record
Abstract
Abstract Edge effects are among the most significant consequences of forest fragmentation. Therefore, understanding the impacts of edge creation on biodiversity is crucial for forest management and biological conservation. In this study, we used trait‐based and phylogenetic approaches to examine the effects of fragmentation on components of diversity and above‐ground biomass of rainforest tree communities in Madagascar in forest edge vs. interior habitats. Tree communities in forest edges showed lower phylogenetic diversity relative to those in interior habitats, suggesting that some clades may be more vulnerable to environmental filtering than others. Functional diversity was also significantly lower on the edge for productivity traits, but not for dispersal traits. Tree communities in the forest edge showed higher divergence of dispersal traits and lower divergence in productivity traits than expected, while functional diversity in interior forest did not differ from random expectations. This suggests that separate mechanisms affect productivity traits vs. dispersal traits in edge habitats. There was no significant difference in above‐ground biomass between edge and interior habitats, suggesting that edge effects have not yet negatively influenced the forest's potential for carbon storage. However, these changes may not have occurred yet, given the slow turnover of tree communities. Synthesis and applications . Our results highlight the role of edge effects in the erosion of functional and phylogenetic diversity of highly diverse tree communities. While above‐ground biomass did not appear to be affected by forest edge in our study, we suggest long‐term monitoring of forests for potential changes in ecosystem functioning. These findings also indicate the need to reduce edge creation and buffer existing edges for holistic biodiversity conservation.
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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) | 0.000 | 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".