Tropical forest fragmentation and isolation: Is community decay a random process?
Bibliographic record
Abstract
Habitat destruction and degradation are the leading causes of species declines and extinctions in the world. Human altered landscapes often leave fragments of previously continuous habitat, which may be of significant conservation value. We assessed the effects of habitat fragmentation on the taxonomic diversity, community composition, and nestedness of avian communities before and after fragment isolation at the Biological Dynamics of Forest Fragments Project research site in the Amazon rainforest. Species loss in 10ha and 100ha fragments was significantly different from random taxonomic loss. In addition, after fragment isolation, but not prior to fragmentation, the species in the 10ha fragments were a nested subset of the species in the 100ha fragments. Finally, within the fragments two distinct communities formed, those on the edge of the fragments and those at the interior of the fragments, indicating that edge species did not penetrate the interior portion of the fragments. The controlled isolation of fragments from continuous forest resulted in rapid changes in the taxonomic diversity and species composition where fragment size served as a driver of species assemblages across the landscape. We suggest that future research continue to assess community level adjustments to habitat fragmentation and investigate the drivers behind the non-random loss of taxonomic groups and the nested structure of species composition of smaller fragments into larger ones after habitat fragmentation.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".