Near-Complete Extinction of Native Small Mammal Fauna 25 Years After Forest Fragmentation
Bibliographic record
Abstract
Tropical forests continue to be felled and fragmented around the world. A key question is how rapidly species disappear from forest fragments and how quickly humans must restore forest connectivity to minimize extinctions. We surveyed small mammals on forest islands in Chiew Larn Reservoir in Thailand 5 to 7 and 25 to 26 years after isolation and observed the near-total loss of native small mammals within 5 years from <10-hectare (ha) fragments and within 25 years from 10- to 56-ha fragments. Based on our results, we developed an island biogeographic model and estimated mean extinction half-life (50% of resident species disappearing) to be 13.9 years. These catastrophic extinctions were probably partly driven by an invasive rat species; such biotic invasions are becoming increasingly common in human-modified landscapes. Our results are thus particularly relevant to other fragmented forest landscapes and suggest that small fragments are potentially even more vulnerable to biodiversity loss than previously thought.
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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 | 0.000 | 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) | 0.001 | 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".