Impact of Forest Fragmentation and Associated Edge Effects on Tropical Forest Biodiversity in North West Madagascar, Assessed via Ecoacoustics
Bibliographic record
Abstract
ABSTRACT Tropical forests harbour up to 50% of the world's terrestrial biodiversity, making them vital refuges for many species. However, tropical forests are one of the world's most threatened habitats; 10 million km 2 of tropical forest has been cleared since 1800, and what remains is now highly fragmented. This poses a major threat to forest‐specialist organisms, many of which are already threatened with extinction. It is therefore imperative that we are able to rapidly assess the impact of forest loss on local biodiversity throughout the tropics. Here, we assessed how forest fragmentation and its associated edge effects impact animal biodiversity in the Sahamalaza‐Iles Radama National Park, North West Madagascar. We used passive acoustic monitoring to collect 6006 h of audio data, from which we calculated six acoustic indices for comparison between (1) continuous and fragmented forest and (2) core and edge forest. We found significant differences in the soundscape (acoustic indices) among forest areas. Some indices associated with overall biodiversity were significantly higher in continuous and core forest in comparison to fragmented and edge forest. Although significantly different among sites, indices associated with specific frequency bands did not show a clear relationship with fragmentation and edge effects. As our results broadly match biodiversity data collected via traditional methods at our study site, this study provides further support for ecoacoustics as a potentially efficient and reliable tool for remote assessment of biodiversity in the tropics. Our results also emphasise the detrimental effects of fragmentation and edge effects on forest animal biodiversity and our need to protect this important habitat.
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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".