Effectiveness of acoustic indices as indicators of vertebrate biodiversity
Bibliographic record
Abstract
Effective monitoring tools are key for tracking biodiversity loss and informing management intervention strategies. Passive acoustic monitoring promises to provide a cheap and effective way to monitor biodiversity across large spatial and temporal scales, however, extracting useful information from long-duration audio recordings still proves challenging. Recently, a range of acoustic indices have been developed, which capture different aspects of the soundscape, and may provide a way to estimate traditional biodiversity measures. Here we investigated the relationship between 13 acoustic indices obtained from passive acoustic monitoring and biodiversity estimates of various vertebrate taxonomic groupings obtained from manual surveys at six sites spanning over 20 degrees of latitude along the Australian east coast. We found a number of individual acoustic indices that correlated well with species richness, Shannon’s diversity index, and total individual count estimates obtained from traditional survey methods. Correlations were typically greater for avian and total vertebrate biodiversity than for anuran and non-avian vertebrate biodiversity. Acoustic indices also correlated better with species richness and total individual count than with Shannon’s diversity index. Random forest models incorporating multiple acoustic indices provided more accurate predictions than single indices alone. Out of the acoustic indices tested, cluster count, mid-frequency cover and spectral density contributed the greatest predictive ability to models. Our results suggest that models incorporating multiple acoustic indices could be a useful tool for monitoring certain vertebrate groups. Further work is required to understand how site-specific variables can be incorporated into models to improve predictive capabilities and how to improve the monitoring of taxa besides avians, particularly anurans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".