Passive acoustic monitoring as a tool for retroactively assessing range boundaries of cryptic species
Bibliographic record
Abstract
When one species is split into two, basic questions arise about where the newly-recognized species occur and where they come into contact. Large and growing archives of passive acoustic monitoring data are an untapped resource for cheaply and efficiently resolving the distributions of vocal species facing taxonomic splits. We used recordings from regions of allopatry to analyze the distinctiveness of two subspecies of Warbling Vireo that have been suggested as different species (Vireo gilvus gilvus and Vireo gilvus swainsoni). We then trained and tested a boosted regression tree and two human observers on the acoustic classification of the songs and re-classified 221 recordings from passive acoustic monitoring in Alberta, Canada, which had been initially identified to the level of species. Analyses from allopatry showed that songs of the two Warbling Vireo taxa were statistically distinct in 10 of 11 acoustic variables, showed quantitative divergence scores consistent with recognized species, and were classifiable with 93–100% accuracy. When existing passive acoustic monitoring data were re-classified, clear geographic patterns emerged, with little to no overlap in the distributions of the two taxa. Our results highlight the potential for passive acoustic monitoring data to be used to map the distributions of taxa facing taxonomic revision. If data from these monitoring programs can be permanently stored and made available for research, they can allow efficient assessments of the distributions of cryptic species without the need for dedicated field work.
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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 | 0.001 |
| 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) | 0.000 | 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".