Uncovering the Calls of Data‐Deficient Masked Owls Using Morphology and Environmental Gradients
Bibliographic record
Abstract
ABSTRACT Aim To test whether vocal characteristics of little‐known and ‘lost’ owl species can be inferred from environmentally driven morphological and vocal correlations, using the Australian Masked Owl ( Tyto novaehollandiae ) as a model system. Location Australasia. Taxon Australian Masked Owl ( T. novaehollandiae superspecies, Tytonidae). Methods We fit linear models to examine the relationship between environmental variables (latitude, temperature, elevation) and wing length for 196 Australian Masked Owl specimens. We also examined the relationship between these environmental variables and acoustic characteristics (call duration and mean dominant frequency) from 700 calls recorded via passive acoustic monitoring. These datasets were then integrated to predict vocalisations for the Papuo‐Moluccan taxa Tyto aurantia , Tyto manusi , Tyto sororcula and Tyto novaehollandiae calabyi . Results Wing length of Australian Masked Owls was greater in females than in males ( R 2 = 0.480, p < 0.001) and increased with cooler temperatures ( R 2 = 0.240, p < 0.001) and higher latitudes ( R 2 = 0.211, p < 0.001). Call duration decreased with decreasing latitude ( R 2 = 0.347, p = 0.013) and temperature ( R 2 = 0.347, p = 0.036). Mean dominant frequency increased at lower latitudes ( R 2 = 0.402, p < 0.001) and warmer temperatures ( R 2 = 0.391, p < 0.001) and decreased with elevation ( R 2 = 0.429, p = 0.032). Main Conclusions Environmental factors influencing morphology correlate with vocal traits, enabling predictions for species lacking reference calls. By correlating small datasets of data‐deficient species with larger datasets from better‐known relatives, our approach provides insights into the expected vocalisations of ‘lost’ Papuo‐Moluccan masked owls, challenges taxonomic boundaries, and offers a broadly applicable framework for biogeographic and taxonomic inference.
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 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".