Using microphone arrays to investigate microhabitat selection by declining breeding birds
Bibliographic record
Abstract
Understanding the microhabitat preferences of animals can help managers to develop better conservation and recovery strategies but this is challenging. Traditional methods are limited by cost, accuracy and human resources. In this study, we investigated avian microhabitat preferences using microphone arrays that are capable of accurately locating vocalizing birds. Our objective was to identify the microhabitat associations of two common species in steep population decline, the Boreal Chickadee Poecile hudsonicus and the Cape May Warbler Setophaga tigrina. We deployed 68 eight‐channel arrays at random locations in Labrador, Canada, during the 2016 avian breeding season. We returned in 2017 to the 18 array locations where the target species had been detected the previous year and characterized the microhabitat at the exact locations where they had been detected. We also characterized the microhabitat at randomly determined control locations. Results show that Boreal Chickadees select trees with greater diameter‐at‐breast‐height that are surrounded by greater stem density. We did not find evidence that Cape May Warblers exhibit microhabitat selection during song production. The study shows that microphone arrays are an effective tool for identifying preferred microhabitat that could be incorporated into future conservation or recovery strategies.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".