Examining the Effectiveness of Automated Acoustic Recording Units for Recording Predator‐Related Disturbances in Colony Nesting Birds: A Case Study
Bibliographic record
Abstract
As habitat destruction and human expansion pushes wildlife to ever shrinking habitats, new methods are needed to monitor and assess the impacts of disturbances on ecosystems. Automated recording units (ARUs) may provide a cost effective and minimally invasive way of monitoring disturbances and behavioral responses under these changing conditions. ARUs are gaining prominence in avian research, replacing in-person observers in various surveys and in tracking the movement of individual birds. Researchers have investigated the reliability of ARUs in these studies, but investigations into their reliability in detecting behavioral events are lacking. The main objective of this case study is to investigate if disturbance data from predation events in Ardea herodias fannini (heron) colonies can adequately be obtained from bioacoustics recordings and used in research to substitute for in-person observations. ARUs were placed at two heron colonies and accompanied by in-person observers. Both sources recorded minor or major predatory disturbances to the colonies with minor disturbances being a single heron responding and major disturbances being multiple herons responding. The records of detected disturbances from each observation method were compared. We found that ARUs were able to distinguish major disturbances from other calls. There was no considerable difference between major disturbances detected by ARUs or by in-person observers. However, the ARUs did have marginally less success when trying to detect minor disturbances. This was attributed to ARUs providing purely auditory cues as opposed to some visual cues that human observers occasionally rely on. When monitoring remote colony nesters with distinct auditory calls, ARUs can provide a cost effective and scalable substitute for in-person observers. These data can be easily repurposed for other research questions, stored for long-term studies to find gradual changes in behavior, or used to study unexpected or rapid changes to an environmental variable.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".