Techniques for monitoring <i>Brachyramphus</i> murrelets: A comparison of radar, autonomous acoustic recording and audio‐visual surveys
Bibliographic record
Abstract
ABSTRACT Conditions in Alaska, USA, pose a challenge for monitoring populations of Brachyramphus murrelets using standard survey methods, because of strong winds, 2 sympatric species, short nights, and variable nesting habitat. We tested 3 methods for monitoring Brachyramphus murrelets breeding in the Kodiak Archipelago, Alaska, in 2010–2012. In addition to standard audio‐visual and radar methods, we tested—for the first time with murrelets in Alaska—the application of autonomous acoustic recorders for monitoring vocal activity. We completed 74 radar, 124 audio‐visual, and 134 autonomous acoustic surveys, focused on presunrise activity peaks; this yielded 26,375 murrelet detections. Marbled ( B. marmoratus ) and Kittlitz's murrelets ( B. brevirostris ) could not be distinguished using combinations of radar and acoustic recordings; therefore, at‐sea surveys will be required to determine localized species proportions. Of the 3 methods, radar sampled the largest area and detected silently flying murrelets, providing the most reliable data on local populations; however, radar identification of murrelets was unreliable in winds exceeding 18 km/hr. Audio‐visual surveys were useful for species identification and to document behaviors associated with local nesting, whereas autonomous acoustic recorders allowed season‐long monitoring of murrelet vocal activity. Within potential forest‐nesting habitat of marbled murrelets, all 3 methods gave similar measures of presunrise murrelet activity, but only radar reliably sampled murrelets commuting between nest and ocean. Because of their low cost and flexible programming, automated sound recorders offer an affordable way to sample vocal activity prior to more intensive or expensive radar and audio‐visual surveys. We recommend that population monitoring and habitat studies of Brachyramphus murrelets in Alaska include combinations of all 3 methods. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".