Determining marine mammal detection functions for a stationary land-based survey site
Bibliographic record
Abstract
Abstract Context The shore-based survey is a common, non-invasive, and low-cost method in marine mammal science, but its scientific applications are currently limited. Such studies typically target populations whose distributions are not random with respect to nearshore sites and involve repeated scans of the same area from single, stationary platforms. These circumstances prohibit the use of classic distance sampling techniques for estimating animal densities or distributions, particularly the derivation of a detection function that describes the probability of detecting targets at various distances from the observer. Aims Here, we present a technique for estimating land-based detection functions, as well as quantifying uncertainty in their parameterisation, on the basis of the range-specific variability of observations from one scan to the next. Methods This Bayesian technique uses Monte Carlo simulation to determine the likelihood of thousands of candidate detection functions, then conducts weighted sampling to generate a posterior distribution estimate of the detection function parameterisation. We tested the approach with both archival and artificial datasets built from known detection functions that reflect whale and porpoise detectability. Key results When the base distribution of targets was random, the whale detection function was estimated without error (i.e. the difference of the median of the posterior and the true value was 0.00), and the porpoise detection function was estimated with an error equal to 4.23% of the true value. When the target base distribution was non-random, estimation error remained low (2.57% for targets concentrated offshore, 1.14% when associated with nearshore habitats). When applied to field observations of humpback whales and Dall’s porpoises from a land-based study in northern British Columbia, Canada, this technique yielded credible results for humpback whales, but appeared to underestimate the detectability of Dall’s porpoises. Conclusion The findings presented here indicate that this approach to detection function estimation is appropriate for long-running surveys in which scan regularity is high and the focus is on large, slow-moving, low herd-size, and easily detectable species. Implications The derivation of a detection function is a critical step in density estimation. The methodology presented here empowers land-based studies to contribute to quantitative monitoring and assessment of marine mammal populations in coastal habitats.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".