Environmental, behavioral, and design-related factors affect accuracy and precision of beluga abundance estimates from aerial surveys
Bibliographic record
Abstract
Abundance estimation of wildlife populations is frequently derived from systematic survey data. Accuracy and precision of estimates, however, depend on the number of replicate surveys, and on adjustments made for animals unavailable to (availability bias), or available but undetected (perception bias) by observers. This study offers a comprehensive analysis of the relative influence of methodological, environmental and behavioral factors on availability bias estimates from photographic and visual aerial surveys of a small cetacean with a highly clumped distribution, the beluga (Delphinapterus leucas). It also estimates the effect of the number of surveys on accuracy and precision of abundance estimates, using 28 replicate visual surveys flown within a 16—29 day window depending on survey year. Availability bias was estimated using detailed dive data from 27 beluga from the St. Lawrence Estuary, Canada, and applied to systematic visual and photographic aerial surveys of this population, flown using various survey platforms. Dive and surface interval durations varied among individuals, and averaged (weighted) 176.6 s (weighted s.e. = 12.6 s) and 51.6 s (weighted s.e. = 4.5 s), respectively. Dive time and instantaneous availability, but not surface time, were affected by local turbidity, seafloor depth, whale behavior (i.e., whether beluga were likely in transit or not), and latent processes that were habitat-specific. Overall, adjustments of availability for these effects remained minor compared to effects from survey design (photographic or visual) and type of platform, and observer search patterns. For instance, mean availability varied from 0.33—0.38 among photographic surveys depending on sightings distribution across the study area, but exceeded 0.40 for all visual surveys. Availability also varied considerably depending on whether observers searched within 0-90° (0.42—0.60) or 170° (0.70—0.80). Simulation-based power analysis indicates a large benefit associated with conducting more than 1 or 2 survey reps, but a declining benefit of conducting > 5—10 survey reps. An increase in sample size from 2, to 5, and 10 reps decreased the CV from 30, to 19 and 13%, respectively, and increased the probability of the abundance estimate being within 15% of true abundance from 0.42, to 0.59 and 0.69 in species like beluga.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".