Estimating the proportion of a beluga population using specific areas from connectivity patterns and abundance indices
Bibliographic record
Abstract
Abstract An understanding of habitat use and connectivity is key to preserving wildlife habitat integrity. In this study, we present an innovative approach to habitat connectivity and quantitative assessment of their use that is applicable to any terrestrial or aquatic species. Specifically, we aimed at estimating the proportion of the St. Lawrence Estuary beluga population (Canada) likely to use specific habitats within their summer range from habitat connectivity. To achieve this goal, we used a long‐term (~30 yr) observational database of beluga herd movements to derive transit corridor and an index of connectivity among habitats. These were then coupled with relative abundance estimates from aerial surveys for each of these habitats. Specifically, our objectives were to (1) verify the existence and location of transit corridors, (2) examine the effect of environmental factors on movement patterns and corridor routes, (3) describe their use by different age classes, and (4) based on connectivity patterns, determine the proportion of the population likely to transit along these corridors and to be present in each habitat. Our results highlighted the existence of a network of transit corridors among habitats that are connected to a central confluence zone, and expanding with three main branches into the three main regions of their summer range (Upper and Lower Estuary, and Saguenay River). They also highlighted the existence of a spatial segregation of sex and age classes in the population. Previously published aerial survey data indicate that on average, <6% of the population can be found within the limits of each habitat at any given time. Using this information within the framework of habitat connectivity provided a minimum estimate of the proportion of the population likely to visit each habitat, with minimum proportions reaching 45% in habitats of the central confluence zone (Île Rouge area). While we detected no evidence of an influence of bottom depth on corridor routes, we identified a tendency of beluga for moving with surface and ebb tide currents. The application of this approach to beluga and other wildlife populations will contribute to better assessing their potential exposure to natural or human‐related stressors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".