Lake Use by Three Avian Piscivores and Humans: Implications for Angler Perception and Conservation
Bibliographic record
Abstract
Humans and colonial piscivorous birds are often perceived to be in conflict over shared aquatic habitats and fisheries resources in inland lakes. We examined angler perception of birds and the relative abundance of American white pelicans (Pelecanus erythrorhynchos), double-crested cormorants (Phalacrocorax auritus), western grebes (Aechmophorus occidentalis), and boats on two lakes in Saskatchewan, Canada. Anglers perceived cormorants to be the biggest threat to fisheries (60%), compared to pelicans (47%), and western grebes (34%). The density of these birds and boats varied significantly between sections of the two study lakes. Boat density was higher in developed sections with shoreline communities (range 0-7/km2) compared to those surrounded by agricultural land or native prairie (0-1/km2). In contrast, cormorant and pelican densities were highest in areas with an undeveloped shoreline (0-22/km2), and were reduced to near zero in developed sections. Western grebes did not follow the same pattern as the other two species; grebe density was generally more uniform within lakes (0-23/km2 in all sections). Boat density was a negative predictor of pelican and cormorant density on one lake, but was a positive predictor for grebes on both lakes. Our results indicate that pelicans and cormorants avoid sections of lakes that have higher levels of human development, potentially altering the location of their foraging sites on the scale of kilometres. In contrast, western grebes were abundant in all areas of the two lakes and did not appear to avoid human development or activity. We conclude that angler perceptions are not congruent with levels of habitat use overlap with birds. In addition, western grebe responses to human activities appear counterintuitive, making interpretations difficult in a conservation context; further study is required.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".