Satellite telemetry reveals habitat selection decisions by black oystercatchers across seasonal, diel, and tidal cycles
Bibliographic record
Abstract
) in four regions of British Columbia, Canada, to examine habitat use and selection decisions across seasonal, diel and tidal cycles. We characterized the shoreline in each region and used GLMMs to investigate how habitat characteristics influenced shoreline use by tracked birds. For individuals, we estimated home range size and the frequency key features of the shoreline were re-visited. Black oystercatchers generally made greater-than-expected use of rocky islets and shoreline with freshwater outflows, less tree cover and greater intertidal area. However, while black oystercatchers preferred islets and shoreline with less tree cover at most/all time periods, they only exhibited preferences for greater intertidal area during low tides, and preferences for shoreline with freshwater outflows during the nonbreeding season, day, and high tides. Individual home ranges, on average, contained 46 km of shoreline (range: 12-156 km) and individuals used 10.4 km (range: 6.7-13.9 km). Individuals made greater use of larger islets with less tree cover that were closer to outflows, and greater use of outflows associated with larger streams, greater intertidal areas and gravel substrates. Black oystercatchers' habitat preferences likely reduce predation risk (rocky islets and shoreline with less tree cover) and increase foraging rewards (shoreline with freshwater outflows, greater intertidal area, and gravel substrates). However, habitat preferences appear sensitive to constraints on movement in the breeding season and changes in foraging rewards across the diel and tidal cycle, highlighting the importance of examining habitat use at multiple temporal scales. Black oystercatchers are considered indicators of rocky intertidal health; therefore, critical habitat is expected to be important for a suite of wildlife dependent on safe and productive coastline.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".