Occupancy trends of overwintering coastal waterbird communities reveal guild‐specific patterns of redistribution and shifting reliance on existing protected areas
Bibliographic record
Abstract
Climate change and anthropogenic stressors are redistributing species and altering community composition globally. Protected areas (PAs) may not sufficiently protect populations of species undergoing distributional shifts, necessitating that we evaluate existing PAs and identify areas for future protection to conserve biodiversity across regional and temporal scales. Coastal waterbirds are important indicators of marine ecosystem health, representing mobile, long-lived, higher trophic-level consumers. Using a 20-year citizen science dataset (1999-2019) with a before-after control-intervention sampling framework for habitat protection, we applied dynamic occupancy models to assess winter occupancy trends along the Pacific coast of Canada. Specifically, we sought to understand potential drivers of regional declines, spatial commonalities among guilds, and changes in habitat use before and after PA designation, as well as between PAs and non-PAs. Occupancy trends varied regionally, with greater declines in the south compared to the north. Regional differences underlined potential range shifts, particularly for species with traits linked to temperature tolerance, movement, and high productivity foraging, as cold-tolerant, migratory benthivores and piscivores wintered farther north relative to 20 years ago or retreated to cold-water fjords. While 21 of 57 (36.8%) species responded positively to PA designation (before-after), greater occupancy declines tended to occur in PAs established pre-1999 relative to non-PAs (control-intervention). Since PAs are currently concentrated in the south, negative associations were most apparent for species retreating northward, but existing PAs may have a stabilizing or transitory effect on southern wintering species shifting into the region from farther south. We emphasize that conservation strategies must balance persistence of current communities with preserving the climate-adapted biodiversity of tomorrow by accounting for community-level effects of species moving into and out of existing PAs. Incorporating range shifts into PA planning by predicting distributional changes will allow conservation practitioners to identify priority habitats, such as cold-water refugia, for persistent wildlife communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".