Piping Plover Habitat Changes and Nesting Responses Following Post-Tropical Cyclone Fiona on Prince Edward Island, Canada
Bibliographic record
Abstract
Climate change is driving regime shifts across ecosystems, exposing species to novel challenges of extreme weather, altered disturbances, food web disruptions, and habitat loss. For disturbance-dependent species like the endangered piping plover (Charadrius melodus), these shifts present both opportunities and risks. While most piping plover populations show net growth following storm-driven habitat creation, similar gains have not been documented in the Eastern Canadian breeding unit. In September 2022, post-tropical cyclone Fiona caused record coastal changes in this region, prompting our study of population and nesting responses within the central subunit of Prince Edward Island (PEI). Using satellite imagery and machine learning tools, we mapped storm-induced change in open sand habitat on PEI and compared nest outcomes across habitat conditions from 2020 to 2023. Open sand areas increased by 9–12 months post-storm, primarily through landward beach expansion. However, the following breeding season showed no change in abundance, minimal use of new habitats, and mixed nest success. Across study years, backshore zones, pure sand habitats, and sandspits/sandbars had lower apparent nest success, while washover zones, sparsely vegetated areas, and wider beaches had higher success. Following PTC Fiona, nest success on terminal spits declined sharply, dropping from 45–55% of nests hatched in pre-storm years to just 5%, partly due to increased flooding. This suggests reduced suitability, possibly from storm-induced changes to beach elevation or slope. Further analyses incorporating geomorphological and ecological data are needed to determine whether the availability of suitable habitat is limiting population growth. These findings highlight the importance of conserving and replicating critical habitat features to support piping plover recovery in vulnerable areas.
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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 | 0.000 | 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) | 0.002 | 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".