Community science for enigmatic ecosystems: Using eBird to assess avian biodiversity on glaciers and snowfields
Bibliographic record
Abstract
Abstract Aim To quantify avian biodiversity and habitat preference and describe behavior in an enigmatic, understudied ecosystem: mountain glaciers and snowfields. Location Mountains in the Pacific Northwest of western North America: British Columbia (CA), Washington and Oregon (USA). Taxon Birds observed within our study area and focal habitat. Methods We used community science data from eBird—an online database of bird observations from around the world—to estimate bird biodiversity and abundance in glacier and snowfield ecosystems as well as nearby, ice-adjacent habitats. We used field notes from eBird users and breeding codes to extend our data set to include insight into habitat usage and behavior. Finally, we compared our community-science approach to previous studies that used traditional survey methods. Results We identified considerable avian biodiversity in glacier and snowfield habitat (46 species) with four specialists that appeared to prefer glaciers and snowfields over nearby, ice-adjacent habitat. Combined with field notes by eBird users, our efforts increased the known global total of avian species associated with ice and snow habitats by 14%. When community science data was compared to traditional methods, we found similar species diversity but differences in abundance. Main conclusions Despite the imminent threat of glacier and snowfield melt due to climate change, species living in these habitats remain poorly studied, likely due to the remoteness and ruggedness of their terrain. Glaciers and snowfields hold notable bird diversity, however, with a specialized set of species appearing to preferentially forage in these habitats. Our results show that community science data can provide a valuable starting point for studying difficult to access areas, but traditional surveys are still useful for more rigorous quantification of avian biodiversity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".