A novel camera trap design for studying wildlife in mountain glacier ecosystems yields new insight for glacier biodiversity in the Pacific Northwest, USA
Bibliographic record
Abstract
Abstract Context The global recession of glaciers and perennial snowfields is reshaping mountain ecosystems. Beyond physical changes to the landscape and altered downstream hydrology, the implications of glacier decline for biodiversity are poorly known. Before predictions can be made about how climate change will affect wildlife in glacier-associated ecosystems, a more thorough accounting of the role that glaciers play in species’ life histories is needed. However, typical approaches for documenting wildlife presence and behavior—remote camera traps—are difficult to use in glaciated terrain due to limited options for securing them (e.g., no trees) and dramatic seasonal changes in snowpack. Aims In this study, we sought to test a novel camera trap designed for glaciated mountain ecosystems. We also aimed to use this approach to gain insight into wildlife and human usage of a mountain glacier in western North America. Methods We deployed an elevational transect of uniquely designed camera traps along the western margin of the Paradise Glacier, a rapidly receding mountain glacier on the south side of Mount Rainier, WA, USA. Our simple camera trap design consisted of a wildlife camera attached to a camouflaged cylindrical cooler filled with snow and rocks. Key results Our camera design proved ideal for a mountain glacier ecosystem and from June to September 2021, we detected at least 16 vertebrate species (seven birds, nine mammals) over 770 trap nights using glacier-associated habitats. Humans, primarily skiers, were the most common species detected, but we also recorded 99 observations of wildlife (birds and mammals). These included three species of conservation concern in Washington: wolverine ( Gulo gulo ), Cascade red fox ( Vulpes vulpes cascadensis ), and white-tailed ptarmigan ( Lagopus leucura ). Conclusions Collectively, our results provide proof-of-concept for a novel camera trap design that is ideal for treeless, perennially snow-covered landscapes and revealed a rich diversity of wildlife using mountain glacier habitat in the Pacific Northwest. We highlight the global need for similar studies to better understand the true scale of biodiversity that will be impacted by glacier recession in mountain ecosystems.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".