Using physiological conditions to assess current and future habitat use of a Subarctic frog
Bibliographic record
Abstract
Abstract Species with especially close dependence on the environment to meet physiological requirements, such as ectotherms, are highly susceptible to the impacts of climate change. Climate change is occurring rapidly in the Subarctic and Arctic, but there is limited knowledge on ectotherm physiology in these landscapes. We investigated how environmental conditions and habitat characteristics influence the physiological conditions and habitat use of wood frogs (Rana sylvatica) in a Subarctic landscape near Churchill, Manitoba (Canada). We used plaster models to estimate water loss rates and surface body temperatures among different habitat types and at specific locations used by radio‐tracked frogs. Water loss (R2= 0.67) and surface temperature (R2= 0.80) of plaster models was similar to that of live frogs. Model‐based water loss rates were greater in tundra habitat than in boreal forest and ecotone habitat. Habitat use of wood frogs was strongly tied with available surface moisture and decreased water loss rates that were observed with plaster models. Environmental conditions, such as wind speed and ground temperature, explained 58% and 91% of the variation in water balance and temperature of plaster models. Maintaining physiological conditions may be challenging for semi‐aquatic ectotherms in environments vulnerable to future climate change. The ability to predict physiological conditions based on environmental conditions, as demonstrated in our study, can help understand how wildlife will respond to climatic changes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".