Of goats and heat, the differential impact of summer temperature on habitat selection and activity patterns in mountain goats (Oreamnos americanus) of different ecotypes
Bibliographic record
Abstract
Abstract Climate change disproportionately affects northern and alpine environments, with faster rates of warming than the global average. In this context, understanding the effect of projected temperature increase on the species inhabiting these ecosystems is essential. Most alpine and northern ungulates are particularly well adapted to low temperatures and must modify their behaviour when temperatures exceed a critical threshold. We analyzed the influence of summer weather conditions, particularly temperature, on the activity patterns and habitat selection of four populations of a mountain specialist, the mountain goat (Oreamnos americanus), representing the two ecotypes found in this species - coastal and continental. We collected GPS location and activity sensor data during 2010-2019 from 223 mountain goats in four different study areas in both the coastal range of Alaska and the Rocky Mountains of Alberta. Mountain goats of each ecotype used different habitat selection tactics to avoid thermal stress. When temperatures increased, mountain goats of both ecotypes decreased selection for alpine meadows, but goats in coastal environments increased selection for open habitats close to snow, while continental goats increased selection for forest environments. With respect to altitude, mountain goats in continental environments selected higher elevation open habitats only when temperature increased. In coastal environments, goats selected higher elevation habitats at all temperatures, but the selection for high elevations was strongest in warm conditions. Mountain goats of both ecotypes reduced the proportion of time spent active when temperatures increased during the middle of the day. Our study shows that mountain goats use diverse tactics that are likely to mitigate thermal stress. Mountain goats living in different ecosystems use alternative tactics. The observed ecotypic variation highlights the importance of assessing the impact of climate change at the population rather than the species level, a pattern that arises because populations are experiencing different environmental constraints.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".