GPS Based Daily Activity Patterns in European Red Deer and North American Elk (Cervus elaphus): Indication for a Weak Circadian Clock in Ungulates
Bibliographic record
Abstract
Long-term tracking using global positioning systems (GPS) is widely used to study vertebrate movement ecology, including fine-scale habitat selection as well as large-scale migrations. These data have the potential to provide much more information about the behavior and ecology of wild vertebrates: here we explore the potential of using GPS datasets to assess timing of activity in a chronobiological context. We compared two different populations of deer (Cervus elaphus), one in the Netherlands (red deer), the other in Canada (elk). GPS tracking data were used to calculate the speed of the animals as a measure for activity to deduce unbiased daily activity rhythms over prolonged periods of time. Speed proved a valid measure for activity, this being validated by comparing GPS based activity data with head movements recorded by activity sensors, and the use of GPS locations was effective for generating long term chronobiological data. Deer showed crepuscular activity rhythms with activity peaks at sunrise (the Netherlands) or after sunrise (Canada) and at the end of civil twilight at dusk. The deer in Canada were mostly diurnal while the deer in the Netherlands were mostly nocturnal. On an annual scale, Canadian deer were more active during the summer months while deer in the Netherlands were more active during winter. We suggest that these differences were mainly driven by human disturbance (on a daily scale) and local weather (on an annual scale). In both populations, the crepuscular activity peaks in the morning and evening showed a stable timing relative to dawn and dusk twilight throughout the year, but marked periods of daily a-rhythmicity occurred in the individual records. We suggest that this might indicate that (changes in) light levels around twilight elicit a direct behavioral response while the contribution of an internal circadian timing mechanism might be weak or even absent.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".