Shifts in diel activity of Rocky Mountain mammal communities in response to anthropogenic disturbance and sympatric invasive white-tailed deer
Bibliographic record
Abstract
Coexistence mechanisms such as temporal niche partitioning among sympatric wildlife species may be impacted by continued human disturbance and invasive species. White-tailed deer expansion in North America, a product of landscape and climate change, may alter other species’ diel activity and have implications for conservation of native species. We asked: 1) To what degree does invasive white-tailed deer temporal activity overlap with other ungulate species (mule deer and moose) in landscapes with high human disturbance, versus landscapes with low human disturbance? 2) To what degree does invasive white-tailed deer and predator activity (wolves, grizzly bears, and coyotes) overlap in landscapes with high human disturbance versus landscapes with low human disturbance? Using detection data from infrared remote cameras, we compared a high-disturbance (2019–2020) and a low-disturbance (2009–2010) landscape in the Canadian Rocky Mountains. We used activity pattern analysis to quantify temporal overlap between sympatric mammal species and white-tailed deer. We found that contrary to our hypotheses, sympatric ungulate species maintained activity and temporal overlap with white-tailed deer in both landscapes. Predators did not match the temporal activity of white-tailed deer in the high-disturbance landscape as closely as in the low-disturbance landscape. We conjecture ungulates may be prioritizing periods of optimal foraging or reducing predation risk rather than temporal niche partitioning from invasive-white-tailed deer. Predator species may be more sensitive to anthropogenic disturbance than ungulates and adjust their temporal activity in high-disturbance landscapes. Native ungulates’ lack of diel activity adaptation to white-tailed deer invasions is an important piece of the behavioural puzzle about how western Nearctic systems change with anthropogenic disturbance and human-mediated range shifts.
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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".