Noise and landscape features influence habitat use of mammalian herbivores in a natural gas field
Bibliographic record
Abstract
Anthropogenic noise is a complex disturbance known to elicit a variety of responses in wild animals. Most studies examining the effects of noise on wildlife focus on vocal species, although theory suggests that the acoustic environment influences non-vocal species as well. Common mammalian prey species, like mule deer and hares and rabbits (members of the family Leporidae), rely on acoustic cues for information regarding predation, but the impacts of noise on their behaviour has received little attention. We paired acoustic recorders with camera traps to explore how average daily levels of anthropogenic noise from natural gas activity impacted occupancy and detection of mammalian herbivores in an energy field in the production phase of development. We consider the effects of noise in the context of several physical landscape variables associated with natural gas infrastructure that are known to influence habitat use patterns in mule deer. Our results suggest that mule deer detection probability was influenced by the interaction between physical landscape features and anthropogenic noise, with noise strongly reducing habitat use. In contrast, leporid habitat use was not related to noise but was influenced by landscape features. Notably, mule deer showed a stronger predicted negative response to roads with high noise exposure. This study highlights the complex interactions of anthropogenic disturbance and wildlife distribution and presents important evidence that the effects of anthropogenic noise should be considered in research focused on non-vocal specialist species and management plans for mule deer and other large ungulates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".