Cascading effects of humans, through wolves, in a multiple land use ecosystem
Bibliographic record
Abstract
Wolves (Canis lupus) have strong influences in terrestrial food webs through predation. Depending on the land use type (e.g. ranching, forestry, oil and gas), humans influence wolf density and distribution. We tested whether human activities affect predator-prey interactions and herbivory in a food chain in Southwest Alberta, Canada. We estimated human distribution using digital camera traps (n = 55). We obtained Global Positioning System telemetry data from wolves (n = 16), elk (n = 110) and cattle (n = 31). We calculated resource Selection Functions using Generalized Linear mixed models (GLmms) to test the spatial relationship between humans, wolves, elk (Cervus elaphus) and cattle and vegetation utilization (n= 148 plots) on the landscape. We found that while elk exhibited anti-predator behavior in response to wolf presence (i.e., drops in distance to cover, z=7.082, P<0.001, and in food quality of habitat used, z=4.454, P<0.001), cattle did not. Anti-predator response by elk confirms wolves can exert ecosystem effects through predation. by directly influenc- ing wolf density and distribution, humans may indirectly influence herbivory patterns of ungulates and ultimately, vegetation utilization. Such effects may be different depending on the land use activity. Poor anti-predator responses suggest that cattle are vulnerable to wolf predation and ensuing ecosystem effects are likely different compared to wild ungulates. Furthermore, predation on domestic cattle elicits intolerance by humans, generating a nega- tive feedback that maintains wolves at low densities
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".