Impacts of Human Disturbance on Large Prey Species: Do Behavioral Reactions Translate to Fitness Consequences?
Bibliographic record
Abstract
Anthropogenic disturbances have been demonstrated to affect animal behavior, distribution, and abundance, but assessment of their impacts on fitness-related traits has received little attention. We hypothesized that human activities and infrastructure cause a decrease in the individual performance of preys because of anthropogenically enhanced predation risk. We evaluated the impacts of commercial logging and road networks on the fitness of a large herbivore known to be sensitive to human disturbance: the forest-dwelling woodland caribou (Rangifer tarandus caribou). For 8 consecutive years (2004-2011) we monitored 59 individuals using GPS telemetry in the Charlevoix region of Québec, Canada. We also used Very High Frequency telemetry locations collected on 28 individuals from 1999-2000. We related habitat selection of adult caribou at various spatio-temporal scales to their probability of dying from predation, and to indices of their reproductive success and energy expenditure. The probability that adult caribou died from predation increased with the proportion of recent disturbances (including cutblocks ≤ 5 years old) in their annual home range. The respective effects of increasing paved and forestry road densities depended upon the overall road density within the home range of caribou. At a finer scale of 10 to 15 days before their death, caribou that were killed by a predator selected for recent disturbances more than individuals that survived, and avoided old mature conifer stands. The home range area of caribou increased with road density. Finally, the composition of the home range of females had no effect on their reproductive success. We show that human activities and infrastructure may influence the individual performance of large prey species in highly managed regions. We outline the need to consider the full set of impacts that human development may have on threatened animal populations, with particular emphasis on predator-prey relationships and population dynamics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".