The Long-term Consequences of Short-term Responses to Disturbance Experiences from Whalewatching Impact Assessment
Bibliographic record
Abstract
Studies often use behavioral responses to detect the impact of given disturbances on animals. However, the observation of these short-term responses can often lead to contradicting results. Here we describe studies focusing on the impacts of whale watching to show how the biological relevance of short-term responses can be inferred from contextual information. They showed that short-term behavioral responses could have long-term consequences for individuals and their populations using information about variation in response magnitude with exposure levels, long term population biology data, and multiple response variables. They showed that the added energetic constraints of the responses can impair life functions and lead to influences on vital rates with the potential to affect population viability. Individuals will manage disturbances as another ecological variable and will assess its costs in relation to other energetic trade-offs associated with the occupancy of the habitat in which the disturbance takes place. This can lead to rapid shift in tactics to cope with the disturbance, such as shift from short-term avoidance tactics to long-term habitat abandonment. When individuals cannot elude proximity to the disturbance, their fitness is reduced as observed through reduced reproductive success. These studies provide mechanisms to inform the US National Research Councils’ Population Consequences of Acoustic Disturbance framework in which the influence of noise impact of on marine mammal conservation can be studied.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".