Tourism affects the behavioural budget of the common dolphin Delphinus sp. in the Hauraki Gulf, New Zealand
Bibliographic record
Abstract
Common dolphins Delphinus sp. are frequently targeted by tourism operations in New Zealand waters, yet there is a paucity of data on potential impacts faced by this species. Transition matrix models, used widely in population ecology, have recently been applied to behavioural transitions in order to provide successful management guidelines. We detail the use of Markov chain models to assess the impact of tourism activities on the behavioural state of common dolphins in the Hauraki Gulf, New Zealand. First-order time discrete Markov chain models were used to describe transition probabilities in both control and impact scenarios. The effect of boat interactions was quantified by comparing transition probabilities of both control and impact chains. Foraging and resting bouts were significantly disrupted by boat interactions to a level that raises concern about the sustainability of this impact. Both the duration of bouts and the overall time spent in these 2 behavioural states decreased. Foraging dolphins took significantly longer to return to their initial behavioural state in the presence of the tour boat. There was also an increased preference to shift behaviour to socialising or milling after tour boat interactions. Impacts identified in the present study are similar to those previously reported for bottlenose dolphins, a coastal species typically considered to be more susceptible to cumulative anthropogenic impacts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".