Effectiveness of Voluntary Conservation Agreements: Case Study of Endangered Whales and Commercial Whale Watching
Bibliographic record
Abstract
The use of voluntary approaches to achieve conservation goals is becoming increasingly popular. Nevertheless, few researchers have quantitatively evaluated their efficacy. In 1998 industry, government agencies, and nongovernmental organizations established a voluntary conservation program for whale watching in the northeast region of the United States, with the intent to avoid collisions with and harassment of endangered whales by commercial and recreational whale-watching vessels. One important aspect of the program was the establishment of 3 speed zones within specific distances of whales. We wanted to determine the level of compliance with this aspect of the program to gauge its efficacy and gain insights into the effectiveness of voluntary measures as a conservation tool. Inconspicuous observers accompanied 46 commercial whale-watching trips from 12 companies in 2003 (n= 35) and 2004 (n= 11). During each trip, vessel position and speed were collected at 5-second intervals with a GPS receiver. Binoculars with internal laser rangefinders and digital compasses were used to record range and bearing to sighted whales. We mapped whale locations with ArcGIS. We created speed-zone buffers around sighted whales and overlaid them with vessel-track and speed data to evaluate compliance. Speeds in excess of those recommended by the program were considered noncompliant. We judged the magnitude of noncompliance by comparing a vessel's maximum speed within a zone to its maximum recorded trip speed. The level of noncompliance was high (mean 0.78; company range 0.74-0.88), some companies were more compliant than others (p= 0.02), noncompliance was significantly higher in zones farther from whales (p < 0.001), and operators approached the maximum speed capabilities of their vessel in all zones. The voluntary conservation program did not achieve the goal of substantially limiting vessel speed near whales. Our results support the need for conservation programs to have quantifiable metrics and frequent evaluation to ensure efficacy.
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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".