Developing conservation action for data-poor species using seahorses as a case study
Bibliographic record
Abstract
In this thesis I explore how to develop conservation action strategically for data-poor marine fishes. The dearth of information about populations, habitats and threats for many marine fishes makes it difficult to know how or where to initiate conservation strategies. My PhD research explores what type of information is essential for conservation management, and how it can be generated and applied for data-poor marine fishes. I use the case study of seahorses (Hippocampus spp), because they are notoriously understudied and yet their trade is regulated under the Convention on International Trade in Endangered Species (CITES). I further focus on Thailand, the largest exporter of seahorses, which has come under considerable international scrutiny. In my first two chapters I generated the spatial data that are vital to support conservation and management efforts. My results showed that using local knowledge to inform a presence / absence study, one that incorporated detection probabilities, was the most expedient way to produce the necessary spatial data. In my next two chapters I explored two approaches to understanding incidental capture of data-poor species in non-selective fishing gear. I found that vulnerability analysis yielded greater return on fewer data than data-poor fisheries stock assessment. However, data-poor fishery stock assessment made it possible to estimate stock status and revise management measures. For my fifth chapter, I applied findings from my previous chapters to meet CITES obligations, by assuming the role of a Thai government agent confronted with the external technical advice that I had generated. I found that implementation was most successful if I addressed three main questions: (1) What are the pressures on species?; (2) Is management in place to mitigate those pressures?; and (3) Are the species responding as hoped to management? My thesis highlights ways that management can move forward with limited data to address conservation issues for marine species. Some of these ways include valuing the use of local knowledge and using new advances in data-poor assessment methods in fisheries. Whenever fisheries are involved, conservationists need to respect the challenges that managers face in simultaneously seeking to protect wild species and meet human needs.
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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 | 0.001 |
| 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".