Ecological impacts of fishing gears in Ko Chang, Trat Province, Thailand
Bibliographic record
Abstract
Knowledge of ecological impacts of fishing, especially in small-scale sector, is not always readily available, making it difficult to employ an ecosystem-based approach to fisheries management and to achieve sustainability. The topic of this dissertation was formulated with the aim to enhance this knowledge through conducting researches in Mu Ko Chang, Trat Province, the Eastern Gulf of Thailand. This study focuses on two main types of impacts, i.e. bycatch and habitat damages, consisting of three research modules: 1) assessing existing knowledge and analyzing the knowledge gap regarding bycatch and habitat impacts of fishing methods in Thai waters; 2) investigating fishing impacts in terms of bycatch and habitat damages, of some fishing gears in Ko Chang, Trat Province, and; 3) analyzing relative ecological impacts of fishing gears and to rank the levels of severity caused by different fishing gears. According to the literature review and gap analysis, a major gap of knowledge on the ecological impacts of fishing gears in Thailand was found, particularly the data on bycatch (retained and discarded) and habitat damages. The onboard surveys of bycatch for nine small-scale fishing gears in Ko Chang revealed that some of the gears, particularly trammel nets, crab traps, and gillnets, produced the higher number of bycatch in terms of biomass and species richness. A study on the impacts of fish traps on coral reefs revealed various possible impacts including physical damages on corals, impacts from sediment dispersion, ecosystem imbalance due to exploitation of reef fish, and marine debris. A comparative study on macrobenthic community between permanent and six-month closure areas exhibited the impacts of fishing activities (trawlers, push nets, and dredges) in terms of the reduction on macrobenthic abundance. An expert consultation workshop was also convened aiming to rate the impacts of fishing gears including large- and small-scale based on existing knowledge, surveyed data, and personal experience of the expert. It showed that otter-board trawls and pair-trawls were rated with the highest score of bycatch and habitat impacts. In terms of small-scale fishing gears, bycatch impacts caused by shrimp trammel nets and crab gill nets were mostly concerned. This dissertation enhances understandings of the ecological impacts of fishing gears on marine ecosystems in Thailand that are highly required for ecosystem-based fisheries management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".