Assessment of spatiotemporal variability of giant clam populations (Cardiidae:<i>Tridacna</i>) from 11 years of monitoring at Koh Tao, Thailand
Bibliographic record
Abstract
ABSTRACT Giant clams (Tridacninae) are an ecologically important species in coral reef habitats across the Indo-Pacific. Numerous examples of giant clam population declines of varying degrees of severity have been documented since the 1970s. These have been attributed to several reasons, such as overexploitation in regional fisheries and ornamental trades, extreme weather events and anomalous marine warming events leading to bleaching. In Thailand, this has led to extensive conservation efforts, such as legal protections and population restocking. Despite these strong measures, to date no long-term studies have been conducted on giant clam populations in Thai waters. We provide results from 11 years (2009–2019) of giant clam population monitoring, at Koh Tao, an island with a well-documented history of coral reef-associated stressors as well as conservation efforts. Surveys were conducted across two depth ranges at 18 reef sites around the island, revealing contrasting trends. Our findings indicate a significant population decline of Tridacna crocea from coral reefs in the 6–8 m depth range, from 1.41 (±0.47) individuals/100 m2 in 2010 to 0.59 (±0.17) individuals/100 m2 in 2019, with, however, no significant change in T. squamosa populations at this depth range. Data from the 3–5 m depth range indicate no significant change in the T. crocea population over the years, but a population increase of T. squamosa from 0.78 (±0.18) individuals/100 m2 in 2009 to 2.07 (±0.38) individuals/100 m2 in 2019. Abundance estimates from these sites indicate extensive heterogeneity in giant clam populations around the island, and highlight the importance of sufficient spatial resolution in identifying population trends.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".