Nesting population trend of the leatherback sea turtle in Bocas del Toro province and Comarca Ngäbe-Buglé, Panama for the period 2002–2022
Bibliographic record
Abstract
Sea turtle biologists have made sustained efforts to understand the global status of leatherback sea turtle populations. However, despite progress in assessments, demographics, and ecology, key uncertainties persist in tracking leatherback population trends. Trend analyses have historically focused on nesting beaches, with nest counts providing a widely used index for population abundance. Here, we analysed 20 years of annual nest abundance at four main nesting beaches (Soropta, Bluff, Playa Larga and Chiriquí) in Bocas del Toro province and the Comarca Ngäbe-Buglé, Panama, which constitute the largest nesting leatherback sea turtle population in Central America. We conducted daily nest counts during the leatherback season. We standardized the Soropta nest counts, as the survey extent varied over time. We calculated catch per unit effort (CPUE) to account for sampling effort differences. We used the Information-Theoretic approach for model selection, based on Akaike’s Information Criterion correction for small sample sizes, using linear regression to assess population trends and discrete rate of population growth (λ). Soropta exhibited a positive nesting trend (8.9% year -1 ; λ = 1.089; 1.076 – 1.10 95% CI). Bluff (- 8.8% year -1 ; λ = 0.911; 0.892 - 0.930 95% CI) and Playa Larga (- 8.3% year -1 ; λ = 0.917; 0.9045 - 0.930% CI) indicated declining nesting populations, while Chiriquí had a stable population (λ =0.993; 0.982 - 1.004 95% CI). For CPUE, the data yielded a stable population for all beaches combined (λ = 0.997; 0.995 – 0.999 95% CI). Overall, distinct nesting trends were observed at each leatherback sea turtle nesting beach. Given that females from different nesting sites mix at shared foraging grounds, this suggests that local factors may be influencing beach-specific nesting trends. The delicate balance of leatherback nesting in Bocas del Toro archipelago, along with its critical importance within the Western Caribbean, makes continuous monitoring and conservation efforts essential in this region, as well as increased protection from governmental agencies. • Conducted beach nest counts on four different beaches • Nesting population is increasing at Soropta beach • At Playa Larga and Bluff Chiriquí nesting populations appear to be declining • Population at Chiriquí beach appears to be stable • Trends are each beach appear to be drive by beach-specific factors • Population at Chiriquí beach remains one of the largest in the Southwest Caribbean sea
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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".