Devil in the details: growth, productivity, and extinction risk of a data-sparse devil ray
Bibliographic record
Abstract
Abstract Devil rays ( Mobula spp.) face rapidly intensifying fishing pressure to meet the ongoing international trade and demand for their gill plates. This has been exacerbated by trade regulation of manta ray gill plates following their 2014 CITES listing. Furthermore, the paucity of information on growth, mortality, and fishing effort for devil rays make quantifying population growth rates and extinction risk challenging. Here, we use a published size-at-age dataset for a large-bodied devil ray species, the Spinetail Devil Ray ( Mobula japanica ), to estimate somatic growth rates, age at maturity, maximum age and natural and fishing mortality. From these estimates, we go on to calculate a plausible distribution of the maximum intrinsic population growth rate ( r max ) and place the productivity of this large devil ray in context by comparing it to 95 other chondrichthyan species. We find evidence that larger devil rays have low somatic growth rate, low annual reproductive output, and low maximum population growth rates, suggesting they have low productivity. Devil ray maximum intrinsic population growth rate ( r max ) is very similar to that of manta rays, indicating devil rays can potentially be driven to local extinction at low levels of fishing mortality. We show that fishing rates of a small-scale artisanal Mexican fishery were up to three times greater than the natural mortality rate, and twice as high as our estimate of r max , and therefore unsustainable. Our approach can be applied to assess the limits of fishing and extinction risk of any species with indeterminate growth, even with sparse size-at-age data.
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".