Murky waters: Assessing the vulnerabilities of Indo‐West Pacific non‐marine elasmobranchs to inform future conservation planning priorities
Bibliographic record
Abstract
Abstract Globally, freshwater environments are imperilled, with freshwater vertebrate species declining at twice the rate of marine and terrestrial populations. Non‐marine elasmobranchs (freshwater obligates and euryhaline generalists) remain understudied and overlooked by conservation efforts. This study aimed to adapt and apply a vulnerability assessment framework to understand the conservation priorities of Indo‐West Pacific non‐marine elasmobranch species. An exposure sensitivity adaptability (ESA) framework was used to assess vulnerability to environmental threats, and an exposure susceptibility productivity (ESP) framework was used to assess vulnerability to fisheries. Resulting species vulnerabilities were categorized into three conservation priority tiers. The general patterns of conservation priority tiering were as follows: (i) large‐bodied euryhaline species occurring in densely populated nations had the highest ESA and ESP vulnerabilities; (ii) freshwater obligates also had high ESA vulnerability rankings, although ESP vulnerability rankings were lower as their smaller body sizes suggest increased population productivity and higher potential for resilience; and (iii) euryhaline species with large range proportions in northern Australia had moderate to low vulnerability rankings across ESA and ESP assessments, as these species benefit from reduced fisheries mortality compared with species occurring in other regions. The outcomes from the vulnerability assessment framework for the conservation priority rankings of species corresponded with their respective International Union for Conservation of Nature (IUCN) Red List status, whereby priority 1 and 2 species also have elevated extinction risks. Environmental threats were at high or moderate levels in all nations assessed, while Cambodia, China, Malaysia, and Myanmar face the highest pressure from inland fisheries. The major knowledge gaps identified included species‐specific productivity estimates, population dynamics (population movements and habitat requirements), and information on mortality from the threats considered. The present ESA–ESP framework was effective for the broad and data‐poor context of Indo‐West Pacific non‐marine elasmobranchs, and the results will be useful for guiding future conservation planning for high‐priority species and nations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".