Exploring gaps, biases, and research priorities in the evidence for reptile conservation actions
Bibliographic record
Abstract
With over 21% of reptile species threatened with extinction, there is an urgent need to ensure conservation actions to protect and restore populations are informed by relevant, reliable evidence. We examined the geographic and taxonomic distribution of 707 studies that tested the effects of actions to conserve reptiles synthesized in Conservation Evidence's Reptile Conservation synopsis. More studies were conducted in countries with higher gross domestic product per capita, more reptile species, and higher proportions of threatened reptile species. Studies were clustered in the United States (43%) and Australia (15%), and no studies were conducted in large parts of Southeast Asia, South America, and sub-Saharan Africa. Taxonomically, 47% of 90 reptile families (mostly Squamata) were not studied at all. Although Squamata and Testudines species featured in approximately 50% of studies, 7 of the 10 most-studied reptiles (constituting 36% of studies) were turtles or tortoises, and there were significantly more studies per species on Testudines than Squamata. There were also significantly more studies on species: classified as least concern (as opposed to all other International Union for Conservation of Nature categories apart from near threatened); not categorized as endemic or insular; with more Wikipedia page views; and lacking data on venomousness. There was no significant relationship between the number of studies and the evolutionary distinctiveness or body mass of species. Our results highlight pressing evidence needs, particularly for underrepresented regions and threatened and data-deficient species (e.g., evolutionarily distinct and globally endangered reptiles in South America, sub-Saharan Africa, and Southeast Asia). To overcome evidence gaps and a lack of basic ecological data, future work should explore how the effects of actions transfer across taxa and regions. We call for greater efforts to coordinate and increase testing and reporting in a strategic manner to inform more effective and efficient conservation actions globally.
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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 | 0.409 | 0.598 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.032 | 0.021 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".