Overcoming data deficiency in reptiles

- We use random forests to predict the extinction risk of Data Deficient reptiles.
- We find that 19% of Data Deficient reptiles are likely to be threatened.
- Global patterns of threatened species richness are robust to data deficiency.
- New information from the Global Reptile Assessment will improve risk models.

We have no information on the risk of extinction of 21% of reptiles listed as Data Deficient on the Sampled Red List Index (SRLI), an indicator developed to track global change in species status. Data Deficient species are of high research priority, because they contribute to uncertainty in estimates of extinction risk and are neglected by conservation programmes. We review the causes of data deficiency in reptiles; the likely status of Data Deficient reptiles; and possible solutions for their re-assessment. We find that 52% of Data Deficient reptiles lack information on population status and trends, and that few species are only known from type specimens and old records. We build a random forest model for SRLI species of known extinction risk, based on life-history, environmental and threat information. The final model shows perfect classification accuracy (100%) in ten-fold cross validation. We use the model to predict that 56 of 292 Data Deficient reptiles (19%) are at risk of extinction, so the overall proportion of threatened reptiles in the SRLI (19%) remains unchanged. Regions predicted to contain large numbers of threatened Data Deficient reptiles overlap with known centres of threatened species richness. However, the model shows lower accuracy (79%) on 29 species recently re-assessed in the Global Reptile Assessment. Predictive models could be used to prioritize Data Deficient species and reptiles not included in the SRLI, and new reptile assessments could be used to improve model predictions through adaptive learning.