Species Distribution Model Predictions of the Critically Endangered Grey Nurse Shark in Australia

Species distribution models (SDMs) are commonly used to forecast how threatened species are influenced by climate change. The grey nurse shark ( Carcharias tauras ) is a critically endangered species inhabiting both the east and west coasts of Australia, with negligible genetic interchange between the two populations. I used Generalized Linear Models (GLM), Maximum Entropy (MaxEnt) models and Boosted Regression Trees (BRT) to predict the distribution of the grey nurse shark. The data were a sample of presence-only data, derived from the known grey nurse shark sighting locations, from the east coasts of Australia, with pseudo-absences generated and bootstrapped from a restricted background. I verified these models using leave-one-out cross validation and model metrics including AICc, BIC, percentage of deviance explained, leave-one-out cross-validated R 2 , AUC, maximum Cohen’s Kappa, specificity and sensitivity. Cross-validated R 2 was used as an overall comparison method across model types. I performed out-of-source validation by comparing model projection with the distributional range of the ragged tooth shark ( Carcharias taurus ) in South Africa. The prediction of the selected model was consistent with the current distributional range of the ragged tooth shark.