Comparing modeled predictions of coral reef diversity along a latitudinal gradient in Mozambique

Introduction Predictive models based on environmental proxy data are being used to predict biodiversity on large and even global scales. Yet, some of the underlying assumptions about the relationship between proxy variables and predictions require investigations and testing the consequences of using model alternatives, data sources, variables choices, and scales, extent, and overlap among the predictions. Mozambican coral reefs provide a good case study to test these assumptions given the paucity of field data, its long coastline, and transitions from tropical to temperate environments. Methods Three modelling formulations and 5 specific models were made using satellite and shipboard measurements and extensive fish and corals field data to test their performance in predicting numbers of fish species and coral taxa from field data. Model predictions were mapped for the 1180 ~6.25 km 2 Mozambican coral reef cells. Predictions were made and mapped 1) based on ~1000 field sites in the Western Indian Ocean (WIO) faunal province model, 2) using environmental variable selected in the WIO model (WIOMOD) but trained only with Mozambican field data (113 sites), and 3) using only Mozambican environmental and field data and standard variable redundancy and selection procedures. Results and discussion Training and testing cross validation of models indicated modest predictive ability (R 2 ~0.42-0.56%) and reasonable transferability. Consequently, there was unexplained variation likely due to small-scale environmental variability finer than the mapped cell scale. Differences between model predictions were caused by different variable rankings and response relationship. For example, the Mozambique-only model predicted more fish but fewer coral taxa, a larger role of water quality and sediments, habitats, and temperature variation, and a lesser role of human influence than the WIOMOD. Therefore, differences between models indicate that large scale models (i.e. provincial or global) can contribute to understanding gross patterns but miss important local environmental and human drivers in transitional environments. Nevertheless, 79% of the fish and 88% of coral taxa cell-level predictions of taxonomic diversity had standardized coefficients of variations of 10%.