Effective prediction of biodiversity in tidal flat habitats using an artificial neural network

Accurate predictions of benthic macrofaunal biodiversity greatly benefit the efficient planning and management of habitat restoration efforts in tidal flat habitats. Artificial neural network (ANN) prediction models for such biodiversity were developed and tested based on 13 biophysical variables, collected from 50 sites of tidal flats along the coast of Korea during 1991–2006. The developed model showed high predictions during training, cross-validation and testing. Besides the training and testing procedures, an independent dataset from a different time period (2007–2010) was used to test the robustness and practical usage of the model. High prediction on the independent dataset (r = 0.84) validated the networks proper learning of predictive relationship and its generality. Key influential variables identified by follow-up sensitivity analyses were related with topographic dimension, environmental heterogeneity, and water column properties. Study demonstrates the successful application of ANN for the accurate prediction of benthic macrofaunal biodiversity and understanding of dynamics of candidate variables.

► We designed and tested prediction models for benthic macrofaunal biodiversity. ► An artificial neural network technique was used with 13 environmental variables. ► Results showed high prediction accuracies in train, cross-validation, and test datasets. ► Influential factors were topographic dimension, environmental heterogeneity, and water column properties. ► Study demonstrated the operational utility of ANN in biodiversity prediction for conservation and restoration efforts.