AN ENVIRONMENTAL PREDICTIVE MODEL FOR MODERN AND ANCIENT TERRESTRIAL ECOSYSTEMS USING LAND SNAIL SHELLS
Network models were developed to reconstruct MAT and MAP from the input variables; i) land nail species, ii) terrain elevation where the shells were collected, iii) the S or N slope of the island, iv) the shell δ18O value, and v) the shell δ13C value in the snail shells. We used 186 stable isotope snail data to develop the models, and 20 data to validate them. The best model to predict MAT had 5 input variables, eleven neurons in the intermediate layer, and one neuron in the output layer. The average values for both phases (training and validation) for correlation coefficient (R2) and Root Mean Square Error (RMSE) were 0.9999 and 0.0 oC, and an Average Percentage Deviation (APD) of 0.05%. The best neural network models developed for MAP has a topology of 5 input neurons, ten neurons in the intermediate layer and one neurons in the output layer. These models presented an average R2 of 0.9999, with a RMSE of 0.5 mm. and an APD of 0.08%.
Our results suggest that ANN modeling using stable isotope compositions of land snail shells can be used to predict accurately average environmental conditions.