A multi‐parameter artificial neural network model to estimate macrobenthic invertebrate productivity and production

Abstract

I developed a new model for estimating annual production‐to‐biomass ratio P/B and production P of macrobenthic populations in marine and freshwater habitats. Self‐learning artificial neural networks (ANN) were used to model the relationships between P/B and twenty easy‐to‐measure abiotic and biotic parameters in 1252 data sets of population production. Based on log‐transformed data, the final predictive model estimates log(P/B) with reasonable accuracy and precision (r2 = 0.801; residual mean square RMS = 0.083). Body mass and water temperature contributed most to the explanatory power of the model. However, as with all least squares models using nonlinearly transformed data, back‐transformation to natural scale introduces a bias in the model predictions, i.e., an underestimation of P/B (and P). When estimating production of assemblages of populations by adding up population estimates, accuracy decreases but precision increases with the number of populations in the assemblage.