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A multi‐parameter artificial neural network model to estimate macrobenthic invertebrate productivity and production
Author(s) -
Brey Thomas
Publication year - 2012
Publication title -
limnology and oceanography: methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.898
H-Index - 72
ISSN - 1541-5856
DOI - 10.4319/lom.2012.10.581
Subject(s) - abiotic component , statistics , artificial neural network , biomass (ecology) , productivity , population , mathematics , production (economics) , environmental science , ecology , econometrics , biology , computer science , artificial intelligence , demography , macroeconomics , sociology , economics
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 ( r 2 = 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.