Brey, Thomas (2015): Global macrozoobenthos production and energy budget data base V150731, with link to database [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.848688,

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Supplement to: Brey, T (2012): A multi-parameter artificial neural network model to estimate macrobenthic invertebrate productivity and production. Limnology and Oceanography-Methods, 10, 581-589, https://doi.org/10.4319/lom.2012.10.581

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.

A data base on somatic production and further energy budget parameters of macro benthic invertebrates from fresh water and marine environments. Data have been extracted from published and grey literature sources. The data base consists of four files:

1. Benthos EBudget 150731 Data: The data base

2. Benthos EBudget 150731 Headers: Explanation of all column headers / parameters

3. Benthos EBudget 150731 Taxa: List of all taxa included in the data base

4. Benthos EBudget 150820 References: List of references