Global macrozoobenthos production and energy budget data base V150731, with link to database


Autoria(s): Brey, Thomas
Data(s)

17/08/2015

Resumo

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.

Formato

application/zip, 187.0 kBytes

Identificador

https://doi.pangaea.de/10.1594/PANGAEA.848688

doi:10.1594/PANGAEA.848688

Idioma(s)

en

Publicador

PANGAEA

Direitos

CC-BY: Creative Commons Attribution 3.0 Unported

Access constraints: unrestricted

Fonte

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

Tipo

Dataset

Palavras-Chave #