Document of bibliographic reference 287827

BibliographicReference record

Type

Bibliographic resource

Type of document

Journal article

BibLvlCode

AS

Title

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.

WebOfScience code

https://www.webofscience.com/wos/woscc/full-record/WOS:000309347600002

Bibliographic citation

Brey, T. (2012). A multi-parameter artificial neural network model to estimate macrobenthic invertebrate productivity and production. Limnol. Oceanogr., Methods 10(8): 581-589. https://dx.doi.org/10.4319/lom.2012.10.581

Is peer reviewed

true

Access rights

open access

Is accessible for free

true