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| Format: | Dataset Open Access |
| Language: | en |
| Published: |
PANGAEA
2015
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| Online Access: | https://doi.org/10.1594/PANGAEA.848688 |
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| _version_ | 1867169106385436672 |
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| author | Brey, Thomas |
| author_facet | Brey, Thomas |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | 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. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_848688 |
| institution | PANGAEA |
| language | en |
| publishDate | 2015 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | Global macrozoobenthos production and energy budget data base V150731, with link to database Brey, Thomas 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. |
| title | Global macrozoobenthos production and energy budget data base V150731, with link to database |
| topic | |
| url | https://doi.org/10.1594/PANGAEA.848688 |