4.7 Article Data Paper

Weight-to-weight conversion factors for benthic macrofauna: recent measurements from the Baltic and the North seas

Journal

EARTH SYSTEM SCIENCE DATA
Volume 14, Issue 1, Pages 1-4

Publisher

COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/essd-14-1-2022

Keywords

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Funding

  1. BMBF project MGF-Ostsee [03F0848A]

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The availability of standardised biomass data is crucial for studying population dynamics, energy flows, fisheries, and food web interactions. This study presents the most detailed and statistically robust list of wet weight (WW), dry weight (DW), and ash-free dry weight (AFDW) ratios. The dataset, consisting of over 17,000 records for 497 taxa, provides an opportunity for reuse and repurposing with reference information. It allows users to combine data with their own local data, quantify natural variability and uncertainty, and can be accessed through an unrestricted repository.
The availability of standardised biomass data is essential for studying population dynamics, energy flows, fisheries and food web interactions. To make the estimates of biomass consistent, weight-to-weight conversion factors are often used, for example to translate more widely available measurements of wet weights into required dry weights and ash-free dry weight metrics. However, for many species and groups the widely applicable freely available conversion factors have until now remained very rough approximations with high degree of taxonomic generalisation. To close up this gap, here for the first time we publish the most detailed and statically robust list of ratios of wet weight (WW), dry weight (DW) and ash-free dry weight (AFDW). The dataset includes over 17 000 records of single measurements for 497 taxa. Along with aggregated calculations, enclosed reference information with sampling dates and geographical coordinates the dataset provides a broad opportunity for reuse and repurposing. It empowers the future user to do targeted sub-selections of data to best combine them with their own local data, instead of only having a single value of conversion factor per region. The dataset can thereby be used to quantify natural variability and uncertainty. The dataset is available via an unrestricted repository from https://doi.org/10.12754/data-2021-0002-01 (Gogina et al., 2021).

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