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

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).

1 Introduction

Research on energy flow, food web interactions, fishery and population dynamics and the role of biodiversity in ecosystem functioning depend on the estimates of biomass and secondary production. This broad range of studies often involves the use of weight-to-weight conversion factors for rapid assessment of required dry-weight-based metrics from less time-consuming and therefore more widely available wet-weight biomass measurements (e.g. Ricciardi and Bourget, 1998, and references therein; Gogina et al., 2020). Conversion factors are derived from subsamples to enable data standardisation and determination of dry weight for a large volume of material. If user-defined sub-selection of a database for conversion factors is possible, it can be combined with the user's own local data, instead of relying on a single average number per large region. With a growing interest in biodiversity in the second half of the last century, primarily efforts from the Baltic Sea were pioneering in publishing compilations of conversion factors for marine macro-invertebrates (Thorson, 1957; Lappalainen and Kangas, 1975; Rumohr et al., 1987); these efforts were later expanded to other geographic regions (Petersen and Curtis, 1980; Tumbiolo and Downing, 1994; Ricciardi and Bourget, 1998; Brey et al., 2010). However, though allowing general biomass estimates for many species and groups, the available widely applicable conversion factors for data standardisation remain very rough approximations of weight-to-weight relationships. For example, the global database for meio-, macro- and megabenthic biomass and densities that was recently published by Stratmann et al. (2020) includes only a small share of measured ash-free dry weights and cites only a handful of publications (including those listed above) that provide such broadly used sets of values for the corresponding conversion. This highlights the importance of the present compilation.

Figure 1 The geographical locations of sites where individuals were collected for reported measurements. Colour of symbols indicates habitats of the Baltic Sea (in red) and the North Sea (in blue). Data points may represent multiple observations at that locality. Projection: ETRS89 Lambert azimuthal equal area.

Table 1 Years of material collection and number of corresponding measurements per region included in the dataset.

Table 2 Weight-to-weight conversion factors for 29 major functional groups, differentiated by region, based on all raw values per taxa included in the group: AFDW: ash-free dry weight; WW: wet weight; DW: whole dry weight; CI: 95 % confidence interval; N: number of values; SPP: number of species (taxa) per group.

Here for the first time we publish the taxonomically most detailed and statically most robust list of ratios of wet weight (WW), dry weight (DW) and ash-free dry weight (AFDW) based on over 17 000 measurements for 497 taxa from the Baltic and the North seas (Gogina et al., 2021). All well-curated raw and aggregated data are currently stored in the open-access repository together with basic usage information. In this data description paper we describe the methods and algorithms used and provide details on metadata, structure and content of the dataset.

Our dataset can assist the studies where information on biomass has a central role by helping to more accurately translate WW into the more relevant AFDW. Data presented here are of use for a range of scientific studies, including

• i.

  facilitating spatial and temporal comparison of secondary production and energy flow in marine ecosystems

• ii.

  assessment of species contribution to ecosystem functioning, supporting the generation of empirical models and predictive mapping of ecosystem services provided by marine benthic macro-invertebrates, by ensuring the most use of best taxonomic resolution and information on biomass.

2 Data availability and usage note

All measurements are available from the Leibniz Institute for Baltic Sea Research Warnemünde (IOW) data repository: https://doi.org/10.12754/data-2021-0002-01 (Gogina et al., 2021). We have included all quality-assured measurement values without prejudice. Reporting errors and updates of the data will be done periodically. Users are encouraged to use the latest version of the dataset according to the “Related” note published in the IOW repository. This contribution is based on data release 2. There are no limitations on the use of these data.

3 Materials and methods

Macrobenthic specimens were collected over the period from 1986 to 2020 in the Baltic and the North Sea (Fig. 1 and Table 1). Following HELOCM guidelines on sampling soft-bottom macrofauna (HELCOM, 2017) most samples that were used for measurements included in the dataset were collected using a Van Veen grab or 1 m dredge (type Kieler Kinderwagen). From hard-bottom habitats samples were partly derived by divers (Beisiegel et al., 2017). Routinely, samples were stored for at least 3 months before weighing. Biomass determination was carried out separately for each taxon. All nesting species like polychaetes or hermit crabs were removed from tubes or shells. Molgula manhattensis, an ascidian species, and phoronids (represented solely by Phoronis sp.) require a special remark. As a rule, both taxa can hardly be separated from the glued grains of sand, which is why an exception has been made here. With these organisms the grains of sand were also commonly weighed in the laboratory routine. However, as desired, the AFDW only specifies the organic content, since sand and ash were deducted from that weight. The biomass of molluscs and echinoderms was measured with shells. The database only includes values based on individuals with a wet weight exceeding 0.5 mg. The dry weight was estimated after drying the formalin material at 60 ^∘C to a constant weight (for 12–24 h, or longer, depending on material thickness). After the determination of dry weight, ash-free dry weight was measured following incineration at 500 ^∘C in a muffle furnace until weight constancy was reached. AFDW is recommended as the most accurate measure of biomass (Rumohr et al., 1987). Species nomenclature has been standardised in line with the World Register of Marine Species (WoRMS Editorial Board, 2021). The database is continuously enlarged, with the main efforts targeted to obtain a sufficient number of measurements for reliable estimates and to cover as many characteristic species per region as possible (Table 2). The groups used in the dataset in order to facilitate the summary should be rather regarded as functional, i.e. not strictly taxonomic, as they vary in rank ranging from the phylum to the order level. A word of caution should also be given regarding mean and confidence interval values reported in Table 2, calculated using the R package “DescTools” (Signorell, 2021) in R (R Core Team, 2021). Here we display the results based on all values of raw measurements of factors for all taxa included in the group. Alternatively, depending on the aims and desired summary level, users are facilitated to obtain from the dataset mean values of conversion factors per group based on mean values per taxon included in the group, thereby avoiding overweighting the reported statistics by dominant species, typically represented by a high number of measurements.