Data sources
Data for the trophic parameters and data categories listed in Tables 1 and 2 were gathered from peer-reviewed scientific publications, grey literature (e.g., agency reports, theses, and dissertations) and unpublished data by the authors of this paper. Data compilation on stomach contents, stable isotopes, FATM, and trophic positions, focussed on mesopelagic organisms, their potential prey and predators. For major and trace elements, energy density and estimates of diet proportions, our search concentrated on mesopelagic taxa. Nevertheless, we also gathered information from small or intermediate-sized epi-, bathy- or benthopelagic species found in the compiled data sources. These species were included because they play key roles in most marine ecosystems, both as important consumers of phytoplankton and zooplankton, and prey for many top predators, and can represent alternative energy pathways to mesopelagic organisms. However, we stress that the data coverage for these species in the current version of the database is very incomplete. Our main interest was on data from the central and eastern North Atlantic, and the Mediterranean, corresponding to the study regions of the SUMMER project. When we could not find suitable data within this region, we extended the geographic scope of our literature search to the western North Atlantic. We did not search for datasets in open access repositories since those data can be easily accessed and extracted. However, some of the data provided by the authors of this paper have been previously deposited in PANGAEA.
DNA sequencing-based methods, such as metabarcoding and direct shotgun sequencing, are emerging as promising tools in dietary analyses due to the high resolution in taxonomic identification of many prey simultaneously, and the potential to provide quantitative diet estimates from relative read abundance29. However, recent studies have shown that various methodological and biological factors can break the correlation between the number and abundance of ingested prey and the prey DNA present in the sample, and lead to biased estimates of taxonomic diversity and composition of diet29,30. Given the uncertainties remaining in the interpretation of DNA sequencing-based diet data, we decided not to include these data in MesopTroph until additional research demonstrates that these techniques can be confidently applied for quantitative diet assessment.
We identified available data sources in the literature through systematic searches on Web of Science, Google Scholar, ResearchGate, and the Google search engine. We used multiple combinations of terms related to specific data categories (Table 3), in conjunction with the common or scientific taxon names (from genus to order), and the ocean basin. For example, the search for stomach content data of fishes belonging to the family Myctophidae was undertaken using the following terms: “stomach content” OR “gut content” OR “prey composition” OR “diet composition”, AND “mesopelagic fish” OR “myctophid” OR “Myctophiformes” OR “Myctophidae”, AND “Atlantic” or “Mediterranean”. For the mesopelagic and predator species known to be numerically abundant in the SUMMER study regions, we performed a second literature search using the common or scientific name of the species, along with the terms “diet”, “feeding habits”, “trophic ecology”, “trophic markers”, or “food web”. We also examined the literature cited within each collected publication to locate additional data sources.
We next screened the full text of the compiled studies and retained data sources that: (1) were collected within the region of interest, (2) reported quantitative data for the trophic parameters of interest, (3) reported the number of samples for pooled or aggregated data, and (4) provided sufficient details on the methodology to enable a quality check. In the case of stable isotope data, we only included data sources reporting both δ13C and δ15N measurements.
Data extraction, cleaning, and formatting
We created a template table for each data category in Microsoft Excel to assemble all datasets into a single file, and to facilitate cleaning and standardization of data records. We added a large number of metadata fields to the tables to annotate details about the sampling (e.g., location, date, methods), sampled specimen(s) (e.g., taxonomy, number and size of individuals, number of replicates, tissue analysed), and data source (e.g., full reference, DOI) for every record.
Data contributors formatted and incorporated their datasets directly into the tables. For published sources, the data and associated metadata were extracted manually or digitized from the article text, tables, or supplementary material into the tables. Extraneous or hidden characters, and values such as “NA” (Not Available) or “ND” (Not Determined), were deleted from the parameter and metadata fields. Measurements of trophic parameters were standardized to the same units (see Tables 1 and 2). Parameter values that were clearly incorrect (e.g., δ15N > 20, or the frequency of occurrence of a prey higher than the number of stomachs sampled) were corrected by searching for the value within the data source. When values could not be corrected, we deleted that data record.
When available, we extracted information at the individual level. However, most studies reported data obtained from pooled samples of the same species. In some cases (e.g., small specimens such as planktonic organisms), a minimum and maximum number of individuals in the sample was provided instead of the actual number of individuals sampled. We added two columns to the tables presenting the minimum and maximum number of individuals in the sample. By filtering the column “Ind No (maximum per sample)” for values >1, users can easily identify records with aggregated data and differentiate them from records where information was drawn from a single individual (i.e., where “Ind No (maximum per sample)” =1). In addition, the tables Stomach contents and Estimates of diet proportions include a field “Sample ID” with a unique identifier of the sample. If data are reported at the individual level (i.e., “Ind No (maximum per sample)” =1) then Sample ID is the individual animal ID. If the data are from a group of individuals (i.e., “Ind No (maximum per sample)” >1), then Sample ID identifies that group.
We standardized the taxonomic classification and nomenclature of fishes and elasmobranchs following the Eschmeyer’s Catalog of Fishes (http://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp)31,32. For the remaining taxa, we used the World Register of Marine Species (http://www.marinespecies.org/)33. Unaccepted or alternate taxon names were replaced by the most up-to-date valid name. When the identification of a taxon was uncertain, the taxonomic level of identification was decreased to a satisfactory level. For example, prey reported as “Cephalopods” were changed to “Cephalopoda”, “Sepiolids” to “Sepiolidae”, and “Myctophum punctatum?” to the genus “Myctophum”.
Stomach contents
Stomach contents analysis is a standard dietary assessment method that potentially enables quantifying diet components with high taxonomic resolution34. Three parameters are typically used to describe diet composition from stomach contents: the number of individuals of a prey type as a proportion of the total number of prey items (%N), the proportion of a prey item by weight or volume (%W), and the proportion of stomachs containing a particular prey item (i.e., percent frequency of occurrence, %F)35. When available, we collected data on the three parameters, as well as on the absolute number, weight, and frequency of occurrence of each prey type in the stomachs of each sampled individual or group of individuals. If stated in the data source, we indicate if prey weights were directly measured or reconstructed from hard remains (fish otoliths and vertebrae, cephalopod beaks), and if they represent dry or wet weight. Some datasets contained records of prey items without corresponding weights or numbers. As a result, the cumulative percent of all prey items did not sum to 100%. This occurred in 11 data records for the cumulative %W, and nine for the cumulative %N. While we checked the accuracy of percentage values and adjusted rounding errors, we did not attempt to fill in missing values nor did we remove records with missing values. When prey values were reported by an upper bound (e.g., “<0.01”), we assigned a value of half of the upper bound to that record and the percent of all prey items in that sample were rescaled to add to 100. Prey types were recorded at the highest taxonomic resolution.
Stable isotopes
Bulk stable isotope ratios, primarily of carbon (δ13C) and nitrogen (δ15N), are increasingly used to examine predator-prey interactions and food web structure. Moreover, advances in isotopic mixing models allow the conversion of isotopic data into estimates of dietary contribution from different food sources34,36. We compiled δ13C and δ15N measured in specific tissues or in the whole body of individual organisms, or mean values (and standard deviations) for pooled samples. When available, the total organic carbon to nitrogen ratio (C/N) was also collected. Because lipids have more negative δ13C values relative to other major biochemical compounds in plant and animal tissues37, many studies correct for the lipid effect by extracting lipids from samples before analysis, or a posteriori, through mathematical corrections38. We searched the original data source to understand how the lipid effect was handled and separately report δ13C and C/N values for untreated and delipidized samples, as well as values that were mathematically-corrected.
Major and trace elements
Organisms accumulate major and trace elements (including metals) directly from the external environment and/or indirectly through diet. As such, their elemental composition can help to infer dietary preferences, solve trophic links, and inform quantitative dietary analysis primarily based on stable isotopes or fatty acids39,40. We entered the individual concentrations (or mean concentrations, for samples with more than one individual) of all elements reported in a study into separate columns. We also collected the moisture content of the samples analysed (expressed in percentage). A column indicates whether concentrations are expressed on a dry mass (weight of the animal tissue after being dried) or wet mass (weight of the animal tissue containing water) basis, to allow the conversion between both types of data if necessary (using the moisture percentage).
Energy density
Information on the energy density of prey is critical for estimating food requirements and consumption by predators, and modelling energy flux through food webs41. We collected data from studies that measured energy density directly by bomb calorimetry methods, and from studies that measured the proximate composition (i.e., the percentage of proteins, lipids and carbohydrates) of sampled tissues and converted these percentages into energy using combustion equivalents reported in the literature. When available, we collected energy density (or mean density, for samples with more than one individual) as a function of both dry and wet mass. When reported, the moisture percentage of samples was included to enable converting energy density between dry and wet mass.
Fatty acid trophic markers (FATM)
Fatty acid (FA) analyses have been long used to qualitatively describe resource use, by tracing distinctive FA signatures of organisms into the lipids of consumers. More recently, quantitative fatty acid signature analysis and Bayesian models are emerging as powerful techniques to estimate the proportional contribution of different resources to consumer’s diet34. The FATM table compiles the proportion of each FA measured in sampled tissues or in the whole body of organisms in relation to total FAs analysed. For pooled samples, we collected the mean and standard deviation (when available) of the percentage FA. In all data records, we included the full range of FAs with values above 0.1% but excluded FAs that were measured together. Care should be taken, however, since these percentages are not absolute but are affected by the number and particular mix of FAs analysed in each study42.
Trophic positions
Trophic position (TP) is a continuous measure of the position of a species in the flow of energy from the bottom to the top of a food web, and estimation of TP is fundamental to the analysis of food webs43. TP has traditionally been estimated using the relative contribution of prey to a consumer’s diet based on stomach content analysis, but isotope analysis (e.g., bulk tissue or compound-specific) is increasingly used to estimate TP, based on the progressive enrichment in heavy isotopes (mainly 15N) from preys to consumers38. We collected TP derived both from stomach content and stable isotope analyses, specifying the type of analysis under the field “Method”. When reported in the original data source, we also present information on the type of models (e.g., additive model with constant isotopic enrichment (TPA), isotopic mixing model, mass-balanced trophic model), baseline taxa, and trophic enrichment factors used to estimate TP44,45.
Estimates of diet proportions
Advances in analytical and statistical techniques now enable quantitative estimation of diet from biochemical tracers such as bulk or compound-specific stable isotopes, fatty acids, or trace elements. Linear or Bayesian mixing models use the biotracer composition (e.g., isotopic composition, fatty acid profiles) of a mixture (i.e., consumer) to estimate the relative proportions of the different sources (i.e., prey) in that mixture36,46. We collected diet proportions (i.e., the relative proportion of a prey item to the diet of a consumer) of small mesopelagic taxa derived from isotope mixing models. We focused on these taxa because their small and soft‐tissued prey are often difficult to identify and quantify through visual methods. In the future, we expect to add estimates of diet proportions from ongoing analysis of other biochemical tracers, and for a wider range of consumers. As such, the table includes columns to specify the type of analysis and models used under “Method” and “Method comment”, respectively.
Metadata
For each data record, we retrieved the sampling location as provided in the source, and geographic coordinates. If coordinates were not reported, the midpoint coordinates of the study area or of data records was determined and appended to the data record. All geographic coordinates were converted to decimal degrees. We categorized the study locations into seven different regions: Mediterranean (when the location within the basin was not specified), Eastern Mediterranean, Western Mediterranean, Atlantic-tropical, Atlantic-temperate, Atlantic-subarctic, and Atlantic-Arctic. Most data sources did not provide the precise date for the collection of each sample, and usually data collection spanned over months or years; thus, the first and last months and years of the sampling were annotated. Some data sources did not provide sufficient details to complete all metadata fields, but we still extracted the trophic parameters. In the current version of MesopTroph, sampling location is missing for seven data records (0.1% of total), sampling year for 218 (4.2%), and sampling month for 748 (14.5%).
We added the phylum, class, order and family for every taxon. Finally, we categorized each taxon as “Mesopelagic”, “Non-mesopelagic” or “Unknown”. Mesopelagic taxa include fish, cephalopods, crustaceans, and gelatinous organisms inhabiting the mesopelagic zone (200–1000 m depth)47,48,49. Non-mesopelagic taxa encompass benthic and pelagic organisms that do not use the mesopelagic zone, as well as elasmobranchs, marine mammals, seabirds and marine turtles. Data records where the taxon was identified only to higher taxonomic ranks that include both mesopelagic and non-mesopelagic organisms were classified as Unknown.
Source: Ecology - nature.com
