DOI: 10.5061/dryad.2547d7wts
title: Avoiding growing pains in reproductive trait databases: the curse of dimensionality

description: Aim: Reproductive output features prominently in many trait databases, but
 the metrics describing it vary and are often untethered to temporal- and
 volumetric-dimensions (e.g., fecundity-per-bout). Using such ambiguous
 reproductive measures to make broadscale comparisons across taxonomic
 groups will only be meaningful if they show a 1:1 relationship with a
 reproductive measure that explicitly includes both a volumetric and
 temporal component (i.e., reproductive mass-per-year). We sought to map
 the prevalence of ambiguous and explicit reproductive measures across
 taxa, and explore their relationships with one another to determine the
 cross-compatibility and utility of reproductive metrics in trait
 databases. Location: Global. Time period: 1990-2021. Major taxa studied:
 We searched for reproductive measures across all Metazoa, and identified
 19,785 Chordata species, along with 440 species of Arthropoda, Cnidaria,
 or Mollusca. Methods: We included 37 databases from which we summarised
 the commonality of reproductive metrics across taxonomic groups. We also
 quantified scaling relationships between ambiguous reproductive traits
 (fecundity-per-bout, fecundity-per-year and reproductive mass-per-bout)
 and an explicit measure (reproductive mass per-year) to assess their
 cross-compatibility. Results: Most species were missing at least one
 temporal or volumetric dimension of reproductive output, such that
 reproductive mass-per-year could be reconstructed for only 4,786
 vertebrate species. Ambiguous reproductive measures were poor predictors
 of reproductive mass-per-year – in no instance did these measures scale at
 1:1. Main Conclusions: Ambiguous measures systematically misestimate
 reproductive mass-per-year. Until more data are collected, we suggest
 authors use the clade-specific scaling relationships provided here to
 convert ambiguous reproductive measures to reproductive mass-per-year.
  
description:   Methodology overview (see paper for full
 methods): We followed the
 guidelines of the Systematic Mapping Methodology (James et al., 2016) to
 determine what reproductive traits are provided in animal databases, and
 to quantify their relationships with one another.
 We
 searched the literature from 2020 to 2021 – we conducted the final search
 in October 2021. First, to identify literature to screen, we trialled a
 combination of search terms in the publication database, Web of Science.
 After trialling 16 search terms, we selected the search term ‘(phylum) AND
 (life history* OR trait) AND (database* OR compil*) NOT (plant*)’, where
 phylum was substituted each time with each metazoan phyla. These search
 terms resulted in a total of 28,028 hits.  To identify the
 eligible databases returned by our literature searches (as previously
 described), we sorted the titles by ‘relevance’, and screened titles and
 abstracts of the first 500 hits of each phylum-specific search term. If
 studies appeared to match the inclusion criteria, they were marked and
 uploaded into the Rayyan Systematic Reviews web application (Ouzzani et
 al., 2016), where each study and its associated database was fully
 reviewed. In total, 3,410 titles and abstracts were reviewed in Web of
 Science, and 240 studies were fully reviewed. Studies were sorted into two
 groups, ‘include’ or ‘exclude’, after fully assessing them; a list of
 assessed articles can be found in supplementary material. Screening and
 eligibility assessments reduced the number of eligible databases to
 42. 
 We coded 42 databases into Microsoft Excel (version 16.46),
 noting the 1) reference information, 2) species information, and 3) trait
 data. For each species in each online database, we coded the species name
 and the following reproductive traits (when available): adult body size
 (mass or length), fecundity measure (fecundity.bout-1, fecundity.time-1,
 reproductive mass.bout-1, or reproductive mass time-1), offspring size
 (mass or length), and reproductive frequency (number of reproductive
 events time-1). We also extracted the numeric values for each trait to
 explore relationships between ambiguous and explicit reproductive
 measures. When not reported directly, we used different reproductive trait
 combinations to calculate unreported reproductive traits. For example,
 fecundity as a rate (i.e., fecundity.time-1) was calculated by multiplying
 fecundity.bout-1 and reproductive frequency. Note that ‘time-1’ can refer
 to ‘year-1’ or ‘day-1’, but can be easily converted into a common
 currency. We converted all measures of fecundity.time-1 and reproductive
 mass.time-1 to a yearly rate, and refer to these hereafter as
 fecundity.year-1 or reproductive mass.year-1, respectively, unless
 otherwise specified. Additionally, only offspring mass (and not offspring
 length) was used to calculate reproductive mass (bout-1 and year-1), as we
 wished to minimize error that can occur from using length-to-mass
 conversions that are not species-specific. After recording the
 observations contained within all eligible databases into a single Excel
 file, we used R studio (version 1.4.1106) (R Core Team 2018) and
 ‘tidyverse’ packages (version 1.3.1) (Wickham et al., 2019) to summarise
 and combine duplicate species observations. In some instances, multiple
 sources are listed for each species because one trait may have been
 identified in one database, but not another – that is, traits reported
 with multiple dimensions (e.g., fecundity.bout-1 and fecundity.year-1) for
 the same species could have originated from different sources. When we
 found the same species had duplicate trait observations across multiple
 databases (e.g., fecundity.bout-1 was reported twice for the same
 species), we defaulted to the oldest database and removed duplicate
 observations from the more recent database(s). We omitted duplicate
 observations to avoid biases in our assessment of the commonality of
 reproductive measures, given that species from well-represented taxa were
 found across multiple databases. However, if users are interested in
 combining multiple trait estimates (e.g., to obtain mean trait values for
 these species), they can refer to the original databases in Appendix 1 and
 our supplemental material. After duplicate species were removed, our final
 database included observations of reproductive traits for 20,225 species
 from 37 studies.   James, K.L.,
 Randall, N.P. & Haddaway, N.R. (2016) A methodology for systematic
 mapping in environmental sciences. Environmental Evidence, 5, 7.
 Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L.,
 François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M.,
 Pedersen, T., Miller, E., Bache, S., Müller, K., Ooms, J., Robinson, D.,
 Seidel, D., Spinu, V. & Yutani, H. (2019) Welcome to the
 Tidyverse. Journal of Open Source Software, 4, 1686.
description: Data are .csv and non-proprietary.