DOI: 10.5061/dryad.djh9w0w29
title: The FloRes Database: A floral resources trait database for pollinator habitat-assessment generated by a multistep workflow

description: Background The decline of pollinating insects in agricultural landscapes
 proceeds due to intensive land use and the associated loss of habitat and
 food sources. The feeding of those insects depends on the spatial and
 temporal distribution of nectar and pollen as food resources. Hence, to
 protect insect biodiversity a spatio-temporal assessment of food quantity
 of their habitats is necessary. Therefore, sufficient data on traits of
 floral resources are required. New information Because floral resources’
 traits of plants are important to quantify food availability, we present
 two databases, the FloRes Database (Floral Resources Database) and the raw
 database, where FloRes was derived from. Both databases contain the plant
 traits (1) flowering period, (2) floral-unit density per day, (3) nectar
 volume per floral unit per day, (4) sugar content per floral unit, (5)
 sugar concentration in nectar, (6) pollen mass or volume per floral unit
 and per day, (7) protein content of pollen and (8) corolla depth. All
 traits are sampled from literature and online databases. The raw database
 consists of 702 specified plant species, 138 unspecified species 37
 species (spec., sp), 22 species pluralis (spp) and for 79 only the genus
 was identified) and two species complexes (agg.). Those 842 taxa belong to
 488 genera and 102 families. Finally, only 27 taxa have a complete set of
 traits, too less for a sufficient assessment of spatio-temporal
 availability of floral food resources. Because information of floral
 resources is scattered throughout many publications with different units,
 we also present our multistep workflow implemented in five consecutive
 R-scripts. The multistep workflow standardizes the trait units of the raw
 database to comparable entities with identical units and aggregates them
 on a reasonable taxonomic level into the second application database, the
 FloRes Database. Finally, the FloRes Database contains aggregated
 information of traits for 42 taxa and, when corolla depth is excluded, for
 70 taxa. This is the first attempt to gather these eight traits from
 different literature sources in one database with a multistep workflow.
 The publication of the multistep workflow enables the users to extend the
 FloRes Database on their own demands with other literature data or newly
 gathered data to improve the quantification of food resources. Especially,
 the combination of pollen, nectar, and open flowers per square meter is,
 as far as we know, a novelty. The FloRes Database can be used to evaluate
 the quantity of food-resource habitats available for pollinators, e.g., to
 compare seed mixtures of agri-environmental measures, such as flower
 strips, considering flower phenology on a daily basis.
description: <strong>Collection of raw data</strong>
 We collected data for eight floral traits (Table 1) from 34
 published articles and their supplementary materials as well as from two
 books, reports, and dissertations each, and from an online database
 (“References_of_raw_data.pdf”, Dryad repository in folder attachment). For
 cultivar plants, the traits were sampled mainly in field experiments. For
 wild flowers, the entities were recorded either in natural, semi-natural
 habitats or botanical gardens. Most research was done in Europe,
 especially Poland and England, though some information came from a
 Northern America database (see “Geographic_Information_raw_data.pdf”,
 Dryad repository in folder attachment). For the
 quantitative traits, we gathered, minimum, maximum, and mean values, if
 available. With the traits 'pollen', 'nectar volume',
 'sugar per flower', and 'flower' or
 'inflorescence density', we recorded the flower unit they
 referred to, i.e., either per single flower or per inflorescence. The
 reference flower unit is very important for scaling nectar volume, nectar
 sugar content, and pollen to the same flower unit, enabling the merging
 and aggregation of trait data from different sources. Furthermore, the
 nomenclature of species varied in the literature. So, we equalized the
 species names in our database in column 'species' in our
 database, but we also included the names used in the original publications
 to facilitate joins and backtrackings with the data source (column
 'species_name_reference' in our database).
 <strong>Data preparation and multistep workflow
 </strong> We compiled the FloRes Database so as
 to include as many species/ taxa with a complete set of traits as possible
 through a multistep workflow in R 4.1.0 (R Core Team 2021)
 using five consecutive scripts: We
 converted flowers and inflorescences per square meter as well nectar and
 pollen per flower or inflorescence to the level of floral units using
 Formula 1 and 2 (Script: 1_Inflorescences.R). This step requires the
 dataset "AgriLand_FlowerDensity_perspecies.csv" of
 <em>Baude et al.
 (2015)</em>. We converted trait
 values to the same physical units for each trait and calculated missing
 trait values from other traits using Equations 3 to 5 and 7 to 9 (Script:
 2_units.R). We took the means (except for flowers per
 square meter where we used the maximum) of multiple trait entries for each
 species (Script: 3_Aggregate_species.R). We either
 unified synonymous species names or grouped species on a reasonable
 taxonomic level (taxon) for the next step to combine and aggregate the
 plant species. Further, we deleted those with few entries (Script:
 4_Selecting_taxa.R). The grouping of species is given in the required
 auxiliary file “Taxa_to_aggregate.csv”, which can be edited.
 We calculated the means of the traits of the synonymous species
 and repeated, now with the more complete dataset, the derivation of traits
 from other traits using Equations 4 and 6 (Script:
 5_Aggregation_selected_taxa.R). The first
 and second scripts were used to convert the data to equal units, whereas
 scripts three to five were used to aggregate and combine the trait data on
 the most suitable taxonomic level, preferably on the species level (Column
 'taxon' in our database). However, we could frequently aggregate
 only on the genus level.
description: For script "1_Inflorescences.R" you are going to need
 the datatable "AgriLand_FlowerDensity_perspecies.csv" from the
 dataset "Flower density values of common British plant species
 [AgriLand]" (<em>Baude et al.
 2015</em>, https://doi.org/10.5285/6c6d3844-e95a-4f84-a12e-65be4731e934).