The FloRes Database: A floral resources trait database for pollinator habitat-assessment generated by a multistep workflow
Baden-Böhm, Franziska; App, Mario; Thiele, Jan (2022), The FloRes Database: A floral resources trait database for pollinator habitat-assessment generated by a multistep workflow, Dryad, Dataset, https://doi.org/10.5061/dryad.djh9w0w29
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.
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.
Collection of raw data
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).
Data preparation and multistep workflow
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 Baude et al. (2015).
- 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.
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]" (Baude et al. 2015, https://doi.org/10.5285/6c6d3844-e95a-4f84-a12e-65be4731e934).
Landwirtschaftliche Rentenbank, Award: Future Resources, Agriculture & Nature Conservation (817759)
Bundesministerium für Ernährung und Landwirtschaft, Award: Monitoring of biological diversity in agricultural landscapes (MonViA)