DOI: 10.5061/dryad.sf7m0cgf7
title: High rates of nectar depletion in summer grasslands indicate competitive conditions for pollinators

description: Competition among pollinators for floral resources is a phenomenon of both
 basic and applied importance. While competition is difficult to measure
 directly under field conditions, it can be inferred indirectly through the
 measurement of floral resource depletion. In this study, we conducted a
 pollinator exclusion experiment to calculate nectar depletion rates in
 summer across 16 grassland sites in the German regions of Franconia and
 Saxony-Anhalt. Overall depletion rates were estimated at 95% in Franconia
 and 79% in Saxony-Anhalt, indicating strong nectar limitation and likely
 competition among pollinators for nectar. Despite being ubiquitous in our
 study regions, honey bees were scarce at our sites at the time of nectar
 sampling. This demonstrates that wild pollinators alone are capable of
 massive nectar depletion, and the addition of managed honey bees to wild
 pollinator communities may intensify already competitive conditions.
 Nevertheless, the manifest diversity of the pollinator communities at our
 sites indicates that other factors, such as non-trophic constraints or
 temporal variation in nectar limitation, can mitigate competitive
 exclusion despite immediate conditions of acute nectar scarcity. 
description: Our study was set in the German regions of Franconia and
 Saxony-Anhalt **(Figure 1A)**. In each region, we selected eight
 semi-natural grassland sites for sampling, ranging in size from 0.1 to
 32.0 hectares in Franconia and from 2.3 to 12.5 hectares in Saxony-Anhalt.
 The sites in Franconia were calcareous grasslands, while those of
 Saxony-Anhalt were set in the distinctive porphyry substrate of that
 region and ranged from 2.3 to 12.5 hectares. In both regions, semi-natural
 grasslands are highly fragmented and embedded in a predominantly
 agricultural matrix characterized by field crops, vineyards (in
 Franconia), deciduous forest patches, and small settlements. Neighboring
 sites were separated by a minimum of 2.6 km in Franconia and 5.7 km in
 Saxony-Anhalt. Sampling protocols differed across
 regions to accommodate local floristics and field work logistics. In
 Franconia, we sampled from 4-19 July, 2022. On each sampling day, we
 arrived at a given site around sunrise (5:00-6:00), prior to the onset of
 foraging by diurnal pollinators, and bagged flowers from the top 1-3 most
 visually abundant flower species. Bags were constructed of synthetic mesh
 similar to mosquito netting, which has a minimal influence on the
 microclimate of the flower. Since our protocol involved bagging flowers
 and then returning to sample nectar on the same day, only one site could
 be sampled per day, and each site was sampled only once during the course
 of the study. The most ubiquitous species in Franconia
 was <em>Centaurea scabiosa</em>, which we sampled at all but
 one of our sites. Other species, sampled opportunistically, include
 <em>Dianthus carthusianorum</em> (one site),
 <em>Echinops sphaerocephalus</em> (two sites),
 <em>Origanum vulgare</em> (one site), <em>Teucrium
 chamaedrys</em> (one site), and a species complex of
 <em>Knautia arvensis</em> and <em>Scabiosa
 columbaria</em> (three sites). Since we failed to distinguish
 <em>Knautia</em> and <em>Scabiosa</em> reliably in
 the field, we lumped them under the subfamily Dipsacoideae in all
 analyses. Only flowers visibly intact and in good
 condition were selected for bagging. Since flowers tended to be patchily
 distributed, we selected flowers opportunistically rather than in a
 spatially systematic fashion, with the goal of representing the major
 patches within each site. The number of flowers bagged at each site varied
 with the abundance of the target species and was constrained by time, but
 we aimed for at least 10 flowers per species (mean: 12, range: 7-20).
 Approximately 5 hours after sunrise (~10:30), we sampled nectar from
 bagged and open flowers using 0.5- or 1.0- uL microcapillary tubes
 (Hirschmann, Eberstadt). Where possible, we selected open flowers
 belonging to the same individual plant as a corresponding bagged flower.
 In all cases, open flowers were selected according to the same standard of
 intactness and condition as the bagged flowers, and we similarly aimed to
 sample at least 10 flowers per species (mean: 14, range: 10-27). For
 species with compound flowers
 (<em>Knautia</em>, <em>Scabiosa</em>, <em>Centaurea</em>, and <em>Echinops</em>), samples for each inflorescence were pooled across 5 florets. Nectar volumes were measured in mm using a digital caliper (HaWe, Aschheim) and converted to uL nectar by multiplying the maximum capacity of the tube by the proportion of its length that was filled. In Saxony-Anhalt, flowers were bagged around 9:00 and sampled around 17:00, thus capturing most of the foraging day but allowing some morning foraging prior to bagging. The only species sampled in Saxony-Anhalt was <em>Scabiosa ochroleuca</em>, which was common at all sites but not among the most abundant flora. Sampling was conducted from 13-28 July, 2022. Aside from these differences, sampling in Saxony-Anhalt followed the same methods described above for Franconia. By midsummer in Germany, the flowering seasons of forests and crops are largely past, and floral resources are mostly restricted to grasslands and settlements (i.e. gardens). Thus, our sampling can be understood to reflect the floral resource conditions of the main foraging habitat available to pollinators at the time of our study. At all sites, we surveyed pollinator communities using standardized 45-minute transect walks. While insects of many taxonomic groups can function as pollinators, our surveys focused on those most characteristically associated with diurnal flower visitation, namely bees (Hymenoptera: Anthophila), butterflies and burnets (Lepidoptera), and hover flies (Diptera: Syrphidae). Specimens were identified live in the field when possible, and specimens that could not be identified to species-level in the field were euthanized and identified later in the laboratory. In Franconia, pollinator surveys began in mid-April and were repeated at monthly intervals until early-August. In Saxony-Anhalt, surveys were performed in mid-May, early-June, and late-July. Prior to modeling, we normalized the nectar measurements by dividing each reading by the mean reading of the bagged flowers within each site and species.
description: # High rates of nectar depletion in summer grasslands indicate competitive
 conditions for pollinators ##
 [https://doi.org/10.5061/dryad.sf7m0cgf7](https://doi.org/10.5061/dryad.sf7m0cgf7) ## Description of the data and file structure All data files are provided in `.rds` format for convenient handling in `R` (and to avoid any nasty tricks Excel might try to play with a regular spreadsheet file). To load an `.rds` files into your `R` environment as a data frame, use the `readRDS()` command. Data frames can easily be exported to `.csv` format using `write.csv()`. Each `.rds` data file is used in one or more of the accompanying RMarkdown files listed in the Code/Software section. ### `wue_nectar.rds` Nectar sampling data from Lower Franconia; 13 columns 1. `site_name`: Name of sampling location; a grassland site 2. `date`: Date of sampling 3. `species`: Species of sampled flower 4. `genus`: Genus of sampled flower 5. `plant`: Individual plant to which sampled flower belonged 6. `index`: Just a row counter 7. `nectar.meanbagged`: Mean volume ($\mu$L) of nectar in bagged flowers for a given species-site-date combo. 8. `treatment`: Whether the sampled flower was bagged or open 9. `microliters.nectar`: Volume of nectar collected from flower 10. `nectar.mc`: Mean-centered nectar volume calculated by dividing `microliters.nectar` by `nectar.meanbagged` 11. `site`: Code for sampling location 12. `site_label`: Label for sampling location used in paper 13. `region`: Study region --- Franconia for all samples ### `ufz_nectar.rds` Nectar sampling data from Saxony-Anhalt; 10 columns 1. `site`: Code for sampling location 2. `date`: Date of sampling 3. `species`: Species of sampled flower 4. `plant`: Individual plant to which sampled flower belonged 5. `bagged.mean`: Mean volume ($\mu$L) of nectar in bagged flowers for a given species-site-date combo. 6. `treatment`: Whether the sampled flower was bagged or open 7. `microliters.nectar`: Volume of nectar collected from flower 8. `nectar.mc`: Mean-centered nectar volume calculated by dividing `microliters.nectar` by `nectar.meanbagged` 9. `site_label`: Label for sampling location used in paper 10. `region`: Study region --- Saxony-Anhalt for all samples ### `wue_transects_sub.rds` Pollinator transect sampling from Lower Franconia; 6 columns 1. \`region``: Study region --- Franconia for all samples 2. `site`: Code for sampling location 3. `site_label`: Label for sampling location used in paper 4. `date`: Date of sampling 5. `pollinator_group`: Functional group of observed pollinator (WB = bees excluding *Apis* and *Bombus*; HB = honey bee, BB = bumble bee, BF = butterfly, SF = syrphid fly) 6. `pollinator_genus`: Genus of observed pollinator ### `ufz_transects_sub.rds` Pollinator transect sampling from Saxony-Anhalt; 24 columns 1. \`region``: Study region --- Saxony-Anhalt for all samples 2. `site`: Code for sampling location 3. `site_label`: Label for sampling location used in paper 4. `date`: Date of sampling 5. `pollinator_group`: Functional group of observed pollinator (WB = bees excluding *Apis* and *Bombus*; HB = honey bee, BB = bumble bee, BF = butterfly, SF = syrphid fly) 6. `pollinator_genus`: Genus of observed pollinator ### `brm_wue_00.rds` Bayesian hierarchical model of Lower Franconia nectar samples (see code file `grassland_nectar_modeling.rmd` below). ### `brm_ufz_01.rds` Bayesian hierarchical model of Saxony-Anhalt nectar samples (see code file `grassland_nectar_modeling.rmd` below). ## Code/Software ### `grassland_nectar_modeling.rmd` This script provides a reproducible workflow for model fitting and validation; uses data files `wue_nectar` and `ufz_nectar` ### `grassland_nectar_visualization.rmd` This script provides a reproducible workflow for model visualization, generating all figures used in the text; uses data files `wue_nectar`, `ufz_nectar`, `wue_transects_sub`, `ufz_transects_sub`, `brm_wue_00.rds`, and `brm_ufz_01.rds`.