DOI: 10.5061/dryad.s1rn8pkgt
title: Data from: Climatic conditions and landscape diversity predict plant-bee interactions and pollen deposition in bee-pollinated plants.

description: Climate change, landscape homogenization and the decline of beneficial
 insects threaten pollination services to wild plants and crops.
 Understanding how pollination potential (i.e. the capacity of ecosystems
 to support pollination of plants) is affected by climate change and
 landscape homogenization is fundamental for our ability to predict how
 such anthropogenic stressors affect plant biodiversity. Models of
 pollinator potential are improved when based on pairwise plant-pollinator
 interactions and pollinator´s plant preferences. However, whether the sum
 of predicted pairwise interactions with a plant within a habitat (a proxy
 for pollination potential) relates to pollen deposition on flowering
 plants has not yet been investigated. We sampled plant-bee interactions in
 68 Scandinavian plant communities in landscapes of varying land-cover
 heterogeneity along a latitudinal temperature gradient of 4–8 C°, and
 estimated pollen deposition as the number of pollen grains on flowers of
 the bee-pollinated plants Lotus corniculatus, and Vicia cracca. We show
 that plant-bee interactions, and the pollination potential for these
 bee-pollinated plants increase with landscape diversity, annual mean
 temperature, plant abundance, and decrease with distances to
 sand-dominated soils. Furthermore, the pollen deposition in flowers
 increased with the predicted pollination potential, which was driven by
 landscape diversity and plant abundance. Our study illustrates that the
 pollination potential, and thus pollen deposition, for wild plants can be
 mapped based on spatial models of plant-bee interactions that incorporate
 pollinator-specific plant preferences. Maps of pollination potential can
 be used to guide conservation and restoration planning.
description: We sampled plant-bee interactions in 68 semi-natural,
 forb-dominated, plant communities. We used linear open landscape features
 such as roadsides as a model system and established one 50×2m transect for
 our surveys in each site. To cover the main flowering period, we sampled
 plant-bee interactions once in May, June and July at each site. To
 standardize sampling times across sites and countries, timing of the first
 sampling was determined by the peak flowering of dandelions
 (<em>Taraxacum officinale</em>). All flower-visiting bees were
 collected from flowers and stored in 96% EtOH prior to
 identification. Specimens within the <em>B. sensu stricto</em>
 subgenus were treated as one morpho-species. Each transect observation
 lasted 30 minutes, adding 30 seconds per collected specimen, to account
 for handling time. Sampling only took place on days with temperatures
 &gt; 15°C, local wind speed &lt; 5 m/s, with little to no cloud
 cover and no rain to standardize sampling conditions between
 networks. In late June to early July 2021, we sampled
 the plant communities by placing ten 1m<sup>2</sup> vegetation
 plots regularly along each transect. In each 1m<sup>2</sup>
 vegetation plot, we recorded the occurrence of bee-pollinated plant
 species, regardless of life stage, within four
 0.25m<sup>2</sup> subplots. Conducting vegetation surveys
 separately from plant-pollinator interaction surveys was necessary for us
 to be able to sample plant-bee interactions at all 68 sites within the
 same time periods. Despite being long after the flowering period of the
 earliest flowering plants, such as <em>Tussilago farfara</em>,
 these plants were recorded during our survey. Our approach allowed us to
 obtain estimates of plant abundances (i.e. number of plots, or subplots
 occupied by a plant), from just one survey per site, regardless of plant
 phenologies. We sampled wilted flowers from two legumes
 (<em>Vicia cracca</em>, <em>Lotus
 corniculatus</em>) along the transects in Norway in July 2021, and
 counted the number of pollen grains deposited on stigmas as a measure of
 total pollen deposition. Flower sampling was conducted independently from
 surveys of plant-pollinator interactions. These legumes were selected
 because they are mainly bee pollinated and had been found in at least 10
 sites during the vegetation surveys.<em> Vicia cracca</em>,
 and <em>Lotus corniculatus </em>occurred at 26, and 25 sites,
 respectively. We collected one flower from 10-15 individuals per species
 per site. Flowers were fixated with 4% formaldehyde alcohol acetic acid
 and stored at 4°C until further processing. Entire flowers were softened
 in 5M NaOH for 24 hours. After softening, the NaOH was removed by
 pipetting and gently washed out with ddH<sub>2</sub>O five
 times. Then, several drops of 0.001g/ml Aniline Blue Fluorochrome (ABF) in
 0.1M K<sub>2</sub>HPO<sub>4</sub> pH 10 buffer
 were dropped on microscope slides. Gynoecia were carefully removed from
 flowers with tweezers, immersed in ABF on the microscope slides, covered
 with aluminum foil, and left for one hour. Following staining, squashing,
 and mounting, we counted the number of pollen grains using a Leica DM2500
 fluorescence microscope. We used the annual mean
 temperature at sampling locations, obtained from the WorldClim database
 (Fick & Hijmans 2017). As a proxy for landscape homogenization (or
 its inverse, landscape diversity), we calculated the Shannon landscape
 diversity within circular buffers with radii of 1 km surrounding each site
 from a European 10 m resolution land cover map (Venter & Sydenham
 2021). As a proxy for availability of high-quality nesting substrates for
 ground nesting bees, which account for the majority of bee species in our
 region, we included the geographic distance to sand dominated geological
 deposits, estimated as the distance to the nearest spatial polygon
 classified as having a high or moderately high infiltration capacity in
 Norway (Geological Survey of Norway<strong> </strong>2011) or
 explicitly classified as being sand dominated in Denmark
 (Landbrugsstyrelsen 2019). We did not include specific predictors for
 cavity nesting bees, because we expected the availability of nesting
 substrates for this group to correspond with landscape
 diversity. We used the number of 1
 m<sup>2</sup> vegetation plots in which a species occurred.
 Plants not recorded during the vegetation survey but on which bees had
 been observed were assigned a plant abundance value of 1, corresponding to
 the lowest value recorded for plants during the plant surveys. To account
 for forbidden links due to phenological mismatches, we included the mean
 Julian day on which bees were observed on the plant across all sites as a
 proxy for plant peak flowering time. As a proxy for flowering time
 duration, we included the standard deviation of Julian days on which
 interactions between the plant and bees had been observed. We used
 pre-existing data on plant and bee associations (Rasmussen et al. 2021) to
 derive plant-bee association scores for both plants and bees. We assembled
 a matrix with information on the number of plant genera for each of 62
 plant families that 281 bee species from Denmark and Norway are known to
 visit (Rasmussen et al. 2021, Wood et al. 2021). We used a detrended
 correspondence analysis (DCA) in the ‘vegan’ package in R (Oksanen et al.
 2022) to ordinate the plant family-bee species matrix and used the plant
 family and bee species DCA-scores along the four axes returned by the
 decorana() function (DCAs 1-4), resulting in four scores for each plant
 species from our surveys (Plant DCA1-4). The DCA scores separated plant
 species according to differences in which bee species they are known to
 interact with. We included the number of 10 km grids
 within a spatial extent (Longitude = [7.97, 13.84], Latitude = [54.37,
 61.81]) slightly exceeding that of our study region (Longitude = [9.47,
 12.34], Latitude = [55.87, 60.32]) where bee species sampled during our
 study had been previously recorded (GBIF, 2024) as a proxy for regional
 commonness. We assigned each bee species a categorical trait with two
 levels to distinguish between eusocial bumblebees and solitary or
 facultatively social bees. We used data from the European bee fauna
 (Scheuchl & Willner 2016) to assign each bee species a
 phenological trait which was a categorical variable with four levels:
 ‘Spring-mid-summer’ indicating activity from April to July; ‘Early-late
 summer’ indicating activity from May to August; ‘Mid-late summer’
 indicating activity from June to August; Entire summer’ indicating
 activity from April to August. Although the phenology of species variesy
 across climatic gradients the relative phenological difference among
 species is likely to be fairly constant. As for plants, each bee species
 was assigned a floral preference score (Bee DCA1-4), extracted from the
 DCA analysis.
 <strong>References</strong> Fick,
 S.E. and Hijmans, R.J. 2017. WorldClim 2: new 1-km spatial resolution
 climate surfaces for global land areas. <em>International Journal of
 Climatology</em> <strong>37</strong>:
 4302–4315. GBIF 2024. Derived dataset GBIF.org (4 June
 2024) Filtered export of GBIF occurrence data
 https://doi.org/10.15468/dd.qww46f Geological Survey of
 Norway (2011). Løsmasser WMS. Retrieved from
 https://kartkatalog.geonorge.no/metadata/loesmasser/3de4ddf6-d6b8-4398-8222-f5c47791a757 Landbrugsstyrelsen. (2019). <em>Jordbundskort 2019</em> Landbrugsgeodata. https://www.geodata-info.dk/srv/eng/catalog.search;jsessionid=E97078277812AF9C3C5A6FD7BA8516E8#/metadata/da22d09d-eec3-44f6-a470-c551e03d512b Oksanen J et al. 2022. vegan: Community Ecology Package. R package version 2.6-4, &lt;https://CRAN.R-project.org/package=vegan&gt;. Rasmussen, C. et al. 2021. Evaluating competition for forage plants between honey bees and wild bees in Denmark. <em>PLoS One</em> <strong>16</strong>: e0250056. Scheuchl, E., and Willner, W. 2016. Taschenlexikon der Wildbienen Mitteleuropas: Alle Arten im Porträt. Quelle & Meyer Verlag. Venter, Z.S. and Sydenham, M.A.K. 2021. Continental-scale land cover mapping at 10 m resolution over Europe (ELC10). <em>Remote Sensing </em><strong>13</strong>, 2301. Wood, T.J. et al. 2021. Global patterns in bumble bee pollen collection show phylogenetic conservation of diet. <em>Journal of Animal Ecology</em> <strong>90</strong>: 2421–2430.
description: # Climatic conditions and landscape diversity predict plant-bee
 interactions and pollen deposition in bee-pollinated plants.
 [https://doi.org/10.5061/dryad.s1rn8pkgt](https://doi.org/10.5061/dryad.s1rn8pkgt) The ModelMetaComNetDF.csv file was used to model plant-bee interactions using a generalized mixed effects model using the formula (R syntax): *glmer(Interaction occurrence ~ Bee Social status × Annual mean temperature + Bee Social status × Landscape Shannon diversity + Plant abundance + BeeDCA1 × PlantDCA1 + BeeDCA2 × PlantDCA2 + BeeDCA3 × PlantDCA3 + BeeDCA4 × PlantDCA4 + Bee Phenology × Plant Phenology + square root (Distance to Sandy soils) + Regional Commonness + (1|Site identity), family = binomial)* To predict onto sites we used a leave-one-out cross validation where for each of the 68 sites, data from the site was removed from the data while fitting the model which was then used to predict the likelihood of occurrence of all bee-plant interactions in the site. the MetaComNetPollen.csv contains information on the number of pollen grains deposited on flowers within sites and was used to test if the sum of predicted occurrences across all bee species was positively related to the number of pollen grains on flowers. Tests were run using the formula (R syntax): *glmmTMB(Pollen grains on stigma ~ Predicted pollination potential × Plant species + (1| Site identity), ziformula=~1, family=nbinom1)* and to test if the spatial predictors of plant-bee interactions predicted pollen deposition a second model was run using the formula (R syntax): *glmmTMB(Pollen grains on stigma ~ Plant abundance + Landscape Shannon diversity + Annual mean temperature + Plant species + square root(Distance to Sandy soils) +(1|Site identity),  ziformula=~1, family=nbinom1)* The R code for running the analyses is stored in the 'R script for running analyses and statistical tests.R' file. ## Description of the data and file structure The dataset contains the files: "ModelMetaComNetDF.csv" used for modelling and predicting plant-bee interactions, "MetaComNetPollen.csv" used for testing if predicted occurrence of pollinators on plants relates to pollen deposition, "Spatial Predictors for map.tif" used for making spatial predictions of pollen deposition, "region sat image.tif" used to for producing maps with locations of study sites, "Denmark sat image.tif" used to for producing maps with locations of study sites, and "Satelite image Norway.tif" used to for producing maps with locations of study sites and pollen deposition prediction maps. Satellite imagery from Map data ©2023 Google via QGIS 2023. **Columns in** **ModelMetaComNetDF.csv** *Site_id*: identifier for the 68 study sites *Pollinator_species*: identifier for each bee species *Plant_species*: identifier for each plant species *SitePlant*: combination of site and plant identifiers *PlantFreq*: number of 1m vegetation plots a plant was recorded in at a site *Occurrence*: occurrence (1) or absence (0) of interaction between plant and bee within the site  *SitePlantPollinator*: combination of site, plant, and bee species identifiers *PlantDCA1-4*: plant species score along DCA axis 1-4 *meanPlantPhenolgyDay*: mean julian day for interactions recorded for the plant species *BeePhenology*: activity period for bee species *Eusocial*: TRUE for bee species within the Bombus genus, FALSE for non-Bombus species. *BeeDCA1-4*: Bee species score along DCA axis 1-4 *RegionalCommonness*: Number of 10km grid cells bee species have been recorded in *coords.x2*: latitudinal coordinates for study site (EPSG:3035) *coords.x1*: longitudinal coordinates for study site (EPSG:3035) *ShannonH*: Landscape diversity within 1km buffer around site *bio1*: Annual mean temperature at site *DistSnd*: Distance in meters to soil deposits classified as sand dominated **Columns in** **MetaComNetPollen.csv** *siteplant*: combination of site and plant identifiers *MCNsiteID*: identifier for the study site where a flower was collected for pollen grain analyses *plant*: identifier for the plant species a flower was collected (TT = Lotus corniculatus, FV = Vicia cracca) *plantSpecimen*: plant specimen the flower was collected from *TubesPicture*: number of pollen tubes in style counted from pictures of the flower (if NA no picture was taken) *PollenPicture*: number of pollen grains on stigma counted from pictures of the flower (if NA no picture was taken) *TubesMic*: number of pollen tubes in style counted through the microscope *PollenMic*: number of pollen grains on stigma counted through the microscope ## Code/Software All analyses were run using R (R Core Team 2022)  with the following R packages: terra (Hijmans 2023), effects (Fox and Weisberg 2019), DHARMa (Hartig 2022), MASS (Venables and Ripley 2002), glmmTMB (Brooks et al. 2017), MuMIn (Bartoń 2023), ncf (Bjornstad 2022), ape (Paradis and Schliep 2019), car (Fox and Weisberg 2019), and boot (Canty and Ripley 2019, Davison and Hinkley 1997). **References** * Bartoń K (2023). *MuMIn: Multi-Model Inference*. R package version 1.47.5, [https://CRAN.R-project.org/package=MuMIn](https://CRAN.R-project.org/package=MuMIn). * Bates, D. et al. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. doi:10.18637/jss.v067.i01 * Bjornstad ON (2022). *ncf: Spatial Covariance Functions*. R package version 1.3-2, [https://CRAN.R-project.org/package=ncf.](https://CRAN.R-project.org/package=ncf.Hartig) * Canty, A. and Ripley, B. (2022). boot: Bootstrap R (S-Plus) Functions. R package version 1.3-28.1. * Davison, A. C. & Hinkley, D. V. (1997) Bootstrap Methods and Their Applications. Cambridge University Press, Cambridge. ISBN 0-521-57391-2 * Hartig, F. (2022) DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.4.6 [https://CRAN.R-project.org/package=DHARMa](https://CRAN.R-project.org/package=DHARMa). * Hijmans R (2023). *terra: Spatial Data Analysis*. R package version 1.7-46, [https://CRAN.R-project.org/package=terra](https://CRAN.R-project.org/package=terra). * Fox, J. and Weisberg, S. (2019). An R Companion to Applied Regression, 3rd Edition. Thousand Oaks, CA [https://socialsciences.mcmaster.ca/jfox/Books/Companion/index.html](https://socialsciences.mcmaster.ca/jfox/Books/Companion/index.html) * Brooks et al. (2017). glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling. The R Journal, 9(2), 378-400. doi: 10.32614/RJ-2017-066. * Paradis E, Schliep K (2019). “ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R.” *Bioinformatics*, *35*, 526-528. doi:10.1093/bioinformatics/bty633 [https://doi.org/10.1093/bioinformatics/bty633](https://doi.org/10.1093/bioinformatics/bty633). * Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0 * R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL [https://www.R-project.org/](https://www.R-project.org/).