DOI: 10.5061/dryad.n02v6wwzn
title: MetaComNet: A random forest-based framework for making spatial prediction of plant-pollinator interactions

description: 1. Predicting plant-pollinator interaction networks over space and time
 will improve our understanding of how environmental change is likely to
 impact the functioning of ecosystems. Here we propose a framework for
 producing spatially explicit predictions of the occurrence and number of
 pairwise plant-pollinator interactions and of the species richness,
 diversity, and abundance of pollinators visiting flowers. We call the
 framework ‘MetaComNet’ because it aims to link metacommunity dynamics to
 the assembly of ecological networks. 2. To illustrate the MetaComNet
 functionality, we used a dataset on bee-flower networks sampled at 16
 sites in southeast Norway along with random forest models to predict
 bee-flower interactions. We included variables associated with climatic
 conditions (elevation) and habitat availability within a 250m radius of
 each site. Regional commonness, site-specific distance to conspecifics,
 social guild, and floral preference were included as bee traits. Each
 plant species was assigned a score reflecting its site-specific abundance,
 and four scores reflecting the bee species that the plant family is known
 to attract. We used leave-one-out cross-validations to assess the models’
 ability to predict pairwise plant-bee interactions across the landscape.
 3. The relationship between observed occurrence or absence of interactions
 and the predicted probability of interactions was nearly proportional
 (GLMlogistic regression slope = 1.09), matching the data well (AUC =
 0.88), and explained 30% of the variation. Predicted probability of
 interactions was also correlated with the number of observed pairwise
 interactions (r = 0.32). The sum of predicted probabilities of bee-flower
 interactions were positively correlated with observed species richness (r
 = 0.50), diversity (r = 0.48), and abundance (r = 0.42) of wild bees
 interacting with plant species within sites. 4. Our findings show that the
 MetaComNet framework can be a useful approach for making spatially
 explicit predictions and mapping plant-pollinator interactions. Such
 predictions have the potential to identify areas where the pollination
 potential for wild plants is particularly high, and where conservation
 action should be directed to preserve this ecosystem function.
description: We sampled bee-flower networks along 16 roadsides (sites) in
 Southeast Norway in 2017. Eight of the study sites were located on sandy
 sediments and the remaining eight were located on clay dominated sediments
 (Skoog 2018). At each study site, flower-visiting bees were collected
 during one hour by two observers along a 50m transect, once during early
 to mid-July and once during early to mid-August in 2017. A total of 910
 interactions between wild bees (n = 45 species) and plants (n = 44
 species) were observed. We only included non-parasitic bees (n = 39) in
 our analyses because parasitic bees do not visit plants for nectar
 <i>and</i> pollen. We estimated the site’s abundance of
 flowering plant species by recording the number of occurrences
 (presence/absence) of each plant species in a grid of four 25x25 cm
 squares within six 1 m<sup>2</sup> quadrats placed in a 2 by
 30 m grid along the roadside, totaling ninety-six squares per
 site. Plant and bee DCA scores were extracted from a
 detrended correspondence analysis on a binary matrix of bee species and
 plant family interactions.  #    The csv file:
 "Sydenham et al MetaComNet data frame.csv"<br> ##      
  Contains the model data frame for running the analyses presented in the
 paper. The data frame consists of the following columns.<br> ###  
          "Site" = Identity of study site<br> ###          
  "SiteBee" = Identity of study site and bee species<br>
 ###            "SitePlant" = Identity of study site and plant
 species <br> ###            "SitePlantBee" = Identity of
 study site and plant species and bee species<br> ###          
  "Number" = Number of recorded interactions between bee species
 and plant species at the study site<br> ###          
  "Occurrence" = Occurrence or absence of recorded interactions
 between bee species and plant species at the study site<br> ###  
          "DCA1" = Plant species DCA score on axis 1<br>
 ###            "DCA2" = Plant species DCA score on axis
 2<br> ###            "DCA3" = Plant species DCA score on
 axis 3<br> ###            "DCA4" = Plant species DCA score
 on axis 4<br> ###            "BeeDCA1" = Bee species DCA
 score on axis 1<br> ###            "BeeDCA2" = Bee species
 DCA score on axis 1<br> ###            "BeeDCA3" = Bee
 species DCA score on axis 1<br> ###            "BeeDCA4" =
 Bee species DCA score on axis 1<br> ###          
  "Solitary"= Bee species is solitary or social
 (Bombus)<br> ###            "PlantFreq" = Frequency of
 plant species in site (number of 0.25cm subplots in which the species was
 recorded)<br> ###            "MASL" = Elevation of study
 site<br> ###            "LnscpH" = Landscape shannon
 diversity within a 250m radius around the site<br> ###          
  "LndscpGR" = Proportion of grassland within a 250m radius
 around the site <br> ###            "DistSand" = Distance
 to sandy soil deposits (deposits with a high infiltration
 capacity)<br> ###            "NearestOcc" = km to the
 nearest known occurrence (from GBIF) of the bee species<br> ###  
          "RegionalCommonness" = number of 10km grid cells within
 the bee species occurs within the region #     The
 shapefile: "Site locations.shp"<br> ##        Contains a
 spatial points dataframe with points for each study site
 #     The csv file: "GBIF wild bee occurrence
 records.csv"<br> ##        Contains occurrence records for wild
 bee species within the wider study region downloaded from GBIF using the
 gbif() function in R package dismo. Records were first downloaded on
 16.07.2021, and then downloaded again on the 17.10.2021 to obtain all
 metadata. These data are used for producing the prediction maps in figure
 4, and were used for estimating regional commonness and distance to
 nearest occurrences in the metacomnet model data frame.
 <br> #    The raster file:
 "StackedPredsForMAP.tif" <br> ##        contains a
 rasterstack for producing the prediction maps in figure 4. Note that the
 raster was aggregated from 10m to 100m resolution when making prediction
 maps. <br> ###        "MASL" = Elevation<br> ###  
      "LnscpH" = Landscape shannon diversity within 250m of
 pixels<br> ###        "LndscpGR" = Landscape shannon
 diversity within 250m of pixels<br> ###        "DistSand"
 = Distance to sandy soil deposits<br> ###        "CellID"
 = Identity of raster cell (first cell in top left corner)
 #### MASL was based on a digital elevation model with a 50m
 resolution (Norwegian mapping authority, 2016)
 ### LnscpH and LndscpGR were calculated from Venter &
 Sydenham (2021) ### DistSand was obtained from the
 infiltration capacity from geological survey maps (Geological Survey of
 Norway, 2011)  #    The raster file:
 "MosaicBlock_UTM32N_T32VPM.tif"<br> ##        Contains a
 satellite image (rgb) for producing figure 4 in the manuscript<br>
 ###             Copernicus Sentinel-2 data (2019)/processed by the
 Norwegian Mapping Authority, downloaded from www.geonorge.no