DOI: 10.5061/dryad.9p8cz8wtm
title: Data from: Pollinator cognition in a plant network

description: Cognitive abilities evolve within the context of ecological communities.
 Honeybees and bumblebees have become model systems for cognitive ecology;
 but pollination is performed by a diverse group of insects under similar
 pressures to forage efficiently in a mixed floral community. We studied
 the colour learning abilities of six species of Hymenoptera (two eusocial
 bumblebees, a cuckoo bumblebee, two wasps, and a leaf-cutter bee) within
 the context of an island plant community. We used records of insect visits
 to flowers in the field to determine the index of specialization of each
 species in the island plant-pollinator network, and measured the spectral
 reflectance of the flowers they visit. Species with higher specialization
 indices in our plant-pollinator network made a larger proportion of
 correct choices in a colour learning task than more generalist species.
 The more generalist species also visited a group of flowers more similar
 to each other in Hymenopteran colour vision space. These results indicate
 that better colour learning abilities may enable insects to forage on
 plants of different colours, whereas more generalist insects are visiting
 flowers that are similar in colour, and therefore are less reliant on
 repeated colour learning to forage efficiently.
description: (a) <em>Plant-pollinator network </em>
 This study was conducted at the Bowdoin Scientific Station on
 Kent Island, an 80-hectare island in New Brunswick, Canada. Kent Island is
 32 km to the closest mainland in Cutler, Maine, 5km from the closest
 larger island of Whitehead NB, and 8km to the larger island of Grand
 Manan, NB. In June and July of 2019, 2022, and 2023 one to two researchers
 collected insects when they were observed visiting flowering plants across
 the island. Across all three collection years insects were identified to
 the lowest taxonomic group possible using keys and by posting photos on
 iNaturalist. Tissues from a subset of 95 insects from 2022 were submitted
 to the Canadian Center for DNA barcoding for identification using the CO1
 barcoding region (BOLD Project "Identifying Insect Flower Visitors in
 Plant Pollinator Network on Kent Island, New Brunswick
 Canada"). We used the ‘bipartite’ package [28] in
 R to visualize the plant-pollinator network and calculate indices of
 specialization of the insect species. To assess how representative a
 network is from a given year we compared the network data between years
 with a Jaccard similarity index for interactions using the ‘vegdist’
 command in the R package ‘vegan’ [29]. We evaluated sampling completeness
 with a Chao 2 estimator [30, 31], using the ‘specpool’ command in the
 ‘vegan’ package. To determine specialization indices for pollinator
 species in the network (d’) [32] we used the ‘dfun’ command in the
 ‘bipartite’ package. (b) <em>Floral Reflectance
 Spectra</em> In 2022 and 2023 we measured the
 reflectance spectra of collected flowers using an Ocean Optics Flame
 miniature spectrometer with a DH-2000 BAL UV–VIS-NIR light source and PTFE
 diffuse reflectance standard (Ocean Optics, Orlando FL USA). For each
 plant species we measured floral reflectance from three different
 representative specimens and used the average reflectance of each
 wavelength from the three specimens for further analysis. We plotted our
 stimuli in the hexagonal colour vision space of the honeybee
 <em>Apis mellifera</em> [33] using the R package ‘pavo 2’
 [34]. We used the honeybee spectral sensitivities for our visual model as
 while there are published spectral sensitivies for some bumblebee species
 [35, 36], they do not include the species we tested, and there are not
 spectral sensitivities published for our wasps or leaf-cutter bees.
 Hymenopteran colour vision is considered quite conserved [37-39], so we
 felt the most appropriate system to use was the well-studied honeybee. To
 compare how similar the two stimuli used in our colour learning experiment
 were to real flower colours, we created vectors of pairwise chromaticity
 distances between a test stimulus and the flowers in the community and
 compared the vectors using a two-sample t-test. We used a linear model to
 examine the relationship between the d’ (index of specialization) of the
 species we tested in our behaviour experiment and the pairwise
 chromaticity distances between flowers they visited in our
 network. (c) <em>Colour Learning
 Experiment</em> We were interested in how
 abilities to associate colours with rewards may relate to the range of
 flower colours insects are visiting in the field. In 2022 and 2023, we
 tested associative colour learning abilities of 6 hymenopteran species:
 <em>Bombus flavidus</em> (N = 15), <em>Bombus
 sandersoni</em> (N = 87), <em>Bombus ternarius</em> (N =
 19), <em>Dolichovespula arenaria</em> (N = 40),
 <em>Dolichovespula norvegicoides</em> (N = 44), and
 <em>Megachile melanophaea </em>(N = 13). Insects collected on
 flowers in the field were returned to the laboratory and transferred into
 clean 50 mL tubes with a modified cap into which we inserted transparency
 paper with two holes ~1.5 cm apart. Insects were allowed to acclimate to
 the new tubes for 30-90 minutes before testing. Colour learning was tested
 using Free-Moving Proboscis Extension Response (FMPER) [40] following
 established testing protocols [41]. Insects were trained to associate
 either blue or yellow strips of paper (conditioned stimulus, CS) with a
 50% sucrose solution reward (unconditioned stimulus, US). Papers were
 Neenah Astrobrights “Sunburst Yellow” and “Blast-Off Blue” (Neenah Paper,
 Atlanta, Georgia, USA). Treatments were balanced such that for each
 species an equal number of individuals received blue as the rewarded CS
 (CS+) and yellow as the CS+.  Each insect received two training trials and
 five choice trials. For the training trials we dipped the designated
 rewarded colour strip in 50% sucrose solution (CS+) and inserted it into
 one side of the cap (we alternated the offered side between individuals).
 We allowed insects to drink from the paper for a maximum of 3 seconds and
 then removed the strip and inserted the alternative colour strip which had
 been dipped in water (CS-) inserted into the opposite side of the cap.
 Insects were allowed to drink the water from the CS- for three seconds and
 then it was removed, or if insects did not drink we removed the strip
 after the insect touched it with an antenna. For the second pre-training
 trial we switched the sides of the presentation of the two stimuli. There
 was a ~10 minute interval between each training trial and between training
 trials and the choice trials. After the two training
 trials we gave each insect five subsequent rewarded choice trials. For
 choice trials we simultaneously inserted the CS+ (with sucrose) and CS-
 (with water) and the first strip the insect touched with its antennae or
 proboscis was recorded as its choice. The insect was allowed to drink for
 3 seconds before the strip was removed. The other strip was left in the
 tube until the insect drank from it for 3 seconds or touched it with its
 antennae. We repeated this for a total of five times, alternating the
 colour presentation sides each time with 10 minutes between each trial. To
 prevent re-testing the same insect we placed a dot of fluorescent pink
 acrylic paint (Sargent Art, Hazleton PA USA) on the thorax of each insect
 and released them back into the field. We analyzed the performance of
 insects using binomial generalized linear mixed-effect models (GLMM) in
 the ‘lme4’ package [42] in R with a choice of the CS+ as 1 and a choice of
 the CS- as 0. Treatment (whether the CS+ was blue or yellow), insect
 species, the interaction between treatment and insect species, trial
 number, and the interaction between trial number and species were included
 as fixed effects and individual insect ID’s were included as a random
 effect.
description: # Pollinator Cognition in a Plant Network Dataset DOI:
 [10.5061/dryad.9p8cz8wtm](10.5061/dryad.9p8cz8wtm) ## Description of the
 data and file structure This dataset consists of three components: a
 plant-pollinator network, floral reflectance measurements, and a learning
 experiment. ### Files and variables #### File:
 Dprime\_Values\_From\_Network.csv **Description:** This is the
 d'prime value of specialization determined for the network organized
 so it can be used for further analyses ##### Variables * species: insect
 species * dprime: d' value calculated for that species from the
 plant-pollinator network * d: d value calculated for that species from the
 plant-pollinator network * dmin: dmin value calculated for that species
 from the plant-pollinator network * dmax: dmax value calculated for that
 species from the plant-pollinator network * color_dprime: d' value
 calculated for that species from the plant-pollinator network when the
 network was created using plant colours instead of plant species ####
 File: Dprime\_and\_Color\_Distances\_for\_Tested\_Species.csv
 **Description:** dprime and color distance data reorganized for stats and
 graphing ##### Variables * dprime: d' value calculated for that
 species from the plant-pollinator network * Species: insect species * dS:
 euclidian distances in bee colour vision space between two plants visited
 by this insect species #### File:
 2019\_2023\_Kent\_Island\_Plant\_Pollinator\_Network.csv
 **Description:** plant-pollinator network for Kent Island ##### Variables
 * Insect_ID: identification number for an individual insect * Year:
 collection year * Month: collection month * Day: collection day *
 Insect_Species: species of insect * Taxonomic_Group: taxonomic group in
 which that species of insect belongs * Plant: plant species on which that
 insect was captured * Common_Name: English common name for the plant
 species on which that insect was captured * CaptureLong (x): longitude
 where the insect was captured on a plant * CaptureLat(y): latitude where
 the insect was captured on a plant #### File: phenonet.csv
 **Description:** phenology dataset to determine when plants were
 seasonally available.  ##### Variables * Week: week of the year when an
 insect was seen on this plant * sec.fine: fine colour segment of the bee
 colour hexagon in which this plant falls * segment: ordering of these
 segments so they would plot properly * forvisplant_fine: fine colour
 segment of the bee colour hexagon in which this plant falls combined with
 plant name * Plant: plant latin name * n: number of observations of
 insects on that plant in that week of the year #### File:
 R\_Script\_Pollinator\_Cognition\_in\_a\_Plant\_Network.R
 **Description:** This is the R script with all the analysis and graphing
 #### File: Floral\_Reflectance\_Kent\_Island\_Plants.csv
 **Description:** Floral reflectance of plants and experimental stimuli
 ##### Variables: Variables were wavelength of light (nm), reflectance of
 each of the plant species (% reflectance at each wavelength), and then the
 experimental stimuli, the cap lids for FMPER tubes and the tabletop. ####
 File: Learning\_Experiment.csv **Description:** learning experiment data
 ##### Variables * Year: year data collected * Insect_Number: individual
 insect number within a year * Insect_ID: individual insect identifier *
 Species: insect species * Plant: plant that insect was captured on *
 Plant_Color_Distance_CSplus: the distance in bee color space between the
 flower the bee was caught on and the test stimulus that received as the
 rewarded stimulus * capture_test_date: day they were captured and tested *
 capture_time: time captured * test_time: time tested * CS_plus_color:
 rewarded color in their learning experiments  * CS_minus_color: unrewarded
 color in their learnind experiments * trial_type: stage of testing -
 either training or choice trial * trial_number: number of trial
 (effectively order of trials received) * choosen_color: which color the
 bees chose (NA indicates Not Applicable for training trials) * correct:
 was the color they chose the rewarded color? (NA indicates Not Applicable
 for training trials) * binom_response: if was correct this is a 1, if they
 chose the unrewarded color this was a 0 (NA indicates Not Applicable for
 training trials) #### File: Plant\_Phenology.csv **Description:** plant
 phenology dataset ##### Variables * Insect_ID: individual insect number *
 Year: year of survey * Month: month insect captured * Day: day insect
 captured * Date: full date insect captured * Week: week of the year insect
 captured * Insect_Species: species of insect * Taxonomic_Group: order the
 insect belongs to * Plant: plant species insect was caught on latin name 
 * Common_Name: plant species insect was caught on common name *
 CaptureLong (x): longitude where insect was caught on plant *
 CaptureLat(y): latitude where insect was caught on plant ## Code/software
 R packages are all detailed in the R file included ## Access information
 Other publicly accessible locations of the data: * n/a Data was derived
 from the following sources: * n/a