DOI: 10.5061/dryad.1c59zw456
title: Data from: Industry needs matter – incorporating stakeholder interests in the selection of flower resources to support pollinators

description: Most pollinator policy initiatives are focused on habitat restoration and
 increasing the availability of floral resources, yet the choice of plant
 species is not always compatible with farming system cultivation and
 management needs.  In this paper, we developed a framework for
 selecting plants to specifically meet stakeholder needs. We trialled 19
 plant species and collected observational data on plant insect visitors,
 plant survival in the orchard environment and potential risks to crops and
 the environment. We used this framework to identify plants suitable to
 incorporate into blueberry cropping systems. Practical implication: Our
 framework ensured plant choice based on informed decisions and allowed
 selection of two plant species that aligned well with industry needs.
 Different plants may be optimal for different conservation aims, hence
 plants selected need ideally to be evaluated for their use by the
 flower-visiting taxa, as well as align with industry growing practices and
 needs.
description: The data set consists of observations of flower visitors to
 candidate flowering plant species incorporated into a blueberry production
 system with the aim to:  1) increase the activity of
 pollinators throughout the blueberry rows in polytunnels, and
 2) support the health of managed honeybees and wild stingless
 bees. 19 plant species, attractive to pollinators, were
 establishment in the blueberry system in May 2019. The plants were first
 placed in blueberry blocks grown under polytunnels and thereafter
 transferred to open field blueberry blocks. For the 36 individual plants
 representing 11 plant species that survived in the blueberry system, and
 co-flowered with the blueberry plants, we conducted standardised
 observations of floral insect visitors. The aim of the standardised
 observations in field trials were to record the actual identity and number
 of visitors that visited the different plant species. The observations
 were conducted in polytunnels in March 2020, approximately 10 months after
 establishment and in open field blocks in March-May 2020. Individual
 plants were observed for five minutes, and all floral visitors were
 recorded, together with the number of open flowers. We conducted eight
 separate days of observations in the polytunnels and five days of
 observations in the open block.  <em>Statistics
 – </em>We modelled the number of visitors to candidate plants using
 generalised linear mixed effect models with the glmmTMB package (Brooks et
 al., 2017) in R (R Core Team, 2017). Random effects reflected experimental
 design, with the effects of plant location nested within row, and the
 crossed effects of observation date. The natural logarithm of the mean
 number of flowers for each plant species was used as an offset. Models
 were specified with a negative binomial error distribution following model
 diagnostics implemented with the DHARMa package (Hartig & Hartig,
 2017). Separate models were fitted for honeybees, stingless bees, and for
 all visitors combined. Due to low visitation to some plant species, only
 plant species receiving greater than one floral visit in aggregate were
 included in the models. Pairwise testing was performed using the emmeans
 package (Lenth & Lenth, 2018).
description: # Data from: Industry needs matter – incorporating stakeholder interests
 in the selection of flower resources to support pollinators
 [https://doi.org/10.5061/dryad.1c59zw456](https://doi.org/10.5061/dryad.1c59zw456) ## Description of the data and file structure 19 plant species, attractive to pollinators, were establishment in the blueberry system. The plants were first placed in blueberry blocks grown under polytunnels and thereafter transferred to open field blueberry blocks. For the 36 individual plants representing 11 plant species that survived in the blueberry system, and co-flowered with the blueberry plants, we conducted standardised observations of floral insect visitors. The observations were conducted in polytunnels in March 2020, approximately 10 months after establishment and in open field blocks in March-May 2020. Individual plants were observed for five minutes, and all floral visitors were recorded, together with the number of open flowers. We conducted eight separate days of observations in the polytunnels and five days of observations in the open block.  .csv file: Includes observations of flower visitors to candidate flowering plant species incorporated into a blueberry production system. .R file is the script we used to analyse the the number of visitors to candidate plants ### Files and variables #### File: data.csv **Description:**  Standardised observation data on identity and number of visitors that visited the candidate plant species. ##### Variables * date: The date for the insect visitor observations * time: The time for the insect visitor observations * block_type: Blueberries were cultivated in both polytunnels and in open field blocks. Candidate plants were observed in both cultivation settings. * plant_species: Scientific species name of candidate plant * common_name: Common species name of candidate plant * tunnel_number: Blueberry tunnel or block number (depending on if grown in poly tunnels or in open field blocks) * tunnel_location_row: Specific row location within the tunnel or field block * pot_number: Position of the pot counted from the edge of the row * hb: Number of observed honey bee, Apis mellifera, during the 5 min observation * sb: Number of observed stingless bee, Tetragonula carbonaria, during the 5 min observation * amegilla: Number of observed insect from the bee genus Amegilla, during the 5 min observation * other_colletidae: Number of observed insects from the bee family colletidae, during the 5 min observation * other_halictidae: Number of observed insects from the bee family halictidae, during the 5 min observation * calliphoridae: Number of observed insects from the family calliphoridae, during the 5 min observation * muscidae: Number of observed insects from the family muscidae, during the 5 min observation * chloropidae: Number of observed insects from the family chloropidae, during the 5 min observation * sarcophagidae: Number of observed insects from the family sarcophagidae, during the 5 min observation * syrphidae: Number of observed insects from the family syrphidae, during the 5 min observation * stripey_moth: Number of observed insects of stripey moth morph, during the 5 min observation * brown_grey_moth: Number of observed insects of brown grey moth morph, during the 5 min observation * wasp_pergidae: Number of observed insects from the family pergidae, during the 5 min observation * flowers: number of flowers on the plant #### File: analysis.R **Description:**  The analysis.R file contains the Rscript for the generalised linear mixed effect models that we used for analysing the number of visitors to candidate plants. We used the glmmTMB package (Brooks et al., 2017). Random effects reflected experimental design, with the effects of plant location nested within row, and the crossed effects of observation date. The natural logarithm of the mean number of flowers for each plant species was used as an offset. Models were specified with a negative binomial error distribution following model diagnostics implemented with the DHARMa package (Hartig & Hartig, 2017). Separate models were fitted for honeybees, stingless bees, and for all visitors combined. Due to low visitation to some plant species, only plant species receiving greater than one floral visit in aggregate were included in the models. Pairwise testing was performed using the emmeans package (Lenth & Lenth, 2018). ## Code/software R (R Core Team, 2017) R Core Team (2017). *R: A Language and Environment for StatisticalComputing*. R Foundation for Statistical Computing, Vienna, Austria. [https://www.R-project.org/](https://www.R-project.org/) Loaded packages: glmmTMB Mollie E. Brooks, Kasper Kristensen, Koen J. van Benthem, Arni Magnusson, Casper W. Berg, Anders Nielsen, Hans J. Skaug, Martin Maechler and Benjamin M. Bolker (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. tidyverse Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” *Journal of Open Source Software*, *4*(43), 1686. doi:10.21105/joss.01686 [https://doi.org/10.21105/joss.01686](https://doi.org/10.21105/joss.01686). emmeans Lenth R (2024). *emmeans: Estimated Marginal Means, aka Least-Squares Means*. R package version 1.10.3, [https://CRAN.R-project.org/package=emmeans](https://CRAN.R-project.org/package=emmeans). DHARMa 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). ggpubr Kassambara A (2023). *ggpubr: 'ggplot2' Based Publication Ready Plots*. R package version 0.6.0, [https://CRAN.R-project.org/package=ggpubr](https://CRAN.R-project.org/package=ggpubr).