DOI: 10.5061/dryad.vx0k6dk36
title: Data from: Floral diversity enhances winter survival of honey bee colonies across climatic regions

description: In temperate climates, winter can be an arduous time for eusocial insects.
 Survival of honey bee colonies during winter depends on a delicate balance
 between hive thermoregulation, managing the food reserves, and timing the
 onset of the new worker bee generation. Winter survival is influenced by
 several factors, most notably colony size, Varroa mites infestation levels
 and the availability of stored food. Importantly, the climatic conditions
 and floral resources of the previous foraging season can also impact honey
 bee health and colony strength before hibernation. This study, conducted
 across Europe, examines how landscape composition and weather conditions
 affect winter survival of honey bee colonies. It uses pollen diversity as
 a proxy for flower resource quality and available foraging days as a
 climatic variable to understand their causal relationships to winter
 survival. We found that landscapes with higher percentages of agricultural
 areas increased pollen diversity collected by honey bees in autumn,
 whereas higher percentages of semi-natural areas increased the diversity
 during summer. Spring and autumn pollen diversity was the main driver for
 winter survival success, emphasizing the importance of diverse flower
 resources for colony health. While we did not find a statistically
 significant effect of weather on winter survival, trends suggest potential
 influences, warranting further research to confirm and clarify the role of
 seasonal foraging on colony health. Our study highlights the critical role
 of including floral resource diversity and weather conditions, in a
 comprehensive framework for studying honey bee hibernation. It suggests
 that increasing plant diversity around apiaries and implementing
 agricultural practices that enhance floral resources can significantly
 improve winter survival, with honey bee colonies benefiting even in
 landscapes with higher agricultural activity, distinct from the needs of
 other pollinators.
description: The study quantified landscape composition around apiaries using
 QGIS with a 2-km buffer and land use data from the CORINE 2018 Land Cover
 map (European Environment Agency and European Environment Agency 2019),
 classifying habitats into four categories. Pollen diversity, used as a
 proxy for floral resource diversity, was assessed through monthly pollen
 collection from March to November 2022 using pollen traps, followed by DNA
 metabarcoding for species identification (Sickel et al.,2015). Weather
 data were obtained using local temperature sensors and precipitation
 records retrieved using the R package <em>climate</em>
 (Czernecki et al., 2020), with seasonal foraging conditions calculated
 based on temperature and precipitation thresholds. Statistical analysis
 employed DAGs to explore relationships between landscape composition,
 floral diversity, weather conditions, and honey bee overwintering success.
 Two GLMMs were fitted: one examining the effect of landscape composition
 on pollen diversity and another assessing the influence of foraging days
 and floral resource diversity on winter survival. Analyses were conducted
 in R using various statistical and visualization packages, ensuring model
 validity through diagnostic checks. All statistical analyses were
 implemented using R v.4.3.1 (R Core Team 2023). Both models were fitted
 using the <em>glmmTMB</em> package (Brooks et al., 2017). The
 package <em>DHARMa</em> (Hartig 2022) allowed us to evaluate
 the models’ performance. Model coefficients from the GLMM were generated
 using the packages <em>broom.mixed</em> (Bolker &
 Robinson 2022) and <em>emmeans (</em>Lenth
 2023<em>).</em> The graphs were generated using R packages
 <em>ggplot</em> (Wickham 2016) and
 <em>ggeffects</em> (Lüdecke 2018). Other packages used are
 specified in the script.
 <strong>REFERENCES</strong>
 Bolker, B., & Robinson, D. (2022). <em>Broom.mixed:
 Tidying methods for mixed models</em>. https://CRAN.R-project.org/package=broom.mixed Brooks, M. E., Kristensen, K., van, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug, H. J., Maechler, M., & Bolker, B. M. (2017). <em>glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling</em>. <em>9</em>. https://doi.org/10.32614/RJ-2017-066 Czernecki, B., Głogowski, A., & Nowosad, J. (2020). <em>Climate: An r package to access free in-situ meteorological and hydrological datasets for environmental assessment</em>. <em>12</em>, 394. https://doi.org/10.3390/su12010394 European Environment Agency, & European Environment Agency. (2019). <em>CORINE Land Cover 2018 (vector), Europe, 6-yearly - version 2020_20u1, May 2020</em>. European Environment Agency. https://doi.org/10.2909/71C95A07-E296-44FC-B22B-415F42ACFDF0 Hartig, F. (2022). <em>DHARMa: Residual diagnostics for hierarchical (multi-level / mixed) regression models</em>. https://CRAN.R-project.org/package=DHARMa Lenth, R. V. (2023). <em>Emmeans: Estimated marginal means, aka least-squares means</em>. https://CRAN.R-project.org/package=emmeans Lüdecke, D. (2018). <em>Ggeffects: Tidy data frames of marginal effects from regression models.</em> <em>3</em>, 772. https://doi.org/10.21105/joss.00772 R Core Team. (2023). <em>R: A language and environment for statistical computing</em>. R Foundation for Statistical Computing. https://www.R-project.org/ Sickel, W., Ankenbrand, M. J., Grimmer, G., Holzschuh, A., Härtel, S., Lanzen, J., Steffan-Dewenter, I., & Keller, A. (2015). Increased efficiency in identifying mixed pollen samples by meta-barcoding with a dual-indexing approach. <em>BMC Ecology</em>, <em>15</em>(1), 20. https://doi.org/10.1186/s12898-015-0051-y Wickham, H. (2016). <em>ggplot2: Elegant graphics for data analysis</em>. https://ggplot2.tidyverse.org
description: # Data from: Floral diversity enhances winter survival of honey bee
 colonies across climatic regions Dataset DOI:
 [10.5061/dryad.vx0k6dk36](10.5061/dryad.vx0k6dk36) ## Description of the
 data and file structure This dataset contains the data and code required
 to replicate the analyses investigating the relationship between landscape
 composition, floral resource diversity, weather conditions, and honey bee
 overwintering survival. Data were collected from multiple apiaries across
 different regions, with landscape composition quantified using QGIS and
 CORINE 2018 Land Cover data within a 2-km buffer around each site. Pollen
 diversity, used as a proxy for floral resource diversity, was assessed
 through monthly pollen collection from March to November 2022, followed by
 DNA metabarcoding for plant species identification. Weather data,
 including temperature and precipitation, were recorded at high temporal
 resolution and aggregated into seasonal foraging conditions. Statistical
 analyses used Directed Acyclic Graphs (DAGs) and Generalized Linear Mixed
 Models (GLMMs) to examine how landscape composition mediates pollen
 diversity and how seasonal foraging opportunities and weather conditions
 influence winter colony survival. The dataset includes raw and processed
 pollen diversity metrics, weather data, land use classifications, and
 winter survival data ## Open source data We have submitted two dataset
 included in *FloralDiversity.zip* : 1\) *dfDiv.csv*: pollen diversity for
 each apiary 2\) *survival.csv*: survival data  and 1 R script
 (*FloralDiversityScript.Rmd*) **Descriptions** *dfDiv.csv* Data: * date:
 date of sampling * country: country of sampling * region: region of
 sampling * apiary: id of each apiary * div_apiary: shannon index of
 diversity for each apiary in each date * weight: weight of sample (gr) *
 nday: day of sampling (from 1st January 2022) * month: month of sampling *
 season: season of sampling (March to May = Sprin, June to August = Summer,
 Septembe to November = Autumn) * landscape_div: shannon index of diversity
 of landscape composition (basing on number of pathes and areas) *
 urbanArea: percentage of urban area in 2 km around the apiary *
 agriculturalArea: percentage of agricultural area in 2 km around the
 apiary * natArea: percentage of natural area (excluding forest,
 eg.grasslands) in 2 km around the apiary * forestArea: percentage of
 forest area in 2 km around the apiary * semiNatArea: percentage of
 semi-natural area in 2 km around the apiary *survival.csv* Data: * year:
 overwintering year (either 2021-2022 or 2022-2023) * country * region *
 landscape: most representative landscape around the apiary  * hive.code:
 id of each hive * apiary: id of each apiary * survival: binary response
 variable indicating with survival or collapse of a colony (1= survived,0=
 not survived) * colony_strenght: estimated number of bees before winter
 (visual estimation) * fdays_growing: sum of foraging days during the
 growing season * fdays_winter: sum of foraging days during winter *
 temp_growing: average temperature during the growing season (Celsius) *
 temp_winter: average temperature during the winter season (Celsius) *
 rain_growing: sum of rain (mm) during the growing season * rain_winter:
 sum of rain (mm) during the winter season * div_spring: average value of
 diversity of pollen collected in spring (Shannon index) *
 div_summer: average value of diversity of pollen collected in
 summer (Shannon index) * div_autumn: average value of diversity of pollen
 collected in autumn (Shannon index) * weight_spring: average weight of
 pollen collected in spring (gr) * weight_summer: average weight of pollen
 collected in summer (gr) * weight_autumn: average weight of pollen
 collected in autumn (gr) NAs indicate missing data **Code** R is required
 to run *FloralDiversityScript.Rmd*. The script include the code for the
 DAG, models and models' diagnostic and models' predictions.
 Annotations are provided throughout the script.