DOI: 10.5061/dryad.cfxpnvxh5
title: Wild pollinators and honeybees respond differently to landscape-scale organic farming and increase sunflower yields

description: Wild pollinators play a critical role in crop production, yet they are
 increasingly threatened by agricultural intensification and habitat loss.
 Hence, identifying effective measures to support pollinators at landscape
 and field scale is crucial for maintaining pollination services and
 ensuring sustainable food production. We assessed how landscape
 composition (area of organic farming, semi-natural habitats and
 mass-flowering crops) and field management (farming system, weed cover and
 weed richness) influence wild pollinators and honeybees in sunflower
 fields. Additionally, we used a pollinator exclusion experiment to assess
 the effects of landscape composition, field management and pollinators on
 seed weight, seed number, pollination services and overall
 yield. Bumblebee abundance increased with organic farming area in
 the landscape, while solitary bee richness increased with semi-natural
 habitat area. Both bumblebees and hoverflies declined in abundance with
 increasing mass-flowering crop area in the landscape. At field level, the
 abundance and richness of solitary bees and hoverflies increased with weed
 richness. Insect pollination in open compared to pollinator-excluded
 treatments increased yields on average by 25%. Pollination services and
 overall yields were not affected by weeds. Overall yields did not differ
 between conventional and organic fields, while pollination services were
 marginally higher in organic fields. Our findings underscore the
 need for multi-scale conservation strategies to sustain pollinators and
 pollination services. Increasing organic farming at the landscape scale
 can support pollinators across both organic and conventional systems but
 cannot replace semi-natural habitats, which remain essential to enhance
 solitary bees in crop fields. Landscape management should therefore
 promote both organic farming and semi-natural habitats. Tolerating
 moderate weed levels within fields can further enhance wild pollinators
 without reducing yields. Farmers should also consider the amount of
 simultaneously mass-flowering crops in the landscape, to avoid dilution
 effects. Our findings provide practical strategies to support different
 groups of wild pollinators through integrative landscape and field
 management and strengthen pollination services in agroecosystems.
description: Site selection The study was carried out in
 2022 on 14 conventional and 15 organic sunflower fields in agricultural
 landscapes around Würzburg (Northern Bavaria, Germany, Fig. 1A). The
 fields were selected in such way that the surrounding landscape
 composition covered the broadest possible gradients in the area of 1)
 annual organic farming, 2) semi-natural habitats and 3) sunflower fields,
 whereby maximizing gradient 1 had highest and gradient 3 had lowest
 priority (Fig. 1B). Conventional farmers applied mineral fertilizer and
 herbicides, but no insecticides in the sunflower fields. Organic farmers
 applied no pesticides or fertilizers, with two exceptions where manure or
 compost were used. The average number of different crops in the typical
 rotation was 2.75 ± 0.48 (mean ± SE) for conventional and 4.3 ± 0.33 (mean
 ± SE) for organic sunflower fields. Minimum distance between study fields
 was 5.8 ± 0.75 km (mean ± SE). In two cases, distance between fields was
 less than 2 km (958 and 1671 m). Field size ranged between 0.8 and 39.9 ha
 and was on average 5.6 ± 1.3 ha (mean ± SE). Neither field size nor
 sunflower coverage differed between conventional and organic sunflower
 fields (t-test, all P &gt; 0.1). Landscape
 composition The study region is dominated by annual
 crop fields, interspersed with semi-natural habitat fragments, villages
 and forests. The most dominant crop in the study region is winter wheat,
 with other typical crops being barley, spelt and oilseed rape (Supporting
 Information, Fig. S1). We calculated landscape composition around each
 field in a 500 m and 1 km radius. These radiuses were chosen to cover the
 typical foraging distance of the studied groups of pollinators (Greenleaf
 et al., 2007; Kendall et al., 2022; Meyer et al., 2009; Zurbuchen et al.,
 2010). Using ArcGIS Pro version 3.2.1 (ESRI, 2023), we calculated the
 proportion of annual organic crop fields and sunflower fields in the study
 year (hereafter “organic farming area” and “sunflower area”), using data
 from the Integrated Administration and Control System (Federal Ministry of
 Food and Agriculture, 2022). To calculate proportions of semi-natural
 habitats (“semi-natural habitat area”), we created a map combining data
 from different sources (Supporting information) which was validated and
 updated via landscape surveys in the study year. The following habitats
 were characterized as semi-natural habitats: orchard meadows, extensively
 managed grasslands, fallows, reed, hedges and forest edges. Landscape
 variables were not correlated, with the exception of organic farming area
 and sunflower area, which were positively correlated (R = 0.54, Fig. S2),
 because sunflower is more often part of organic than of conventional crop
 rotations. We included both, organic farming area and sunflower area, as
 variables in the same models since they were expected to have opposite
 effects on pollinators. Pollinator, flower and yield
 surveys We sampled bees and hoverflies in two rounds in
 July 2022 during sunflower bloom. We differentiated between honeybees,
 bumblebees, all other wild bees (hereafter referred to as “solitary bees”)
 and hoverflies since these groups differ considerably in their ecology.
 Pollinators were surveyed using transect walks, standardized by time and
 area. Each transect consisted of 30 minutes searching time and covered
 300 m<sup>2</sup> (300 m length, 1 m width). One half of the
 transect (15 minutes, 150 m<sup>2</sup>) was walked along the
 edge of the field, the other half was walked in the center of the field,
 along the sowing row lines. Individuals were identified in the field or
 caught using a net for identification in the laboratory. For each
 flower-visiting pollinator recorded during the transect walks, we recorded
 whether it visited a sunflower or a weed. Additionally, we conducted
 flower surveys of weeds and sunflowers on the same transects in each
 round. Weed richness was assessed by counting the number of distinct
 non-sunflower morphospecies per transect. Weed and sunflower coverage were
 measured by the size (i.e. area) of one representative flower or floral
 unit per species and multiplying it by the number of flowers or floral
 units in the transect. If flower size varied within a transect, we
 calculated flower coverage for each section based on the respective
 representative flower size and summed them up for total coverage. We
 summed flower coverage across all weed species per transect. We also
 measured sun and shade temperature during each round. Pollinator surveys
 were always conducted between 9:00 am and 5:30 pm, when shade temperatures
 were at least 18° C and wind speeds did not exceed 3 Bft. The order (i.e.
 time of the day) in which sunflower fields were visited was randomized
 between rounds to avoid effects of sampling time. Pollinator abundances
 were not affected by sampling temperature (Table S1, Fig. S3). To assess
 the contribution of insect pollination to sunflower yield components, we
 conducted pollinator exclusion experiments on 28 of our study fields (14
 conventional, 14 organic). In mid-June (i.e. before flowering), we marked
 eight sunflower heads of different plants along the edge and in the center
 of each field. All plants had a minimum distance of 2 m to each other and
 plants in the center of the field had a minimum distance of 20 m to all
 edges. Half the heads per location were bagged using a fine mesh net
 before bud opening, to allow wind and self-pollination only. After
 sunflower bloom, we removed the nets. Shortly before harvest, we
 hand-harvested the marked flowers. Seeds were extracted from the heads;
 empty seeds were sorted out and the head diameter was measured. Then, the
 seeds were dried at 55° C for 48 hours. Seeds per head were weighed using
 a fine scale and counted using a counting machine. Seed number and seed
 weight per plant were averaged for each field and location (center vs.
 edge). We calculated the yield of open and bagged flowers as average seed
 weight per flower per location, multiplied by flower density per location.
 Pollination services were calculated as the difference in yield between
 open and bagged flowers per location and field. To estimate overall yield,
 we used the yield calculated from open pollinated flowers in the field
 center. Statistical analyses We
 summed pollinator abundances per group across rounds and calculated
 cumulative species richness for pollinators and weeds per field. Weed and
 sunflower coverage were averaged per field across rounds. We calculated
 Chao1 estimators for species diversity for each wild pollinator group
 using the ‘vegan’ package (Oksanen et al., 2022). Since the Chao1
 estimator was highly correlated with measured species richness for all
 pollinator groups (r &gt; 0.7, <em>P</em> &lt; 0.001,
 Fig. S4), we used measured species richness in all our models.
 We used generalized linear models (GLMs) to identify effects of
 landscape composition and field-scale management on pollinators, weeds,
 pollination services and overall yield. For pollinator abundance models,
 we used negative binomial distributions, to account for
 overdispersion (Venables & Ripley, 2002). We used Gamma
 distributions for pollinator richness and Poisson distribution for
 solitary bee richness models. For weed, pollination services and overall
 yield models, we used Gaussian distributions. We fitted separate models
 for abundance and richness of each pollinator group, weed coverage
 (log-transformed), weed richness, pollination services, and overall yield
 as responses. We implemented a multi-step modelling
 approach, because field-scale predictors were partly moderated by
 landscape-scale predictors, which could obscure the actual relationships
 between predictor and response variables (Arif & MacNeil, 2023).
 In the first model set, we included landscape-composition variables
 (organic farming area, semi-natural habitat area and sunflower area within
 a 1 km radius), farming system (conventional vs. organic), as well as the
 interactions of each landscape variable with farming system. For solitary
 bees, we ran an additional model set using landscape variables at a 500 m
 radius, because foraging distances in this pollinator group can be smaller
 than 1 km (Zurbuchen et al., 2010). To assess the
 effects of field-scale management on pollinators, we calculated a second
 set of models with the predictors sunflower coverage, weed coverage, weed
 richness, and honeybee abundance as predictors. To identify effects of
 weeds on pollination services and overall yield, we calculated models with
 weed coverage and weed richness as predictors. In all models from the
 second set, we included all predictors from the first set of landscape
 models as control variables to obtain unbiased causal estimates for the
 field-scale variables (Arif & MacNeil, 2023). In the models for
 pollination services we included field ID as random effect.
 To assess pollinator resource use (sunflower vs. weeds), we
 summed the number of pollinators recorded on sunflowers and the number of
 pollinators recorded on weeds per pollinator group per field. We then
 calculated negative binomial models using flower resource (i.e. sunflower
 vs. weeds), farming system and their interaction as predictors.
 To test whether seed number, seed weight, and yield differ
 between bagged and open flowers,  we calculated GLMs (Brooks et al., 2017)
 with treatment (i.e. bagged vs. open), farming system and their
 interaction as fixed effects, and field ID as random effect. For models
 with seed weight or seed number, we used normal distribution and for
 models with yield, we used Gamma distribution. To evaluate the effects of
 pollinator abundance on pollination services, we calculated linear mixed
 effects models (Bates et al., 2015) with pollinator abundance as fixed
 effect, and field ID as random effect. We assessed the
 significance of predictors for all models using a Type II ANOVA from the
 ‘car’ package, since our focus was to test the main effects of predictors,
 while still accounting for potential interactions (Fox & Weisberg,
 2019). Residuals of all models were checked using ‘DHARMa’ (Hartig, 2022).
 Statistical analyses were done in R version 4.3.1 (R Core Team,
 2023).
description: # Wild pollinators and honeybees respond differently to landscape-scale
 organic farming and increase sunflower yields Dataset DOI:
 [10.5061/dryad.cfxpnvxh5](10.5061/dryad.cfxpnvxh5) ## Description of the
 data and file structure Here, we aimed to disentangle effects on wild
 pollinators, weeds, and yield components acting at the landscape scale and
 the local field scale. At the landscape scale, we assessed the effects of
 landscape composition, quantified as the area of organic farming,
 semi-natural habitats, and mass-flowering crops, within 500 m and 1 km
 radiuses around the field center. At the field scale, we assessed effects
 of field-scale management, based on farming system (conventional vs.
 organic), weed coverage, weed richness, and sunflower coverage. We
 surveyed flower visitation of three groups of wild pollinators
 (bumblebees, solitary bees, and hoverflies) and honeybees in conventional
 and organic sunflower fields along a gradient of surrounding landscape
 composition. We surveyed pollinators (bumblebees, solitary bees,
 hoverflies, and honeybees) and flowering plants via transect walks on
 conventionally and organically managed sunflower fields. We performed two
 rounds of sampling during the flowering period of the sunflowers. For all
 flower-visiting pollinators, we recorded whether they were visiting a
 sunflower or a weed. On the same fields, we conducted pollinator exclusion
 experiments on 16 plants per field. We harvested the bagged (pollinator
 exclusion) and control (open pollination) flowers and counted and weighed
 the seeds per flower. Additionally, we calculated the yield of open and
 bagged flowers as average seed weight per flower per location, multiplied
 by flower density per location. Pollination services were calculated as
 the difference in yield between open and bagged flowers per location and
 field. To estimate overall yield, we used the yield calculated from open
 pollinated flowers in the field center. ### Files and variables #### File:
 flower.survey.csv **Description:** We conducted flower surveys on the same
 transects we surveyed pollinators, in two rounds during sunflower
 flowering. For this, we identified each flowering plant to genus or
 morphospecies level and estimated flower cover by measuring flower size of
 one representative flower and then estimating the number of flowers in the
 transect. We distinguished between sunflowers and weeds. Species richness
 of flowering plants was cumulated across rounds. Sunflower and weed cover
 were averaged per field across rounds. ##### Variables * plotID: unique ID
 of each sunflower field * sf.cov: Estimated sunflower cover of the
 transect, averaged across rounds [%] * weed.cov: Estimated weed cover of
 the transect, averaged across rounds [%] * flower.rich: Total flower
 species richness (sunflower + weeds) in the transect, cumulated across
 rounds * weed.rich: Species richness of weeds in the transect, cumulated
 across rounds * sf.cov.log: log-transformed sunflower cover *
 weed.cov.log: log-transformed weed cover #### File: flower.count.csv
 **Description:** As part of the flower survey, we counted the average
 number of sunflowers per square meter for each transect. ##### Variables *
 plotID:  unique ID of each sunflower field * location: center vs. edge.
 Within-field location * flower.count.sqm: number of sunflowers per square
 meter #### File: transect.size.csv **Description:** Exact sizes of sampled
 transects. All transects were planned to cover of 300 m2. Half of which
 was walked along the edge, and half in the center of the field. In
 individual cases, transects slightly varied in size. ##### Variables *
 plotID:  unique ID of each sunflower field * location: center vs. edge.
 Within-field location * transect.sqm: transect size in square meter ####
 File: pollinator.table.csv **Description:** Data of the pollinator
 surveys. We sampled bees and hoverflies in two rounds in July 2022 during
 the flowering period of the sunflowers. We differentiated between
 honeybees, bumblebees, all other wild bees (hereafter referred to as
 “solitary bees”), and hoverflies. For pollinator sampling, we used
 transect walks, standardized by time and area. Each transect consisted of
 30 minutes searching time and covered 300 m2 (300 m length, 1 m width).
 One half of the transect (15 minutes, 150 m2) was walked along the edge of
 the field, the other half was walked in the center of the field, along the
 sowing row lines. We summed pollinator abundances per group across rounds
 and calculated cumulative species richness per field This data is already
 combined with data on landscape composition. We calculated landscape
 composition around the center of each field in a 500 m and 1 km radius. We
 calculated the proportion (hereafter referred to as "area") of
 annual organic agriculture, sunflower fields, and semi-natural habitats in
 the study year. ##### Variables * plotID: unique ID of each sunflower
 field * management: conventional (conv.) vs. organic (org.). Type of
 farming system. Organic management prohibits the use of synthetic
 pesticides and fertilizers. * BB.abd: bumblebee abundance, summed across
 sampling rounds * HB.abd: honeybee abundance, summed across sampling
 rounds * HF.abd: hoverfly abundance, summed across sampling rounds *
 WB.abd: solitary bee abundance, summed across sampling rounds * BB.rich:
 bumblebee richness, cumulated across sampling rounds * HF.rich: hoverfly
 richness, cumulated across sampling rounds * WB.rich: solitary bee
 richness, cumulated across sampling rounds * al.1000:  area of annual
 agriculture in a 1km radius [%] * org.1000: area of annual organic
 agriculture in a 1km radius [%] * osr.1000: area of oilseed rape fields in
 a 1km radius [%] * sf.1000: area of sunflower fields in a 1km radius [%] *
 snh.1000: area semi-natural habitats in a 1km radius [%] * crop.div.1000:
 crop diversity in a 1km radius * al.500: area of annual agriculture in a
 500m radius [%] * org.500: area of annual organic agriculture in a 500m
 radius [%] * sf.500: area of sunflower fields in a 500m radius [%] *
 osr.500: area of sunflower fields in a 500m radius [%] * snh.500: area of
 semi-natural habitats in a 500m radius [%] * crop.div.500: crop diversity
 in a 500m radius #### File: resource_use.csv **Description:** Data is
 gained from pollinator surveys (see description of pollinator.table.csv).
 Here, we filtered only pollinators we recorded visiting a flower. For
 this, we differentiated between sunflowers and all other flowers (i.e.,
 weeds). ##### Variables * plotID: unique ID of each sunflower field *
 management: conventional (conv.) vs. organic (org.). Type of farming
 practice. Organic management prohibits the use of synthetic pesticides and
 fertilizers. * resource.type: sunflower vs. weed. visited flower type *
 BB.abd: number of bumblebees recorded on the respective flower type,
 summed across sampling rounds * HB.abd: number of honeybees recorded on
 the respective flower type, summed across sampling rounds * HF.abd: number
 of hoverflies recorded on the respective flower type, summed across
 sampling rounds * WB.abd: number of solitary bees recorded on the
 respective flower type, summed across sampling rounds #### File:
 exclosure.csv **Description:** Data of the pollinator exclosure
 experiment. Before flowering, we marked 16 flowers per field (8 along the
 edge, 8 in the center of the field). Half of the flowers at each location
 were bagged to allow only self and wind-pollination. Shortly before
 sunflower harvest, we hand-harvested the marked plants. Seeds were dried.
 We counted the number of seeds, measured total seed weight, and measured
 head diameter of each flower. All variables were averaged across the four
 plants per location per field. ##### Variables * plotID: unique ID of each
 sunflower field * management: conventional (conv.) vs. organic (org.).
 Type of farming practice. Organic management prohibits the use of
 synthetic pesticides and fertilizers. * location: center vs. edge.
 Within-field location * treatment: open vs. bagged. Treatment of the
 pollinator-exclosure experiment. Open: no pollinator exclusion, i.e.,
 control; bagged: pollinator exclusion * mean.number: average number of
 seeds per flower * mean.weight: average seed weight per flower [g] *
 mean.size: average diameter of sunflower head [cm] #### File:
 yield.survey.csv **Description:** Data is received via a survey with the
 participating farmers. The farmers of a subset of our surveyed sunflower
 fields gave us information on their harvested yield and management
 practices.  NA: Data not available. In some cases, farmers were unable to
 remember or did not record their exact management practices. In these
 cases, the data included NAs. ##### Variables * plotID: unique ID of each
 sunflower field * yield: Sunflower yield per field, measured as seed
 weight per hectare [quintal/ha]. Measured and provided by the farmers. *
 field.size.sqm: area of the sunflower field [ha] * fertilizer: type of
 fertilizer used on the field in the study year * fertilizer.amount: amount
 of each fertilizer used on the field in the study year, as reported by the
 farmers through interviews [mixed units, depending on the fertilizer] *
 totalN: Total amount of fertilizer used on the field, as total Nitrogen
 [kg/ha], calculated based on the farmers' statements *
 fertility.degree: Soil appraisal. An estimation of the agricultural
 productivity potential (i.e., yield potential) of land based on soil
 quality, terrain, and climatic factors. Unit: index from 0-100 where
 higher numbers indicated greater yield potential. * herbicide: type of
 herbicide used on the field in the study year * herbicide.amount: amount
 of herbicide used on the field in the study year [kg/ha] * herbicide.time:
 time in the year of herbicide application #### File: crop.comp.2021.csv
 **Description:** Crop composition of the study region in the year
 preceding our study. The study region was defined as the landscape in a
 5km radius around all our studied sunflower fields. Information on crop
 composition was received from the Integrated Administration and Control
 System. ##### Variables * crop: Crops growing in the study region * area:
 Area each crop covered in the study region [ha] * year: Year for which
 crop composition was sampled. #### File: crop.comp.2022.csv
 **Description:** Crop composition of the study region in the study year.
 The study region was defined as the landscape in a 5km radius around all
 our studied sunflower fields. Information on crop composition was received
 from the Integrated Administration and Control System. ##### Variables *
 crop: Crops growing in the study region * area: Area each crop covered in
 the study region [ha] * year: Year for which crop composition was sampled.
 #### File: crop.comp.2023.csv **Description:** Crop composition of the
 study region in the year following our study. The study region was defined
 as the landscape in a 5km radius around all our studied sunflower fields.
 Information on crop composition was received from the Integrated
 Administration and Control System. ##### Variables * crop: Crops growing
 in the study region * area: Area each crop covered in the study region
 [ha] * year: Year for which crop composition was sampled. #### File:
 field.sizes.csv **Description:** Dataset with the area of each surveyed
 sunflower field. ##### Variables * management: conventional (conv.) vs.
 organic (org.). Type of farming system. Organic management prohibits the
 use of synthetic pesticides and fertilizers. * field.size: area of the
 surveyed sunflower field [ha] * min.dist: minimum distance of each plot to
 the closest plot [m] * plotID: unique ID of each sunflower field ####
 File: pol2.csv **Description:** Data of the pollinator surveys. We sampled
 bees and hoverflies in two rounds in July 2022 during the flowering period
 of the sunflowers. We differentiated between honeybees, bumblebees, all
 other wild bees (hereafter referred to as “solitary bees”), and
 hoverflies. For pollinator sampling, we used transect walks, standardized
 by time and area. Each transect consisted of 30 minutes searching time and
 covered 300 m2 (300 m length, 1 m width). One half of the transect
 (15 minutes, 150 m2) was walked along the edge of the field, the other
 half was walked in the center of the field, along the sowing row lines.
 This dataset is based on the same surveys as
 "pollinator.table.csv", only that pollinator abundance and
 richness is separated for sampling rounds. ##### Variables *
 plotID: unique ID of each sunflower field * round: sampling round (1 or 2)
 * BB.abd: bumblebee abundance * HB.abd: honeybee abundance * HF.abd:
 hoverfly abundance * WB.abd: solitary bee abundance * BB.rich: bumblebee
 richness * HF.rich: hoverfly richness * WB.rich: solitary bee richness
 #### File: temp.transects.csv **Description:** Dataset with measured sun
 and shade temperatures during pollinator surveys. This data is already
 merged with the abundances of the respective pollinator groups, separated
 between sampling rounds (see description of pol2.csv). ##### Variables *
 plotID: unique ID of each sunflower field * round: sampling round (1 or 2)
 * temp.sun: temperature measured in the sun [%] * temp.shade: temperature
 measured in the shade [%] * BB.abd: bumblebee abundance * HB.abd: honeybee
 abundance * HF.abd: hoverfly abundance * WB.abd: solitary bee abundance *
 BB.rich: bumblebee richness * HF.rich: hoverfly richness * WB.rich:
 solitary bee richness #### File: pol.comp.csv **Description:** Species
 composition of pollinators, gained from pollinator surveys, performed via
 transect walks. We summarized the total abundance of each species per
 field to calculate Chao1 richness indices. ##### Variables *
 plotID: unique ID of each sunflower field * group: Pollinator group. BB:
 Bumblebee, WB: Solitary wild bee, HF: Hoverfly, HB: Honeybee * species:
 Identified pollinator species * abundance: total abundance of each species
 per sunflower field, cumulated across sampling rounds #### File: pol3.csv
 **Description:** Data of the pollinator surveys. We sampled bees and
 hoverflies in two rounds in July 2022 during the flowering period of the
 sunflowers. We differentiated between honeybees, bumblebees, solitary
 bees, and hoverflies. For pollinator sampling, we used transect walks,
 standardized by time and area. Each transect consisted of 30 minutes
 searching time and covered 300 m2 (300 m length, 1 m width). One half of
 the transect (15 minutes, 150 m2) was walked along the edge of the field,
 the other half was walked in the center of the field, along the sowing row
 lines. This dataset is based on the same surveys as
 "pollinator.table.csv", only that pollinator abundance and
 richness is separated for field location (i.e., center vs. edge of the
 field), but averaged between rounds. ##### Variables * plotID: unique ID
 of each sunflower field * management: conventional (conv.) vs. organic
 (org.). Type of farming system. Organic management prohibits the use of
 synthetic pesticides and fertilizers. * location: center vs. edge.
 Within-field location * BB.abd: bumblebee abundance * HB.abd: honeybee
 abundance * HF.abd: hoverfly abundance * WB.abd: solitary bee abundance *
 BB.rich: bumblebee richness * WB.rich: solitary bee richness *
 HF.rich: hoverfly richness * flower.rich: Total flower species richness
 (sunflower + weeds) in the transect, cumulated across rounds *
 sf.cov: Estimated sunflower cover of the transect, averaged across rounds
 [%] * weed.cov: Estimated weed cover of the transect, averaged across
 rounds [%] ## Code/software **R version 4.3.1** **R studio** used R
 packages: tidyverse: for data wrangling car: Type-II ANOVA lme4: linear
 mixed effect models MASS: negative binomial models glmmTMB: models with
 Gamma distribution and random effects mgcv: GAMs (for temperature
 analysis) MuMIn: extract AICcs of models performance: calculate pseudo-R2
 for GLMs DHARMa: model diagnostics ggeffects: model effects emmeans:
 interaction effects vegan: species composition and Chao1 calculation
 flextable: contruct tidy output tables ggplot2: plotting of results egg:
 arranging plots ggpubr: arranging plots GGally: correlation plots
 **Reproducible script is supplied under Sunflower_Analyses.Rmd**
 **Sunflower_Analyses.html**: R Markdown output of all analyses used.  ##
 Access information Data was derived from the following sources: Data on
 crop composition, organic farming, and mass-flowering crops was calculated
 using data from the Integrated Administration and Control System (Federal
 Ministry of Food and Agriculture, 2022). To create a map of semi-natural
 habitats, we combined data from the Integrated Administration and Control
 System (IACS; Federal Ministry of Food and Agriculture, 2022), from the
 Bavarian biotope map (Bayrisches Landesamt für Umwelt [LfU], 2023), from
 the official cadastral information system (ALKIS; Bayerisches Landesamt
 für Digitalisierung, Breitband und Vermessung [LDBV], 2021), and from
 CORINE landcover data (European Environment Agency [EEA], 2018). This map
 was then validated and updated via a landscape survey in the study year.
 Landscape variables were calculated using ArcGIS Pro version 3.2.1 (ESRI,
 2023). * ESRI. (2023). *ArcGIS Pro* (Version 3.1.2) [Computer software].
 [https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview](https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview) * Federal Ministry of Food and Agriculture. (2022). *Integrated Administration and Control System*. [https://agriculture.ec.europa.eu/common-agricultural-policy/financing-cap/assurance-and-audit/managing-payments_en](https://agriculture.ec.europa.eu/common-agricultural-policy/financing-cap/assurance-and-audit/managing-payments_en) * Bayrisches Landesamt für Umwelt. (2023). *Biotopkartierung Flachland - Umweltdaten und Geodatendienste*. [https://www.lfu.bayern.de/umweltdaten/geodatendienste/pretty_downloaddienst.htm?dld=biotopkartierung.xml](https://www.lfu.bayern.de/umweltdaten/geodatendienste/pretty_downloaddienst.htm?dld=biotopkartierung.xml) * Bayerisches Landesamt für Digitalisierung, Breitband und Vermessung. (2021). *Tatsächliche Nutzung - Daten der Bayrischen Vermessungsverwaltung*. [https://www.ldbv.bayern.de/produkte/kataster/tat_nutzung.htmlhttps://www.ldbv.bayern.de/produkte/kataster/tat_nutzung.html](https://www.ldbv.bayern.de/produkte/kataster/tat_nutzung.htmlhttps://www.ldbv.bayern.de/produkte/kataster/tat_nutzung.html) * European Environment Agency. (2018). *CORINE Land Cover*. [https://land.copernicus.eu/en/products/corine-land-cover](https://land.copernicus.eu/en/products/corine-land-cover)