DOI: 10.5061/dryad.4mw6m906s
title: Data from: Plant-pollinator interactions along an urbanization gradient from cities and villages to farmland landscapes

description: Urbanization affects pollinator diversity and plant-pollinator networks by
 changing resource availability locally and in the surrounding landscape.
 We experimentally established (N = 12) standardized plant communities in
 farmland, villages and cities to identify the relative role of local and
 landscape effects on plant-pollinator communities along this urbanization
 gradient. We found that the number of flower visits by solitary bees, but
 not bumblebees, were highest in cities and lowest in farmland, with
 villages being intermediate, whereas syrphid flies exhibited lowest
 numbers in cities. Villages supported the richest pollinator communities,
 as they appeared to benefit from both farmland and city communities.
 Plant-pollinator network metrics such as robustness, interaction evenness
 and interaction diversity decreased with increasing urbanization, although
 local plant richness increased towards urban areas. In conclusion,
 pollinator communities were most diverse and stable in farmland and
 village sites, despite the high plant richness in cities. The different
 composition of pollinator communities along the urbanization gradient
 suggests considering all three landscape types for conservation schemes.
description: <strong>Study sites</strong>
 Insect observations were conducted on an urbanization gradient
 from farmland and villages to cities, including grassy field margins in
 pure farmland, and gardens (c. 800-1000 m²) in villages and cities.
 Farmland sites were at least 500 m from the nearest house. Village sites
 were close to the village edge and were surrounded by a 500 m buffer
 comprising approximately 50% urban and 50% farmland. City sites were at
 least 500 m from the city edge and were surrounded by a buffer of 100%
 urban area. The urbanization gradient was constructed in this way to test
 the influence of amount of farmland in the landscape and the level of
 urbanization. N=12 sites were used: four farmland sites (maximum distance
 30 km from Göttingen), two villages (two gardens each: Dransfeld
 (51°50'06.01"N, 9°76'23.95"E) and Diemarden
 (51°48'72.82"N, 9°98'05.67"E) and two cities (two
 gardens each: Göttingen and Einbeck (51°49'13.29"N,
 9°52'6.14"E), separated by a minimum of 500 m inside the city
 border). <strong>Experimental plant
 plots</strong> Experimental plant patches were
 established in April 2015 (size 80 x 80 cm) in each of the 12 sites. We
 standardized soil conditions by using a soil mix at all sites (mix from
 volcanic clay, peat, lime carbonate and NPK fertilizer; 180 mg/L N, 180
 mg/L P; 260 mg/L K; 130 mg/L Mg and 100 mg/L of S with a pH of 5.9).
 Approximately 30 mL of NPK fertilizer was added when the seeds were
 planted, which contained equal parts N (8%) and P (8%). The numbers of
 plant seeds used were standardized to approximately 20 seeds per plant
 species and were evenly scattered over the soil. The plant species used
 were Phacelia tanacetifolia (Benth.) and Sinapis arvensis (L.). Plant
 patches were watered once a week with 10 L of water and fertilised once
 more after one month. The perennial garden plants Veronica spicata (L.)
 and Astilbe chinensis (Maxim.) were bought from a commercial supplier
 (Baumschule Jenssen, Göttingen) and transplanted to the experimental plots
 in June. This mixture of four plant species included plants with high
 quality pollen and nectar that are attractive to pollinators. The plant
 species covered a wide range of flower types, both open and tubular, and a
 mixture of colors: yellow, white and purple (Pritsch 2007). All plant
 species flowered simultaneously at the start of July for two
 weeks.   <strong>Insect
 observations</strong> Insect observations were
 run in early July 2015 for 15-minute intervals at two different times of
 the day (total observation hours = 6): morning (10-11:30) and midday
 (12:45-14:30), these times were centered on midday (13:15), calculated as
 the midpoint between sunrise and sunset. Observations were run in early
 July as midsummer is when pollinator richness is high in urban and
 agriculture areas. Six sites were visited each day, three per time-period,
 and the order in which they were visited was randomized. Observations were
 conducted on a corner of each plant plot (50 x 50 cm) that included all
 plant species. We observed all insect pollinators that visited a flower,
 identified them to genus or species level and counted the number of visits
 (landing on a single flower equals one visit) for each insect until it
 left the plant plot. If it was not possible to identify an individual to
 species level, we identified it as accurately as possible (e.g. to genus
 level) and assigned a morphospecies to it. We also recorded which plant
 species each insect pollinator was observed on. Insect pollinators
 included: solitary bees (i.e. non-bumblebees), bumblebees, butterflies,
 syrphid flies, non-syrphid flies, wasps and honeybees. To assess the plant
 species richness neighboring the plant plots, we counted all plant species
 within a radius of 20 m that were flowering at that time.
 <strong>Statistical analyses</strong>
 We found no differences in pollinator richness and their
 abundance between morning and midday observations; thus, abundances were
 summed for every observation day. All analyses were performed using R
 (version 3.5.1; R Core Team 2018). The response variables (number of
 pollinator visits, plant richness and pollinator morphospecies richness)
 were modelled as functions of landscape type (a factor with three levels)
 and plant species richness (numeric). Plant species richness was
 influenced by landscape type and was always tested in separate
 models. We used a series of generalized least squares
 (GLS) models and linear mixed-effects models (LME; all fitted using REML)
 to account for potential spatial non-independence in our data: a
 GLS model without heterogeneity, a GLS model with spherical
 autocorrelation (longitude, latitude), and an LME model with random
 effects for every site (N=12). These models were ranked using AICc
 (Information Theoretic approach). For all variables, the models with the
 lowest AICc were simple GLS models without a correlation structure or
 random effect. We tested which distribution fitted each
 response variable best, using the fitdistrplus package. In all cases,
 models with an untransformed response variable had lowest AICc values,
 except for plant richness and pollinator morphospecies richness for which
 a log-normal distribution had lowest AICc values. Corresponding negative
 binomial or poisson models did not fit the data adequately. Results were
 plotted using the effects package. The proportional
 abundance of the seven different pollinator groups was tested using
 multinomial models against the explanatory variables. Bipartite networks
 (N=12) were created from the plant-pollinator interactions for each site
 and their structure analyzed with network level metrics using the
 bipartite package. The network level metrics used were: robustness,
 interaction evenness and Shannon diversity of interactions. Robustness is
 a measure of the stability of the network, specifically it is calculated
 by measuring the area below the extinction curve generated as a measure of
 the robustness of the network to the loss of species.