DOI: 10.5061/dryad.msbcc2g51
title: Data from: Urbanisation and agricultural intensification modulate plant-pollinator network structure and robustness

description: Land use change is a major pressure on pollinator abundance, diversity,
 and plant-pollinator interactions. Far less is known about how land use
 alters the structure of plant-pollinator networks and their robustness to
 plant-pollinator coextinctions. We analyzed the structure of
 plant-pollinator networks sampled in 12 landscapes along an urbanisation
 and agricultural intensity gradient, from early spring to late summer
 2021, and used a stochastic coextinction model to correlate
 plant-pollinator coextinction risk with network structure (species and
 network-level metrics) and landscape context. Networks in intensively
 managed (i.e. agricultural and urban) landscapes had a lower risk of
 initiating a coextinction cascade, while networks in less-intensively
 managed landscapes may be less robust. Network structure modulated the
 frequency and severity of coextinctions and species loss, while the
 strength of species interactions increased robustness. Urban networks were
 more species-rich and symmetrical due to the high diversity of ornamental
 plants, while intensively managed agricultural landscapes had smaller,
 more tightly connected, and nested networks. Network structure modulated
 the frequency of extinctions, which was decreased by greater linkage
 density, interaction asymmetry, and interaction dependence in the
 networks, while once an extinction occurred, nestedness and linkage
 density propagated the degree of the coextinction cascade and species
 loss. At the species level, species strength was inversely correlated with
 extinction risk, implying that generalist species with a high number of
 interactions with specialists had the lowest extinction risk.
description: The dataset describes plant-pollinator networks in 12 landscapes
 in Bourgogne (eastern France), collected in three different seasons. Per
 sampling site and sampling period, all pollinators on flowers were caught
 on a 1 km transect with 2 hours of catching time. The landscape was 1 km
 in radius and landscape metrics were calculated based on this distance.
 Pollinators were caught within the inner 500 m radius of this landscape.
 Network metrics were calculated using the bipartite package in R. Z-score
 standardization was done using 10,000 random networks created by the
 vaznull function in bipartite. A Stochastic
 Coextinction Model (SCM) was run on the data to test network robustness.
 The R code for this is given. For further details, we refer to the
 metadata files.
description: # Urbanisation and agricultural intensification modulate plant-pollinator
 network structure and robustness
 We analysed
 plant-pollinator networks in 12 landscapes along a
 rural-urban-agricultural gradient during three times of the year and
 looked how network structure affected robustness against coextinctions by
 running a Stochastic Coextinction Model (SCM). We analysed both the
 network level and the individual species level (i.e. species chance of
 coextinction depending on network position). The dataset does not contain
 any threatened species, and specimen data is given with a 500 m precision.
 ## Description of the data and file structure This dataset contains the
 data including raw landscape, plant-pollinator network and network
 analysis data on which the paper was based. It also holds the R-script
 containing all data-analyses. Contains following files: Habitat areas.csv:
 EUNIS land cover of selected sites at 1 km radius Masterfile insects
 v2.0.csv: File with all plant-pollinator interactions observed on the
 transect Masterfile networkmetrics.csv: File with all plant-pollinator
 network metrics per site, per sampling period pollinator dependence.csv:
 File with interaction dependence scores for plants and pollinators
 specieslevel pollinators.csv: species-level traits from all
 plant-pollinator networks Each of these files has got an additional .xlsx
 file with more specific metadata Furthermore, there are following R
 scripts: Landscape analysis.R: Was used for the PCA-analysis of the
 landscape composition SCM function.R: The Stochastic Coextinction Model
 function described in the paper Networklevel models.R: Statistical
 analysis of network structure in relation to network robustness and
 landscape composition Specieslevel models.R: Statistical analysis of
 species-level traits on coextinction chance ## Sharing/Access information
 This dataset is provided under a CC0 1.0 Universal (CC0 1.0) Public Domain
 Dedication ## Code/Software The data was analysed in R. See above for a
 further explanation on the different scripts used.