Map and location of the experimental trap.

<p>1.A. Map of the Île-de-France region and locations of the study plots and B. Experimental trap used in the study.</p

Models tested using the GLM procedure and the associated Akaike Information Criterion (AIC) obtained by backward stepwise selection procedure.

<p>Models tested using the GLM procedure and the associated Akaike Information Criterion (AIC) obtained by backward stepwise selection procedure.</p

Uncoupled responses of butterfly density and parasitism rate to urbanization.

<p>Relationship between the proportion of artificial urban landscape and A. <i>Pieris brassicae</i> density (number of individuals/m²) and B. the parasitism rate. Black squares figure the relation between parasitism rate and proportion of artificial urban landscape and full black and bold line represents linear regression these two variables (R = −0.85). Pale grey triangles figure the relation between the proportion of artificial urban landscape and <i>Pieris brassicae</i> density and dotted grey line represents linear regression these two variables (R = −0.18).</p

Flower visitor communities data

This table provide the data of the 1606 flower visitors communities analysed as described in the main text of the manuscript. Field “collection_id” is a unique ID number for each flower visitor community; field “long” and “lat” provide longitude and latitude of the site sampled. Field “volunteer_id” gives a unique ID number for each volunteer observing data. Field “prop_ins_withCSI” gives, for each flower visitor community, the proportion of insect that were available for the calculation of the CSI (i.e. for each collection, the proportion of insects with a defined specialisation index). Field “prop_urban_1km” is the proportion of urban areas within 1km of the sampling sites. Fields “richness_flovis” and “CSI_flovis” are respectively the richness and the Community Specialisation Index of the flower visitor communities

Deguines et al 2016_Hymdata

This table provide the data of the 1159 Hymenoptera communities analysed as described in Appendix 1. Field “collection_id” is a unique ID number for each Hymenoptera community; field “long” and “lat” provide longitude and latitude of the site sampled. Field “volunteer_id” gives a unique ID number for each volunteer observing data. Field “prop_Hym_withCSI” gives, for each Hymenoptera community, the proportion of insect that were available for the calculation of the CSI (i.e. for each collection, the proportion of insects with a defined specialisation index). Field “prop_urban_1km” is the proportion of urban areas within 1km of the sampling sites. Fields “richness_Hym” and “CSI_Hym” are respectively the richness and the Community Specialisation Index of the Hymenoptera communities

The Whereabouts of Flower Visitors: Contrasting Land-Use Preferences Revealed by a Country-Wide Survey Based on Citizen Science

<div><h3>Background</h3><p>In the past decade, accumulating evidence of pollinator decline has raised concerns regarding the functioning of terrestrial ecosystems and the sustainability of crop production. Although land-use changes have been advanced as the major causes, the affinities of most wild pollinators with the main land-use types remain unknown. Filling this gap in our knowledge is a prerequisite to improving conservation and management programmes.</p> <h3>Methodology/Principal Findings</h3><p>We estimated the affinity of flower visitors with urban, agricultural and natural land-uses using data from a country-wide scale monitoring scheme based on citizen science (Spipoll). We tested whether the affinities differed among insect orders and according to insect frequency (frequent or infrequent). Our results indicate that the affinities with the three land-use types differed among insect orders. Apart from Hymenopterans, which appeared tolerant to the different land-uses, all flower visitors presented a negative affinity with urban areas and a positive affinity with agricultural and natural areas. Additionally, infrequent taxa displayed a lower affinity with urban areas and a higher affinity with natural areas than did frequent taxa. Within frequent taxa, Hymenoptera and Coleoptera included specialists of the three land-use types whereas Diptera and Lepidoptera contained specialists of all but urban areas.</p> <h3>Conclusions/Significance</h3><p>Our approach allowed the first standardised evaluation of the affinity of flower visitors with the main land-use types across a broad taxonomical range and a wide geographic scope. Our results suggest that the most detrimental land-use change for flower visitor communities is urbanisation. Moreover, our findings highlight the fact that agricultural areas have the potential to host highly diverse pollinator communities. We suggest that policy makers should, therefore, focus on the implementation of pollinator-friendly practices in agricultural lands. This may be a win-win strategy, as both biodiversity and crop production may benefit from healthier communities of flower visitors in these areas.</p> </div

Sampling sites and land-use types spatial distributions.

<p>The spatial distribution of (A) the 2131 collections (i.e. sampling sites) analysed in 2010, and (B) the urban, agricultural and natural land-use types in France, represented in dark, medium and light grey, respectively.</p

Deguines et al 2016_Dipdata

This table provide the data of the 843 Diptera communities analysed as described in Appendix 1. Field “collection_id” is a unique ID number for each Diptera community; field “long” and “lat” provide longitude and latitude of the site sampled. Field “volunteer_id” gives a unique ID number for each volunteer observing data. Field “prop_Dip_withCSI” gives, for each Diptera community, the proportion of insect that were available for the calculation of the CSI (i.e. for each collection, the proportion of insects with a defined specialisation index). Field “prop_urban_1km” is the proportion of urban areas within 1km of the sampling sites. Fields “richness_Dip” and “CSI_Dip” are respectively the richness and the Community Specialisation Index of the Diptera communities

Number of insect taxa recorded among orders and by taxonomic resolution.

<p>The distributions of frequent (Freq) and infrequent (Infreq) taxa among orders and by taxonomic resolution. The corresponding numbers of observations (number of pictures) are in brackets.</p

Deguines et al 2016_Lepdata

This table provide the data of the 337 Lepidoptera communities analysed as described in Appendix 1. Field “collection_id” is a unique ID number for each Lepidoptera community; field “long” and “lat” provide longitude and latitude of the site sampled. Field “volunteer_id” gives a unique ID number for each volunteer observing data. Field “prop_Lep_withCSI” gives, for each Lepidoptera community, the proportion of insect that were available for the calculation of the CSI (i.e. for each collection, the proportion of insects with a defined specialisation index). Field “prop_urban_1km” is the proportion of urban areas within 1km of the sampling sites. Fields “richness_Lep” and “CSI_Lep” are respectively the richness and the Community Specialisation Index of the Lepidoptera communities