Dataset DOI: 10.5061/dryad.rjdfn2zr7

Description of the data and file structure

The unit of the statistical analyses is the point count within each bird monitoring plot. The postcode of the plots is provided to link the bird data with pesticide purchase data, which are reported at the postcode level. Environmental data are aggregated within a 1 km radius around the point counts.

All environmental data ("env_data.csv"), pesticide data ("pesticide_purchases_data.csv" and "pesticide_residues_data.csv"), and bird count data ("bird_data_FBBS_export.csv") used in the analyses are provided in the dataset. All data are also publicly available elsewhere, except for the PPDB.

Scripts to prepare the selected data on pesticide purchases (“0_dataBNVD.Rmd”), pesticide residues (“0_dataNaiades.Rmd”), environmental data ("0_dataenv.Rmd”), and to merge them with the bird data for analyses ("1_data_merging.Rmd") are provided.

The scripts for the intermediate steps to identify postcodes around the point counts ("0_CP_SAU_for_buffer-plots.Rmd") or the water stations ("0_CP_SAU_for_buffer-waterstations.Rmd"), as well as the scripts to compute the alternative pesticide metrics ("0_pesticide_metrics_1_PHR.Rmd", “0_pesticide_metrics_2_buffers.Rmd”), are also provided.

Finally, all the scripts to run the main models ("2_models_obs_nomead_QuCP_all.Rmd"), the alternative models computed at a different spatial scale (“2_models_obs_nomead_Qu10km_all.Rmd”), or with the more complex pesticides metrics ("2_models_obs_nomead_PHRCP_all.Rmd") are provided.

In parallel, the script to analyse the relationship between pesticide purchase and pesticide residues is also provided (“1_comp_naiades.Rmd").

Files and variables

File: bird_data_FBBS_export.csv

Description: Bird count data from the French Breeding Bird Survey (FBBS).

Variables

• plot: Unique identifier of the bird monitoring plot
• point: Unique identifier of the point count
• year: Year of the observation
• lon_point: Longitude coordinate of the point (WGS84)                         
• lat_point: Latitude coordinate of the point (WGS84)
• code_sp: Unique identifier of the species
• scientific_name: Latin name of the species
• abundance: Maximum number of individuals per species counted over the two sessions, including observations from all distance classes
• habitat_primary: Code of the level-1 description of the dominant land cover within 200 meters of each point count by the observer
• habitat_primary_details: Code of the level-2 description of the dominant land cover within 200 meters of each point count by the observer
• habitat_primary_details_eng_name: Name of the level-2 description of the dominant land cover within 200 meters of each point count by the observer

File: env_data.csv

Description: Environmental data used to characterize the structure and composition of the agricultural area around the bird monitoring plots from data: on the agricultural parcels from the “Registre Parcellaire Graphique” data (“rpg_”, year 2017), on the crop types from the OSO-CES - THEIA data (“oso_”, year 2017), on Small Woody Features (swf_”year 2015), on crop practices and landscape features by municipality from Agreste data (“agr_”, year 2010), as well as from climate data of WorldClim 2.1 data (“worldclim_”, year 2015-2018).

Variables

• "plot": Unique identifier of the bird monitoring plot
• "point": Unique identifier of the point count
• "postcode": Postcode of the bird monitoring plot
• "rpg2017_1km_agri_area_m2": Surface of agricultural area within 1 km around the point count, in square meters
• "rpg2017_1km_parcels_count": Number of agricultural parcels within 1 km
• "rpg2017_1km_parcels_perimeter_m": Mean perimeter of parcels within 1 km, in meters
• "oso2017_1km_annual_crops_prop": Proportion of annual crops within 1 km
• "oso2017_1km_meadows_prop”: Proportion of meadows within 1 km
• "oso2017_1km_perannual_crops_prop": Proportion of perannual crops within 1 km
• "swf2015_mean_density": Mean density of Small Woody Features
• "worldclim_mean_prec_apr_may_2015_2018": Mean precipitation during April–May 2015-2018, in mm
• "worldclim_mean_max_temp_apr_may_2015_2018": Mean maximum temperature during April–May 2015-2018, in °C
• "agr_conv_tillage": proportion of agricultural areas under conventional tillage
• "agr_fertilization": proportion of agricultural areas using mineral fertilization.

File: pesticide_purchases_data.csv

Description: Purchase data from the French national bank of pesticide sales (« Banque Nationale des Ventes de produits phytopharmaceutiques par les Distributeurs agréés », BNV-D) with values of DT50 and LD50 of the active substances provided by the PPDB data.

Variables

• "CAS_number": Unique CAS registry number identifying the active substance
• "substance_quantity_kg": Reported quantity of the active substance in kilograms
• "postcode": Postcode of the purchase
• "year": Year of the purchase
• "soil_DT50_typical_days": rate of pesticide degradation in the soil, expressed as the time in days taken for 50% (DT50) of the pesticide to disappear.
• "birds_acute_LD50_mgkg": Acute toxicity (LD50) for birds, in mg/kg of body weight. Dose of an active substance required to kill 50% of the test population.

File: pesticide_residues_data.csv

Description: Pesticide residues data from the national monitoring of the quality of surface water in French rivers and water bodies (“Naïades”).

Variables

• "waterstation_id": Unique identifier of the surface water monitoring station
• "CAS_number": Unique CAS registry number identifying the active substance
• "max_quantity": Mean maximum value of the pesticide doses (µg/L) per sampling water station and substance
• "longitude": Longitude coordinate of the monitoring water station (WGS84)
• "latitude": Latitude coordinate of the monitoring water station (WGS84)

File: 0_dataBNVD.Rmd

Description: Script to prepare the BNV-D data

File: 0_dataNaiades.Rmd

Description: Script to prepare the Naïades data

File: 0_dataenv.Rmd

Description: Script to prepare the environmental data (RPG, OSO, Agreste, Worldclim)

File: 1_data_merging.Rmd

Description: Script to merge pesticides and environmental data with bird data for analyses

File: 0_CP_SAU_for_buffer-plots.Rmd

Description: Script to identify postcodes around the point

File: 0_CP_SAU_for_buffer-waterstations.Rmd

Description: Script to identify postcodes around the water stations

File: 0_pesticide_metrics_1_PHR.Rmd

Description: Script to compute the alternative pesticide metric at a different spatial scale

File: 0_pesticide_metrics_2_buffers.Rmd

Description: Script to compute the more complex pesticide metrics

File: 2_models_obs_nomead_QuCP_all.Rmd

Description: Script to run the main models

File: 2_models_obs_nomead_Qu10km_all.Rmd

Description: Script to run the alternative models computed at a different spatial scale

File: 2_models_obs_nomead_PHRCP_all.Rmd

Description: Script to run the alternative models computed with the more complex pesticide metrics

File: 1_comp_naiades.Rmd

Description: Script to analyse the relationship between pesticide purchase and pesticide residues

Code/software

Scripts are written in R language and provided in R Markdown file format.

References:

R Core Team (2024). R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. <https://www.R-project.org/

rmarkdown: Dynamic Documents for R. (2025) R package version 2.29.2, https://github.com/rstudio/rmarkdown.

Xie Y, Allaire J, Grolemund G (2018). R Markdown: The Definitive Guide. Chapman and Hall/CRC, Boca Raton, Florida. ISBN 9781138359338, https://bookdown.org/yihui/rmarkdown.

Access information

• All environmental, pesticide, and birds data are publicly available online, except the database on Pesticide Properties Database (PPDB), which can be purchased on sitem.herts.ac.uk/aeru/ppdb/en/purchase_database.htm:
• Climate data were obtained from CRU-TS 4.03 data from Harris et al. (2014), downscaled with WorldClim 2.1, Fick & Hijmans, 2017).
  Downloaded from: https://www.worldclim.org/data/monthlywth.html#, accessed on 14/04/2022.
• Data on the land use and structure of agricultural parcels were obtained from the data of the “Registre Parcellaire Graphique (RPG)” produced by the French Ministry of Agriculture and Food Sovereignty (“Ministère de l’Agriculture et de la Souveraineté Alimentaire”).
  Downloaded from: https://www.data.gouv.fr/datasets/rpg, accessed on 29/03/2022
• Data on crop types were obtained from the land use data (Occupation du Sol de la France métropolitaine, OSO) from CES-OSO – THEIA.
  Downloaded from: https://www.theia-land.fr/ces-occupation-des-terres/occupation-des-sols/, accessed on 15/04/2022.
• Data on Small Woody Features were obtained from Copernicus Land Monitoring Service (CLMS), European Environment Agency (EEA).
  Downloaded from: https://land.copernicus.eu/en/products/high-resolution-layer-small-landscape-features/small-woody-features-2015, accessed on 23/04/2025
• Data on crop practices and landscape features by municipality (“Méthodes de culture, éléments de paysage par commune”) obtained from Recensements Agricoles (RA), Agreste – Ministère de l'Agriculture et de la Souveraineté alimentaire.
  Downloaded from: https://agreste.agriculture.gouv.fr/agreste-web/disaron/!searchurl/searchUiid/search/, accessed on 20/02/2024
• Map of French postcodes (“Fond de carte des codes postaux ») from Géoclip.
  Downloaded from: https://www.data.gouv.fr/api/1/datasets/r/029656d6-0fcd-48ec-917d-d511e1f36ff6, accessed on 10/11/2020.
• Data on pesticides purchases from the French national bank of pesticide sales (« Banque Nationale des Ventes de produits phytopharmaceutiques par les Distributeurs agréés », BNV-D).
  Downloaded from: https://ventes-produits-phytopharmaceutiques.eaufrance.fr/search, accessed on 08/12/2023.
• Data on pesticide residues from the national monitoring the quality of surface water in French rivers and water bodies (“Naïades”).
  Downloaded from: http://www.naiades.eaufrance.fr/), accessed on 13/03/2023.
• Database on Pesticide Properties Database (PPDB) can be purchased on sitem.herts.ac.uk/aeru/ppdb/en/purchase_database.htm, accessed on 08/12/2023.
• French Breeding Bird Survey.
  Accessible under request at https://pecbms.info/country/france/, accessed on 09/07/2020.
• Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.
• Harris, I., Osborn, T.J., Jones, P.D., Lister, D.H. (2020). Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data 7: 109