DOI: 10.5061/dryad.rjdfn2zr7
title: Common birds have higher abundances in croplands with lower pesticide purchases

description: This dataset combines environmental data, pesticide purchase data, and
 bird abundance data to study the relationship between pesticide purchases
 and bird counts in croplands in France, while accounting for other
 variables related to agricultural intensification. The relationship
 between pesticide purchases and pesticide residues is also analyzed. Bird
 count data are obtained from the French Breeding Bird Survey (FBBS).
 Pesticide purchase data are supplied by the French national bank of
 pesticide sales (BNV-D), and data on pesticide residues come from the
 national monitoring of surface water quality in French rivers and water
 bodies (Naïades). Physicochemical properties of pesticide active
 substances were extracted from the database on Pesticide Properties
 Database (PPDB). Environmental data on agricultural parcels are from the
 “Registre Parcellaire Graphique” data (RPG), crop types from OSO-CES -
 THEIA data (OSO), Small Woody Features (SWF), crop practices and landscape
 features by municipality from Agreste data, as well as climate data
 (Worldclim).
description: # Common birds have higher abundances in croplands with lower pesticide
 purchases Dataset DOI:
 [10.5061/dryad.rjdfn2zr7](https://doi.org/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.
 &lt;[https://www.R-project.org/](https://www.R-project.org/) rmarkdown:
 Dynamic Documents for R. (2025) R package version 2.29.2,
 [https://github.com/rstudio/rmarkdown](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](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#](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](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/](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](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/](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](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](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/](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/,](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](https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.5086%22). * 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](https://doi.org/10.1038/s41597-020-0453-3)