DOI: 10.5061/dryad.fxpnvx148
title: Habitat availability is insufficient to explain regional variations in white stork breeding habitat preference

description: Understanding species-habitat associations is key for making predictions
 of species distributions of relevance to ecology and conservation.
 Regional differences in species habitat preferences can hinder the
 transferability of habitat models in space and time, but our ability to
 account for these differences will depend on the mechanisms underlying
 them (differences in habitat availability, genetics, culture). Here, we
 modelled the large-scale breeding distribution of an expanding species,
 the white stork Ciconia ciconia in France, applying machine-learning
 algorithms to an extensive dataset of the distribution of nests spanning
 the whole country. Specifically, we assessed the transferability of the
 models across different geographic zones and contrasted the modelled
 nesting habitat preferences of the species across these zones. Finally, we
 assessed whether local differences in model transferability were related
 to habitat availability in each zone. Our models generally had good
 calibration performances, but were not equally transferable to all zones.
 Additionally, environmental variables did not have the same effects in the
 different zones, with particularly striking differences between Alsace and
 the rest of France. This included a certain preference for urban areas in
 Alsace – absent from other zones - that was consistent with their tendency
 to nesting on buildings in that zone. Differences in habitat availability
 between Alsace and the rest of France, as well as connectivity within the
 French white stork metapopulation appeared to be insufficient to explain
 the lack of transferability of models to this zone, suggesting some
 possible local historical and cultural effects on habitat selection.
description: # Habitat availability is insufficient to explain regional variations in
 white stork breeding habitat preference Dataset DOI:
 [10.5061/dryad.fxpnvx148](10.5061/dryad.fxpnvx148) ## Description of the
 data and file structure The aim of the study was to model the nesting
 distribution of white storks in France. Nest location data were collected
 nationwide and combined with land use data, using species distribution
 models to infer nesting habitat preferences across different regions of
 France. ### Files and variables #### File: data.zip **Description:** This
 folder contains a table and two shapefiles: * final_dataset_nest_locs.csv:
 locations of white stork nests; * locs_buffer_with_vars.gpkg: nest
 locations with associated extracted environmental variables; *
 grid_buffer_with_vars.gpkg: extracted environmental variables across a 1km
 x 1km grid covering France; **Column names:** * *annee*: year of census; *
 *departement*: department of census; * *support_clean*: support categories
 (following Table S1 in the manuscript) - "arbre" = tree,
 "plateforme ou poteau" = platform or pole, "pylone" =
 pylon, "batiment" = building, "autre" = other; *
 *longitude* and *latitude*: coordinates in WGS84 - coordinates are rounded
 to the 3rd decimal in the public dataset for privacy reasons (nests
 potentially falling on private property); * *id_ini*: nest location ID, to
 be used to merge datasets; * *problems*: potential problems in the
 original coordinates (solved issues are indicated); *
 *dist_nearest_pylon*: distance to the nearest RTE pylon; Note that some
 column names and column contents are in French because the census was
 carried out by French ornithological associations. They were not
 translated to ensure the reproducibility of analyses. Column names and
 nest support categories (in the *support_clean* column) are translated
 above. ## Code/software #### File: scripts.zip **Description:** This
 folder contains scripts to run the analyses as described in the methods of
 the published manuscript. * 00_main_nest_distribution_models.R is the main
 script that calls all others. * 01_final_dataset.R is the script to clean
 the dataset to be used in subsequent analyses. This is provided for
 illustration only, but the output of this code is provided
 (final_dataset_nest_locs.csv) and can be used to run subsequent R scripts.
 * 01b_support_cleaning_function.R is a function to clean and harmonise the
 text describing the type of supports. * 02_matching_pylons_with_rte.R is a
 code to match the nest location data with the database of electric pylons,
 to refine the categorisation of nesting supports. This requires a database
 of RTE pylons that can be downloaded from the OpenData Réseau-Énergies
 website (odre.opendatasoft.com). * 03_zones_separation.R is a code to run
 the DBSCAN algorithm and manually refine the definition of the 13 zones
 used in the analyses. * 04_pseudo_absences_by_region.R is a code to
 generate pseudo-absences. * 05_variable_extraction_function.R is a
 function called in 06 variable extractions.R * 06_variable_extractions.R
 is a code to extract environmental variables around each nest location and
 pseudo-absence, and around the centroid of each 1km x 1km grid cell. This
 requires environmental layers that can be downloaded from
 [www.data.gouv.fr/fr/datasets/corine-land-cover-edition-2018-france-metropolitaine/](http://www.data.gouv.fr/fr/datasets/corine-land-cover-edition-2018-france-metropolitaine/) and [https://geoservices.ign.fr/bdtopo](https://geoservices.ign.fr/bdtopo) * 07_habitat_models_functions.R is a function called in 08 habitat models.R and contains ad-hoc functions for use within the biomod2 package. * 08_habitat_models.R is a code to run all species distribution models.