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Supplementary information for "Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability"

Reproducibility data for the manuscript "Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability", it contains data and script for : <ol> <li> The R script <code>01_HDSfreq_Calibration.R</code> of the developed approach to estimate national breeding bird population size using Hierarchical Distance Sampling (HDS) and the secondary candidate set model selection method (Morin et al., 2020) </li> <li> The R script <code>02_pglmm_figures.R</code> for the calibration of the Phylogenetic Generalised Mixed Model (PGLMM) used in the manuscript to compare previous population size estimates to ones modelled using <code>01_HDSfreq_Calibration.R</code>, while accounting for species phylogenetic relatedness </li> <li> The R script <code>03_results_tables.R</code>, used to generate supplementary tables S2.1-3 and S6.1-2. </li> </ol> <ul> <li> Column names are highlighted in italics. </li> </ul> <h2>Data description</h2> <h5>A. BirdPhylo_Burleigh_et_al.tre</h5> A phylogenetic tree from Burleigh et al., 2015. Phylogenetic distances are used as random effect for the PGLMM in script <code>02_pglmm_figures.R</code> <h5>B. Conservation_status.txt</h5> A <code>.txt</code> file of the conservation status for France (Statut_FR) and Europe (Statut_EU) for the studied species retrieved from (UICN France et al., 2016). Only Statut_FR is used for the table S6.1. <h5>C. FBBS_trends_20122023.txt</h5> A <code>.txt</code> file containing species trend of the French Breeding Bird Survey data from 2012 to 2023. <ul> <li> Species names (English, French) associated with FBBS trend estimated using data collected from 2012 - 2023 </li> <li> Hab_specialization, determined from Julliard et al. 2006 approach </li> <li> infPrec, supPerc, estimate, se, pval : Species trends over 2012-2023 period in % | lower and upper confidence intervals, mean, standard error and significance </li> </ul> <h5>D. PrepData_HDS.RData</h5> A file containing <code>.RData</code> environment required to run <code>01_HDSfreq_Calibration.R</code> script, it contains : <ul> <li> ATLAS12 : A dataframe with breeding status information from 2012 breeding bird atlas (used to restrict model prediction grid, in regard of 2012 known breeding locations) </li> <li> ConcordTBL : A concordance table for species names (English, French and scientific notation) </li> <li> EPOC_ODF : observation dataset, each line corresponds to detected individuals <ul> <li> UUID, Ref, ID_liste, ID, ID_place, Grid_10x10 : Columns used to identify observations, lists, sites, locations, 10x10 grids </li> <li> ID_species_Biolovision, Nom_espece, english_name, scientific_name : Species ID and names </li> <li> Date, Day, Month, Year, Julian_date, Obs_hour, Hour_list, Complete_checklist, Commentary, Project_name, Scheme, Observer, List_time, List_diversity, List_abundance : Lists and Observation related effort covariates and metadata </li> <li> X_Lambert93_m, Y_Lambert93_m : Observation locations in <code>(crs = 2154)</code> </li> <li> GPS_loc_observer : Logical, TRUE : location of observers corresponds to true GPS information ; FALSE : observer's location approximated as the barycenter of observations </li> <li> X_barycentre_L93, Y_barycentre_L93 : Observers location in <code>(crs = 2154)</code> </li> <li> Use_distance_sampling, Observation_distance_m, Distance_bin_logical, Distance_class_0_25, Distance_class_25_100, Distance_class_100_200, Distance_class_200_more : Distance sampling related informations </li> <li> Abudance_brut, Estimate, Number, Nb_male_identified, Nb_female_identified, Nb_juvenile_identified, Nb_grounded, Nb_flying, Nb_auditory, Nb_NA : Observation metadata, used in case of a priori filter over male detection. </li> </ul> </li> <li> grid_pred_envvar : Prediction grid with environmental covariates, see appendix S3 of the manuscript, covering metropolitan France </li> <li> grid_pred.sf : corresponding sf object </li> <li> L93_10x10 : sf object corresponding to 10x10 grid used in 2012 atlas </li> <li> ObsVar_EPOCODF : dataframe specifying lists effort covariates </li> <li> OCCU_EPOC_ODF : Environmental covariate agregated over lists </li> <li> OCCU_EPOC_ODF_sites_envvar : Environmental covariate agregated over sites </li> <li> table.pheno : species table specifying related phenology filter </li> </ul> <h5>E. ReadOutput_HDSfreq_comparison.csv</h5> A <code>.csv</code> table of species population size estimated using 2021-2023 EPOC-ODF data over areas determined as breeding in the 2012 atlas. Predictions were restrained over location known as breeding in 2012 for the sake of comparison. <ul> <li> HDS_estimUnfenced_XXX : average pop. size estimated with confidence interval before prediction post-treatment (describe in fig 2. of the manuscript) </li> <li> HDS_estim_ExtrapolFence_XXX : average pop. size estimated with confidence interval after prediction post-treatment </li> <li> NB_data_calib : Number of observations (distance data, not sites) used for calibration </li> <li> MALE_FILTERING : (logical) indicating if female individuals could be detected in the same proportion of males during list recording. FALSE : we considered that estimated pop.size corresponded to the number of individuals leading to a division by 2 for the comparison with the previous atlas (in pairs). (cf . line 88-90 in <code>02_pglmm_figures.R</code>) </li> <li> EcartDTF_filtrage_maleOnly : If MALE_FILTERING == T, proportion of the remaining data used for calibration after removal of list with individual tagged as female/juvenile (in %) </li> <li> Max_dist_breaks : Maximal distance for detection function, after right-side truncation of 5% </li> <li> Chat : Coefficient of overdisperion of the best model in the second candidate set </li> <li> MED_MEAN_Prob_Detect (.._SE) : weighted averaged median of intercept from the availability state from HDS models, weigthed AICc-wise </li> <li> MED_MEAN_Density : weighted averaged median of intercept from the abundance state from HDS models, weigthed AICc-wise </li> <li> Significant_phi/lambda : Categorial (Significant/Near/Not), are availability/abundance intercepts significatively different from 0 (significant : alpha = 0.05, near : alpha = 0.1) </li> <li> KeyFun_used : Key function used for distance sampling </li> <li> Mixtured_used : Mixture used in the abundance state for HDS </li> </ul> <h5>F. ReadOutput_HDSfreq_comparison_20212022.csv</h5> A <code>.csv</code> table of species population size estimated using 2021-2022 EPOC-ODF data over areas determined as breeding in the 2012 atlas. Used for the robustness analysis of HDS estimated population size, see appendix S2 and table S2.2 of the manuscript. <h5>G. ReadOutput_HDSfreq_EstimMetropole.csv</h5> A <code>.csv</code> table of species population size estimated using 2021-2023 EPOC-ODF data over metropolitan France. <ul> <li> HDS_estimUnfenced_XXX : average pop. size estimated with confidence interval before prediction post-treatment (describe in fig 2. of the manuscript) </li> <li> HDS_estim_ExtrapolFence_XXX : average pop. size estimated with confidence interval after prediction post-treatment </li> <li> MALE_FILTERING : (logical) indicating if female individuals could be detected in the same proportion of males during list recording. FALSE : we considered that estimated pop.size corresponded to the number of individuals leading to a division by 2 for conversion to pop. size in breeding pairs </li> <li> Chat : Coefficient of overdisperion of the best model in the second candidate set </li> <li> KeyFun_used : Key function used for distance sampling </li> <li> Mixtured_used : Mixture used in the abundance state for HDS </li> </ul> <h5>H. TABLE_SpeciesFilters_and_2012Estimates.txt</h5> A <code>.txt </code>table containing species names (English, French and scientific notation), filters and 2012 French atlas pop. size estimates <ul> <li> debut_jour : starting day of the month for phenology filter </li> <li> debut_mois : starting month for phenology filter </li> <li> fin_jour : ending day of the month for phenology filter </li> <li> fin_mois : ending month for phenology filter </li> <li> Estim_low/up_Atlas2012 : Lower and Upper interval of estimated pop. size in 2012 (number in breeding pairs) </li> <li> gregarious : logical (0,1) specifying if the species is considered gregarious during its breeding season </li> </ul> <h5>I. sessionInfo_script_XX</h5> User R session information, obtained from <code>sessionInfo()</code> R function, used for running R script. <h2>Code</h2> <h5>A. <code>01_HDSfreq_Calibration.R</code></h5> R script showcasing data formatting and model calibration of the HDS based upon frequentist aproach from <code>unmarked</code> R package. For more details of the model calibration approach, see appendix S4 of the manuscript. <h5>B. <code>02_pglmm_figures.R</code></h5> Script for the calibration of the PGLMM and generation of figure 5 of the manuscript. <h5>C. <code>03_results_tables.R</code></h5> Script to generate tables depicted in Appendices S2 (S2.1-3) and S6 (S6.1-2) <h5>D. <code>HDS_functions.R</code></h5> R script called in <code>01_HDSfreq_Calibration.R</code>, contains 2 functions: <ul> <li> <code>Try_HDS()</code> : Function implementing a try-catch permitting calibration of multiple species in a loop. <ul> <li> Species with non convergent models are skipped sending a notification to the user R interface. </li> <li> Used in all sub-candidate sets (i.e. "null", "p", "phi", "lambda") </li> <li> When phase="ALL" corresponding to the second candidate set (i.e. ensemble of best model candidates, with delta_AIC <= 10, from previous sub-candidate sets), it permits the use of previous sub-candidates set coefficients as starting values, with <code>StartValues </code>argument </li> <li> Later part of the function hack the call of the unmarkedFit class, in order to accommodate from calibrating a gdistsamp using characters formulas </li> </ul> </li> <li> <code>fitstats()</code> : Function from unmarked::parboot(), available with <code>help(parboot)</code>. Allow estimation of multiple goodness-of-git statistic (Freeman-Tukey, Chi-squared and Sum of Squared Estimate of errors) through parametric bootstrap. In the manuscript, only chi-squared metric is used. </li> </ul> <h5>E. <code>dsmextra_modif_function.R</code></h5> R script called in <code>01_HDSfreq_Calibration.R</code>. Miscellaneous adjustment of core function from <code>dsmextra </code>package (main change being the integration of tolerance argument (<code>tol</code>) in the chain of function. <h5>F. <code>misc_unmarked.R</code></h5> R script called in <code>01_HDSfreq_Calibration.R</code>. modify Setmethods for unmarked function, in particular for <code>unmarked::parboot</code>, allowing parallelization of parametric bootstrap with prior unmarked version (<code>unmarked < 1.3.0</code>). <h2>References</h2> Data was derived from the following sources: <ul> <li> Burleigh, J.G., Kimball, R.T., Braun, E.L., 2015. Building the avian tree of life using a large-scale, sparse supermatrix. Molecular Phylogenetics and Evolution 84, 53–63. <a href="https://doi.org/10.1016/j.ympev.2014.12.003">https://doi.org/10.1016/j.ympev.2014.12.003</a> </li> </ul> Other sources : <ul> <li> Julliard, R., Clavel, J., Devictor, V., Jiguet, F., Couvet, D., 2006. Spatial segregation of specialists and generalists in bird communities. Ecology Letters 9, 1237–1244. <a href="https://doi.org/10.1111/j.1461-0248.2006.00977.x">https://doi.org/10.1111/j.1461-0248.2006.00977.x</a> </li> <li> Morin, D.J., Yackulic, C.B., Diffendorfer, J.E., Lesmeister, D.B., Nielsen, C.K., Reid, J., Schauber, E.M., 2020. Is your ad hoc model selection strategy affecting your multimodel inference? Ecosphere 11, e02997. <a href="https://doi.org/10.1002/ecs2.2997">https://doi.org/10.1002/ecs2.2997</a> </li> <li> UICN France, MNHN, LPO, SEOF, ONCFS, 2016. La Liste rouge des espèces menacées en France - Chapitre Oiseaux de France métropolitaine. Paris, France. </li> </ul>

ShareScore

40/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship: 8
Harmonization: 4
Access: 16
Reuse readiness: 8
Engagement: 4