DOI: 10.5061/dryad.fxpnvx14z
title: Data from: Phenology of migrating game birds in Italy based on citizen science data: mean maps and grid maps for 23 species

description: Studying the timing of the seasonal movements of migratory birds, known as
 migration phenology, is crucial for managing and conserving migratory bird
 populations. To effectively protect these species during these critical
 periods, it is vital to employ reliable methods for assessing their
 migration phenology. Citizen science, which involves the participation of
 skilled volunteers in scientific data gathering, is a valuable resource
 for migration studies. It allows the collection of large amounts of data
 across extensive geographic areas, overcoming some limitations of other
 datasets and analytical methods. In this study we analysed pre- and
 post-breeding migration phenology of 23 game bird species in Italy, using
 citizen science data from the www.Ornitho.it portal. These findings
 highlight the value of citizen science data in obtaining migration timing
 and the importance of employing multiple methods to estimate it. This
 approach is particularly valuable for species subject to hunting, which
 require well-informed management.
description: We retrieved the data available on the Ornitho.it databank for 23
 species.  The method is based on the approach first
 proposed by Ambrosini et al. (2014) and refined to fit partial migratory
 species and citizen science data by Ambrosini et al. (2023).
 The study area (the Italian peninsula and Sardinia, Sicily and
 the minor Italian islands) was first divided into cells of 0.5° by 0.5°
 degrees (latitude – longitude). Cells containing fewer than 20 records
 collected in fewer than 10 different days in a year (the minimum required
 sample size) were merged with adjacent cells (full details are reported in
 the Supporting information). This procedure, identical to the one used in
 Ambrosini et al. (2023), ultimately generated cells of different sizes,
 inversely proportional to data density. These cells are represented in the
 grid maps. Data that were spatially isolated were also identified and
 discarded to avoid the generation of unreasonably large cells. The number
 of records per day was then transformed first into the proportion of
 records per calendar date and then into the cumulative proportions of
 records at each date and cell. These were then modelled using a binomial
 Generalized Linear Mixed Model (GLMM) with a cloglog link function and
 exponential spatial covariance structure to account for spatial
 autocorrelation in the data. Day-of-the-year (centred to its mean value)
 was the only fixed predictor, while the random part of the model included
 cell ID as a random factor. In addition, the random part included a random
 slope for the day-of-the-year within cell (i.e. random intercept and slope
 model). The procedure briefly described above and
 reported in full detail in the Supporting information thus returns the
 date when a given proportion of observations is estimated at each cell
 after considering the observations expected from birds that are stationary
 in the cell, if any. These values were then spatially interpolated using a
 grid with 0.5° × 0.5° (latitude x longitude) cells, the inverse distance
 weighting algorithm, with weights calculated using the Shepard method
 (Shepard 1968), and a leave-one-out validation routine to choose the best
 power function for interpolation. Finally, the resulting interpolated map
 was downscaled to obtain the expected values at cells of 0.1° × 0.1° of
 size (latitude × longitude, approximately corresponding to 10 × 8 km)
 using the bilinear method. Since this procedure included a predefined set
 of parameters with arbitrarily selected values, we selected alternative
 values for these parameters and re-run the analyses using all 27 possible
 combinations (see the Supporting information for an overview of these
 parameters, their meanings, and the different values used). This produced
 27 final maps per species, for each predicted proportion of arrived
 migrants (i.e. PRED values as outlined in the Supporting information)
 which were then averaged. These 27 maps for all 23 game species (621 in
 total) can be found in the mean maps folder.
description: # Data from: Phenology of migrating game birds in Italy based on citizen
 science data: mean maps and grid maps for 23 species Dataset DOI:
 [10.5061/dryad.fxpnvx14z](10.5061/dryad.fxpnvx14z) ## Description of the
 data and file structure The mean_maps folder contains 10 maps for each of
 the 23 game species included in our analyses. For every species, five maps
 represent the progression of pre-breeding migration and five represent
 post-breeding migration across Italy, derived from citizen-science
 observations submitted to [www.Ornitho.it](http://www.Ornitho.it). Each
 set of maps corresponds to a different proportion of observations (0%, 1%,
 5%, 10%, and 50%), representing the onset of migration for pre-breeding
 and the end of migration for post-breeding. In addition, the dataset
 includes all grid maps generated during the intermediate analytical steps
 (in the grid_maps folder). These grid maps result from the procedure
 described in Ambrosini et al. (2023), which creates spatial cells of
 varying sizes inversely proportional to encounter density. For each
 species, 18 grid maps are provided (nine for pre-breeding and nine for
 post-breeding migration) generated using the parameter combinations
 detailed below. In total, the dataset comprises 414 grid maps across the
 23 species. Description of file naming convention (see Table S1 for
 explanations of specific procedure names): Each file name in grid_maps
 follows this structure: "Ornitho.it code of the species–scientific
 name of the species_All individuals regardless of sex–All available
 data_Cell size in degrees before any potential spatial
 aggregation_StartD_NP_DN \_Total number of observations_Migration phase
 (pre- or post-breeding)_Name of dataset used (in this case ORN stands for
 Ornitho.it)" ### Files and variables #### File: mean_maps.zip
 **Description:**  Description of file naming convention (see Table S1 for
 explanations of specific procedure names): Each file name follows this
 structure: "Ornitho.it code of the species–scientific name of the
 species_All individuals regardless of sex–All available
 data_percentile_Name of dataset used (in this case ORN stands for
 Ornitho.it)" #### File: grid_maps.zip **Description:**  Description
 of file naming convention (see Table S1 for explanations of specific
 procedure names): Each file name follows this structure: "Ornitho.it
 code of the species–scientific name of the species_All individuals
 regardless of sex–All available data_Cell size in degrees before any
 potential spatial aggregation_StartD_NP_DN \_Total number of
 observations_Migration phase (pre- or post-breeding)_Name of dataset used
 (in this case ORN stands for Ornitho.it)" ## Code/software R ##
 Access information Other publicly accessible locations of the data: * 
 
 Data was derived from the following sources: * Ornitho.it data