DOI: 10.5061/dryad.r2280gbns
title: Data from: Continental patterns of phenotypic variation along replicated urban gradients: A mega-analysis

description: Individual variation among and within natural populations can have
 eco-evolutionary implications by, for example, affecting species
 interactions or evolutionary potential. Urban systems present a unique
 opportunity to evaluate how environmental change shapes variation since
 urban phenotypic differentiation is widely documented on contemporary
 timescales. We introduce and test three hypotheses to determine how
 urbanization affects phenotypic variation at different population levels.
 Combining 21 long-term datasets in a mega-analysis approach, we synthesize
 how urbanization impacts variation in tarsus length and lay date among and
 within subpopulations of great and blue tits (Parus major, Cyanistes
 caeruleus) at a continental scale. Our synthesis reveals that urbanization
 is associated with increased phenotypic variation within subpopulations by
 11% on average, and by as much as 25% across the species and traits
 examined. We also find some evidence (for tarsus length in great tits)
 that urbanization increases differentiation between subpopulations. We did
 not, however, find that urbanization increases differences between
 subpopulations in their within-subpopulation variation. Our synthesis
 provides novel insights into how urban contexts impact individual
 diversity at different spatial scales and we highlight future directions
 that could establish the genetic and environmental effects that underlie
 these continental patterns of urban phenotypic variation.
description: Dataset resulting from trans-European collaboration includes
 combined long-term data from multiple populations of great tits and blue
 tits along urbanization gradients across Europe. 
description: # Continental patterns of phenotypic variation along replicated urban
 gradients: A mega-analysis By Thompson, M.J., Martin, J.G.A, Biard, C.,
 Bleu, J., Branston, C. J., Capilla-Lasheras, P., Dingemanse, N.J.,
 Dominoni, D.M., Eens, M., Eeva, T., Evans, K.L., Isaksson, C., Liker, A.,
 Massemin, S., Matthysen, E., Mouchet, A., Perret, S., Senar, J.C., Seress,
 G., Szulkin, M., Vincze, E., Watson, H., Réale, D., Charmantier, A. ##
 Description of the data and file structure Raw data to generate the
 formatted data below used for statistical models can be accessed through
 SPI-Birds ([https://spibirds.org](https://spibirds.org)) or by contacting
 data owners directly. Data processing steps for each dataset are described
 below. Data list 1."ThompsonEA_UrbanVp_AdultTarsus.csv" /
 "ThompsonEA_UrbanVp_AdultTarsus_RanObs.csv"
 2."ThompsonEA_UrbanVp_NstTarsus.csv"
 3."ThompsonEA_UrbanVp_layDate.csv"
 1."ThompsonEA_UrbanVp_AdultTarsus.csv": Dataset on
 individual-level tarsus length of Adult great tits and blue tits,
 resulting from trans-European collaboration with long-term data from
 multiple populations along urbanization gradients across Europe. Data
 "ThompsonEA_UrbanVp_AdultTarsus_RanObs.csv" has identical
 structure aside from the column "Tarsus_mean" (see below). Data
 processing included the i) conversion of tarsus measurements from the
 Paris study system using equation S2 in supplementary, ii) removal of four
 outliers of tarsus length from raw data: 10.4mm (Barcelona ID BH8112),
 10.5mm (Barcelona ID BS2981), 11.73mm (Malmo ID 2KT47318), 29.2mm (Malmo
 ID 2KS92199), iii) retention of only one observation per individual by
 taking the mean tarsus length of a given individual across observations in
 the dataset (or random observation for the "_RanObs" version of
 the dataset), iv) assignment of nest boxes into cluster IDs using a
 clustering algorithm, and v) modification of ID codes. Please see methods
 in main text and supplementary materials for more details concerning these
 processing steps. Column: Description *City : City code pertaining to
 study system where ANT = Antwerp, BUD = Budapest, GLA = Glasgow, GOT =
 Göteborg, HAR = Harjavalta, HEL = Helsingborg, Malmo = Malmö, MON =
 Montpellier, MUN = Munich, PAR = Paris, STR = Strasbourg, VES = Veszprém,
 WAR = Warsaw *BreedingSeason : Year data collected in yyyy format *ID_clu
 : Cluster (=subpopulation) ID code *Habitat : Habitat type category urban
 vs. forest *ID : Individual ID code (note only one observation per
 individual in dataset) *Tarsus_mean : Mean tarsus length in millimeters
 (Other dataset uses Tarsus_mod instead which is just a random observation
 of tarsus length per individual instead) *Species : Species where great
 tit = PARMAJ or blue tit = CYACAE *Sex : Sex were F = female and M = male
 *mean_propurb_1000: Mean proportion urbanization of each cluster measured
 as impervious surface area within 1000m radius circular buffers of each
 nest box *mean_propurb_100 : Mean proportion urbanization of each cluster
 measured as impervious surface area within 100m radius circular buffers of
 each nest box *mean_shdi_1000 : Mean Shannon diversity index of each
 cluster measured using number of land cover types within 1000m radius
 circular buffers of each nest box *mean_shdi_100 : Mean Shannon diversity
 index of each cluster measured using number of land cover types within
 1000m radius circular buffers of each nest box *mean_lat : Mean latitude
 of each cluster *area : Area in squared meters of each cluster *years_clu
 : Number of years of data collection for each cluster
 2."ThompsonEA_UrbanVp_NstTarsus.csv": Dataset on
 individual-level tarsus length of nestling great tits and blue tits,
 resulting from trans-European collaboration with long-term data from
 multiple populations along urbanization gradients across Europe. Data
 processing included the i) conversion of tarsus measurements from the
 Paris study system using equation S2 in supplementary, ii) removal of nine
 outliers of tarsus length from raw data: 4.6mm (Munich ID 18307), 5.3mm
 (Munich ID 1871), 5.7mm (Munich ID 611), 4.5mm (Munich ID 614), 5.3mm
 (Munich ID 846), 2.1mm (Paris ID 8949165), 2.53mm (Malmo ID 2KR97129),
 3.30mm (Malmo ID 2KX12867), 29.7mm (Malmo ID 2KS91854), iii) including
 only observations of nestlings that were measured between 13 - 17 days
 old, iv) the retention of only one random nestling observation for each
 brood (observation per nest box in a given year) in the dataset, v)
 assignment of nest boxes into cluster IDs using a clustering algorithm,
 and vi) modification of ID codes. Please see methods in main text and
 supplementary materials for more details concerning these processing
 steps. Column: Description *City : City code pertaining to study system
 where ANT = Antwerp, BUD = Budapest, GLA = Glasgow, GOT = Göteborg, HAR =
 Harjavalta, HEL = Helsingborg, Malmo = Malmö, MON = Montpellier, MUN =
 Munich, PAR = Paris, STR = Strasbourg, VES = Veszprém, WAR = Warsaw
 *BreedingSeason : Year data collected in yyyy format *ID_clu : Cluster
 (=subpopulation) ID code *Habitat : Habitat type category urban vs. forest
 *ID : Individual ID code (note only one observation per individual in
 dataset) *Tarsus_mod : Tarsus length in millimeters taken from one random
 nestling per each brood *Species : Species where great tit = PARMAJ or
 blue tit = CYACAE *ChickAge_comb : Age of chick in days when measured
 *mean_propurb_1000: Mean proportion urbanization of each cluster measured
 as impervious surface area within 1000m radius circular buffers of each
 nest box *mean_propurb_100 : Mean proportion urbanization of each cluster
 measured as impervious surface area within 100m radius circular buffers of
 each nest box *mean_shdi_1000 : Mean Shannon diversity index of each
 cluster measured using number of land cover types within 1000m radius
 circular buffers of each nest box *mean_shdi_100 : Mean Shannon diversity
 index of each cluster measured using number of land cover types within
 1000m radius circular buffers of each nest box *mean_lat : Mean latitude
 of each cluster *area : Area in squared meters of each cluster *years_clu
 : Number of years of data collection for each cluster
 3."ThompsonEA_UrbanVp_layDate.csv": Dataset on individual-level
 lay date of female great tits and blue tits, resulting from trans-European
 collaboration with long-term data from multiple populations along
 urbanization gradients across Europe. Data processing included the i)
 assignment of nest boxes into cluster IDs using a clustering algorithm ,
 ii) inclusion of first clutches only determined by selecting observations
 that were within 30 days of the first lay date within a given year,
 cluster, and species, iii) retention of only one observation of each known
 individual by taking the first observation of that individual that
 appeared in the dataset, and iv) modification of ID codes. Please see
 methods in main text and supplementary materials for more details
 concerning these processing steps. Column: Description *City : City code
 pertaining to study system where ANT = Antwerp, BUD = Budapest, GLA =
 Glasgow, GOT = Göteborg, HAR = Harjavalta, HEL = Helsingborg, Malmo =
 Malmö, MON = Montpellier, MUN = Munich, PAR = Paris, STR = Strasbourg, VES
 = Veszprém, WAR = Warsaw *BreedingSeason : Year data collected in yyyy
 format *ID_clu : Cluster (=subpopulation) ID code *Habitat : Habitat type
 category urban vs. forest *ID : Individual ID code (note only one
 observation per individual in dataset) *LayDate_julian : Laying date in
 julian days (days since Jan 1) from the first observation of each
 individual in the dataset *Species : Species where great tit = PARMAJ or
 blue tit = CYACAE *Age : Age category where Adult = Ad and Yearling /
 Juvenile = Juv *mean_propurb_1000: Mean proportion urbanization of each
 cluster measured as impervious surface area within 1000m radius circular
 buffers of each nest box *mean_propurb_100 : Mean proportion urbanization
 of each cluster measured as impervious surface area within 100m radius
 circular buffers of each nest box *mean_shdi_1000 : Mean Shannon diversity
 index of each cluster measured using number of land cover types within
 1000m radius circular buffers of each nest box *mean_shdi_100 : Mean
 Shannon diversity index of each cluster measured using number of land
 cover types within 1000m radius circular buffers of each nest box
 *mean_lat : Mean latitude of each cluster *area : Area in squared meters
 of each cluster *years_clu : Number of years of data collection for each
 cluster ## Model object .rds files Model objects (22 total) include output
 from Bayesian statistical models fitted using the package "brms"
 ([https://cran.r-project.org/web/packages/brms/index.html](https://cran.r-project.org/web/packages/brms/index.html)) and are provided to reduce processing time to obtain the study's results. Model object file names include information on the type of model fitted where models have been run separately for each trait (tarsAd = Adult tarsus length, tarsNst = Nestling tarsus length, LD = Lay Date) and species (GT = great tit, BT = blue tit). Each of these trait-species model combinations further evaluate three different effects of urbanization (Hab = habitat type urban vs forest, 1000 = continuous impervious surface area at 1000m scale, 100 = continuous impervious surface area at 100m scale). See further descriptions below and where model results are reported in main text and supplementary. Note that saved model objects (.rds files) are called in the scripts and can be downloaded here in the file list. CompositeModel_tarsAdGT_Hab_pub.rds / CompositeModel_tarsAdBT_Hab_pub.rds CompositeModel_tarsNstGT_Hab_pub.rds / CompositeModel_tarsNstBT_Hab_pub.rds CompositeModel_LDAdGT_Hab_pub.rds / CompositeModel_LDAdBT_Hab_pub.rds: Model results for effect of urbanization via habitat type (Hab) shown in Table 1 of main text for adult tarsus length (tarsAd), nestling tarsus length (tarsNst), and lay date (LD) for each species (great tit models on left of dash and blue tit models on right of dash above). CompositeModel_tarsAdGT_Hab_RanObs_pub.rds / CompositeModel_tarsAdBT_Hab_RanObs_pub.rds: Model results for effect of urbanization via habitat type (Hab) shown in Table S4 of supplementary for adult tarsus length (tarsAd) for each species (great tit models on left of dash and blue tit models on right of dash above) when instead selecting a random observation per individual rather than their mean. CompositeModel_tarsAdGT_1000_pub.rds / CompositeModel_tarsAdBT_1000_pub.rds CompositeModel_tarsNstGT_1000_pub.rds / CompositeModel_tarsNstBT_1000_pub.rds CompositeModel_LDAdGT_1000_pub.rds / CompositeModel_LDAdBT_1000_pub.rds: Model results for effect of urbanization via impervious surface area at 1000m scale (1000) shown in Table S5 of supplementary for adult tarsus length (tarsAd), nestling tarsus length (tarsNst), and lay date (LD) for each species (great tit models on left of dash and blue tit models on right of dash above). CompositeModel_tarsAdGT_100_pub.rds / CompositeModel_tarsAdBT_100_pub.rds CompositeModel_tarsNstGT_100_pub.rds / CompositeModel_tarsNstBT_100_pub.rds CompositeModel_LDAdGT_100_pub.rds / CompositeModel_LDAdBT_100_pub.rds: Model results for effect of urbanization via impervious surface area at 100m scale (100) shown in Table S6 of supplementary for adult tarsus length (tarsAd), nestling tarsus length (tarsNst), and lay date (LD) for each species (great tit models on left of dash and blue tit models on right of dash above). CompositeModel_tarsAdGT_Hab_MUC_pub.rds / CompositeModel_tarsNstGT_Hab_MUC_pub.rds: Model results for effect of urbanization via habitat type (Hab) shown in Table S8 of supplementary for adult (tarsAd) and nestling tarsus length (tarsNst) for great tits when excluding data from the larger Munich cluster. ## Code/Software Data compiled and analyzed using R script: "ThompsonEA_UrbanVp_Script.R" with free open software R ([https://www.r-project.org/](https://www.r-project.org/)). ## License Data and code provided are available under a Creative Commons Zero v1.0 Universal License (CC0 1.0; [https://choosealicense.com/licenses/cc0-1.0/](https://choosealicense.com/licenses/cc0-1.0/)).