DOI: 10.5061/dryad.fxpnvx0zt
title: Data from: Climatic predictors of long-distance migratory birds’ breeding productivity across Europe

description: Ongoing climate changes represent a major determinant of demographic
 processes in many organisms worldwide. Birds, and especially long-distance
 migrants, are particularly sensitive to such changes. To better understand
 these impacts on long-distance migrants’ breeding productivity, we tested
 three hypotheses focused on (i) the shape of the relationships with
 different climate variables, including previously rarely tested quadratic
 responses, and on regional differences in these relationships predicted by
 (ii) mean climatic conditions and (iii) by the rate of climate change in
 respective regions ranging from Spain to Finland. We calculated breeding
 productivity from constant effort ringing sites from 11 European countries
 covering 34 degrees of latitude, and extracted temperature- and
 precipitation-related climate variables from E-OBS and NASA MODIS
 datasets. To test our hypotheses, we fitted GLMM and Bayesian
 meta-analytic models. We revealed hump-shaped responses of productivity to
 temperature, growing degree-days, green-up onset date, and precipitation
 anomaly, and negative responses to intense and prolonged rains across the
 regions. The effects of March temperature and April growing degree-days
 were more negative in cold than in warm regions, except that one with the
 highest accumulated heat, whereas increasing June precipitation anomalies
 were associated with higher productivity in both dry and wet regions. The
 rate of climate warming was unrelated to productivity responses to
 climate. The influence of climate on bird productivity proved to be
 frequently non-linear, as expected by ecological theory. To explain the
 differences between regions, the rate of climate change is less important
 than regional interannual variability in climate (which is predicted to
 increase), but this may change with the progression of climate change in
 the future. Productivity declines in long-distance migratory songbirds are
 particularly expected if out-of-norm water excess increases in frequency
 or strength.
description: # Bird\_breeding\_productivity\_data
 [https://doi.org/10.5061/dryad.fxpnvx0zt](https://doi.org/10.5061/dryad.fxpnvx0zt) This folder contains data sets (**Bird_prod_data.csv, Clim_mean_prod_lin.csv, Clim_mean_prod_poly.csv, Clim_trend_PCA_prod_lin.csv, Clim_trend_PCA_prod_poly.csv**), models (.rds files; see below for their naming scheme) and code (**R-script_bird_prod.R**) related to the article: *Climatic predictors of long-distance migratory birds’ breeding productivity across Europe* ## Description of the data and file structure The data is stored in subfolder "Data" **Bird_prod_data.csv** * *Reg*: breeding region; CZP = the Czech Republic, DEG-DKC = Germany and Denmark, ESP = Spain, FRP_N = northern part of France, FRP_S = central & southern part of France, GBT_N = northern parts of Great Britain – Wales and England, Scotland, Northern Ireland – and Ireland, GBT_S = southern parts of Great Britain – England and Wales, HGB = Hungary, NLA = the Netherlands, SFH = Finland, SVS = Sweden - *EURING*: species code * *Year*: year corresponding to breeding season - *Species*: species name (see also Table 3 in the article) * *Site*: site code - *Ad*: number of adults * *Juv*: number of juveniles - *TotalEPR*: water availability in wintering grounds (called ETr in the article) * *Ad_scaled*: Number of adults standardized to mean = 0 and SD = 1 for each species and site - *T3, T4, T5, T6*: temperature in March, April, May, June * *GDD10_3, GDD10_4, GDD10_5, GDD10_6*: growing degree-days in March, April, May, June - *GOD*: green-up onset date * *Rain_anom_3, Rain_anom_4, Rain_anom_5, Rain_anom_6*: precipitation anomaly in March, April, May, June, abbreviated as ΔR in the article - *R10_5, R10_6*: number of heavy rain days in May, June * *R20_5, R20_6*: number of very heavy rain days in May, June - *R1c_5, R1c_6*: number of consecutive rain days 1mm in May, June * *R2c_5, R2c_6*: number of consecutive rain days 2mm in May, June **Clim_mean_prod_lin.csv** * *reg*: breeding region - *clim_var*: abbreviation of climate variable * *mean_val*: mean value of the climate variable - *Est_prod_lin*: estimate of the linear term in the relationship between breeding productivity and climate variable * *SE_prod_lin*: standard error of the estimate of the linear term in the relationship between breeding productivity and climate variable **Clim_mean_prod_poly.csv** * *reg*: breeding region - *clim_var*: abbreviation of climate variable * *mean_val*: mean value of the climate variable - *Est_prod_poly*: estimate of the quadratic term in the relationship between breeding productivity and climate variable * *SE_prod_poly*: standard error of the estimate of the quadratic term in the relationship between breeding productivity and climate variable **Clim_trend_PCA_prod_lin.csv** * *reg*: breeding region - *clim_change*: climate warming variable derived from the first axis of PCA (Principal Component Analysis), for months of March, April, May, June * *Est_trend*: slope of the linear temporal trend of climate warming variable over the study period **Clim_trend_PCA_prod_poly.csv** * reg: breeding region - clim_change: climate warming variable derived from the first axis of PCA (Principal Component Analysis), for months of March, April, May, June * Est_trend: slope of the quadratic temporal trend of climate warming variable over the study period Fitted models (88 files) are stored in subfolder "Models" Naming scheme of the models is: **Hyp2 or Hyp3**: models for testing Hypothesis 2 or Hypothesis 3, respectively  **resp1 or resp2**: response variable of the model was derived from the relationship between breeding productivity and the linear term of the climate variable (i.e. *Est_prod_lin*, see above in Clim_mean_prod_lin.csv) or the quadratic term of the climate variable (i.e. *Est_prod_poly*, see above in Clim_mean_prod_poly.csv), respectively **lin or poly**: models employ linear or polynomial (quadratic) terms of climate variables, respectively **T, GDD10, ΔR, GOD**: climate variables used in testing Hypothesis 2 or Hypothesis 3, i.e. temperature, growing degree-days, precipitation anomaly, and green-up onset date, respectively **3, 4, 5, 6**: months of March, April, May, or June **warm_PCA1** (for Hypothesis 3 only): climate warming variable was derived from the first axis of PCA (Principal Component Analysis), suffixes 3, 4, 5 or 6 means months of March, April, May, and June ## Code/Software The code file "R-script_bird_prod.R" is an R script created by version 4.3.1, allowing to run all our analyses. It consists of the following parts: * loading the libraries * loading the data set Bird_prod_data.csv and preparing the variables for testing Hypothesis 1 * fitting the models for testing Hypothesis 1 * performing the model averaging * extraction of the marginal effects of climate variables * calculation of the temporal variance explained by climate variables * loading the data sets Clim_mean_prod_lin.csv and Clim_mean_prod_poly.csv and preparing the variables for testing Hypothesis 2 * fitting the models for testing Hypothesis 2 * extraction of parameters from the fitted models * loading the data sets Clim_trend_PCA_prod_lin.csv and Clim_trend_PCA_prod_poly.csv and preparing the variables for testing Hypothesis 3 * fitting the models for testing Hypothesis 3 * extraction of parameters from the fitted models