Data from: Climatic predictors of long-distance migratory birds’ breeding productivity across Europe
Data files
Aug 06, 2024 version files 389.40 MB
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Data.zip
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Models.zip
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R-script_bird_prod.R
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README.md
Abstract
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.
README: Bird_breeding_productivity_data
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