Code from: The role of intrinsic factors in explaining range shifts of European breeding birds: A meta-analysis

Warmer, Femke1; Van Vliet, Wessel1; Van Hooft, Pim 1 ; Hof, Anouschka 1

Published Dec 05, 2025 on Dryad. https://doi.org/10.5061/dryad.wstqjq2z5

Data files

Dec 05, 2025 version files 37.08 KB

Abstract

Species are shifting their distribution ranges in response to climate and land-use change. However, the observed range shift patterns are idiosyncratic in rate and direction. Species traits, such as ecological, life-history and movement traits, may play an important role in determining range shifts by influencing a species’ capacity to shift successfully. Whilst several studies investigate the role of different species traits in driving range shifts, they generally consider few traits and range shift types. Range shift types such as abundance shift and centroid shift are generally not taken into account. Drivers of range shifts may however differ per range shift type. We conducted a meta-analysis to uncover the role of intrinsic factors (9 species functional traits and 5 spatial abundance characteristics), in explaining six contemporary range shift types ( range size changes: expansion/contraction, relative change and rate of change; latitudinal shifts: abundance shift, centroid shift and range margin shift) in European breeding birds (n=270). We found that the role of intrinsic factors in explaining contemporary range shifts in European breeding birds is indeed range shift type dependent. Natal dispersal distance and clutch size were for instance positively related with range size changes, while diet breadth and conservation status showed both negative and positive relationships depending on the range shift type. Acknowledging limitations regarding unevenness of data availability across the study region; the region of study was an important predictor for range size changes, suggesting a relative importance of local context and extrinsic drivers. Future trait-based analyses of range shifts would benefit from accounting for intraspecific variation in functional traits across time and space, the inclusion of additional traits like phenological traits, exposure to environmental pressures, and competitive ability, and should be investigated across multiple scales and for multiple types of range shifts.

Dataset DOI: 10.5061/dryad.wstqjq2z5

Description of the data and file structure

R scripts from: The role of intrinsic factors in explaining range shifts of European breeding birds: a meta-analysis. Refer to the material and methods section for data collection. The underlying data used for this study were sourced from freely available publications as listed in the paper.

Files and variables

File: Warmer_et_al._Appx7.R

Description: R script for the calculation of the species thermal maximum, minimum, and range, as well as the calculation of the historical range variables.

This script filters bird-occurrence records and extracts climate data to calculate each species’ thermal maximum, minimum, and range using buffered temperature values. It also derives historical range metrics (range size and northern limit) from presence records.

File: GBIF_EBCCdata.csv

Description: Species data used in the R script in Warmer_et_al._Appx7.R

File: myspecies.csv

Description: reduced species data used in the R script in Warmer_et_al._Appx7.R

File: Warmer_et_al._Appx8.R

Description: R script for model selection and multi-model inference of the Range size change dataset, including the three range shift types: Change-type, Relative-change, and Rate-of-change.

This script prepares ecological and trait data, builds mixed-effects models for three response variables, and performs model selection using AICc-based dredging with multi-model averaging. It outputs averaged coefficients, variable importance, diagnostics, and confidence intervals for identifying predictors of range size change.

File: ExpContr.csv

Description: Expansion contraction data used in the R script in Warmer_et_al._Appx8.R

File: Warmer_et_al._Appx9.R

Description: The accompanying text file shows the R script for model selection and multi-model inference of the Abundance shift dataset.

This script cleans and standardizes bird abundance–shift data, recodings, and scaling traits before fitting a linear model. It then performs AICc-based model selection and multi-model averaging to identify key predictors of abundance shift.

File: Abundance_shift.csv

Description: Abundance shift data used in the R script in Warmer_et_al._Appx9.R

File: Warmer_et_al._Appx10.R

Description: The accompanying text file shows the R script for model selection and multi-model inference of the Centroid shift dataset.

This script preprocesses centroid-shift data by recoding factors and scaling traits, then fits a transformed linear model to predict geographic centroid movements. It uses AICc-based model selection and multi-model averaging to identify the traits and ecological variables most strongly associated with centroid shifts.

File: Centroid_shift.csv

Description: Centroid shift data used in the R script in Warmer_et_al._Appx10.R

File: Warmer_et_al._Appx11.R

Description: The accompanying text file shows the R script for model selection and multi-model inference of the Northern range margin shift dataset.

This script filters northern range-margin data, recodes ecological and trait variables, and scales predictors before fitting a linear model of margin-shift rates. It then uses the AICc model selection and multi-model averaging to identify which species traits and environmental factors best explain northward margin shifts.

File: Range_margins.csv

Description: Range margin shift data used in the R script in Warmer_et_al._Appx11.R

Code/software

All data analyses were performed in R version 4.1.2 (R Core Team, 2021) using the packages lme4 version 1.1-27.1 (Bates et al., 2015), MuMIn version 1.43.17 (Barton, 2020), DHARMa version 0.4.5 (Hartig, 2022), and emmeans version 1.7.2 (Lenth, 2022). The packages ggplot2 (Wickham, 2016) and gridExtra version 2.3. (Auguie, 2017) were used for data visualisation, and dplyr version 1.0.7 (Wickham et al., 2021) was used during data manipulation.

Auguie, B. (2017). gridExtra: Miscellaneous Functions for "Grid" Graphics. R package version 2.3. In https://CRAN.R-project.org/package=gridExtra

Barton, K. (2020). MuMIn: Multi-Model Inference. R package version 1.43.17. In https://CRAN.R-project.org/package=MuMIn

Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. https://doi.org/10.18637/jss.v067.i01

Lenth, R. V. (2022). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.7.2. In https://CRAN.R-project.org/package=emmeans

R Core Team. (2021). R: A language and environment for statistical computing. In R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org

Wickham, H., François, R., Henry, L., & Müller, K. (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.7. In https://CRAN.R-project.org/package=dplyr

Access information

Data were extracted from the literature. See "The role of intrinsic factors in explaining range shifts of European breeding birds: a meta-analysis" Appendix 1 for a full overview of the used search queries and the systematic review flow diagram. See Appendix 2 for the full list of literature used.