DOI: 10.5061/dryad.wstqjq2z5
title: Code from: The role of intrinsic factors in explaining range shifts of European breeding birds: A meta-analysis

description: 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.
description: # The role of intrinsic factors in explaining range shifts of European
 breeding birds: A meta-analysis Dataset DOI:
 [10.5061/dryad.wstqjq2z5](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](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](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](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](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/](https://www.R-project.org/) Wickham, H. (2016). *ggplot2: Elegant Graphics for Data Analysis*. Springer-Verlag New York. [https://ggplot2.tidyverse.org](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](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.