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Global impacts of climate change on avian functional diversity


Stewart, Peter et al. (2021), Global impacts of climate change on avian functional diversity, Dryad, Dataset,


Climate change is predicted to drive geographical range shifts, leading to fluctuations in species richness worldwide. However, the effect of these changes on functional diversity remains unclear, in part because comprehensive species-level trait data are generally lacking at global scales. Here we use morphometric and ecological traits for 8268 bird species to estimate the impact of climate change on avian functional diversity (FD). We show that future bird assemblages are likely to undergo substantial shifts in trait structure, with a magnitude of change greater than predicted from species richness alone, and a direction of change varying according to geographical location and trophic guild. For example, our models predict that FD of insect predators will increase at higher latitudes with concurrent losses at mid-latitudes, whereas FD of seed dispersing birds will fluctuate dramatically across the tropics. Our findings highlight the potential for climate change to drive continental-scale shifts in avian functional diversity with implications for ecosystem function and resilience.


These data comprise the raw data and results of the article: Stewart et al. (2021) Global impacts of climate change on avian functional diversity. Ecology Letters (in press).

The methods used to generate these data are fully described in main text and supplementary methods of the paper, and all R code is provided below. Please see the usage notes for details.

Code to reproduce the species distribution models from Hof et al. (2018), which we used in our analyses, is available at:

Usage Notes

All data and code are contained within subfolders of a compressed (zipped) folder, as shown below.

Please see the three metadata files within the data subfolders (metadata_dispersal_data.txt, metadata_raw_data.txt, and metadata_results_data.txt) for all data file descriptions, variable descriptions and other usage information.

  • Code
    • 1. Dispersal model - Contains all code to model dispersal distances for each species.
      • CODE_NonVolant - model for flightless species
      • CODE_Volant - model for flying species
      • _MACOSX - versions for Mac OS X
    • 2. Creating presence absence matrices - Contains all code to go from the SDM's ( to the presence-absence matrices.
      • Threshold the projected distributions
      • Clip projected distributions by modelled dispersal
      • Summarise projections into presence-absence matrix
      • Cleaning presence-absence matrices
    • 3. Functional diversity analyses - Contains all code to go from the presence-absence matrices to the final results. The R scripts within were run in numerical order.
      • 1. Traits dendro and PCA
      • 2. Create_no_dispersal_scenario
      • 3. Calculate SR FD and FRic
      • 4. Create subsets for hypervolumes
      • 5. Hypervolume code and subsets - contains all code and data necessary to perform the Gaussian hypervolume calculations. We recommend running this in parallel on an HPC. 
      • 6. Binding hypervolume results together
      • 7. Merge results dataframes and calculate metric changes
      • 8. Create figures
  • Data
    • Dispersal_model_data
      • metadata_dispersal_data.txt - contains all metadata for the dispersal dataset
    • Raw_data
      • metadata_raw_data.txt - contains all metadata for the raw data
    • Results_data
      • metadata_results_data.txt - contains all metadata for the results datasets




NERC Environmental Bioinformatics Centre, Award: NE/I028068/1

NERC Environmental Bioinformatics Centre, Award: NE/P004512/1

BMBF, Award: FKZ 01LS1617A

Bavarian Climate Research Network*

Deutsche Forschungsgemeinschaft, Award: HO 3952/3-1

BMBF, Award: FKZ 01LS1617A

Bavarian Climate Research Network