TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Experimental Trial Data

This file contains [female] subject metadata, stimulus [masculine and feminine male] metadata, and all trial data for all 107 experimental trials. Use in conjunction with R Code to reproduce all main analyses and the analyses of the effects of familiarity (co-group-membership) on look-time

Color Data

The file contains RGB values for all stimulus male faces. Use in conjunction with the equations for calculating color and luminance, which can be found in subsection Methods > Potential confounds of facial masculinit

Facial measures

File containing feature measurements of all male and female faces, along with color and luminance data. Use this file to reproduce masculinity scores (in conjunction with SPSS Code) and analyses of facial masculinity by age, color, and luminance analyses (in conjunction with R Code)

R Code for statistical analyses

This file contains all R code necessary to carry out the statistical analyses used throughout the paper, aside from the calculation of masculinity scores, which can be found in the SPSS Code file. Brief explanations of each procedure are also provided

SPSS Code for Facial Masculinity

This file contains the syntax for the regression of facial features (N=8) on sex, including saving the unstandardized predicted variables, which we used as facial masculinity scores. Load the Facial Measures file in SPSS and run this code to reproduce the analysis