A global dataset of mesophyll conductance measurements and accompanying leaf traits

This dataset contains a compilation of published measurements of leaf mesophyll conductance (gm) and accompanying leaf structural, anatomical, biochemical, and physiological traits as presented in: 

Knauer, J., Cuntz, M., Evans, J.R., Niinemets, Ü., Tosens, T., Veromann-Jürgenson, L.-L., Werner, C. and Zaehle, S. (2022), Contrasting anatomical and biochemical controls on mesophyll conductance across plant functional types, New Phytologist, doi:10.1111/nph.18363. 

Note that the compilation aims to represent unstressed, young, but fully expanded and high light-adapted leaves, albeit these criteria were not always explicitly stated. The reported measurements are assumed to be independent (i.e. from different set of plants) if one or several of species, cultivar/variety/genotype, population, measurement year (if annual/deciduous), age class (if woody), or growth environment is different. Only one (aggregated) gm value per set of plants is reported in the dataset. For further details on data collection and processing see reference above.

The file gm_dataset_Knauer_et_al_2022.xlsx contains the following sheets:

- data: the main dataset including all variables and associated information such as species, measurement conditions, growing conditions etc.

- columns_descriptions_units: a description of all columns in sheet 'data' as well as associated units (if applicable).

- references: literature references of the data. Column 'refkey' can be used for cross-referencing with column 'refkey' in sheet 'data'.

- references_methods: literature references for measurement methods of gm. Column 'refkey' can be used for cross-referencing with column 'method_reference' in sheet 'data'.

- references_rubisco_parameters: literature references for rubisco parameters used in the studies. Column 'refkey' can be used for cross-referencing with columns 'Rubisco_constants_Ci_reference' and 'Rubisco_constants_Cc_reference' in sheet 'data'.

The xlsx file can be imported into software environments such as R or python for further analysis. To read into R (tested for R version 4.1.2), a package such as readxl needs to be installed and loaded first, after which individual tabs can be imported using the read_xlsx() function. In python, the file can be imported using the pandas.read_excel command available from the pandas package.

The file aggregate_by_method.R provides code to read the xlsx dataset and aggregate by measurement method as described in the reference above. 

For questions or comments please contact Dr. Jürgen Knauer ([e-mail]).