Cox, A.J.F. et al
Leaf functional trait data from three experimental sites in the Colombian Andes, June-August 2019
Cite this dataset as:
Cox, A.J.F.; González-Caro, S.; Meir, P.; Hartley, I.P.; Restrepo, Z.; Villegas, J.C.; Sanchez, A.; Mercado, L.M. (2024). Leaf functional trait data from three experimental sites in the Colombian Andes, June-August 2019. NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/55ed98e7-f150-43c8-9b93-d639c1c8af3e
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This dataset is available under the terms of the Open Government Licence
https://doi.org/10.5285/55ed98e7-f150-43c8-9b93-d639c1c8af3e
This dataset includes key leaf functional trait data collected from three common garden sites along an elevation/temperature gradient in the Colombian Andes. From eight species, there are a selection of leaf structural (leaf mass per unit area, leaf thickness, leaf dry matter content, leaf area, leaf width) nutrient (nitrogen and phosphorus, expressed on area- and mass-bases) and water-use efficiency (13C and g1) traits. Values for these traits were obtained by a combination of laboratory analysis, raw measurements with handheld equipment, and processing with packages in the ‘R’ environment.
Publication date: 2024-10-21
View numbers valid from 21 October 2024 Download numbers valid from 21 October 2024 (information prior to this was not collected)
Format
Comma-separated values (CSV)
Spatial information
Study area
Spatial representation type
Tabular (text)
Spatial reference system
WGS 84
Temporal information
Temporal extent
2019-06-01 to 2019-08-31
Provenance & quality
Leaf material was collected from eight species at the experimental sites. Immediately after collection, leaves were sealed in individual plastic bags with damp cotton wool to prevent premature drying.
For leaf structural traits, the following procedures were followed. High-precision electronic callipers were used to measure leaf thickness from three areas of each leaf, avoiding the central vein, with the average of these three measurements taken to be leaf thickness (mm). Images were taken of each leaf using a CanoScan LiDE 300 flatbed scanner and then processed using the ‘R’ environment (v4.2.1; R Core Team, 2022) to calculate leaf area (cm2) and leaf width (mm): leaf area was obtained using the ‘LeafArea’ package in R which automates the ImageJ programme while leaf width values were calculated using the ‘LeafJ’ plugin for ImageJ. Following scanning, leaves were weighed to give their wet weight before being dried in an oven to constant mass for approximately 48 hours at 70°C, then re-weighed to give their dry weight. Leaf dry matter content (mg g-1) was calculated as the quotient of leaf dry weight to leaf wet weight while leaf mass per unit area (g m-2) was calculated by dividing dry weight by LA.
For leaf nutrients and 13C values, leaves were ground into a powder using a shaker mill and ~1 mg of sample for each was then packed into tin capsules and run through an infrared mass spectrometer. This returned %N values, and thus, mass-based nitrogen content, as well as 13C. For leaf P content, 0.1-0.2 g of sample was subjected to ICP-OES analysis with a unique Dichroic Spectral; this returned mass-based phosphorus content. Both leaf N and P can be expressed on an area-basis by dividing by LMA.
For g1, the unified stomatal model was then fitted to diurnal course An and gs measurements in the R environment using the ‘plantecophys’ package to obtain the model parameter g1. The An and gs measurements were collected using LI-6400XT portable photosynthesis system with the ambient conditions (photosynthetically active radiation, relative humidity, air temperature and 410ppm CO2) for each leaf replicated within the cuvette of the LI-6400XT before the leaf was inserted, allowing readings to be taken almost immediately.
For leaf structural traits, the following procedures were followed. High-precision electronic callipers were used to measure leaf thickness from three areas of each leaf, avoiding the central vein, with the average of these three measurements taken to be leaf thickness (mm). Images were taken of each leaf using a CanoScan LiDE 300 flatbed scanner and then processed using the ‘R’ environment (v4.2.1; R Core Team, 2022) to calculate leaf area (cm2) and leaf width (mm): leaf area was obtained using the ‘LeafArea’ package in R which automates the ImageJ programme while leaf width values were calculated using the ‘LeafJ’ plugin for ImageJ. Following scanning, leaves were weighed to give their wet weight before being dried in an oven to constant mass for approximately 48 hours at 70°C, then re-weighed to give their dry weight. Leaf dry matter content (mg g-1) was calculated as the quotient of leaf dry weight to leaf wet weight while leaf mass per unit area (g m-2) was calculated by dividing dry weight by LA.
For leaf nutrients and 13C values, leaves were ground into a powder using a shaker mill and ~1 mg of sample for each was then packed into tin capsules and run through an infrared mass spectrometer. This returned %N values, and thus, mass-based nitrogen content, as well as 13C. For leaf P content, 0.1-0.2 g of sample was subjected to ICP-OES analysis with a unique Dichroic Spectral; this returned mass-based phosphorus content. Both leaf N and P can be expressed on an area-basis by dividing by LMA.
For g1, the unified stomatal model was then fitted to diurnal course An and gs measurements in the R environment using the ‘plantecophys’ package to obtain the model parameter g1. The An and gs measurements were collected using LI-6400XT portable photosynthesis system with the ambient conditions (photosynthetically active radiation, relative humidity, air temperature and 410ppm CO2) for each leaf replicated within the cuvette of the LI-6400XT before the leaf was inserted, allowing readings to be taken almost immediately.
Licensing and constraints
This dataset is available under the terms of the Open Government Licence
Cite this dataset as:
Cox, A.J.F.; González-Caro, S.; Meir, P.; Hartley, I.P.; Restrepo, Z.; Villegas, J.C.; Sanchez, A.; Mercado, L.M. (2024). Leaf functional trait data from three experimental sites in the Colombian Andes, June-August 2019. NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/55ed98e7-f150-43c8-9b93-d639c1c8af3e
Correspondence/contact details
Authors
Villegas, J.C.
Universidad de Antioquia
Sanchez, A.
Universidad del Rosario
Other contacts
Rights holder
University of Exeter
Custodian
NERC EDS Environmental Information Data Centre
info@eidc.ac.uk
Publisher
NERC EDS Environmental Information Data Centre
info@eidc.ac.uk