Spectral and trait data for Rapid estimates of leaf litter chemistry using reflectance spectroscopy
Data files
Apr 26, 2024 version files 12.72 MB
Abstract
Measuring the chemical traits of leaf litter is important for understanding plants’ roles in nutrient cycles, including through nutrient resorption and litter decomposition, but conventional leaf trait measurements are often destructive and labor-intensive. Here, we develop and evaluate the performance of partial least-squares regression (PLSR) models that use reflectance spectra of intact or ground leaves to estimate leaf litter traits, including carbon and nitrogen concentration, carbon fractions, and leaf mass per area (LMA). Our analyses included more than 300 samples of senesced foliage from 11 species of temperate trees, including needleleaf and broadleaf species. Across all samples, we could predict each trait with moderate-to-high accuracy from both intact-leaf litter spectra (validation R2 = 0.543-0.941; %RMSE = 7.49-18.5) and ground-leaf litter spectra (validation R2 = 0.491-0.946; %RMSE = 7.00-19.5). Notably, intact-leaf spectra yielded better predictions of LMA. Our results support the feasibility of building models to estimate multiple chemical traits from leaf litter of a range of species. In particular, the success of intact-leaf spectral models allows non-destructive trait estimation in a matter of seconds, which could enable researchers to measure the same leaves over time in studies of nutrient resorption.
README: Spectral and trait data for Rapid estimates of leaf litter chemistry using reflectance spectroscopy
https://doi.org/10.5061/dryad.hdr7sqvrk
Description of the data and file structure
This dataset contains reflectance spectra of leaf litter from the Forests and Biodiversity 1 experiment at Cedar Creek Ecosystem Science Reserve, as well as a residential property in Minneapolis. The same litter samples were measured while intact (whole) or ground (powdered). Reflectance spectra were measured with a Spectral Evolution PSR+ 3500 (Haverhill, MA, United States), using either a leaf clip (intact leaves) or a benchtop reflectance probe (ground leaves) supplied by the same company.
The two main files for intact and ground spectra (intact_spec.csv and ground_spec.csv) contain metadata (sample information and functional traits) and the spectra themselves. Each row is a sample. More context for the sample information and functional traits (including units) can be found in the metadata_fields.csv file; detailed measurement information can be found in the associated paper.
Columns with numerical names (400 to 2400) are wavelength bands of the reflectance spectrum. Reflectance values are expressed as the fraction of light reflected from 0 to 1, and the wavelength units are nm. Each reflectance spectrum is the average of multiple replicate measurements of a sample. The reflectance spectra have been automatically resampled to 1 nm resolution and jump-corrected in sensor overlap regions.
Data use
The spectra and trait data together have been used to calibrate and validate a set of partial least-squares regression models used for predicting traits from spectra, found at: https://doi.org/10.5061/dryad.hdr7sqvrg. The README of that repository gives an example of how to read in the data .csv files using the package spectrolab v. 0.0.18 in R 4.2.1, which is copied below. In spectrolab, the class spectra allows users to attach and retrieve metadata from spectral data using the function meta().
library(spectrolab)
spec_df<-read.csv("mydata.csv")
name_var<-1 ## index for the column that contains sample names
meta_vars<-2:20 ## adjust as needed: indices for columns that contain metadata (including traits)
band_names<-400:2400 ## wavelengths of spectral bands corresponding to remaining columns
## you can also use the as_spectra command, but it's a bit more finicky
## with data frames because the column names of bands must contain a letter
spec<-spectra(value = spec_df[,-c(name_vars,meta_vars)],
band_names = 400:2400,
names = spec_df[,name_var],
meta = spec_df[,meta_vars])
test.spectra<-as.matrix(spec)
Sharing/Access information
An identical version of these data is available at EcoSIS here. Please contact Shan Kothari at shan.kothari [at] umontreal [dot] ca or quercusacerifolia [at] gmail [dot] com with any questions.