Spectral and trait data for Rapid estimates of leaf litter chemistry using reflectance spectroscopy

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 ..., , , # Spectral and trait data for Rapid estimates of leaf litter chemistry using reflectance spectroscopy

[https://doi.org/10.5061/dryad.hdr7sqvrk](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 (incl...