Kothari, ShanBeauchamp-Rioux, RosalieLaliberté, EtienneCavender-Bares, Jeannine2022-06-232022-06-232022-06-23https://hdl.handle.net/11299/228067This submission contains the partial least-squares regression (PLSR) model coefficients to accompany the paper “Reflectance spectroscopy allows rapid, accurate, and non-destructive estimates of functional traits from pressed leaves” by Kothari et al. (2022) Methods in Ecology & Evolution. Each set of models for a given trait comprises a .csv file containing 100 models (rows) × wavelengths. The 100 models are derived from a jackknife analysis as described in the paper. To generate trait estimates using a model set, you apply them to the data (see below) and can take the mean or the full distribution of estimates.More than ever, ecologists seek to employ herbarium collections to estimate plant functional traits from the past and across biomes. However, many trait measurements are destructive, which may preclude their use on valuable specimens. Researchers increasingly use reflectance spectroscopy to estimate traits from fresh or ground leaves, and to delimit or identify taxa. Here, we extend this body of work to non-destructive measurements on pressed, intact leaves, like those in herbarium collections. Using 618 samples from 68 species, we used partial least-squares regression to build models linking pressed-leaf reflectance spectra to a broad suite of traits, including leaf mass per area (LMA), leaf dry matter content (LDMC), equivalent water thickness, carbon fractions, pigments, and twelve elements. We compared these models to those trained on fresh- or ground-leaf spectra of the same samples. Here, we present the model coefficients and a README that provides examples of how to apply them to other data.Attribution 4.0 International (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/pressed leavesreflectance spectroscopyPLSR model coefficientsherbarium specimensDatasethttps://doi.org/10.13020/ycga-7e47