Open Collections

Abstract

There are ~400 million plant specimens held in ~3,500 herbaria worldwide today. Along with their long-established role in cataloguing species taxonomy and distribution, herbarium specimens are increasingly being used to measure ecologically important plant traits, often via spectral analyses. Such analyses quantify the interaction between plant matter and electromagnetic radiation across wavelengths, revealing spectral signatures that are linked to traits via statistical modelling. Once models are developed, these vast collections may facilitate leaf functional trait estimation across wide spatial and temporal scales. These methods may also allow for the analysis of foreign, extirpated, or even extinct species non-destructively within local herbaria. But for this to occur on a wide scale, proof of concept must be provided for the use of older or more degraded specimens, as many specimens in herbaria exhibit some degree of degradation. This research aims to provide the basis for the use of a wider range of specimens in spectral analyses. To do this, we devised an experiment wherein Ginkgo biloba specimens were prepared and placed in four temperature treatments of varying intensity and measured annually to determine whether their spectra can accurately estimate functional traits as specimens degrade. We employed partial least squares regression (PLSR) analyses to assess whether dried-leaf reflectance spectra can estimate a suite of structural/water-related- and chemical-based functional traits over time and across treatments. Our results indicate that, across models, leaf mass per area and cellulose were consistently the best estimated whereas pigment and lignin contents were consistently poorly predicted. We found that collection season, years since collection, nor storage condition affected PLSR model performance and trait estimation, despite trends in spectra indicating degradation. This provides promise for the use of a larger proportion of the global stock of herbarium specimens in spectral analyses, providing non-destructive, low-cost, efficient means of trait estimation.