Near‐infrared spectroscopy aids ecological restoration by classifying variation of taxonomy and phenology of a native shrub. Issue 5 (24th November 2021)

• Record Type: Journal Article
• Authors: Robb, Brecken C.; Olsoy, Peter J.; Mitchell, Jessica J.; Caughlin, T. Trevor; Delparte, Donna M.; Galla, Stephanie J.; Fremgen‐Tarantino, Marcella R.; Nobler, Jordan D.; Rachlow, Janet L.; Shipley, Lisa A.; Forbey, Jennifer S.
• Is Part Of: Restoration ecology. Volume 30:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-24
• Subjects: ecological restoration -- functional restoration -- near‐infrared spectroscopy -- phenotypes -- sagebrush; Restoration ecology -- Periodicals; Reclamation of land -- Environmental aspects -- Periodicals; 333.7153
• DOI: 10.1111/rec.13584
• Languages: English
• ISSNs: 1061-2971

Abstract

Abstract : Plant communities are composed of complex phenotypes that not only differ among taxonomic groups and habitats but also change over time within a species. Restoration projects (e.g. translocations and reseeding) can introduce new functional variation in plants, which further diversifies phenotypes and complicates our ability to identify locally adaptive phenotypes for future restoration. Near‐infrared spectroscopy (NIRS) offers one approach to detect the chemical phenotypes that differentiate plant species, populations, and phenological states of individual plants over time. We use sagebrush ( Artemisia spp.) as a case study to test the accuracy by which NIRS can classify variation within taxonomy and phenology of a plant that is extensively managed and restored. Our results demonstrated that NIRS can accurately classify species of sagebrush within a study site (75–96%), populations of sagebrush within a subspecies (99%), annual phenology within a population (>99%), and seasonal phenology within individual plants (>97%). Low classification accuracy by NIRS in some sites may reflect heterogeneity associated with natural hybridization, translocation of nonlocal seed sources from past restoration, or complex gene‐by‐environment interactions. Advances in our ability to detect and interpret spectral signals from plants may improve both the selection of seed sources for targeted conservation and the capacity to monitor long‐term changes in vegetation.