Using an Ultraviolet Light Test to Improve Sagebrush Identification and Predict Forage Quality for Wildlife

Rosentreter, Roger; Robb, Brecken C.; Forbey, Jennifer S.

doi:10.3398/064.081.0204

Cited by 1 publication

(1 citation statement)

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“…Unfortunately, classifying sagebrush taxa based on morphology alone is challenging (Rosentreter et al 2021) and misclassification may lead to errors in land cover maps (Fremgen‐Tarantino et al 2021). Although genetic tools for classification of sagebrush are emerging (Richardson et al 2012), there is a need for inexpensive and rapid approaches to identify and monitor specific sagebrush taxa in the field.…”

Section: Introductionmentioning

confidence: 99%

Near‐infrared spectroscopy aids ecological restoration by classifying variation of taxonomy and phenology of a native shrub

Robb

Olsoy

Mitchell

et al. 2021

Restoration Ecology

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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.

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Section: Introductionmentioning

confidence: 99%