Spectral Fingerprints Predict Functional Phenotypes of a Native Shrub

Abstract

Landscapes are rapidly changing. To understand these changes and how they may influence coexisting herbivores, it is critical that we improve the ways in which we monitor changes in plant species, populations, and functional phenotypic traits over space and time. Near infrared spectroscopy (NIRS) is proving to be a valuable tool when it comes to this goal. NIRS is noninvasive and can provide high-resolution temporal information, including structural and chemical characteristics, on objects that are otherwise expansive, inaccessible, or imperceptible. We used the threatened sagebrush-steppe ecosystem, which spans over 43 million hectares of the Western United States, as a case study to test the accuracy in which NIRS can measure and classify functional phenotypic traits of sagebrush (Artemisia spp.) populations. Sagebrush habitats are known to have extreme levels of genetic and chemical heterogeneity and plasticity. Yet, our results showed that NIRS can classify species of sagebrush within a site, populations of sagebrush within a species across sites, and phenology (both seasonally and annually) of sagebrush within a population. These taxonomic, geographic, and phenological phenotypes are functionally important in many ways, including determining species composition and distribution, identifying developmental stages of individual plants, potentially detecting past and present anthropogenic and environmental stressors, and predicting interactions with herbivores. Even so, habitat use by coexisting herbivores is not always explained by these relatively crude phenotypes. Specifically, herbivores make foraging decisions based on specific concentrations of chemical phenotypes that have functional consequences for herbivores. Our research further demonstrated that NIRS can predict concentrations of individual chemical compounds and classes of compounds, in the forms of both nutrients and toxins, in sagebrush plants across species and populations. As such, we further tested if NIRS could directly predict browsing by coexisting sagebrush herbivores, in the form of bite marks on plants. Although NIRS was not able to predict herbivore foraging behavior, it shows promise for predicting foraging behavior indirectly through predicted concentrations of phytochemicals and directly with finer tuned field validation and model calibration. To monitor the threats of climate and anthropogenic disturbances on ecosystems, it is essential we find better ways to quantify the functional phenotypes that mediate interactions among plants, herbivores, and the environment. We show that NIRS can be a powerful tool in achieving this aim.

Scaling Up Sagebrush Chemistry With Near-Infrared Spectroscopy and Uas-Acquired Hyperspectral Imagery

“…Future work should take advantage of larger sample size, sites with more chemical diversity, and a better balance between species and chemo-types. Additionally, the continued success of NIRS in lab environments shows potential for scaling to the field to classify sagebrush and predict chemistry (Robb, 2020). We recommend using existing datasets (either NIRS or lab-based chemistry) to set up which bands are more important for the goal in hand (i.e., differentiation between chemotypes or predicting chemistry of interest) to select the sensor best suited to that purpose.…”

“…For monoterpenes, a subset of 100 mg of sample was transferred to a headspace vial and analysed using headspace gas chromatography (Agilent 7694 Headspace Sampler, Agilent 6890 Series GC). See Robb (2020) and Olsoy et al (2020) for more details on chemical analysis.…”

“…Sagebrush leaves contain a complex mixture of plant chemicals to protect against herbivory, including volatile monoterpenes and phenolics, but are also a good source of crude protein. This chemistry is highly variable among and within sites (Robb, 2020, Olsoy et al, 2020 and influences diet and habitat selection by wild herbivores at varying spatial scales (Frye et al, 2013;Ulappa et al, 2014;Fremgen-Taratino et al, 2020). To better understand plant-herbivore interactions, we need to map this plant chemistry across the landscape.…”

“…One sensor technology for predicting plant and leafscale chemistry is near-infrared spectroscopy (NIRS). The spectral signatures measured with NIRS depend on the number and type of C-H, N-H, and O-H chemical bonds, and can be related to plant defensive and nutritional chemistry (Foley et al, 1998;Moore et al, 2010;Robb, 2020). Unoccupied aerial systems (UAS) have emerged as a viable option for habitat mapping of vegetation and chemical traits at moderately large extents (Anderson and Gaston, 2013;Manfreda et al, 2018).…”

“…Recent attempts at landscape mapping of diet quality involved classifying sagebrush structural morphotypes with unique chemical profiles, but relied on regression kriging (Olsoy et al, 2020), a type of spatial interpolation requiring a large amount of leaf sampling and laboratory analysis that does not directly predict plant chemical concentrations. NIRS (Olsoy et al, 2016;Robb, 2020) and airborne hyperspectral sensors (Mitchell et al, 2012b) have potential to link near-and short-wavelength infrared signals to plant chemistry. Recent technological advances have miniaturized hyperspectral sensors and allowed for UAS platforms to capture high-resolution imagery at these longer wavelengths.…”