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.
A case study in the functional consequences of scaling the sizes of realistic cortical models
