Spectral Fingerprints Predict Functional Phenotypes of a Native Shrub

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

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

Sagebrush identification can be improved by using a relatively easy ultraviolet (UV) light test on specimens. Sagebrush produces a variety of water-soluble polyphenols called coumarins, which fluoresce a blue color under UV light and can help differentiate species, subspecies, and hybrids. We tested 16 different sagebrush taxa (including species and subspecies) from herbarium specimens and found 3 taxa (low sagebrush, Artemisia arbuscula; Wyoming sagebrush, A. tridentata wyomingensis; and basin sagebrush, A. t. tridentata) that were often misidentified. We show that the UV light test can greatly improve identification of these species. Moreover, given that the UV+ chemicals that discriminate taxa are also considered an indirect biomarker of sagebrush palatability for some herbivores, the UV light test can be used to predict forage quality for threatened species like sage-grouse (Centrocercus spp.) and pygmy rabbits (Brachylagus idahoensis). Collecting voucher specimens of sagebrush at wildlife study sites and comparing their UV intensity to historical herbarium specimens could help identify both current and changing availability of palatable sagebrush for wildlife. We found that even herbarium specimens \u3e80 years old still fluoresce under UV light

Scaling Up Sagebrush Chemistry with Near-Infrared Spectroscopy and UAS-Acquired Hyperspectral Imagery

Sagebrush ecosystems (Artemisia spp.) face many threats including large wildfires and conversion to invasive annuals, and thus are the focus of intense restoration efforts across the western United States. Specific attention has been given to restoration of sagebrush systems for threatened herbivores, such as Greater Sage-Grouse (Centrocercus urophasianus) and pygmy rabbits (Brachylagus idahoensis), reliant on sagebrush as forage. Despite this, plant chemistry (e.g., crude protein, monoterpenes and phenolics) is rarely considered during reseeding efforts or when deciding which areas to conserve. Near-infrared spectroscopy (NIRS) has proven effective in predicting plant chemistry under laboratory conditions in a variety of ecosystems, including the sagebrush steppe. Our objectives were to demonstrate the scalability of these models from the laboratory to the field, and in the air with a hyperspectral sensor on an unoccupied aerial system (UAS). Sagebrush leaf samples were collected at a study site in eastern Idaho, USA. Plants were scanned with an ASD FieldSpec 4 spectroradiometer in the field and laboratory, and a subset of the same plants were imaged with a SteadiDrone Hexacopter UAS equipped with a Rikola hyperspectral sensor (HSI). All three sensors generated spectral patterns that were distinct among species and morphotypes of sagebrush at specific wavelengths. Lab-based NIRS was accurate for predicting crude protein and total monoterpenes (R2 = 0.7–0.8), but the same NIRS sensor in the field was unable to predict either crude protein or total monoterpenes (R2 \u3c 0.1). The hyperspectral sensor on the UAS was unable to predict most chemicals (R2 \u3c 0.2), likely due to a combination of too few bands in the Rikola HSI camera (16 bands), the range of wavelengths (500–900 nm), and small sample size of overlapping plants (n = 28–60). These results show both the potential for scaling NIRS from the lab to the field and the challenges in predicting complex plant chemistry with hyperspectral UAS. We conclude with recommendations for next steps in applying UAS to sagebrush ecosystems with a variety of new sensors

Spectral Fingerprints Predict Functional Chemistry of Native Plants Across Sagebrush-Steppe Landscapes

Landscapes are changing and under threat from anthropogenic activities, decreasing land cover, contaminated air and water quality, and climate change. These changes impact native communities and their functions at all spatial scales. A major functional trait being affected across these communities is nitrogen. Nitrogen supports plant nutrient cycling and growth, serves as an indicator for crude protein and productivity, and offers quality forage for wild and domestic herbivores. We need better ways to monitor nitrogen across space and time. Current monitoring is elaborate, time-consuming, and expensive. We propose drawing from agricultural methodologies to incorporate near-infrared spectroscopy as a technique in detecting and monitoring nitrogen concentrations across a threatened shrub-steppe ecosystem. We are currently developing calibration equations for nitrogen in sagebrush across four species (Artemisia tridentata wyomingensis, A. tripartita, A. arbuscula, A. nova), three study sites and two seasons. Preliminary results suggest that nitrogen can be accurately predicted across all sites, species, and seasons, explaining 75-90% of the variation in nitrogen. These results indicate that near infrared spectroscopy offers a rapid, noninvasive diagnostic tool for assessing nitrogen in wild systems. This advancing technology is important because it economizes the collection of ecological data in rapidly changing landscapes and provides land managers and researchers with valuable information about the health and sustainability of their lands

Remotely-Sensing Chemical Diversity and Function of Native Plants Across Sagebrush-Steppe Landscapes

Plant chemical diversity provides ecosystem services by supporting wildlife diversity and offering sources for novel medicines. Current mapping of phytochemicals can be expensive, time-intensive and provides only a snapshot of available diversity. To overcome this, I will use handheld and airborne instruments collecting near infrared spectra and hyperspectral imagery to remotely sense chemical diversity within plants and ecosystems. I hypothesize that greater plant chemical diversity will be correlated with greater habitat use by wildlife and greater bioactivity of plant extracts. This research provides a powerful tool to map chemical diversity, target wildlife conservation and direct the discovery of novel medicines

Unifying Community Detection Across Scales from Genomes to Landscapes

Biodiversity science encompasses multiple disciplines and biological scales from molecules to landscapes. Nevertheless, biodiversity data are often analyzed separately with discipline-specific methodologies, constraining resulting inferences to a single scale. To overcome this, we present a topic modeling framework to analyze community composition in cross-disciplinary datasets, including those generated from metagenomics, metabolomics, field ecology and remote sensing. Using topic models, we demonstrate how community detection in different datasets can inform the conservation of interacting plants and herbivores. We show how topic models can identify members of molecular, organismal and landscape-level communities that relate to wildlife health, from gut microbes to forage quality. We conclude with a future vision for how topic modeling can be used to design cross-scale studies that promote a holistic approach to detect, monitor and manage biodiversity

Stomaching Toxic Guts: An Avian Herbivore Strategy for Limiting Exposure to Plant Secondary Metabolites

Avian herbivores are known to use behavioral strategies to combat chemically defended plants while foraging, but this alone is insufficient to ensure survival with such dietary specialization. Many flora consumed by herbivores contain plant secondary metabolites (PSMs), so even under a highly selective foraging regime that reduces exposure, herbivores must ingest some of these potentially toxic compounds. Although physiological defenses to PSMs are well known in mammalian herbivores, the same cannot be said for avian herbivores. We used the greater sage-grouse (Centrocercus urophasianus) to test the hypothesis that avian herbivores regulate the absorption of PSMs by limiting the absorption of ingested sagebrush chemicals throughout the entire digestive tract. We used gas chromatography to detect monoterpenes (a toxic constituent of plant chemical defenses) in the sagebrush browsed by sage-grouse, in the contents within the different segments of the sage-grouse digestive tract, and from sage-grouse fecal droppings. The major monoterpenes detected in browsed sagebrush were found throughout the entire digestive tract, except the ceca, and in the excreta of the sage-grouse. The monoterpenes detected in the contents of the digestive tract and in the fecal droppings were approximately the same proportion as what was consumed. Results indicate that sage-grouse limit the absorption of ingested PSMs in the small intestine and thereby excrete these compounds unmetabolized in the feces. Regulated absorption of PSMs may be one strategy to increase their tolerance to chemically defended plants. However, further investigation is required to determine the extent to which PSMs are absorbed and elucidate other physiological strategies that may also function to limit systemic exposure to PSMs in sage-grouse

The Influence of Plant Defensive Chemicals, Diet Composition, and Winter Severity on the Nutritional Condition of a Free-Ranging, Generalist Herbivore

When consuming plants, herbivores must deal with both low nutritional quality from cell wall constituents and potentially toxic plant secondary metabolites, which are often inversely related. Herbivores that consume a highly nutritious, but chemically defended plant, may consume high levels of toxins that require energy for detoxification. Alternatively, herbivores may avoid consuming high levels of toxins by consuming a diverse diet that may be lower in overall nutritional quality. In this study, we assessed the relationship among nutritional restriction, detoxification and diet diversity in a free-ranging wild herbivore. We collected urine deposited in the snow (hereafter, snow-urine) and feces by free-ranging moose Alces americanus, a generalist browser, during winter. We used the ratio of urinary urea nitrogen to creatinine (UN:C), measured in snow-urine samples, as an indicator of nutritional restriction, and the ratio of glucuronic acid to creatinine (GA:C), as an indicator of investment in detoxification. We used microhistology to determine diet composition from fecal pellets. GA:C and UN:C were positively associated, suggesting that nutritional condition tends to be worse for individuals investing more in detoxification. We found, after accounting for the influence of winter severity, diet diversity and UN:C to be negatively related, suggesting that increasingly diverse diets were associated with improved nutritional condition. Overall, the most important predictor of UN:C was winter severity and proportion of diet comprised of balsam fir Abies balsamea. Physiological indicators of nutritional restriction tended to be worse during severe winters and among individuals that had consumed more balsam fir. These results highlight complex relationships among environmental conditions, foraging decisions, and costs of detoxification that can influence nutritional condition of herbivores

Mapping Foodscapes and Sagebrush Morphotypes with Unmanned Aerial Systems for Multiple Herbivores

Context The amount and composition of phytochemicals in forage plants influences habitat quality for wild herbivores. However, evaluating forage quality at fine resolutions across broad spatial extents (i.e., foodscapes) is challenging. Unmanned aerial systems (UAS) provide an avenue for bridging this gap in spatial scale. Objectives We evaluated the potential for UAS technology to accurately predict nutritional quality of sagebrush (Artemisia spp.) across landscapes. We mapped seasonal forage quality across two sites in Idaho, USA, with different mixtures of species but similar structural morphotypes of sagebrush. Methods We classified the sagebrush at both study sites using structural features of shrubs with object-based image analysis and machine learning and linked this classification to field measurements of phytochemicals to interpolate a foodscape for each phytochemical with regression kriging. We compared fine-scale landscape patterns of phytochemicals between sites and seasons. Results Classification accuracy for morphotypes was high at both study sites (81–87%). Forage quality was highly variable both within and among sagebrush morphotypes. Coumarins were the most accurately mapped (r2 = 0.57–0.81), whereas monoterpenes were the most variable and least explained. Patches with higher crude protein were larger and more connected in summer than in winter. Conclusions UAS allowed for a rapid collection of imagery for mapping foodscapes based on the phytochemical composition of sagebrush at fine scales but relatively broad extents. However, results suggest that a more advanced sensor (e.g., hyperspectral camera) is needed to map mixed species of sagebrush or to directly measure forage quality

Near-Infrared Spectroscopy Aids Ecological Restoration by Classifying Variation of Taxonomy and Phenology of a Native Shrub

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 (\u3e99%), and seasonal phenology within individual plants (\u3e97%). 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