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

High fidelity one‐pot DNA assembly using orthogonal serine integrases

Background: Large serine integrases (LSIs, derived from temperate phages) have been adapted for use in a multipart DNA assembly process in vitro, called serine integrase recombinational assembly (SIRA). The versatility, efficiency, and fidelity of SIRA is limited by lack of a sufficient number of LSIs whose activities have been characterized in vitro. Methods and Major Results: In this report, we compared the activities in vitro of 10 orthogonal LSIs to explore their suitability for multiplex SIRA reactions. We found that Bxb1, ϕR4, and TG1 integrases were the most active among the set we studied, but several others were also usable. As proof of principle, we demonstrated high-efficiency one-pot assembly of six DNA fragments (made by PCR) into a 7.5 kb plasmid that expresses the enzymes of the β-carotenoid pathway in Escherichia coli, using six different LSIs. We further showed that a combined approach using a few highly active LSIs, each acting on multiple pairs of att sites with distinct central dinucleotides, can be used to scale up “poly-part” gene assembly and editing. Conclusions and Implications: We conclude that use of multiple orthogonal integrases may be the most predictable, efficient, and programmable approach for SIRA and other in vitro applications

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