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

Understanding Tradeoffs Between Food and Predation Risks in a Specialist Mammalian Herbivore

Understanding habitat use by animals requires understanding the simultaneous tradeoffs between food and predation risk within a landscape. Quantifying the synergy between patches that provide quality food and those that are safe from predators at a scale relevant to a foraging animal could better reveal the parameters that influence habitat selection. To understand more thoroughly how animals select habitat components, we investigated tradeoffs between diet quality and predation risk in a species endemic to sagebrush Artemisia spp. communities in North America, the pygmy rabbitBrachylagus idahoensis. This species is a rare example of a specialist herbivore that relies almost entirely on sagebrush for food and cover. We hypothesized that pygmy rabbits would forage in areas with low food risk (free of plant secondary metabolites, PSMs) and low predation risk (high concealment). However, because of relatively high tolerance to PSMs in sagebrush by pygmy rabbits, we hypothesized that they would trade off the risk of PSM-containing food to select lower predation risk when risks co-occurred. We compared food intake of pygmy rabbits during three double-choice trials designed to examine tradeoffs by offering animals two levels of food risk (1,8-cineole, a PSM) and predation risk (concealment cover). Rabbits ate more food at feeding stations with PSM-free food and high concealment cover. However, interactions between PSMs and cover suggested that the value of PSM-free food could be reduced if concealment is low and the value of high concealment can decrease if food contains PSMs. Furthermore, foraging decisions by individual rabbits suggested variation in tolerance of food or predation risks

Modeling Trade-Offs Between Plant Fiber and Toxins: A Framework for Quantifying Risks Perceived by Foraging Herbivores

When selecting habitats, herbivores must weigh multiple risks, such as predation, starvation, toxicity, and thermal stress, forcing them to make fitness trade-offs. Here, we applied the method of paired comparisons (PC) to investigate how herbivores make trade-offs between habitat features that influence selection of food patches. The method of PC measures utility and the inverse of utility, relative risk, and makes trade-offs and indifferences explicit by forcing animals to make choices between two patches with different types of risks. Using a series of paired-choice experiments to titrate the equivalence curve and find the marginal rate of substitution for one risk over the other, we evaluated how toxin-tolerant (pygmy rabbit Brachylagus idahoensis) and fiber-tolerant (mountain cottontail rabbit Sylviagus nuttallii) herbivores differed in their hypothesized perceived risk of fiber and toxins in food. Pygmy rabbits were willing to consume nearly five times more of the toxin 1,8-cineole in their diets to avoid consuming higher levels of fiber than were mountain cottontails. Fiber posed a greater relative risk for pygmy rabbits than cottontails and cineole a greater risk for cottontails than pygmy rabbits. Our flexible modeling approach can be used to (1) quantify how animals evaluate and trade off multiple habitat attributes when the benefits and risks are difficult to quantify, and (2) integrate diverse risks that influence fitness and habitat selection into a single index of habitat value. This index potentially could be applied to landscapes to predict habitat selection across several scales