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Abstract Understanding interactions between environmental stress and genetic variation is crucial to predict the adaptive capacity of species to climate change. Leaf temperature is both a driver and a responsive indicator of plant physiological response to thermal stress, and methods to monitor it are needed. Foliar temperatures vary across leaf to canopy scales and are influenced by genetic factors, challenging efforts to map and model this critical variable. Thermal imagery collected using unoccupied aerial systems (UAS) offers an innovative way to measure thermal variation in plants across landscapes at leaf‐level resolutions. We used a UAS equipped with a thermal camera to assess temperature variation among genetically distinct populations of big sagebrush (Artemisia tridentata), a keystone plant species that is the focus of intensive restoration efforts throughout much of western North America. We completed flights across a growing season in a sagebrush common garden to map leaf temperature relative to subspecies and cytotype, physiological phenotypes of plants, and summer heat stress. Our objectives were to (1) determine whether leaf‐level stomatal conductance corresponds with changes in crown temperature; (2) quantify genetic (i.e., subspecies and cytotype) contributions to variation in leaf and crown temperatures; and (3) identify how crown structure, solar radiation, and subspecies‐cytotype relate to leaf‐level temperature. When considered across the whole season, stomatal conductance was negatively, non‐linearly correlated with crown‐level temperature derived from UAS. Subspecies identity best explained crown‐level temperature with no difference observed between cytotypes. However, structural phenotypes and microclimate best explained leaf‐level temperature. These results show how fine‐scale thermal mapping can decouple the contribution of genetic, phenotypic, and microclimate factors on leaf temperature dynamics. As climate‐change‐induced heat stress becomes prevalent, thermal UAS represents a promising way to track plant phenotypes that emerge from gene‐by‐environment interactions. 

Abstract How intensely animals use habitat features depends on their functional properties (i.e., how the feature influences fitness) and the spatial and temporal scale considered. For herbivores, habitat use is expected to reflect the competing risks of starvation, predation, and thermal stress, but the relative influence of each functional property is expected to vary in space and time. We examined how a dietary and habitat specialist, the pygmy rabbit (Brachylagus idahoensis), used these functional properties of its sagebrush habitat—food quality, security, and thermal refuge—at two hierarchical spatial scales (microsite and patch) across two seasons (winter and summer). At the microsite and patch scales, we determined which plant functional traits predicted the number of bites (i.e., foraging) by pygmy rabbits and the number of their fecal pellets (i.e., general habitat use). Pygmy rabbits used microsites and patches more intensely that had higher crude protein and aerial concealment cover and were closer to burrows. Food quality was more influential when rabbits used microsites within patches. Security was more influential in winter than summer, and more at Cedar Gulch than Camas. However, the influence of functional properties depended on phytochemical and structural properties of sagebrush and was not spatiotemporally consistent. These results show function‐dependent habitat use that varied according to specific activities by a central‐place browsing herbivore. Making spatially explicit predictions of the relative value of habitat features that influence different types of habitat use (i.e., foraging, hiding, and thermoregulating) will improve how we predict patterns of habitat use by herbivores and how we monitor and manage functional traits within habitats for wildlife. 

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 (Artemisiaspp.) 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 (>99%), and seasonal phenology within individual plants (>97%). 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.