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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. 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. 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. 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. 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.

Understanding the influence of large and small-scale heterogeneity on species distribution and abundance is one of the major foci of landscape ecology research in fragmented environments. Although a large number of studies have addressed this issue individually, little effort has been made to synthesize the vast amount of literature published in the last decade. We reviewed 122 focal patch studies on 954 species published between 1998 and 2009 to determine the probability of species responding significantly to landscape, patch, and within-patch variables. We assessed the influence of taxonomic, life history, and methodological variables on probability of response to these 3 levels. Species in diverse taxa responded at high rates to factors at all three levels, suggesting that a multi-level approach is often necessary for understanding species response in patchy systems. Mammals responded at particularly high rates to landscape variables and therefore may benefit more than other taxa from landscape-level conservation efforts in fragmented environments. The probability of detecting a species response to landscape context, patch, and within-patch factors was influenced by a variety of methodological aspects of the studies such as type of landscape metric used, type of response variable, and sample size. Study design issues rarely are discussed by authors as reasons why a particular study did not find an effect of a variable, but should be given more consideration in future studies.