High‐quality information on predator–prey relationships is fundamental in understanding food webs, community assembly and ecosystem functioning. Recent analytical advances have made it possible to develop new trait‐based approaches to study trophic relationships and evaluate trait matching between predators and prey. Here, we develop a novel analytical approach based on generalized linear mixed‐effects models (GLMM) to test the importance of prey availability and to identify the set of prey traits that best explain the occurrence and number of prey in the predator's diet. We demonstrate that the approach by using an extensive dataset on prey availability, prey traits and gut content collected in all known populations of Vipera graeca, a little‐known, endangered snake of alpine grasslands in the Pindos Mountains of the Balkan Peninsula. We show that V. graeca is a unique, venomous snake specialized on bush‐crickets and grasshoppers (Orthoptera). Prey selection GLMMs showed that the ideal prey of V. graeca is abundant, large‐bodied, has poor escape abilities (flightless, slow‐moving and bad jumper) and prefers loose grasslands (as opposed to bare ground/rock or closed sward). Vipers restrict their feeding to periods of high Orthoptera abundance in the late summer and need to reach a certain body size to become able to catch large‐sized prey. Our analytical approach provides a framework for trait matching between predators and prey and unprecedented fine‐scale information on the importance of prey traits in prey selection by a specialist predator. The narrow trophic niche of V. graeca likely increases the vulnerability of this cold‐adapted snake to extinction. A free Plain Language Summary can be found within the Supporting Information of this article.

1. Understanding animals’ selection of microhabitats is important in both ecology and biodiversity conservation. However, there is no generally accepted methodology for the characterisation of microhabitats, especially for vegetation structure. 2. Here we present a method that objectively characterises vegetation structure by using automated processing of images taken of the vegetation against a whiteboard under standardised conditions. We developed an R script for automatic calculation of four vegetation structure variables derived from raster data stored in the images: leaf area (LA), height of closed vegetation (HCV), maximum height of vegetation (MHC), and foliage height diversity (FHD). 3. We demonstrate the applicability of this method by testing the influence of vegetation structure on the occurrence of three viperid snakes in three grassland ecosystems: Vipera graeca in mountain meadows in Albania, V. renardi in loess steppes in Ukraine and V. ursinii in sand grasslands in Hungary. 4. We found that the variables followed normal distribution and there was minimal correlation between those. Generalized linear mixed models revealed that snake occurrence was positively related to HCV in V. graeca, to LA in V. renardi and to LA and MHC in V. ursinii, and negatively to FHD in V. renardi, and to HCV in V. ursinii. 5. Our results demonstrate that biologically meaningful vegetation structure variables can be derived from automated image processing. Our method minimises the risk of subjectivity in measuring vegetation structure, allows upscaling if neighbouring pixels are combined, and is suitable for comparison of or extrapolation across different grasslands, vegetation types or ecosystems.