Automated image processing for quantitative characterization of grassland vegetation structure: microhabitat selection in threatened meadow and steppe vipers

Abstract

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.