Quantifying vegetation structure
Vegetation structure was recorded in the field by taking photographs of the vegetation against a whiteboard applying standardised settings. The whiteboard was made of plexiglass of size 0.25 m (width) ×1 m (height), installed in a vertical position on its shorter edge at all viper presence and random locations. The vegetation against the whiteboard was photographed with a digital SLR camera (55 mm focal length and maximum f/11 aperture) fixed at a height of 0.5 m in a distance of 4 m from the whiteboard (Fig. 2) as in Volesky et al. (1999).
The resulting photographs were pre-processed (cropping, white adjustment, retouching) with the GIMP 2.8.18. image editing software. Next, we applied image processing using an automated for loopwritten in the R statistical environment (version 3.6.1., R Core Team, 2019). The script is available in Supplementary Material (SM). The script first retrieved the images by the ‘load.image’ function of the ‘imager’ package (Barthelme, 2019), converted it to a black and white image by the ‘grayscale’ function, and then to a binary (0-1) image by the ‘threshold’ function of the ‘imager’ package. The resulting image was converted to a data frame using the ‘as.data.frame’ function and the coordinates of every image pixel covering the whiteboard were calculated (0.25×1 m, average resolution: 1 to 1.5 megapixels image-1). The resulting data frame had three columns for each image, the x and y pixel coordinates (in cm) and the pixel value (0 = white, 1 = black).
We used the data frame to calculate four variables to quantify particular attributes of the vegetation structure. At first, leaf area, referred to as LA hereafter, a frequently used quantity in vegetation characterisation (Volesky, 1999), was calculated as the count of black pixels rescaled to cm2 units. At second, we calculated visual obstruction readings (VOR), developed primarily for prairie vegetation based on the Robel pole method (Benkobi et al., 2000; Vermeire & Gillen, 2001). This method takes two readings by eye at a height of 1 m from a distance of 4 m from the pole with height tick-marks: (i) the height at which the pole is first visible, i.e., not obstructed by vegetation (low reading) and (ii) the maximum height reached by the vegetation (high reading). The average of the two readings strongly correlates with prairie phytomass (Benkobi et al., 2000; Vermeire & Gillen, 2001). In our study, we modified the lower reading by calculating the maximum height at which 95% of the whiteboard is mantled by the vegetation, and we refer to this as the height of closed vegetation (HCV) (Fig. 2) to avoid confusion with the VOR reading terminology. We chose 95% as a threshold because glint on some leaves in the image could return white cells and could thus reduce the true coverage. At third, the high reading was calculated as the maximum height of the vegetation (MHV) regardless of its width, cover, or surface area (Fig. 2). Finally, to characterise the vertical distribution of vegetation, we calculated foliage height diversity (FHD) (Karr & Roth, 1971) as the Shannon diversity of the number of black cells in each pixel row using the ‘diversity’ function of the ‘vegan’ package (Oksanen et al., 2019). Calculating FHD from values in each pixel row circumvents the problem of arbitrarily choosing counting intervals, e.g. ten 10-cm height intervals in each of which cover is estimated or measured for the calculation of FHD (MacArthur et al., 1962; Karr & Roth, 1971).