Image analysis
To image and quantify the growth of the extraradical fungal network, we covered the central compartment of a random subset of plates (A5:n =12, B12: n =12, Agg: n =17) with a sterile cellophane sheet to facilitate 2D imaging (Fig. 1c). We monitored plates for fungal growth in the focal and partner compartment and checked weekly for fungal cross-over into the central compartment. After approximately 20 days, the first hyphae crossed the plastic barrier to the central compartment. We then imaged the entire fungal network in the central compartment using a 5x objective on a Leica Wild M8 preparation microscope, taking images with an Olympus SC180 camera.
To obtain representative images of each of the fungal strains, we selected three spatial locations with a dimension of 5x5 mm2 (640x640 px2) across the central fungus-only compartment in each of the treatments. The locations ranged across the space connecting the partner compartment barrier to the center of the central compartment (Fig. 1b). Using MATLAB, we applied morphological operations to the images, binarized the images, removed isolated cluster (background noise) and extracted the network skeleton of the extraradical fungi. We calculated the mass fractal dimension (Dm ) of every spatial area using the box-counting technique (Hitchcocket al. 1996; Falconer 2003; Boddy & Donnelly. 2008; Boudaet al. 2016), with a square grid size ranging from 8 to 64 pixels, i.e. from 1/10 to1/80 times the total square area. We then estimated the fractal dimension by:
N(s) \(\propto\ \)s - Dm
Where s corresponds to the grid size and N(s) the total number of boxes that contain fungal hyphae. We calculated the density of the network (surface percentage) as the ratio between the surface occupied by the network and the total square area.