We have pioneered network analysis of foraging basidiomycete fungi growing in unconstrained microcosms (Bebber et al. 2007a; Bebber et al. 2007b; Boddy et al. 2009; Boddy et al. 2010; Fricker et al. 2008a; Fricker et al. 2007; Fricker et al. 2008b; Heaton et al. 2010; Rotheray et al. 2008). We have shown using graph-theoretic analysis of digitised networks, that these indeterminate, de-centralized systems can yield adaptive networks with both high transport capacity and robustness to damage, but at a relatively low cost, through a ‘Darwinian’ process of selective reinforcement of key transport pathways and recycling of redundant routes (Bebber et al. 2007a). Furthermore, fungal networks are able to dynamically modify link strengths and local connectivity when subject to experimental attack to readjust the balance between transport capacity, robustness to damage and resource allocation, resulting in increased resilience as the environment becomes more challenging (Boddy et al. 2010; Rotheray et al. 2008).

Currently network extraction is a laborious manual exercise. We have recently developed high-throughput, automated imaging, visualisation and network analysis protocols to extract biological network organisation using Phase Congruency Tensors (PCTs) to specifically enhance curvi-linear features in the image (Obara, 2012a-c). This approach can rapidly extract networks with 104-5 links in a few minutes with high fidelity.

The underlying mechanisms leading to the emergence of adaptive behaviour in macroscopic mycelial networks are unknown. However, models based on growth-induced mass flow through the experimentally determined macroscopic networks provide a high level of explanatory power for the transport of added radiolabel (Heaton et al. 2010; Heaton et al. 2012). We infer that bio-physical hydraulic coupling and internal flows observed in macroscopic networks may act as the central mechanism enabling coordinated growth across the complete range of scales in networked organisms.