  We investigate the application of mesoscopic response functions (MRFs) to
characterize a large set of networks of fungi and slime moulds grown under a
wide variety of different experimental treatments, including inter-species
competition and attack by fungivores. We construct 'structural networks' by
estimating cord conductances (which yield edge weights) from the experimental
data, and we construct 'functional networks' by calculating edge weights based
on how much nutrient traffic is predicted to occur along each edge. Both types
of networks have the same topology, and we compute MRFs for both families of
networks to illustrate two different ways of constructing taxonomies to group
the networks into clusters of related fungi and slime moulds. Although both
network taxonomies generate intuitively sensible groupings of networks across
species, treatments and laboratories, we find that clustering using the
functional-network measure appears to give groups with lower intra-group
variation in species or treatments. We argue that MRFs provide a useful
quantitative analysis of network behaviour that can (1) help summarize an
expanding set of increasingly complex biological networks and (2) help extract
information that captures subtle changes in intra- and inter-specific
phenotypic traits that are integral to a mechanistic understanding of fungal
behaviour and ecology. As an accompaniment to our paper, we also make a large
data set of fungal networks available in the public domain.
