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Analysis of fungal networks

Heaton, Luke, Obara, Boguslaw, Grau, Vincente, Jones, Nick, Nakagaki, Toshiyuki, Boddy, Lynne and Fricker, Mark D. 2012. Analysis of fungal networks. Fungal Biology Reviews 26 (1) , pp. 12-29. 10.1016/j.fbr.2012.02.001

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Abstract

Mycelial fungi grow as indeterminate adaptive networks that have to forage for scarce resources in a patchy and unpredictable environment under constant onslaught from mycophagous animals. Development of contrast-independent network extraction algorithms has dramatically improved our ability to characterise these dynamic macroscopic networks and promises to bridge the gap between experiments in realistic experimental microcosms and graph-theoretic network analysis, greatly facilitating quantitative description of their complex behaviour. Furthermore, using digitised networks as inputs, empirically-based minimal biophysical mass-flow models already provide a high degree of explanation for patterns of long-distance radiolabel movement, and hint at global control mechanisms emerging naturally as a consequence of the intrinsic hydraulic connectivity. Network resilience is also critical to survival and can be explored both in silico by removing links in the digitised networks according to particular rules, or in vivo by allowing different mycophagous invertebrates to graze on the networks. Survival depends on both the intrinsic architecture adopted by each species and the ability to reconnect following damage. It is hoped that a comparative approach may yield useful insights into not just fungal ecology, but also biologically inspired rules governing the combinatorial trade-off between cost, transport efficiency, resilience and control complexity for self-organised adaptive networks in other domains.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Biosciences
Subjects: Q Science > QH Natural history
Uncontrolled Keywords: Fungal network; Mass-flow modelling; Mycophagous invertebrate grazing; Network resilience; Nutrient translocation; Transport network
Publisher: Elsevier
ISSN: 1749-4613
Last Modified: 04 Jun 2017 04:04
URI: http://orca-mwe.cf.ac.uk/id/eprint/32076

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