Quantification  of  fungal  growth:  models,  experiments,  and  observations

In Section I, mathematical modelling is presented as a useful complement to experimental research, since it helps to focus attention on the fundamental properties of the system and enables predictions to be made under a wide range of conditions. Growth models should be based on reasonable biological assumptions regarding growth mechanisms and generate predictions that can be tested experimentally, however, difficulties on hyphal observations in natural soil occur. In Chapter 2, a fungal growth model is introduced in relation to soil organic matter decomposition, along lines previously described, but with detailed attention to carbon and nitrogen dynamics. The model describes the colonisation and decomposition of substrate, subsequent uptake of nutrients, and incorporation into fungal biomass. The overall-steady states of the variables are obtained by standard mathematical procedures, and the conditions for existence of the steady states have a clear biological interpretation. In Chapter 3, this fungal growth model is simplified by assuming that the nutrient dynamics are much faster than the dynamics of fungal growth and substrate, implying that the system will reach a quasi-steady state relatively quickly. A quasi-steady state approximation allows the derivation of a fungal invasion criterion, which was not possible for the original model. Importantly, the invasion criterion takes two forms: one for systems where carbon is limiting, another for systems where nitrogen is limiting. Carbon sources are the primary object of competition in soil, and competition for nitrogen may occur in substrates of a high carbon: nitrogen ratio such as woody plant residues. In Chapter 4 it is assumed that only carbon is limiting fungal growth, and nitrogen dynamics are excluded from the model. The resulting model is then fitted to data on growth of the soil-borne plant pathogen Rhizoctonia solani, obtained using a model system. The model produces a good fit to these experimental data.

In Section II, quantification tools other than mathematical modelling are presented. The quantitative epidemiology of the macrofungus Armillaria spp. is reviewed in Chapter 5. Armillaria root rot is a serious disease in many forests and horticultural tree crops world-wide, and many Armillaria species spread largely through rhizomorph growth in soil. Consequently, there is much interest in determining how different silvicultural practices influence disease incidence and options for avoiding or restricting the spread of the disease. A necessary condition for better management of Armillaria root rot is an improved understanding of the ecological significance of the extended rhizomorph networks that arise from spread. Two maps of rhizomorph networks of Armillaria lutea, growing in soil over an area of 25 m² of a tree plantation, are presented and analysed from an ecological perspective in Chapter 6. Both networks had numerous branches and anastomoses resulting in cyclic paths, i.e. regions of the system that start and end at the same point. Network characteristics like total rhizomorph length, number of cyclic paths, fractal dimension, etc. are determined, and in Chapter 7 other possible applications of graph-theoretic concepts are explored. In particular both exploitative and explorative foraging strategies of Armillaria are apparent, and we speculate about the underlying reasons and interpretations for these. The introduction of graph-theoretic properties to fungal growth may lead to an improved ecological understanding of fungal networks in general, when relevant biological interpretations can be drawn. Finally, the main conclusions are given in Chapter 8.

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