Many mycological models have attempted to represent and explain the growth and development of hyphae and mycelia in relation to their abiotic environment. The reader is directed to Ritz and Crawford (1999) for a review of some of the experimental approaches and mathematical models developed to date, and so these will not be considered in any detail here. Most have focussed on the foraging and space-filling properties of mycelia developing on resources distributed discretely or as gradients. Reaction-diffusion models have been derived to reflect hyphal growth and branching (Regalado et al. 1996) and different colony morphologies (Davidson et al. 1996). More recently a simple stochastic model accommodating some biological processes (inhibition by toxic metabolic products and nutrient uptake) was able to reproduce a variety of fungal growth patterns observed on solidified media (Lopez and Jensen 2002). However, there is a paucity of theoretical models that accommodate environmental topography, translocational source-sink relationships, and exploitation vs. exploration growth morphs, either individually or in combination. In an attempt to address these issues, work is currently being undertaken to develop a generic spatially explicit model adopting a process-based approach, in which the fungal individual is defined by a set of measurable traits that describe physiological processes such as nutrient uptake, redistribution and growth (Figure 2). Each individual may be described by a characteristic set of traits and therefore community diversity may be accommodated within the model. Being spatially explicit where biomass is located in cells on a discrete spatial grid, the model permits regions of mycelia to interact within a neighborhood and change according to the local environment and context. Thus, the ethos of the modeling framework is that any system can be described by a set of processes that can approximate the system. By linking these processes to parameters that may be measured experimentally (physiological traits), a mechanistic understanding of those parameters influencing the overall organisation of the system can be obtained.
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