Modeling Fungal Community Dynamics

Modeling approaches for fungal communities, have received far less attention, perhaps because this is a most ambitious project, summed-up by Frankland (1998) as "unravelling the unpredictable." Indeed, the work of Halley et al. (1996) represents one of the few attempts to model interactions within a multispecies fungal decay community. In this work, a computer based simulation model known, as a "cellular automaton" was developed to predict the decomposition of wheat straw by four saprotrophic fungi. The model was based on real experimental data obtained by Robinson et al. (1994) during studies on resource capture by interacting fungal colonisers of straw. Although this model was capable of reproducing some of the behaviors exhibited during the

Figure 2 (a) Image showing the underlying soil pore space derived from real data using a CAT scan—with white to black scale representing pore to solid respectively. (b) Image showing the resulting fungal biomass gradient distribution obtained from the theoretical model with the mycelium growing from a single point and resource (bottom right hand corner) with white to black scale representing high to low biomass, respectively. The model is based on a set of biological processes, intended to characterize any fungal individual. By linking these processes to parameters that can be measured experimentally, a mechanistic understanding of which parameters are influencing the overall organization of the system can be obtained (Falconer R, Bown J, Crawford JW, and White NA, unpublished data).

Figure 2 (a) Image showing the underlying soil pore space derived from real data using a CAT scan—with white to black scale representing pore to solid respectively. (b) Image showing the resulting fungal biomass gradient distribution obtained from the theoretical model with the mycelium growing from a single point and resource (bottom right hand corner) with white to black scale representing high to low biomass, respectively. The model is based on a set of biological processes, intended to characterize any fungal individual. By linking these processes to parameters that can be measured experimentally, a mechanistic understanding of which parameters are influencing the overall organization of the system can be obtained (Falconer R, Bown J, Crawford JW, and White NA, unpublished data).

experimental study, validation of the model was only possible on a qualitative basis. Furthermore, the model admittedly ignored important aspects of the fungal community such as sporulation and the coordinated behavior of the mycelium.

More recent modeling studies have attempted to examine and accommodate the complex and coordinated behavior exhibited by indeterminate mycelia. A stochastic cellular automaton for modeling the dynamics of two-species microcosm communities of differing patch size, revealed the significance of local and nonlocal interactions in generating the emergent behavior of mycelial systems (Bown et al. 1999). Importantly, the experimental system allowed for detailed quantitative spatial analysis of the community. This revealed that despite the predictability of the final interaction outcome irrespective of patch size, the finer-scale dynamics were highly dependent on non-local interactions (Bown et al. 1999; Sturrock et al. 2002). Furthermore, experimental studies indicate that fungi occupying large domains commonly display a higher combative success compared to those occupying smaller domains when challenged by the same species, and in vitro that younger mycelia may be less combative than more mature growth (Holmer and Stenlid 1993; Stahl and Christensen 1992). Consequently, despite the benefits of in vitro interaction studies in understanding the factors that influence the community dynamics of fungi, a major limitation exists in how such small-scale studies relate to the behavior at larger community scales. At larger scales it is probable that contrasting emergent behaviors may arise because individuals in a group or patch behave differently to individuals that are isolated. Processes such as modification of the environment, resource translocation and hyphal networking or anastomosis (Rayner 1996) may all influence community development. Issues relating to scale have been a central problem in ecology over the last fifty years. Nevertheless, the importance of appreciating the relationships across different scales should be emphasized, as understanding and predicting large-scale ecosystem events, will have origins in and consequences for fine-scale phenomena (Levin 1992). Therefore, in the development of a complete understanding of fungal ecology it is necessary to address issues relating to scale and to adopt a hierarchical framework (Allen and Hoekstra 1992; Swift 1976).

In vitro mycelial interaction studies, particularly those evident on agar media, have often formed the basis of understanding or predicting ex situ fungal community dynamics. Furthermore, these have often centred on the pair-wise interaction between a limited number of individuals under experimentally defined environmental conditions (Boddy 2000). Data produced from combative interaction studies are often used to rank species in order of their combative ability, and hence to indicate sequences of fungal colonisation in the field. Correlation between antagonistic behavior in artificial culture and in the natural environment is mixed, and may be related to the spatial or temporal scale at which the data are collected or aggregated. Nevertheless, such studies represent a valuable approach to understanding fungal ecology, providing that any limitations are recognized,

Figure 3 (a)-(c) Example maps showing the spatial distribution of fungal species in a 3 X 3 (equal proportion) tessellated agar tile interaction array at the onset of the experiment. Bold lines denote air-gaps between individual tiles. Dimension of each tile is 1 cm2. Symbols indicate the species inoculated onto each tile. (d)-(f) Plots of first and second principal components from analysis of interface classes and state transition classes of tessellated agar tile arrangements (a)-(c). S = 0 week, □ = 1 week, K = 3 weeks, O = 5 weeks incubation at 15°C. From Sturrock et al. (2002).

Figure 3 (a)-(c) Example maps showing the spatial distribution of fungal species in a 3 X 3 (equal proportion) tessellated agar tile interaction array at the onset of the experiment. Bold lines denote air-gaps between individual tiles. Dimension of each tile is 1 cm2. Symbols indicate the species inoculated onto each tile. (d)-(f) Plots of first and second principal components from analysis of interface classes and state transition classes of tessellated agar tile arrangements (a)-(c). S = 0 week, □ = 1 week, K = 3 weeks, O = 5 weeks incubation at 15°C. From Sturrock et al. (2002).

qualified and considered. According to general ecological terminology combative hierarchies may be either transitive (following a strict linear order of hierarchy) or intransitive (no strict linear hierarchical order) (Boddy 2000). Consequently, predicting the interaction outcome of more than two species may become extremely difficult, if not impossible, if the species under investigation display an intransitive hierarchy (White et al. 1998). Thus, the expansion from two-species interactions to three or more species may allow for the emergence of "higher-order interactions" involving greater complexity and even different biological phenomena, often resulting in shifts in interactive functioning (Culver 1992). Furthermore, the initial spatial configuration of three-species communities of equal inoculum and patch size, has been shown to impact on community development and reprodu-cibility, and may contribute to the persistence of individuals which would otherwise be expected (based on binary pairings) to be eliminated from the system (Figure 3; Sturrock et al. 2002). In addition, fungal individuals can often display a variety of responses when subjected to ostensibly the same set of conditions (Figure 3; Halley et al. 1996; Rayner et al. 1995; White et al. 1998). Such stochasticity may be interpreted as offering the mycelium an ecological advantage within a dynamic and unpredictable heterogeneous environment. The complex stochastic and sensitive nature of community interactions is particularly evident within experimental community systems involving more than two species, which tend to demonstrate more heterogeneity among replicates with the same initial arrangement compared to binary-pairings (Sturrock et al. 2002; White et al. 1998). Agreement between agar based studies and those occurring in natural substrata is also likely to be influenced by environmental complexity (Boddy 2000). Water potential, gaseous regime, temperature, and resource quality have all been shown to significantly influence the outcome of interactions. Importantly, the sensitivity of the interaction outcome may not be solely associated with influence on colony extension rate, and that improved growth temperature regimes are not necessarily conducive to superior combative physiology (Sturrock et al. 2002).

From the above the limitations implicit within fungal models are clear. Frankland (1998) remarked that community development would become "predictable only when the mechanisms are understood and can be related to the multivariate situation." However, the contrary approach should be considered, explicitly that the process of developing a working model, will highlight the important underlying biological processes and mechanism driving community development, and identify new hypotheses for further investigations.

Detoxification and Weight Loss

Detoxification and Weight Loss

Detoxification is something that is very important to the body, but it is something that isn't understood well. Centuries ago, health masters in the East understood the importance of balancing and detoxifying the body. It's something that Western medicine is only beginning to understand.

Get My Free Ebook


Post a comment