November  2020, 25(11): 4411-4426. doi: 10.3934/dcdsb.2020104

Modelling fungal competition for space:Towards prediction of community dynamics

1. 

ORCID: 0000-0003-4452-7106, Department of Mathematics, Swansea University, Bay Campus, Swansea, SA1 8EN, UK

2. 

ORCID: 0000-0003-1544-0407, Department of Biosciences, Swansea University, Singleton Park Campus, Swansea, SA2 8PP, UK

3. 

ORCID: 0000-0003-0486-5450, Department of Mathematics, Swansea University, Bay Campus, Swansea, SA1 8EN, UK

* Corresponding author: Chenggui Yuan

Received  October 2019 Revised  November 2019 Published  April 2020

Fund Project: DAK was supported by a student scholarship from Swansea University

Filamentous fungi contribute to ecosystem and human-induced processes such as primary production, bioremediation, biogeochemical cycling and biocontrol. Predicting the dynamics of fungal communities can hence improve our forecasts of ecological processes which depend on fungal community structure. In this work, we aimed to develop simple theoretical models of fungal interactions with ordinary and partial differential equations, and to validate model predictions against community dynamics of a three species empirical system. We found that space is an important factor for the prediction of community dynamics, since the performance was poor for models of ordinary differential equations assuming well-mixed nutrient substrate. The models of partial differential equations could satisfactorily predict the dynamics of a single species, but exhibited limitations which prevented the prediction of empirical community dynamics. One such limitation is the arbitrary choice of a threshold local density above which a fungal mycelium is considered present in the model. In conclusion, spatially explicit simulation models, able to incorporate different factors influencing interaction outcomes and hence dynamics, appear as a more promising direction towards prediction of fungal community dynamics.

Citation: Diogenis A. Kiziridis, Mike S. Fowler, Chenggui Yuan. Modelling fungal competition for space:Towards prediction of community dynamics. Discrete & Continuous Dynamical Systems - B, 2020, 25 (11) : 4411-4426. doi: 10.3934/dcdsb.2020104
References:
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L. Boddy, Interspecific combative interactions between wood-decaying basidiomycetes, FEMS Microbiology Ecology, 31 (2000), 185-194.  doi: 10.1111/j.1574-6941.2000.tb00683.x.  Google Scholar

[2]

L. Boddy, Saprotrophic cord-forming fungi: Meeting the challenge of heterogeneous environments, Mycologia, 91 (1999), 13-32.  doi: 10.2307/3761190.  Google Scholar

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G. P. Boswell, Modelling combat strategies in fungal mycelia, Journal of Theoretical Biology, 304 (2012), 226-234.  doi: 10.1016/j.jtbi.2012.03.036.  Google Scholar

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J. Hiscox and L. Boddy, Armed and dangerous – chemical warfare in wood decay communities, Fungal Biology Reviews, 31 (2017), 169-184.  doi: 10.1016/j.fbr.2017.07.001.  Google Scholar

[14]

J. HiscoxM. SavouryI. P. VaughanC. T. Müller and L. Boddy, Antagonistic fungal interactions influence carbon dioxide evolution from decomposing wood, Fungal Ecology, 14 (2015), 24-32.  doi: 10.1016/j.funeco.2014.11.001.  Google Scholar

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[17]

D. A. KolesidisL. BoddyD. C. EastwoodC. Yuan and M. S. Fowler, Predicting fungal community dynamics driven by competition for space, Fungal Ecology, 41 (2019), 13-22.  doi: 10.1016/j.funeco.2019.04.003.  Google Scholar

[18]

K. L. McGuire and K. K. Treseder, Microbial communities and their relevance for ecosystem models: Decomposition as a case study, Soil Biology and Biochemistry, 42 (2010), 529-535.  doi: 10.1016/j.soilbio.2009.11.016.  Google Scholar

[19]

H. V. Moeller and K. G. Peay, Competition-function tradeoffs in ectomycorrhizal fungi, PeerJ, 4 (2016), e2270. doi: 10.7717/peerj.2270.  Google Scholar

[20]

J. OlivaM. MessalL. Wendt and M. Elfstrand, Quantitative interactions between the biocontrol fungus Phlebiopsis gigantea, the forest pathogen Heterobasidion annosum and the fungal community inhabiting Norway spruce stumps, Forest Ecology and Management, 402 (2017), 253-264.  doi: 10.1016/j.foreco.2017.07.046.  Google Scholar

[21]

N. J. B. Plomley, Formation of the colony in the fungus Chaetomium, Australian Journal of Biological Sciences, 12 (1959), 53-64.  doi: 10.1071/BI9590053.  Google Scholar

[22]

J. I. Prosser, N. A. R. Gow and G. M. Gadd (eds.), Kinetics of filamentous growth and branching, in The Growing Fungus, Springer, Dordrecht, (1995), 301–318. doi: 10.1007/978-0-585-27576-5_14.  Google Scholar

[23]

J. I. Prosser and A. P. J. Trinci, A model for hyphal growth and branching, Microbiology, 111 (1979), 153-164.  doi: 10.1099/00221287-111-1-153.  Google Scholar

[24]

W. S. Rasband, ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, available from: https://imagej.nih.gov/ij/ (cited 17 Dec 2018). Google Scholar

[25]

T. StellaS. CovinoM. ČvančarováA. FilipováM. PetruccioliA. D'Annibale and T. Cajthaml, Bioremediation of long-term PCB-contaminated soil by white-rot fungi, Journal of Hazardous Materials, 324 (2017), 701-710.  doi: 10.1016/j.jhazmat.2016.11.044.  Google Scholar

[26]

W. Thompson and A. D. M. Rayner, Extent, development and function of mycelial cord systems in soil, Transactions of the British Mycological Society, 81 (1983), 333-345.  doi: 10.1016/S0007-1536(83)80085-0.  Google Scholar

[27]

R. Toral and P. Colet, Introduction to master equations, in Stochastic Numerical Methods: An Introduction for Students and Scientists, Wiley-VCH, Weinheim, (2014), 235–260. doi: 10.1002/9783527683147.ch8.  Google Scholar

[28]

A. van der WalT. D. GeydanT. W. Kuyper and W. de Boer, A thready affair: Linking fungal diversity and community dynamics to terrestrial decomposition processes, FEMS Microbiology Reviews, 37 (2013), 477-494.  doi: 10.1111/1574-6976.12001.  Google Scholar

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V. Volpert and S. Petrovskii, Reaction–diffusion waves in biology, Physics of Life Reviews, 6 (2009), 267-310.  doi: 10.1016/j.plrev.2009.10.002.  Google Scholar

show all references

References:
[1]

L. Boddy, Interspecific combative interactions between wood-decaying basidiomycetes, FEMS Microbiology Ecology, 31 (2000), 185-194.  doi: 10.1111/j.1574-6941.2000.tb00683.x.  Google Scholar

[2]

L. Boddy, Saprotrophic cord-forming fungi: Meeting the challenge of heterogeneous environments, Mycologia, 91 (1999), 13-32.  doi: 10.2307/3761190.  Google Scholar

[3]

G. P. Boswell, Modelling combat strategies in fungal mycelia, Journal of Theoretical Biology, 304 (2012), 226-234.  doi: 10.1016/j.jtbi.2012.03.036.  Google Scholar

[4]

J. L. BownC. J. SturrockW. B. SamsonH. J. StainesJ. W. PalfreymanN. A. WhiteK. Ritz and J. W. Crawford, Evidence for emergent behaviour in the community-scale dynamics of a fungal microcosm, Proceedings of the Royal Society B – Biological Sciences, 266 (1999), 1947-1952.  doi: 10.1098/rspb.1999.0871.  Google Scholar

[5]

R. W. BuchkowskiM. A. BradfordA. S. GrandyO. J. Schmitz and W. R. Wieder, Applying population and community ecology theory to advance understanding of belowground biogeochemistry, Ecology Letters, 20 (2017), 231-245.  doi: 10.1111/ele.12712.  Google Scholar

[6]

I. H. ChapelaL. Boddy and A. D. M. Rayner, Structure and development of fungal communities in beech logs four and a half years after felling, FEMS Microbiology Ecology, 53 (1988), 59-69.  doi: 10.1111/j.1574-6968.1988.tb02648.x.  Google Scholar

[7]

M. J. A. ChoudhuryP. M. Trevelyan and G. P. Boswell, A mathematical model of nutrient influence on fungal competition, Journal of Theoretical Biology, 438 (2018), 9-20.  doi: 10.1016/j.jtbi.2017.11.006.  Google Scholar

[8]

D. Coates and A. D. M. Rayner, Fungal population and community development in cut beech logs. Ⅲ. Spatial dynamics, interactions and strategies, New Phytologist, 101 (1985), 183-198.  doi: 10.1111/j.1469-8137.1985.tb02825.x.  Google Scholar

[9]

F. A. DavidsonB. D. SleemanA. D. M. RaynerJ. W. Crawford and K. Ritz, Context-dependent macroscopic patterns in growing and interacting mycelial networks, Proceedings of the Royal Society B – Biological Sciences, 263 (1996), 873-880.  doi: 10.1098/rspb.1996.0129.  Google Scholar

[10]

R. E. FalconerJ. L. BownN. A. White and J. W. Crawford, Modelling interactions in fungi, Journal of the Royal Society Interface, 5 (2008), 603-615.  doi: 10.1098/rsif.2007.1210.  Google Scholar

[11]

M. D. FrickerL. L. M. HeatonN. S. Jones and L. Boddy, The mycelium as a network, Microbiology Spectrum, 5 (2017), 1-32.  doi: 10.1128/microbiolspec.FUNK-0033-2017.  Google Scholar

[12]

J. M. HalleyH. N. CominsJ. H. Lawton and M. P. Hassell, Competition, succession and pattern in fungal communities: Towards a cellular automaton model, Oikos, 70 (1994), 435-442.  doi: 10.2307/3545783.  Google Scholar

[13]

J. Hiscox and L. Boddy, Armed and dangerous – chemical warfare in wood decay communities, Fungal Biology Reviews, 31 (2017), 169-184.  doi: 10.1016/j.fbr.2017.07.001.  Google Scholar

[14]

J. HiscoxM. SavouryI. P. VaughanC. T. Müller and L. Boddy, Antagonistic fungal interactions influence carbon dioxide evolution from decomposing wood, Fungal Ecology, 14 (2015), 24-32.  doi: 10.1016/j.funeco.2014.11.001.  Google Scholar

[15]

L. Holmer and J. Stenlid, The importance of inoculum size for the competitive ability of wood decomposing fungi, FEMS Microbiology Ecology, 12 (1993), 169-176.  doi: 10.1111/j.1574-6941.1993.tb00029.x.  Google Scholar

[16]

P. Kennedy, Ectomycorrhizal fungi and interspecific competition: Species interactions, community structure, coexistence mechanisms, and future research directions, New Phytologist, 187 (2010), 895-910.  doi: 10.1111/j.1469-8137.2010.03399.x.  Google Scholar

[17]

D. A. KolesidisL. BoddyD. C. EastwoodC. Yuan and M. S. Fowler, Predicting fungal community dynamics driven by competition for space, Fungal Ecology, 41 (2019), 13-22.  doi: 10.1016/j.funeco.2019.04.003.  Google Scholar

[18]

K. L. McGuire and K. K. Treseder, Microbial communities and their relevance for ecosystem models: Decomposition as a case study, Soil Biology and Biochemistry, 42 (2010), 529-535.  doi: 10.1016/j.soilbio.2009.11.016.  Google Scholar

[19]

H. V. Moeller and K. G. Peay, Competition-function tradeoffs in ectomycorrhizal fungi, PeerJ, 4 (2016), e2270. doi: 10.7717/peerj.2270.  Google Scholar

[20]

J. OlivaM. MessalL. Wendt and M. Elfstrand, Quantitative interactions between the biocontrol fungus Phlebiopsis gigantea, the forest pathogen Heterobasidion annosum and the fungal community inhabiting Norway spruce stumps, Forest Ecology and Management, 402 (2017), 253-264.  doi: 10.1016/j.foreco.2017.07.046.  Google Scholar

[21]

N. J. B. Plomley, Formation of the colony in the fungus Chaetomium, Australian Journal of Biological Sciences, 12 (1959), 53-64.  doi: 10.1071/BI9590053.  Google Scholar

[22]

J. I. Prosser, N. A. R. Gow and G. M. Gadd (eds.), Kinetics of filamentous growth and branching, in The Growing Fungus, Springer, Dordrecht, (1995), 301–318. doi: 10.1007/978-0-585-27576-5_14.  Google Scholar

[23]

J. I. Prosser and A. P. J. Trinci, A model for hyphal growth and branching, Microbiology, 111 (1979), 153-164.  doi: 10.1099/00221287-111-1-153.  Google Scholar

[24]

W. S. Rasband, ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, available from: https://imagej.nih.gov/ij/ (cited 17 Dec 2018). Google Scholar

[25]

T. StellaS. CovinoM. ČvančarováA. FilipováM. PetruccioliA. D'Annibale and T. Cajthaml, Bioremediation of long-term PCB-contaminated soil by white-rot fungi, Journal of Hazardous Materials, 324 (2017), 701-710.  doi: 10.1016/j.jhazmat.2016.11.044.  Google Scholar

[26]

W. Thompson and A. D. M. Rayner, Extent, development and function of mycelial cord systems in soil, Transactions of the British Mycological Society, 81 (1983), 333-345.  doi: 10.1016/S0007-1536(83)80085-0.  Google Scholar

[27]

R. Toral and P. Colet, Introduction to master equations, in Stochastic Numerical Methods: An Introduction for Students and Scientists, Wiley-VCH, Weinheim, (2014), 235–260. doi: 10.1002/9783527683147.ch8.  Google Scholar

[28]

A. van der WalT. D. GeydanT. W. Kuyper and W. de Boer, A thready affair: Linking fungal diversity and community dynamics to terrestrial decomposition processes, FEMS Microbiology Reviews, 37 (2013), 477-494.  doi: 10.1111/1574-6976.12001.  Google Scholar

[29]

V. Volpert and S. Petrovskii, Reaction–diffusion waves in biology, Physics of Life Reviews, 6 (2009), 267-310.  doi: 10.1016/j.plrev.2009.10.002.  Google Scholar

Figure 1.  Examples from the empirical system of fungal mycelia. (a) One-species setting of an extending mycelium inoculated at the centre of the 22.4 $ \times $ 22.4 cm dish. (b) Transparency of drawn boundaries of three-species mycelia inoculated randomly at 49 sites indicated by the letters H (for Hf), T (Tv) and V (Vc). (c) The transparency of the mycelial boundaries of (b), processed in ImageJ for obtaining species relative occupancy at time $ t = 0 $ d
Figure 2.  One-species empirical dynamics compared to the model dynamics from the ODE (17). Circles are for the Tv empirical relative cover of the dish, and curves are for the relative abundance of Tv in a well-mixed culture with the ODE model. (a) A single Tv mycelium inoculated at the centre of a dish in the empirical system, extending before reaching the edges of the dish. (b) Three Tv mycelia closely inoculated at the dish centre, fusing to form one mycelium which extended and covered the dish. The initial conditions were equal to the relative cover of Tv on the empirical dishes at time $ t = 0 $ d. The extension rate parameter $ e_A \approx 0.35 $ d$ ^{-1} $
Figure 3.  One-species empirical dynamics compared to the model dynamics with the one-species PDE (34). Circles are for the Tv empirical relative cover of the dish, and curves are for Tv from the PDE model. (a) A single Tv mycelium inoculated at the centre of a dish in the empirical system (inset is the PDE model's solution at $ t = 12 $ d), extending before reaching the edges of the dish (as in Fig. 2a). (b) Three Tv mycelia closely inoculated at the dish centre (inset is a numerical solution of the PDE model at $ t = 12 $ d), fusing to form one mycelium which extended and covered the dish (as in Fig. 2b). The PDE model for Tv had initial conditions similar to the empirical setting, with growth rate $ \epsilon_A = 2.16 $ d$ ^{-1} $, and diffusion coefficient $ \delta_A = 0.017 $ cm$ ^2 $ d$ ^{-1} $. It was assumed that a mycelium is present when its density $ A > 0.01 $. The relative cover in the PDE solution was estimated by Monte Carlo integration of the mycelium present (the curve in each panel is 95% confidence region of the mean relative cover in the PDE solution from 100 Monte Carlo integrations)
Figure 4.  Prediction of the three-species empirical community dynamics with the PDE (35–37). The data points are for the empirical relative cover of the species in time, and the curves are for the PDE model. The inset shows the PDE numerical solution at time $ t = 40 $ d. Colour–point (of each species): red–circle (Hf), cyan–square (Tv), and blue–$ \times $ (Vc). The PDE model had initial conditions similar to the empirical setting, with the following parameter values (species A was Hf, B was Tv, and C was Vc): $ \epsilon_A = 0.78 $ d$ ^{-1} $, $ \epsilon_B = 2.16 $ d$ ^{-1} $, $ \epsilon_C = 1.14 $ d$ ^{-1} $, $ \delta_A = 0.0062 $ cm$ ^2 $ d$ ^{-1} $, $ \delta_B = 0.017 $ cm$ ^2 $ d$ ^{-1} $, $ \delta_C = 0.0091 $ cm$ ^2 $ d$ ^{-1} $, $ \rho_A = 0.22 $ d$ ^{-1} $, and $ \rho_B = 0 $ d$ ^{-1} $. Local extension and replacement rates were taken from the mean boundary extension and replacement rates in the empirical dishes, as for the extension in the one-species PDE. It was assumed that a mycelium is present when its density is greater than 0.5. The relative cover in the PDE solution was estimated by Monte Carlo integration of the mycelium present (the curves are 95% confidence regions of the mean relative cover in the PDE solution from 100 Monte Carlo integrations)
Table 1.  Summary of the features and performance of the models considered in the present work
Model Advantages Disadvantages Predictability
ODE 1. Simplicity 1. Non-spatial 1. Low for even one-species
2. Determinism
3. Math. liability
PDE 1. Spatial 1. Arbitrary mycelial presence (density threshold)
2. Challenging set-up of exact initial conditions
3. Challenging measurement-parameterisation
4. Cannot model each mycelium separately
1. High for one-species
2. Low for three-species
2. Determinism
3. Math. liability
Model Advantages Disadvantages Predictability
ODE 1. Simplicity 1. Non-spatial 1. Low for even one-species
2. Determinism
3. Math. liability
PDE 1. Spatial 1. Arbitrary mycelial presence (density threshold)
2. Challenging set-up of exact initial conditions
3. Challenging measurement-parameterisation
4. Cannot model each mycelium separately
1. High for one-species
2. Low for three-species
2. Determinism
3. Math. liability
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