Toward Modeling the Resistance and Resilience of "Below-ground" Fungal Communities: A Mechanistic and Trait-Based Approach.

The role of fungi in shaping ecosystems is well evidenced and there is growing recognition of their importance among scientists and the general public. Establishing and separating the role of key local (soil chemical, biological, and physical properties) and global (climate, dispersal limitation) drivers in fungal community structure and functioning is currently a source of frustration to mycologists. The quest to determine niche processes and environmental characteristics shaping fungal community structure, known to be important for plant and animal communities, is proving difficult, resulting in the acknowledgment that niche neutral processes (climate, dispersal limitations) may dominate. The search for predictable patterns in fungal community structure may have been restricted as the "appropriate" scales at which to measure community structure and characterize the environment have not been fully determined yet, and the focus on taxonomy makes it difficult to link environmental characteristics to fungal traits. While key determinants of microbial community composition have been uncovered for some functional groups, the differential response of functional groups is largely unknown. Before we can truly understand what drives the development of microbial community structure, an understanding of the autecology of major fungal taxa and how they interact with their immediate environment (from the micro- up to kilometer scale) is urgently needed. Furthermore, key information and empirical data is missing at the microscale due to experimental difficulties in mapping this heterogeneous and opaque environment. We therefore present a framework that would help generate this much-needed empirical data and information at the microscale, together with modeling approaches to link the spatial and temporal scales. The latter is important as we propose that there is much to be gained by linking our understanding of fungal community responses across scales, in order to develop species and community-environment-function predictive models.

Microscale heterogeneity explains experimental variability and non-linearity in soil organic matter mineralisation.

Soil respiration represents the second largest CO2 flux from terrestrial ecosystems to the atmosphere, and a small rise could significantly contribute to further increase in atmospheric CO2. Unfortunately, the extent of this effect cannot be quantified reliably, and the outcomes of experiments designed to study soil respiration remain notoriously unpredictable. In this context, the mathematical simulations described in this article suggest that assumptions of linearity and presumed irrelevance of micro-scale heterogeneity, commonly made in quantitative models of microbial growth in subsurface environments and used in carbon stock models, do not appear warranted. Results indicate that microbial growth is non-linear and, at given average nutrient concentrations, strongly dependent on the microscale distribution of both nutrients and microbes. These observations have far-reaching consequences, in terms of both experiments and theory. They indicate that traditional, macroscopic soil measurements are inadequate to predict microbial responses, in particular to rising temperature conditions, and that an explicit account is required of microscale heterogeneity. Furthermore, models should evolve beyond traditional, but overly simplistic, assumptions of linearity of microbial responses to bulk nutrient concentrations. The development of a new generation of models along these lines, and in particular incorporating upscaled information about microscale processes, will undoubtedly be challenging, but appears to be key to understanding the extent to which soil carbon mineralization could further accelerate climate change.

Characterising the tumour morphological response to therapeutic intervention: an ex vivo model.

In cancer, morphological assessment of histological tissue samples is a fundamental part of both diagnosis and prognosis. Image analysis offers opportunities to support that assessment through quantitative metrics of morphology. Generally, morphometric analysis is carried out on two-dimensional tissue section data and so only represents a small fraction of any tumour. We present a novel application of three-dimensional (3D) morphometrics for 3D imaging data obtained from tumours grown in a culture model. Minkowski functionals, a set of measures that characterise geometry and topology in n-dimensional space, are used to quantify tumour topology in the absence of and in response to therapeutic intervention. These measures are used to stratify the morphological response of tumours to therapeutic intervention. Breast tumours are characterised by estrogen receptor (ER) status, human epidermal growth factor receptor (HER)2 status and tumour grade. Previously, we have shown that ER status is associated with tumour volume in response to tamoxifen treatment ex vivo. Here, HER2 status is found to predict the changes in morphology other than volume as a result of tamoxifen treatment ex vivo. Finally, we show the extent to which Minkowski functionals might be used to predict tumour grade. Minkowski functionals are generalisable to any 3D data set, including in vivo and cellular systems. This quantitative topological analysis can provide a valuable link among biomarkers, drug intervention and tumour morphology that is complementary to existing, non-morphological measures of tumour response to intervention and could ultimately inform patient treatment.

Fungal colonization in soils with different management histories: modeling growth in three-dimensional pore volumes.

Despite the importance of fungi in soil functioning they have received comparatively little attention, and our understanding of fungal interactions and communities is lacking. This study aims to combine a physiologically based model of fungal growth with digitized images of internal pore volume of samples of undisturbed soil from contrasting management practices to determine the effect of physical structure on fungal growth dynamics. We quantified pore geometries of the undisturbed-soil samples from two contrasting agricultural practices, conventionally plowed (chisel plow) (CT) and no till (NT), and from native-species vegetation land use on land that was taken out of production in 1989 (NS). Then we modeled invasion of a fungal species within the soil samples and evaluated the role of soil structure on the progress of fungal colonization of the soil pore space. The size of the studied pores was > or =110 microm. The dynamics of fungal invasion was quantified through parameters of a mathematical model fitted to the fungal invasion curves. Results indicated that NT had substantially lower porosity and connectivity than CT and NS soils. For example, the largest connected pore volume occupied 79% and 88% of pore space in CT and NS treatments, respectively, while it only occupied 45% in NT. Likewise, the proportion of pore space available to fungal colonization was much greater in NS and CT than in NT treatment, and the dynamics of the fungal invasion differed among the treatments. The relative rate of fungal invasion at the onset of simulation was higher in NT samples, while the invasion followed a more sigmoidal pattern with relatively slow invasion rates at the initial time steps in NS and CT samples. Simulations allowed us to elucidate the contribution of physical structure to the rates and magnitudes of fungal invasion processes. It appeared that fragmented pore space disadvantaged fungal invasion in soils under long-term no-till, while large connected pores in soils under native vegetation or in tilled agriculture promoted the invasion.

Modelling fungal colonies and communities: challenges and opportunities.

This contribution, based on a Special Interest Group session held during IMC9, focuses on physiological based models of filamentous fungal colony growth and interactions. Fungi are known to be an important component of ecosystems, in terms of colony dynamics and interactions within and between trophic levels. We outline some of the essential components necessary to develop a fungal ecology: a mechanistic model of fungal colony growth and interactions, where observed behaviour can be linked to underlying function; a model of how fungi can cooperate at larger scales; and novel techniques for both exploring quantitatively the scales at which fungi operate; and addressing the computational challenges arising from this highly detailed quantification. We also propose a novel application area for fungi which may provide alternate routes for supporting scientific study of colony behaviour. This synthesis offers new potential to explore fungal community dynamics and the impact on ecosystem functioning.

Modelling interactions in fungi.

Indeterminate organisms have received comparatively little attention in theoretical ecology and still there is much to be understood about the origins and consequences of community structure. The fungi comprise an entire kingdom of life and epitomize the indeterminate growth form. While interactions play a significant role in shaping the community structure of indeterminate organisms, to date most of our knowledge relating to fungi comes from observing interaction outcomes between two species in two-dimensional arena experiments. Interactions in the natural environment are more complex and further insight will benefit from a closer integration of theory and experiment. This requires a modelling framework capable of linking genotype and environment to community structure and function. Towards this, we present a theoretical model that replicates observed interaction outcomes between fungal colonies. The hypotheses underlying the model propose that interaction outcome is an emergent consequence of simple and highly localized processes governing rates of uptake and remobilization of resources, the metabolic cost of production of antagonistic compounds and non-localized transport of internal resources. The model may be used to study systems of many interacting colonies and so provides a platform upon which the links between individual-scale behaviour and community-scale function in complex environments can be built.

Biomass recycling and the origin of phenotype in fungal mycelia.

Fungi are one of the most important and widespread components of the biosphere, and are essential for the growth of over 90% of all vascular plants. Although they are a separate kingdom of life, we know relatively little about the origins of their ubiquitous existence. This reflects a wider ignorance arising from their status as indeterminate organisms epitomized by extreme phenotypic plasticity that is essential for survival in complex environments. Here we show that the fungal phenotype may have its origins in the defining characteristic of indeterminate organisms, namely their ability to recycle locally immobilized internal resources into a mobilized form capable of being directed to new internal sinks. We show that phenotype can be modelled as an emergent phenomenon resulting from the interplay between simple local processes governing uptake and remobilization of internal resources, and macroscopic processes associated with their transport. Observed complex growth forms are reproduced and the sensitive dependence of phenotype on environmental context may be understood in terms of nonlinearities associated with regulation of the recycling apparatus.