Introduction

The majority of natural environments which fungi inhabit are heterogeneous in both space and time with respect to many factors, and this is particularly the case in the soil habitat. Purely structural non-uniformity is exemplified by the complex spatial architecture of soil, where a myriad of connecting and blind pore networks and tortuous surfaces prevail across a wide range of scales. Temperature and moisture profiles canvary markedly both seasonally and over short timescales (Marshall &. Holmes, 1988), for example on a day with patchy cloud, temperatures near the surface can be highly dynamic. Biological heterogeneity is also the norm, with considerable spatio-temporal variation in microbial and faunal community structures being a characteristic of most soils. Nutrient resources are generally distributed patchily, and also may show seasonal variation, since they are often linked to plant growth cycles.

The degree of heterogeneity in these parameters is intimately linked to the scale under consideration. In spatial terms, all environments are heterogeneous at certain scales: at the atomic level, even a solution of salt is effectively non-uniform, and at the greatest of scales, it is apparent that the structure of the known universe is heterogeneous(Saunders et al., 1991). In mycological terms, the spatial scale of reference is not as readily definable as it might at first seem, since myceliaare composed of operational units represented by hyphae of the order of a few microns in diameter, and thalli that may extend typically several centimetres, and occasionally several kilometres (Smith, Bruhn & Anderson, 1992).

Introduction

Sexual reproduction is a major factor aiding adaptability and fitness in organisms throughout the natural world, and the fungi are no exception in exploiting its potential. Fungal mycelia in natural environments, unless they are self-fertile, are faced with the problem of finding a compatible partner. Their major senses are chemical, i.e. taste and smell, so we can imagine each mycelium, in for example the soil, exuding its own specific repertoire of chemicals, to announce its presence to potential mates. These chemicals have to be at least reasonably specific to fungal species, and completely specific to mating type within that species, so that attempts at mating stand a good chance of being successful. Thus potentially there are probably as many different chemicals as there are species. Such specific chemicals can be termed ‘hormones’ used in the context as defined by Raper (1952) for fungi substances produced by the affected plant or by others of the same species … performing indispensable regulatory roles in the sexual process’. An alternative term, increasingly used as a synonym in the fungal literature, is ‘pheromone’ for a chemical acting at a distance (cf., insect sex attractants). The very small number of such compounds that have been identified to date fall into two classes: isoprenoids (derived from mevalonic acid) among the ‘lower fungi’ (a very diverse phylogenetic group). and hydrophobic peptides, mostly isoprenylated, among Ascomycetes and Basidiomycetes. Reviews of various aspects of these fungal hormones/pheromones include those of Raper (1952), Machlis (1972), Gooday (1974), Van den Ende (1984) Gooday & Adams (1993), Gooday (1994) and Duntzse, Betz & Nientiedt (1994).

Introduction

Mycelial fungi are adapted for the colonization of solid surfaces (Carlile, 1994). These are unlike aquatic environments because natural mixing processes are insufficient for effective colonization of solid substrates. A common colonization and dispersal method, typical of the majority of ascomycetes (but by no means restricted to them) is the production of very large numbers of spores, allowing the faeile, but random, creation of new foci for mycelial exploitation of substrate. The disadvantage of this process is that the whole mycelium cannot be convened to spores when a particular mycelium has exhausted all the substrate in its range.

Within the basidiomycetes an alternative strategy has evolved, which is the formation of very large colonies that behave in a non-random manner with respect to colonization and, crucially, employ long-range translocation mechanisms to enable the re-use of scarce resources where new sub-strate is detected. This phenomenon is best described for some wood-degrading fungi of the forest floor and the dry-rot fungus Serpula lacrymans. In both cases wood substrate is dispersed in an otherwise nutrient-poor environment, requiring ‘foraging’. This is an unusual process in microorganisms (and in plants), where growth or movement towards detectable centres of nutrient is the common pattern. Again, the responses of the wood-degrading fungi have to be different in that their substrate is non-diffusing and cannot be detected chemotactically.

Introduction

Fungi form cells by inserting cross walls called septa. Even the so-called cenocytic fungi (Chytrids and Zygomyoetes) delimit sporangia, zoospores and other structures with septa. ln the filamentous Basidiomycetes and Ascomycetes, septa are formed along the mycelial thallus denning cell compartments of a uniform length and nuclear content. In all true fungi, septa are formed by a similar process. A site is chosen within the cell for the assembly of the septum. Actin is recruited to this site and can be seen as a complex of dots (in the case of unicellular yeasts) (Adams & Pringle, 1984; Marks & Hyanns, 1985) or as a micro filamentous belt in the case of higher fungi (Girbardt. 1979). The actin cytoskeleton facilitates the highly localized, circumferential deposition of cell wall material outside the plasma membrane. In unicellular yeast such as Saccharomyces cerevisiae or Schizasaccharamyces pombe a primary cell wall layer is depositedfollowed by the symthesis of a secondary septal wall on either side of the primary septum. The primary wall is enzymatically removed to allow cell separation. In the mycelia of higher fungi, septation is incomplete, leaving a complex structure called the septal pore. There is no obvious stage of mondary septal wall synthesis and no cell separation. Cell separation does occur during the septation processes that produce aerial spores.

This chapter focuses on the mechanisms controlling the pattern of septation in the filamentous fungus, Aspergillus nidulans.

Introduction

The genetic nature of the ‘fungal individual’ has intrigued biologists for over 50 years. To some extent this is because fungi have an indeterminate growth form and the physical shape of a ‘fungus’ can not usually be described a priori in the same way that a cat or a tree is characterized and identified. Most filamentous fungi are cryptic organisms whose visible portion consists of ephemeral fruit-bodies that occur in a temporally and spatially discontinuous manner and the boundaries of individuals are, therefore, not immediately evident to the casual observer (see Chapter 1). An early view of the fungal individual was recognized in the ‘unit mycelium’ concept (Buller, 1958; Raper, 1966). Within this view, a basidiomyoete could be a mosaic of several different nuclear genotypes operating within a physiologically integrated individual. Studies that examine the distribution of genetic markers within fungal populations support an alternative view of fungal individualism (Todd 8:.Rayner, 1980; Rayner, 1991), in which mycelia occupy discrete territories in space, have cellular non-self recognition systems and are genetically distinct. Reports that basidiomycete genotypes can occur throughout extensive geographic areas (Adams, 1974; Shaw & Roth, 1976; Anderson et al., 1979; Dickman & Cook, 1989; Smith, Bruhn 3Anderson, 1992) are surprising since some fungi must now be thought of as large, and consequently ancient, individuals. Intrinsic to this notion, such individuals must be genetically stable, at least to the extent that they are recognizable as genetic units. possibly to the extent that they are potentially immortal. In this chapter, genetic stability and factors that may influence genetic stability in fungi will he discussed.

Introduction

All cells have evolved regulatory mechanisms which allow them to respond to external signals and are essential for cell multiplication and survival. In mammalian cells, an array of signal transduction cascades have been described that respond to growth factors and hormones (Mooibroek & Wang, 1988; Su & Karin, 1996). More recently, highly homologous signalling cascades have been reported in the yeasts Saccharomyces cerevisiae and Schizosucchuromyces pombe, which are involved in a variety of cellular processes including mating, hyper and hype-osmotic sensing, invasive filamentous growth and cell wall integrity (Nishida & Gotoh, 1993; Roberts & Fink, 1994; Waskiewicz & Cooper, 1995; Su & Karin, 1996; Cahil, Janknecht & Nordheim,1996). The presence of such highly conserved signal transduction pathways suggests that these signal cascades may first have evolved in eukaryotic microbes and have been conserved and adapted during eukaryotic evolution (Janssens, 1987; Kincaid, 1991; Csaba, I994;Gadd, 1995; Rasmussen et al., 1996).

One of the most highly studied signal transduction pathways in mammalian cells is the phosphoinositide cycle which has been the centre of intense research since the first report that inositol 1, 4, 5-trisphosphate (Ins(1, 4, 5)P3), acts as a second messenger, mobilizing Ca2+ from intracellular stores in response to a variety of growth factors, hormones and other ligands (for reviews see Berridge & Irvine, 1984; Nishizuka, 1984;Divecha & Irvine, 1995).

Introduction

One of the most characteristic features of the fungal mycelium is its highly polarized mode of growth. Mycologists have devoted considerable effort towards understanding the basic mechanisms underlying hyphal elongation and branching, and these subjects have been reviewed extensively (Trinci, 1979; Prosser, 1983; Trinci, Wiebe & Robson, 1994; Gow, 1994; Trinci et al., Chapter 5, this volume). One approach which has been employed to investigate these mechanisms is the identification and characterization of mutants defective in hyphal morphogenesis. Such mutants are relatively easy to detect since they typically cause severe alterations in colony morphology. Indeed, a useful benefit of early experiments in the biochemical genetics of Neurospora crassa was the generation and description of a large collection of colonial mutants (Murray dr. Srb, 1962). Although these mutants have been characterized to a limited extent, the nature of the affected genes is in most cases unknown. Recently, with rapid progress being made in understanding cellular morphogenesis in the yeasts Saccharomyces cerevisiae andSchizosoccharomyces pombe (Snell & Nurse, 1994; Simanis, 1995;Roemer, Vellier&Snyder, 1996), there has been renewed interest in understanding the genetic basis of filamentous growth in fungi. Sincemolecular genetic analyses in the model filamentous fungi Aspergillusnidulans and N. crassa have yielded considerable insight into metabolic control, development, and mitosis (Bennett&Lasure, 1991; Martinelli & Kinghorn, 1994), it is reasonable to presume that this approach will alsocontribute towards achieving a thorough understanding of hyphal morphogenesis.

Introduction

Fungi comprise a significant proportion of the soil microbial community as decomposer organisms and plant symbionts (mycorrhizas), playing fundamental roles in carbon mineralization and other biogeochemical cycles (Wainwright, 1988), and are often dominant in acidic soils where toxic metals may he speciated into mobile forms (Morley et al., 1996). Anthropogenic activities, including fossil fuel combustion, mineral mining and processing, and production of industrial effluents and sludges, biocides and preservatives (Gadd & Griffiths, 1978; Gadd, 1992), release a variety of toxic metal species into aquatic and terrestrial ecosystems and this can have significant effects on the biota as well as resulting in metal transfer to higher organisms, plants and animals(Wainwright & Gadd, 1997). Metals and their compounds can interact with fungi in various ways depending on the metal species, organism and environment, while metabolic activity can also influence speciation and mobility. Certain mechanisms may mobilize metals into forms available for cellular uptake or leaching from the system, e.g. complexation with citric acid, other metabolites and siderophores (Francis, 1994). Metalsmay also be immobilized by, for example, sorption onto cell components, exopolymers, transport and intracellular sequestration or precipitation, both intra- and extracellular (Morley & Gadd, 1995; Sayer & Gadd, 1997). The apparently opposing phenomena of metal solubilization and immobilization are key components of biogeochemical cycles for toxic metals, whether indigenous or introduced into a given location, since both mobility and toxicity can be affected.

Introduction

For so long neglected in the development and promulgation of evolutionary theory, there are increasing signs that mycelial fungi can bring new insights into the origins of phenotypic diversity and change. They challenge some of our most fundamental assumptions about natural selection and its significance relative to other processes in determining the direction of evolutionary pathways. This is because of the way mycelia are physically organized as versatile systems of interconnected tubes that can span heterogeneous environments in which energy is often invery variable supply (Rayner 1994; Rayner, Griffith & Ainsworth, 1995a).

Current models of evolutionary change effectively treat the boundaries of living systems and their components as fixed (that is, determinate). Consequently, the dynamic processes underlying change are assumed to be driven by purely external forces acting on discrete objects – genes and individuals (see Dawkins, 1995). However, such discretist models of evolutionary and ecological dynamics are potentially very misleading because all known life forms, from single cells to communities, are dynamic systems which assimilate supplies of free energy from their surroundings and distribute this energy into growth, development, reproduction and movement. They achieve this by possessing boundaries through which they regulate energy exchange with their surrounding and other life forms (Rayner, 1997a). For life forms to thrive and survive as energy supplies wax and wane, these boundaries have to be capable of enhancing gains through the proliferation of assimilative free surface in energy-rich environments whilst minimizing losses by various means of containment in inhospitable environments.

Introduction

The most important criteria that determine how sampling will proceed in the study of spatial pattern are the question being asked and the scale at which we wish to answer it. Secondly, the kind of analysis that is required to answer the question must be considered because particular methods of analysis require certain kinds of data. Then, the sampling method will be determined by the interaction of a number of factors including the morphology, size and density of the plants of interest; the topography, accessibility, and area of the study site; the availability of time, money, technological and field assistance. It will be influenced fundamentally by whether the spatial pattern is to be treated as the arrangement of points in continuous space or as a mosaic of domains. We must also consider how much disturbance the sampling technique will cause, because we will want to minimize disturbance in long-term studies or in ecologically sensitive areas.

The methods used will also depend very much on whether the focus is on the spatial pattern of plants relative to a fixed frame of reference, on the elucidation of a community's response to an environmental gradient, or on the spatial arrangement of plants relative to other plants (species association). Kenkel et al. (1989) make the important point that the considerations for sampling design that are traditionally emphasized in statistics textbooks may not apply in studies of spatial pattern, because they are designed to provide efficient and unbiassed estimates of parameters such as mean cover or diversity.

Introduction

Vegetation is patchy at a range of spatial and temporal scales, and so even within what might be recognized as a single plant community, the plants of different species are not really expected to be arranged homogeneously and independently. Natural groupings of species may arise from biological interactions or from shared and divergent responses to abiotic factors. In some cases, the community is viewed as a mosaic of patches, with each phase of the mosaic being characterized by a set of species' abundances. This phenomenon in plant communities has given rise to the patch dynamics approach to studies of vegetation (van der Maarel 1996).

The existence of nonrandomness in species arrangement is the context in which the multivariate analysis method of classification takes place. Classification can be used to organize samples, like quadrats, into hierarchical categories based on the similarity of species composition. The composition and strength of the associations within and between groupings is an important aspect of the plant community's structure. It is reasonable to begin to investigate this structure by examining the relationships of pairs of species because these pairwise interactions can then be amalgamated loosely or exclusively into larger groupings. We will therefore examine methods designed to evaluate the joint spatial pattern of pairs of species and thus the scales at which they are positively or negatively associated.

When we look at the spatial pattern of a single species, we are examining the arrangement in space of two mosaic phases, places where the species is present (perhaps at variable density) and places where the species is absent.

Introduction

The preceding three chapters have examined various aspects of the study of spatial pattern in one dimension using data acquired by several sampling methods including strings of contiguous quadrats. In fact, we know that in real vegetation, spatial pattern exists in at least two, if not three, dimensions. We know, also, that spatial pattern may be anisotropic, exhibiting different characteristics in different directions. Depending on the application, we may want to retain that anisotropy in the analysis, or we may want to average over all possible directions to look at the overall spatial pattern.

There are several approaches to two-dimensional analysis available, most of which are adaptations of methods initially developed for one-dimensional analysis. It is interesting that one of the earliest methods, blocked quadrat variance (BQV), was originally proposed in a two-dimensional version (Greig-Smith 1952), for the analysis of spatial pattern in grids of quadrats.

The practical problem associated with two-dimensional analysis is the collection of data. Studies of pattern in one dimension have usually sampled the vegetation with strings of small contiguous square quadrats from as few as 36 (Usher 1983) to 1001 (Dale and Zbigniewicz 1995). The amount of work necessary for one-dimensional studies is often great, and to extend them into two dimensions would, in many instances, be impractical. Most of the two-dimensional analysis methods described here may be most useful when applied to data collected by means such as the digitizing of images or the direct acquisition of digital images.

Introduction

In this chapter, we will present and discuss methods designed to examine the spatial pattern of groups of species or of whole plant communities. While it is true that plant communities are made up of individual species, we do not expect to be able to capture the essential features of the spatial structure of the whole community by compiling information on the spatial patterns of single species. Similarly, while we tend to think of species interactions as being pairwise, we know that the relationship between two species, A and B, can be modified by the presence and absence of other species (Dale et al. 1991). We cannot, therefore, in studies of plant communities, restrict our examination of species interactions only to pairs. In stead, we must find ways to look at the spatial structure and pattern of vegetation more holistically, by looking at many species simultaneously.

In Chapter 3, we described how the spatial pattern of a single species can be studied using methods that examine the effects of distance or block size on a calculated variance, with low variance indicating similarity and high variance indicating dissimilarity. In analyzing the spatial pattern of a single species using the data from a string of contiguous quadrats, the information for each quadrat is a single value, either some measure of the species' density, or simply 0 for absence and 1 for presence. A technique like two-term local quadrat variance (TTLQV) combines the quadrats into blocks of a range of sizes to determine which block size maximizes the difference between adjacent blocks of quadrats.

Introduction

In this chapter, we will discuss the arrangement of plants on environmental gradients. In this context, an environmental gradient is a monotonic directional change in the intensity of an environmental factor with distance. It is the class of gradients that Keddy (1991) calls ‘spatially continuous’ and includes cases that may give rise to obvious zonation in the community.

Obviously the concept of spatial pattern is somewhat different in this context than in previous chapters, but it still refers to nonrandomness that has a certain predictability. As we move along a gradient, we do not expect to see the repeated alternation of different phases of a mosaic, but rather we expect species to become present and perhaps abundant where they were previously absent and then to become absent again. The predictability is in the way that species come and go along the gradient and the relationship between the ranges and densities of the species.

In Chapter 1, we discussed the importance of spatial pattern, as an area of study, pointing out that there are two facets to consider: (1) making inferences about processes based on observed pattern, and (2) the effect current spatial pattern has on future processes and interactions. The same two categories apply to the study of pattern on gradients. The potential positions of individual species are determined by their physiological responses to the gradient. Then, the arrangements of species on gradients can be used to examine questions about the forces that structure these communities, the interaction between species whether positive or negative, and the niche relations of the species in the community.

Summary of recommendations

In order to study spatial pattern and to answer questions about the relationship between the pattern and the processes that either give rise to it or are affected by it, we need to be able to detect pattern reliably and to quantify its characteristics. The highest quality data for pattern analysis come from strings or grids of relatively small contiguous quadrats in which some quantitative measure of species' densities has been recorded, or from the direct mapping of individual plant units such as stems. Even with the best data, no single method of analysis can quantify all the important characteristics of pattern.

For contiguous quadrat data, three-term local quadrat variance (3TLQV) and new local variance (NLV) form a good combination of methods to evaluate single-species pattern (Chapter 3). These methods detect the scales of pattern and the size of the smaller phase; the intensity associated with a 3TLQV peak can be used to evaluate the consistency of the pattern. For two species, three-term local quadrat covariance (3TLQC) is recommended; paired quadrat covariance (PQC) cannot be used because of the effects of resonance peaks, and the correlation coefficient cannot be used by itself to detect scale (Chapter 4). Multispecies pattern is best investigated using the modified multiscale ordination (MSO) technique based on 3TLQV; among its advantages are an evaluation of the evenness of the species' contributions to the multispecies pattern (Chapter 5).

Introduction

Several methods have been proposed to detect the scale of pattern in vegetation; most of them analyze density data in strings or rectangular arrays of contiguous quadrats by examining how variance depends on the size of blocks of quadrats which are lumped together in the analysis (e.g., Greig-Smith 1952; Hill 1973; Usher 1975). In this chapter, we will review and illustrate the basic methods for studying the spatial pattern of a single species in one dimension along which there is no environmental gradient. The kind of data under consideration are therefore density or presence/absence data collected in a string of contiguous quadrats (see Chapter 2).

Data

We will begin by considering a standard pattern consisting of a regular square wave and let the scale of the pattern be B quadrat units. Throughout the transect, gaps of B quadrats, each of density 0, alternate regularly with patches of B quadrats, each with density d. There are several ways in which this basic pattern can be modified to be made less regular:

1. The pattern is ‘unbalanced’ with the patch:gap ratio different from 1:1 but the patch size (p quadrats) and the gap size (g quadrats) are both constant for the entire length of the transect. For a given value of d, unbalanced patterns have a lower intensity than balanced patterns. In forest communities, the spatial pattern of the canopy will often be unbalanced in this way with the patches of canopy being considerably larger than the gaps between them.