Guild Structure and Community Organization
(For an outline, click here)
To what extent are species overdispersed in niche/resource space? Do clusters of functionally similar species exist? Root (1967) coined the term "guild" to describe groups of functionally similar species in a community, such as foliage-gleaning insectivorous birds. In competitive communities, guilds would represent arenas with the potential for intense interspecific competition, with strong interactions within guilds but weaker interactions with the remainder of their community. Techniques for objectively defining a guild remain in their infancy, although the "single-linkage" criterion of cluster analysis allows a guild to be defined operationally as follows. A guild is a group of species separated from all other such clusters by an ecological distance greater than the greatest distance between the two most disparate members of the guild concerned. This conservative definition allows complex hierarchical patterns of nesting of smaller guilds within larger ones.
Another very useful technique that depicts some of a community's "connectedness" involves ranking each species' neighbors in niche space from the nearest to the most distant (Inger and Colwell 1977). When overlap is plotted against such nearness ranks in niche space, very similar species (such as those belonging to the same guild) fall out together, whereas species on the periphery of niche space have low overlap with the remainder of the community and tend to fall well below other species. Mean overlap among all members of an assemblage decays monotonically with nearness rank in niche space. The standard deviation in overlap may, however, increase, decrease, or even rise and then fall as one moves from the nearest niche neighbor to the most distant. Inger and Colwell (1977) argue that humps in such standard deviation curves are indicative of guild structure. The reasoning behind this assertion is that, because close-in neighbors in niche space tend to belong to the same guilds, standard deviation in overlap is primarily with guilds at low ranks and thus small. But, if a community contains two or more guilds of different size (number of species with membership in a given guild), at a rank one beyond that of the smallest guild, standard deviation should increase because both within-guild overlap pairs and some between-guild pairs occur at the same nearness rank. At still higher ranks, standard deviation in overlap falls off because now all overlaps are between members of different guilds (all overlap values are homogeneously small).
Community ecology remains in its infancy and lags far behind other organismal and suborganismal disciplines due to its innate complexity, multi-dimensionality, extensive scale in space and time, multiple causality, as well as serious empirical difficulties of data collection and analysis. Pristine natural systems are vanishing rapidly before we understand how they are organized or how they behave. The study of communities must of necessity remain to some degree abstract and complex. Community ecology is also extremely promising and important, as well as exceedingly urgent. Major new insights lie just around the corner. But community ecology is not for the faint at heart: it is one of the most challenging and most difficult of all sciences. We are still in the process of developing a vocabulary. Examples of macrodescriptors include relative abundance, species diversity, trophic levels, and food webs. Identification of appropriate aggregate variables is not only essential, but also constitutes a double-edged sword: such macrodescriptors enable progress but simultaneously constrain its direction(s). At this early stage in community ecology, it is perhaps safest not to become overly "locked in" by words and concepts. A diversity of approaches seems desirable.
Many community-level properties are probably simply epiphenomena that arise from pooling component populations: examples would presumably include trophic levels, guilds, subwebs, and ecological pyramids. But, do communities also possess truly emergent properties that transcend those of mere collections of populations? For example, are patterns of resource utilization among coexisting species co-adjusted so that they mesh together in a meaningful way? If so, truly emergent community-level properties arise as a result of orderly interactions among component populations. This fundamental question needs to be answered. Either way, transcendent phenomena or epiphenomena cannot be studied at the level of individuals or populations.
A major pitfall for community ecologists is that communities are not designed directly by natural selection (as are individual organisms). We must keep clearly in mind that natural selection operates by differential reproductive success of individual organisms. It is tempting, but dangerously misleading, to view ecosystems as "superorganisms" that have been "designed" for efficient and orderly function. However, antagonistic interactions at the level of individuals and populations (such as competition, predation, parasitism and even mutualisms) must frequently impair certain aspects of ecosystem performance while enhancing other properties. Natural selection does not necessarily produce a "balance of nature," and it does not work at the ecosystem level. Rather, natural selection works at the level of individuals. There are a multitude of antagonistic interactions among the members of a system, for example prey-predator interactions. Selection operating on individual prey organisms favors escape ability, which in turn reduces the rate of flow of matter and energy through that trophic level, decreasing ecological efficiency but increasing community stability. In contrast, predators evolve so as to be better able to capture their prey, which increases the efficiency of flow of energy of through the trophic levels but reduces the stability of a system. In the coevolution of a predator and its prey, to avoid extinction, the prey must remain a step ahead of its predator. As a corollary, the community-level properties of ecological efficiency and community stability may in fact be inversely related because natural selection operates at the level of individual predators and prey. Moreover, the apparent constancy and low level (10 - 15%) of ecological efficiency may be a result of the "compromise" reached between prey and predators.
A second possible example of how community-level properties could emerge from those of individuals concerns terrestrial succession. A fast rate of photosynthesis and hence a rapid growth rate are presumably incompatible with shade tolerance, and hence competitive ability in a light-limited situation. In contrast, shade tolerance and an ability to compete require slower rates of photosynthesis and growth. Such physiological tradeoffs at the level of individuals could very well dictate many of the sequential patterns of species replacement (i.e., colonizing species to climax species) that characterize terrestrial succession. More thought needs to be devoted towards attempts to connect community properties with those of individuals in populations.
The traditional ecological approach to population interactions has been to consider species in pairs. While fruitful, this focus has diverted attention away from a more holistic systems-level approach. We must make an effort to understand indirect interactions within complex networks of interacting species. (To read about indirect interactions, click here)
Properties of complex networks of interacting species need to be evaluated. Approaches that incorporate mutualism and variable intensity in interactions must be developed. Competitive interactions should also be included and distinguished from the vertical ones that operate between trophic levels. Strong interactions may usually be more important than weak ones, but the cumulative effects of many weak interactions (as in diffuse competition and diffuse coevolution) could nevertheless be considerable. The extent to which indirect effects can and actually do balance direct effects needs to be ascertained. Whereas the reductionistic approach has been quite successful in other scientific disciplines, it will not lead to generalizations in community ecology. A holistic approach is required.
Analysis of Community Structure
Next, I present a hybrid protocol for the analysis of community structure that incorporates resource availability, electivities, nearness rank of neighbors in niche space, null models, and Monte Carlo statistical methodology (Winemiller and Pianka 1990). This new approach facilitates graphical comparisons of very different systems.
The raw data for analysis of community structure are resource utilization matrices. A resource matrix contains a lot of vital information about a system. It identifies which species eat which other species as well as which species are potential competitors because they share common foods. A resource matrix thus describes the food web structure of the system. Considerable tedious work is required to put together a satisfactory resource matrix. Statistical samples of all the species in the system must be collected; if the system is changing in time, this needs to be done quickly; to follow changes in the community through time, adequate samples at different times are necessary. Entries in the resource matrix are used to calculate probabilities. These vary between zero and one, reflecting the probability that a given consumer species, say 1, will use a particular resource state, say 2. Some utilization probabilities, or uij's, will obviously be zeros because some consumers don't use some resources.
Simple dietary proportions, or pi's, weight uncommon or very abundant resources disproportionately. Ivlev (1961) suggested resource utilization should somehow be standardized in terms of relative availabilities. Resource availabilities are not easy to measure in the field. Insects can be sampled with sweep nets, DeVac vacuum cleaners, tanglefoot sticky traps, pit traps, or burliese funnels: each technique yields very different results. Some insects are simply more easily pit trapped than others, whereas others are captured by burliese funnels more than others, etc. Pefaur and Duellman (1980; pers. comm.) studied Andean herps from Columbia to Argentina. They fenced study plots and collected all frogs and squamates (lizards and snakes). Inside these plots, all conspicuous insects that a human observer could find were also collected, and saved with the intention of using these as standards against the stomach contents of the herps. Humans actually collected only a very few of the insects that were eaten by the herpetofaunas, only about 10% (Duellman, pers. comm.). Incredibly enough, 90% of the insects that were in stomachs in fact were not even collected by diligent humans! It is a gross and dangerously misleading oversimplification to accept the idea that a single resource vector exists out there that describes a system. Each species experiences its own resource availabilities that depend to some extent on that species' use of space and time as well as its behavior and foraging mode.
Various solutions to this problem have been proposed. Colwell and Futuyma (1971) suggested a technique that weights resources in proportion to their use in the overall system. Lawlor (1980a) proposed exploiting the resource totals in the resource matrix as a measure of resource availability. This constitutes a sort of a bioassay. In a system of a hundred species, the diet summed over all the component species represents an estimated resource availability vector. This is used to compute probabilistic analogues of electivity and an analysis can proceed that is unbiased by resource availability (Winemiller and Pianka, 1990).
A classic paper in community ecology was published on Thailand herps (Inger and Colwell 1977). Inger and Colwell (1977) said there is "no standard protocol for community ecology." That statement is still true today, a dozen years later. Even so, in that paper, Inger and Colwell (1977) made a giant step. They suggested a nearest neighbor approach to look at communities; each species' overlap with every other species is ranked from the closest neighbor in niche space to those increasingly more distant. These generate monotonically declining curves for all the species in the system (Fig. 6). Some species have high overlap well out into niche space whereas overlap in others falls off rapidly (such consumers are very distinct with low overlap with most of the other members of the system).
Winemiller and Pianka (1990) developed a hybrid approach that uses simply the mean overlap at a given rank across all species in the system. A system in the Kalahari desert with 15 species of lizards in depicted in Figure 6. This system can be represented with a single curve of the arithmetic average over all fifteen species at each rank in niche space.
Another promising technique involves what are called null models (Colwell and Winkler 1984; Gotelli and Graves 1996). One of the big challenges is to find something with which to compare a given community. It is extremely difficult to compare a system with someone else's system: that is in fact what provoked us to devise these techniques, to compare the fish with the lizards. Sale (1974) suggested scrambling the elements of a resource matrix according to some rules to create what I have since come to call pseudo-communities (Pianka, 1986); these are then compared with the original prototype to look for differences to see how the original system is in fact organized. Sale's algorithm involved scrambling all the utilization coefficients, whatever their values are for each consumer in the system (zeros or positive). So one simply takes the first consumer and randomly rearranges all its elements. Rearranged utilization probability u(31) could fall with equal probability into any slot in that species' utilization vector and u(11) would be the same. The nice thing is, with a computer, one can easily perform this rearrangement a hundred times, and use the bootstrap approach and exploit Monte Carlo statistics to generate a distribution against which the observed can be compared (Felsenstein, 1985; Pimm, 1983). Thus one can actually do some statistics and say whether or not any differences are significant.
Lawlor (1980b) suggested a slightly different algorithm which turns out to be equally instructive: Lawlor's algorithm leaves the zero structure of the resource matrix intact. So that if consumer 1 does not eat resource state 3, a zero must remain in cell u(31); it is frozen and not allowed to change. Elements in the resource matrix are scrambled but only among the resources that are actually used by a given species. (We call this Lawlor's method or the "conserved-zero" approach and the Sale method the "scrambled-zero" algorithm because it destroys the zero structure.)
To exploit these techniques on our real fish and lizard systems (Winemiller and Pianka, 1990), we constructed a test set of hypothetical model systems that had understandable known structure. We built model systems both with and without guilds and "bench tested" our methodology on these. Figure 7 depicts three systems of two guilds of equal size--five species in each, simple little model systems just to see how the randomization algorithms affect them. At the top, there are two guilds with very high, almost total, overlap. At the bottom there are two guilds with low overlap. In the middle, overlap is intermediate. When the zeros are scrambled, of course, guild structure is destroyed and the result is that the scrambled-zero algorithm results in increased overlap at distant ranks in the niche space. Effectively they float, because the original system (the prototype) had niche segregation in it which was destroyed when the resource matrix elements were scrambled.
We assembled another set of three systems with guilds of different sizes; these behave somewhat the same (Fig. 8). We also put together systems like these without any guild structure, but with resource partitioning (Fig. 9). It became harder to get pseudo-communities to float, although some conserved zero pseudo-communities did float, which we interpret as evidence of niche segregation.
We were also interested in the phenomenon of core resources. Both Winemiller's fish and my lizards exploit certain core resources extensively. Among the lizards, these are termites and ants, especially termites. Among the fish, mayflies constitute a core resource. So we created some systems with extensive or total overlap on certain core resources and then unique resources used by each species that were partitioned (Fig. 10).
To sum up the effects of these algorithms: With consumers piled up on a certain resource state (core resources or guilds with everything within a guild eating the same things), the scrambled-zero algorithms tended to sink, and fall below the observed. But, when resources are partitioned, the conserved-zero pseudo-communities tend to be above the observed system (i.e., they "float").
Results from Real Systems
Winemiller studied ichthyofaunas of aquatic systems in Venezuela and in Costa Rica. One of his study sites has over eighty species of fish in it over the course of an entire annual cycle. Winemiller discovered how to collect virtually an entire freshwater aquatic assemblage. He and I pulled a seine through Cano Maraca during the dry season and captured over a thousand fish of dozens of species (plus a "bonus" of a couple of large caiman!) His sample sizes are on the order of three hundred to five hundred. Winemiller could not examine the stomachs of all these fish, but went through statistical subsamples and separated his data into wet versus dry season resource matrices. Prey content by volume was estimated to the finest discriminatory abilities possible, given our own expertise, usually insect orders.
We examined 18 different resource matrices, with two or three from each of eight sites: a wet and dry season for each of four fish sites and microhabitat plus diet matrices for each of four lizard sites. Numbers of fish species on these sites vary from 19 to 59 and numbers of lizard species vary from 15 to 39. We had between 40 and 217 resource states among the sites analyzed.
An Australian desert site, the L-area near Laverton, Western Australia, has 32 species of lizards. My favorite study area is Red Sands, near Yamarna Homestead in Western Australia. I have collected 53 species of lizards there so far. The hummock grass tussock plant growth form (spinifex) is very important in the Australian desert. These tussocks, as large as a meter in diameter, house certain lizards that virtually never leave them. Some lizards are highly adapted to spinifex and swim through it with ease. Each lizard collected, some 3000 in Australia and another 2000 from the Kalahari, was weighed and measured in the field, individually tagged, and then permanently preserved by injection with formaldehyde. These specimens are all safely deposited in major museums where they are valuable material for systematics research. Many of my lizards have been dissected by people interested in functional anatomy. When the lizards are returned eventually to the laboratory, each lizard is measured--ten different body measurements are made on them for anatomical analyses, and then each lizard is dissected and its reproductive state is noted, and relative clutch mass is estimated (testicular cycles can be deduced from serial samples like this), but the most important thing for present purposes is that stomachs are pulled. A competent entomologist went through the stomach contents on the Australian lizards, identifying 100,000 or more prey items to the finest categories possible.
Costa Rican fish assemblages are shown in Figure 11 during two seasons, wet and dry, based on two different resource matrices (some fish species present in the wet are not there during the dry season). Mean overlap in the observed system is shown in the upper panel of each graph with solid circles. Overlap at each rank in niche space is plotted, the average similarity between consumers at the first, second, third, etc. rank. Pseudo-communities are shown with the open symbols, the conserved zeros are open circles; the scrambled zeros are open triangles. In the lower panels of each figure, the percentage of pseudo-communities that either float or sink are plotted. In this case, sinking of the scrambled zero pseudo-communities is interesting, as is floating of conserved zero pseudo-communities which reflects niche segregation. The dashed lines in the bottom panels are at 5% and 95% confidence levels, so when a pseudo-community lies above the upper dashed line or below the lower dashed line, there is a statistically significant difference between the pseudo-communities and the observed system. At close ranks in niche space, conserved-zero systems don't float but farther out they clearly differ from observed systems and to some extent, except for the lower right panel in the figure, the scrambled-zero psuedo-communities almost invariably sink, at most ranks.
Venezuelan fish float and sink even better (Fig 12). Realize that these represent an enormous amount of information in one graph. In one case, at Maraca (top panel), 29,000 fish went into the figure. Winemiller lived down there for a full year, collected many thousand fish, brought them back and spent an entire year going through vast numbers of stomachs. All this information can be represented on a single page with a simple graph that one can examine and interpret with a little bit of training.
These aquatic systems are highly organized, with guild structure, core resources, plus niche segregation. Consumers are piled up on certain core resources, reflected in the sinking of the scrambled-zero pseudo-communities. But those same consumers are also segregated out on those core resources that they do use, with different species using the same core resources but with different probabilities.
Australian lizards are depicted in Figure 13. At the very top are microhabitats, in the middle are standard dietary resource matrices (nineteen prey categories, largely insect orders). At the bottom are expanded dietary resource matrices with 201 different prey categories represented at Red Sands and 217 prey categories recognized at the L area on the right. Some interesting things emerge from these plots. Scrambled-zero pseudo-communities tend to sink in all cases which indicates piling up on certain core resources and is indicative of guild structure. Conserved-zero pseudo-communities float pretty well in microhabitats which indicates niche segregation: different species use different microhabitats and they float fairly unequivocally, except at the closest ranks at Laverton. In the middle plot, though, pseudocommunities do not show very much floating because food resource states are too crudely differentiated resulting in a piling up of consumers on some resource states. The same data are shown in the bottom panel, but with finer discrimination of prey resource states: note that conserved zero pseudocommunities float as they did in the fish, indicating segregation.
Kalahari desert systems are more loosely organized than those in the Australian desert (Fig. 14). There are fewer lizard species in the Kalahari and prey could not be distinguished to categories as fine as those in Australia: only 46 different prey resource states were recognized. For microhabitats, there is some hint of floating in the conserved zero pseudo-communities at Tsabong, Botswana on the right (Fig. 14b), but not at Bloukrans, South Africa on the left (Fig. 14a). Examining diet (bottom panel) shows there is not much niche segregation. There isn't any at all at Tsabong, but there seems to be a little, at least at more distant ranks, at Bloukrans. All in all, the systems we examined tend to be fairly highly organized. This technique should facilitate analyses of other systems and allow comparisons with our own.
Another neglected area with promise in community ecology is the effect of large-scale regional factors on the diversity and community structure at the local level scale. Most ecologists have focused on local-level processes.
(To read about ecomorphology, click here)
References on Guild Structure and Community Organization
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Gotelli, N. J. and G. R. Graves. 1996. Null Models in Ecology. Smithsonian Institution Press, Washington, D.C.
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Wilbur, H. M. 1984. Complex life cycles and community organization in amphibians. Chapter 7 (pp. 195-224) in Price, P.W., C.N. Slobodchikoff, and W.S. Gaud (eds.) A new ecology: novel approaches to interactive systems. Wiley, New York.
Winemiller, K. O. and E. R. Pianka. 1990. Organization in natural assemblages of desert lizards and tropical fishes. Ecological Monographs 60: 27-55.
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