Quantifying the community-level consequences of competition

Folia Geobot. Phytotax., Praha, 30: 231-242, 1995

QUANTIFYING THE COMMUNITY-LEVEL CONSEQUENCES OF COMPETITION

Deborah E. Goidberg 1), Roy Turkington 2) & Linda Olsvig-Whittaker 3)

1) Department of Life Sciences, Ben Gurion University, PO.B. 653, Beer Sheva 84105, Israel; Current address: Department of Biology, University of Michigan, Ann Arbor, M148109, USA; E-mail DEGOLD@ UMICH.EDU

2) Department of Botany, University of British Columbia, Vancouver, B.C., V6T lZ4, Canada; E-mail RO YT@ UNIXG. UBC. CA

3) Mitrani Center for Desert Ecology, Blaustein Institute for Desert Research, Sede Boqer Campus 84990, Israel; Current address: Nature Reserves Authority, 78 Yirmiahu Street, Jerusalem, Israel; E-mail LINDA @ BG UMAIL BGU.A C.IL

Keywords: Community-density experiments, Competition, Perennial grasslands, Plant communities, Productivity gradients. Species composition, Species diversity, Yield-density experinaents

Abstract: Changes in plant community structure after changes in some aspect of the environment such as nutrients or grazing is often ascribed to changes in competitive relationships among the plants. However, very rarely is competition measured directly in such experiments. To distinguish between the direct effects of environmental treatments and changes in competitive relationships, it is necessary to quantify the influence of competition on community structure and compare the magnitude and direction of this influence between environments. We describe an experimental approach to accomplish this that is based on the classic yield-density experiment of agronomy. The approach is called the community-density experiment and requires experimental establishment of a gradient in total initial community density such that absolute densities of each species increase but initial relative abundances of each species stay constant along the gradient. We define various indices of the magnitude of community-level consequences of increasing density that can be compared among environments such as different fertilizer or grazing treatments. We also discuss various practical ways of achieving the experimental density gradient that are suitable for different kinds of communities.

INTRODUCTION

Numerous studies have measured the impact of various environmental treatments on plant community structure, including many of the papers in this symposium. Such studies often attribute changes in species composition or diversity among environments to changes in competitive interactions (WILSON 1978, FRENCH 1979). A large literature documenting the occurrence of competition in plant communities in general and in grasslands in particular is certainly consistent with this assumption (RaSSER 1969, FOWLER 1986, AARSSEN & EPP 1990, TURKINGTON & MEHRHOFF 1990, GOLDBERG & BARTON 1992). However, almost no studies have actually distinguished between the effects of competition and the direct effects of the environmental treatments on COmmunity structure. One or both of two necessary components for such tests are usually lacking: (a) quantification of the effects of competition alone on the entire community rather than on one or a few selected target species (see CONN'ELL 1983,

232 D,E. Gotdberg et al.

SCHOENER 1983, GUREVrrCH & UNNASCH 1989, GOLDBERG & BARTON 1992), and (b) direct assessment of the difference in magnitude of the effects of competition among environments, i.e., tests of a competition x environment interaction (GOLDBERG & BARTON 1992, GOLDBERG t~r SCHEINER 1993).

The lack of direct tests of community-level consequences of competition may be due to the lack of appropriate experimental designs or analytical approaches. The relatively few existing experiments on community-level consequences of competition all use a similar approach: removal of a single species at a time (usually a dominant) and measurements of abundance or diversity of the remaining species (review in GUREVITCH & UNNASCH 1989, GOLDBERG & BARTON 1992). While a very useful method for assessing the influence of the dominant on the rest of the community, this approach does not assess interactions among all the species in the community. GOLDBERG (1994) suggested an alternative approach that requires experimental low-density monocultures of all species that occur within the community as well as an additive mixture of all species; this approach is obviously feasible only for relatively simple communities (see also JOLn~FE et al. 1984 for a related technique for pairwise interactions). In this paper, we describe a very different experimental approach called the community-density series that is suitable even for highly diverse communities because it does not require separate monoculture experiments for all species in the community. We then discuss how the method can be used to distinguish between alternative hypotheses about patterns in the magnitude of competition over gradients such as predation, disturbance, and productivity (e.g. CONNELL 1975, GRrME 1973, 1988, MENGE & SUTHERLAND 1976, 1987, NEWMAN 1973, OKSANEN et al. 1981, SOUTHWOOD 1977, 1988, TILMAN 1988). Finally, we will discuss specific application of the design to perennial grassland systems.

THE COMMUNITY-DENSITY SERIES

The basic experimental approach is a simple extension of the single species yield-density experiment to multi-species assemblages. In a typical yield-density experiment, seeds of a single species are sown over a wide range of densities and yield is measured at some point, in time (usually the end of a growing season) (HARPER 1977, chapter 6). Typical results for total biomass yield exhibit three phases along a gradient of planting density (Fig. 1 ). At low planting densities, the final biomass yield is strictly proportional to initial planting density, indicating no effects of competition in this linear phase. Above some threshold density (DD, the slope changes from linear to positive but with a negative second derivative, i.e., biomass continues to increase but the amount of increase with each added individual declines with increasing density, indicating some competition. Finally, above a second threshold density (Din), further increases in initial planting density do not result in any further increase in biomass, i.e., a biomass carrying capacity (Brn) is reached. All these changes in total biomass yield as a function of initial planting density could be either a function of mortality (final density < initial density) and/or differential growth (smaller individuals at higher density, i.e., plasticity).

An experiment using the community-density series is analogous to a yield-density experiment, except that the initial density of the entire community rather than the density of a single species is manipulated. The absolute density of the entire community and of each species changes along the community density gradient, but the relative initial density of each

Community-level consequences of competition 233

species stays constant. One can then plot total community yield along the initial community density (ICD) gradient to determine the threshold density at which competition starts to influence community biomass or other measures of total yield (Dc), as well as the asymptotic total community yield or carrying capacity (Bin; Fig. 2a). More complex community parameters can then be related to the experimentally-extended gradient of initial community densities (Fig. 2b). Of primary interest are parameters describing species or functional group composition such as indices from multivariate ordinations (e.g. canonical correspondence analysis; TER BRAAK 1987-1992). However many other parameters could be used ranging from relatively simple ones such as diversity or dominance indices to more complex indices of spatial patchiness in species composition.

The basic assumption of the community-density series is that the potential for interactions among individuals increases with increasing density. If this assumption is correct, the

IT.

Initial dens~

Fig. 1. Typical results from a yield-density experiment, where yield is measured as total biomass. In a yield-density experiment, seeds of a single species are planted at different densities and a measure of final yield is plotted as a function of initial planting density. The results show 3 phases defined by the two threshold densities Dc and Din. At densities less than Do yield is a linear function of planting density, indicating no competition. At densities between Dc and Din, yield still increases with planting density, but at an ever-decreasing rate, indicating some competition. The addition of a single individual adds a smaller increment to total biomass at high density than at lower densities. At Din, total yield reaches an asymptotic value (Bin) where increasing density has no further effect on yield. Bm is a biomass carrying capacity.

value of the community parameters at low ICD (below Dc) characterize the "null community" (ZOI3EL 1992), i.e., what the community would look like in the absence of competitive interactions among individuals. As initial community density increases above De, deviations of the community parameters from these null values indicate the consequences of interactions among individuals at the community level (Fig. 2b). As with any experiment manipulating density, these interactions can be positive (facilitative) or negative (competition) and results only indicate the net outcome of all such interactions. However, because this net effect is typically negative and, for convenience, we will simply refer to competit ion or the effects of competition.

For any particular community parameter, several indices from these graphs can be used to quantify the magnitude of the effects of competition on a single community in a single environment, and to compare these magnitudes among communities or environments:

The threshold density at which competition begins, De Dc is the density at which a deviation from strict linearity becomes apparent in yield-lCD

relationships (Fig. 2a) or at which a deviation from a flat line (slope = 0) becomes apparent in relationships between other community parameters and ICD (Fig. 2b). For example, we

234 D.E. Goldberg et al.

(a)

Bm

De D n Dm initial oommunity density

(b)

r E

13 D n D m Initial community density

Fig. 2. Hypothetical results from a community density series experiment for (a) final biomass yield and (b) other community parameters such as diversity or dominance. Dc is the competition threshold density: i.e., the density at which we first detect deviation from linearity in biomass-initial density relationships or deviation from slope = 0 (flat lines) in other community parameter-initial density relationships. Dn is the mean naturally-occurring density of the community. Dm is the density at which the asymptotic total community bioma.ss is reached. En is the difference in the community parameter between no competition (ICD < Dc) and natural density (Dn); i.e., the magnitude of the effect of competition at natural density.

could hypothesize that Dc for biomass yield will be lower in more productive environments because individuals are larger and therefore will begin to overlap in zones of influence at a lower density than in less productive environments. Alternatively, we could argue, perhaps less convincingly, that the threshold density will be higher in more productive environments because resources are more available and thus it takes more individuals to reach a point where the resources are depleted and become limiting.

The slope of the relationship between some community parameter and initial community density, for ICD > Dc

Larger absolute values of this slope indicate bigger effects of competition. For simplicity, Fig. 2b depicts the curve as linear once ICD is greater than Dc but there is no reason to expect such strict linearity. In addition, depending on the community parameter under consideration, competition could be indicated by positive or negative slopes. For diversity measures, we usually expect increasing density to increase competitive exclusion, thereby reducing diversity. Thus, a negative slope is predicted (but see below for special problems in testing species richness). However, for indices of community composition, species or groups of species that are good competitors will, by definition, increase in relative abundance with ICD, and therefore exhibit a positive slope with

ICD, while poor competitors will exhibit a negative slope.

The deviation of the community parameter between the null community and natural density, En

Comparisons of threshold densities or slopes only assess the potential for competition to affect the community at a given ICD. To assess whether competition actually influences the community in the field, it is necessary to locate the position of the natural range of ICD values (Dn in Fig. 2) on the full ICD gradient and then calculate En, the difference in the value of various community parameters between Dn and any ICD < De. Qualitatively, we can also ask


i . . . . . . . . . .

V~Bi ~ B

" " - .

A

Dn(A) On(B) Initml r density

Fig. 3. A hypothetical example of different results of a comparison between two environments (A and B) when two different indices of community-level effects of competition are compared. In this example, the per-individual effect on diversity (slope) is higher in environment A, but the effect at natural density (En) is higher in environment B because the naturally-occurring density is so low in environment A (e.g., because of herbivory or disturbance).

if the typical range of Dn values for a given community falls above or below Dc for that environment? Or, because density is typically very patchy in real plant communities, what proportion of patches in the field have ICD values above or below the threshold competition value?

The community-density series can yield a wide diversity of results from even a simple comparison of the effects of competition among only two different environments or community types. First, it is possible that these three indices (Dc, slope, En) can give very different results when compared among communities or environments. For example, Fig. 3 shows a case where environment A has a steeper slope of species diversity vs. ICD than does environment B once past the threshold density, which is similar for both environments. However, the naturally- -occurring ICD in environment A (Dn (A))

is so low that competition barely occurs at all, reflected in the very small value for En (A). Such a low/9,, could be due to low productivity or high rates of disturbance or herbivory.

Second. a single index such as Dc or En could differ among community parameters when compared among the same set of environments or communities. Fig. 4a shows an example where total community biomass is first affected by competition at lower densities in environment B[Dc (B) < Dc (A)], while species composition (Fig 4b) is first affected at lower densities in environment A [De (A) < Dc (B)]. Such a scenario might arise if species were more similar in competitive ability for the limiting resource in environment B so that, although individual growth is strongly reduced at even relatively low densities in environment B, this reduction is similar enough among species that no species have strong advantages over others that would result in a change in their relative abundances. At high enough density, however, even small differences among species will have an impact on their relative competitive ability and species composition will be affected even in environment B.

Third, comparisons among communities or environments are straightforward when they have similar values of the community parameter in the null communities (no competition) as in Figs. 3 and 4. However, if values are not similar, comparisons could be made in two ways: (a) the absolute difference between no competition and a higher ICD for both communities [D,~ (A,B); Fig. 5a] or, (b) a relative difference, where values at high competition are standardized to values at low competition (Fig. 5b). This standardization could have a big impact on patterns of community-level effects of competition (GOLDBERG 8s SCHEINER 1993, GRACE 1993). For example, in Fig. 5, community A has a lower diversity than community B even at the lowest ICD, although they have similar natural densities. When compared in


(a)

B . , ' - ' "" ..................................................

(b)

o~) oc(A)

InJt~ oomn~n~

tO(A) De(a) Inllial community density

Fig, 4. A hypothetical example of different results of a comparison between two environments when different community parameters (but the same index) are compared. In this example, the competition threshold density (De) is higher in environment A for final yield (a) but higher in environment B for a species composition index (b).

absolute terms, En is similar between the two communities (Fig. 5a). However, in relative terms, the effect is much larger in community A (Fig. 5b).

The examples in Figs. 3-5 of differences in interpretation among different parameters or indices of the magnitude of the effects of competition in communities suggest that extreme caution must be used in testing hypotheses about patterns among environments or community types. Empirical tests of an hypothesis must use indices and parameters that match those on which the hypothesis is based. For example, depending on whether an absolute or relative index was used, both CAMPBELL & GRIME (1992) and TURrdNGTON et al. (1993) found contrasting results for patterns in the effects of competition at the population level along nutrient and disturbance gradients. CAMPBELL & GRIME (1992) argued that GRIME'S (1977) prediction of smaller effects of competition at high disturbance or low nutrients (high stress) is based on having less absolute biomass of competing plants and therefore only the absolute index was appropriate. WILSON 8z TILMAN (1991), however, used a relative index to test the same hypothesis. While this may be

inappropriate in terms of testing GRIME's hypothesis, the absolute index will usually necessarily be higher in more productive environments (GRACE 1993) and so the hypothesis may itself be trivially true.

For most community parameters, the above analyses are straightforward in principle, even when the parameters themselves are the result of a complex analysis such as a multivariate ordination to characterize species composition. However, quantifying the effect of density on the number of species and other measures of diversity presents a special problem in analysis because increasing density effectively increases sample size. Therefore, all else being equal, the number of species should increase with density (up to some value) simply because more individuals are sampled. This sampling effect may well overwhelm or at least partially counteract the negative effect of density on species diversity that is expected because of increa,~ing competitive exclusion or at least increasing dominance.

The problem of comparing species richness between samples with different numbers of individuals may be solved by using the rarefaction technique, first introduced by SANDERS (1968) and later corrected by HURLBERT (1971) and SIMBERI-.OFF (1972). The technique estimates the number of species expected in a random sample of individuals taken from some


(a)

| i

D n (A,B)

Ir~aJ comnu~ den~

(b)

J o n ~ , ~

Jnit~ ~ ~

Fig. 5. A hypothetical example of different results of a comparison between two environments (A and B) when absolute (a) vs. relative (b) values of a community parameter are compared. In this example, species diversity is lower in environment A at all initial community density (ICD) values, although the threshold competition density (Dc), natural density (Dn), and diversity-lCD slope are identical in the two environments. However, if the diversity values are standardized to a percentage of the value in no competition, the diversity-ICD slope is steeper in environment A. Consequently the magnitude of the relative effect at natural density (En) is also greater in environment A.

larger pool, thus making it possible to generate an entire curve of species richness estimates as a function of sample size. This expected curve of purely sampling effects can then be compared to the observed curve of richness versus ICD. If increasing density increases the potential for competitive exclusion, the difference between the expected and observed curves of richness vs. density should increase with ICD. KREBS (1989) summarizes the rarefaction method and provides a FORTRAN computer program for doing the calculations. The major potential problem in applying the method is deciding what constitutes the larger pool that is used to generate the expected curve of richness vs. ICD. Ideally, the species composition of the initial community should be pooled along the ICD gradient, because this is measured before any interactions occur. One additional problem is that if species are aggregated due to noncompetitive interactions, the actual number of species per unit area may be less than the expected number, even in the absence of any competitive effects (J. LEPL pers. comm.).

CARRYING O U T T H E DESIGN

There are two main practical issues associated with using the community- -density series: (a) defining initial densities and (b) manipulating initial community density while keeping initial relative abundances constant. In this section, we suggest methods for both of these, with a special emphasis on perennial grassland communities.

Defming initial density. It is important to use a gradient of initial, rather than final, density to define the gradient in potential interactions because competition can cause mortality and this mortality is often differential among species. Therefore relative abundances of species are unlikely to be the same along a gradient in final community density. However, when establishing an initial community density gradient, "initial" can mean a variety of things, depending on life histories of the organisms and the time scale of observation. For an annual


plant community, seed density will generally be the most appropriate stage so that any density-dependent germination can be taken into account. For conununities of long-lived perennials, "initial" can mean the beginning of a given growing season (e.g., density of all ramets) or it could be the density at the initiation of succession after some major disturbance, in which case seed density is again the relevant stage. Regardless of life history stage, it is not necessary that "initial community density" be an actual density count of individuals; other measures of abundance such as initial cover, initial biomass, or initial number of ramets could also be used. It is also not necessary that the measure used for initial abundance be independent of past effects of competition because the community-density series only quantifies subsequent effects of competition in any case. However, it is necessary that any past effects of competition are constant along the current competition gradient (i.e. constant with experimental ICD).

Generating an ICD gradient. The critical aspect of generating an ICD gradient is that initial relative abundances of each species are constant along a gradient where their absolute abundances are increasing. This allows any subsequent differences in relative abundances or diversity to be ascribed to the effects of density. Natural gradients in total community density are not satisfactory because the differences in ICD among locations may be due to some environmental factor that could directly influence the magnitude of the effects of competition or initial relative abundances, Therefore it is necessary to manipulate total community density in common conditions. There are a number of ways this could be done, each with different advantages and limitations and suitable in different types of communities and/or for different questions.

(I) Using mixtures of propagules (usually seeds) and adding different amounts of this "concentrated community" to achieve a range of densities from below to above natural density. These propagule mixtures can be obtained in several ways:

(a) Extract the seedbank from soil and litter. We have been successfully using this technique to manipulate initial community density in experiments with sand dune annuals in Israel. Sand is a particularly easy matrix to work with because it can be removed or added and so used to concentrate or dilute the seedbank but KROPA(2 (1966) and ROBERTS (1981) describe techniques for separating the seedbank from other soil types. NEILL (pers. comm.) has used a similar technique with plankton communities from freshwater lakes. The seedbank extraction technique is most appropriate for annual communities that start from seed every year or for studying the succession of perennial grasslands or other communities after a soil disturbance.

(b) Collect current year's seed production by harvesting seed heads before seed release. This would ensure that only species represented as mature adults in the local community are included and so probably exclude many ruderal species that have either dormant seed in the soil or widely dispersed seed. This would be appropriate for examining the potential of seedling-seedling interactions within a perennial community to influence the adult community structure (does differential success in seedling interactions correspond to patterns in adult relative abundance?).

(c) In managed pastures, use the recommended mixture from seed suppliers. Regardless of seed collection method, starting the community from seed measures the

effect of competition on community development and therefore is most suitable for questions about competition and succession or the influence of initial conditions. Across generations, we would expect initially low density plots to have high per-capita reproduction and therefore catch up in density relatively rapidly. However, do the different initial densities give different


species an initial advantage, which carries through subsequent generations? I.e., are there persistent effects of initial density on successional patterns and how long do these effects last? The mixed propagule approach is obviously not appropriate if the question concerns the effect of competition on an already existing community because relative competitive abilities among species (and therefore the community-level consequences of competition) may differ depending on whether adult-adult, adult-seedling, or seedling-seedling competition is considered (GOLDBERG 1990). For such questions, manipulation of density of established plants as described in the next section will be more appropriate.

(2) Removing/adding established individuals. Alternatively, a protocol for removing or adding individuals can be established that would maintain relative abundances while decreasing or increasing total density; the latter would be more limited especially in communities which are visually quite saturated with few openings. For example, to achieve a density half that of natural, remove every second individual encountered of each species. Such removal experiments would have all the problems attendant on any removal experiment, such as potential artifacts of roots left in the ground that may either release or immobilize nutrients during decomposition [e.g., TURKINGI"ON 1989, although MCLELLAN et al. (1995) found these effects to be fairly minor in a recent study]. A removal experiment is particularly difficult to interpret in communities with highly clonal organisms for two reasons: (a) it is sometimes difficult to know whether one is removing competitors that are preempting resources from some target individual or removing ramets still connected to the target that may be donating resources to the target, and (b) it is difficult to remove individuals completely because of clonal connections or underground perennating tissues and therefore regrowth of"removed" competitors often occurs. If species differ in amount of regrowth, this is particularly problematical for the community density series where it is critical to control relative species abundances. Species with such potential for regrowth will have a biased representation in the developing manipulated community and their abundance will be less a reflection of competitive ability than a result of poor experimental protocol.

An addition experiment could be tractable in a community with gaps in the vegetation, i.e. where the individual plants are spaced. There may be some difficulties, however, in communities with continuous vegetation cover. Firstly, the actual act of transplanting may damage existing individuals. Secondly, it may not be feasible to appreciably increase the density above natural levels. Nevertheless, even in communities which have apparently continuous cover such as pastures or temperate grasslands, upon close inspection it is common to find numerous small spaces at ground level. These may be adequate to increase densities, and to do it without damaging existing individuals. Thirdly, in patchy field plots there is the potential for integration effects from parts of a plant living in poor patches to parts growing in good patches. This may cause the response of the plant to its local density conditions to be obscured (SLADE & HUTCHINGS 1987, TURKINGTON &KLEtN 1991).

Despite all these problems of both addition and removal experiments, they are the most common types of field experiment on competition and have a long history of use for studying the individual-level consequences of competition in perennial grasslands and other communities (AARSSEN & EPr' 1990, GOLDBERG & BARTON 1992, GUREVIXCH et al. 1992). They seem to be the primary options (other than the logistically difficult (3) below) currently available lbr studying the individual or community-level interactions among already-established adults in perennial communities and therefore we reluctantly recommend


their use, with all the caveats discussed above. Some of the problems can be dealt with by careful experimental technique.

(3) Reconstruction of the community by transplanting established plants. This would involve quantifying the relative abundances of the species in the natural community, collecting transplants of the appropriate age/size distributions from all the species in the community, and then planting predetermined numbers of individuals of each species in experimental plots such that total density varies but relative abundances of each species are constant along the total density gradient. This would be a logistic nightmare for plant communities of even moderate diversity and introduce many potential artifacts due to transplant effects, soil disturbance, etc. This may, however, be an option for less diverse communities with organisms less likely to be affected by handling/transplanting.

Finally, we note that for any of these techniques, many of the questions of interest to plant ecologists and to pasture managers will require comparing the effects of competition on community structure among environments-either sites that differ in some environmental characteristic such productivity or herbivory or experimental treatments such as fertilizer or grazer manipulation (GOLDBERG & BARTON 1992). Therefore experimental designs will typically be factorials involving several levels of an environmental factor (site or treatment) fully crossed with several levels of initial community density (at least 2 levels are required: natural density and a single very low density, although more levels are strongly preferred so that all the parameters in Fig. 2, including slope, can be calculated). For example, to test whether the effects of competition at natural densities are stronger in fertilized plots, a minimal design is a two way factorial of competition treatments (natural community density, very low community density) x fertilizer treatments (fertilized, unfertilized). If natural densities are very different between fertilized and unfertilized plots, this design is inadequate to separate per-plant effects of competition from total density effects, For management-related questions, this may not be an important distinction. Where the distinction is important, it is necessary to have an entire density gradient so that slopes can be estimated and then compared. For generalizing results, it is also useful to have a wide range of densities, including some higher than natural, because densities vary between years.

CONCLUSIONS

The community-density series potentially allows us to fill a big empirical gap by testing a wide variety of hypotheses about patterns in the influence of competition on entire communities. In addition, simply describing the design by itself opens up a series of important questions because it is immediately clear that different ways of quantifying the "influence" of competition at the community level may well give different patterns along productivity gradients, herbivory gradients, or any other type of gradient (Figs. 3-5). Therefore, even without using the design empirically, it can be helpful by forcing us to define exactly what we mean by "influence of competition" in the context of any particular hypothesis. As with the example of changes in the importance of competition along productivity gradients, to a large extent, some controversies may be a simple consequence of different definitions of "influence".

Acknowledgments: We are grateful to the participants in the Bedrichov workshop for stimulating discussions on the community-density series experimental approach, and especially for discussions on how to apply it in


perennial grasslands. We also thank the Binational Science Foundation (US-Israel), the Keren Kayemet LeYisroel, and the University of Michigan for supporting ongoing empirical tests of the method.

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Quantifying the community-level consequences of competition

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