Complexity and stability in practice whole communities

Turning to the aggregate, whole community level, evidence is largely consistent in supporting the prediction that increased richness in a community increases stability (decreases variability), though a number of studies have failed to detect any consistent relationship (Cottingham et al., 2001; Worm & Duffy, 2003).

First, returning to McNaughton's (1978) studies of US and Serengeti grasslands, the effects of perturbations were quite different when viewed in ecosystem (as opposed to population) terms. The addition of fertilizer significantly increased primary productivity in the species-poor field in New York State (+53%), but only slightly and insignificantly changed productivity in the species-rich field (+16%); and grazing in the Serengeti significantly reduced the standing crop biomass in the species-poor grassland (— 69%), but only slightly reduced that of the species-rich field (—11%). Similarly, in Tilman's (1996) Minnesota grasslands, in contrast to the weak negative effect found at the population level, there was a strong positive effect of richness on the stability of community biomass (Figure 20.10b).

no consistent answers data support the models: aggregates are more stable in richer communities

1 If 150

o cn

TE 100

10 15

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80 70 60 50 40 30 20

10 15

Species richness

Field A

Field A

10 12

10 12

Field B

Field B

2 4 6 8 10 12 14 16 0 2 4 6 8 10121416182022 Average species richness

Figure 20.11 Variation (i.e. 'instability') in productivity (standard deviation of carbon dioxide flux) declined with species richness in microbial communities observed over a 6-week period. Richness is described as 'realized' because it refers to the number of species present at the time of the observation, irrespective of the number of species with which the community was initiated. (After McGrady-Steed et al., 1997.)

Figure 20.10 (a) The coefficient of variation of population biomass for 39 plant species from plots in four fields in Minnesota over 11 years (1984-94) plotted against species richness in the plots. Variation increased with richness but the slope was very shallow. (b) The coefficient of variation for community biomass in each plot plotted against species richness for each of the four fields. Variation consistently decreased with richness. In both cases, regression lines and correlation coefficients are shown. *, P < 0.05; **, P < 0.01; ***, P < 0.001. (After Tilman, 1996.)

McGrady-Steed et al. (1997) manipulated richness in aquatic microbial communities (producers, herbivores, bacterivores and predators) and found that variation in another ecosystem measure, carbon dioxide flux (a measure of community respiration) also declined with richness (Figure 20.11). On the other hand, in an experimental study of small grassland communities perturbed by an induced drought, Wardle et al. (2000) found detailed community composition to be a far better predictor of stability than overall richness.

Figure 20.11 Variation (i.e. 'instability') in productivity (standard deviation of carbon dioxide flux) declined with species richness in microbial communities observed over a 6-week period. Richness is described as 'realized' because it refers to the number of species present at the time of the observation, irrespective of the number of species with which the community was initiated. (After McGrady-Steed et al., 1997.)

Studies of the response of a community to a perturbation (e.g. McNaughton, 1978) or of variations in the community in response to year-to-year variations in the environment (e.g. Tilman, 1996), are focused largely on the resistance of communities to change. A quite different perspective examines the resilience of communities to perturbations in ecosystem characteristics such as the energy or nutrient levels contained within them. O'Neill (1976), for example, considered the community as a three-compartment system consisting of active plant tissue (P), heterotrophic organisms (H) and inactive dead organic matter (D). The rate of change in the standing crop in these compartments depends on transfers of energy between them (Figure 20.12a). Inserting real data from six communities representing tundra, tropical forest, temperate deciduous forest, a salt marsh, a freshwater spring and a pond, O'Neill subjected the models of these communities to a standard perturbation: a 10% decrease in the initial standing crop of active plant tissue. He then monitored the rates of recovery towards equilibrium, and plotted these as a function of the energy input per unit standing crop of living tissue (Figure 20.12b).

The pond system, with a relatively low standing crop and a high rate of biomass turnover, was the most resilient. Most of its plant populations have short lives and rapid rates of population increase. The salt marsh and forests had intermediate values, whilst tundra had the lowest resilience. There is a clear relationship importance of the nature - not just the richness - of the community

Net primary production

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Transport Respiration

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Freshwater

_

spring

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deciduous

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forest •• Salt marsh

Tundra

• i

i I

10-2 10-1 100 101 102

10-2 10-1 100 101 102

Energy input per unit of standing crop (energy units)

Figure 20.12 (a) A simple model of a community. The three boxes represent components of the system and arrows represent transfers of energy between the system components. (b) The rate of recovery (index of resilience) after perturbation (as a function of energy input per unit standing crop) for models of six contrasting communities. The pond community was most resilient to perturbation, tundra least so. (After O'Neill, 1976.)

cycling rather than energy flow. Here too, then, stability seems more influenced by the nature of the species in the community than by simple measures such as overall richness.

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