Definition of Stability

B. Patten and Odum (1981) proposed that a number of time-invariant or regularly oscillating ecosystem parameters represent potential goals for stabilization. These included total system production (P) and respiration (R), P: R ratio, total chlorophyll, total biomass, nutrient pool sizes, species diversity, population sizes, etc. However, the degree of spatial and temporal variability of these parameters remains poorly known for most, even intensively studied, ecosystems (Kratz et al. 1995).

Kratz et al. (1995) compiled data on the variability of climatic, edaphic, plant, and animal variables from 12 Long Term Ecological Research (LTER) sites, representing forest, grassland, desert, lotic, and lacustrine ecosystems in the United States. Unfortunately, given the common long-term goals of these projects, comparison was limited because different variables and measurement techniques were represented among these sites. Nevertheless, Kratz et al. offered several important conclusions concerning variability.

First, the level of species combination (e.g., species, family, guild, total plants or animals) had a greater effect on observed variability in community structure than did spatial or temporal extent of data. For plant parameters, species- and guild-level data were more variable than were data for total plants; for animal parameters, species-level data were more variable than were guild-level data, and both were more variable than were total animal data. As discussed for food-web properties in Chapter 9, the tendency to ignore diversity, especially of insects (albeit for logistic reasons), clearly affects our perception of variability. Detection of long-term trends or spatial patterns depends on data collection for parameters sufficiently sensitive to show significant differences but not so sensitive that their variability hinders detection of differences.

Second, spatial variability exceeded temporal variability. This result indicates that individual sites are inadequate to describe the range of variation among ecosystems within a landscape. Variability must be examined over larger spatial scales. Edaphic data were more variable than were climatic data, indicating high spatial variation in substrate properties, whereas common weather across landscapes homogenizes microclimatic conditions. This result also could be explained as the result of greater biotic modification of climatic variables compared to substrate variables (see the following text).

Third, biotic data were more variable than were climatic or edaphic data. Organisms can exhibit exponential responses to incremental changes in abiotic conditions (see Chapter 6). The ability of animals to move and alter their spatial distribution quickly in response to environmental changes is reflected in greater variation in animal data compared to plant data. However, animals also have greater ability to hide or escape sampling devices.

Finally, two sites, a desert and a lake, provided a sufficiently complete array of biotic and abiotic variables to permit comparison. These two ecosystem types represent contrasting properties. Deserts are exposed to highly variable and harsh abiotic conditions but are interconnected within landscapes, whereas lakes exhibit relatively constant abiotic conditions (buffered from thermal change by mass and latent heat capacity of water, from pH change by bicarbonates, and from biological invasions by their isolation) but are isolated by land barriers. Comparison of variability between these contrasting ecosystems supported the hypothesis that deserts are more variable than lakes among years, but lakes are more variable than deserts among sites.

Kratz et al. (1995) provided important data on variation in a number of ecosystem parameters among ecosystem types. However, important questions remain. Which parameters are most important for stability? How much deviation can be tolerated? What temporal and spatial scales are relevant to ecosystem stability?

Among the parameters that could be stabilized as a result of species interactions, net primary production (NPP) and biomass structure (living and dead) may be particularly important. Many other parameters, including energy, water and nutrient fluxes, trophic interactions, species diversity, population sizes, climate, and soil development, are directly or indirectly determined by NPP or biomass structure (Boulton et al. 1992; see Chapter 11). In particular, the ability of ecosystems to modify internal microclimate, protect and modify soils, and provide stable resource bases for primary and secondary producers depends on NPP and biomass structure. Therefore, natural selection over long periods of co-evolution should favor individuals whose interactions stabilize these ecosystem parameters. NPP may be stabilized over long time periods as a result of compensatory community dynamics and biological interactions, such as those resulting from biodiversity and herbivory (see later in this chapter).

No studies have addressed the limits of deviation, for any parameter, within which ecosystems can be regarded as qualitatively stable. Traditional views of stability have emphasized consistent species composition, at the local scale, but shifts in species composition may be a mechanism for maintaining stability in other ecosystem parameters, at the landscape or watershed scale. This obviously is an important issue for evaluating stability and predicting effects of global environmental changes. However, given the variety of ecosystem parameters and their integration at the global scale, this issue will be difficult to resolve.

The range of parameter values within which ecosystems are conditionally stable may be related to characteristic fluctuations in environmental conditions or nutrient fluxes. For example, biomass accumulation increases ecosystem storage capacity and ability to resist variation in resource availability (J. Webster et al. 1975) but also increases ecosystem vulnerability to some disturbances, including fire and storms. Complex ecosystems with high storage capacity (i.e., forests) are the most buffered ecosystems, in terms of regulation of internal climate, soil conditions, and resource supply, but also fuel the most catastrophic fires under drought conditions and suffer the greatest damage during cyclonic storms. Hence, ecosystems with lower biomass, but rapid turnover of matter or nutrients, may be more stable under some environmental conditions. Species interactions that periodically increase rates of nutrient fluxes and reduce biomass (e.g., herbivore outbreaks) traditionally have been viewed as evidence of instability but may contribute to stability of ecosystems in which biomass accumulation or rates of nutrient turnover from detritus are destabilizing (de Mazancourt et al. 1998, Loreau 1995).

No studies have addressed the appropriate temporal and spatial scales over which stability should be evaluated or whether these scales should be the same for all ecosystems. Most studies of ecosystem processes represent periods of <5 years, although some ecosystem studies now span 40 years. The long time scales representing processes such as succession exceed the scale of human lifetimes and have required substitution of temporal variation by spatial variation (e.g., chronosequences within a landscape). Data from such studies have limited utility because individual patches have unique conditions and are influenced by the conditions of surrounding patches (Kratz et al. 1995, Woodwell 1993). Therefore, temporal changes at the patch scale often follow different successional trajectories.

Boulton et al. (1992) compared rates and directions of benthic aquatic invertebrate succession following flash floods of varying magnitude among seasons in a desert stream in Arizona, United States, over a 3-year period. Several flash floods occurred each year, but the interval between floods was long relative to the life spans of the dominant fauna. Invertebrate assemblage structure changed seasonally but was highly resistant and resilient to flooding disturbance (i.e., displacements resulting from flooding were less than were seasonal changes). By summer, robust algal mats supported dense invertebrate assemblages that were resistant to flooding disturbance. By fall, algal mat disruption made the associated invertebrate community more vulnerable to flooding disturbance. Assemblages generally returned to preflood structure, although trajectories varied widely. Long-term community structure was relatively consistent, despite unpredictable short-term changes.

Van Langevelde et al. (2003) proposed a model of African savanna dynamics in which alternate vegetation states cycle over time as a result of the interactive effects of fire and herbivory. Positive feedback between grass biomass and fire intensity is disrupted by grazing, which reduces fuel load, fire intensity, and tree mortality. Increased woody vegetation causes a change in state from grass dominance to tree dominance. Browers respond to increased tree abundance, reducing woody biomass and stimulating grass growth, causing the cycle to repeat. Such a system may be relatively stable over long time periods but appear unstable over short transition periods.

Although individual patches may change dramatically over time, or recover to variable endpoints, the dynamic mosaic of ecosystem types (e.g., successional stages or community types) at the landscape or watershed scale may stabilize the proportional area represented by each ecosystem type (see Chapter 10). Changing land-use practices have disrupted this conditionally stable heterogeneity of patch types at the landscape scale.

Finally, the time frame of stability must be considered within the context of the ecosystem. For example, forests appear to be less stable than grasslands because of the long time period required for recovery of forests to predistur-bance conditions compared to rapid refoliation of grasses from surviving underground rhizomes. However, forests usually are disturbed less frequently. NPP

may recover to predisturbance levels within 2-3 years, although biomass requires longer periods to reach predisturbance levels (e.g., Boring et al. 1988, Scatena et al. 1996, J. Zimmerman et al. 1996).

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