9.4. Quantitative Measures of StabilityIn the previous section we have discussed qualitative indicators of stability. According to these indicators, a model is either stable if its trajectory converges to an equilibrium state or unstable if it diverges from the equilibrium after small disturbances. However, real populations never converge to an equilibrium because of the random noise associated with weather and other stochastic factors. Thus, qualitative stability has a vague biological meaning. Ecologists are more interested in quantitative indicators of stability which represent the ability of the population to resist environmental fluctuations.Robert May (1973) suggested to measure system stability by the maximum real part of eigenvalues of the linearized model. It was shown that this value correlates with the variance of population fluctuations in stochastic models. Sharov (1991, 1992) suggested measures of m and vstability that characterize the stability of the mean (m) and variance (v) of population density (initially these measures were called as coefficients of buffering and homeostasis, see Sharov [1985, 1986]). Later they were reinvented by Ives (1995a, 1995b). They can be used to predict the effect of environmental changes (e.g., global warming or pest management) on the mean and variance of population numbers Mstability (MS) was defined as the ratio of the change in mean log population density, N, as a response to the change in mean value of some environmental factor, v.
Mstability is the reciprocal of the sensitivity of mean population density to the mean value of factor v. Logtransformation of population density is important because it makes population models closer to linear. For example, if v is temperature which is going to change by 2 degrees due to global warming, and log population density per ha (log base e) will increase from 1 to 1.5, then the sensitivity is S=(1.51)/2=0.25, and mstability MS=4. The population with higher mstability will change less than the population with low mstability under the same changes in average factors. Strong population regulation increases mstability because regulating mechanisms will resist to the changes in population density. Let's assume that regulation is caused by interspecific competition. Then, if conditions become favorable for the population, then the organisms will increase their reproduction rate. However, as population density increases, mortality due to competition increases too and partially compensates increased reproduction rates. If conditions become less favorable, then density will decline and mortality due to competition will decrease and partially compensate the decrease in reproduction rates. If population dynamics is described by a mathematical model then mstability can be estimated from that model. The simplest example is the logistic model. Mean population density in the logistic model equals to carrying capacity, K. If the factor v affects K, then . If the factor v affects population growth rate, r, but does not affect carrying capacity, then mean population density will not respond to factor change, and thus, mstability will be infinitely large. Vstability (VS) was defined as a ratio of the variance of additive random noise to the variance of log population numbers : Population that has smaller fluctuations of population numbers than another population that experience the same intensity of additive environmental noise has a higher vstability. To estimate vstability in the Ricker's model we can use the linearized model at the equilibrium point:
where N is log population density, and is the white noise with a zero mean. Noise is not correlated with log population numbers. Thus:
This graph shows that vstability equals to zero at r=0 and r=2 (these are the boundaries of quantitative stability). Vstability has a maximum at r = 1. References
Ives, A.R. 1995. Predicting the response of populations to environmental change. Ecology 76: 926941.
