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Stochastic Growth Rate (stochastic + growth_rate)
Selected AbstractsTemporal autocorrelation and stochastic population growthECOLOGY LETTERS, Issue 3 2006Shripad Tuljapurkar Abstract How much does environmental autocorrelation matter to the growth of structured populations in real life contexts? Interannual variances in vital rates certainly do, but it has been suggested that between-year correlations may not. We present an analytical approximation to stochastic growth rate for multistate Markovian environments and show that it is accurate by testing it in two empirically based examples. We find that temporal autocorrelation has sizeable effect on growth rates of structured populations, larger in many cases than the effect of interannual variability. Our approximation defines a sensitivity to autocorrelated variability, showing how demographic damping and environmental pattern interact to determine a population's stochastic growth rate. [source] Environmental variance, population growth and evolutionJOURNAL OF ANIMAL ECOLOGY, Issue 1 2010Shripad Tuljapurkar N. Jonzén, T. Pople, K. Knape & M. Skjöld (2009) Stochastic demography and population dynamics in the red kangaroo (Macropus rufus). Journal of Animal Ecology, 79, 109,116. Environmental fluctuations on time scales of one to tens of generations are increasingly recognized as important determinants of population dynamics and microevolution. Jonzén et al. in this issue analyse how the vital rates of red kangaroos depend on annual rainfall, and estimate the elasticities of stochastic growth rate to the means and variances of the vital rates, as well as to the mean and variance of rainfall. Their results demonstrate how ecological and evolutionary studies can benefit from including explicit environmental drivers when modelling populations, and from the use of mean and variance elasticities. [source] Life table response experiment analysis of the stochastic growth rateJOURNAL OF ECOLOGY, Issue 2 2010Hal Caswell Summary 1.,Life table response experiment (LTRE) analyses decompose treatment effects on a dependent variable (usually, but not necessarily, population growth rate) into contributions from differences in the parameters that determine that variable. 2.,Fixed, random and regression LTRE designs have been applied to plant populations in many contexts. These designs all make use of the derivative of the dependent variable with respect to the parameters, and describe differences as sums of linear approximations. 3.,Here, I extend LTRE methods to analyse treatment effects on the stochastic growth rate log ,s. The problem is challenging because a stochastic model contains two layers of dynamics: the stochastic dynamics of the environment and the response of the vital rates to the state of the environment. I consider the widely used case where the environment is described by a Markov chain. 4.,As the parameters describing the environmental Markov chain do not appear explicitly in the calculation of log ,s, derivatives cannot be calculated. The solution presented here combines derivatives for the vital rates with an alternative (and older) approach, due to Kitagawa and Keyfitz, that calculates contributions in a way analogous to the calculation of main effects in statistical models. 5.,The resulting LTRE analysis decomposes log ,s into contributions from differences in: (i) the stationary distribution of environmental states, (ii) the autocorrelation pattern of the environment, and (iii) the stage-specific vital rate responses within each environmental state. 6.,As an example, the methods are applied to a stage-classified model of the prairie plant Lomatium bradshawii in a stochastic fire environment. 7.,Synthesis. The stochastic growth rate is an important parameter describing the effects of environmental fluctuations on population viability. Like any growth rate, it responds to differences in environmental factors. Without a decomposition analysis there is no way to attribute differences in the stochastic growth rate to particular parts of the life cycle or particular aspects of the stochastic environment. The methods presented here provide such an analysis, extending the LTRE analyses already available for deterministic environments. [source] | ||||||||||