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Deterministic Environment (deterministic + environment)
Selected AbstractsHatching fraction and timing of resting stage production in seasonal environments: effects of density dependence and uncertain season lengthJOURNAL OF EVOLUTIONARY BIOLOGY, Issue 3 2001M. Spencer Many organisms survive unfavourable seasons as resting stages, some of which hatch each favourable season. Hatching fraction and timing of resting stage production are important life history variables. We model life cycles of freshwater invertebrates in temporary pools, with various combinations of uncertain season length and density-dependent fecundity. In deterministic density-independent conditions, resting stage production begins suddenly. With uncertain season length and density independence, resting stage production begins earlier and gradually. A high energetic cost of resting stages favours later resting stage production and a lower hatching fraction. Deterministic environments with density dependence allow sets of coexisting strategies, dominated by pairs, each switching suddenly to resting stage production on a different date, usually earlier than without density dependence. Uncertain season length and density dependence allow a single evolutionarily stable strategy, around which we observe many mixed strategies with negatively associated yield (resting stages per initial active stage) and optimal hatching fraction. [source] Scheduling Part-Families Under FMS: To Mix or Not to Mix?INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 2 2001Henry C. Co This paper considers the issue of whether to mix part-types in one or several of the families to be produced in a flexible manufacturing system (FMS). For this input control problem in an FMS, we have derived conditions that support the mixing of a part-type, which can share the setup of other part-types, in deterministic environment. The problem is identified as a special economic lot scheduling problem (ELSP), and is formulated as a linear programming problem. Analytical insights are derived by considering the special case with three part-families. The results are illustrated with a numerical example. [source] CONSTANT EFFORT AND CONSTANT QUOTAFISHING POLICIES WITH CUT-OFFS IN A RANDOM ENVIRONMENTNATURAL RESOURCE MODELING, Issue 2 2001CARLOS A. BRAUMANN ABSTRACT. Consider a population subjected to constant effort or constant quota fishing with a generaldensity-dependence population growth function (because that function is poorly known). Consider environmental random fluctuations that either affect an intrinsic growth parameter or birth/death rates, thus resulting in two stochastic differential equations models. From previous results of ours, we obtain conditions for non-extinction and for existence of a population size stationary density. Constant quota (which always leads to extinction in random environments) and constant effort policies are studied; they are hard to implement for extreme population sizes. Introducing cut-offs circumvents these drawbacks. In a deterministic environment, for a wide range of values, cutting-off does not affect the steady-state yield. This is not so in a random environment and we will give expressions showing how steady-state average yield and population size distribution vary as functions of cut-off choices. We illustrate these general results with function plots for the particular case of logistic growth. [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] | ||||||||||