Pattern of variation in avian population growth rates
A central question in population ecology is to understand why population growth rates differ over time. Here, we describe how the long-term growth of populations is not only influenced by parameters affecting the expected dynamics, for example form of density dependence and specific population growt...
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Veröffentlicht in: | Philosophical transactions of the Royal Society of London. Series B. Biological sciences 2002-09, Vol.357 (1425), p.1185-1195 |
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Sprache: | eng |
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Zusammenfassung: | A central question in population ecology is to understand why population growth rates differ over time. Here, we describe how the long-term growth of populations is not only influenced by parameters affecting the expected dynamics, for example form of density dependence and specific population growth rate, but is also affected by environmental and demographic stochasticity. Using long-term studies of fluctuations of bird populations, we show an interaction between the stochastic and the deterministic components of the population dynamics: high specific growth rates at small densities r1 are typically positively correlated with the environmental variance σe2. Furthermore, θ, a single parameter describing the form of the density regulation in the theta-logistic density-regulation model, is negatively correlated with r1. These patterns are in turn correlated with interspecific differences in life-history characteristics. Higher specific growth rates, larger stochastic effects on the population dynamics and stronger density regulation at small densities are found in species with large clutch sizes or high adult mortality rates than in long-lived species. Unfortunately, large uncertainties in parameter estimates, as well as strong stochastic effects on the population dynamics, will often make even short-term population projections unreliable. We illustrate that the concept of population prediction interval can be useful in evaluating the consequences of these uncertainties in the population projections for the choice of management actions. |
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ISSN: | 0962-8436 1471-2970 |
DOI: | 10.1098/rstb.2002.1119 |