Abstract

A central goal in ecology is to predict population dynamics from demographic information. Based on the asymptotic population growth rate λ, calculated from a projection matrix model as a descriptor of the population dynamics, we analyze published data of 49 species of birds to determine how λ is influenced by variation in different demographic traits. Across species, the mean elasticity of the adult survival rate was significantly larger than the mean elasticity of the fecundity rate. The contribution of the fecundity rate to the population growth rate increased with increasing clutch size and decreasing adult survival rate, while the greatest contribution of adult survival rate occurred among long-lived species that matured late and laid few eggs. This represents a continuum from “highly reproductive species” at one end to “survivor species” at the other end. In addition, a high contribution of adult survival rate was found in some relatively long-lived species with early age at maturity (and a large clutch size) which was assumed to represent a bet-hedging strategy, i.e., producing a large number of offspring in some occasional good years. In a retrospective analysis, interspecific differences in the effects of actual temporal variation in adult survival rate and fecundity rate on the variability of λ were analyzed. These effects are expected to be large when the variance or the sensitivity of the trait is large. Because there was a negative relationship among species, both for the adult survival rate and the fecundity rate between the variability and the sensitivity of the trait, contribution of a trait to the variance in λ decreased with sensitivity. Similarly, within species, less temporal variation was found in traits with high elasticities than in traits with less contribution to λ. In some species, covariance among matrix elements also influenced the contribution of a demographic trait to λ. Monitoring schemes of bird demography should be designed in such a way that temporal variances and covariances among demographic traits can be estimated. Furthermore, it is important in such schemes to include data from a combination of traits that either have large sensitivities or high temporal variation.

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