A central goal in ecology is to predict population dynamics from demographic information. Based on the asymptotic population growth rate , 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 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 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 decreased with sensitivity. Similarly, within species, less temporal variation was found in traits with high elasticities than in traits with less contribution to . In some species, covariance among matrix elements also influenced the contribution of a demographic trait to . 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.

The food limitation hypothesis of population regulation states that a stable equilibrium will exist between a population and its food resources due to a density—dependent decrease in fecundity and/or increase in mortality. This hypothesis was tested for the moose (Alces alces) by comparing regional variation in life history characteristics in four Norwegian study areas, chosen to represent a gradient both in summer and winter range conditions. The rate of body growth was most rapid in the northern study area with the best summer ranges. Lowest body growth occurred in the population living under the poorest winter conditions. After snow—free winters the rate of body growth increased substantially, leading to large annual variations in selective regimes. The peak timing of ovulation of old females in the autumn showed a latitudinal delay. Females in the alpine population, with the poorest winter conditions, had significantly later mean calving dates and produced fewest calves per year. Gestation length appears to be dependent on nutritional condition of females during pregnancy. Mortality was highest in the northern study are where most of the deaths occurred during the summer. Very few calves died during the winter. These results suggest that a stable high—density equilibrium between moose and their food resources as expected from the food limitation hypothesis is unlikely. The decrease in fecundity and the increase in mortality under poor nutritional conditions during the winter has only a small effect on the population growth rate and is therefore unlikely to have a strong regulatory effect. In the absence of large predators, this will lead to large fluctuations in population size that will overshoot the carrying capacity.

The evolution of population dynamics in a stochastic environment is analysed under a general form of density-dependence with genetic variation in r and K, the intrinsic rate of increase and carrying capacity in the average environment, and in s e 2 , the environmental variance of population growth rate. The continuous-time model assumes a large population size and a stationary distribution of environments with no autocorrelation. For a given population density, N, and genotype frequency, p, the expected selection gradient is always towards an increased population growth rate, and the expected fitness of a genotype is its Malthusian fitness in the average environment minus the covariance of its growth rate with that of the population. Long-term evolution maximizes the expected value of the density-dependence function, averaged over the stationary distribution of N. In the q-logistic model, where density dependence of population growth is a function of N q , long-term evolution maximizes E[N q ]Z[1Ks e 2 /(2r)]K q . While s e 2 is always selected to decrease, r and K are always selected to increase, implying a genetic trade-off among them. By contrast, given the other parameters, q has an intermediate optimum between 1.781 and 2 corresponding to the limits of high or low stochasticity.

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Estimation of intra- and interspecific interactions from time-series on species-rich communities is challenging due to the high number of potentially interacting species pairs. The previously proposed sparse interactions model overcomes this challenge by assuming that most species pairs do not interact. We propose an alternative model that does not assume that any of the interactions are necessarily zero, but summarizes the influences of individual species by a small number of community-level drivers. The community-level drivers are defined as linear combinations of species abundances, and they may thus represent e.g. the total abundance of all species or the relative proportions of different functional groups. We show with simulated and real data how our approach can be used to compare different hypotheses on community structure. In an empirical example using aquatic microorganisms, the community-level drivers model clearly outperformed the sparse interactions model in predicting independent validation data.