Quantifying the impact of density, extrinsic climatic fluctuations, and demography on population fluctuations is a persistent challenge in ecology. We analyzed the effect of these processes on the irregular pattern of population crashes of Soay sheep on the St. Kilda archipelago, United Kingdom. Because the age and sex structure of the population fluctuates independently of population size, and because animals of different age and sex respond in different ways to density and weather, identical weather conditions can result in different dynamics in populations of equal size. In addition, the strength of density-dependent processes is a function of the distribution of weather events. Incorporating demographic heterogeneities into population models can influence dynamics and their response to climate change.
In studies of wild animals, one frequently encounters both census and mark-recapture-recovery data. We show how a state-space model for census data in combination with the usual multinomial-based models for ring-recovery data provide estimates of productivity not available from either type of data alone. The approach is illustrated on two British bird species. For the lapwing, we calibrate how its recent decline could be due to a decrease in productivity. For the heron, there is no evidence for a decline in productivity, and the combined analysis increases significantly the strength of logistic regressions of survival on winter severity.
In this paper we compare and contrast the objectives of principal component analysis and exploratory factor analysis. This is done through consideration of nine examples. Basic theory is presented in appendices. As well as covering the standard material, we also describe a number of recent developments. As an alternative to factor analysis, it is pointed out that in some cases it may be useful to rotate certain principal components if and when that is appropriate.
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