Recent advances in PVA methodology include the development of techniques for assessing model reliability, synthesis of PVA with decision theory, new models for sex-structured populations and populations with density dependence, and experimental tests of qualitative and quantitative model predictions. New methods for estimating model parameters from time series with observation errors will be particularly useful. There are many areas for further development, however. First, future research should focus on developing biologically based models of population growth. In particular, it is crucial to develop a better understanding of how the variance in population growth rate due to demographic stochasticity changes with population size. Additionally, statistical methods for model selection averaging over different models would be useful in cases where the sources of variation and the drivers of population fluctuations are relatively unknown. Second, modeling tools need to be extended to better represent species with complicated life histories including quiescent stages and seasonal life histories. This has been done to some extent for plants that propagate by seed. However, the majority of existing models assume that the effect of density dependence is negligible. Thus, integrating nonlinear population dynamics and complicated life histories in reliable ways is an important topic for research. Third, although population extinction is generally the result of interactions between habitat degradation and population growth, PVA focuses on demographic aspects. New tools for modeling the effect of habitat quality on demographic parameters, spatial boundaries, and habitat refuges are important topics for research and development. Finally, most PVAs are parametrized using time series of population counts. But, such data are scarce for the threatened and endangered species that are the focus of PVA. Further, time series contain information only about fluctuations of populations at observed population sizes - extrapolations based on such models are particularly unreliable. Thus, new tools need to be developed to incorporate additional information about species' demography from knowledge of their ecologies and natural histories. The suite of tools that are available for performing PVA is developing rapidly and users should recognize that a large variety of models and techniques are available for population forecasting.
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