Empirical and theoretical approaches are needed to solve the current problem of increased extinction risk for many species. Thus, this thesis focuses on: (1) ways to estimate population trends for a large number of species, and (2) a predictive framework for identifying vulnerable populations from species traits or life history traits to allow for more proactive conservation actions. I estimated long-term population trends and range-abundance dynamics of longhorn beetles using Natural History Collections. In general, negative population trends were not accompanied by declines in range, but range increased among species with increasing populations. The analysis also exemplified how the results can be used in the red listing process. Linking life history traits and two metrics of extinction risk (population trend and red list classification) in long horn beetles showed that generation time, overwintering stage, larval host plant specialisation, adult activity period and body size were related to extinction risk, often with interaction effects between predictor variables. Variability in population size is an important factor affecting population extinction risk. I modelled the effects of demographic and environmental stochasticity on extinction risk in small populations, for a large range of life history types. Extinction risk due to demographic stochasticity increased with increasing fecundity and decreasing age of maturation, whereas effects of adult survival interacted with maturation age. Including environmental stochasticity showed that the qualitative relationship between extinction risk and life history types changed, but also that combined effects of both stochasticities on extinction risk were most significant in short-lived life histories. The results suggest that data from Natural History Collections can be used to estimate long-term population trends, and that population declines may be underestimated if estimated from changes in range. My studies also suggest that life history traits and species traits can be used to predict population vulnerability to extinction and, hence, that certain groups of species are more vulnerable to extinction than others