Detecting population-level consequences of ongoing environmental change without long-term monitoring

The frequent lack of correspondence between measured population stage struc tures and those predicted from demographic models has usually been seen as an embarrassment, resulting from poor data, or a testimony to the failings of overly simplistic models. However, such mismatches can also arise due to natural or anthropogenic changes in the: environment, thus providing the d ata needed to test hypotheses about the ecological effects of local or glob al environmental change. Here, we present a method that allows this type of comparison to rigorously test for the population-level effects of past and ongoing environmental change in situations where no long-term monitoring d ata exist. Our approach hinges on the fact that changing environmental cond itions will cause population size structure to lag behind that predicted by current demographic rates. We first develop the methods needed to calculat e the likelihood of an observed population structure, given different stoch astic models of demography responding to environmental changes. We next use simulated data to explore the method's power in the face of estimation err ors in current vital rates, environmental noise, and other complications. W e conclude that this method holds promise when applied to slowly growing, l ong-lived species and when model structures are used that allow for realist ic time lags in population structure. Researchers using this approach shoul d also be careful to assess the importance of other phenomena (rare catastr ophes, recent founding of populations, genetic changes, and density depende nce) that may compromise the method's accuracy. Although large data sets ar e required for the method to be accurate and powerful, the data required wi ll be readily obtainable for abundant and easily sampled species. While mos t ecological efforts to detect global environmental changes have focused on long-term monitoring, or indicators such as tree rings that are unique to organisms not present in all biomes, this method allows tests for past and ongoing changes in situations where neither past monitoring data nor unique indicator species are available.