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

The frequent lack of correspondence between measured population stage structures 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 data needed to test hypotheses about the ecological effects of local or global 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 data exist. Our approach hinges on the fact that changing environmental conditions will cause population size structure to lag behind that predicted by current demographic rates. We first develop the methods needed to calculate the likelihood of an observed population structure, given different stochastic models of demography responding to environmental changes. We next use simulated data to explore the method's power in the face of estimation errors in current vital rates, environmental noise, and other complications. We conclude that this method holds promise when applied to slowly growing, long-lived species and when model structures are used that allow for realistic time lags in population structure. Researchers using this approach should also be careful to assess the importance of other phenomena (rare catastrophes, recent founding of populations, genetic changes, and density dependence) that may compromise the method's accuracy. Although large data sets are required for the method to be accurate and powerful, the data required will be readily obtainable for abundant and easily sampled species. While most 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.

Duke Scholars

Author William F. Morris Biology