Long-term population dynamics of exotic species: a cross-site multi-species investigation
The population dynamics of introduced species may dictate trends in those species' impacts on invaded ecosystems and societies. For instance, if there are lags in the population growth of an exotic plant, future impacts may vastly outweigh current impacts. In contrast, crashes in the populations of exotic species may reduce future impacts below current levels. Cyclical, equilibrial, and irregular dynamics may also exhibit significantly different trends in impacts. These classes of dynamics have all been demonstrated for one or more exotic species, but the relative frequencies of occurrence of each have been largely unstudied. Similarly, the drivers of these different dynamics and the consequences for native populations have only been studied within specific systems and for specific species.
The synthesis of data from multiple introduced species in different ecosystem types could lend valuable insight into understanding these fundamental dynamics. For this working group, we have constructed an initial, growing dataset consisting of annual (or twice-annual) abundance estimates for 20 (and counting) exotic populations for which long-term data exist. After adding more data from within and beyond the LTER network, we will analyze this dataset using clustering and other techniques to inspect for general patterns (i.e. are invasive populations growing, contracting or remaining stable on average?, how common are lags, crashes, cycles, etc, across a range of exotic species?). We also hope to identify the common drivers of these different dynamics, and determine if there are fundamental differences in the dynamics between native and introduced species. By using long-term cross-site data from any exotic species, this work will significantly expand our basic understanding of invasion biology and lend insight into predictive models of future abundances and impacts, thus helping management agencies and other stakeholders develop a clearer picture of the future.