Demographic experimental biogeography: Using demographic models to make the most of short-term experiments

Background/Question/Methods

Most studies of population dynamics use long-term observational data. This is for good reason, because a species’ vital rates are affected by many factors that may vary over time, including climate, competition, and density dependence. However, most ecological field studies are still short term and most only measure individual vital rates as responses to treatments (e.g. growth rates or flowering). This is problematic because different vital rates may in fact respond differently to changing conditions (e.g. a warming experiment). Moreover, when multiple vital rates are measured, they are typically analyzed separately, thereby missing the integrated effects of changing individual vital rates on population dynamics. In this project, I explore how we might use integral projection models (IPMs) to draw stronger inference about treatment effects in experimental field ecology. While it is unreasonable to expect robust predictions of population growth rates with limited data, we may nonetheless be able to use IPMs to make more general inferences from short-term experiments. 

Results/Conclusions

I will discuss results from two field experiments as a backdrop for more general discussion of experimental biogeography. The first is a transplant experiment aimed at understanding the role of competitive interactions in shaping species’ abilities to shift in distribution with changing climate. The second experiment manipulates snow-melt dates to test species’ phenological responses to changing growing season length. In both experiments we use short-term experimental manipulation to induce responses in perennial plants. I show here how integration of demographic responses into IPMs enables conclusions inaccessible without the use of demographic models. I place this approach in the context of how most experimental field ecology is still conducted and analyzed. I argue that will a little bit of foresight, some targeted data collection, and these emerging demographic tools, we can make better use of the many (and laborious) ecological experiments.