Impacts of polyandry and mate limitation on female fecundity and the population dynamics of the aspen leaf miner

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Populations of some species can persist at low numbers for long periods of time and then quickly rise to very high numbers. When these species inflict damage on human resources, their population "explosions" become a practical concern. It is often difficult to study populations when they are very small because individuals are so rare and hard to find. Yet, understanding what keeps a species rare and what happens to shift populations into periods of rapid growth is important for predicting and controlling outbreaks of many kinds. This research examines possible reasons for dramatic cycles of abundance in the aspen leaf miner, a tiny species of moth that is usually rare but can outbreak and cause extensive damage to aspen trees. In particular, investigators will test whether females in small populations are unable to mate as often and lay as many eggs as females in large populations. If confirmed, low mating success in small populations may be important in controlling other forest pests and slowing the spread of invasive species. Due to its high level of polyandry and sustained low and high population phases, the aspen leaf miner (Phyllocnistis populiella) feeding on quaking aspen (Populus tremuloides) provides an excellent system for exploring potential demographic impacts of mate-finding Allee effects on large amplitude population fluctuations. This project will use a combination of experimental and observational approaches to address whether mate-finding Allee effects and polyandry contribute to sustaining low phases of the aspen leaf miner. Specifically it will employ: 1) mating experiments to examine whether multiple mating increases female fecundity, and 2) observational studies across populations with varying densities to address whether potential and realized polyandry increases with population density. A better understanding of the mechanisms keeping populations at low density or releasing them into growth phases will aid predictions of where and when outbreaks are likely to occur.