Collaborative Research: SG: Effects of altered pollination environments on plant population dynamics in a stochastic world

A major challenge in biology is understanding how random fluctuations in the environment affect populations and their ability to persist. For plants, this is compounded by declines in pollinators, which have been documented across the world. There is concern that many flowering plant species will decline alongside their pollinators because almost 90% of flowering plants rely on animal pollination to make seeds, grow new plants, and prevent extinction. Short-term, year-to-year fluctuations in pollinators and other environmental factors (like precipitation) during a longer-term decline make the challenge of identifying a true plant decline even harder. Despite the strong dependence of plants on pollinators, pollinator declines may not immediately cause plant declines, and this can prevent us from detecting the warning signs of plant extinction. For example, plants may appear to do better immediately after a pollinator decline by reinvesting energy that would have been used to make seeds into improving their survival. This project will amplify natural variation in pollination for thousands of plants, follow their fates, and use mathematical models to understand how plants persist despite year-to-year changes in pollination and other environmental factors. This project will help scientists to understand why some plant species are more at risk from pollinator declines than other species. The project will leverage 9–11 years of field-based data on plant demography and pollinator abundance to measure and experimentally impose stochastic fluctuations in pollinator abundance. Individual-based population models (integral projection models) will be parameterized with data from a long-term, ongoing field experiment in which two species of perennial plants receive different pollination treatments: (i) increased pollination, (ii) reduced pollination, and (iii) an unmanipulated control. All plants are tagged with a unique identification number and their demographic status assessed annually. Projections of the population models will demonstrate the consequences of stochasticity in pollination services for plant population dynamics. Further analyses based on model-based population projections will reveal which demographic vital rates (survival, growth, reproduction) are responsible for changes in population dynamics when pollinator abundances fluctuate, in addition to how various components of stochasticity – the mean, variance, and autocorrelation of pollinator abundances – affect plant population dynamics. This project will shed new light on how species interactions shape population dynamics in a stochastic world. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.