How important is "colored" stochasticity for plant population dynamics?

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Ecologists need to predict the impacts of human-caused change on plant and animal populations. These predictions are often made using demographic models, which combine vital rates (birth, growth, and death rates) into estimates of how population size changes in response to changes in the environment. Variation in vital rates is usually modeled as "white noise", meaning that rates in one year are not correlated with rates in the recent past or recent future. However, there are good reasons to believe that variation may be "red", meaning good years tend to follow good years (due to trends in environmental conditions or carry-over through physiological condition), and there is evidence that some species experience "blue" noise, meaning bad years tend to follow good years (for example, if reproduction depletes stored resources). In the few cases where researchers have looked for "colored" noise in plant and animal populations, these effects have substantially changed predictions about population dynamics. This project will develop methods for estimating "colored" stochasticity and also evaluate how important this variation is for population dynamics. The research will compare population dynamics of three species of perennial wildflowers, representing different extremes: 1) a perennial wildflower of arid environments, which experiences high costs of reproduction, typical of "blue noise"; 2) a wildflower that lives along meandering rivers, which experiences directional changes in the environment, typical of "red noise"; and 3) an orchid near the northern edge of its global distribution. In this this latter species the color of noise is unknown, but likely important for life-history evolution and responses to climate change. As part of this project, the researcher will also evaluate if and how hundreds of short-term (3-10 year) studies of different plant species (more than 300 have been published to date) can be used to infer long-term effects of environmental change for plants in general. Additional broader impacts include developing statistical methods for analysis of short time series, educating students in mathematical biology and ecological forecasting, and promoting international awareness through collaboration with botanists in Oulanka, Finland (near the intersection of the Arctic Circle with the Finland/Russia border) and with statistical ecologists in Brisbane, Australia.