Imagine your favorite mushroom hunting spot. Is porcini and chanterelle risotto in your future, or will you only find enough for a slice of toast? Predicting the coming autumn’s bounty, surprisingly, is analogous to sending astronauts to the moon. In both cases, the value of interest, be it abundance or trajectory, are inferred from imprecise measurements. Independently, we reasonably expect their current, unknown values to be linked to their previous states: in engineering parlance, they are dynamical systems, or in other words, are autocorrelated. With repeated observations, predictions based on previous observations and an autocorrelation function can be compared to incoming data and any discrepancies used to refine future predictions. These elements – a recursive algorithm, set of observations, and a dynamic process – comprise a state space model, which made their grand debut with the Apollo 11 lunar landing. Some decades later, these models are increasingly applied to the messy data often collected by ecologists.

In this pitch, Alexis shares insights gained from studying the dynamics of ca. 2,000 organisms in a 34-year environmental DNA archive. While an improvement over previous methods, she believes we have yet to realize the full potential state space models in ecology. This is due, in part, because the decades of progress made by engineers and physicists are effectively inaccessible to outsiders. Whether you’re fluent in measure theory or have ever cursed your noisy, autocorrelated data, Alexis is looking for your help to design, derive, and develop the tools to launch ecology’s moonshot moment.