UC Davis

There is a critical need in conservation, and in ecology in general, to accurately predict the adaptive potential and subsequent demographic outcomes for species facing environmental changes. The ability of a species to adapt to change depends, essentially, on two factors: the rate of environmental changes and the genetic mechanisms that control any traits under selection. However, our ability to inform demographic forecasts using genetic data has not yet been well validated. Here, we use simulations to show that existing genetic methodologies for demographic forecasting do not perform well, and instead propose a novel method of genetic architecture estimation (True Genomic Architecture Computation, or TGAC), which estimates the underlying genetic architecture of a trait without directly attempting to identify causal loci. We show that this method can be used to generate far more accurate demographic forecasts than existing alternatives.Monarch Butterflies (Danaus plexippus) are one such species of particular conservation concern. Due in part to long-term environmental and land use changes, migratory populations of the monarchs have dramatic declines over the last several decades, prompting concern that the migratory life history of the species may go “quasi-extinct” over the coming years as selective factors begin to favor residency over migration. However, one relatively new non-native monarch population in Australia rapidly re-acquired a migratory life history following hundreds of generations of residency and successive bottlenecks. We here show that reproductive diapause, a key migratory trait, is strongly associated with a novel genetic mechanism in the population. This may imply that the species might be able to rapidly re-evolve migration in other places as well should conditions change in the future to favor it.