Seminars

May 16 2019

5:00 pm TLSB 1100

EcoEvoPub Series

Graduate Student Presentations

Summary

Jenny Hazlehurst
University of California, Riverside

Title: Documenting Plant-pollinator Interactions in California: Opportunities for Citizen Science

California is home to over 2,000 species of rare or threatened plants that are potentially animal-pollinated. However, for the vast majority of these, the identity or phenology of pollinators is not well documented. Documenting the pollinators of these focal species and the diet and behavior of key pollinator species using tools like citizen science projects and DNA metabarcoding can help to generate valuable plant-pollinator interaction data for conservation practitioners.


Chris Kyriazis
Department of Ecology and Evolutionary Biology, UCLA

Title: Purge, Perish, or Rescue: Modeling Inbreeding Depression and Extinction in Small and Isolated Populations

Inbreeding depression has increasingly been recognized as a major threat to extinction for small and isolated populations. The two main mechanisms for reducing inbreeding depression are purging (removal of deleterious variants by purifying selection) and genetic rescue (increase in fitness due to the introduction of migrants). The widespread fragmentation and isolation of wildlife populations has increased attention on purging and genetic rescue as important tools for averting extinction. However, the efficacy of these approaches remains unclear due to both inconsistent empirical results and unrealistic assumptions of theoretical models. Here, we use population genetic simulations incorporating realistic genomic structure and stochastic non-Wright Fisher population dynamics. We track the time to extinction following a population contraction under a variety of ancestral and modern population sizes. As expected, we found that population persistence is highly dependent on the carrying capacity of the modern population, whereby small populations go extinct much more rapidly than large populations. Strikingly, we also find that time to extinction is strongly governed by the ancestral population size through its role in determining levels of recessive deleterious variation and the strength of inbreeding depression. Simulations that included genetic rescue of the modern population witnessed an overall increase in time to extinction\; however, the effectiveness of genetic rescue was similarly found to be highly dependent on the source population size and levels of recessive deleterious variation. These results provide a novel conceptual framework for predicting the fate of small and isolated populations, highlighting the importance of a populations demographic history and levels of recessive deleterious variation in determining extinction risk. They also have significant implications for designing wildlife management strategies, particularly for the ongoing reintroduction of wolves on Isle Royale, a population that has recently been driven to extinction by inbreeding depression.



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



 



this is idtest: