Award: OCE-1260169

This project set out to answer the question of: where does the next generation of coral reef organisms come from if the current population is removed? We compiled the most extensive co-sampled multispecies connectivity dataset (66 species, ranging from corals and lobsters to fishes and dolphins) to ask: 1) what shared patterns of genetic connectivity emerge when examining the diverse set of species co-sampled across the Hawaiian Archipelago? 2) What are the factors driving these shared patterns of exchange that can inform resource management? 3) Do genetic, oceanographic and ecological measures of connectivity agree within or across species? And 4) Do historical bottlenecks from sea level change impact genetics consistently to result in shared patterns of genetic structure in the majority of species? We show that there are 4 primary categories of population structure (panmixia, isolation-by-distance, chaotic and regional population structure), and that these common categories encompass all 66 species of coral reef organisms regardless of life history or taxonomy (Selkoe et al. 2014). We further show, using a seascape genetics approach, that genetic diversity correlates with species diversity, and that large areas of intact reef with high coral cover harbor more genetic diversity than fragmented or degraded reefs (Selkoe et al. 2016). We created the first oceanographic dispersal models for the entire Hawaiian Archipelago and showed that biophysical models predict the location of genetic breaks in the unpopulated low islands and atolls of protected Northwestern Hawaiian Islands, but not in the heavily populated high islands of the Main Eight (Wren et al. 2016). Finally, we find that contrary to the hypothesis that most coral reef species should show population expansion as sea level recovered after the last glacial maximum approximately 18,000 years ago, only about half of the species in Hawai?i match this expectation. These results imply that management of large, intact, and undisturbed areas of coral reef habitat is able to conserve both species and genetic biodiversity. Further, contrary to the single archipelagic management strategy advocated by State and Federal Agencies at the outset of this project, these studies have shown limited dispersal among adjacent islands in the Hawaiian Archipelago (particularly in the populated Main Hawaiian Islands), supporting a transition to island-specific management and a growth of community managed areas. We further show that despite the fact that resource managers rarely consider genetic information explicitly, it can fundamentally change conservation priorities (Beger et al. 2014). Broader Impacts: In sum, this project has supported four Pacific Islanders, two female post-docs, four female graduate students, and two soft-money female faculty members; we have successfully placed one female student into a permanent agency position (NOAA), two of the post-docs into tenure-track faculty positions and all three of our undergraduate interns (1 Native Hawaiian) have successfully gone on to graduate school placement. We trained 3 post-docs (2 into faculty positions), 5 graduate students (2 completed dissertations), and 3 undergraduates (2 senior theses) under this project. The research findings were presented at multiple national and international public talks, professional meetings, and departmental seminars to date. The findings of this work were communicated to the public both in talk radio (http://www.cosee-ie.net/programs/allthingsmarineradioshow/ ) and public television broadcasts (https://www.youtube.com/watch?v=idwR098oOaE ). Finally, 37 peer-reviewed publications resulted from this work to date, and more are in the works. These data have already played a major role in management decisions made by the State of Hawai?i and resulted in multiple legislative measures introduced to change management policy throughout the State. Data from this research is deposited at https://www.bco-dmo.org/project/552879 and genetic data is also archived to DIPnet and are publically available from the GeOMe portal (http://www.geome-db.org/query ). Last Modified: 05/17/2017 Submitted by: Robert J Toonen