Aim: Due to their spatial isolation, oceanic islands are natural systems to study evolutionary divergence. The Ogasawara Islands belong to the most isolated archipelagos on Earth and are well-known for their high terrestrial endemicity, however, less is known about the marine realm. Here, we analyze the degree of oceanographic isolation of the archipelago based on genetic data of a reef-building coral and a biophysical dispersal model.

Location: North-Western Pacific (Ogasawara, Ryukyu, Daito Islands, Guam)

Taxon: Galaxea fascicularis L.

Method: Three to 15 specimens were sampled at several sites in Ogasawara and its closest potential migration sources in southern Japan and the Mariana Islands (Guam) and RAD-sequenced. 108 specimens from the common Pacific lineages ‘L’ (Ryukyu- and Daito Islands, Guam) and ‘Ogasawara’ (Ogasawara) were analyzed with population genetics and demographic modeling. Oceanographic dispersal was investigated by inverse particle tracking using a Lagrangian particle advection simulation based on ROMS and applying biological dispersal parameters of G. fascicularis.

Results: The G. fascicularis population in Ogasawara is genetically highly differentiated from the next closest reefs in the region and has diverged from the Ryukyu Islands under very little, asymmetric, eastward migration. Inverse particle tracking confirmed the oceanographic isolation of Ogasawara and showed that the islands are rarely but most likely reached by settlers from the Ryukyu Islands by long-distance-dispersal of exceptionally long-lived larvae (>44 days), with no dispersal vice versa.  

Main conclusions: Ogasawara is a dispersal sink location and the high degree of genetic differentiation in Galaxea has resulted from strong oceanographic isolation. This research highlights how oceanographic features impact species-level genetic differentiation even in well-dispersed taxa such as broadcast-spawning corals, and they are likely even more pronounced in less vagile organisms. These findings suggest the Ogasawaran Archipelago should be considered an important priority for marine conservation, alongside its high importance for terrestrial conservation.  

In order to investigate dispersal patterns in a wider geographic context and to understand the influence of contemporary dispersal on genetic composition, a biophysical dispersal model was developed using the Regional Ocean Modeling System (ROMS; Shchepetkin & McWilliams, 2005) and life-history parameters of the larval stage of Galaxea fascicularis. ROMS modeling was performed as described by Mitarai et al. (2016) and dispersal was assessed using an inverse particle tracking approach. More than 15,000 fluid particles (one particle per day per site from June 1 thru September 30 over the years of 2008–2017) were introduced from the grid mid points of our 18 sampling sites in Ogasawara (Haha Island), Ryukyu (Tanega, Miyako, Iheya Islands), Daito (Minami-Daito) and Guam (Table 1). We estimated dispersal for corals in general using a maximal pelagic larval duration (PLD) of 60 days as maximal longevity of most coral larvae seem to lie between 50 and 70 days (Markey, Abdi, Evans, & Bosserelle, 2016). Thus, particles were tracked back in time for 60 days by integrating the fluid particle velocity interpolated from the daily-mean, surface velocity fields. Lagrangian probability density functions (Lagrangian PDFs) were computed from the time series of the obtained Lagrangian particle locations by using the method described in (Mitarai et al. 2009). The distribution of coral reefs was inferred from the World Conservation Monitoring Center (UNEP-WCMC, 2010) and the SangoMap collaborative citizen science project (https://www.sangomap.jp/), and projected onto the ROMS grid points. The potential amounts of immigrants from each source for a site were quantified for an advection time of 3–60 d and a mortality rate of 5% per day, mimicking known development characteristics of coral larvae (Babcock & Heyward, 1986; Markey et al., 2016). The results of four example locations (Haha Island, Tanega Island, Minami-Daito, Guam) were plotted using the Matplotlib toolkit (Hunter, 2007) in Python. Expected connection times from a given source and destination site were calculated following the method of Mitarai et al. (2009).

References

Babcock, R. C., & Heyward, A. J. (1986). Larval Development of Certain Gamete-Spawning Scleractinian Corals. Coral Reefs, 5(3), 111-116.

Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in science & engineering, 9(03), 90-95.

Markey, K. L., Abdo, D. A., Evans, S. N., & Bosserelle, C. (2016). Keeping It Local: Dispersal Limitations of Coral Larvae to the High Latitude Coral Reefs of the Houtman Abrolhos Islands. Plos One, 11(1), e0147628.

Mitarai, S., Siegel, D.A., Watson, J., Dong, C., & McWilliams, J.C. (2009). Quantifying connectivity in the coastal ocean with application to the Southern Californian Bight. Journal of Geophysical Research - Oceans, 114, C10026.

Mitarai, S., Watanabe, H., Nakajima, Y., Shchepetkin, A. F., & McWilliams, J. C. (2016). Quantifying dispersal from hydrothermal vent fields in the western Pacific Ocean. Proceedings of the National Academy of Sciences of the United States of America, 113(11), 2976-2981.

Shchepetkin, A. F., & McWilliams, J. C. (2005). The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean modelling, 9(4), 347-404.

UNEP-WCMC, W. C., WRI, TNC (2010). Global distribution of warm-water coral reefs, compiled from multiple sources including the Millennium Coral Reef Mapping Project. Version 3.0. Includes contributions from IMaRS-USF and IRD (2005), IMaRS-USF (2005) and Spalding et al. (2001).