1:45 PM - 2:10 PM
[ACG40-01] Toward modeling propagule dispersal of corals, seagrasses, macroalgae, and mangroves
★Invited Papers
Ocean circulation processes are one of the major factors that determine propagule (larvae, spores, etc.) dispersal, recruitment, and population connectivity of marine species, including corals, seagrasses, macroalgae, and mangroves. Regional ocean circulation models have been increasingly employed to assess potential dispersal of nearshore marine species, given specific biological traits of target species (hatch locations, spawning timing, propagule duration, ontogenetic behaviors, etc.). Estimated dispersal patterns have been compared with empirical ecological data, such as recruitment time series of coastal species and genetic structures of marine populations. Optimized networks of marine protected areas have been designed based upon potential larval supplies estimated from dispersal modeling, among other things (habitat maps, etc.).
This biophysical modeling approach makes several fundamental assumptions. First, it is assumed that regional ocean circulation models can accurately predict paths of passively drifting materials (Lagrangian descriptions) in the ocean. Despite its importance, Lagrangian descriptions of ocean circulation models have rarely been examined. Second, drifting materials are assumed to be transported only by local oceanic flow. Unlike fish larvae, propagules of corals, seagrasses, macroalgae, and mangroves float to the surface, and disperse there for finite periods, during which the influence of winds, waves, and other wind-induced surface phenomena are significant. Most ocean circulation models, however, have not fully integrated these important surface processes. These uncertainties limit the credibility and value of such biophysical modeling.
We present empirical examinations of dispersal models using two datasets: 1) trajectories of >200 GPS drifting buoys (Pacific Gyre, Microstar drifters) that we deployed near reef crests of fringing reefs in Okinawa Prefecture, and 2) satellite images of pumice rafting from the Fukutoku-Okanoba submarine volcano, introduced on August 13, 2021. Lagrangian descriptions of widely-used atmosphere/ocean circulation models were examined against the empirical data, with a focus on contributions from winds, ocean currents, and wind-driven currents. This study suggests the best available approach for modeling propagule dispersal of corals, seagrasses, macroalgae, and mangroves, while identifying missing components in the current modeling framework.
This biophysical modeling approach makes several fundamental assumptions. First, it is assumed that regional ocean circulation models can accurately predict paths of passively drifting materials (Lagrangian descriptions) in the ocean. Despite its importance, Lagrangian descriptions of ocean circulation models have rarely been examined. Second, drifting materials are assumed to be transported only by local oceanic flow. Unlike fish larvae, propagules of corals, seagrasses, macroalgae, and mangroves float to the surface, and disperse there for finite periods, during which the influence of winds, waves, and other wind-induced surface phenomena are significant. Most ocean circulation models, however, have not fully integrated these important surface processes. These uncertainties limit the credibility and value of such biophysical modeling.
We present empirical examinations of dispersal models using two datasets: 1) trajectories of >200 GPS drifting buoys (Pacific Gyre, Microstar drifters) that we deployed near reef crests of fringing reefs in Okinawa Prefecture, and 2) satellite images of pumice rafting from the Fukutoku-Okanoba submarine volcano, introduced on August 13, 2021. Lagrangian descriptions of widely-used atmosphere/ocean circulation models were examined against the empirical data, with a focus on contributions from winds, ocean currents, and wind-driven currents. This study suggests the best available approach for modeling propagule dispersal of corals, seagrasses, macroalgae, and mangroves, while identifying missing components in the current modeling framework.