Mesoscale flow variability and its impact on connectivity for the island of Hawai`i

Abstract

[1] Understanding population connectivity is a contemporary challenge in marine ecology. Connectivity results from a combination of biological traits and physical mechanisms, at different life stages. We focus on the transport of particles around an oceanic island, simulating transport at early life stages of marine organisms. We aim to investigate through case studies how mesoscale features influence particle transport, recruitment, and connectivity. We determine particle dispersion by using an individual-based model and the flow fields derived from a regional implementation of an ocean circulation model. To understand the underlying physical processes of transport, we locate coherent structures in the flow field, identify recurrent physical features, and observe how particle transport is related to them. Our results show that the varying eddying flow increases connectivity among populations located on different sides of the island. Both the flow field and dispersal patterns are highly variable. In this scenario, eddy events influence transport in distinct ways, and the timing of release plays an important role in dispersal. Our results highlight the need for modeling studies to use hydrodynamical model flows that represent the scales of variability affecting transport and dispersion.

Modeling Fine-Scale Coral Larval Dispersal and Interisland Connectivity to Help Designate Mutually-Supporting Coral Reef Marine Protected Areas: Insights from Maui Nui, Hawaii

“…Together, these comparisons provide confidence the model accurately captures the fine-scale processes that occur at the spatial scales being modeled and thus made it possible to conduct inter-model run comparisons to evaluate the influence of the differences in forcing on the resulting dispersal patterns. These promising finescale (order∼km) model-data comparisons, which have never been conducted in such a complex environment, contrast with numerous modeling efforts focused on larval dispersal patterns where no such model-data comparisons were made (Oliver et al, 1992;Treml et al, 2008;Vaz et al, 2013;Thomas et al, 2014;Kool and Nichol, 2015) and thus model accuracy and the resulting projections and findings are unknown.…”

“…If this is the case, the short time scales of dispersal shown here are important because both larval density ( Figure 6) and recruitment success decrease quickly with time and distance (Sammarco and Andrews, 1988) due to mixing and horizontal dispersion (Wolanski et al, 1989;Oliver et al, 1992). It has been demonstrated (Andutta et al, 2012;Tay et al, 2012) that reefs and islands in close proximity often result in decreased flushing and thus more exposure time in areas where settlement can occur successfully; these features are generally not captured with large-scale models (Vaz et al, 2013;Wood et al, 2014). In this case, the flushing between islands appears to be somewhat offset by areas in the lee of the high islands that result in increased retention that was not captured in previous modeling studies (Andutta et al, 2012;Tay et al, 2012) where topographic steering of winds was not a factor.…”

“…Overall, there seems to be a general northwesterly predominant directionality in dispersal in Maui Nui; such a predominant directionality in dispersal due to relatively consistent forcing has been observed in other locations (Gaines et al, 2003;Peliz et al, 2007). The decision to model such a relatively short maximum corallarval dispersal competency period (72 h) compared to many other studies (Oliver et al, 1992;Vaz et al, 2013;Kool and Nichol, 2015) was demonstrated to be sound because the modeled larvae densities generally decreased more than two orders of magnitude within 24 h (Figure 6). The exponential decrease in density during the initial 24 h is due purely to dispersion, as there is no settling or mortality during this time frame.…”

“…The timescale of modeled flow and pelagic larval duration used in this effort was relatively short in comparison to many other studies (Oliver et al, 1992;Vaz et al, 2013;Kool and Nichol, 2015), and it was selected not only because it minimizes the exponential decrease in competency and larval density (due to dilution) but also because the probability of effective connectivity of a substantial number of coral larvae that would need to successfully be transported from one reef to another to support normal mortality decreases exponentially with time and distance. The importance of such dilution in the marine environment is generally greatly underestimated: For example, if 1 million coral larvae that are 1-mm in diameter were evenly distributed in a 1-m thick surface layer that covered a relatively small reef area of 250 m by 250 m, the coral larval density would already be on the order of 1 × 10 −8 ; once that water mass starts to be advected by currents, dispersion starts and dilution goes up exponentially.…”

“…Insolation-driven heating and nocturnal cooling of the islands cause the general Trade wind speeds in the vicinity of the islands to vary from almost negligible at night to more than 15 m s −1 in the afternoon, compared to about 5 m s −1 in the open ocean. These relatively rapid diurnal variations in wind speed result in a very thin (<1 m) winddriven surface layer (Storlazzi et al, 2006b) that constrains positively-buoyant particles, such as coral larvae during their planktonic dispersal stage (Storlazzi et al, 2006a), varies greatly over small spatial and temporal scales, and is not captured in larger-scale oceanographic models (Tay et al, 2012;Vaz et al, 2013;Wood et al, 2014). The wave climate in Maui Nui during the period of coral spawning in the boreal summer is dominated by two wave regimes: northeast Trade wind waves and Southern Ocean swell (Moberly and Chamberlain, 1964).…”