Shallow tropical bays in the Caribbean, like Orient Bay and Galion Bay in Saint Martin, are often sheltered by coral reefs. In the relatively calm environment behind the reefs, seagrass meadows grow. Together, these ecosystems provide valuable ecosystem services like coastal protection, biodiversity hotspots, nursery grounds for animals and enhancing tourism and fisheries. However, sea-level rise imperils these ecosystems and the services they provide because of changing hydrodynamic conditions, with potential effects on the interdependencies between these ecosystems. By means of a hydrodynamic model that accounts for the interaction with vegetation (Delft3D Flexible Mesh), the impact of sea-level rise (0.87 m in 2100) is investigated for three scenarios of future reef development (i.e. keep-up, give-up and catch-up). If coral reefs cannot keep up with sea-level rise, the wave height and flow velocity increase significantly within associated bays, with the wave height doubling locally in case of eroding reefs in our model simulations. Since the presence of seagrass strongly depends on the hydrodynamic conditions, the response of seagrass to the future hydrodynamic conditions is projected using a habitat suitability model that is based on a logistic regression. The spatial character of the bays determines the response of seagrass. In Orient Bay, which is deeper and partly exposed to higher waves, the seagrass will likely migrate from the deeper parts to shallow areas that become suitable for seagrass because of the surf zone moving landward. In contrast, the conditions for seagrass worsen in Galion Bay for the catch-up and give-up scenario; due to the shallowness of this bay, the seagrass cannot escape to more suitable areas, resulting in significant seagrass loss. It is shown that healthy coastal ecosystems are able to limit the change in hydrodynamic conditions due to sea-level rise. Therefore, preserving these ecosystems is key for ensuring the resilience of shallow tropical bays to sea-level rise and maintaining their ecosystem services.

Global oceanic pH is lowering, which is causing great concern for the natural functioning of marine ecosystems. Current pH predictions are based on open ocean models; however, coastal zones are dynamic systems with seawater pH fluctuating temporally and spatially. To understand how coastal ecosystems will respond in the future, we first need to quantify the extent that local processes influence the pH of coastal zones. With this study, we show that over a single diurnal cycle, the total pH can fluctuate up to 0.2 units in a shallow seagrass-dominated bay, driven by the photosynthesis and respiration of the vegetation. However, these biologically controlled pH fluctuations vary significantly over small distances. Monitoring conducted at neighboring sites with contrasting hydrodynamic regimes highlights how water motion controls the extent that the local pH is altered by the metabolism of vegetation. The interactive effects of hydrodynamics and vegetation were further investigated with an in situ experiment, where the hydrodynamics were constrained and thus the local water residence time was increased, displaying the counteractive effect of hydrodynamics on the pH change caused by vegetation. With this research, we provide detailed in situ evidence of the spatial variation of pH within marine ecosystems, highlighting the need to include hydrodynamic conditions when assessing the pH-effects of vegetation, and identifying potential high-pH refuges in a future low pH ocean.

Jamaica is one of the few remaining countries in the Caribbean region with an abundant population of Lobatus gigas (queen conch) able to sustain a lucrative fishery. Efforts to understand and maintain queen conch populations must involve an investigation into genetic connectivity. This connectivity facilitates population replenishment and continuity via the transport of veliger larvae by ocean currents. Due to the lack of knowledge in this regard to queen conch populations in Jamaica, the fine-scale population structure of Lobatus gigas populations in the country's Exclusive Economic Zone (EEZ) has been analysed by comparing the allele frequencies of nine microsatellite loci on a total of 459 individuals collected across twelve sites encompassing nearshore and offshore locations. Samples were grouped into five broad scale geographic clusters for statistical analysis. Our findings indicate that a weak but significant population structure exists (Global Fst = 0.004, p = 0.01) suggesting that mainland Jamaica acts as a weak divide between populations north and south of the island. Greater levels of connectivity are suggested between north coast populations and those present at the Formigas Bank, an offshore site northeast of the island. The island's primary conch fishing ground located offshore on Pedro Bank, receives limited gene flow from the other sampled populations and may be heavily dependent on local recruitment or receive recruits from sources external to Jamaica's EEZ. An analysis of surface ocean currents strongly supports these three findings and further that conch populations on Pedro Bank very likely receive recruits from sources distinct to those that supply nearshore populations. Further genetic studies into the recruitment patterns and sources for the community on Pedro Bank are therefore critical to ensure sustainable management of this commercially threatened population. Decades of intense fishing pressure has resulted in the establishment of the Allee effect on the island shelf, significantly hampering reproduction and consequently recruitment. If the question of recruitment on Pedro Bank is not addressed, further development of the Allee effect there and eventual population exhaustion are inevitable. These findings, their implications and recommendations for the management of the queen conch fishery in Jamaica are discussed.

The Yucatan Channel connects the Caribbean Sea with the Gulf of Mexico and is the main outflow region of the Caribbean Sea. Moorings in the Yucatan Channel show high-frequent variability in kinetic energy (50–100 days) and transport (20–40 days), but the physical mechanisms controlling this variability are poorly understood. In this study, we show that the short-term variability in the Yucatan Channel transport has an upstream origin and arises from processes in the North Brazil Current. To establish this connection, we use data from altimetry and model output from several high resolution global models. A significant 40–70 day variability is found in the sea surface height in the North Brazil Current retroflection region with a propagation toward the Lesser Antilles. The frequency of variability is generated by intrinsic processes associated with the shedding of eddies, rather than by atmospheric forcing. This sea surface height variability is able to pass the Lesser Antilles, it propagates westward with the background ocean flow in the Caribbean Sea and finally affects the variability in the Yucatan Channel volume transport.

The approaches taken to describe and develop spatial discretisations of the domains required for geophysical simulation models are commonly ad hoc, model- or application-specific, and under-documented. This is particularly acute for simulation models that are flexible in their use of multi-scale, anisotropic, fully unstructured meshes where a relatively large number of heterogeneous parameters are required to constrain their full description. As a consequence, it can be difficult to reproduce simulations, to ensure a provenance in model data handling and initialisation, and a challenge to conduct model intercomparisons rigorously. This paper takes a novel approach to spatial discretisation, considering it much like a numerical simulation model problem of its own. It introduces a generalised, extensible, self-documenting approach to carefully describe, and necessarily fully, the constraints over the heterogeneous parameter space that determine how a domain is spatially discretised. This additionally provides a method to accurately record these constraints, using high-level natural language based abstractions that enable full accounts of provenance, sharing, and distribution. Together with this description, a generalised consistent approach to unstructured mesh generation for geophysical models is developed that is automated, robust and repeatable, quick-to-draft, rigorously verified, and consistent with the source data throughout. This interprets the description above to execute a self-consistent spatial discretisation process, which is automatically validated to expected discrete characteristics and metrics. Library code, verification tests, and examples available in the repository at https://github.com/shingleproject/Shingle</a. Further details of the project presented at http://shingleproject.org.

In this paper we present the Oceanographic Multipurpose Software Environment (OMUSE). OMUSE aims to provide a homogeneous environment for existing or newly developed numerical ocean simulation codes, simplifying their use and deployment. In this way, numerical experiments that combine ocean models representing different physics or spanning different ranges of physical scales can be easily designed. Rapid development of simulation models is made possible through the creation of simple high-level scripts. The low-level core of the abstraction in OMUSE is designed to deploy these simulations efficiently on heterogeneous high-performance computing resources. Cross-verification of simulation models with different codes and numerical methods is facilitated by the unified interface that OMUSE provides. Reproducibility in numerical experiments is fostered by allowing complex numerical experiments to be expressed in portable scripts that conform to a common OMUSE interface. Here, we present the design of OMUSE as well as the modules and model components currently included, which range from a simple conceptual quasi-geostrophic solver to the global circulation model POP (Parallel Ocean Program). The uniform access to the codes' simulation state and the extensive automation of data transfer and conversion operations aids the implementation of model couplings. We discuss the types of couplings that can be implemented using OMUSE. We also present example applications that demonstrate the straightforward model initialization and the concurrent use of data analysis tools on a running model. We give examples of multiscale and multiphysics simulations by embedding a regional ocean model into a global ocean model and by coupling a surface wave propagation model with a coastal circulation model.