Genetic connectivity in the dominant Caribbean reef-building coral, Orbicella (prev. Montastrea) annularis.
We have just completed a project focusing on evaluating patterns of connectivity in the dominant Caribbean reef-building coral, Orbicella annularis (previously Montastrea annularis). This work has been critical in demonstrating how, while marine connectivity (as measured by gene-flow) is correlated with distance, physical barriers, including oceanic currents and river outflows, can play key roles in shaping both macro and micro (local) patterns of marine connectivity. This work and a related study has recently been published:
Understanding the larval connectivity of aquatic organisms is one of the most challenging yet important goals of ecology, evolution and conservation science. Over evolutionary timescales, larval dispersal in ocean currents has influenced the global distribution of many taxa and impediments to dispersal have resulted in the emergence of new species. On ecological scales, larval connectivity is a vital demographic process underpinning the persistence of populations. Patterns of larval connectivity define the size of metapopulations and guide the appropriate scale at which a population should be managed.
Two approaches have been used to infer levels of larval connectivity among aquatic (generally marine) populations. The first uses oceanographic models of currents to predict potential levels of dispersal among sites and the second uses population genetics to infer aspects of connectivity from observed gene flow. Both methods have their limitations. Predictions of oceanographic models are difficult to test without genetic data and, whilst patterns of gene flow can identify distinct populations, they provide limited insight into larval flux, which is important for ecological and conservation studies. Although both approaches to inferring population connectivity are highly complementary, they have rarely been applied simultaneously. This is largely because of the sampling constraints to measuring gene flow at appropriate scales and the sheer computational complexity of creating realistic, vertically-stratified models of larval dispersal. In this project, a multi-scale analysis of population gene flow was combined with a published state-of-the-art model of larval dispersal.
The focus of the study was the scleractinian coral, Montastraea annularis (s.s.) which is found throughout the Caribbean Sea and is a major reef-building species. Focusing the project on this coral had a number of theoretical and applied advantages. Firstly, this species should –in theory– be an excellent test of the circulation model. M. annularis is a massive, long-lived coral (often exceeding 100 years in age) that depends on annual spawning events for colony dispersal. Not only are these spawning events easier to predict than the brooding behaviour of many other corals, but its mound-like morphology assures that dispersal is not confounded by asexual methods of reproduction. In contrast, many branching species of corals propagate by colony fragmentation and high levels of local clonality can complicate the measurement of gene flow. Perhaps more importantly, the motility of coral larvae is weak compared to ocean or coastal currents, which greatly simplifies the modelling of their dispersal. Fish larvae, on the other hand, are competent swimmers, which reduces the certainty of predicted larval dispersal. A focus on M. annularis is also consistent with on-going modelling of the community dynamics of Montastraea reefs and studies of reserve impacts and the ecology of this ecosystem (see work by Prof P.J. Mumby). In particular, outcomes of the study will provide fresh insight into the metapopulation dynamics of this coral, which is severely threatened by mass coral bleaching and rising sea temperatures.
Members of the gorup are partners in the EU-funded FP7 project, Future of Reefs in a Changing Environment (FORCE); the University of Exeter is the lead partner in this project and the MEEG laboratory is participating in genetic connectivity analysis.
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