| Summary: | Connectivity was assessed during ATLAS for a diversity of organisms, from the corals that structure Vulnerable Marine Ecosystems (VMEs) to economically important fishery species using two main pathways. Predicted connectivity patterns were obtained through simulated larval Lagrangian particle modelling, based on oceanographic data gained in WP1 and reproductive knowledge produced in WP4. Realised connectivity was inferred using population genetics on sets of samples gathered before and during ATLAS, focusing on a subset of the target species initially listed, for which enough samples could be gathered to perform comprehensive population genetics analysis. Lagrangian modelling of larval dispersal within ATLAS unravelled the effect of long-term ocean variability (Atlantic Meridional Overturning Circulation - AMOC, subpolar gyre strength - SPG and North Atlantic Oscillation - NAO) and larval behaviour on particle transport pathways and population connectivity (Fox et al., 2016), the contribution of man-made structures to connectivity (Henry et al., 2018) and the application of these results to marine planning and the development of ecologically coherent marine protected area networks. This work has underlined the crucial need for data on reproductive and larval biology to inform these predictions (Fox et al., 2016). This proved to be even more important for deep-sea species due to the vast extent of the water column through which larvae can disperse. Very different outcomes can be expected depending not only on the timing of reproduction or the length of pelagic larval duration (PLD), but also on the behaviour of larvae remaining on the seafloor or migrating more or less along the water column. The relationship between PLD and “realised connectivity” as estimated through population genetics is far from easily predictable, despite some relationship existing (Riginos et al., 2011). This is likely to be worse in the deep sea as exemplified by recent models where extensive PLD resulted in extreme variance of predicted ...
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