Lagrangian Flow Network: a new tool to evaluate connectivity and understand the structural complexity of marine populations

Vincent Rossi1, Mélodie Dubois2,3, Enrico Ser-Giacomi3, Pedro Monroy3, Cristobal Lopez3 and Emilio Hernandez-Garcia3, (1)CNRS-MIO, Mediterranean Institute of Oceanography, Marseille, France, (2)CRIOBE, USR 3278, EPHE-CNRS-UPVD, 58 Av. Paul Alduy, 66860 Perpignan, France, (3)IFISC Institute for Cross-Disciplinary Physics and Complex Systems, Palma de Mallorca, Spain
Abstract:
Assessing the spatial structure and dynamics of marine populations is still a major challenge for ecologists. The necessity to manage marine resources from a large-scale perspective and considering the whole ecosystem is now recognized but the absence of appropriate tools to address these objectives limits the implementation of globally pertinent conservation planning. Inspired from Network Theory, we present a new methodological framework called Lagrangian Flow Network which allows a systematic characterization of multi-scale dispersal and connectivity of early life history stages of marine organisms. The network is constructed by subdividing the basin into an ensemble of equal-area subregions which are interconnected through the transport of propagules by ocean currents. The present version allows the identification of hydrodynamical provinces and the computation of various connectivity proxies measuring retention and exchange of larvae. Due to our spatial discretization and subsequent network representation, as well as our Lagrangian approach, further methodological improvements are handily accessible. These future developments include a parametrization of habitat patchiness, the implementation of realistic larval traits and the consideration of abiotic variables (e.g. temperature, salinity, planktonic resources...) and their effects on larval production and survival. While the model is potentially tunable to any species whose biological traits and ecological preferences are precisely known, it can also be used in a more generic configuration by efficient computing and analysis of a large number of experiments with relevant ecological parameters. It permits a better characterization of population connectivity at multiple scales and it informs its ecological and managerial interpretations.