Enrico Ser-Giacomi
Enrico Ser-Giacomi

Ubiquitous abundance scaling of plankton distributions and ocean dynamics from a network theory approach

Enrico Ser-Giacomi
enrico.sergiacomi@gmail.com
Laboratoire d'Océanographie et du Climat: Expérimentations et approches numériques (LOCEAN). Unité Mixte de Recherche 7159 CNRS / IRD / Université Pierre et Marie Curie/MNHN. Institut Pierre Simon Laplace. Boîte 100 - 4, place Jussieu 75252 PARIS Cedex 05.

I will first focus on scaling properties obtained from the analysis of Species Abundance Distributions (SADs) of planktonic organisms. Using the dataset gathered by the Tara Oceans expedition for marine microbial eukaryotes (protists) we explore how SADs of planktonic local communities vary across the global ocean. We find that the decay in abundance of more than the 99% of species is commonly governed by a power-law. Moreover, the power-law exponent varies by less than 10% across locations and does not show biogeographical signatures suggesting that large-scale ubiquitous ecological processes could govern the assembly of such communities.

I will then introduce a Network Theory framework developed for the characterization of fluid transport dynamics in the ocean. The discretization of the sea surface in small equal-sized cells brings to the construction of a new kind of network networks, called Lagrangian Flow Networks (LFNs), that describe water exchanges between different regions of the seascape. Using Network Theory concepts & tools we can study dispersion and mixing at both local and global scales evidencing relationships between network measures and dynamical properties of the flow. Among possible applications, such a framework provides a systematic characterization of the dispersal of planktonic life-stages of marine organisms which helps to understand the connectivity and structural complexity of marine populations.

I will finally discuss possible perspectives to investigate the effects of ocean transport and mixing on planktonic community assembly in the Mediterranean using the LFN methodology.