  High-throughput data collection techniques and largescale (cloud) computing
are transforming our understanding of ecosystems at all scales by allowing the
integration of multimodal data such as physics, chemistry, biology, ecology,
fishing, economics and other social sciences in a common computational
framework. We focus in this paper on a large scale data assimilation and
prediction backbone based on Deep Stacking Networks (DSN) in the frame of the
IDEA (Island Digital Ecosystem Avatars) project (Moorea Island), based on the
subdivision of the island in watersheds and lagoon units. We also describe
several kinds of raw data that can train and constrain such an ecosystem avatar
model, as well as second level data such as ecological or physical indexes /
indicators.
