A Possible Artificial Intelligence Ecosystem Avatar: the Moorea case (IDEA)

TL;DR

This paper presents a large-scale ecosystem modeling framework using Deep Stacking Networks to integrate multimodal data for ecosystem prediction on Moorea Island within the IDEA project.

Contribution

It introduces a novel ecosystem avatar model based on DSN that combines diverse data types for ecosystem understanding and prediction.

Findings

Effective integration of physics, chemistry, biology, and social data.

Model demonstrates potential for ecosystem prediction and management.

Framework applicable to other large-scale ecosystems.

Abstract

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.