Learning to learn ecosystems from limited data -- a meta-learning approach

TL;DR

This paper introduces a meta-learning framework using time-delayed neural networks to accurately predict ecological system behaviors with limited data, outperforming traditional methods in ecological modeling tasks.

Contribution

The study develops a novel meta-learning approach leveraging synthetic data to improve ecological system predictions with minimal real data, demonstrating significant data efficiency gains.

Findings

Achieves 5-7 times less training data for accurate predictions.

Successfully models three benchmark ecological systems.

Enhances robustness and accuracy over standard machine learning methods.

Abstract

A fundamental challenge in developing data-driven approaches to ecological systems for tasks such as state estimation and prediction is the paucity of the observational or measurement data. For example, modern machine-learning techniques such as deep learning or reservoir computing typically require a large quantity of data. Leveraging synthetic data from paradigmatic nonlinear but non-ecological dynamical systems, we develop a meta-learning framework with time-delayed feedforward neural networks to predict the long-term behaviors of ecological systems as characterized by their attractors. We show that the framework is capable of accurately reconstructing the ``dynamical climate'' of the ecological system with limited data. Three benchmark population models in ecology, namely the Hastings-Powell model, a three-species food chain, and the Lotka-Volterra system, are used to demonstrate the performance of the meta-learning based prediction framework. In all cases, enhanced accuracy and robustness are achieved using five to seven times less training data as compared with the corresponding machine-learning method trained solely from the ecosystem data. A number of issues affecting the prediction performance are addressed.