Learning to learn ecosystems from limited data.

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, its variant, 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 have been achieved using five to seven times less training data as compared with the corresponding machine-learning method trained solely from the ecosystem data. In addition, two real-world ecological benchmark datasets: the microbial time-series dataset and global population dynamics database, are tested to demonstrate the applicability of the meta-learning framework to the real world. A number of issues affecting the prediction performance are addressed.