Learning to learn ecosystems from limited data -- a meta-learning approach
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 25 of its 98 references that are in the map. First posted on arXiv 2 Oct 2024.
Builds on
25 of 98 references.
References · 25
- 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
- 2019A Comprehensive Survey on Transfer Learningcited by 1,154
- 2018On First-Order Meta-Learning Algorithmscited by 1,003
- 2020Meta-Learning in Neural Networks: A Surveycited by 944
- 2018Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learningcited by 270
- 2024Reinforcement learningcited by 236
- 2021Next Generation Reservoir Computingcited by 160
- 2021Seeing biodiversity: perspectives in machine learning for wildlife conservationcited by 129
- 2018Meta-Learning for Low-Resource Neural Machine Translationcited by 105
- 2020Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Datacited by 99
- 2020Meta-learning framework with applications to zero-shot time-series forecastingcited by 70
- 2020Machine learning prediction of critical transition and system collapsecited by 54
13 more
- 2019Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computerscited by 43
- 2021Meta-Learning Dynamics Forecasting Using Task Inferencecited by 37
- 2019Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radiuscited by 34
- 2021Anticipating synchronization with machine learningcited by 22
- 2021Parallel Machine Learning for Forecasting the Dynamics of Complex Networkscited by 17
- 2018Machine-learning inference of fluid variables from data using reservoir computingcited by 16
- 2019Dynamical learning of dynamicscited by 16
- 2023Model-free tracking control of complex dynamical trajectories with machine learningcited by 13
- 2022Machine Learning and Deep Learning -- A review for Ecologistscited by 11
- 2022Catch-22s of reservoir computingcited by 11
- 2023Metalearning generalizable dynamics from trajectoriescited by 9
- 2020Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Testscited by 7
- 2023Machine-learning parameter tracking with partial state observationcited by 4
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.