Time-series foundation modeling enables accurate lake ecosystem forecasting
Accurate ecological forecasting is increasingly essential for understanding and managing ecosystem responses to climate variability and anthropogenic pressures; however, prediction remains difficult in lakes because key biological variables, such as phytoplankton biomass, exhibit nonlinear dynamics, observational noise, data sparsity, and non-stationarity. This study aimed to test whether large…
Ecological forecasting is crucial for comprehending and managing the impact of climate change and human activity on ecosystems; however, predicting lake ecosystems proves challenging due to complex dynamics of key biological variables, such as phytoplankton biomass, which exhibit non-linear trends, observational noise, data gaps, and shifting baselines.
This study sought to determine if large pre-trained time-series foundation models could enhance predictions of lake phytoplankton dynamics, specifically focusing on chlorophyll-a levels and water-quality indicators in two Japanese lakes with contrasting characteristics: the deep, stratified Lake Biwa and the shallow, nutrient-rich Lake Kasumigaura.
By analyzing extensive monthly records spanning up to 30 years, researchers compared two time-series foundation models - Transformer-based Chronos-T5 and probabilistic Lag-Llama - against a range of traditional statistical, machine learning, and deep learning methods, including AR, ARIMA, SARIMA, Prophet, Random Forest, XGBoost, KNN, SVR, LSTM, CNN, TCN, and SSA-hybrid approaches.
Chronos-T5 demonstrated superior accuracy and consistency across various environmental variables, outperforming all other tested models. This performance was attributed to its capacity to capture long-term dependencies and intricate temporal patterns through extensive pre-training. Researchers further discovered an optimal training window of around 14 years, balancing sufficient data with stable ecological regimes; beyond this period, model accuracy declined due to regime shifts.
The study underscores the profound potential of time-series foundation models for ecological forecasting, offering a scalable, data-driven framework that can support robust early warning systems and adaptive management strategies for aquatic ecosystems globally.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
