Understanding and forecasting lake dynamics is essential for monitoring water quality and ecosystem health in lakes and reservoirs. While machine learning models trained on ecological time-series data have shown promise, they tend to be task-specific and struggle with generalization across diverse aquatic environments. Current research is limited to single-lake single-variable models, inconsistent observation frequencies, and a lack of foundation models that can generalize across ecosystems, hindering reproducibility and transferability. To address these challenges, we introduce LakeFM, a foundation model for lake ecosystems, pre-trained on multi-variable and multi-depth data drawn from a combination of simulated and observational lake datasets. Through empirical results and qualitative analysis, we demonstrate that LakeFM learns meaningful representations spanning both fine-grained variable-level dynamics and broader lake-level patterns. Furthermore, it achieves competitive—and in some cases superior—forecasting performance compared to existing time-series foundation models