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Using neural ordinary differential equations to predict complex ecological dynamics from population density data

Simple models have been used to describe ecological processes for over a century. However, the complexity of ecological systems makes simple models subject to modeling bias due to simplifying assumptions or unaccounted factors, limiting their predictive power. Neural Ordinary Differential Equations (NODEs) have surged as a machine-learning algorithm that preserves the dynamic nature of the data \cite{chen_neural_2018}. Although preserving the dynamics in the data is an advantage, the question of how NODEs perform as a forecasting tool of ecological communities is unanswered. Here we explore this question using simulated time series of competing species in a time-varying environment. We find that NODEs provide more precise forecasts than ARIMA models. {We also find that untuned NODEs have a similar forecasting accuracy as untuned Long-Short Term Memory neural networks (LSTMs) and both are outperformed in accuracy and precision by EDM models. However, we also find NODEs generally outperform all other methods when evaluating with the interval score, which evaluates precision and accuracy in terms of prediction intervals rather than pointwise accuracy.} We also discuss ways to improve the forecasting performance {of NODEs}. The power of a forecasting tool such as NODEs is that it can provide insights into population dynamics and should thus broaden the approaches to studying time series of ecological communities.

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