reserBUGS: A reservoir computing framework for probabilistic forecasting of ecological abundance time series

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Abstract Forecasting species population dynamics is a central challenge in computational ecology, yet existing approaches rarely combine flexible nonlinear modelling, support for count-based ecological data, and systematic uncertainty quantification within a single, scalable framework. Here we introduce reserBUGS , an open-source Python framework for ecological forecasting based on reservoir computing, a recurrent neural network architecture in which only a simple readout layer is trained while a fixed high-dimensional dynamical system encodes temporal memory and nonlinear dependencies. reserBUGS integrates species abundance time series with environmental covariates retrieved automatically from global climate products, generates probabilistic ensemble forecasts, and provides tools for forecast evaluation and reliability assessment. We evaluated reserBUGS using insect abundance time series from available biodiversity monitoring datasets, comparing its performance against seven statistical and machine-learning baselines over one- to five-year forecast horizons. Reservoir-based models consistently outperformed alternatives in both predicting future abundance and capturing forecast uncertainty, with environmental predictors increasing the proportion of stable forecasts and contributing additional predictive value beyond historical abundance dynamics alone, particularly at 3-4-year forecast horizons. Probabilistic forecasts further enabled the identification of conditions associated with reduced predictive skill, providing a practical basis for communicating forecast confidence to end users. While default configurations already achieved competitive performance across a taxonomically and geographically diverse set of time series, hyperparameter optimisation revealed substantial room for performance gains through series-specific tuning. reserBUGS offers a computationally efficient and extensible framework for ecological forecasting that is well suited to the short, heterogeneous time series typical of biodiversity monitoring programmes. Its combination of flexible nonlinear modelling, probabilistic uncertainty quantification, and automated environmental data integration addresses key practical barriers to the adoption of modern forecasting methods in conservation and ecological research. Author summary Predicting how animal populations will change in the future is crucial for conservation, yet it remains surprisingly difficult. Population sizes fluctuate due to a complex mix of factors — climate, habitat, species interactions, and chance — making future dynamics hard to anticipate, especially when the available data are limited. We present reserBUGS , a computational tool designed to forecast animal population abundance. The tool is built around a technique called reservoir computing, which uses a large network of randomly connected artificial neurons to capture complex temporal patterns in data. We tested reserBUGS on thousands of insect population time series collected worldwide, and covering hundreds of species from butterflies to beetles. We found that our approach predicted future population sizes more accurately than commonly used statistical and machine-learning alternatives. Incorporating environmental information — such as temperature and precipitation — further improved forecast accuracy, particularly for predictions several years into the future. By providing estimates of forecast uncertainty, users can identify situations where predictions are likely to be less accurate. reserBUGS will be a useful tool for ecologists and conservation practitioners who need to anticipate population trends and make informed management decisions under uncertainty.

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