de Aledo, J. G. | Search bioRxiv

bioRxiv2026

Available time-series of terrestrial animals provide low reliability in population trend estimates

Monitoring biodiversity through time is critical for understanding the impacts of global change, yet the reliability of available time-series for estimating population trends remains poorly studied. We simulated population dynamics to study how monitoring duration, inter-annual variability, and temporal autocorrelation influence trend reliability. We then inferred the expected reliability of 16,450 empirical time-series of terrestrial animals based on those simulations. Longer, less variable, and negatively autocorrelated time-series produced more reliable trend estimates. Time-series shorter than 20 years led to large magnitude errors and low power in estimating population trends, despite generally low sign uncertainty. Overall, 80% of the empirical time-series were too short and/or noisy to identify meaningful temporal trends, yet reliability varies among taxa. Our results show important limitations in the available biodiversity data, stressing the need to sustain long-term monitoring to better estimate population trends, a top priority in the current biodiversity crisis.

ecology↗

bioRxiv2026

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

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.

bioinformatics↗