Long-term information is crucial for enhancing our ability to predict ecosystem trajectories under adaptive management and climate change scenarios. In this study, we assessed how the systematic incorporation of information affects our predictive capacity regarding the response of a target variable, offering insights into ecosystem dynamics and highlighting the importance of long-term data. We analyzed over 20 years of aboveground net primary production (ANPP) data across three highly dynamic dryland ecosystems in a grassland-to-shrubland transition zone. Our approach, widely applicable for testing long-term observations, involved modeling probability distributions, temporal semivariograms, and copula-based dependency functions between annual precipitation and ANPP. Our results indicate non-linear trends in prediction capacity as more data are included, demonstrating emergent unexpected responses not evident in short-term observations. These dynamic and non-stationary responses pose significant challenges for prediction, even with over 20 years of data, underscoring the need for ongoing measurements. Our findings emphasize the importance of long-term temporal variability for understanding trends and resilience of ecosystem processes. Quantitative methodologies for assessing predictive capacity and identifying trends are essential for making informed decisions regarding the continuation or termination of long-term monitoring initiatives. We strongly advocate for the sustained support of long-term ecological research, as it is crucial for deepening our understanding of ecosystem responses and for guiding effective management and policy decisions.
Keywords: Carbon cycle; Copula-based modeling; Forecasting; LTER; Precipitation variability.
© 2025. The Author(s).