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Abstract Global environmental change is causing a decline in biodiversity with profound implications for ecosystem functioning and stability. It remains unclear how global change factors interact to influence the effects of biodiversity on ecosystem functioning and stability. Here, using data from a 24-year experiment, we investigate the impacts of nitrogen (N) addition, enriched CO₂(eCO₂), and their interactions on the biodiversity-ecosystem functioning relationship (complementarity effects and selection effects), the biodiversity-ecosystem stability relationship (species asynchrony and species stability), and their connections. We show that biodiversity remains positively related to both ecosystem productivity (functioning) and its stability under N addition and eCO₂. However, the combination of N addition and eCO₂diminishes the effects of biodiversity on complementarity and selection effects. In contrast, N addition and eCO₂do not alter the relationship between biodiversity and either species asynchrony or species stability. Under ambient conditions, both complementarity and selection effects are negatively related to species asynchrony, but neither are related to species stability; these links persist under N addition and eCO₂. Our study offers insights into the underlying processes that sustain functioning and stability of biodiverse ecosystems in the face of global change. 

Over the past decade, deep reinforcement learning (RL) techniques have significantly advanced robotic systems. However, due to the complex architectures of neural network models, ensuring their trustworthiness is a considerable challenge. Programmatic reinforcement learning has surfaced as a promising approach. Nonetheless, synthesizing robot-control programs remains challenging. Existing methods rely on domain-specific languages (DSLs) populated with user-defined state abstraction predicates and a library of low-level controllers as abstract actions to boot synthesis, which is impractical in unknown environments that lack such predefined components. To address this limitation, we introduce RoboScribe, a novel abstraction refinement-guided program synthesis framework that automatically derives robot state and action abstractions from raw, unsegmented task demonstrations in high-dimensional, continuous spaces. It iteratively enriches and refines an initially coarse abstraction until it generates a task-solving program over the abstracted robot environment. RoboScribe is effective in synthesizing iterative programs by inferring recurring subroutines directly from the robot’s raw, continuous state and action spaces, without needing predefined abstractions. Experimental results show that RoboScribe programs inductively generalize to long-horizon robot tasks involving arbitrary numbers of objects, outperforming baseline methods in terms of both interpretability and efficiency.