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Tropical cyclones (e.g., hurricanes and tropical storms), are considered one of the world's most destructive climatological forces, causing substantial damage especially in urban areas. However, for some arid ecosystems, tropical cyclones represent natural disturbance events, providing important sources of fresh water that support ecosystem functioning. For subsistence populations living in these regions, it is unclear whether they experience these events negatively due to the associated damages or positively within a predictable disturbance regime. Here, we assess these phenomena with traditional ranchers from Baja California Sur, Mexico, following Hurricane Kay (September 2022). We find that despite significant damage caused by the hurricane, nearly the entire sample perceived this tropical cyclone event as a net positive on their lives. This traditional ranching population has a culture that is adapted to the seasonal tropical cyclone disturbance regime, and expects extreme rain events annually to support ecosystem functioning, and therefore their economic livelihoods. To these ranchers, the climate shock is not when the hurricanes come, but rather, when hurricanes do not come. We situate our results within a disturbance ecology framework, highlighting the role of increasing aridity and probability of drought in the North American Arid West. 

Granular hydrogel matrices have emerged as promising candidates for cell encapsulation, bioprinting, and tissue engineering. How- ever, it remains challenging to design and optimize these materials given their broad compositional and processing parameter space. Here, we combine experimentation and computation to create granular matrices composed of alginate-based bioblocks with controlled structure, rheological properties, and injectability pro- files. A custom machine learning pipeline is applied after each phase of experimentation to automatically map the multidimensional input-output patterns into condensed data-driven models. These models are used to assess generalizable predictability and define high-level design rules to guide subsequent phases of development and characterization. Our integrated, modular approach opens new avenues to understanding and controlling the behavior of complex soft materials.