Cross-Species CRB Landscape Predicts Gravitropic Precision Hierarchy Across Statolith-Based Plant Organs

Plants don't have eyes or ears, but they can sense gravity with remarkable precision. They do this using tiny starch-filled particles inside specialized cells — called statoliths — that settle toward the ground and trigger the plant to grow in the right direction. Scientists have studied this for decades, but mostly in a descriptive way: we know it happens, we can observe when it goes wrong in mutants, but we lack a deep mathematical framework for predicting *how well* any given plant should be able to sense gravity based on its biology. This hypothesis borrows a powerful tool from statistics and engineering called the Cramér-Rao bound. In engineering, this formula tells you the theoretical limit on how precisely any sensor can measure something, given the noise and information available to it. Think of it like asking: given this camera's lens size and sensor quality, what's the sharpest photo it could ever take? The hypothesis proposes applying this same logic to plant gravity sensors — plugging in biological parameters like how many statolith particles a plant has, how they move, and how the plant's cells are arranged, to calculate a theoretical precision limit for gravity sensing. The prediction is that this formula should correctly rank different plant species and mutants from most to least precise at sensing gravity, matching what we actually observe in nature. What makes this exciting is that it would transform how we think about plant gravity sensing — from 'here's what we observe' to 'here's the mathematical reason it has to work this way.' It's the difference between noticing that a taller antenna picks up better radio signals and understanding *why* based on physics. No one has ever applied this kind of information-theoretic analysis to plant gravity sensing before.

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