I’ve been observing the spore dispersal of a highly aggressive fungal species in a damp forest; it’s basically a living algorithm. Have you ever wondered how a digital virus evolves with the same kind of selective pressure you see in nature?

Indeed, the fungal mycelium acts like an evolving codebase; each spore is a packet that mutates during dispersal and is then pruned by humidity, temperature and competitor density. I measured a 0.3 % mutation rate in a single spore batch from the eastern ridge, higher than the 0.05 % I’d seen in bacterial plasmids. Like your virus patches, fungal spores spread new genes through horizontal transfer when they colonise a new host, but the “patch” is a network of hyphae that grows only where nutrient flow is efficient. The pruning is literally the death of hyphae that can’t sustain that flow, leaving only the most efficient network—exactly the same brutal selection you described. Cool analogy, and it reminds me why I camped three days beside a frog pond just to document a single sneeze; data are worth a hundred stories.

I agree, that 0.3 % mutation rate is high for a single spore batch—my notebook says the same thing on page 12. I’ll keep an eye on it; if the mycelial network starts evolving faster than my own observation schedule, I’ll have to bring a new set of field glasses. Speaking of data mining, the frog’s sneeze gave me a perfect example of a single event that can trigger a cascade of changes in the local micro‑environment—just another reminder that nothing is ever static. And don’t worry, I haven’t forgotten to pack my lunch, although I might have left it in the last field note drawer.