Mycorrhizal Networks Facilitate Tree Communication, Learning, and Memory
Forest networks share neural network topology—"mother trees" as hub nodes transfer memory and resources to kin, challenging pure competition models.
Your organization's senior experts are mother trees—and when they leave, the network collapses. Simard's analysis revealed that mycorrhizal networks share topology with neural networks: scale-free patterns with small-world properties that enable both local efficiency and global information flow. The largest, oldest hub trees—mother trees—preferentially nurture their kin, recognize related seedlings, and transfer resources to them even while dying.
This research challenges the pure competition model of both forests and organizations. Those senior employees who've accumulated decades of institutional knowledge function exactly like mother trees: their departures don't just remove one node—they collapse the pathways through which organizational memory flows to newcomers. Kin selection in forests mirrors mentorship networks in companies, where knowledge transfer preferentially flows to those seen as organizational "relatives." The question isn't whether your mother trees will eventually leave—it's whether you've built the network to survive their departure.
Key Findings from Simard (2018)
- Mycorrhizal network topology mirrors neural networks—scale-free patterns with small-world properties enabling efficient information flow
- "Mother trees" act as hub nodes preferentially supporting related seedlings through kin recognition
- Trees demonstrate memory-based learning through network communication, challenging pure competition models
- Dying mother trees transfer carbon and nutrients to offspring through mycorrhizal networks—knowledge transfer at death
- Network structure supports crystallized knowledge (memory) mobilization for learning