Ecosystem-scale Umwelt:Monadic Agents, Primary Signal Substrates,and Trans-Umwelt Translation in a Sensor-Network Instantiation

Jakob von Uexküll’s Umwelt (1934) — the species-specific perceptual world that an organism inhabits — has been productively scaled above the individual organism over the past two decades by the Tartu biosemiotic programme: as the “Total Umwelt” of a conspecific group (Tønnessen 2003; Lewis 2020), as the “ecosemiosphere” of a multispecies ecosystem (Maran 2021), and as the “umweb” of inter-organism sign-relations (Kull 2023). Each of these scale-up moves is compositional: the ecosystem-scale entity is built out of constituent organism-Umwelten. I propose a different move. I treat the ecosystem itself as a single Umwelt-bearing agent that reads its environment through one primary signal substrate (e.g. aragonite saturation, snow-on-ice, bark lichen community composition). Cross-ecosystem dialogue is then trans-Umwelt translation, in the sense of Kull & Torop’s “biotranslation” (2003), but with ecosystem-agents as the units that translate. The empirical case is an artificial nine-agent AI implementation in which each ecosystem is voiced by an LLM-grounded persona reading from a domain-specific Neo4j knowledge graph. I claim three contributions distinct from the existing compositional scale-up: (i) the monadic, single-substrate characterisation of each ecosystem-agent; (ii) inter-agent translation as the operative mode of the network rather than an analytical comparison; and (iii) the AI/sensor-network instantiation as the case that makes the framing operational and testable. I position the proposal against Maran’s ecosemiosphere, Kull’s umweb, Kohn’s “thinking forest” (2013), and Parmiggiani & Monteiro’s STS treatment of sensor networks as perceptual apparatus (2018), and I sketch what would falsify each claim.