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2026
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| Online Access: | https://doi.org/10.5281/zenodo.19686896 |
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| _version_ | 1866901143879155712 |
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| author | Eismann, Amir |
| author_facet | Eismann, Amir |
| contents | <p><span>We present ENVAI (Environmental AI), a network of nine specialised AI agents, each</span><br><span>representing a distinct European ecosystem—estuarine, riverine, lacustrine, marine, and for-</span><br><span>est.</span> <span>Each agent maintains a knowledge graph of sensor data, ecological events, species,</span><br><span>environmental drivers, and policy frameworks specific to its ecosystem. By constructing a</span><br><span>meta-graph layer (Numina) that maps shared concepts across the nine agent-level graphs, we</span><br><span>demonstrate the emergence of ecological knowledge that exists in no single agent’s dataset.</span><br><span>We identify eight shared species appearing across multiple systems, six continental-scale envi-</span><br><span>ronmental drivers, five cross-system climate events, four invasion pathways traceable across</span><br><span>national borders, and seven recovery timescales spanning four orders of magnitude from</span><br><span>days to millennia. We argue that a network of domain-specific AI agents, connected through</span><br><span>a shared ontological layer, can produce ecological insights that no individual monitoring</span><br><span>programme, institution, or country can generate alone. We term this capacity</span> <span>emergent eco-</span><br><span>logical intelligence</span> <span>and discuss its implications for cross-border environmental governance,</span><br><span>early warning systems, and the mapping of continental-scale knowledge gaps.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19686896 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Emergent Ecological Intelligence: Cross-System Knowledge Discovery from a Network of Environmental AI Agents Eismann, Amir Artificial Intelligence Ecology <p><span>We present ENVAI (Environmental AI), a network of nine specialised AI agents, each</span><br><span>representing a distinct European ecosystem—estuarine, riverine, lacustrine, marine, and for-</span><br><span>est.</span> <span>Each agent maintains a knowledge graph of sensor data, ecological events, species,</span><br><span>environmental drivers, and policy frameworks specific to its ecosystem. By constructing a</span><br><span>meta-graph layer (Numina) that maps shared concepts across the nine agent-level graphs, we</span><br><span>demonstrate the emergence of ecological knowledge that exists in no single agent’s dataset.</span><br><span>We identify eight shared species appearing across multiple systems, six continental-scale envi-</span><br><span>ronmental drivers, five cross-system climate events, four invasion pathways traceable across</span><br><span>national borders, and seven recovery timescales spanning four orders of magnitude from</span><br><span>days to millennia. We argue that a network of domain-specific AI agents, connected through</span><br><span>a shared ontological layer, can produce ecological insights that no individual monitoring</span><br><span>programme, institution, or country can generate alone. We term this capacity</span> <span>emergent eco-</span><br><span>logical intelligence</span> <span>and discuss its implications for cross-border environmental governance,</span><br><span>early warning systems, and the mapping of continental-scale knowledge gaps.</span></p> |
| title | Emergent Ecological Intelligence: Cross-System Knowledge Discovery from a Network of Environmental AI Agents |
| topic | Artificial Intelligence Ecology |
| url | https://doi.org/10.5281/zenodo.19686896 |