Operationalizing Nature Based Intelligence for Ecosystem Early Warning
GSI Journals Serie C: Advancements in Information Sciences and Technologies, cilt.8, sa.2, ss.53-66, 2026 (Hakemli Dergi)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 8 Sayı: 2
- Basım Tarihi: 2026
- Dergi Adı: GSI Journals Serie C: Advancements in Information Sciences and Technologies
- Derginin Tarandığı İndeksler: Index Copernicus
- Sayfa Sayıları: ss.53-66
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Çukurova Üniversitesi Adresli: Evet
Özet
Environmental early-warning systems often treat data quality, anomaly detection, adaptation, and provenance as separate tasks. That separation is problematic in non-stationary ecosystems, where unusual observations may represent either unreliable data or legitimate ecological novelty, and where unrestricted adaptation can normalize persistent degradation. We introduce Nature-Based Intelligence (NBI) as a computational decision-intelligence paradigm grounded in five operational principles: multiscale memory, context-conditioned normality, relational coherence, conservative adaptation, and auditable feedback. We formalize these principles in NBI-EW, an early-warning architecture combining fast and slow state representations, Mahalanobis stress energy, debiased Sinkhorn distributional displacement, optional spatial coherence, gated reference updating, and tamper-evident provenance. The engineering precursor, XAI Kernel v11, passes 48 unit and regression tests; these checks concern software behaviour, and ecological performance remains untested. We specify a leakage-controlled validation protocol using the ICOS Drought-2018 eddy-covariance product to test early detection of drought-associated functional stress at matched false-alarm rates. Temporal and site-transfer performance will be evaluated separately.