<p>Spatial variation is a fundamental property of regional ecosystem assessments, yet model-derived uncertainty is rarely examined as a spatially structured field. Common practices such as averaging, thresholding, or masking treat uncertainty as residual noise and obscure information embedded in its spatial organization. This study examines whether the spatial configuration of uncertainty itself contains interpretable diagnostic information for spatial inference at the regional scale. Uncertainty fields derived from model-based degradation rankings are transformed into region-specific quantile space using empirical cumulative distribution functions, preserving internal ordering without external calibration, probability estimation, or causal attribution. Fixed interpretative zones are defined in quantile space, and their spatial configuration is characterized using the Structural Coherence Index, a deterministic adjacency-based coherence descriptor that summarizes whether uncertainty forms coherent clusters, fragmented patterns, or diffuse organization. Although overall uncertainty magnitude appears comparable across regions, spatial coherence profiles differ substantially. Regions with similar global uncertainty levels exhibit distinct spatial regimes, including localized concentration, fine-scale fragmentation, and diffuse organization. These contrasts demonstrate that spatial organization conveys information beyond magnitude-based summaries alone. The results show that model-derived uncertainty can be treated as an independent data-geometric diagnostic layer, reflecting internal properties of spatial inference under heterogeneous and partially observable conditions. By focusing on spatial organization under fixed adjacency rules rather than on magnitude or threshold-based classification, the proposed framework supports interpretation of regional ecosystem assessments without decision rules, early-warning assumptions, or modification of underlying models.</p>