Year: 2025
Type: preprint
Abstract: Ecosystem service (ES) capacity matrices are widely used for ES assessment and mapping due to their operational simplicity. However, standard matrices assign uniform capacity scores to ecosystem types without accounting for local variations in ecosystem condition, leading to systematic assessment errors. We present a methodological framework for integrating ecosystem condition into capacity matrices through ecosystem-specific condition thresholds. Ecosystem condition is characterized using two components: structural condition (e.g. vegetation cover, productivity, canopy height) and biological condition (e.g. biodiversity intactness, soil organic carbon, natural habitat proportion). We classify ecosystem services into five functional groups based on their dependence on these condition components and develop allocation tables linking condition states (low, standard, high) to adjusted capacity matrices. The framework employs ecosystem-specific percentile thresholds (20th-80th) to define condition classes, avoiding systematic bias between ecosystem types while capturing within-type quality variation. Application to a 430-km² study area in the Hauts-de-France region (France) demonstrates that condition-adjusted capacity assessments differ from unadjusted matrices by 0.58-0.81 units on average (12-16% of the capacity range), with maximum local differences reaching 2.1 in a 0 to 5 scale units (42% of range). Validation against six independent reference datasets shows correlation improvements ranging from 0.035 to 0.132 (mean 0.078) when incorporating condition adjustments. The approach provides an operational method for implementing the SEEA EA conceptual linkage between ecosystem condition and service capacity, with applications in natural capital accounting, land-use planning, and environmental impact assessment.
Authors: Philip Roche, C. Sylvie Campagne
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDGs: Life on land, Climate action
Open Access status: gold
OpenAlex ID: W4417102059
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