Accounting for forest condition in Europe based on an international statistical standard

Forest ecosystems are vital to biodiversity and climate regulation globally, yet deforestation and degradation persist, driving biodiversity loss and exacerbating climate change. In Europe, forests have expanded and accumulated biomass, including deadwood, but continue to face pressures such as eutrophication, drought, and tree cover loss that undermine condition and ecosystem service provision. Recognizing the need to assess and manage ecosystem assets, the UN Statistical Commission adopted the SEEA Ecosystem Accounting (SEEA EA) standard in March 2021, providing a spatially based statistical framework for ecosystem extent, condition, and services consistent with national accounts. This study aims to operationalize SEEA EA guidelines to assess and map forest ecosystem condition across Europe, using a standardized index (0–1) anchored to reference conditions observed in primary or least disturbed protected forests. The purpose is to create a consistent, repeatable, and policy-relevant account to track trends, support conservation and restoration decisions, and integrate ecological values into decision-making.

Multiple European efforts have assessed forest health, condition, and integrity using diverse indicators and methods, often integrating remote sensing and in situ data. However, comparability and uptake across sectors are limited without a harmonized standard. SEEA EA provides guidance on selecting condition variables, defining reference conditions, and aggregating indicators into an index rooted in ecosystem integrity. Prior work highlights key condition indicators (e.g., productivity, structure, biodiversity, connectivity) and limitations in data availability and temporal consistency. This paper builds on these insights by applying the internationally adopted SEEA EA standard to produce a pan-European, spatially explicit forest condition account, facilitating cross-country comparability and policy relevance.

The assessment follows SEEA EA guidelines. Accounting area and typology: Forests were delineated using CORINE Land Cover (CLC) classes: broad-leaved forest, coniferous forest, mixed forest, and transitional woodland and shrub, for years 2000 and 2018. To reflect biogeographic variability in reference conditions, these four classes were intersected with 11 biogeographic regions (Alpine [with separate Scandinavian mountains], Arctic, Atlantic, Black Sea, Boreal, Continental, Macaronesian, Mediterranean, Pannonian, Steppic), yielding 44 forest ecosystem types. All maps used ETRS89-Lambert Azimuthal Equal Area projection. Condition variables: Seven variables were selected to represent abiotic, biotic, and landscape-level characteristics per the SEEA Ecosystem Condition Typology: (1) vegetation water content (NDWI), (2) soil organic carbon (SOC), (3) species richness of threatened forest birds, (4) tree cover density (TCD), (5) forest productivity (NDVI), (6) forest connectivity (Forest Area Density, FAD), and (7) landscape naturalness. Selection criteria included thematic representativeness, directionality (clear relation to condition), and availability as spatially explicit, regularly updated datasets. Data sources and processing: - NDWI (water content): Three-year means to smooth interannual variability. For 2000, average of 2001–2003; for 2018, average of 2017–2019. 30 m resolution. - SOC: OCTOP 2003 (1 km) and LUCAS 2015 point samples interpolated via Gaussian kriging in ArcGIS to 1 km. Values normalized (0–1). OCTOP used to approximate 2000; LUCAS-based for 2018. Gaps (e.g., Iceland, part of Türkiye) filled with forest-type averages; no data for Macaronesian islands. - Threatened forest birds richness: Based on EU Birds Directive Article 12 (2008–2012) and Red List, selecting 24 species and 9 subspecies associated with forests. Data filtered for quality; Poland and Romania excluded from calibration due to incomplete coverage. Predictors (15 after collinearity filtering) included climate, topography, land cover shares, landscape diversity (Shannon), and NDVI metrics. A GLM (quasi-Poisson) with stepwise forward selection was calibrated on 70% of 40,575 10×10 km cells; validated on 30%, with repeated 10-fold cross-validation. Model explained 55% deviance (pseudo-R²), RMSE ≈2.13–2.15, correlation 0.74. Projections for 2000 and 2018 were produced at 5×5 km, updating dynamic predictors (forest, cropland share, summer NDVI) and holding climate constant. - Tree cover density: 100 m data; 2012 used to approximate 2000; higher TCD assumed to reflect better condition. - NDVI (productivity): MODIS MOD13Q1 (250 m). For 2000, mean of 2001–2003; for 2018, mean of 2017–2019. - Forest connectivity: FAD computed as the proportion of forest within a 23×23 cell neighborhood (529 ha) centered on each forest cell, using Guidos Toolbox; summarized per forest type. - Landscape naturalness: CLC 1 ha grid aggregated into agriculture, natural, and developed; naturalness computed as proportion of natural cells in 529 ha neighborhoods, categorized into 12 classes and summarized per forest type. Correlation between landscape variables (connectivity and naturalness) was moderate (r=0.61); other pairwise correlations were low. Reference levels and scaling: For each forest type, variables were rescaled to [0,1] using lower reference levels from the ambient distribution (degraded state) and upper reference levels from primary forest sites; where absent, least-disturbed sites in IUCN protected areas (categories Ia, Ib, II) with <5% tree cover loss since 2000 were used. Year 2000 served to define reference levels. The seven indicators were aggregated into a forest condition index via weighted sum with predefined weights: birds 0.22, trees 0.21, ndvi 0.13, fad 0.13, soc 0.12, lm 0.11, ndwi 0.08. Sensitivity analysis: One-at-a-time ±10% perturbations of weights and reference levels were applied; average sensitivity across forest types was reported. Uncertainty assessment: A semi-quantitative uncertainty level (low to high) was assigned per forest type, based on: (i) area of reference sites, (ii) representativeness of reference sites in elevation/slope/temperature/precipitation (z-score thresholds), (iii) share of forest area with naturally occurring forest class, and (iv) share of reference area with naturally occurring forest class. Spatial patterns of uncertainty were mapped.

- Spatial patterns: Among 1,964,211 km² of European forests, high condition clusters in eastern Alps, Carpathians, Scandinavia, and Black Sea shores; lower condition in Atlantic plain, British Isles, and Iberian Peninsula. - Temporal change: 63% of forest area showed increased condition from 2000 to 2018 (average increase 4.3% in those areas), while 37% declined (average loss −3.8%); 2.8% lost >10%. Across all 44 forest types, the mean condition rose from 0.566 (2000) to 0.585 (2018), a +1.9% increase. - By forest type: Condition ranged 0.31–0.78. Using a Mann–Whitney U test on 1000 random, non-autocorrelated cells, 33 forest types had significantly higher condition in 2018 vs 2000; seven showed no significant change; the four Macaronesian forest types declined significantly (−7.5% on average). - By forest class: Transitional woodland and shrub had lower condition than broad-leaved, coniferous, and mixed forests, mainly due to lower tree cover density relative to references. - By biogeographic region: Black Sea, Alpine, Continental, and Boreal regions scored above the European average; Atlantic, Mediterranean, and Macaronesian below; Arctic and Steppic lowest. Regional differences were strongly driven by species richness of threatened forest birds (highest-weighted indicator), with particularly low scores in Arctic and Macaronesian regions. - Indicator levels (Europe-wide averages, rescaled 0–1): 2000 vs 2018: NDWI 0.63→0.64; SOC 0.15→0.24; threatened forest birds 0.50→0.53; tree cover density 0.52→0.55; NDVI 0.75→0.80; connectivity 0.63→0.64; landscape naturalness 0.76→0.77. Variables closest to upper references were vegetation water content, productivity, connectivity, and naturalness; birds and tree cover reached ~50% of upper references; SOC averaged ~20% of upper reference. - Regional insets: Boreal box decreased from 0.625 (2000) to 0.605 (2018) (−2%), mainly due to lower productivity; Alpine box increased 0.648→0.682 (+3.4%) across all variables; Iberian Peninsula decreased slightly 0.515→0.512, driven by reduced tree cover density. - Sensitivity: Forest condition index was stable to parameter changes (<2.5% change). The largest effects were from weights: +10% SOC weight reduced the index by 8.1% on average; +10% landscape naturalness weight increased it by 5.7%. - Uncertainty: Reference sites tended to be at higher elevation, steeper slopes, colder temperatures, and with different precipitation than non-reference forests. 72.6% of forest area matched the naturally occurring forest class; this share varied by type (e.g., only 13% of Continental coniferous area coincided with naturally occurring conifer forest). Overall, 25% of forest area had low uncertainty, 55% low–medium, 15% medium–high, and 5% high uncertainty.

Applying the SEEA EA standard, European forests overall are in moderate condition relative to reference conditions, with two-thirds of the area improving modestly but one-third declining. Despite relatively high productivity and connectivity, persistent pressures are evident through low soil organic carbon, reduced tree cover density, and low richness of threatened forest birds relative to reference conditions. These patterns align with increased canopy mortality and disturbance regimes in recent decades and suggest that enhanced management, conservation of sensitive species, and restoration efforts—especially to rebuild soil carbon and structurally complex habitats—are needed. The selection of indicators and three-year windows captures fire impacts and productivity shifts but underscores the need to consider climate-driven changes (e.g., drying in the Mediterranean reducing productivity; warming in boreal regions boosting growth). The strong influence of bird richness on the index highlights the importance of biodiversity-sensitive management. Sensitivity analyses indicate robustness to parameter choices overall, yet index values are notably affected by indicator weights, particularly SOC and landscape naturalness, which should be considered in policy applications. Uncertainty arises from scarcity and representativeness of reference sites and mismatches with potential natural vegetation in certain regions; dynamic, periodically updated reference conditions and stricter protection of reference sites are recommended. Regular, standardized accounts can inform restoration targets, track progress, and integrate ecosystem condition into economic decision-making.

This study operationalizes a SEEA EA-compliant, spatially explicit forest condition account across Europe, providing the first large-scale test and a consistent EU-wide baseline for monitoring trends (2000–2018). By integrating seven regularly updated variables and reference-based scaling, it offers a policy-relevant tool to identify degraded areas, prioritize conservation and restoration, and mainstream ecological values in decision-making. The approach is globally applicable given the availability of analogous datasets and IUCN typologies; in the absence of primary forest references, strict protected areas can serve as proxies. For Europe, the accounts directly support the European Green Deal, biodiversity and forest strategies, and LULUCF targets, and can underpin mandatory reporting on forest condition. Future work should expand in situ monitoring, incorporate additional management-sensitive indicators (e.g., deadwood, age structure, species composition), refine weighting schemes, and adopt dynamic reference conditions, while enhancing protection of primary and old-growth forests to anchor reference baselines.

- Data availability constrained inclusion of additional informative indicators (e.g., deadwood, tree species richness, defoliation, tree growth, age structure), which often lack EU-wide, regularly updated spatial datasets. - Reliance on remote sensing can bias condition assessments; increased, regular in situ observations are needed. - Indicator weighting is inherently subjective; results are sensitive to weights, especially for SOC and landscape naturalness. - Reference conditions may be biased: primary forests are scarce and often in climatically less favorable or isolated areas; even reference sites may be affected by novel disturbances. Regional data gaps (e.g., Atlantic, Black Sea, Arctic) elevate uncertainty. - It remains challenging to disentangle anthropogenic from natural disturbances in observed condition changes. - Approximations were necessary for some years (e.g., NDVI/NDWI 3-year means; TCD 2012 for 2000; SOC 2003/2015), potentially smoothing short-term variability. - Considered but excluded pressure indicators (e.g., critical loads exceedances) could misrepresent legacy effects; condition accounts preferably use state variables (e.g., pollutant concentrations).