Boreal forests play a vital role in the global carbon cycle, yet the stability of this sink is uncertain during a period of rapid global environmental change. Swedish forest biomass has increased in recent decades, yet it remains unclear whether primary and managed secondary forests have changed similarly, and to what extent changes are driven by stand-age or shifts in growth curves. This is because intensive forest management practices in Sweden such as thinning, draining and fertilisation may obscure the effect of environmental changes, while rotational clear-cutting reduces the stand age. Here, we combine extensive National Forest Inventory data from 1983 to 2022 with random forest models to disentangle potential drivers of biomass change across forest types by isolating growth-curve and stand-age distribution effects. Our results indicate that management suppresses potential growth curve enhancement at a given stand-age as well as reducing the stand-age itself, leading to little net biomass change in managed secondary forests but large increases in primary forests. With the continued logging of unprotected primary forests, as well as the projected increase in warming, this has significant implications for the future boreal forest carbon stock.

Boreal forests mitigate climate change by sequestering atmospheric carbon. Recent declines in the boreal carbon sink and tree growth suggest that this mitigating influence is weakening, yet the underlying drivers remain unresolved. Increasing drought frequency is one likely driver. Yet most studies have focused on managed forests, overlooking the significant but shrinking areas of old-growth forests, which recent evidence suggests exhibit greater drought resistance. We analysed tree rings from Norway spruce, Scots pine, and Birch across soil-moisture gradients in 12 Swedish old-growth forests to evaluate temporal trends and climatic drivers of tree stem growth during 2012-2021. Contrary to previous studies, we found no overall recent forest growth decline, only a species-specific decline for Norway spruce in the warmest region. Following the extreme 2018 drought, Norway spruce showed pronounced growth reductions that peaked one year after the event (a lagged response) and persisted for at least three years (a legacy effect) in warm, dry areas. In contrast, Scots pine and Birch were less affected. Warming and drought (i.e. periods when evaporative demand exceeds water availability) jointly affected growth, particularly in warmer climates, but with differences among species in timing and magnitude. Tree growth in old-growth forest appeared relatively resistant to warming and drought, especially in wetter locations, although Norway spruce appears increasingly vulnerable in already warm regions. We further found that to avoid misleading interpretations of climatic effects, statistical models had to account for both the diminishing legacy effect (by including an autocorrelation structure), and the lagged drought response (by including a lagged hydroclimatic variable). Together, our findings emphasise the critical role of tree species composition and landscape-level hydrological conditions in shaping climate sensitivity of tree growth, and suggest that old-growth forests may sustain C sequestration into long-lived pools under climate change and thereby be valuable models for climate-adapted forestry.

Primary forests, with little to no direct human impact, can serve as baselines to assess land-use effects. However, most have been converted for other land-uses and the locations and characteristics of the remaining forests are poorly characterized. This study presents a map of Swedish primary forests, explores their persistence in a managed landscape, and assesses their suitability as baselines for studying land-use impacts. We mapped 384 primary forests across Sweden and assessed their naturalness using historical records. To evaluate factors influencing their persistence, we analyzed their accessibility by measuring proximity to historical roads and timber floating rivers, and their boulder coverage. Lastly, we assessed the suitability of primary forests as baselines by comparing their topographic characteristics with those of managed secondary forests. Most primary forest land (93.6

Many key ecosystem functions are affected by plant roots and their traits. However, understanding of the patterns in, and drivers of, root traits lags far behind equivalent knowledge of above-ground tissues, particularly in boreal old growth forests. We surveyed community-level root traits across 11 old growth forests spanning a wide latitudinal range in Sweden, encompassing a similar to 7(o)C range in mean annual temperature. In each forest, three plots were selected in locations with low, intermediate and high soil moisture index (SMI). We then applied principal components, redundancy analysis and linear regression to quantify how root traits co-varied with stand abiotic and biotic properties, including plant foliar area in the same stands. Stand hydrology did not affect overall mass based root trait values (tissue density, nitrogen content, carbon:nitrogen ratio, root tips, length and area per unit dry root mass), but appeared to strongly mediate the responses of these traits to environmental drivers across forests. Overall, measured root and leaf area per unit ground area were not clearly related to each other or to tree biomass across plots and forests. Measured root surface area per unit ground area was much greater than literature estimates from similar forests, and similar to 3 times greater than leaf area per unit ground area recorded from the same forests. Our results provide a valuable baseline for root traits in old growth boreal forests, redefining what are considered typical or natural root properties for the biome and highlighting key environmental controls.

Accurately distinguishing between natural forests and planted forests underpins effective biodiversity monitoring and climate policy, yet their spectral similarity and limited reference data challenge large-scale classification efforts. This study focuses on Guangxi Province, China, combining Sentinel-2 imagery, multi-source ecological indicators—including vegetation traits such as leaf chlorophyll content and LAI—and over 12 million visually interpreted forest stands to map the distribution of natural and planted forests. We assessed pixel- and object-based classification models under various sample scenarios, with accuracy independently validated using additional samples interpreted from high-resolution Google Earth imagery. Model evaluation relied primarily on random splits, supplemented with spatially stratified validation across ecological sub-regions to assess spatial generalization. Results show that the object-based random forest classifier achieved the highest accuracy (OA: 84.52%, F1: 84.04%), with topographic and vegetation functional traits as key predictors. Spatially, natural forests dominate mountainous zones, while planted forests prevail in flatter areas. This work delivers the first 10 m resolution forest type map for Guangxi based on a uniquely large training and validation dataset and demonstrates that combining functional traits with robust sampling improves classification performance. Our framework supports scalable forest monitoring and contributes to improved management and conservation strategies.

Boreal forests provide considerable global land carbon storage and uptake, but they are being rapidly transformed to managed secondary forests, with poorly quantified implications for ecosystem carbon storage. Here we present data from extensive mapping and field inventories of carbon storage in primary forests in Sweden and use multiple methods to show that primary forests store ~72% (70 to 74% across methods) more carbon than managed secondary forests in vegetation, deadwood, soils, and harvested wood products combined. Soils constitute both the largest carbon store and the largest difference between these forest types. The total carbon storage difference between primary and managed secondary forests is 2.7 to 8.0 times larger than previous estimates. Our results challenge estimated past and future contributions of boreal forest management to atmospheric carbon dioxide concentrations.

AbstractForests have seen a strong greening trend worldwide, and previous studies have attributed this mainly to land‐use conversions such as afforestation. However, for the greening of existing forests, the role of human interventions is unclear. Here we paired neighboring natural and planted forests in Southern China to minimize the differences between the forest types and analyzed the vegetation index EVI2 from Landsat over 1987 to 2021. The EVI2 trends observed in natural forests can be seen as mainly responses to large‐scale environmental changes, whereas the difference between the forest types represents the impact caused by human interventions. We found that though the mean EVI2 of planted forests was comparable to that of natural forests, the greening trends were overall 7.0% lower in planted forests. Our results suggest that human interventions associated with planted forests did not accelerate their greening, indicating the necessity for refined policies to enhance future forest greening.

Aim: Earth observation-based estimates of land-atmosphere exchange of carbon are essential for understanding the response of the terrestrial biosphere to climatic change and other anthropogenic forcing. Temperature, soil water content and gross primary production are the main drivers of ecosystem respiration (R-eco), and the main aims of this study are to develop an R-eco model driven by long-term global-scale Earth observations and to study R-eco spatiotemporal dynamics 1982-2015.Location: Global scale.Time Period: 1982-2015.Major Taxa StudiedTerrestrial ecosystems.Methods: We parameterized and applied a global R-eco model for 1982-2015 using novel Earth observation-based data. We studied the relationships between R-eco measured at field sites globally and land surface temperature, gross primary production and soil water content. Trends 1982-2015 were quantified, and the contributions from terrestrial regions to the spatiotemporal variability were evaluated.Results: The R-eco model (LGS-R-eco) captured the between-site and intra- and interannual variability in field-observed Reco and soil respiration well in comparison with other Earth observation-based products. The global annual R-eco was on average 105.6 +/- 2.3 Pg C for 1982-2015, which is close to 105 Pg C according to residuals of the carbon exchange processes within the global carbon budgets. The trend in global terrestrial R-eco 1982-2015 was 0.19 +/- 0.02 Pg C y-1, with the strongest positive trends found in cropland areas, whereas negative trends were primarily observed for savannah/shrublands of Southern Africa and South America. Trends were especially strong during the eighties and nineties, but substantially smaller 1998-2015.Main Conclusions: The LGS-R-eco model revealed a substantial increase in global R-eco during recent decades. However, the growth rates of global R-eco were slower during 1998-2015, partially explaining the reduced growth rates of atmospheric CO2 during this period. The LGR-R-eco product may be an essential source for studying carbon sources and sinks and functioning of the Earth system.

Soil organic matter decomposition and its interactions with climate depend on whether the organic matter is associated with soil minerals. However, data limitations have hindered global-scale analyses of mineral-associated and particulate soil organic carbon pools and their benchmarking in Earth system models used to estimate carbon cycle–climate feedbacks. Here we analyse observationally derived global estimates of soil carbon pools to quantify their relative proportions and compute their climatological temperature sensitivities as the decline in carbon with increasing temperature. We find that the climatological temperature sensitivity of particulate carbon is on average 28% higher than that of mineral-associated carbon, and up to 53% higher in cool climates. Moreover, the distribution of carbon between these underlying soil carbon pools drives the emergent climatological temperature sensitivity of bulk soil carbon stocks. However, global models vary widely in their predictions of soil carbon pool distributions. We show that the global proportion of model pools that are conceptually similar to mineral-protected carbon ranges from 16 to 85% across Earth system models from the Coupled Model Intercomparison Project Phase 6 and offline land models, with implications for bulk soil carbon ages and ecosystem responsiveness. To improve projections of carbon cycle–climate feedbacks, it is imperative to assess underlying soil carbon pools to accurately predict the distribution and vulnerability of soil carbon.

The ongoing climate change can modulate the behavior of global vegetation and influence the terrestrial biosphere carbon sink. Past observation-based studies have mainly focused on the linear trend or interannual variability of the vegetation greenness, but could not explicitly deal with the effect of natural decadal variability due to the short length of observations. Here we put the variabilities revealed by remote sensing-based global leaf area index (LAI) from 1982 to 2015 into a long-term perspective with the help of ensemble Earth system model simulations of the historical period 1850–2014, with a focus on the low-frequency variability in the global LAI during the growing season. Robust decadal variability in the observed and modelled LAI was revealed across global terrestrial ecosystems, and it became stronger toward higher latitudes, accounting for over 50% of the total variability north of 40°N. The linkage of LAI decadal variability to major natural decadal climate modes, such as the El Niño–Southern Oscillation decadal variability (ENSO-d), the Pacific decadal oscillation (PDO), and the Atlantic multidecadal oscillation (AMO), was analyzed. ENSO-d affects LAI by altering precipitation over large parts of tropical land. The PDO exerts opposite impacts on LAI in the tropics and extra-tropics due to the compensation between the effects of temperature and growing season length. The AMO effects are mainly associated with anomalous precipitation in North America and Europe but are mixed with long-term climate change impacts due to the coincident phase shift of the AMO which also induces North Atlantic basin warming. Our results suggest that the natural decadal variability of LAI can be largely explained by these decadal climate modes (on average 20% of the variance, comparable to linear changes, and over 40% in some ecosystems) which also can be potentially important in inducing the greening of the Earth of the past decades.

The determinants of fire-driven changes in soil organic carbon (SOC) across broad environmental gradients remains unclear, especially in global drylands. Here we combined datasets and field sampling of fire-manipulation experiments to evaluate where and why fire changes SOC and compared our statistical model to simulations from ecosystem models. Drier ecosystems experienced larger relative changes in SOC than humid ecosystems—in some cases exceeding losses from plant biomass pools—primarily explained by high fire-driven declines in tree biomass inputs in dry ecosystems. Many ecosystem models underestimated the SOC changes in drier ecosystems. Upscaling our statistical model predicted that soils in savannah–grassland regions may have gained 0.64 PgC due to net-declines in burned area over the past approximately two decades. Consequently, ongoing declines in fire frequencies have probably created an extensive carbon sink in the soils of global drylands that may have been underestimated by ecosystem models.

Globally, northern peatlands are major carbon deposits with important implications for the climate system. It is therefore crucial to understand their spatial occurrence, especially in the context of peatland degradation by land cover change and climate change. This study was aimed at mapping peatlands in the forested landscape of Sweden by modelling soil data against lidar-based terrain indices. Machine learning methods were used to produce nationwide raster maps at 10 m spatial resolution indicating the presence or not of peatlands. Four different definitions of peatlands were examined: 30, 40, 50 and 100 cm thickness of the organic horizon. Depending on peatland definition, testing with a hold-out dataset indicated an accuracy of 0.89-0.91 and Matthew's correlation coefficient of 0.79-0.81. The final maps showed a national forest peatland extent of 60 292-71 996 km(2), estimates which are in the range of previous studies employing traditional soil maps. In conclusion, these results emphasize the possibilities of mapping boreal peatlands with lidar-based terrain indices. The final peatland maps are publicly available at (Rimondini et al., 2023) and may be employed for spatial planning, estimating carbon stocks and evaluating climate change mitigation strategies.

Abstract Widespread changes in the intensity and frequency of fires across the globe are altering the terrestrial carbon (C) sink 1–4 . Although the changes in ecosystem C have been reasonably well quantified for plant biomass pools 5–7 , an understanding of the determinants of fire-driven changes in soil organic C (SOC) across broad environmental gradients remains unclear, especially in global drylands 3,4,7–9 . Here, we combined multiple datasets and original field sampling of fire manipulation experiments to evaluate where and why fire changes SOC the most, built a statistical model to estimate historical changes in SOC, and compared these estimates to simulations from ecosystem models. We found that drier ecosystems experienced larger relative changes in SOC than humid ecosystems—in some cases exceeding losses from plant biomass pools—primarily explained by high fire-driven declines in tree biomass inputs in dry ecosystems. Ecosystem models provided more mixed insight into potential SOC changes because many models underestimated the SOC changes in drier ecosystems. Upscaling our statistical model predicted that soils in 1.57 million km 2 savanna-grassland regions experiencing declines in burned area over the past ca. two decades may have 23% more SOC, equating to 1.78 PgC in topsoils. Consequently, ongoing declines in fire frequencies have likely created an extensive carbon sink in the soils of global drylands that may have been underestimated by ecosystem models.

Dead standing trees (DSTs) generally decompose slower than wood in contact with the forest floor. In many regions, DSTs are being created at an increasing rate due to accelerating tree mortality caused by climate change. Therefore, factors determining DST fall are crucial for predicting dead wood turnover time but remain poorly constrained. Here, we conduct a re-analysis of published DST fall data to provide standardized information on the mean time to fall (MTF) of DSTs across biomes. We used multiple linear regression to test covariates considered important for DST fall, while controlling for mortality and management effects. DSTs of species killed by fire, insects and other causes stood on average for 48, 13 and 19 years, but MTF calculations were sensitive to how tree size was accounted for. Species' MTFs differed significantly between DSTs killed by fire and other causes, between coniferous and broadleaved plant functional types (PFTs) and between managed and unmanaged sites, but management did not explain MTFs when we distinguished by mortality cause. Mean annual temperature (MAT) negatively affected MTFs, whereas larger tree size or being coniferous caused DSTs to stand longer. The most important explanatory variables were MAT and tree size, with minor contributions of management and plant functional type depending on mortality cause. Our results provide a basis to improve the representation of dead wood decomposition in carbon cycle assessments.

Boreal forest ecosystems are predicted to experience more frequent summer droughts due to climate change, posing a threat to future forest health and carbon sequestration. Forestry is a regionally dominant land use where the managed secondary forests are typically even-aged forests with low structural and tree species diversity. It is not well known if managed secondary forests and unmanaged primary forests respond to drought differently in part because the location of primary, unmanaged, forests has remained largely unknown. Here we employed a unique map detailing over 300 primary forests in Sweden. We studied impacts of the 2018 nationwide drought by extracting and analyzing a high-resolution remote sensing vegetation index over the primary forests and over buffer zones around the primary forests representing secondary forests. We controlled for topographical variations linked to soil moisture, which was a strong determinant of drought responses, and analyzed Landsat-derived EVI2 anomalies during the drought year from a multiyear non-drought baseline. We found that primary forests were less affected by the drought compared to secondary forests. Our results indicate that forestry may exacerbate the impact of drought in a future climate with more frequent and extreme hydroclimatic events.

Soils contain the largest actively-cycling terrestrial carbon pool, which is itself composed of chemically heterogeneous and measurable pools that vary in their persistence. Fundamental uncertainties in terrestrial carbon-climate feedbacks still depend on the timing, sign, and magnitude of the response of soil carbon, and its underlying pools, to environmental change. However, model comparisons typically focus on benchmarking only bulk soil carbon stocks and climatological temperature sensitivities. Underlying microbial and mineral-associated pools, and their response to global change, have received increasing attention among empirical studies, yet data limitations still hinder benchmarking of these pools and processes in models at ecosystem- to global-scales. Here we examined the distribution of carbon within particulate and mineral-associated fractions across an ensemble of global soil biogeochemical models, and compared model estimates to a global database of soil fractions. We found that, while bulk soil carbon stocks were seemingly comparable in magnitude and geographic distribution across the models and observations, the spread in underlying pools was much more pronounced. Indeed, the ensemble of models varied nearly 6-fold in the proportion of carbon in mineral-associated fractions, and the majority of models greatly underestimated mineral-associated carbon stocks compared to the observations. Latitudinal differences between the models resulted in divergent pool-specific climatological temperature sensitivities, with implications on projections to global change scenarios. Our study elucidates key structural and theoretical differences between models that drive divergent soil carbon projections, and clearly highlights the need to benchmark underlying carbon pools, in addition to bulk soil carbon stocks.

Abstract Land ecosystems contribute to climate change mitigation by taking up approximately 30% of anthropogenically emitted carbon. However, estimates of the amount and distribution of carbon uptake across the world's ecosystems or biomes display great uncertainty. The latter hinders a full understanding of the mechanisms and drivers of land carbon uptake, and predictions of the future fate of the land carbon sink. The latter is needed as evidence to inform climate mitigation strategies such as afforestation schemes. To advance land carbon cycle modeling, we have developed a matrix approach. Land carbon cycle models use carbon balance equations to represent carbon exchanges among pools. Our approach organizes this set of equations into a single matrix equation without altering any processes of the original model. The matrix equation enables the development of a theoretical framework for understanding the general, transient behavior of the land carbon cycle. While carbon input and residence time are used to quantify carbon storage capacity at steady state, a third quantity, carbon storage potential, integrates fluxes with time to define dynamic disequilibrium of the carbon cycle under global change. The matrix approach can help address critical contemporary issues in modeling, including pinpointing sources of model uncertainty and accelerating spin‐up of land carbon cycle models by tens of times. The accelerated spin‐up liberates models from the computational burden that hinders comprehensive parameter sensitivity analysis and assimilation of observational data to improve model accuracy. Such computational efficiency offered by the matrix approach enables substantial improvement of model predictions using ever‐increasing data availability. Overall, the matrix approach offers a step change forward for understanding and modeling the land carbon cycle.