Higher aboveground carbon stocks at forest edges

Tree-size dimension inequality shapes aboveground carbon stock across temperate forest strata along environmental gradients

Boreal forests play an important role in climate change mitigation, biodiversity conservation and the provision of vital ecosystem services. Changing climate is likely to increase the frequency and the severity of forest disturbances. Hence, increasing disturbances may offset the past and ongoing efforts to increase forest-based mitigation and halt biodiversity loss. Therefore, understanding the dynamics of forest ecosystems and predicting their responses to management, changing climate and disturbance regimes is vital.  While forest disturbance risk prevention measures i.e., adaptive management, offer solutions to safeguard future timber yields, the effects of adaptive management on biodiversity, climate change mitigation potential of forests and other ecosystem services have received little attention. In addition, it remains unknown whether climate change alters disturbance regimes in a way that cancels out efforts to increase and preserve carbon stocks and protect forest biodiversity. In this study we contrast the effects of mitigation versus adaptation forest management on the resilience of boreal forest ecosystems in a changing climate. We address the following questions: i) How timber harvests, forest carbon stocks and disturbed volumes develop in different forest management and land-use options that emphasize either adaptation or mitigation under different climate scenarios? ii) What are the synergies and trade-offs in ecosystem service and biodiversity indicators in adaptation and mitigation options? To address these questions, we used the process-based forest landscape and disturbance model iLand to dynamically simulate interactions of forest management, climate change and disturbances. We simulated combinations of seven forest management scenarios and three climate scenarios with ten replicate runs for 80 years in Finland. The forest management scenarios included a business-as-usual scenario and mitigation and adaptation scenarios with changes in rotation lengths and in the shares of deciduous trees in regeneration. Mitigation managements resulted in on average 6 to 15% higher carbon stocks over the simulation period compared to business-as-usual even when disturbances were accounted for but even halved the annual harvests. Mitigation management generally increased the amount of deadwood (3-21%) and large diameter trees (10-52%) compared to business-as-usual management but the severity of climate change reduced the positive trend on large diameter trees. Adaptive management reduced especially the bark beetle disturbances but, in some cases, the disturbed volumes were even higher than business-as-usual management because of increased wind damages. Generally, over the simulation period, adaptive management had a small positive impact on deadwood and mixed effects on large diameter trees.  Scenic beauty was impacted very little by climate change or management. Our findings highlight the complex interactions between disturbance risk prevention, biodiversity, carbon storage and sequestration and other ecosystem services. The results help to guide forest managers and policymakers in planning conservation and mitigation efforts, maximizing multiple benefits and enhancing forest resilience under a changing climate.

Biological carbon sequestration by forest ecosystems plays an important role in the net balance of greenhouse gases, acting as a carbon sink for anthropogenic CO2 emissions. Nevertheless, relatively little is known about the abiotic environmental factors (including climate) that control carbon storage in temperate and boreal forests and consequently, about their potential response to climate changes. From a set of more than 94,000 forest inventory plots and a large set of spatial data on forest attributes interpreted from aerial photographs, we constructed a fine-resolution map (∼375 m) of the current carbon stock in aboveground live biomass in the 435,000 km2 of managed forests in Quebec, Canada. Our analysis resulted in an area-weighted average aboveground carbon stock for productive forestland of 37.6 Mg ha−1, which is lower than commonly reported values for similar environment. Models capable of predicting the influence of mean annual temperature, annual precipitation, and soil physical environment on maximum stand-level aboveground carbon stock (MSAC) were developed. These models were then used to project the future MSAC in response to climate change. Our results indicate that the MSAC was significantly related to both mean annual temperature and precipitation, or to the interaction of these variables, and suggest that Quebec’s managed forests MSAC may increase by 20% by 2041–2070 in response to climate change. Along with changes in climate, the natural disturbance regime and forest management practices will nevertheless largely drive future carbon stock at the landscape scale. Overall, our results allow accurate accounting of carbon stock in aboveground live tree biomass of Quebec’s forests, and provide a better understanding of possible feedbacks between climate change and carbon storage in temperate and boreal forests.

Insect outbreaks are major drivers of natural disturbances in forest ecosystems. Outbreaks can have both direct and indirect effects on the composition of soil arthropod communities through canopy opening, nutrient addition and predator-prey interactions. In this study, we aimed to understand the effects of forest tent caterpillar (Malacosoma disstria; FTC) outbreaks through cascading effects on ant communities in both temperate and boreal forests in Canada. Pitfall traps and Berlese funnels were used to compare the ant communities, as well as the surrounding arthropod communities, between control and outbreak sites in boreal and temperate forests (in Quebec, Canada). Using the Sørensen dissimilarity index, we determined the alpha and beta diversity of the ant community. Other arthropods collected in the traps were counted to evaluate the richness and abundance of potential prey for the ants and other potential predators of the FTC. We used an indicator species analysis to examine the species associated with sites defoliated by the outbreak. In the boreal forest, we found that FTC outbreaks caused decreases in species richness and increases in the evenness of ant communities in defoliated sites. In the boreal forest sites, species composition varied significantly between control and outbreak sites. This pattern was driven in part by the presence of other predators. A similar, but weaker pattern was observed in the temperate forest. We saw no changes in the beta diversity in the boreal forest, but did see a significant decrease in the temperate forest between the outbreak sites and the control sites. Ant species in the boreal forest tended to exhibit a more marked preference for either control or previously defoliated sites than species in the temperate forest. Our study showed that disturbances such as insect outbreaks can drive changes in the ant community. While we saw small effects of outbreaks, manipulation experiments using resource addition could help us validate the mechanisms behind these relationships.

The relative strengths of the effects of forest edges and recreational use on understorey vegetation were studied at sub-xeric boreal urban forest edges in the greater Helsinki region, Finland. The study was performed at northern, eastern, southern and western edges, and vegetation sample plots were placed on, next to and away from paths with different trampling intensities 0–107 m from the forest edges. We found that human trampling altered vegetation more than the effects of forest edges. Vegetation changed dramatically on paths and the effects of path edges were seen in seemingly untrampled vegetation at least up to 4 m from the path edge. However, our results suggested that the effect of the edge may penetrate up to 50 m into forest interiors. Changes in vegetation composition indicated that the effects of the edge were stronger at eastern, southern and western than at northern edges.

Tropical agroforestry systems can mitigate climate change through carbon storage while supporting biodiversity and improving livelihoods. This study evaluates the potential of coffee agroforestry systems in the Gayo Highlands, Indonesia, to achieve carbon storage levels comparable to secondary and primary forests. Vegetation data are collected across three locations, covering coffee agroforestry, secondary forest, and primary forest. We found that primary forests had the highest carbon stocks, with 223.53 tC ha−1 in Location 1, 194.68 tC ha−1 in Location 2, and 542.55 tC ha−1 in Location 3. Coffee agroforestry in Location 1 had carbon stocks comparable to secondary forest, while in Location 3, it showed the lowest carbon stock (6.26 tC ha−1). Higher carbon stocks were linked to greater tree species richness, larger DBH, and higher basal areas. Additionally, we observed a positive relationship between tree basal area and aboveground carbon stock, confirming that forests with higher basal area values exhibit greater carbon storage capacity. The findings underscore the critical role of mature trees in carbon storage and highlight the value of preserving mature forest species in coffee agroforestry systems.

Monitoring temporal changes of aboveground carbon (AGC) stocks distribution in subtropical thicket is key to understanding the role of vegetation in carbon sequestration. The main objectives of this research paper were to model and quantify the temporal changes of AGC stocks between 1972 and 2010 in the Great Fish River Nature Reserve and its environs, Eastern Cape Province, South Africa. We used a method based on the integration of remote sensing and geographical information systems to estimate AGC stocks in a time series framework. A non-linear regression model was developed using Normalised Difference Vegetation Index values generated from SPOT 5 High Resolution Geometric satellite imagery of 2010 as an independent variable and AGC stock estimates from field plots as the dependent variable. The regression model was used to estimate AGC stocks from satellite imagery for 1972 (Landsat TM), 1982 (Landsat 4 TM), 1992 (Landsat 7 ETM), 2002 (Landsat ETM+) and 2010 (SPOT 5) satellite imagery. AGC stocks for the respective years were compared by means of change detection analysis at the subtropical thicket class level. The results showed a decline of AGC stocks in all the classes from 1972 to 2010. Degraded and transformed thicket classes had the highest AGC stock losses. The decline of AGC stocks was attributed to thicket transformation and degradation, which were attributed to anthropogenic activities.

The ‘mass ratio’ hypothesis states that ecosystem functioning is driven by the functional traits of the most dominant species in communities. Thus, we aimed to evaluate (i) How topographical conditions and stand age determine changes in tree community composition, richness, abundance and carbon dominant (CD) species, and (ii) Assess whether community-weighted mean of functional trait values of CD species explain aboveground carbon (AGC) stock. We used community-weighted mean of wood density and maximum stem diameter to evaluate the effect of functional dominance in AGC stock. We found that different topographic conditions and stand age change community composition, richness, abundance and CD species along the late-secondary stage. Our results showed that functional trait values of CD species determine AGC stock. Thus, the proportion of CD species was shaped by topography and stand age, whereas carbon stock by the dominant species’ functional traits (wood density and diameter). This study advances our understanding of the mechanisms that drive carbon stock in tropical forests and supports the ‘mass ratio’ hypothesis. We emphasize the relevance of the trait-based approach to understand forest functioning and trait functional composition and taxonomic identity for carbon storage, recovery and increase in secondary Atlantic Forests.

BackgroundAlthough great efforts have been made to quantify mangrove carbon stocks, accurate estimations of below-ground carbon stocks remain unreliable. In this study, we examined the distribution patterns of mangrove carbon stocks in China and other countries using our own field survey data and datasets from published literature. Based on these data, we investigated the possible relationships between above-ground carbon stock (AGC) and below-ground carbon stock (BGC) for mangrove forests, aiming to provide a scientific basis for estimation of total mangrove carbon stocks.ResultsThe average above-ground carbon stock in each region was sizeable (ranging from 12.0 to 150.2 Mg/ha), but average below-ground carbon stock was dominant (ranging from 46.6 to 388.6 Mg/ha), accounting for 69–91% of total carbon stock at the sites studied in China. Significant positive relationships were found between above-ground and below-ground mangrove carbon stocks, with the best fitting equation as BGC = 1.58 * AGC + 81.06 (Mg/ha, R2 = 0.62, p < 0.01, n = 122) for China. Such linear relationships vary for mangrove forests of different types and locations, from different geographical regions in China to other countries worldwide.ConclusionThe positive relationship we found between above- and below-ground carbon stocks of mangrove forests in China and worldwide can facilitate more accurate assessments of mangrove blue carbon stocks at regional or global scales using modern techniques including remote sensing.

Temperate and boreal forests are increasingly suffering from anthropic degradation. Ectomycorrhizal fungi (EMF) are symbionts with most temperate and boreal forest trees, providing their hosts with soil nutrients and water in exchange for plant carbon. This group of fungi is involved in woody plants’ survival and growth and helps plants tolerate harsh environmental conditions. Here, we describe the current understanding of how EMF can benefit temperate and boreal forest restoration projects. We review current evidence on promising restoration plans that actively use EMF in sites contaminated with heavy metals, affected by soil erosion, and degraded due to clearcut logging and wildfire. We discuss the potential role of this group of fungi for restoring sites invaded by non-native plant species. Additionally, we explore limitations, knowledge gaps, and possible undesired outcomes of the use of EMF in forest restoration, and we suggest how to further incorporate this fungal group into forest management. We conclude that considering EMF – host interactions could improve the chances of success of future restoration programs in boreal and temperate forests.

Improved forest management may offer climate mitigation needed to hold warming to below 2°C. However, uncertainties persist about the effects of harvesting intensity on forest carbon sequestration, especially when considering interactions with regional climate and climate change. Here, we investigated the combined effects of harvesting intensity, climatic aridity, and climate change on carbon stocks in Douglas-fir [Pseudotsuga menziesii Mirb. (Franco)] stands. We used the Carbon Budget Model of the Canadian Forest Sector to simulate the harvest and regrowth of seven Douglas-fir stand types covering a 900 km-long climate gradient across British Columbia, Canada. In particular, we simulated stand growth under three regimes (+17%, −17% and historical growth increment) and used three temperature regimes [historical, representative concentration pathways (RCP) 2.6 and RCP 8.5]. Increasing harvesting intensity led to significant losses in total ecosystem carbon stocks 50 years post-harvest. Specifically, forests that underwent clearcutting were projected to stock about 36% less carbon by 2,069 than forests that were left untouched. Belowground carbon stocks 50 years into the future were less sensitive to harvesting intensity than aboveground carbon stocks and carbon losses were greater in arid interior Douglas-fir forests than in humid, more productive forests. In addition, growth multipliers and decay due to the RCP’s had little effect on total ecosystem carbon, but aboveground carbon declined by 7% (95% confidence interval [−10.98, −1.81]) in the high emissions (RCP8.5) scenario. We call attention to the implementation of low intensity harvesting systems to preserve aboveground forest carbon stocks until we have a more complete understanding of the impacts of climate change on British Columbia’s forests.

Forest carbon emission mitigation schemes seek to protect tropical forest, combat effects of climate change, and offer potential cash and development opportunities. Reducing emissions from deforestation and degradation (REDD+) projects based on a foundation of accurate carbon stock assessment provide such an opportunity for Papua New Guinea. The objective of this study was to quantify the carbon stock of the central forests of Manus Island, Papua New Guinea, and identify factors that underpin any observed variation within it. We employed the Winrock Standard Operating Procedures for Terrestrial Carbon Measurement for plots and associated measurements. In 75 variable-radius nested plots (total area = 14.4 ha), we assessed above-ground and total carbon stock of stems ≥5 cm diameter at breast height via general linear models in a model-selection framework. The top models described variation in average carbon stock at 95% lower and upper confidence interval in above-ground biomass solely in terms of forest type: primary hill forest 165.0 Mg C ha–1 (148.3–183.7, n = 48), primary plain forest 100.9 Mg C ha–1 (78.0–130.6, n = 10) and secondary hill forests 99.7 Mg C ha–1 (80.9–122.9, n = 17). To a lesser extent, above-ground carbon stock increased with slope and varied idiosyncratically by the nearest village. Our estimates are comparable with published studies for Papua New Guinea and the wider tropical region. These data should strengthen pre-existing knowledge and inform policies on carbon accounting for REDD+ projects in the region.

Landscape fragmentation has created large areas of forest edge. Understanding how soil organic carbon (SOC) stocks within forest edges respond to fragmentation is essential to assess carbon budgets; however, the causes and magnitude of edge effects on SOC stocks have been poorly characterized. Here, we quantify the edge effects on SOC stocks within and across urban to rural forest patches from three fragmented urban forests to a large patch of rural forest. The SOC stocks within 20 m of the rural forest edge (1.86kgCm−2) is on average 80% lower than the interiors of rural forest (10.47kgCm−2). We found that biotic factors, including annual litterfall mass (R2 > 0.94), peak leaf area index (R2 > 0.92), and fine-root mass density (R2 > 0.77), explained the spatial variation in SOC stocks within the rural forest. In urban forests, human activities at forest edges led to contrasting edge effects on SOC stocks, for instance, the SOC stocks at the east edges (4.74 kgC m−2) were 56% greater than at the west edges (3.03 kgC m−2) explained by the adjacent land uses (e.g., paved roads vs. non-paved soils) and in-situ litterfall management. We also found significant differences in summer soil temperature (ΔTS > 2.8 °C) and soil moisture (ΔVWC > 0.05 m3 m−3) between the east and west forest edges. Our results reveal that the factors responsible for the edge effects on SOC stocks in rural forests are biotic factors, while heterogeneous human activities at the local scale lead to complex edge effects on urban forest SOC stocks.

The correct estimation of forest aboveground carbon stocks (AGCs) allows for an accurate assessment of the carbon sequestration potential of forest ecosystems, which is important for in-depth studies of the regional ecological environment and global climate change. How to estimate forest AGCs quickly and accurately and realize dynamic monitoring has been a hot topic of research in the forestry field worldwide. LiDAR and remote sensing optical imagery can be used to monitor forest resources, enabling the simultaneous acquisition of forest structural properties and spectral information. A high-density LiDAR-based point cloud cannot only reveal stand-scale forest parameters but can also be used to extract single wood-scale forest parameters. However, there are multiple forest parameter estimation model problems, so it is especially important to choose appropriate variables and models to estimate forest AGCs. In this study, we used a Duraer coniferous forest as the study area and combined LiDAR, multispectral images, and measured data to establish multiple linear regression models and multiple power regression models to estimate forest AGCs. We selected the best model for accuracy evaluation and mapped the spatial distribution of AGC density. We found that (1) the highest accuracy of the multiple multiplicative power regression model was obtained for the estimated AGC (R2 = 0.903, RMSE = 10.91 Pg) based on the LiDAR-estimated DBH; the predicted AGC values were in the range of 4.1–279.12 kg C. (2) The highest accuracy of the multiple multiplicative power regression model was obtained by combining the normalized vegetation index (NDVI) with the predicted AGC based on the DBH estimated by LiDAR (R2 = 0.906, RMSE = 10.87 Pg); the predicted AGC values were in the range of 3.93–449.07 kg C. (3) The LiDAR-predicted AGC values and the combined LiDAR and optical image-predicted AGC values agreed with the field AGCs.

ABSTRACTThe Sentinel-1 satellites provide the formerly unprecedented combination of high spatial and temporal resolution of dual polarization synthetic aperture radar data. The availability of dense time series enables the derivation and analysis of temporally filtered annual backscatter signals. The study concentrates on the use of Sentinel-1 seasonal backscatter signatures for forest area estimation and forest type classification. A classification method based on time series similarity measures is introduced and tested in three test areas covered by various forest types including broadleaf temperate, boreal and montane forests. The results are compared with two European-wide Copernicus high resolution layers, namely forest type and tree cover density (TCD). The correspondence of forest/non-forest maps and TCD is high in all test areas, with overall accuracies for forest/non-forest classification between 86% and 91% and Pearson correlation coefficients for TCD between 0.68 and 0.74. The forest type classification (non-forest, coniferous and broadleaf forest classes) provides best results in temperate forests with an overall accuracy of 85%; in boreal forest, the accuracy decreases to only 65%. Generally, the method provides reliable results for forest area estimation, including regions where methods based on static parameters are often problematic (mountainous areas), and it enables forest type classification in temperate forests.