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• Disturbances have altered community dynamics in boreal forests with unknown consequences for belowground ecological processes. Soil fungi are particularly sensitive to such disturbances; however, the individual response of fungal guilds to different disturbance types is poorly understood. • Here, we profiled soil fungal communities in lodgepole pine forests following a bark beetle outbreak, wildfire, clear-cut logging, and salvage-logging. Using Illumina MiSeq to sequence ITS1 and SSU rDNA, we characterized communities of ectomycorrhizal, arbuscular mycorrhizal, saprotrophic, and pathogenic fungi in sites representing each disturbance type paired with intact forests. We also quantified soil fungal biomass by measuring ergosterol. • Abiotic disturbances changed the community composition of ectomycorrhizal fungi and shifted the dominance from ectomycorrhizal to saprotrophic fungi compared to intact forests. The disruption of the soil organic layer with disturbances correlated with the decline of ectomycorrhizal and the increase of arbuscular mycorrhizal fungi. Wildfire changed the community composition of pathogenic fungi but did not affect their proportion and diversity. Fungal biomass declined with disturbances that disrupted the forest floor. • Our results suggest that the disruption of the forest floor with disturbances, and the changes in C and nutrient dynamics it may promote, structure the fungal community with implications for fungal biomass–C.

Fires shape the biogeochemistry and functioning of many ecosystems, and fire frequencies are changing across much of the globe. Frequent fires can change soil carbon (C) and nitrogen (N) storage by altering the quantity and chemistry of plant inputs through changes in plant biomass and composition as well as the decomposition of soil organic matter. How decomposition rates change with shifting inputs remains uncertain because most studies focus on the effects of single fires, where transient responses may not reflect responses to decadal changes in burning frequencies. Here, we sampled seven sites exposed to different fire frequencies. In four of the sites, we intensively sampled both soils and plant communities across four ecosystems in North America and Africa spanning tropical savanna, temperate coniferous savanna, temperate broadleaf savanna, and temperate coniferous forest ecosystems. Each site contained multiple plots burned frequently for 33–61 years and nearby plots that had remained unburned over the same period replicated at the landscape scale. Across all sites, repeatedly burned plots had 25–185% lower bulk soil C and N concentrations but also 2–10-fold lower potential decomposition of organic matter compared to unburned sites. Soil C and N concentrations and extracellular enzyme activities declined with frequent fire because fire reduced both plant biomass inputs into soils and dampened the localized enrichment effect of tree canopies. Examination of soil extracellular enzyme activities revealed that fire decreased the potential turnover of organic matter in the forms of cellulose, starch, and chitin (𝑃 < 0.0001) but not polyphenol and lignin (𝑃 = 0.09), suggesting a shift in soil C and N cycling. Inclusion of 𝛿13C data from three additional savanna sites (19–60 years of altered fire frequencies) showed that soil C losses were largest in sites where estimated tree inputs into soils declined the most (𝑟² = 0.91, 𝑃 < 0.01). In conclusion, repeated burning reduced C and N storage, consistent with previous studies, but fire also reduced potential decomposition, likely contributing to slower C and N cycling. Trees were important in shaping soil C and N responses across sites, but the magnitude of tree effects differed and depended on how tree biomass inputs into soil responded to fire.

[This corrects the article DOI: 10.1371/journal.pone.0263488.].[This corrects the article DOI: 10.1371/journal.pone.0263488.].

Coniferous forest soils have an indispensable ecological role in the global cycles of nutrients on Earth. Despite the fact that microbial communities in this ecosystem were subject of multiple studies, the involvement of individual taxa in the processes of organic matter transformation and the functional roles of dominant and active bacteria are largely unknown. Here, we have performed a comprehensive isolation effort to obtain multiple dominant bacterial taxa from a Picea abies forest soil and provide their physiological characterization. This information allows us to link ecological traits with groups of microorganisms. In the study, conventional culture techniques at acidic pH and low-nutrient content led to the recovery of 299 bacterial isolates. The isolates represented operational taxonomic units (OTUs) that contained 20 and 32 % of all bacterial genomes detected in the litter and soil by 16S amplicon analysis, including some of those bacterial strains representing the most abundant and active OTUs. These included also several isolates of the still underexplored phylum of the Acidobacteria, all of them belonging to the subdivision 1 of the phylum. Acidobacterial isolates produced the widest range of enzymes among all isolates and highest enzyme activities in acidic conditions. Moreover, members of the Acidobacteria represented more than 50 % of the isolates able to grow on disaccharides produced during the breakdown of cellulose, chitin, and starch. Our results indicate that Acidobacteria may play an important ecological role by degrading polysaccharides of plant and fungal origin in the important ecosystems of acidic coniferous forests.

Here, we investigated the capabilities of a lightweight unmanned aerial vehicle (UAV) photogrammetric point cloud for estimating forest biophysical properties in managed temperate coniferous forests in Japan, and the importance of spectral information for the estimation. We estimated four biophysical properties: stand volume (V), Lorey’s mean height (HL), mean height (HA), and max height (HM). We developed three independent variable sets, which included a height variable, a spectral variable, and a combined height and spectral variable. The addition of a dominant tree type to the above data sets was also tested. The model including a height variable and dominant tree type was the best for all biophysical property estimations. The root-mean-square errors (RMSEs) for the best model for V, HL, HA, and HM, were 118.30, 1.13, 1.24, and 1.24, respectively. The model including a height variable alone yielded the second highest accuracy. The respective RMSEs were 131.74, 1.21, 1.31, and 1.32. The model including a spectral variable alone yielded much lower estimation accuracy than that including a height variable. Thus, a lightweight UAV photogrammetric point cloud could accurately estimate forest biophysical properties, and a spectral variable was not necessarily required for the estimation. The dominant tree type improved estimation accuracy.

In tree Aboveground Biomass (AGB) estimation, the traditional harvest method is accurate but unsuitable for a large-scale forest. The airborne Light Detection And Ranging (LiDAR) is superior in obtaining the point cloud data of a dense forest and extracting tree heights for AGB estimation. However, the LiDAR has limitations such as high cost, low efficiency, and complicated operations. Alternatively, the overlapping oblique photographs taken by an Unmanned Aerial Vehicle (UAV)-loaded digital camera can also generate point cloud data using the Aerial Triangulation (AT) method. However, limited by the relatively poor penetrating capacity of natural light, the photographs captured by the digital camera on a UAV are more suitable for obtaining the point cloud data of a relatively sparse forest. In this paper, an electric fixed-wing UAV loaded with a digital camera was employed to take oblique photographs of a sparse subalpine coniferous forest in the source region of the Minjiang River. Based on point cloud data obtained from the overlapping photographs, a Digital Terrain Model (DTM) was generated by filtering non-ground points along with the acquisition of a Digital Surface Model (DSM) of Minjiang fir trees by eliminating subalpine shrubs and meadows. Individual tree heights were extracted by overlaying individual tree outlines on Canopy Height Model (CHM) data computed by subtracting the Digital Elevation Model (DEM) from the rasterized DSM. The allometric equation with tree height (H) as the predictor variable was established by fitting measured tree heights with tree AGBs, which were estimated using the allometric equation on H and Diameter at Breast Height (DBH) in sample tree plots. Finally, the AGBs of all of the trees in the test site were determined by inputting extracted individual tree heights into the established allometric equation. In accuracy assessment, the coefficient of determination (R2) and Root Mean Square Error (RMSE) of extracted individual tree heights were 0.92 and 1.77 m, and the R2 and RMSE of the estimated AGBs of individual trees were 0.96 and 54.90 kg. The results demonstrated the feasibility and effectiveness of applying UAV-acquired oblique optical photographs to the tree AGB estimation of sparse subalpine coniferous forests.

At the ecosystem scale, water-use efficiency (WUE) is defined broadly as the ratio of carbon assimilated to water evaporated by an ecosystem. WUE is an important aspect of carbon and water cycling and has been used to assess forest ecosystem responses to climate change and rising atmospheric CO2 concentrations. This study investigates the influence of meteorological and radiation variables on forest WUE by analyzing an 18 year (1998-2015) half-hourly time series of carbon and water fluxes measured with the eddy covariance technique in an old-growth conifer forest in the Pacific Northwest, USA. Three different metrics of WUE exhibit an overall increase over the period 1998-2007 mainly due to an increase in gross primary productivity (GPP) and a decrease in evapotranspiration (ET). However, the WUE metrics did not exhibit an increase across the period from 2008 to 2015 due to a greater reduction in GPP relative to ET. The strength of associations among particular meteorological variables and WUE varied with the scale of temporal aggregation used. In general, vapor pressure deficit and air temperature appear to control WUE at half-hourly and daily time scales, whereas atmospheric CO2 concentration was identified as the most important factor controlling monthly WUE. Carbon and water fluxes and the consequent WUE showed a weak correlation to the Standard Precipitation Index, while carbon fluxes were strongly dependent on the combined effect of multiple climate factors. The inferred patterns and controls on forest WUE highlighted have implications for improved understanding and prediction of possible adaptive adjustments of forest physiology in response to climate change and rising atmospheric CO2 concentrations.

The dynamic monitoring of forest resources is an integral component of forest resource management and forest eco-system stability maintenance. In recent years, LiDAR (Light Detection and Ranging) has been increasingly utilized in precision forest surveys due to its great penetrating ability and capacity to detect forest vertical structure information. However, the present airborne LiDAR data individual tree segmentation algorithms are not highly adaptable to forest types, particularly in mixed coniferous and broad-leaved forest zones, where the accuracy of individual tree extraction is low, and trees are incorrectly recognized and missed. In order to address these issues, in this study, spectral images and LiDAR data of a red pine conifer–broadleaf mixed forest in the Changbai Mountain Nature Reserve in Jilin Province were chosen, and the normalized point cloud was segmented iteratively using the distance-threshold-based individual tree segmentation method to obtain the initial segmented individual tree vertices. For individual trees with deviations in the initial vertex identification position, and unidentified individual trees, identification anchor points of real tree vertices are added within the canopy of the trees. These identification anchor points have strong position directivity in LiDAR data, which can mark the individual trees whose vertices were misidentified or missed during the initial individual tree segmentation process and identify these two tuples. The tree vertices may be inserted precisely based on the 3D shape of the individual tree point cloud, and the seed-point-based individual tree segmentation method is used to segment the normalized point cloud and finish the extraction of individual trees in red pine mixed conifer forests. The results indicate that, compared to the previous individual tree segmentation approach based on the relative spacing between individual trees, this study enhances the accuracy of individual tree segmentation from 83% to 96%. The extremely high segmentation accuracy indicates that the proposed method can accurately identify individual trees based on remote sensing techniques to segment forest individual trees, can provide a data basis for subsequent individual tree information extraction, and has great potential in practical applications.

The paper addresses the study of dark coniferous forests of the southern taiga, which were analyzed with regard to severity of stand weakening caused by stem pests, up to stand death. To assess the carbon stock of grass cover, the mass fraction of carbon in grass species and the aboveground mass of grass were measured. The carbon stock of grass cover was shown to vary during the change in its main microgroups caused by stand weakening. The carbon content in grass species abundant in different grass microgroups exhibits a narrow range of variation of 33.4–44.7 %, with an average of 41.1±0.9 %. The amount of carbon stored in the grass cover of dark coniferous forests in uplands ranges from 16.2 to 32.9 gC/m2. It was revealed that in weakened stands, the grass cover stores 2–5 fold more carbon due to an increase in grass biomass. Considering that most of the grass carbon released to the atmosphere within a year, the efficiency of carbon sink in forest areas with dead stands becomes lower, despite an increase in grass mass, compared to forest areas where the stands are not damaged by stem pests.

Research Highlights: This study carried out a feasibility analysis on the tree height extraction of a planted coniferous forest with high canopy density by combining terrestrial laser scanner (TLS) and unmanned aerial vehicle (UAV) image–based point cloud data at small and midsize tree farms. Background and Objectives: Tree height is an important factor for forest resource surveys. This information plays an important role in forest structure evaluation and forest stock estimation. The objectives of this study were to solve the problem of underestimating tree height and to guarantee the precision of tree height extraction in medium and high-density planted coniferous forests. Materials and Methods: This study developed a novel individual tree localization (ITL)-based tree height extraction method to obtain preliminary results in a planted coniferous forest plots with 107 trees (Metasequoia). Then, the final accurate results were achieved based on the canopy height model (CHM) and CHM seed points (CSP). Results: The registration accuracy of the TLS and UAV image-based point cloud data reached 6 cm. The authors optimized the precision of tree height extraction using the ITL-based method by improving CHM resolution from 0.2 m to 0.1 m. Due to the overlapping of forest canopies, the CSP method failed to delineate all individual tree crowns in medium to high-density forest stands with the matching rates of about 75%. However, the accuracy of CSP-based tree height extraction showed obvious advantages compared with the ITL-based method. Conclusion: The proposed method provided a solid foundation for dynamically monitoring forest resources in a high-accuracy and low-cost way, especially in planted tree farms.