Mixed Conifer-broadleaf Forests Research Articles

Influence of photosynthetic active radiation on sap flow dynamics across forest succession stages in Dinghushan subtropical forest ecosystem

With the intensification of global change, forests are subjected to varying degrees of drought or high-temperature stress, which has an indelible impact on the growth of trees. However, knowledge on the response of sap flow to environmental changes in different types of forests is still rare, especially in China's subtropical forest ecosystem. Consequently, studying how different tree species regulate their sap flow in response to shifting environmental conditions is essential for understanding forest transpiration, water use efficiency, and drought stress resilience. Therefore, this study aimed to investigate the sap flow dynamics of seven tree species in five forest plots, i.e., pine forest (PF), two types of mixed conifer-broadleaf forests (MF1+MF2), monsoon evergreen broadleaved forest (MEBF), and montane monsoonal evergreen broad-leaf forest (MOBF) at Dinghushan National Reserve in Southern China, using the heat dissipation probe technique and synchronous environmental factor recordings. Results demonstrated a significant influence of photosynthetic active radiation (PAR) on sap flow across all tree species, with mean PAR values ranging from over 1200 to 425 μmol m−2 s−1, establishing it as the principal driving factor. This observation underscores the heightened responsiveness or sensitivity of tree species to variations in PAR as the forest undergoes development and maturation. The correlation between vapor pressure deficit (VPD) and tree sap flow decreased as succession progressed. Moreover, the influence of soil water content (SWC) on sap flow stability against environmental changes increased. Similar patterns were also found between the two MF, with MF-2 displaying ecological characteristics and environmental conditions more closely aligned with those of the late-successional MEBF. The study reveals the intricate relationship between environmental factors and sap flow regulation in tree species within a subtropical forest ecosystem. Addressing a comparative gap in sap flow correlation among dominant tree species at Dinghushan, it establishes a hydro-physiological foundation for understanding tree species substitution during forest succession. The results provide key insights for forest management and climate-related research. Future studies should delve into the long-term implications of observed sap flow dynamics, exploring their impact on tree species adaptability amid ongoing environmental changes.

Improving the Estimation of Structural Parameters of a Mixed Conifer–Broadleaf Forest Using Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle Red Green Blue (RGB) Imagery

Forest structural parameters are crucial for assessing ecological functions and forest quality. To improve the accuracy of estimating these parameters, various approaches based on remote sensing platforms have been employed. Although remote sensing yields high prediction accuracy in uniform, even-aged, simply structured forests, it struggles in complex structures, where accurately predicting forest structural parameters remains a significant challenge. Recent advancements in unmanned aerial vehicle (UAV) photogrammetry have opened new avenues for the accurate estimation of forest structural parameters. However, many studies have relied on a limited set of remote sensing metrics, despite the fact that selecting appropriate metrics as powerful explanatory variables and applying diverse models are essential for achieving high estimation accuracy. In this study, high-resolution RGB imagery from DJI Matrice 300 real-time kinematics was utilized to estimate forest structural parameters in a mixed conifer–broadleaf forest at the University of Tokyo Hokkaido Forest (Hokkaido, Japan). Structural and textual metrics were extracted from canopy height models, and spectral metrics were extracted from orthomosaics. Using random forest and multiple linear regression models, we achieved relatively high estimation accuracy for dominant tree height, mean tree diameter at breast height, basal area, mean stand volume, stem density, and broadleaf ratio. Including a large number of explanatory variables proved advantageous in this complex forest, as its structure is influenced by numerous factors. Our results will aid foresters in predicting forest structural parameters using UAV photogrammetry, thereby contributing to sustainable forest management.

Patterns and determinants of calcium concentrations in forest litter and different soil horizons in warm-temperate China

Calcium is an essential nutrition for the integrity and structure of plant cells, and is generally neglected because of its high abundance in temperate forest soil. To date, little is known about the changes in soil Ca and the main natural drivers in the conserved forests. Here, the concentrations of Ca in forest litter and different soil horizons (0–10, 10–20, 20–30, and 30–50 cm) from 234 forest plots were measured. Spatial variations in litter and soil Ca concentrations beneath different forest types were determined, and a structural equation model (SEM) was constructed to evaluate the causal pathways of the biotic and abiotic drivers. The results showed that litter Ca concentration beneath broadleaved forests and mixed broadleaf-conifer forests (22.62 and 19.76 mg/g, respectively) were significantly greater than that beneath the coniferous forest (15.35 mg/g). The Ca concentrations in the soil significantly increased with latitude (soil depth < 30 cm) and longitude. However, litter and soil Ca concentrations significantly declined with elevation. The basal area of the forest and soil C/N were the dominant factors regulating the concentration of Ca in litter and soil profiles, respectively. The SEM highlighted the important effects of Ca concentration in deep soil (soil depth > 20 cm) on litter Ca concentration. The basal area and soil C/N had negative and positive direct effects on Ca concentration in topsoil (soil depth < 20 cm), respectively. The precipitation and temperature in the growing season had indirect effects by affecting the soil C/N and basal area. These results collectively suggest that different forest types influence the redistribution of Ca belowground, and the process is mainly mediated by the basal area and soil C/N. The afforestation in the study area with admixture of conifers and broadleaved trees is a desirable management for the improvement of forest productivity and soil fertility.

Organic carbon sequestration by Fe oxides in aggregates in a Pinus massoniana conifer-broadleaf mixed forest

Stand conversion and Fe/Al oxides are potential variables that collectively determine soil organic carbon (SOC) sequestration. However, the mechanism of SOC sequestration provided by Fe/Al oxides in the conversion of Pinus massoniana plantations (PMs) into mixed conifer-broadleaf forests remains unknown. The increase in SOC content (4.08–151.80%) in PM-converted mixed forest was related to the significant increase in the content of all OC fractions. Light fraction (LF) contents were generally higher in larger aggregates and mixed forest aggregates, reflecting the greater plant C input and the driving effect of soil aggregation in mixed conifer-broadleaf forests. This plant C input to the soil as the core of aggregate formation provides physical protection from particulate organic carbon (POC), but this physical protection was achieved by the greater free Fe oxides (FeD) and amorphous Fe oxides (FeO) content in the mixed forests acting as an aggregate stabiliser. Potential processes are driven by pH-driven FeD and FeO binding to silt to improve aggregate stability and pore occlusion, which promotes physical protection of coarse intra-aggregate particulate organic carbon (c-iPOC). Moreover, this process reduces macroaggregate turnover (c-iPOC/f-iPOC ratio), allowing the transfer of c-iPOC in macroaggregates to microaggregate-associated fine intra-aggregate particulate organic carbon (f-iPOC). The higher mineral-associated organic carbon (MOC) content of conifer-broadleaf mixed forest soils was mainly derived from silt+clay and controlled by chemisorption of Fe/Al oxides (especially complex Fe oxides, FeP), but Fe/Al oxides do not provide physical protection for MOC. In addition, LF, c-iPOC and f-iPOC were significantly positively correlated with SOC content, reflecting the sensitivity of aggregate structure development and stabilization in predicting SOC sequestration, but these OC fractions will shift more towards MOC as conifer-broadleaf mixed forests develop. Overall, Fe oxides provide SOC accumulation and long-term stability by OC fractions associated with physical protection and chemisorption in the conversion of PM into mixed conifer-broadleaf forests.

Association between Soil Physicochemical Properties and Bacterial Community Structure in Diverse Forest Ecosystems.

Although the importance of the soil bacterial community for ecosystem functions has long been recognized, there is still a limited understanding of the associations between its community composition, structure, co-occurrence patterns, and soil physicochemical properties. The objectives of the present study were to explore the association between soil physicochemical properties and the composition, diversity, co-occurrence network topological features, and assembly mechanisms of the soil bacterial community. Four typical forest types from Liziping Nature Reserve, representing evergreen coniferous forest, deciduous coniferous forest, mixed conifer-broadleaf forest, and its secondary forest, were selected for this study. The soil bacterial community was analyzed using Illumina MiSeq sequencing of 16S rRNA genes. Nonmetric multidimensional scaling was used to illustrate the clustering of different samples based on Bray-Curtis distances. The associations between soil physicochemical properties and bacterial community structure were analyzed using the Mantel test. The interactions among bacterial taxa were visualized with a co-occurrence network, and the community assembly processes were quantified using the Beta Nearest Taxon Index (Beta-NTI). The dominant bacterial phyla across all forest soils were Proteobacteria (45.17%), Acidobacteria (21.73%), Actinobacteria (8.75%), and Chloroflexi (5.06%). Chao1 estimator of richness, observed ASVs, faith-phylogenetic diversity (faith-PD) index, and community composition were distinguishing features of the examined four forest types. The first two principal components of redundancy analysis explained 41.33% of the variation in the soil bacterial community, with total soil organic carbon, soil moisture, pH, total nitrogen, carbon/nitrogen (C/N), carbon/phosphorous (C/P), and nitrogen/phosphorous (N/P) being the main soil physicochemical properties shaping soil bacterial communities. The co-occurrence network structure in the mixed forest was more complex compared to that in pure forests. The Beta-NTI indicated that the bacterial community assembly of the four examined forest types was collaboratively influenced by deterministic and stochastic ecological processes.

Detection of the Infection Stage of Pine Wilt Disease and Spread Distance Using Monthly UAV-Based Imagery and a Deep Learning Approach

Pine wood nematode (PWN) is an invasive species which causes pine wilt disease (PWD), posing a significant threat to coniferous forests globally. Despite its destructive nature, strategies for the management of PWD spread lack a comprehensive understanding of the occurrence pattern of PWNs. This study investigates the outbreak timing and spread distances of PWD on a monthly scale. Two regions (A and B) in southeastern China, characterized by varying mixed ratios of coniferous and broadleaf trees, were examined. Infected trees were classified into early, middle, late, and dead stages. Monthly unmanned aerial vehicle (UAV) RGB data covering one year and three deep learning algorithms (i.e., Faster R-CNN, YOLOv5, and YOLOv8) were employed to identify the stress stages and positions of the trees. Further, each month, newly infected trees were recorded to calculate spread distances from the location of surrounding trees. The results indicate that the YOLOv5 model achieved the highest accuracy (mean average precision (mAP) = 0.58, F1 = 0.63), followed by Faster R-CNN (mAP = 0.55, F1 = 0.58) and YOLOv8 (mAP = 0.57, F1 = 0.61). Two PWD outbreak periods occurred between September–October and February of the following year, with early and middle-stage outbreaks in August and September and late and dead-tree outbreaks occurring between October and February of the following year. Over one year, the nearest spread distance for PWD-infected trees averaged 12.54 m (median: 9.24 m) for region A in September and 13.14 m (median: 10.26 m) for region B in October. This study concludes that February through August represents the optimal period for PWD control. Additionally, mixed conifer–broadleaf forests with a higher proportion of broadleaf trees prove beneficial in mitigating PWD outbreaks and reducing the number of infected trees. This work demonstrates the effectiveness of integrating monthly UAV-based imagery and deep learning algorithms for monitoring PWD outbreak times and spread distances, offering technical support for forest pest prevention and management.

Distribution characteristics and diversity of myxomycetes in three parallel rivers in Yunnan, China.

Three Parallel Rivers is one of the world's biodiversity hotspots. However, the research on myxomycetes diversity is scarce in this area. Random sampling was used to investigate myxomycetes' diversity and distribution characteristics in this area. One hundred and seventeen species, including three varieties, were obtained, belonging to 28 genera, nine families, and six orders, with Arcyria cinerea and Physarum viride being the dominant species. Moreover, four species and one variety were first reported in China. Twenty-six species and one variety were first reported in Yunnan Province. The species' most commonly utilized substrate for fruiting bodies was decaying wood, and Cribraria was the dominant genus. The species diversity was most abundant in mixed broadleaf-conifer forests. Species similarity between coniferous and broad-leaved forests was much higher than the pairwise comparison of other forest types. NMDS analysis shows that substrate and forest types had insignificant effects on myxomycetes communities, while river valley had a significant effect. The myxomycetes community similarity between river valleys is unrelated to geographical proximity.

Climatic drivers of litterfall production and its components in two subtropical forests in South China: A 14-year observation

Understanding the climatic triggers of litterfall production is essential for simulating nutrient cycling in forest ecosystems. However, the mechanisms underlying litterfall dynamics in subtropical forests remain unclear because of the complex canopy phenological strategies. Herein, we explored this by investigating a unique litterfall dataset (including components such as leaves, branches, flowers and fruits, and miscellaneous, etc.) spanning a 14-year observation period (2005–2018) in two typical subtropical evergreen forest ecosystems in South China: a mixed conifer-broadleaf forest (MF) and a monsoon evergreen broadleaved forest (MEBF). We found that the annual total litterfall production, often dominated by leaf litterfall, was significantly higher in MF (10.21 ± 2.09 t∙hm−2) than in MEBF (7.74 ± 2.21 t∙hm−2) (P < 0.01). Generally, forest litterfall production occurred mostly during the wet season and was profoundly correlated with the monthly maximum wind speed (rMF = 0.70, rMEBF = 0.71, P < 0.01), it contributed exceeded 80% to the total litterfall and the leaf and branch components. Whereas during the dry season, the forest litterfall process was dramatically weakened, showing a significant correlation with the mean temperature (rMF = 0.48, rMEBF = 0.68, P < 0.01). Further analysis showed that during the dry season, the cumulative contribution rates of temperature factors to the total litterfall and the leaf and branch components exceeded 60%, indicating a leading role of temperature in triggering forest litterfall processes. Notably, when excluding the typhoon disturbances, it is observed that temperature, alongside wind speed, emerges as a significant determinant of forest litterfall production during the wet season. Those two factors collectively account for almost 60% of the total litterfall and the leaf and branch components. Our findings have potential significance in improving our understanding of carbon and nutrient cycling in subtropical forest ecosystems under climate change conditions.

Community vertical stratification drives temporal taxonomic and phylogenetic beta diversity in a mixed broadleaf-conifer forest

Temporal change of beta diversity provides a better understanding of the extent and consequences of species composition in forest communities with the ongoing global climate change. However, relatively little is known about temporal beta diversity changes across vertical stratification in the forest. In this study, we divided more than 5,000 tree individuals from a mixed broadleaf-conifer forest into four vertical strata (i.e., shrub, subcanopy, lower canopy, and upper canopy) to quantify how vertical stratification drives the temporal change of taxonomic and phylogenetic beta diversity. We found that taxonomic beta diversity significantly decreased while phylogenetic beta diversity showed an insignificant increase after 5 years. When considering vertical stratification, taxonomic beta diversity in the subcanopy, lower canopy, and upper canopy significantly changed with inconsistent directions, but phylogenetic beta diversity in the shrub significantly increased. Moreover, the significant decrease in taxonomic beta diversity is mainly driven by changes in species composition in shrub and subcanopy stratification (with 85.89% contributions). The changes in phylogenetic beta diversity are driven by shifts in the shrub and upper canopy (with 96.02% contributions). Our study suggests that taking community vertical stratification into consideration contributes to a better understanding of temporal beta diversity in forest communities.

Robust management strategies promoting ecological resilience and economic efficiency of a mixed conifer-broadleaf forest in Southwest Germany under the risk of severe drought

Robust decision-making in forestry seeks solutions that reduce the risk of environmental damage and economic losses, which matters for designing forest adaptation measures. We propose a state-of-the-art methodology to identify robust drought adaptive strategies. First, we used a process-based model with an ensemble of climate change scenarios to simulate managed forest dynamics. Second, we quantified the ecological resilience and financial return as net present value (NPV), applying business-as-usual (BAU) and alternative adaptation strategies to regenerate the overstorey layer (trees ≥ 30 cm dbh) of the forest (e.g., “active”, “reactive”, and “do-nothing”). Afterward, we analyzed robustness by searching for the strategy with minimum worst-case losses of ecological and economic signposts. Our analysis found a reduced forest drought resilience under BAU and “do-nothing” strategies, determining a high probability of economic failure (∼ −49% and ∼ −67% loss in NPV respectively). While, an early “active” strategy would increase the NPV (∼ +10%), and a highly-intense “reactive” strategy would have minimal losses (∼ −2%). Finally, we found that local conditions influence the signpost performance magnitude but do not vary the robust solution. We conclude that our methodology facilitates applying a robustness analysis in forestry, and drought adaptation should occur rather sooner than later, considering constant revision.

Seasonal changes in total mercury and methylmercury in subtropical decomposing litter correspond to the abundances of nitrogen-fixing and methylmercury-degrading bacteria.

Previous research has found total mercury (THg) and methylmercury (MeHg) levels increase with litterfall decay, thus suggesting litterfall decomposition plays an essential role in the biogeochemical transformation of mercury (Hg). However, it remains unclear how Hg accumulates in the decaying litter, how bacterial taxa networks vary and what roles various microorganisms play during litterfall decomposition, especially nitrogen (N)-fixing, MeHg-degrading and Hg-methylating microbes. Here, we demonstrated as degradation proceeded, a gradually-complex network evolved for litterfall bacteria for the subtropical mixed broadleaf-conifer (MBC) forest, whereas a relatively static network existed for the evergreen broadleaf (EB) forest. N-fixing and MeHg-degrading bacteria dominated throughout litterfall decomposition process, with relative abundances of N-fixing genera and nifH copies maximum and relative abundances of MeHg-degrading bacteria and merAB copies minimum in summer. Hence, N-fixing bacteria likely mediate THg increase in the decomposing litterfall, while MeHg enhancement may be regulated by aerobic MeHg-degrading microbes which can transform MeHg to inorganic divalent Hg (Hg2+) or further to elemental Hg (Hg0). Together, this work elucidates variations of N-fixing and MeHg-degrading microbes in decaying litterfall and their relationships with Hg accumulation, providing novel insights into understanding the biogeochemical cycle of Hg in the forest ecosystem.

Diversity of Dictyostelid Cellular Slime Molds, Including Two Species New to Science, in Forest Soils of Changbai Mountain, China.

Dictyostelid cellular slime molds (dictyostelids) are protists that are common inhabitants of most soils, where they feed upon bacteria. Changbai Mountain is the highest mountain in northeast China. Soil samples collected on Changbai Mountain yielded 11 isolates representing six species of dictyostelid samples. Two of these species (Dictyostelium robusticaule and Heterostelium recretum) were found to be new to science, based on morphology, SSU rDNA sequences, and an ATPase subunit 1 gene (atp1) phylogeny. The present study also demonstrated that the increased accuracy and lower costs associated with the use of atp1 sequences make them a complement of SSU rDNA sequences for identifying dictyostelids. Changbai Mountain is characterized by a higher diversity of dictyostelids than indicated by the few previous reports. Moreover, the data for Changbai Mountain, compared with comparable data for Taiwan, suggest that differences in diversity at the family level are possibly related to latitude. Mixed broadleaf-conifer forests produced more isolates and species than broadleaf forests at the same elevation and also had the highest species richness, which indicates an effect of vegetation on dictyostelids. However, the pattern of slightly decreasing diversity with increasing elevation in dictyostelids was also apparent. IMPORTANCE Dictyostelium robusticaule and Heterostelium recretum are two new species of dictyostelids reported in this study. The potential use of atp1 sequences is a complement of SSU rDNA sequences for the identifying dictyostelids. A pattern of slightly decreasing diversity with increasing elevation in dictyostelids was observed, with the conditions that exist at lower elevations apparently more suitable for dictyostelids, whereas differences of diversity observed at the family level are possibly related to latitude.

Effects of Forest Vegetation Restoration on Soil Organic Carbon and Its Labile Fractions in the Danxia Landform of China

The Danxia landform is a unique red bed landform in China. The effects of vegetation restoration on soil organic carbon (SOC) components are still poorly understood in the Danxia landform region of southwest China. In this study, soil samples were collected from selected five different vegetation restoration types (shrub (SH), mixed conifer–broadleaf forest (MCBF), evergreen broad-leaved forest (EBF), Chinese fir forest (CFF), and bamboo forest (BF)) at 0–30 cm depth to discuss the concentrations and stocks of SOC and its labile organic carbon (LOC) fractions ((dissolved organic C (DOC), microbial biomass C (MBC), and easily oxidized organic C (EOC)) and their relationship with soil physicochemical properties and enzyme activities. The results indicated that the contents of SOC and LOC fractions as well as SOC stocks declined with increasing soil depth in five vegetation restoration types. At 0–30 cm depth, BF and CFF showed higher the average concentrations and total stocks of SOC and EOC compared with SH, EBF, and MCBF. The highest average DOC content was in BF, but no significant differences was observed in the total DOC stocks among five vegetation restoration types. BF and EBF showed significantly greater average MBC concentrations and total MBC stocks than other vegetation restoration types. SOC and its LOC fractions were positively correlated with soil moisture and three enzyme activities in different degrees under the five vegetation restoration types and closely related with total nitrogen (TN) and total phosphorus (TP) except for TP of CFF and BF and negatively affected by pH (except for CFF and the DOC and MBC of MCBF) and BD. Generally, soil TN, TP, and invertase were found to be the main driver factors for soil carbon accumulation. However, the overall levels of SOC and its labile fractions indicate that BF had the strongest carbon storage capacity, followed by CFF and EBF. This study can provide a good reference for ecosystem management and the selection of appropriate restoration strategies in Danxia landform regions.

Four new species of Mycenasect.Calodontes (Agaricales, Mycenaceae) from northeast China.

Species of Mycenasect.Calodontes are representative of the Mycena genus as a whole and are easily recognised by the pinkish, reddish, purplish to brownish pileus and larger basidiomata. Furthermore, the colour of the pileus in the species of sect. Calodontes often has a transition or changes in different stages and the combination of the colour of the pileus with cystidia and basidiospores can be used to recognise taxa within this section. To date, 19 species of Mycenasect.Calodontes have been reported worldwide. Including our recent description of M.yuezhuoi, five species of sect. Calodontes have been recorded in China. During examination of specimens collected in coniferous forests or mixed broadleaf-conifer forests in temperate regions of China, additional taxa assigned to sect. Calodontes were identified. Four new species are recognised, based mostly on characters of the pileus and cystidia. Phylogenetic analysis of sequence data from multiple DNA regions (ITS + rpb1 + tef1) supported the morphological evidence. Here, we propose M.polycystidiata, M.rufobrunnea, M.shengshanensis and M.subulata as new species in Mycenasect.Calodontes. Morphological descriptions, line drawings, habitat photos and comparisons with closely-related taxa are provided. A key to the 23 known species of sect. Calodontes is presented.

Thinning can increase shrub diversity and decrease herb diversity by regulating light and soil environments.

Tree thinning affects the light environment, which in turn affects the growth and survival of understory vegetation, thus improving species diversity and nutrient cycling, as well as the ecological habitat factors. However, the response of understory vegetation to the thinning intensity and short-time effects in the temperate broadleaf-conifer mixed forest is not completely clear. In this study, four permanent plots with a total area of 4 hm2 were established in a mixed broadleaf-conifer forest in northeast China, with thinning intensities of 20% (light thinning, LT), 35% (medium thinning, MT), 55% (heavy thinning, HT) and the unthinned plot (CK), respectively, in accordance with the basal area. The responses of species diversity to changes in understory vegetation were conducted by a structural equation model (SEM). The results showed that compared with CK, thinning significantly increased the photosynthetically active radiation (PAR) and the light quality (R/FR) (p < 0.05), while decreased the contents of soil total nitrogen (TN), total phosphorous (TP), organic matter (OM), nitrate nitrogen (NN), ammonia nitrogen (AN) and pH. The degree of fragmentation of light factors among the treatment plots gradually decreased as thinning intensity increased. Among all the thinning treatments, PAR and R/FR were found to be the optimal light condition when the forest thinning intensity was 55%. The light condition was found to have a significant negative correlation with soil TN, TP, OM, and AN. While the soil nutrients were positively correlated with herbaceous layer diversity but negatively correlated with shrub layer diversity. The soil nutrients were lost after thinning in a short time and herb diversity decreased, but shrub diversity increased significantly compared with unthinned plots. For the understory vegetation, the species diversity of shrub and herb layer were showed to be more sensitive to soil nutrients than light environment.

Database and primer selections affect nematode community composition under different vegetations of Changbai Mountain

High-throughput sequencing technology is increasingly used in the study of nematode biodiversity. However, the annotation difference of commonly used primers and reference databases on nematode community is still unclear. We compared two pairs of primers (3NDf/C_1132rmod, NF1F/18Sr2bR) and three databases (NT_v20200604, SILVA138/18s Eukaryota and PR2_v4.5 databases) on the determination of nematode community from four different vegetation types in Changbai Mountain, including mixed broadleaf-conifer forest, dark coniferous forest, betula ermanii Cham and alpine tundra. Our results showed that the selection of different primers and databases influenced the annotation of nematode taxa, but the diversity of nematode community showed consistent pattern among different vegetation types. Our findings emphasize that it is necessary to select appropriate primer and database according to the target taxonomic level. The difference in primers will affect the result of nematode taxa at different classification levels, so sequencing analysis cannot be used for comparison with studies using different primers. In terms of annotation effect in this study, 3NDf/C_1132rmod primers with NT_v20200604 database could provide more information than other combinations at the genus or species levels.

Individual Tree Segmentation and Tree Height Estimation Using Leaf-Off and Leaf-On UAV-LiDAR Data in Dense Deciduous Forests

Accurate individual tree segmentation (ITS) is fundamental to forest management and to the studies of forest ecosystem. Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) shows advantages for ITS and tree height estimation at stand and landscape scale. However, dense deciduous forests with tightly interlocked tree crowns challenge the performance for ITS. Available LiDAR points through tree crown and appropriate algorithm are expected to attack the problem. In this study, a new UAV-LiDAR dataset that fused leaf-off and leaf-on point cloud (FULD) was introduced to assess the synergetic benefits for ITS and tree height estimation by comparing different types of segmentation algorithms (i.e., watershed segmentation, point cloud segmentation and layer stacking segmentation) in the dense deciduous forests of Northeast China. Field validation was conducted in the four typical stands, including mixed broadleaved forest (MBF), Mongolian oak forest (MOF), mixed broadleaf-conifer forest (MBCF) and larch plantation forest (LPF). The results showed that the combination of FULD and the layer stacking segmentation (LSS) algorithm produced the highest accuracies across all forest types (F-score: 0.70 to 0.85). The FULD also showed a better performance on tree height estimation, with a root mean square error (RMSE) of 1.54 m at individual level. Compared with using the leaf-on dataset solely, the RMSE of tree height estimation was reduced by 0.22 to 0.27 m, and 12.3% more trees were correctly segmented by the FULD, which are mainly contributed by improved detection rate at nearly all DBH levels and by improved detection accuracy at low DBH levels. The improvements are attributed to abundant points from the bole to the treetop of FULD, as well as each layer point being included for segmentation by LSS algorithm. These findings provide useful insights to guide the application of FULD when more multi-temporal LiDAR data are available in future.

UAV-Based Characterization of Tree-Attributes and Multispectral Indices in an Uneven-Aged Mixed Conifer-Broadleaf Forest

Unmanned aerial vehicles (UAVs) have contributed considerably to forest monitoring. However, gaps in the knowledge still remain, particularly for natural forests. Species diversity, stand heterogeneity, and the irregular spatial arrangement of trees provide unique opportunities to improve our perspective of forest stands and the ecological processes that occur therein. In this study, we calculated individual tree metrics, including several multispectral indices, in order to discern the spectral reflectance of a natural stand as a pioneer area in Mexican forests. Using data obtained by UAV DJI 4, and in the free software environments OpenDroneMap and QGIS, we calculated tree height, crown area, number of trees and multispectral indices. Digital photogrammetric procedures, such as the ForestTools, Structure from Motion and Multi-View Stereo algorithms, yielded results that improved stand mapping and the estimation of stand attributes. Automated tree detection and quantification were limited by the presence of overlapping crowns but compensated by the novel stand density mapping and estimates of crown attributes. Height estimation was in line with expectations (R2 = 0.91, RMSE = 0.36) and is therefore a useful parameter with which to complement forest inventories. The diverse spectral indices applied yielded differential results regarding the potential vegetation activity present and were found to be complementary to each other. However, seasonal monitoring and careful estimation of photosynthetic activity are recommended in order to determine the seasonality of plant response. This research contributes to the monitoring of natural forest stands and, coupled with accurate in situ measurements, could refine forest productivity parameters as a strategy for the validity of results. The metrics are reliable and rapid and could serve as model inputs in modern inventories. Nevertheless, increased efforts in the configuration of new technologies and algorithms are required, including full consideration of the costs implied by their adoption.

Intensive forest harvest increases N2O emission from soil: A meta-analysis

A comprehensive evaluation of the impact of forest harvest on soil N2O flux at the global scale is currently unavailable. In this study, by using a global meta-analysis of 64 observations from 29 peer-reviewed publications over the last 30 years, we elucidated how forest harvest impacted soil N2O flux and related soil properties. Intensive forest harvest significantly increased soil N2O flux with a mean effect size of 0.85 and the increase was associated with elevated soil temperature, increased concentration of NO3−-N and decreased soil pH. The effect on soil N2O flux lasted for around six years, and soil N2O emission depended on existing forest types in the late stage (the recovery period >6 years) after harvest. Forest harvest clearly increased soil N2O flux, with effect size ranging 0.73–2.12, in broadleaf forests but did not significantly affect it in coniferous and mixed broadleaf-conifer forests. In addition, the time required for the complete recovery from harvest in terms of N2O flux was difficult to define because of the scarcity of long-term experiments. Overall, this study provides the first formal global-scale assessment on how forest harvest intensity, forest types and the time since harvest affect soil N2O flux. The findings of this study help to narrow the knowledge gap regarding the effect of forest harvest on soil N2O flux, and to facilitate decision making to decrease soil N2O emission post forest harvest.

Soil moisture dominated the temporal dynamics of litter moisture content in subtropical forests: a 7-year observation in south China

Study regionDinghushan watershed, China. Study focusThe dynamics of litter moisture content play an important role in understanding the forest’s internal environmental changes, forest fire forecast and prediction of general forest health and so on. These characteristics have not been extensively analyzed in lower subtropical China and therefore, three typical subtropical evergreen forest ecosystems, Pinus massoniana coniferous forest (PF), mixed conifer-broadleaf forest (CBF), and monsoon evergreen broadleaved forest (MBF), were chosen to explore the changes of the litter moisture content and its response to environmental factors. New hydrological insights for the regionOver a 7-year observation period, the mean annual litter moisture content was 72.0 ± 7.3%, 87.9 ± 11.3%, and 100.5 ± 15.7% for PF, CBF, and MBF respectively. The litter moisture content increased while the litter standing mass decreased after vegetation restoration. The litter moisture content in the three forests during the wet season was substantially higher than that in the dry season. Compared to the soil layer, the coefficient of variation of the litter moisture content during the dry and wet seasons was higher as compared to the topsoil (0–30 cm) water content, indicating the litter layer was more sensitive to changes in external environmental factors. Further analysis revealed that soil water content was the dominating factor controlling the dynamics of litter moisture content both during the dry and wet seasons. Analyses of the litter moisture can be very useful in forests around the world as a guideline in indicating the change in a forest’s environment.