Chinese Forest Research Articles

China’s Climate Change Policy Attention and Forestry Carbon Sequestration Growth

Forest carbon sinks play an important role in climate change mitigation and adaptation. The Chinese government has demonstrated its proactive approach to addressing climate change by setting development targets for low-carbon transformation and making solemn commitments to peak carbon emissions before 2030 and to achieve carbon neutrality by 2060. In this article, the Chinese Government’s Work Reports and Five-Year Plans are used to construct an index named China’s Climate Change Policy Attention (CCPA), which reflects the government’s emphasis on climate change and forestry in China. This article aims to examine the impact of CCPA, the adjusted Climate Policy Uncertainty (CPU) index from the U.S., and the Economic Policy Uncertainty (EPU) index from China on the Chinese forest carbon stocks (FCS), comprehensively. On this basis, we are interested in clarifying the channels by which CCPA promotes the growth of forest carbon sequestration in China. Specifically, panel data from 30 provinces in China from 2000 to 2017 are used for empirical analysis, and the following results are obtained: (1) The baseline regression indicates that CCPA significantly promotes the growth of FCS, while CPU or EPU inhibits its growth. (2) The interactive regression shows that the effect of CCPA on the growth of FCS can mitigate the inhibiting impact of CPU or EPU. (3) Mediation analysis documents that CCPA promotes FCS growth by increasing the forest tending area, expanding the existing forest area, increasing renewable energy consumption, and improving green investment. (4) Heterogeneity analysis reveals a clear differentiation in the effect of CCPA on FCS under different situations. Finally, policy implications are proposed based on the results. This article is expected to provide a theoretical basis for the Chinese government to develop relevant policies from the perspective of promoting FCS growth.

Microbial communities and functions changed in rhizosphere soil of Pinus massoniana provenances with different carbon storage.

The average carbon storage of Pinus massoniana is much higher than the average carbon storage of Chinese forests, an important carbon sink tree species in subtropical regions of China. However, there are few studies on the differences in rhizosphere microorganisms of P. massoniana with different carbon storages. To clarify the relationships between plant carbon storage level, environmental parameters and microbial community structure, we identified three carbon storage levels from different P. massoniana provenances and collected rhizosphere soil samples. We determined chemical properties of soil, extracellular enzyme activity, and microbial community structures at different carbon storage levels and examined how soil factors affect rhizosphere microorganisms under different carbon storage levels. The results revealed that soil organic carbon (SOC), nitrate nitrogen (NO3--N), ammonium nitrogen (NH4+-N) contents all increased with increasing carbon storage levels, while pH decreased accordingly. In contrast, the available phosphorus (AP) content did not change significantly. The soil AP content was within the range of 0.91 ~ 1.04 mg/kg. The microbial community structure of P. massoniana changed with different carbon storage, with Acidobacteria (44.27%), Proteobacteria (32.57%), and Actinobacteria (13.43%) being the dominant bacterial phyla and Basidiomycota (73.36%) and Ascomycota (24.64%) being the dominant fungal phyla across the three carbon storage levels. Soil fungi were more responsive to carbon storage than bacteria in P. massoniana. C/N, NH4+-N, NO3--N, and SOC were the main drivers (p < 0.05) of changes in rhizosphere microbial communities. The results revealed that in the rhizosphere there were significant differences in soil carbon cycle and microorganism nutrient preferences at different carbon storages of P. massoniana provenance, which were significantly related to the changes in rhizosphere microbial community structure. Jiangxi Anyuan (AY) provenance is more suitable for the construction of high carbon storage plantation.

PH is the major predictor of soil microbial network complexity in Chinese forests along a latitudinal gradient

Patterns in the network structure of microbial communities may give indications of their degree of interaction and interdependence, and their resilience to environmental disturbance. Geographical differences in network patterns of forest communities may reflect broad-scale differences in ecosystem structure and responses to environmental change. We analyzed a large dataset of 144 samples of soil bacterial and fungal communities characterized by 16S and ITS amplicon sequencing from forests in eastern China, along a 4,100 km latitudinal transect. This showed that fungal-bacterial network complexity was most closely related to soil pH. Results showed that pH was the strongest predictor of variation in community network structure across the forest soils of eastern China. Lower network complexity and stability were found around neutral pH, which was the strongest environmental predictor. This may be due to the optimal conditions that neutral soils provide for microorganism survival, reducing the necessity for physiological interdependence, consequently leading to fewer positive connections. Additionally, neutral soils provide more generalized niches, resulting in less specific niche exclusion, showing up as fewer negative connections. Unlike the trend for overall connectivity, the richness and relative abundances of keystone taxa were significantly and negatively correlated with pH. Overall, both connectivity and numbers of keystones were consistent in suggesting that network complexity was greatest overall at acidic pH. It appears that at the broad scale studied here, pH plays a more significant role in shaping microbial community integration than such factors as ecosystem productivity or climate.

Carbon budget of different forests in China estimated by an individual-based model and remote sensing.

Forests play a key role in the regional or global carbon cycle. Determining the forest carbon budget is of great significance for estimating regional carbon budgets and formulating forest management policies to cope with climate change. However, the carbon budget of Chinese different forests and their relative contributions are not completely clear so far. We evaluated the carbon budget of different forests from 1981 to 2020 in China through combining model with remote sensing observation. In addition, we also determined the relative contribution of carbon budget of each forest type to all forests in China. Eight forest types were studied: evergreen coniferous forest (ECF), deciduous coniferous forest (DCF), coniferous and broad-leaved mixed forest (CBF), deciduous broad-leaved forest (DBF), evergreen broad-leaved forest (EBF), evergreen deciduous broad-leaved mixed forest (EDBF), seasonal rain forest (SRF), and rain forest (RF). The results indicated that the Chinese forests were mainly carbon sink from 1981 to 2020, particularly the annual average carbon budget of forest from 2011 to 2020 was 0.191 PgC·a-1. Spatially, the forests' carbon budget demonstrated obvious regional differences, gradually decreasing from Southeast China to Northwest China. The relative contributions of carbon budget in different forests to all forests in China were different. During 2011-2020, the ECF forests contributed the most carbon budget (34.45%), followed by DBF forests (25.89%), EBF forests (24.82%), EDBF forests (13.10%), RF forests (2.23%), SRF forests (3.14%) and CBF forests (1.14%). However, the DCF forests were found mainly as carbon source. These results contribute to our understanding of regional carbon budget of forests.

Metagenomics reveals the underestimated role of bacteria in the decomposition of downed logs in forest ecosystems

Downed logs are important structural and functional elements in the material flow of global forest ecosystems. They provide a rich natural substrate resource for microorganisms; however, current laboratory-based methods cannot fully elucidate the complex process of microbial decomposition of downed logs. Therefore, our study investigated the microbial community structure, succession patterns, and underlying environmental factors controlling the decomposition of Pinus tabulaeformis and Quercus aliena var. acuteserrata downed logs at different stages of decomposition in Chinese forest ecosystems. The genomes of the microorganisms present on the logs during different stages were therefore sequenced, and functional genes were annotated and analyzed using the Kyoto Encyclopedia of Genes and Genomes and Carbohydrate-Active enZYmes databases. We found that bacterial abundance was always higher than fungal abundance throughout the decomposition process, reflecting their strong competitive ability. Microbial community structure and function were similar in the early stage (I) and late stages (IV, V) of log decomposition, but those of the middle stages (II, III) differed. With the decomposition of downed logs of Q. aliena var. acuteserrata, there was a fungal succession pattern from Ascomycota to Basidiomycota to Mucoromycetes, but this trend did not occur for P. tabulaeformis. The density, moisture and elemental content of downed logs were the main factors affecting the microbial community. Finally, microbially mediated functions were mainly related to the metabolism of amino acids, carbohydrates and various small molecule carbohydrate enzymes, cellulases, and chitinases in glycoside hydrolases; the taxa sources of the main functions were primarily Proteobacteria and Firmicutes bacteria, reflecting the functional importance of bacteria in the process of downed log decomposition. We conclude that bacteria always play an important role in the process of downed log decomposition and that their role may be far underestimated. Therefore, we believe that the next step should be to expand the study of downed log bacteria, to further clarify the ecological functions of the bacteria on downed logs in natural environments and their relationship with fungi, which may help shed light on the complex decomposition process of downed logs.

Maximum potential of vegetation carbon sink in Chinese forests

Forest vegetation is essential in sequestering carbon dioxide (CO2) from the atmosphere and mediating global warming. The carbon (C) sink potential of forest vegetation in different provinces is vital for policymakers to develop C-neutral technical routes and regional priorities in China; however, the mechanism remains unclear. In this study, we compiled the public data on forest vegetation biomass or storage along forest succession series between 2003 and 2022 and obtained the spatial variation of the maximum C storage(BCmax) of forest vegetation using classic logistic equation and nonlinear fitting. Furthermore, the C sink potential (∆Cpot) of the Chinese forest vegetation was calculated based on the differences between the BCmax and intensive field-investigated data in the 2010s. The results showed that the BCmax in the Chinese forest vegetation was approximately 19.03 Pg. The BCmax in southwest and northeast China were higher than those in other regions. The ∆Cpot was estimated as 8.83 Pg. Moreover, 1 km × 1 km spatial raster data for ∆Cpot were produced using the spatial raster calculation. Similarly, the per capita ∆Cpot of regions with low economic development (southwest, central, and southern Chinese provinces) were five to ten times higher than those of regions with a higher economic level. The ∆Cpot correlated negatively with gross domestic product (GDP)across all Chinese provinces. Our findings provide new insights into the ∆Cpot of the Chinese forest vegetation under natural restoration and emphasize that some differences in financial and political support among different provinces facilitate achieving a large ∆Cpot for C neutrality.

Evidence of advancing spring xylem phenology in Chinese forests under global warming

Evidence of advancing spring xylem phenology in Chinese forests under global warming

Spatial redistribution and enantiomeric signatures of hexachlorocyclohexanes in Chinese forest soils: Implications to environmental behavior and influencing factors

Hexachlorocyclohexanes (HCHs) are a group of highly persistent pesticides. The concentrations of HCHs and the enantiomeric fractions of α-HCH in the O- and A-horizons from 30 mountains across China were analyzed in this study. The concentrations of total HCHs ranged from 0.061 to 46.9 ng/g (mean 2.12 ng/g) and 0.046 to 16.1 ng/g (mean 0.792 ng/g) in the O- and A-horizons, respectively. The HCH residues were mainly derived from the historical applications of technical HCH and lindane. Higher concentrations of HCHs were typically found in northern China, and no significant correlations were found between historical technical HCH usage and HCH isomer concentrations in either the O- or A-horizons (p > 0.05). Conversely, the concentrations of HCH isomers were significantly correlated with the environmental parameters (temperature and precipitation), thus indicating a typical secondary distribution pattern. Some HCH isomers tended to be transported northward under the long-term effect of monsoon. Chiral α-HCH was non-racemic in soils and showed preferential degradation of (−) α-HCH in both the O- and A-horizons. The transformation from γ-HCH to α-HCH might alter the enantiomeric signatures of α-HCH in soils. Moreover, the deviation from racemic of α-HCH was positively correlated with the C/N ratio in the A-horizon (p < 0.01), thus suggesting that the C/N ratio could alter the microbial activity and significantly affect the enantioselective degradation extent of α-HCH in soils.

Impact of Global Supply Chain Crisis on Chinese Forest Product Enterprises: Trade Trends and Literature Review

The global supply chain crisis caused by COVID-19 and the trend of “anti-globalization” continuously impact international trade. As one of the central processing centers of global primary products, China’s forest product enterprises import raw materials and export manufactured products, which are vulnerable to the worldwide supply chain crisis. This article reviews research on the influence of the pandemic and the Sino–US trade war on the international trade of wooden products and China’s forestry enterprises that import raw timber materials and export wooden products. Furthermore, with the analysis of trade data, we found that China’s forest product enterprises are facing a shortage of wood materials and rising import prices. Regarding export, they face the problems of increased trade costs and fluctuating product demand. Moreover, the influence of the global supply chain crisis causes China’s trade diversion effect. This article comprehensively analyzes how the global supply chain crisis affects wood-based product enterprises and offers suggestions for further developing China’s forestry enterprises.

Integrating multiple plant functional traits to predict ecosystem productivity

Quantifying and predicting variation in gross primary productivity (GPP) is important for accurate assessment of the ecosystem carbon budget under global change. Scaling traits to community scales for predicting ecosystem functions (i.e., GPP) remain challenging, while it is promising and well appreciated with the rapid development of trait-based ecology. In this study, we aim to integrate multiple plant traits with the recently developed trait-based productivity (TBP) theory, verify it via Bayesian structural equation modeling (SEM) and complementary independent effect analysis. We further distinguish the relative importance of different traits in explaining the variation in GPP. We apply the TBP theory based on plant community traits to a multi-trait dataset containing more than 13,000 measurements of approximately 2,500 species in Chinese forest and grassland systems. Remarkably, our SEM accurately predicts variation in annual and monthly GPP across China (R2 values of 0.87 and 0.73, respectively). Plant community traits play a key role. This study shows that integrating multiple plant functional traits into the TBP theory strengthens the quantification of ecosystem primary productivity variability and further advances understanding of the trait-productivity relationship. Our findings facilitate integration of the growing plant trait data into future ecological models.

Response of wood decomposition to different forms of N deposition in subtropical forests

AimsWood decomposition plays an important role in ecosystem soil fertility and nutrient cycling, but how different forms of nitrogen (N) affect these biogeochemical processes is still unclear. The effects of N deposition on wood decomposition have been widely studied, but the decomposition process and biotic driver response to different forms of N have rarely been studied.MethodsIn this study, we conducted a two-year field factorial fertilization experiment with different N forms in a subtropical Chinese forest. Glycine and urea were selected as organic N (ON), and ammonium nitrate was selected as inorganic N (IN). Six different ratios (control, 0:10, 3:7, 5:5, 7:3, 10:0) of IN:ON with equal N amounts were uniformly added to the studied wood.ResultsWe found that both forms of N deposition, i.e., ON and IN, accelerated the wood decomposition rates across the four studied species, and the magnitude of the increase was species specific. Mixed fertilizer with ON and IN resulted in the highest responses in the wood decomposition rate, which was 1.73- and 1.48-fold higher than that in the control and in response to IN addition alone across species. The ON + IN treatment resulted in the highest faunal and microbial community abundance of the decomposing wood.ConclusionIn summary, our results indicate that different forms of anthropogenic N enrichment can promote wood decomposition through the modification of microbial and faunal communities in the wood decomposition process. Our results show that future studies need to consider N forms and components when estimating exogenous N deposition effects on the woody material nutrient cycle and terrestrial ecosystem carbon cycles.

Monitoring of Drought Stress in Chinese Forests Based on Satellite Solar-Induced Chlorophyll Fluorescence and Multi-Source Remote Sensing Indices

Greenhouse gas emissions have largely changed the global climate, leading to an increase in the frequency and extent of droughts. Forests are essential natural resources, and they play an important role in maintaining ecological security. Effectively monitoring drought stress in forests can help promote sustainable forestry development. Solar-induced chlorophyll fluorescence is a spectral signal released by vegetation photosynthesis after light absorption. In this study, we used solar-induced chlorophyll fluorescence data (SIF), canopy fluorescence yield (SIFyield) data, vegetation indices (NDVI, EVI), leaf area index (LAI), and fraction of absorbed photosynthetically active radiation (fPAR) to study forest drought stress in the Yunnan, Fujian, Shaanxi, and Heilongjiang provinces in China, respectively. The temporal and spatial ranges of drought stress indicated by the Standardized Precipitation-Evapotranspiration Index (SPEI) values were used as a reference (SPEI ≤ −0.5 indicates the occurrence of drought). Firstly, the standardized anomalous values of SIF, SIFyield, NDVI, EVI, LAI, and fPAR were calculated. The temporal and spatial response abilities of each variable to drought stress were analyzed. Secondly, the correlation between each variable and the drought indicator SPEI was quantified. Finally, the validity and variability of SIF and other variables for drought monitoring were analyzed and verified with a random forest classification model. The results showed that on a temporal scale, SIFyield showed an earlier response to drought stress than other variables and the abnormal change of SIFyield was higher than other variables by 10% or more. Spatially, the range of drought areas indicated by SIFyield and SPEI had more coincident areas than other variables. The overall correlation between SIFyield and SPEI was also higher during the drought period, especially during late drought periods when other variables showed negative correlations. For SIFyield, the correlation coefficients of the Yunnan, Fujian, Shaanxi, and Heilongjiang provinces were 0.57, 0.43, 0.32, and 0.49, respectively. Additionally, the variable importance assessment using a random forest model also indicated that SIFyield is more sensitive to forest droughts. We concluded that SIFyield is an effective tool for monitoring forest drought stress in various regions of China and that it can provide a scientific basis for forest drought monitoring.

Patterns and controlling factors of soil carbon sequestration in nitrogen-limited and -rich forests in China-a meta-analysis.

Soil organic carbon (SOC) management has the potential to contribute to climate change mitigation by reducing atmospheric carbon dioxide (CO2). Understanding the changes in forest nitrogen (N) deposition rates has important implications for C sequestration. We explored the effects of N enrichment on soil carbon sequestration in nitrogen-limited and nitrogen-rich Chinese forests and their controlling factors. Our findings reveal that N inputs enhanced net soil C sequestration by 5.52-18.46 kg C kg-1 N, with greater impacts in temperate forests (8.37-13.68 kg C kg-1 N), the use of NH4NO3 fertilizer (7.78 kg Ckg-1 N) at low N levels (<30 kg Ckg-1 N; 9.14 kg Ckg-1 N), and in a short period (<3 years; 12.95 kg C kg-1 N). The nitrogen use efficiency (NUE) varied between 0.24 and 13.3 (kg C kg-1 N) depending on the forest type and was significantly controlled by rainfall, fertilizer, and carbon-nitrogen ratio rates. Besides, N enrichment increased SOC concentration by an average of 7% and 2% for tropical and subtropical forests, respectively. Although soil carbon sequestration was higher in the topsoil compared to the subsoil, the relative influence indicated that nitrogen availability strongly impacts the SOC, followed by dissolved organic carbon concentration and mean annual precipitation. This study highlights the critical role of soil NUE processes in promoting soil C accumulation in a forest ecosystem.

Mapping China’s planted forests using high resolution imagery and massive amounts of crowdsourced samples

Tree planting has been suggested as a potentially effective solution for mitigating climate change. China has implemented the world’s largest afforestation and reforestation project since the 1970s, but high-resolution maps of China’s planted forests remain unavailable. In this study, we explored the use of multi-source remote sensing images and crowdsourced samples to produce the first high-resolution (30-m) map of China’s planted forests. We constructed a Google Earth Engine (GEE)-based mapping framework using spectral, temporal, structural, textural and topographic features derived from Landsat and Sentinel-1 time series imagery, Digital Elevation Model (DEM) and Chinese Forest Canopy Height (CFCH) data. Over 300,000 high-quality crowdsourced samples were collected for training the mapping pipeline. Validation against independent field samples indicated an accuracy of 84.93 % and an F1 score of 0.85. The uncertainty map of each pixel was also constructed and showed that the areas of low and medium uncertainties accounted for 38.27 % and 50.98 % of the total area, respectively, indicating the high estimation reliabilities of the planted forest map. We show that China’s planted forests in the year of 2020 had a total area of 769853.01 km2, accounting for 31.30 % of the world’s total planted forests. The majority (77.45 %) of China’s planted forests were located in the Eastern, Center-South, and Southwestern regions. By further assessing the performance of the image features used to map the planted forests, we found that temporal features are key to identifying the planted forests in East and Center-South of China, where they are mainly timber plantations. However, structural and textural features were more useful for locating the planted forests in North and Northeast of China, where are dominated by planted shelterbelts. Our study demonstrated that combining crowdsourced samples with high-resolution satellite images allows mapping planted forests with unprecedented resolution (30-m) across large areas. Our map could contribute to the sustainable management of China’s forests and a more accurate quantification of the carbon balance of China’s natural ecosystems.

Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019

Over the past 2 to 3 decades, Chinese forests are estimated to act as a large carbon sink, yet the magnitude and spatial patterns of this sink differ considerably among studies. Using 3 microwave (L- and X-band vegetation optical depth [VOD]) and 3 optical (normalized difference vegetation index, leaf area index, and tree cover) remote-sensing vegetation products, this study compared the estimated live woody aboveground biomass carbon (AGC) dynamics over China between 2013 and 2019. Our results showed that tree cover has the highest spatial consistency with 3 published AGC maps (mean correlation value R = 0.84), followed by L-VOD ( R = 0.83), which outperform the other VODs. An AGC estimation model was proposed to combine all indices to estimate the annual AGC dynamics in China during 2013 to 2019. The performance of the AGC estimation model was good (root mean square error = 0.05 Pg C and R 2 = 0.90 with a mean relative uncertainty of 9.8% at pixel scale [0.25°]). Results of the AGC estimation model showed that carbon uptake by the forests in China was about +0.17 Pg C year −1 from 2013 to 2019. At the regional level, provinces in southwest China including Guizhou (+22.35 Tg C year −1 ), Sichuan (+14.49 Tg C year −1 ), and Hunan (+11.42 Tg C year −1 ) provinces had the highest carbon sink rates during 2013 to 2019. Most of the carbon-sink regions have been afforested recently, implying that afforestation and ecological engineering projects have been effective means for carbon sequestration in these regions.

The Chinese Forest Biodiversity Monitoring Network: 20-year achievements and outlook

The Chinese Forest Biodiversity Monitoring Network: 20-year achievements and outlook

A dataset of meteorological observations at Zhejiang Tiantong National Forest Ecosystem Observation and Research Station (2003 – 2017)

Evergreen broad-leaved forests are one of the main types of Chinese forests. The meteorological data of them are of great value for the study on the function and ecological process of evergreen broad-leaved forest ecosystem, and can serve as a major data source for weather forecast and disaster warning. Zhejiang Tiantong National Forest Ecosystem Observation and Research Station located in the subtropical broad-leaved evergreen forest in Tiantong Forest Park in eastern China. Since April 2003, we had collected long-term meteorological data by setting up the automatic meteorological observation system. This dataset contains the monthly data (177 entries) and annual data (15 entries) from 2013 to 2017, including 18 meteorological observation indexes, such as atmospheric temperature, atmospheric humidity, precipitation, atmospheric pressure, total radiation, net radiation, reflected radiation, UV radiation, photosynthetically active radiation, sunshine hours and soil temperature in different depths (0 cm, 5 cm, 10 cm, 15 cm, 20 cm, 40 cm, 60 cm and 100 cm). It is expected to provide data support for studying the ecological process, evaluating ecosystem service function and increasing disaster warning accuracy of local forests in Tiantong zone.

Single-nucleus RNA and ATAC sequencing uncovers the molecular and cellular characteristics in the musk gland of Chinese forest musk deer (Moschus berezovskii).

The Chinese forest musk deer (FMD; Moschus berezovskii) is an endangered artiodactyl mammal. Musk secreted by the musk gland of male has extremely high economic and medicinal value. However, the molecular and cellular characteristics of the musk gland have not been studied. Here, we investigated the diversity and transcriptional composition of musk gland cell types and the effect of cell type-specific chromatin accessibility on gene expression using single-nucleus RNA sequencing (snRNA-seq) and single-nucleus ATAC sequencing (snATAC-seq) association analysis. Based on uniform manifold approximation and projection (UMAP) analysis, we identified 13 cell types from the musk gland, which included two different acinar cells (cluster 0 and cluster 10). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that many pathways related to musk secretion were enriched in acinar cells. Our analysis also revealed acinar cell core transcription factors and core target genes, and further constructed acinar cell-specific regulatory networks. In cluster 0, 11 core target genes (Nedd4l, Adcy9, Akr1c1, Vapb, Me1, Acsl1, Acss3, Srd5a1, Scnn1a, Acadm, and Nceh1) possibly related to musk secretion were regulated by 24 core transcription factors (SP3, NFIC, NR6A1, EHF, RUNX1, TFAP2A, RREB1, GRHL2, NFIB, ELF1, MAX, KLF5, REL, HES1, POU2F3, TFDP1, NR2C1, ATF7, MEIS1, NR4A2, NFIA, PBX1, ZNF652, and NFKB1). In cluster 10, four core target genes (Akr1c1, Pcca, Atp1b1, and Sgk1) possibly related to musk secretion were regulated by 10 core transcription factors (BARX2, EHF, PBX1, RUNX1, NFIB, FOXP1, KLF3, KLF6, ETV6, and NR3C2). Moreover, the credibility of snRNA-seq and snATAC-seq data was verified by fluorescence in situ hybridization and immunohistochemistry. Finally, cell communication analysis demonstrated that the two types of acinar cells mainly have communications in musk secretion-related processes. In conclusion, we provided important insights and invaluable resources for the molecular and cellular characteristics of the musk gland, which will lay a foundation for the study of musk secretion mechanism in the future.

Fine-scale forest biological hazard in China show significant spatial and temporal heterogeneity

To date, there is a lack of fine-scale forest biological hazard risk (FBHR) data for Chinese forests on a national scale. This study explored the spatial and temporal patterns of the FBHR in prefecture-level cities in China. Data on the forest biological hazard (FBH) damage area and forest cover area of each prefecture-level city in China from 2007 to 2019 were used. Based on the statistical analysis characteristics of the FBH rate of each prefecture-level city in recent years, the spatial pattern of the FBHR of prefecture-level cities in China was obtained by optimal fitting. The results of the study show that the overall average annual occurrence of FBHs in China from 2007 to 2019 was 11,420 thousand hectares. Although the incidence rate showed a decreasing trend, the occurrence area showed a decreasing and then increasing trend. The incidence of FBHs in each prefecture-level city in China showed a pattern of “heavy in the north and light in the south”; the overall incidence of FBHs showed a pattern of “high in the central part and low in the north and south”. The FBHR at the prefecture level in China showed a circular scattering spatial pattern, and significant geographical differences were observed, with an average annual expected incidence of approximately 1.3‰. Among them, the highest FBHR at the prefecture level, was mainly concentrated along the Henan, Anhui and Shandong borders. A high FBHR was distributed in the Jiangsu, Xinjiang, and Ningxia-Neimenggu areas. A medium FBHR was distributed in the North China Plain, Liaoning and Sichuan. A low FBHR was distributed in Central China, North China, Xinjiang and Liaoning. An extremely low FBHR was concentrated in Heilongjiang-Jilin-Qinghai-Gansu, southern East China, Southwest China and South China. The results of this study reveal the FBHR patterns on a refined scale in China and provide a reference for managing, preventing, and controlling the FBH.

Effects of Forest Fire Prevention Policies on Probability and Drivers of Forest Fires in the Boreal Forests of China during Different Periods

Fire prevention policies during different periods may lead to changes in the drivers of forest fires. Here, we use historical fire data and apply the boosted regression tree (BRT) model to analyze the spatial patterns and drivers of forest fires in the boreal forests of China from 1981 to 2020 (40 years). We divided the fire data into four periods using the old and new Chinese Forest Fire Regulations as a dividing line. Our objectives here were: to explore the influence of key historical events on the drivers of forest fires in northern China, establish a probability model of forest fire occurrence, and draw a probability map of forest fire occurrence and a fire risk zone map, so as to interpret the differences in the drivers of forest fires and fire risk changes over different periods. The results show that: (1) The model results from 1981 to 2020 (all years) did not improve between 2009 and 2020 (the most recent period), indicating the importance of choosing the appropriate modeling time series length and incorporating key historical events in future forest fire modeling; (2) Climate factors are a dominant factor affecting the occurrence of forest fires during different periods. In contrast with previous research, we found that here, it is particularly important to pay attention to the relevant indicators of the autumn fire prevention period (average surface temperature, sunshine hours) in the year before the fire occurrence. In addition, the altitude and the location of watchtowers were considered to have a significant effect on the occurrence of forest fires in the study area. (3) The medium and high fire risk areas in our three chosen time periods (1981–14 March 1988; 15 March 1988–2008; 2009–2020) have changed significantly. Fire risks were higher in the east and southeast areas of the study area in all periods. The northern primeval forest area had fewer medium-risk areas before the new and old regulations were formulated, but the medium-risk areas increased significantly after the old regulations were revised. Our study will help understand the drivers and fire risk distribution of forest fires in the boreal forests of China under the influence of history and will help decision-makers optimize fire management strategies to reduce potential fire risks.