Forestry research最新文献

Pinus armandii Franch., a key native conifer in China, faces severe Dendroctonus armandi infestation, threatening forest ecosystems. Reducing infestation probability and enhancing resistance are essential for transforming Pinus armandii forests. This study investigates the correlation between stand structure, topographic factors, and the probability of Pinus armandii infestation by Dendroctonus armandi. Based on these correlations, it selects suitable mixed-species combinations of native tree species with low infestation probabilities that are adapted to regional characteristics. A random survey was conducted in 58 plots (6,021 trees) in Shennongjia. Logistic regression and analysis of variance (ANOVA) revealed: (1) Infestation rate increased with elevation, peaking at 84.38% above 2,000 m; between 1,500-2,100 m, probability rose 4.3% per 100 m elevation gain; (2) Steeper slopes (> 25°) reduced infestation (46.03%), with risk decreasing 1.9% per 1° slope increase (0°-40°); (3) Larger DBH (> 30 cm) trees had higher infestation (82.93%), increasing 4.5% per 1 cm DBH; (4) Higher species mingling (four neighboring non-Pinus trees) lowered infestation to 63.39%, reducing risk by 54.3% per mingling unit; (5) Healthy Pinus armandii were frequently neighbored by Litsea pungens, Carpinus cordata, Phellodendron chinense, and Betula platyphylla. Prioritizing slopes > 25° and elevations < 2,000 m for afforestation, mixed with Litsea pungens, Carpinus cordata, or Betula platyphylla, can mitigate infestation. These findings provide actionable strategies to enhance Pinus armandii forest resilience against Dendroctonus armandi threats.

Adventitious root formation is a crucial developmental process in the clonal propagation of economically significant horticultural and woody species. This process is antagonistically regulated by auxin and cytokinin, yet the underlying molecular regulatory mechanisms remain poorly understood. In this study, we investigated the role of PagARF3.1, a homolog of Arabidopsis auxin response factor 3, in hybrid poplar (Populus alba × Populus glandulosa clone cv. '84K'), focusing on its involvement in adventitious root formation. GUS staining analysis revealed that PagARF3.1 was expressed in adventitious root tips, pericycle cells, early root primordia, and outgrowing roots. Knockdown of PagARF3.1 delayed adventitious root formation and reduced root biomass in transgenic plants, whereas overexpression of PagARF3.1 promoted earlier rooting and increased the number of adventitious roots. Real-time quantitative polymerase chain reaction analysis indicated that the expression levels of PagIPT5a and PagIPT5b were significantly elevated in PagARF3.1 RNAi lines and reduced in overexpression lines. Yeast one-hybrid assays and ChIP-PCR analysis demonstrated that PagARF3.1 directly binds to the promoter regions of PagIPT5a and PagIPT5b, thereby regulating their expression. These findings collectively demonstrate that PagARF3.1 acts as a positive regulator of adventitious root formation by repressing IPT-mediated cytokinin biosynthesis.

The swift progress of close-range remote sensing necessitates a quantitative evaluation of its accuracy in estimating forest above-ground biomass (AGB) across diverse scales, forest types, methodologies, and variables. These evaluations will enhance the effectiveness of remote sensing in forest monitoring, reveal the carbon sequestration capability of forest vegetation, and underscore the critical function of forests as terrestrial carbon sinks. In this study, we designated R ² as the effect size for the meta-analysis given that it is commonly regarded as a measure for estimating accuracy in AGB research, which indicates the explanatory capacity of independent variables. Utilizing 187 global investigations and 233 datasets, this research systematically assessed the accuracy (R ²) of ground light detection and ranging (LiDAR), unmanned aerial vehicles (UAVs), spectra, and red-green-blue (RGB) sensors across the single-tree, plot, and stand scales. The discrepancies in accuracy across the various research methods and the independent variables in the allometric growth equation were also assessed. The research indicated that ground lidar exhibited the best accuracy across all studies and was the most effective approach at both the single-tree and plot scales. Nonetheless, as the scale of the research broadened, both accuracy and sample size diminished. Furthermore, the variations from different approaches among different forest types were substantial; therefore, it was necessary to model these forest types explicitly. By integrating diameter at breast height (DBH or D) and tree height (H) as independent variables in the allometric growth equation, the method showed improved estimation accuracy. The estimation of AGB must address the issue of accumulated error arising from the interconversion of DBH and H, single-tree segmentation, and specific allometric growth equations, which are subsequently compounded at the plot and stand levels. Close-range remote sensing is currently the most efficient method for estimating forest AGB, surpassing conventional measurement techniques. Yet, due to sensor limitations, no single sensor achieved optimal results independently. The integration of multi-source data and scale adaptation strategies further enhanced the efficacy of close-range remote sensing, surpassing the conventional survey methods. Moving forward, efforts should prioritize cross-platform data standardization, deep learning model refinement, and the establishment of non-destructive validation systems to support high-precision forest carbon monitoring, in alignment with carbon management goals.

CRISPR/Cas9-mediated genome editing has revolutionized tree improvement by enabling precise trait modification, accelerating breeding cycles, and enhancing forestry sustainability. Fraxinus mandshurica, valued for its desirable traits and adaptability, serves as a strategic focus for the National Reserve Forest Project (NRFP) and forestry germplasm resource breeding and quality improvement in China. Developing a species-specific genome editing system is crucial for valuable yet recalcitrant species like F. mandshurica. In this study, the development of a species-specific CRISPR/Cas9 platform is presented for F. mandshurica, which incorporates endogenous promoter engineering, sgRNA optimization, light quality modulation, and temperature control protocols to enhance genome editing efficiency. Truncated endogenous FmU6 promoter variants (FmU6-6-4 and FmU6-7-4) drove sgRNA expression at levels 3.36 and 3.11 times higher than that of the AtU6-26 promoter. The expression of Cas9 was controlled by the endogenous constitutive FmECP3 promoter, exhibiting an activity 5.48 times greater than the positive control. A highly active sgRNA4 targeting FmPDS1/2 was identified, demonstrating a cleavage efficiency of 36.10%. Heat treatment at 37 °C effectively increased the Cas9 cleavage efficiency to 7.77 times that observed at 22 °C. Chimeric albino mutants with an editing efficiency of 18.2% were obtained through transient and stable transformations, combined with light quality optimization and heat treatment during different regeneration stages. The mutation types included nucleotide insertions, deletions, and substitutions, leading to early termination codons and truncated FmPDS1/2 protein. Additionally, mutations in FmPDS1/2 resulted in albino phenotypes and a reduction in chlorophyll content to 46.44%-58.88%. This optimized system provides a robust platform for functional genomics studies and trait improvement in F. mandshurica, with potential applications in forestry biotechnology.

To cope with winter stress from low temperatures and excessive light, evergreen conifers employ seasonal adjustments in photosynthetic function. Understanding these regulatory mechanisms is critical for predicting coniferous forest responses to climate change and their role in the global carbon cycle. To assess variation in cold acclimation strategies, the following were analyzed: pigment composition, photochemical efficiency of photosystem II, and photosynthetic parameters in three to five provenances (Prv) of Cryptomeria japonica grown in two common gardens (CGs) with contrasting climates. All Prv exhibited winter chlorophyll reduction, increased xanthophyll cycle, lutein pigments, and rhodoxanthin accumulation, reflecting conserved photoprotective responses. However, needle chlorophyll concentrations were unexpectedly higher in the colder site, especially in the northernmost Prv, suggesting genotype-specific plasticity. Higher rhodoxanthin levels in the hotter sites indicated a trade-off between the xanthophyll cycle and rhodoxanthin-mediated protection governed by winter severity. Despite these differences, values of photochemical efficiency of photosystem II were similar among Prv within each garden, though consistently higher in the hotter garden. No significant variation in photosynthetic capacity was detected among the three Prv measured. This local adaptation is further supported by high phenotypic plasticity in pigment composition and leaf morphology. These findings highlight the diverse and flexible mechanisms by which C. japonica regulates pigment composition, enabling sustained photosynthesis across seasonal extremes, and suggest a role for both winter cold and summer heat in shaping local adaptation in this widely distributed conifer.

Construction of gene regulatory networks (GRNs) is essential for elucidating the regulatory mechanisms underlying metabolic pathways, biological processes, and complex traits. In this study, we developed and evaluated machine learning, deep learning, and hybrid approaches for constructing GRNs by integrating prior knowledge and large-scale transcriptomic data from Arabidopsis thaliana, poplar, and maize. Among these, hybrid models that combined convolutional neural networks and machine learning consistently outperformed traditional machine learning and statistical methods, achieving over 95% accuracy on the holdout test datasets. These models not only identified a greater number of known transcription factors regulating the lignin biosynthesis pathway but also demonstrated higher precision in ranking key master regulators such as MYB46 and MYB83, as well as many upstream regulators, including members of the VND, NST, and SND families, at the top of candidate lists. To address the challenge of limited training data in non-model species, we implemented transfer learning, enabling cross-species GRN inference by applying models trained on well-characterized and data-rich species to another species with limited data. This strategy enhanced model performance and demonstrated the feasibility of knowledge transfer across species. Overall, our findings underscore the effectiveness of hybrid and transfer learning approaches in GRN prediction, offering a scalable framework for elucidating regulatory mechanisms in both model and non-model plant systems.

Liriodendron is a highly valued ornamental genus renowned for its distinctive tulip-shaped flowers. Despite its horticultural importance, the molecular mechanisms underlying interspecific variation in petal coloration, namely green petals in Liriodendron chinense (Hemsl.) Sargent, an orange-yellow basal band in Liriodendron tulipifera Linn., and an extended orange-yellow band in their hybrid, remain poorly understood. By integrating morphological, transcriptomic, and metabolomic analyses, we found that orange-yellow pigmentation during petal development is closely associated with chlorophyll degradation and carotenoid biosynthesis. The expression of chlorophyll synthesis genes PORA1 and PORA2 showed a strong positive correlation with chlorophyll content, and their downregulation led to disrupted chloroplast structure and reduced chlorophyll levels. Concurrently, carotenoid biosynthesis genes CRTISO and LCYE were markedly upregulated during the formation of the colored petal band. These results highlight the synergistic roles of chlorophyll and carotenoid metabolism in determining petal color patterning in Liriodendron, providing a genetic basis for the targeted breeding of ornamental traits.

The seed dormancy of Malus sieversii belongs to the comprehensive type of dormancy, abscisic acid (ABA) is one of the important hormones to lift the seed dormancy. The ABA receptor pyrabactin resistance-like (PYL) protein is involved in seed germination and various stress responses. Fourteen MsPYL genes were evaluated in the genome of Malus sieversii. Phylogenetic analyses demonstrated that MsPYL proteins can be divided into three groups. The promoter regions of MsPYL genes contain cis-acting elements associated with expression induction by abiotic stresses such as abscisic acid, drought, and low temperature. Arabidopsis transgenic lines overexpressing MsPYL8 exhibited a markedly reduced germination rate, an extended growth period, and a reduction in the biomass of the aboveground parts compared to the wild type. Additionally, we found that MsPYL8 interacted with 30 proteins, including low-temperature-induced 65 kDa protein-like (LTI) and late embryogenesis abundant (LEA). The results demonstrated that PYL8 binds to ABA during seed germination, inhibits the dephosphorylation activity of protein phosphatases type 2C (PP2C), and activates the transmission of sucrose non-fermenting-1-related protein kinases 2s (SnRK2s) to abscisic acid insensitive 5 (ABI5) signaling, thereby activating the expression of ABA-responsive genes to regulate ABA signal transduction. The screening and validation of PYL8 interaction with LTI, and LTI interaction with ABI5, provides a foundation for further elucidation of the mechanism of PYL gene involvement in Malus sieversii seed dormancy lifting under the regulation of the ABA signaling pathway.

Stand structural diversity, encompassing spatial and non-spatial dimensions, is a key indicator of forest carbon storage, yet its relative impacts on multiple carbon pools remain unclear. Additionally, whether structural diversity consistently influences carbon storage across overstory, understory, and soil layers is uncertain. This study examined carbon storage dynamics across 13 secondary Masson pine forests within the Fengyang Mountain Nature Reserve. Principal component analysis was used to classify the stands into three types based on their spatial and non-spatial structural diversity: Type I (high spatial and high non-spatial diversity), Type II (high spatial but low non-spatial diversity), and Type III (low spatial and low non-spatial diversity). Total carbon storage was highest in Type I, while carbon storage in the understory layers was lowest in this type. Spatial structural diversity had a stronger influence on carbon storage than non-spatial diversity, with the uniform angle index primarily affecting overstory carbon storage, and the crowding index influenced understory carbon storage. Random forest analysis identified biomass and structural diversity as major predictors of carbon storage. Partial least squares path modeling revealed that spatial structural diversity indirectly increased overstory and soil carbon storage, but reduced understory carbon storage by modulating biomass. Our results highlight that spatial structural diversity is a dominant driver of carbon storage in forest ecosystems, with contrasting effects on overstory, understory, and soil layers, underscoring its critical role in regulating forest carbon dynamics.

ASYMMETRIC LEAVES1 (AS1) is a central regulator of leaf polarity establishment in Arabidopsis. However, the functions of its ortholog genes in Populus are unclear. In this study, we performed gene expression analysis and found that the AS1 ortholog gene PagAS1a exhibited significantly higher expression levels in the secondary phloem than in the secondary xylem within the wood formation zone. Subcellular localization analysis showed that PagAS1a localizes in the nuclei. Compared to the wild type, both PagAS1a overexpression (PagAS1a-OE), and dominant repression (PagAS1a-SRDX) plants exhibited enhanced height and stem diameter growth. Analysis of stem cross-sections revealed that both overexpression and dominant repression of PagAS1 promoted secondary xylem development. We performed transcriptome sequencing (RNA-seq) for gene expression analysis, and identified 2,736 significantly differentially expressed genes (DEGs) in PagAS1a-OE plants. GO analysis revealed that genes associated with photosynthesis, lignin biosynthetic, and cellulose biosynthetic pathways were significantly enriched in up-regulated DEGs in PagAS1a-OE, while genes associated with lignin catabolic, and phenylpropanoid catabolic pathways were significantly enriched in down-regulated DEGs. Together, our results suggest that the AS1 ortholog PagAS1a is an important regulator of secondary xylem development and plant growth in Populus.