Transcriptome analysis reveals rootstock-driven effects on growth and photosynthesis in Camellia chekiangoleosa: A phenotypic and biochemical perspective.

Camellia chekiangoleosa is a significant oil-bearing tree species, known for its high oleic acid content and shorter reproductive cycle compared to traditional oil-tea plants. However, there are few studies on the molecular mechanism and compatibility of the interaction between oil-Camellia scion and rootstock, which poses certain challenges to the cultivation and promotion of oil-Camellia. This study systematically evaluates the effects of hetero-grafting Camellia chekiangoleosa scions onto divergent rootstocks (Camellia chekiangoleosa, Camellia oleifera, and Camellia yuhsienensis).

Different Types of Planted Forest Accumulate Specific Heavy Metals in Western Beijing.

Biological enrichment is a promising strategy for mitigating heavy metal pollution; however, the characteristics of different tree species, their elevation-related heavy metal content, and their capacity to accumulate these metals remain unclear. Moreover, the factors influencing heavy metal pools require further investigation. In this study, we measured the concentrations of copper (Cu), iron (Fe), manganese (Mn), and zinc (Zn) in ground surface litter (GSL) and soil from four forest types (Pinus tabuliformis, Populus davidiana, Larix principis-rupprechtii, Betula platyphylla).

Developing a generalized nonlinear mixed-effects biomass model at stand-level under different age conditions for Chinese fir based on LiDAR and ground survey data in southern China.

Introduction: Chinese fir is a crucial afforestation and timber species in southern China. Accurate estimation of its stand biomass is vital for forest resource assessment, ecological industry development, and ecosystem management. However, traditional biomass prediction methods often face limitations in terms of accuracy and efficiency, highlighting the need for more robust modeling approaches.

Assessing the impact of climate change on the potential distribution of Mast. in southwest China: a Maxent modeling approach.

, which is widespread in southwestern China, is valuable for studying under different future climate scenarios to assess potential distribution shifts in response to climate warming. Understanding these changes can provide theoretical support for species conservation, rational utilization, ecological restoration, and management of habitats. The Maxent model was optimized using the package of ENMeval to adjust the Regularization Multiplier (RM) and Feature Class Combinations (FC) parameters.

A lightweight Deeplab V3+ network integrating deep transitive transfer learning and attention mechanism for burned area identification.

Complete and accurate burned area map data are needed to document spatial and temporal patterns of fires, to quantify their drivers, and to assess the impacts on human and natural systems. To achieve the the purpose of identifying burned area accurately and efficiency from remote sensing images, a lightweight deep learning model is proposed based on Deeplab V3 + , which employs the combination of attention mechanism and deep transitive transfer learning (DTTL) strategy. The lightweight MobileNet V2 network integrated with Convolutional Block Attention Module (CBAM) is designed as the backbone network to replace the traditional time-consuming Xception of Deeplab V3 +.

Ecological Interactions Between and Insect Pollinators Across Heterogeneous Habitats.

is an important woody oil plant in southern China, and developing its industry can enhance forest resource uses and increase edible oil supply. This study investigated the floral characteristics of different varieties, analysed the species and diversity of flower-visiting insects in different habitats, identified the main pollinators and their flower-visiting behaviours, and explored the relationship between pollinating insects and their floral characteristics. The floral lifespan of individual flowers was 5-8 d across cultivars, which is essentially the same.

Mapping Temperate Savanna in Northeastern China Through Integrating UAV and Satellite Imagery.

Temperate savannas are globally distributed ecosystems that play a crucial role in regulating the global carbon cycle and significantly contribute to human livelihoods. This study aims to develop a novel method for identifying temperate savannas and to map their distribution in Northeastern China. To achieve this objective, Unmanned Aerial Vehicle (UAV) imagery was integrated with Sentinel-2 and Sentinel-1 satellite imagery using Random Forest  (RF) regression and Classification and Regression Tree (CART) algorithms.

Incorporating site suitability and carbon sequestration of tree species into China's climate-adaptive forestation.

Strategic selection and precise matching of climate-resilient tree species are crucial for maximizing the mitigation and adaptation potential of Climate-Smart Forestry. However, current forestation plans often overlook species-specific environmental shifts, leading to suboptimal long-term carbon sequestration. Here we developed a climate-adaptive optimization framework to guide tree species selection and planting in China, based on projected habitat suitability and range shifts under future climate scenarios.

Soil microbial community and influencing factors of different vegetation restoration types in a typical agricultural pastoral ecotone.

Microbial network complexity is an important indicator for assessing the effectiveness of vegetation restoration. However, the response of the microbial network complexity of bacteria and fungi to different vegetation restoration types is unclear. Therefore, in this study, we selected four vegetation restoration types ( var.

A global product of 150-m urban building height based on spaceborne lidar.

Urban building height, as a fundamental 3D urban structural feature, has far-reaching applications. However, creating readily available datasets of recent urban building heights with fine spatial resolutions and global coverage remains a challenging task. Here, we provide a 150-m global urban building heights dataset around 2020 by combining the spaceborne lidar (Global Ecosystem Dynamics Investigation, GEDI), multi-sourced data (Landsat-8, Sentinel-2, and Sentinel-1), and topographic data.

Thinning-induced decrease in fine root biomass, but not other fine root traits in global forests.

In forest ecosystems, changes in the expression of tree absorptive root traits following management interventions are expected to influence post-thinning forest structure and function. Fine root traits are expected to be especially responsive to forest thinning-one of the most common forest management interventions and the focus of our research here-influencing tree-level responses to environmental change, and thereby contributing to post-thinning stand-level dynamics and ecosystem processes. However, there remains limited understanding surrounding whether or not forest thinning influences the expression of root morphological, chemical, and physiological traits associated with belowground resource acquisition.

Impact of atmospheric particulate matter retention on physiological characters of five plant species under different pollution levels in Zhengzhou.

Atmospheric particulate matter (PM) pollution has become a major environmental risk, and green plants can mitigate air pollution by regulating their enzymatic activity, osmoregulatory substances, photosynthetic pigments, and other biochemical characteristics. The present investigation aims to evaluate the mitigation potential of five common evergreen tree species (, , , , ) against air pollution and to assess the effect of dust retention on plant physiological functions exposed to three different pollution levels (road, campus, and park). The results found that the amount of dust retained per unit leaf area of the plants was proportional to the mass concentration of atmospheric particulate matter in the environment, and that dust accumulation was higher on the road and campus than in the park.

Cold threat and moisture deficit induced individual tree mortality via 25-year monitoring in seminatural mixed forests, northeastern China.

Accurately predicting tree mortality in mixed forests sets a challenge for conventional models because of large uncertainty, especially under changing climate. Machine learning algorithms had potential for predicting individual tree mortality with higher accuracy via filtering the relevant climatic and environmental factors. In this study, the sensitivity of individual tree mortality to regional climate was validated by modeling in seminatural mixed coniferous forests based on 25-year observations in northeast of China.

A two-dimensional four-quadrant assessment method to explore the spatiotemporal coupling and coordination relationship of human activities and ecological environment.

Human activities that involve diverse behaviors and feature a variety of participations and collaborations usually lead to varying and dynamic impacts on the ecological environment. Quantitative analysis of the dynamic changes and complex relationships between human activities and the ecological environment (eco-environment) can provide crucial insights for ecological protecting and balance maintaining. We proposed a two-dimensional four-quadrant assessment method based on the dynamic changes in Human Activity Index (HAI) - Environmental Ecological Condition Index (EECI) to analyze the dynamic trends and coupling coordination degree (CCD) between HAI and EECI.

Individual tree diameter growth modelling for natural secondary forest in Changbai Mountains, Northeast China.

Individual modelling is a foundational approach to study the natural forest growth and in this paper, we develop a serial distance-depended individual tree model for some species in natural forest which would provide prediction and characteristics for natural species. The data used to develop individual model for natural mixed forests were collected from 712 remeasured 10-year periodic permanent sample plots of in Baihe Forest Bureau of Changbai Mountains, northeast China. Based on analyzing relationship between diameter increment of individual trees with tree size, competitive status, and site condition and finding out the major independent variables, the growth models for individual trees of 15 species in the natural mixed forests, that have good predicting precision, and easily applicable, were developed using stepwise regression method.

Maximizing carbon sequestration potential in Chinese forests through optimal management.

Forest carbon sequestration capacity in China remains uncertain due to underrepresented tree demographic dynamics and overlooked of harvest impacts. In this study, we employ a process-based biogeochemical model to make projections by using national forest inventories, covering approximately 415,000 permanent plots, revealing an expansion in biomass carbon stock by 13.6 ± 1.

Deforestation in Latin America in the 2000s predominantly occurred outside of typical mature forests.

The role of tropical forests in the global carbon budget remains controversial, as carbon emissions from deforestation are highly uncertain. This high uncertainty arises from the use of either fixed forest carbon stock density or maps generated from satellite-based optical reflectance with limited sensitivity to biomass to generate accurate estimates of emissions from deforestation. New space missions aiming to accurately map the carbon stock density rely on direct measurements of the spatial structures of forests using lidar and radar.

Forest carbon storage and sink estimates under different management scenarios in China from 2020 to 2100.

Forests play a crucial role in mitigating climate change through carbon storage and sequestration, though environmental change drivers and management scenarios are likely to influence these contributions across multiple spatial and temporal scales. In this study, we employed three tree growth models-the Richard, Hossfeld, and Korf models-that account for the biological characteristics of trees, alongside national forest inventory (NFI) datasets from 1994 to 2018, to evaluate the carbon sink potential of existing forests and afforested regions in China from 2020 to 2100, assuming multiple afforestation and forest management scenarios. Our results indicate that the Richard, Hossfeld, and Korf models provided a good fit for 26 types of vegetation biomass in both natural and planted Chinese forests.

Combating land degradation through human efforts: Ongoing challenges for sustainable development of global drylands.

Drylands, as highly vulnerable ecosystems, support environmental functions and human well-being. Nevertheless, widespread land degradation and desertification present significant global and regional environmental challenges, with limited consensus on their area and degree. This study used time-series vegetation productivity and meteorological data from 2000 to 2020 to quantify global land degradation trends and driving factors in drylands.

Identifying Regenerated Saplings by Stratifying Forest Overstory Using Airborne LiDAR Data.

Identifying the spatiotemporal distributions and phenotypic characteristics of understory saplings is beneficial in exploring the internal mechanisms of plant regeneration and providing technical assistances for continues cover forest management. However, it is challenging to detect the understory saplings using 2-dimensional (2D) spectral information produced by conventional optical remotely sensed data. This study proposed an automatic method to detect the regenerated understory saplings based on the 3D structural information from aerial laser scanning (ALS) data.

Assessment of ecosystem quality changes based on optimizing key indicators in Nanwenghe National Nature Reserve, Heilongjiang, China.

Net primary productivity (NPP) is an indicator to reflect the production capacity of terrestrial ecosystems, as well as a key indicator for ecological quality. NPP at large scale is difficult to be measured. At present, most of the assessment of ecosystem quality uses NPP products with low resolution, which cannot capture the detailed characteristics of the ecosystem and is not conducive to the assessment of ecosystem quality at small-scale.

Modelling the effects of topographic heterogeneity on distribution of Nitraria tangutorum Bobr. species in deserts using LiDAR-data.

Microclimate ecology is attracting renewed attention because of its fundamental importance in understanding how organisms respond to climate change. Many hot issues can be investigated in desert ecosystems, including the relationship between species distribution and environmental gradients (e.g.

From Laboratory to Field: Unsupervised Domain Adaptation for Plant Disease Recognition in the Wild.

Plant disease recognition is of vital importance to monitor plant development and predicting crop production. However, due to data degradation caused by different conditions of image acquisition, e.g.