Mapping the current and future distributions of Onosma species endemic to Iran

Climate change may cause shifts in the natural range of species especially for those that are geographically restricted and/or endemic species. In this study, the spatial distribution of five endemic and threatened species belonging to the genus Onosma (including O. asperrima, O. bisotunensis, O. kotschyi, O. platyphylla, and O. straussii) was investigated under present and future climate change scenarios: RCP2.6 (RCP, representative concentration pathway; optimistic scenario) and RCP8.5 (pessimistic scenario) for the years 2050 and 2080 in Iran. Analysis was conducted using the maximum entropy (MaxEnt) model to provide a basis for the protection and conservation of these species. Seven environmental variables including aspect, depth of soil, silt content, slope, annual precipitation, minimum temperature of the coldest month, and annual temperature range were used as main predictors in this study. The model output for the potential habitat suitability of the studied species showed acceptable performance for all species (i.e., the area under the curve (AUC)>0.800). According to the models generated by MaxEnt, the potential current patterns of the species were consistent with the observed areas of distributions. The projected climate maps under optimistic and pessimistic scenarios (RCP2.6 and RCP8.5, respectively) of 2050 and 2080 resulted in reductions and expansions as well as positive range changes for all species in comparison to their current predicted distributions. Among all species, O. bisotunensis showed the most significant and highest increase under the pessimistic scenario of 2050 and 2080. Finally, the results of this study revealed that the studied plant species have shown an acute adaptability to environmental changes. The results can provide useful information to managers to apply appropriate strategies for the management and conservation of these valuable Iranian medicinal and threatened plant species in the future.     

热处理对低温胁迫下黄瓜活性氧代谢和膜脂组分的影响

为探讨热处理减轻黄瓜果实低温冷害的作用机理,本试验研究了47℃热水浸泡5 min处理对黄瓜果实在4℃贮藏期间活性氧代谢和膜脂组分的影响。结果表明,47℃热水5 min处理可显著抑制黄瓜果实冷害的发生,贮藏15 d后果实冷害指数较对照(CK)低20.87%。此外,热处理还可抑制黄瓜相对电导率及丙二醛(MDA)含量的上升,提高黄瓜果实超氧化物歧化酶(SOD)、过氧化物酶(DOD)、过氧化氢酶(CAT)和抗坏血酸过氧化物酶(APX)的活性,抑制超氧阴离子($\mathop{{O}}_{2}^{{\mathop{}_{\ ·}^{-}}}$)及过氧化氢(H₂O₂)的产生,同时降低脂氧合酶的活性,保持较低的饱和脂肪酸含量、较高的不饱和脂肪酸含量和膜脂不饱和度。综上,热处理可维持黄瓜果实活性氧代谢的平衡,抑制膜脂的过氧化作用,从而提高黄瓜果实的抗冷性,减轻果实冷害损伤。     

Response of soil respiration to nitrogen addition in two subtropical forest types

Anthropogenic activities have increased nitrogen (N) deposition in terrestrial ecosystems, which directly and indirectly affects soil biogeochemical processes, including soil respiration. However, the effects of the increases in N availability on soil respiration are not fully understood. In this study, soil respiration was measured using an infrared gas analyzer system with soil chambers under four N treatments (0, 5, 15, and 30 g N m^-2 year^-1 as control, low N (LN), moderate N (MN), and high N (HN), respectively) in camphor tree and slash pine forests in subtropical China. Results showed that soil respiration rates decreased by 37% in the camphor tree forest and 27% in the slash pine forest on average on an annual base, respectively, in the N-fertilized treatments when compared with the control. No significant differences were found in the soil respiration rate among the LN, MN, and HN treatments in both forest types as these fertilized plots reached an adequate N content zone. In addition, soil microbial biomass carbon (C) content and fine root biomass declined in N-treated plots compared to the control. Our results indicated that elevated N deposition might alter the tree growth pattern, C partitioning, and microbial activity, which further affect soil C sequestration by reducing soil respiration in subtropical forests of China.     

Alpha-shape算法构建枣树点云三维模型

为了实现枣树智能化修剪作业，该研究提出了基于点云配准的自然光照环境下的果树三维重构方法，并针对传统最近点迭代（Iterative Closest Point，ICP）算法对待配准点云的空间位置要求苛刻的问题，提出了改进的点云配准算法。首先，使用彩色深度（RGB-D）相机采集不同角度下的枣树彩色和深度图像，并通过信息融合实现相应角度下的点云获取。其次，对点云进行背景去除和滤波处理，基于直方图设定分割阈值，提取单株枣树点云，并将放置在树根附近的标靶球作为标记，使用人工标记法进行两站点云初配准。最后，在初配准基础上计算点云的曲面法向量和曲率，由曲率相近的点构成配对点对，使用k维树最近点迭代（k dimensional-tree-Iterative Closest Point，kd-tree-ICP）算法完成精配准，对点云使用Alpha-shape算法面片化，实现表面重构。利用上述方法对多棵枣树进行全局配准并完整重构果树模型。试验结果表明，通过引入初配准，有效提高了点云配准的准确性和稳定性，配准误差均控制在1.0 cm以内，平均配准误差为0.78 cm；重构模型真实感较强，在外观上更加接近真实树，枝干相对误差控制在7%以内。该研究重构模型精度较高，可为枣树智能修剪提供可视化研究基础和技术支持。     

光谱特征变量和BP神经网络构建油用牡丹种子含水率估算模型

为了进一步提高种子含水率的高光谱估算精度，该研究测定了156份油用牡丹种子的近红外吸收光谱及其对应的含水率值，分析了近红外吸收光谱、一阶微分光谱、水分吸收特征参数与含水率的相关关系，构建了基于特征波长吸收光谱、特征波长一阶微分光谱、水分特征吸收参数和BP神经网络的油用牡丹种子含水率估算模型，并对模型进行了验证；再结合一元线性回归（SLR，Single Linear Regression）、逐步多元线性回归（SMLR，Stepwise MultipleLinear Regression）、偏最小二乘回归（PLSR，Partial Least Squares Regression）模型与BP神经网络（BPNN，BP Neural Network）模型进行比较。结果表明：1）油用牡丹种子含水率的吸收光谱特征波长位于1 410、1 900、1990 nm，一阶微分光谱特征波长位于1 150、1 950、2 080 nm；2）以DF2080和AD2140为自变量建立的一元线性回归模型预测效果较优，在能够满足水分估算精度的情况下，是最优的选择方法。3）将优选的特征参数作为输入，实测含水率值作为输出，构建BP神经网络模型，其建模与验模R2分别为0.978和0.973，RMSE分别为0.22%和0.242%，而RPD值分别为6.478和5.889，与其他模型相比，BP神经网络模型的建模及预测精度均最高，是估算油用牡丹种子含水率的最优模型，其次为逐步多元线性回归模型。研究结果表明BP神经网络模型对种子含水率具有更好的预测能力，是估算油用牡丹种子含水率的有效方法。     

Regional and local scale variations in soil organic carbon stocks in West Greenland

The soil organic carbon (SOC) pool of the Northern Hemisphere contains about half of the global SOC stored in soils. As the Arctic is exceptionally sensitive to global warming, temperature rise and prolonged summer lead to deeper thawing of permafrost‐affected soils and might contribute to increasing greenhouse gas emissions progressively. To assess the overall feedback of soil organic carbon stocks (SOCS) to global warming in permafrost‐affected regions the spatial variation in SOCS at different environmental scales is of great interest. However, sparse and unequally distributed soil data sets at various scales in such regions result in highly uncertain estimations of SOCS of the Northern Hemisphere and here particularly in Greenland. The objectives of this study are to compare and evaluate three controlling factors for SOCS distribution (vegetation, landscape, aspect) at two different scales (local, regional). The regional scale reflects the different environmental conditions between the two study areas at the coast and the ice margin. On the local scale, characteristics of each controlling factor in form of defined units (vegetation units, landscape units, aspect units) are used to describe the variation in the SOCS over short distances within each study area, where the variation in SOCS is high. On a regional scale, we investigate the variation in SOCS by comparing the same units between the study areas. The results show for both study areas that SOCS are with 8 kg m^?2 in the uppermost 25 cm and 16 kg m^?2 in the first 100 cm of the soil, i.e., 3 to 6 kg m^?2 (37.5%) higher than existing large scale estimations of SOCS in West Greenland. Our approach allows to rank the scale‐dependent importance of the controlling factors within and between the study areas. However, vegetation and aspect better explain variations in SOCS than landscape units. Therefore, we recommend vegetation and aspect for determining the variation in SOCS in West Greenland on both scales.     

林木氮素吸收偏好性及其形成机制研究进展

森林土壤中可被林木吸收利用的氮（N）素主要以铵态氮（NH₄+-N）和硝态氮（NO₃--N）的形态存在。受全球气候变暖、氮沉降和人类活动等因素的影响，NH₄+和NO₃-的分布存在很大的时间波动性和空间异质性，且NH₄+-N和NO₃--N亏缺已成为限制林地生产力的主要因素。在森林土壤N亏缺和N异质分布的逆境中，在林木长期进化过程中形成了对不同形态N素的吸收偏好，且这种吸收偏好会随生长环境条件而发生改变。特别是对于NH₄+和NO₃-这2种主要形态的偏好选择性已被证明是决定林木生产力、竞争、共存和生态演替的重要因素之一。对不同树种在N异质分布环境下的N吸收偏好和形成机制的研究，是揭示林木N素营养遗传特性和提高林地N素利用效率的关键。文中从森林土壤中N的主要形态及其分布特征、林木对不同形态N素的吸收偏好和形成机制、林木N吸收偏好的影响因素3个方面进行总结阐述，并对未来研究方向进行展望，以期为我国人工林培育中不同树种的造林配置和合理N素施肥技术提供理论依据和参考。     

运用植被指数时序特征对落叶松人工林分类

为探究植被指数时序特征是否有利于落叶松人工林提取,以孟家岗林场为研究试验区域,根据落叶松人工林季相和物候特征,利用Landsat8OLI影像数据提取研究区内5种植被的归一化植被指数(I NDV)、差值植被指数(I DV)、比值植被指数(I RV)、增强型植被指数(I EV),构建相应的植被指数时序特征。采用最大似然和随机森林两种方法对单一时相影像和加入植被指数时序特征的影像进行对比试验。结果表明:影像中加入植被指数时序特征后,最大似然算法的分类总体精度为89.53%,Kappa系数为0.87,比单一时序特征的影像分类精度提高了13.35%;随机森林算法的森林类型分类总体精度为93.22%,Kappa系数为0.92,比单一时序特征的影像分类精度提高了19.8%。因此,加入植被指数时序特征后能得到更高的落叶松人工林提取精度。     

基于GF-1卫星数据与面向对象分类的达里诺尔湿地自然保护区土地覆盖信息提取

以达里诺尔湿地自然保护区为研究区，基于国产GF-1遥感影像，采用面向对象和传统目视解译的分类方法对研究区土地覆盖遥感信息进行提取，并对其结果进行对比分析，采取混淆矩阵对面向对象分类结果进行精度验证。结果表明：（1）充分利用了GF-1遥感影像的光谱信息，面向对象分类采取试错法确定最优分割尺度为550，形状和紧致度因子分别为0.6和0.5，各波段权重均为1。（2）面向对象分类总体分类精度达98.22％，KAPPA系数为0.96；（3）面向对象分类方法可快速准确提取类型较为复杂的土地覆盖信息，为内陆湿地精准快速提取研究区土地覆盖分类信息提供参考，以期为湿地遥感业务化监测提供技术规范。     

生态文明视域下草原治理体系构建研究

草原是我国面积最大的陆地生态系统，既是我国重要的生态安全屏障，也是广大农牧民赖以生存的家园。为深入贯彻习近平生态文明思想，落实山水林田湖草沙系统治理的理念，推进草原保护修复和高效利用，本研究结合"十四五"林业和草原保护发展规划纲要，提出了构建草原监测评价体系、草原保护体系、草原生态修复体系、草原执法监管体系、现代草业体系、支撑保障体系等草原治理体系，旨在为推进草原治理体系和治理能力现代化提供参考。