Distribution Characteristics of Soil Total Nitrogen in Low-efficiency Forest Land in the Northern Windy Desert Area of Jingbian County

[Objectives]The paper was to investigate the distribution characteristics of soil total nitrogen in low-efficiency forest land in the northern windy desert area of Jingbian County.[Methods]The distribution of soil total nitrogen in the 0-40 cm soil layer of 5 towns in the northern windy desert area of Jingbian County was studied through field sampling and laboratory detection.[Results]The average soil total nitrogen contents of Hongdunjie Town,Haizetan Town,Huanghaojie Town,Ningtiaoliang Town,Dongkeng Town and windy desert area in the 0-20 cm soil layer were 0.259,0.224,0.242,0.248,0.431 and 0.275 g/kg,respectively.The soil total nitrogen content in Dongkeng Town was higher than those in other towns,while there was little difference among other regions.The average total nitrogen contents in the 20-40 cm soil layer were 0.239,0.285,0.113,0.262,0.349 and 0.241 g/kg,respectively.The soil total nitrogen content in Huanghaojie Town was slightly lower than those in other towns,while that in Dongkeng town was higher.The variation coefficient of soil total nitrogen content in the survey area was greater than 30%,and there was great difference in spatial distribution.With the increase of soil depth,there was little change in soil total nitrogen content,namely the soil total nitrogen contents in 5 towns and windy desert area were not statistically different in the 0-40 cm soil layer.According to the nutrient grading standard of the second national soil survey,the soil total nitrogen content in the survey area was in the deficiency grade.[Conclusions]The research will provide a scientific guidance for the healthy and sustainable development of vegetation in arid regions.

Power Transformer Fault Diagnosis Using Random Forest and Optimized Kernel Extreme Learning Machine

Power transformer is one of the most crucial devices in power grid.It is significant to determine incipient faults of power transformers fast and accurately.Input features play critical roles in fault diagnosis accuracy.In order to further improve the fault diagnosis performance of power trans-formers,a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study.Firstly,the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration,gas ratio and energy-weighted dissolved gas analysis.Afterwards,a kernel extreme learning machine tuned by the Aquila optimization algorithm is implemented to adjust crucial parameters and select the optimal feature subsets.The diagnosis accuracy is used to assess the fault diagnosis capability of concerned feature subsets.Finally,the optimal feature subsets are applied to establish fault diagnosis model.According to the experimental results based on two public datasets and comparison with 5 conventional approaches,it can be seen that the average accuracy of the pro-posed method is up to 94.5%,which is superior to that of other conventional approaches.Fault diagnosis performances verify that the optimum feature subset obtained by the presented method can dramatically improve power transformers fault diagnosis accuracy.

Carbon and Nitrogen Transformations in Surface Soils Under Ermans Birch and Dark Coniferous Forests

Soil samples were taken from an Ermans birch （Betula ermanii）-dark coniferous forest （Picea jezoensis and Abies nephrolepis） ecotone growing on volcanic ejecta in the northern slope of Changbai Mountains of Northeast China, to compare soil carbon （C） and nitrogen （N） transformations in the two forests. The soil type is Umbri-Gelic Cambosols in Chinese Soil Taxonomy. Soil samples were incubated aerobically at 20℃ and field capacity of 700 g kg^-1 over a period of 27 weeks. The amount of soil microbial biomass and net N mineralization were higher in the Ermans birch than the dark coniferous forest （P 〈 0.05）, whereas the cumulative C mineralization （as CO2 emission） in the dark coniferous forest exceeded that in the Ermans birch （P 〈 0.05）. Release of the cumulative dissolved organic C and dissolved organic N were greater in the Ermans birch than the dark coniferous forest （P 〈 0.05）. The results suggested that differences of forest types could result in considerable change in soil C and N transformations.

Effects of six years of simulated N deposition on gross soil N transformation rates in an old-growth temperate forest

Elevated atmospheric nitrogen(N) deposition has been detected in many regions of China, but its effects on soil N transformation in temperate forest ecosystems are not well known. We therefore simulated N deposition with four levels of N addition rate(N0, N30, N60, and N120) for6 years in an old-growth temperate forest in Xiaoxing’an Mountains in Northeastern China. We measured gross N transformation rates in the laboratory usingN tracing technology to explore the effects of N deposition on soil gross N transformations taking advantage of N deposition soils. No significant differences in gross soil N transformation rates were observed after 6 years of N deposition with various levels of N addition rate. For all N deposition soils, the gross NH~+ immobilization rates were consistently lower than the gross N mineralization rates,leading to net N mineralization. Nitrate(NO~-) was primarily produced via oxidation of NH~+(i.e., autotrophic nitrification), whereas oxidation of organic N(i.e., heterotrophic nitrification) was negligible. Differences between the quantity of ammonia-oxidizing bacteria and ammonia-oxidizing archaea were not significant for any treatment, which likely explains the lack of a significant effect on gross nitrification rates. Gross nitrification rates were much higher than the total NO~- consumption rates,resulting in a build-up of NO~-, which highlights the high risk of N losses via NO~- leaching or gaseous N emissions from soils. This response is opposite that of typical N-limited temperate forests suffering from N deposition,suggesting that the investigated old-growth temperate forest ecosystem is likely to approach N saturation.

基于改进YOLOv5s的CNN-Swin Transformer森林野生动物图像目标检测算法

【目的】为提高野生动物在复杂森林环境中的检测精度,促进森林野生动物保护技术发展,提出一种基于YOLOv5s网络模型、针对陷阱相机所摄取森林野生动物图像的改进检测算法。【方法】以包含湖南壶瓶山国家级自然保护区几种典型森林野生动物在内的数据集为研究对象,首先,对真实标注框图像进行裁剪、归一化和缩放处理,随机将2~4张裁剪图像拼贴组成新的数据集元素,以丰富和增强数据集图像信息;其次,使用一种基于通道注意力思想的加权通道拼接方法,在通道拼接时引入权重改变通道数量,通过反向传播训练方法不断更新权重以增加重要特征信息的通道层数;接着,引入Swin Transformer模块与CNN网络相结合,为卷积神经网络特征提取加入自注意力机制,融合2种网络特征提取层的优势,提高特征提取的感受野;最后,选择更优的α-DIoU损失函数替代GIoU损失函数,针对边界框重叠面积和中心点距离造成的损失,引入新的几何因素惩罚项。【结果】在相同试验条件和数据集下,相比原YOLOv5s网络模型,改进算法极大提高检测的平均准确率和平均回归率,均值平均精度由74.1%提升至88.4%,获得14.3%的精度提升,同时也超过YOLOv3、YOLOXs、RetinaNet、Faster R-CNN等其他流行目标检测算法。【结论】针对陷阱相机所摄取森林野生动物图像背景与目标对比度低、遮挡重叠严重,致使检测误检率、漏检率高等问题,在检测算法中提出一系列改进措施,为我国森林野生动物的保护和数据获取提供一种新的可行性方案和思路。

Formation mechanisms and remediation techniques for low-efficiency artificial shelter forests on the Chinese Loess Plateau

The construction of artificial shelter forests(ASFs)has resulted in substantial ecological,economic,and societal benefits to the Chinese Loess Plateau(CLP).However,the health and benefits of ASFs are being increasingly threatened by the formation of low-efficiency artificial shelter forests(LEASFs).In this study,LEASFs are systematically analyzed in terms of their status,formation mechanisms,and developmental obstacles.The key restoration techniques and schemes were summarized to improve the quality and efficiency of LEASFs.LEASFs are formed by relatively complex mechanisms,but they arise mainly due to poor habitat conditions,improper tree species selections,mismatch between stands and habitat,extensive forest management measures,and human interferences.The restoration and improvement of LEASFs are hindered by water deficits,mismatch between stands and habitat,single management purpose,and low efficiency.LEASFs are becoming more complex due to their wide range,the challenges associated with their restoration,and insufficient technological measures for their restoration.The key techniques of the quality and efficiency improvement of LEASFs include basic forest tending methods,near-natural restoration,multifunction-oriented improvement,and systematic restoration.An understanding on the formation mechanisms of LEASFs and a scientific approach toward their restoration are urgently needed and critical for the ecological protection and high-quality development of LEASFs on the CLP.Based on these analyses,we recommend strengthening the monitoring and supervision of LEASFs,considering the bearing capacity of regional water resources,implementing multiple restoration techniques,promoting multifunction-oriented ecological development,and exploring new management concepts to achieve the sustainable development of ASFs on the CLP.

基于Transformer改进YOLOv5的山火检测方法研究

构建智慧生态林业中核心环节为对森林火灾的监测及防范,为了第一时间扑灭火源防止火势蔓延并将可能发生的山火隐患于第一时间消除,提出了两种适用于无人机高空巡检的森林火灾检测模型YOLO_MC与YOLO_MCLite。其中YOLO_MC可对标准图像中的明火及烟雾进行检测,并基于YOLO_MC模型进行轻量化设计,提出适用于热图像中高温区域的检测模型YOLO_MCLite。在网络结构的设计中,首先在常规的卷积神经网络中融合加入了Transformer模型,提升了主干网络对于全局特征信息的感知能力;同时对Transformer模型进行轻量化设计,首先在网络结构上通过分组计算的形式减少tokens数量以降低计算量,其次通过通道注意力机制对特征块的通道数进行去冗余并提权来减少tokens的维度参数以降低计算复杂度,并且采用蒸馏算法从所设计的网络中提取出超轻量化网络应用于无人机红外影像的森林高温点检测,以预防森林火灾的发生。经过实验得出以下数据:所设计的两个检测模型中,其中适用于标准图像中对于明火及烟雾的检测准确率可达94.8%,适用于热图像对高温点的检测准确率可达97.2%,并且在英伟达JETSON TX2嵌入式设备上测试两个模型的帧率分别达到了22.5以及32.4。实验结果表明本文所设计网络能够对森林火灾进行有效检测并可以通过检测高温点及时预防火灾发生。

Distribution Characteristics of Soil Organic Carbon in Degraded Forest Land in the Sandstorm Area of Jingbian County,Shaanxi Province

[Objectives]To explore the distribution characteristics of soil organic carbon in degraded forest land in the sandstorm area of Jingbian County,Shaanxi Province.[Methods]The distribution characteristics and abundance of 0-20 cm shallow soil organic carbon in 5 towns in the sandstorm area in the north of Jingbian County were studied by field sampling and indoor detection.[Results]The average soil organic carbon contents in Hongdunjie Town,Haizetan Town,Huanghaojie Town,Ningtiaoliang Town and Dongkeng Town were 2.93,3.21,2.53,2.54 and 4.08 g/kg,respectively,which were all lower than the national background value(31.00 g/kg).The coefficients of variation of soil organic carbon content in Hongdunjie Town,Huanghaojie Town and Dongkeng Town were 59.04%,35.97%and 47.55%,respectively,with higher coefficients of variation and larger differences in spatial distribution.The organic carbon content of Haizetan Town and Dongkeng Town was above the abundance,accounting for 70%and 50%,which were relatively rich,while the soil organic carbon content of Hongdunjie was relatively scarce.The average content of soil organic carbon in the sandstorm area was 3.03 g/kg,which was also lower than the national background value.The coefficient of variation was 46.53%,showing high coefficient of variation and large difference in spatial distribution.In addition,20.41%of the average content of soil organic carbon in the sandstorm area was in the deficient level,and 79.59%were in the medium or above level.[Conclusions]The study of distribution characteristics of soil organic carbon in degraded forest land in the sandstorm area of Jingbian County will better serve the precise management of soil resources.

基于EMD-gcForest模型的变压器油中溶解气体浓度预测方法

首先,考虑到气体浓度序列的波动性,利用经验模态分解(Empiricalmode decomposition,EMD)方法,将油中溶解气体浓度序列分解为不同特征尺度的本征模态函数(Intrinsic mode function,IMF)分量和1个剩余分量;然后,采用多粒度级联森林(Multi-grained cascadeforest,gcForest)模型对分解得到各子序列分量分别进行预测;最后,叠加所有各子序列分量的预测值作为最终结果。算例分析结果表明,相较传统预测方法,所提的EMD-gcForest方法具有较高的预测精度和泛化能力。

融合YOLOv5s与Swin Transformer的森林火灾检测

针对传统的森林火灾检测方法检测精度不佳、可靠性低等不足,提出一种基于YOLOv5s融合Swin Transformer的火灾检测方法.为实现森林火灾的实时性检测,提出了YOLOv5s-SwinT的改进识别方法.基于Transformer模型的应用,解决了卷积神经网络的运算局部性以及全局特征提取等不足.融合Swin Transformer与YOLOv5s卷积神经网络模型,并将其应用于森林火灾检测的机器视觉任务中.引入α-IoU损失函数替换GIOU损失函数,并在骨干网络中融入CA注意力机制轻量模块,提升了整体网络的特征提取能力以及获取高质量和高精准度的定位图像区域,进行边界框生成及预测,改善小目标检测漏检及检测精度差等问题.实验结果表明,融合YOLOv5s-Swin T的改进识别方法在森林火灾检测任务中,可实现mAP值达到74.2%,相比YOLOv5s提高了4.5%,并设计了GUI界面直接部署到PC端实现实时火灾检测需求,为森林火灾检测视觉任务提供了一种有效的检测方法.

基于IHHT‑RF的配电网单相接地故障选线方法

小电流系统发生单相接地故障时故障特征易受高接地过渡电阻、小初相角等弱故障条件影响而导致选线准确率低。为此,提出一种基于改进希尔伯特黄变换—随机森林(improved Hilbert⁃Huang transform⁃random forest,IHHT⁃RF)的配电网单相接地故障选线方法。首先,提取每条线路在故障发生时的电流暂态信号,通过IHHT提取纯净的暂态电气量,构造标准差、能量熵和幅值畸变度3类特征向量;然后,将特征向量输入RF分类器建立故障选线模型,把故障选线问题转化为二分类问题;最后,将测量数据输入RF分类器中得出分类结果,实现故障线路的自动识别。仿真结果表明,该选线方法综合利用暂态信号的幅值、频率和能量等特征信息,不受弱故障条件、馈线结构等因素的影响,能有效提高故障选线的准确率,具有较强的适应性和可靠性。

基于二维散射变换的湖相碳酸盐岩储层厚度预测方法研究

济阳坳陷沙四段湖相碳酸盐岩受湖盆内沉积环境和构造运动等因素控制,储层厚度变化大,非均质性强,油气开发难度大.为了准确预测湖相碳酸盐岩储层的厚度,本文提出一种基于二维散射变换和随机森林的储层厚度预测方法.首先,引入二维散射变换提取地震时频属性,该变换是在二维小波变换的基础上,通过迭代小波分解和非线性操作来实现的.与传统的二维小波变换对比,散射变换提取的时频属性具有局部形变稳定性以及对噪声鲁棒性的优点,有助于提高储层厚度预测的准确率.在此基础上,在有限测井数据的条件下,利用随机森林算法建立多尺度时频属性与测井解释厚度之间的非线性关系,实现湖相碳酸盐岩储层预测.模型数据的预测结果表明,与基于传统地震振幅属性的厚度预测和基于二维小波变换的储层厚度预测对比,本文所提的厚度预测方法具有最优的性能.叠后三维地震数据的预测结果表明,与基于传统地震振幅属性的厚度预测和基于二维小波变换的储层厚度预测对比,本文所提方法的厚度预测结果与实际钻井数据误差更小,提高了储层厚度预测的精度,清晰刻画了灰礁、灰滩与灰泥等三种沉积亚相的空间展布,有利于后续井位部署和优化.

环境承载力中介下森林转型赋能区域生态宜居——作用路径与效应识别

基于中国1999—2018年4次森林资源清查结果及相关统计数据,对中国31个省域(不含港澳台地区)的森林转型指数、环境承载力和生态宜居水平进行测评,然后借助中介效应模型探索森林转型赋能区域生态宜居的作用路径,并测度环境承载力对森林转型赋能区域生态宜居水平的中介效应。结果显示:1999—2018年中国森林转型指数整体呈现稳中有升的发展态势,但不同地区之间水平差异比较明显,西南、东北地区森林转型效果最为显著;中国环境承载力的“U型”曲线演变特征明显,其中西南、华南、华东地区环境承载力较强,华东、华中、华南、华北地区环境承载力在回升阶段表现突出,东北、西南和西北地区在经济快速发展时期,环境承载力呈现下降趋势,突显出资源环境系统脆弱性;中国生态宜居水平总体呈现波动上升态势,其中西南、华北、华东地区上升态势最为明显;环境承载力在森林转型对区域生态宜居水平影响中具有中介作用,森林转型通过提升环境承载力部分解释了其对区域生态宜居水平的促进作用。

生态转型模式对吕梁山红枣经济林土壤团聚体及其有机碳分布的影响

为探究吕梁山区红枣经济林不同生态转型模式对土壤团聚体及其有机碳分布的影响,试验选取该地区5种主要的生态转换模式,分别为枣林撂荒(AF)、枣林补种黄芪-柴胡(MM)、补种苜蓿(AL)、补植油松(CP)、补植侧柏(PO),并以清耕枣林为对照(CK),测定并分析各样地0~20 cm土层中土壤团聚体稳定性、团聚体有机碳与土壤总有机碳含量及其相关性。结果表明:(1)与CK相比,除AF模式外,其他生态模式提高了>2.0 mm及>0.25 mm团聚体含量,增量分别为3.75%~9.31%和1.68%~10.36%;同时提高了团聚体的平均重量直径、几何平均直径,增量分别为0.05~0.19、0.03~0.10 mm,CP模式提高幅度最大;(2)所有生态模式显著提高了土壤有机碳含量,增量为0.24~4.72 g·kg^(-1),提高幅度为9.25%~182.10%,同时提高了土壤各粒径团聚体有机碳含量;(3)土壤有机碳的贡献率主要来自于>0.25 mm团聚体,所有生态模式均提高了土壤>0.25 mm团聚体有机碳的贡献率,增量为4.20%~22.96%;(4)土壤有机碳及>0.25 mm团聚体有机碳含量与土壤团聚体稳定性参数呈显著正相关关系,红枣林补植油松模式最有利于土壤有机碳、>0.25 mm团聚体有机碳含量及团聚体稳定性的提高。

森林根际土壤细菌的分离、鉴定及生物活性筛选

【目的】从无量山国家级自然保护区森林根际土壤发掘具有多种生物活性的功能菌株,探究其开发应用潜力。【方法】采集无量山地区25个区域植物的根际土壤,采用选择培养基,分离鉴定磷酸盐溶解、固氮、溶锌和拮抗等活性菌株,进一步测定菌株分泌铁载体、ACC脱氨酶和吲哚乙酸等生物活性,并验证促番茄种子发芽和生长效果。【结果】分离鉴定得到解磷菌70株,固氮菌27株,解钾菌8株,拮抗镰刀菌的菌株51株。其中,YIM B08401和YIM B08402形态学结合生理生化特性和16S rDNA序列测序,鉴定为白色伯克霍尔德氏菌(Burkholderia alba)和青岛假单胞菌(Pseudomonas qingdaonensis),两个菌株均具有磷酸盐溶解、固氮、溶锌和分泌铁载体的活性,最大可溶性磷含量为(455.63±59.65)mg/L和(878.95±64.78)mg/L;两株菌的促种子发芽试验结果接近,施加稀释10倍、10^(2)倍和10^(3)倍的发酵上清液后,发芽率都维持在82%-93%,明显高于对照组的56%和49%,施加菌株发酵液的处理组相较于空白对照组的长度都有显著增加。盆栽实验证明,两株菌株促生效果最明显的处理组在地上部长度、鲜重、干重、茎粗、根长、根鲜重、根干重方面的数据都显著优于对照组,YIM B08401的上述指标相对于对照组分别显著增加了89%、495%、268%、62%、53%、385%和469%,YIM B08402的上述指标相对于对照组分别显著增加了118%、528%、477%、55%、37%、413%和747%。此外,菌株YIM B08401还具有拮抗病原菌和分泌ACC脱氨酶活性,YIM B08402则还具有分泌吲哚乙酸的活性。【结论】无量山森林根际土壤蕴含具有多种生物活性的功能菌株,白色伯克霍尔德氏菌(B. alba, YIM B08401)和青岛假单胞菌(P. qingdaonensis, YIM B08402)具备开发为新型微生物肥料的应用潜力。

黄土高原次生林演替对团聚体有机碳含量及化学稳定性的影响

次生林演替过程中土壤团聚体有机碳的积累机制和化学稳定性研究较少。为探明次生林演替对土壤团聚体有机碳含量及其化学组成稳定性的影响,选取黄土高原次生白桦林(演替初期),山杨辽东栎混交林(演替中期)和辽东栎林(演替后期)为研究对象,分析演替过程中不同粒径土壤团聚体有机碳含量变化特征。采用傅里叶红外光谱技术(FTRI)测定活性(AC)和非活性(IC)有机碳化学组成,以(IC/AC)作为有机碳化学组成稳定性指标,并分析其影响因素。结果表明:次生林演替过程中土壤团聚体有机碳含量表现出逐渐增加的趋势且各群落间差异显著(P<0.05),以演替后期的中等粒径团聚体为最高(37.63 g/kg)。土壤团聚体AC中多糖体有机碳含量最高(55.87%),而IC中芳香族有机碳含量最高(94.45%),演替过程中IC与AC总体变化趋势均呈现先降后增。IC/AC随着演替的进行呈先降低后升高的趋势,其中演替后期微团聚体有机碳化学组成稳定性最强达到了3.95。微团聚体含量(WM)与土壤全氮、全磷、全钾一起,显著促进了团聚体有机碳化学组成稳定性(P<0.05)。综上,次生林演替有利于促进土壤团聚体有机碳的积累以及有机碳化学稳定,其中微团聚体起到了关键性作用。

基于GJO特征量优选的AO-RF的变压器故障诊断模型

在变压器故障诊断过程中,进行合理的特征优选,将有助于提高诊断模型的诊断精度,为此,文中提出了一种基于金豺优化算法(golden Jackal optimization,GJO)特征量优选与AO-RF的变压器故障诊断模型。首先,采用GJO对构建的21维变压器油中溶解气体特征量进行优选;然后,根据GJO得到的特征优选结果,采用天鹰算法(aquila optimizer,AO)优化随机森林(random forest,RF)的变压器故障诊断模型对变压器故障进行诊断,并与不同特征量、不同故障诊断模型的诊断结果进行了对比。实验结果表明:GJO优选特征量相比21维原始特征、三比值法、无编码比值法以及AO优选特征量的故障诊断准确率可提高1.12%~25.78%,kappa系数可提高0.02~0.24;AO-RF故障诊断模型较RF、SVM、ELM、SSA-RF、WOA-RF、GJO-RF模型的诊断准确率可提高1.84%~15.86%,kappa系数可提高0.02~0.16,验证了所提方法的有效性和准确性。

杉木人工林套种不同树种对林分生长和植物多样性的影响

研究杉木林间伐后,于林下套种香梓楠、华盖木、红豆杉、缅茄4种树种,对比未套种杉木纯林,分析套种后林分的生长状况和植物多样性,为杉木人工林近自然改造与可持续经营提供参考依据。结果表明:香梓楠、华盖木、红豆杉与杉木形成异龄复层混交林,香梓楠胸径、树高生长量及保存率最高,分别达到10.5 cm、9.8 m、82.22%,显著高于其他树种;缅茄胸径、树高生长量及保存率最低,分别为2.4 cm、1.7 m、15.66%。套种缅茄、香梓楠、华盖木对杉木胸径有促进作用,套种缅茄后杉木树高显著提高。5种杉木林分林下植被中共出现33科52属60种植物,草本层出现植物15科25属27种,以禾本科和菊科植物为主;灌木层出现植物18科27属33种,以大戟科、茜草科和桑科植物为主;4种杉木混交林林下出现植物比杉木纯林增加24种。杉木×香梓楠、杉木×华盖木林下植物多样性比杉木×缅茄和杉木纯林显著提高。综合杉木间伐后套种不同树种表现,套种香梓楠对林分生长和植物多样性更具优势,可作为今后杉木近自然改造的参考模式。

蒙古栎低质林改造后2010—2023年林地土壤质量的综合评价及未来6a土壤质量变化趋势预测——以2009年改造的蒙古栎低质林为例

在黑龙江省大兴安岭林区的加格达奇林业局翠峰林场174林班,以蒙古栎(Quercus mongolica)低质林为例,2009年进行不同采伐带宽度的顺山皆伐,每条采伐带设置在近似相同海拔高度,皆伐带带长均为300 m,带宽分别为6、10、14、18 m,采伐后在每种采伐带分别补植了西伯利亚红松(Pinus sibirica)、兴安落叶松(Larix gmelinii)、樟子松(Pinus sylvestris),共设置12个不同改造样地;2010—2023年,测定12个不同改造样地的7个土壤养分指标(有机质质量分数、全氮质量分数、全磷质量分数、全钾质量分数、速效氮质量分数、速效磷质量分数、速效钾质量分数),应用模糊综合评价法、主成分分析法对土壤质量进行综合评价;结合2010—2023年对样地土壤养分质量评价结果,采用非线性自回归外部输入(NARX)神经网络模型预测2024—2029年各样地的土壤养分质量状况,分析未来6 a土壤养分质量变化趋势。结果表明:2023年测定的改造样地的土壤养分质量,91.67%的改造样地满足功能标准。补植樟子松样地中,只有采伐宽度为14 m的样地土壤养分质量相对稳定,其他采伐宽度的样地土壤养分质量状态不稳定。在2024—2029年,除了采伐宽度为10、18 m宽带中补植樟子松的样地以及采伐宽度为14 m宽带中补植西伯利亚红松的样地土壤养分不稳定外,其他样地未来6 a总体看土壤养分质量均较好,为Ⅰ级。构建的评价模型框架,可为低质林改造样地土壤养分质量综合评价及预测提供技术支持,弥补了土壤观测的数据盲区。

亚热带森林转换对土壤氮转化关键功能微生物群落的影响

近年来,由于林地开发和商品林建设等原因,我国亚热带地区大量天然林和次生林经皆伐改造为林分结构简单、树种单一的人工林。氮(N)素是维持森林植被生长和系统初级生产力的重要因子,土壤微生物驱动了森林土壤N转化的关键过程。然而,目前亚热带森林转换对土壤N转化微生物群落的影响仍不清晰。以湖南芦头森林生态系统国家定位观测研究站内典型次生林(CS)及由其转换而成的油茶(YC)、黄桃(HT)、杨梅(YM)和杉木(SM)四种人工林为研究对象,采用实时荧光定量PCR和高通量测序等方法,研究了各林分土壤性质、固N菌和氨氧化微生物功能基因丰度、群落特征及相互关系,旨在探讨亚热带森林转换后土壤N转化关键过程(固N和氨氧化作用)的功能微生物群落变化及驱动因素。结果表明:森林转换显著改变了土壤碳(C)、N含量,降低了土壤nifH基因丰度、固N菌和氨氧化细菌的群落α多样性,但提高了氨氧化微生物amoA基因丰度和氨氧化古菌的群落α多样性;并且,森林转换通过改变各功能微生物优势菌群(如变形菌、蓝细菌、泉古菌和奇古菌等)的相对丰度,显著影响了土壤固N菌和氨氧化微生物的群落组成;冗余分析和结构方程模型表明,土壤有机碳、全氮、铵态氮含量和pH是驱动土壤固N菌和氨氧化微生物群落变化的关键因素。森林转换后,合理的施肥方式有利于人工林土壤固N菌和氨氧化微生物的群落恢复。研究结果为转换后单一人工林土壤养分恢复、生产力的提高和可持续经营提供了科学依据。