遵义烟区不同海拔下植烟土壤细菌群落及影响因素分析

为探究不同海拔下植烟土壤细菌群落结构特征及影响细菌群落结构的主要因素,在贵州省遵义地区选择海拔800（ZL）、1000（ZS）和1200m（ZH）的3块植烟土壤,采用16S rRNA高通量测序法对植烟土壤微生物多样性进行分析。结果表明,ZL和ZH处理植烟土壤细菌α多样性均低于ZS处理;3个海拔下植烟土壤优势门为放线菌门（Actinobacteriota）、变形菌门（Proteobacteria）、绿弯菌门（Chloroflexi）和酸杆菌门（Acidobacteriota）,放线菌门的相对丰度以ZL处理最高,较ZH处理提高了34.22%;变形菌门表现则相反,ZH处理变形菌门丰度较ZL处理显著提高。绿弯菌门与酸杆菌门丰度在3个海拔处理中表现较为一致,均为ZS>ZL>ZH。聚类热图分析结果显示,土壤含水率和pH与Armatimonadota和蓝细菌门（Gyanobacteria）存在显著正相关关系,与变形菌门存在显著负相关关系;变形菌门、厚壁菌门（Firmicutes）和髌骨菌门（Patescibacteria）与土壤微生物量氮存在显著正相关关系,而与速效钾存在极显著负相关关系。     

抗青枯病花生种质资源的鉴定与筛选

为筛选适宜河南省信阳市种植的抗青枯病花生品种,对27份花生品种青枯病田间病圃进行鉴定,计算其发病率和病情指数,从而进行抗病性鉴定和筛选。结果表明,27个供试品种中,中高抗品种共10个占总数的37%,3个品种类型各筛选出2个抗病品种。6个抗病品种发病规律表明,7天和14天病情指数增长较快,21天后基本无变化。高油型花生‘远杂9102’和‘信花425’,高油酸型花生‘信花14号’和‘驻花11号’,普通型花生‘远杂21号’和‘豫花149号’为适宜信阳种植的抗病品种。     

商品化猪丹毒疫苗分类及特点

文章介绍了5种商品化猪丹毒疫苗的种类、毒株组成、抗原含量及相应免疫程序,为了解各种类型的猪丹毒商品化疫苗提供参考。     

湖北省杉木人工林立地类型划分及评价

以湖北省杉木人工林为研究对象,选择166块具有代表性的杉木人工林标准地,并测定样地的常规立地因子。利用数量化理论Ⅰ的方法建立了杉木优势高与立地因子之间的关系模型,对研究区杉木人工林进行立地类型划分及立地质量评价。结果表明,所选择的5个立地因子与优势高数量化拟合的复相关系数为0.639;海拔、腐殖层厚度和坡位这3个立地因子对优势高的贡献率达84.99%,且影响均达到极显著水平;采用海拔、腐殖层厚度和坡位3个因子构建研究区杉木人工林立地类型,共划分为22类;按照优、良、中、差4个评价等级对166块样地所属的立地类型进行立地质量评价,得出湖北省杉木人工林在中海拔、土壤湿润、土壤养分含量较高的立地条件下生产力较高,研究区立地质量整体处于中等偏上水平的结论。     

中国杜鹃花品种分类问题与思考

杜鹃花具有极高的观赏价值，是世界级名花。经过数百年的人工选育，全球杜鹃花品种已超过3万个。我国虽然拥有丰富的杜鹃花资源，但园艺化程度与国外差距明显，其中一个重要原因就是国内杜鹃花品种分类及品种名称较为混乱，阻碍了其进一步应用和发展。文中综述了近年来中国杜鹃花品种分类研究的成果与存在的不足，并展望了研究方向。分析认为，综合植物系统分类学研究及品种亲本来源信息是建立科学品种分类体系的基础，同时运用分子生物学技术和形态性状数量分析等方法进行归类将是重要的研究方向。     

基于极化分解和集成学习的PolSAR影像分类

为实现Pol SAR数据极化信息的充分利用,以进一步改善分类效果,该研究提出了一种基于极化分解和集成学习的Pol SAR影像分类方法。该方法首先利用多种极化分解方法从Pol SAR影像中提取极化参数;将提取的极化参数组合成一幅多通道影像;然后对多通道影像进行分割和特征提取,分别提取出各目标极化分解方法所对应的特征;并进行特征选择和分类,得到各目标极化分解方法的分类结果;最后利用集成学习技术对各分类结果进行集成。该研究以吉林省长春市部分区域为研究区,Radarsat2影像为数据源,将提出的方法应用于土地覆被分类中,取得了较好的分类效果,总体精度和Kappa系数分别达到了92.49%和0.90。此外,该研究还将提出方法与其他基于多种极化分解的分类方法进行比较,对比方法的总体精度和Kappa系数分别为90.74%和0.88,比提出方法分别低1.75%和0.02,对比结果进一步证明了提出方法的优越性。     

基于随机森林的高寒湿地地区土地覆盖遥感分类方法

高寒湿地是青藏高原典型独特的生态系统,是全球气候变化的敏感地带和预警区。利用遥感技术快速、准确地分类提取高寒湿地的土地覆盖信息,对当地生态安全监测和保护具有重要意义。本文以若尔盖湿地国家级自然保护区为研究区,首先,以高分一号(GF-1)遥感影像为数据源,融合光谱特征、水体指数、地形特征、植被指数和纹理信息等26个变量进行随机森林(Random forest,RF)分类实验;然后,根据袋外数据(Out of bag,OOB)的特征变量重要性得分和精度评价结果,选出高寒湿地地区土地覆盖类型的最优分类方案和特征;最后,对特征变量进行降维,并基于相同的变量,采用极大似然法(Maximum likelihood classification,MLC)、支持向量机(Support vector machine,SVM)、人工神经网络(Artificial neural network,ANN)和RF等方法进行分类,比较不同方法的优适性。结果表明:结合GF-1影像光谱、水体、植被、纹理特征和地形信息,使用26个变量的RF模型的分类精度最高,总体精度(Overall accuracy,OA)为90.07%,Kappa系数为0.86;通过RF模型的变量重要性分析可以有效选出重要的特征信息,在降低特征变量维度的同时,还能保证较高的分类精度; 4种分类方法中,RF算法是高寒湿地地区较合适的分类方法,OA比MLC基准方法高17.63个百分点,比SVM和ANN等机器学习算法分别高6.98、6.56个百分点。     

Bacteria and antibiotic resistance detection in fractures of wild birds from wildlife rehabilitation centres in Spain

Anatomic adaptations make birds more prone to open fractures with exposed bone parts losing vascularization. As a result of this exposure, fractures are colonized by different microorganisms, including different types of bacteria, both aerobic and anaerobic, causing osteomyelitis in many cases. For this reason, antibiotic treatment is common. However, carrying out antibiotic treatment without carrying out a previous antibiogram may contribute to increased resistance against antibiotics, especially in migratory wild birds. In this paper, bacterial counts regarding fracture type, bacterial identification and antibiotic resistance have been analysed in wild birds from wildlife rehabilitation centres in Spain. The results obtained showed that open fractures had higher bacterial counts (CFU/mL) than closed ones. Bacteria in family Enterobacteriaceae, identified were Escherichia spp., Enterobacter spp., Shigella spp., Hafnia alvei, Proteus mirabilis, Leclercia adecarboxylata and Pantoea agglomerans. Other bacteria present in wild birds’ fractures were Aeromonas spp., Enterococcus spp. Bacillus wiedmannii and Staphylococcus sciuri. All species found presented resistance to at least one of the antibiotics used. Wild birds can be implicated in the introduction, maintenance and global spreading of antibiotic resistant bacteria and represent an emerging public health concern. Results obtained in this paper support the idea that it is necessary to take this fact into account before antibiotic administration to wild animals, since it could increase the number of bacteria resistant to antibiotics.     

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.