Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests

Forestry science has a long tradition of studying the relationship between stand productivity and abiotic and biotic site characteristics, such as climate, topography, soil and vegetation. Many of the early site quality modelling studies related site index to environmental variables using basic statistical methods such as linear regression. Because most ecological variables show a typical non-linear course and a non-constant variance distribution, a large fraction of the variation remained unexplained by these linear models. More recently, the development of more advanced non-parametric and machine learning methods provided opportunities to overcome these limitations. Nevertheless, these methods also have drawbacks. Due to their increasing complexity they are not only more difficult to implement and interpret, but also more vulnerable to overfitting. Especially in a context of regionalisation, this may prove to be problematic. Although many non-parametric and machine learning methods are increasingly used in applications related to forest site quality assessment, their predictive performance has only been assessed for a limited number of methods and ecosystems.In this study, five different modelling techniques are compared and evaluated, i.e. multiple linear regression (MLR), classification and regression trees (CART), boosted regression trees (BRT), generalized additive models (GAM), and artificial neural networks (ANN). Each method is used to model site index of homogeneous stands of three important tree species of the Taurus Mountains (Turkey): Pinus brutia, Pinus nigra and Cedrus libani. Site index is related to soil, vegetation and topographical variables, which are available for 167 sample plots covering all important environmental gradients in the research area. The five techniques are compared in a multi-criteria decision analysis in which different model performance measures, ecological interpretability and user-friendliness are considered as criteria.When combining these criteria, in most cases GAM is found to outperform all other techniques for modelling site index for the three species. BRT is a good alternative in case the ecological interpretability of the technique is of higher importance. When user-friendliness is more important MLR and CART are the preferred alternatives. Despite its good predictive performance, ANN is penalized for its complex, non-transparent models and big training effort.     

Learning habitat models for the diatom community in Lake Prespa

Habitat suitability modelling studies the influence of abiotic factors on the abundance or diversity of a given taxonomic group of organisms. In this work, we investigate the effect of the environmental conditions of Lake Prespa (Republic of Macedonia) on diatom communities. The data contain measurements of physical and chemical properties of the environment as well as the relative abundances of 116 diatom taxa. In addition, we create a separate dataset that contains information only about the top 10 most abundant diatoms. We use two machine learning techniques to model the data: regression trees and multi-target regression trees. We learn a regression tree for each taxon separately (from the top 10 most abundant) to identify the environmental conditions that influence the abundance of the given diatom taxon. We learn two multi-target regression trees: one for modelling the complete community and the other for the top 10 most abundant diatoms. The multi-target regression trees approach is able to detect the conditions that affect the structure of a diatom community (as compared to other approaches that can model only a single target variable). We interpret and compare the obtained models. The models present knowledge about the influence of metallic ions and nutrients on the structure of the diatom community, which is consistent with, but further extends existing expert knowledge.     

用改进的TRAP法测定树木端粒酶活性

TRAP法是一种常用的测定端粒酶活性的方法,但并不太适合测定木本植物的端粒酶活性.因此以油松针叶为实验材料,对TRAP法进行改进.参照前人的研究通过对比实验证明,在端粒酶提取过程中将PEG8000加入上清液,可获得高效的端粒酶.在此基础上,对PCR先导引选择TS、模板浓度选择100 ng,最终获得油松针叶高质量的端粒酶PCR产物.应用SYBR Green I替代银染液对电泳凝胶进行染色,获得条带清晰、重复性好的电泳结果.通过对油松针叶、银杏叶片、胡杨愈伤组织、沙冬青悬浮细胞等4种木本植物实验材料进行测定,证明该改进的技术体系可能是适合于木本植物端粒酶活性测定的稳定的、灵敏度高的可行方法.图5表1参17     

酸铝胁迫下常绿杨根冠超微结构变化

中国的亚热带地区气候温暖湿润、雨量充沛,是中国速生丰产林基地,森林土壤多呈偏酸性和酸性,为富铝化酸性土壤,容易造成植物铝毒害。A-61/186（Populus×enramericana cv.）是澳大利亚 L．D．Pryor 博士选育的半常绿杨树无性系（semi-evergreen poplar clone）,母本为美国南方种的落叶型美洲黑杨（Populus deltoids Marsh）,父本为原产智利的常绿品种欧洲杨智利变种（Populus nigra cv.Chile）,是中国林科院郑世锴研究员于1987年从巴基斯坦引入中国的3个常绿杨品系之一,适合热带亚热带种植。以常绿杨A-61/186（Populus×euramericana cv.）根冠细胞为材料,采用水培酸铝胁迫实验,在胁迫30 d、60 d和90 d时在透射电子显微镜下进行根冠细胞超微结构的观察及拍片,研究在酸铝胁迫下浓度和作用时间对其超微结构的影响,探究铝毒条件下杨树根冠细胞的超微结构变化,并为根冠细胞铝毒亚显微水平评价提供资料。结果表明,随着铝浓度的升高,根冠细胞壁溶解断裂;细胞基质由凝胶态向分散碎块转变;淀粉粒数量有减少的趋向,淀粉质体数量有增加的趋向,而淀粉粒堆重心偏向由明显到不明显的趋向。随着铝作用时间的延长,根冠细胞在60 d浓度0.370 mmol·L^-1淀粉粒急剧减少;在90 d浓度0.666 mmol·L^-1淀粉粒质体急剧减少;淀粉粒堆或淀粉质体堆无重心偏向。     

古树名木保护地方立法评析与建议

对我国出台的15部古树名木保护地方性法规和规章出台时间、主要内容进行了归纳分析。结果发现:法规和规章中存在着管理体制不顺、权责利不对等、处罚标准不合理等问题。在研究基础上,提出完善古树名木保护立法的建议:加快推进古树名木保护国家立法,逐步完善地方立法,进一步理顺保护管理体制,建立古树名木保护补偿制度,加强科学研究与技术推广等。     

Removal of atmospheric particles by leaves and twigs of urban trees: Some preliminary observations and assessment of research needs

This paper reviews the various lines of evidence that support the hypothesis that above-ground plant parts remove particulates from the atmosphere. Some scanning electron microscope observations of the particulate burden on leaves of London planetree are discussed. The authors present an outline of the kinds of research required to support or reject the hypothesis that urban woody plants are important air filters.     

路面预防性养护时机确定方法探讨

介绍了路面预防性养护时机的确定方法 :应用行驶质量指数或破坏指数、基于时间或路况、效益费用评估、生命周期费用分析、决策树或决策矩阵、评分排序方法进行路面预防性养护时机的确定 ;挖掘了目前路面预防性养护时机选取方法中存在的不足。根据路面破坏的复杂性以及目前路面预防性养护时机选取方法的不足 ,提出应对措施 ,路面预防性养护时机研究应该在一个多标准、基于路面性能的基础上进行 ;在路面结构性能良好的情况下 ,基于预防性养护的路面功能性能衰变是进行路面预防性养护时机研究的关键     

北京不同古树类型和级别对土壤物理性质的影响

以北京的代表性古树白皮松、侧柏、油松和国槐为研究对象,分析不同树种类型和不同级别古树土壤理化特性以及土壤理化特性之间的关系,以期为古树土壤健康评价和复壮管理提供科学的依据。研究结果表明:研究区土壤含水量为10.62%,容重为1.20 g/cm3,紧实度为3.94 kg/m2,土壤孔隙度、毛管孔隙度和非毛管孔隙度分别为20.72%、11.77%和8.96%,各指标都呈中等变异;在6个物理指标中,土壤紧实度、土壤含水量和土壤容重与其他几个物理指标相关性较高;不同树种类型和不同古树级别对古树土壤物理特性影响并不显著。     

Impact of fertilization on chestnut growth, N and P concentrations in runo water on degraded slope land in South China

Growing fruit trees on the slopes of rolling hills in South China was causing serious environmental problems because of heavy application of chemical fertilizers and soil erosion. Suitable sources of fertilizers and proper rates of applications were of key importance to both crop yields and environmental protection. In this article, the impact of four fertilizers, i.e., inorganic compound fertilizer, organic compound fertilizer, pig manure compost, and peanut cake （peanut oil pressing residue）, on chestnut （Castanea mollissima Blume） growth on a slope in South China, and on the total N and total P concentrations in runoff waters have been investigated during two years of study, with an orthogonal experimental design. Results show that the organic compound fertilizer and peanut cake promote the heights of young chestnut trees compared to the control. In addition, peanut cake increases single-fruit weights and organic compound fertilizer raises single-seed weights. All the fertilizers increased the concentrations of total N and total P in runoff waters, except for organic compound fertilizer, in the first year experiment. The observed mean concentrations of total N varied from 1.6 mg/L to 3.2 mg/L and P from 0.12 mg/L to 0.22 mg/L, which were increased with the amount of fertilizer applications, with no pattern of direct proportion. On the basis of these experiment results, organic compound fertilizer at 2 kg/tree and peanut cake at 1 kg/tree are recommended to maximize chestnut growth and minimize water pollution.     

北京1o个常绿树种颗粒物吸附能力研究

大气中颗粒物PM( particuIate matter)不仅造成环境污染,还对人体造成严重的危害.城市绿色植物作为大气过滤器,能够有效地提高城市空气质量,保护人体健康.因此了解不同树种对空气颗粒物的吸附滞纳作用是必要的.以北京植物园10种常绿植被为研究对象,应用空气气溶胶再发生器( QRJZFSQ_I)测定了北京市常见6种乔木和4种灌木叶片对空气总悬浮颗粒物( TSP)﹑PM10﹑PM2.5和PM1.0的吸附能力.结果表明①不同树种叶片表面附着颗粒物的能力差异明显,最高的是雪松( Cedrus deodara)和油松( pinus tabuliformis),吸附量分别是(18.95±0.71)μg.cm-2和(14.61±0.78)μg.cm-2,冷杉( Abies fabri)最小,为(8.02±0.4)μg.cm-2;②不同树种叶片单位面积对不同颗粒物的附着能力也存在差异,附着PM10能力最强的是油松和雪松,附着PM2.5能力最强的是雪松﹑铺地柏( Juniperus procumbens)﹑龙柏( Juniperus chinensis cv. kaizuka)和油松,附着PM1.0能力最强的是雪松﹑铺地柏﹑冷杉和油松;③不同月份叶片上附着的各粒级颗粒物( PM10﹑ PM2.5 )占TSP的比例不同.其中PM10在4~6月之间主要表现两种变化趋势,一是先上升后下降,主要为灌木树种;二是逐渐上升,主要的树种是乔木树种.而PM2.5则没有这种明显的变化趋势.