呼伦贝尔沙地樟子松径向生长特征的VS模型模拟分析

利用Vaganov-Shashkin模型对呼伦贝尔地区4个样点的沙地樟子松标准化宽度年表进行模拟研究,在2000年以前时段拟合度较好,而2000年以后时段拟合度较差。进而选取2000年以前的模拟结果进行径向生长过程分析。结果表明: 呼伦贝尔沙地樟子松主要的生长季为每年的5—9月,温度对每年樟子松生长初期与末期具有显著影响,而在树木生长季旺盛期,土壤湿度的不足是制约树木生长的主要因素。极端窄年树木径向生长速率受土壤湿度的影响较极端宽年明显,生长季中期7—8月树木径向生长速率在宽窄年份均呈降低趋势,表明该时期沙地樟子松生长均受到不同程度的干旱胁迫。研究结果与我国半干旱地区树木年轮生理模型分析特征相符,模型对呼伦贝尔沙地樟子松径向生长模拟具有一定的适用性。     

蔷薇科果树类受体激酶的研究进展

类受体激酶(receptor like kinase,RLK)参与调控植物几乎所有的生命活动,是植物生长发育和环境适应的“中央处理器”。该文对近年来国内外有关蔷薇科果树RLK基因鉴定、进化特征及其在各器官生长发育、非生物和生物逆境中的作用及调控机制等方面的研究进展进行了综述。蔷薇科果树基因组中存在数目庞大的RLKs,不同树种间的RLK数目和各亚家族成员数目都存在较大差异,而且蔷薇科果树RLK存在极为普遍的部分重复和串联重复现象,是导致家族成员迅速变化的重要原因。有研究发现,一些RLKs调控蔷薇科果树器官发育和对环境的适应性。在器官发育方面,LRR RLK亚家族成员调控根系发育,CrRLK1L、LysM RLK和LRR RLK亚家族部分成员参与调控果实发育,CrRLK1L亚家族成员参与调控花粉管发育,LRR RLK、LysM RLK、L LEC RLK和B Lectin RLK亚家族部分成员调控蔷薇科果树对生物逆境的适应。今后RLK功能研究可侧重于蔷薇科果树特色性状,通过提高目标基因的筛选和验证的效率,加速主效RLKs的筛选进程,并通过筛选主效RLKs诱导方式和加速分子育种进程等途径,将研究成果应用于实际生产。     

High-altitude tree growth responses to climate change across the Hindu Kush Himalaya

兴都库什喜马拉雅地区高海拔树木生长对气候变化的响应 高海拔地区快速升温可能导致树木对温度响应更为敏感,而限制高海拔地区树木生长的关键气候因子以及气候变化对树木生长产生多大程度的影响尚不清楚。本研究在兴都库什喜马拉雅地区收集了73 个样点的树轮数据,包括3个优势属的树种(Abies属、Juniperus属和Picea属),样点海拔均在3000 m以上。 将时间动态规整(dynamic time warping)的方法用于建立亚区域年表,以考虑不同站点年表之间变化的同步 性。同时,定量分析了气候因子对树木生长的贡献以及树木生长与气候因子关系的时空动态。研究结果发现,73个站点年表可以聚为3类,且与其所处的生物气候区相对应,即西喜马拉雅地区,中东喜马拉雅地区和藏东南地区。在干旱的西喜马拉雅地区,树木生长与冬、春两季的降水呈正相关关系,而在湿润的藏东南地区,树木生长与冬季温度和春季降水呈正相关关系。树木生长受最低温度的影响最大,特别是冬季温度,其重要性从西到东呈现递增趋势。滑动窗口相关分析表明,在中西喜马拉雅地区,影响树木生长的冬季温度信号在减弱,然而在藏东南地区该信号随着1980年以来的快速升温而增强。本研究结果表明,若该地区升温持续,在西喜马拉雅地区可能会因变暖引起的水分亏缺而造成森林衰退,而在藏东南地区因树木生长得益于变暖而使得森林扩张。     

Molecular dating for phylogenies containing a mix of populations and species by using Bayesian and RelTime approaches

Simultaneous molecular dating of population and species divergences is essential in many biological investigations, including phylogeography, phylodynamics and species delimitation studies. In these investigations, multiple sequence alignments consist of both intra‐ and interspecies samples (mixed samples). As a result, the phylogenetic trees contain interspecies, interpopulation and within‐population divergences. Bayesian relaxed clock methods are often employed in these analyses, but they assume the same tree prior for both inter‐ and intraspecies branching processes and require specification of a clock model for branch rates (independent vs. autocorrelated rates models). We evaluated the impact of a single tree prior on Bayesian divergence time estimates by analysing computer‐simulated data sets. We also examined the effect of the assumption of independence of evolutionary rate variation among branches when the branch rates are autocorrelated. Bayesian approach with coalescent tree priors generally produced excellent molecular dates and highest posterior densities with high coverage probabilities. We also evaluated the performance of a non‐Bayesian method, RelTime, which does not require the specification of a tree prior or a clock model. RelTime's performance was similar to that of the Bayesian approach, suggesting that it is also suitable to analyse data sets containing both populations and species variation when its computational efficiency is needed.     

Direct response of tree growth to soil water and its implications for terrestrial carbon cycle modelling

Wood growth constitutes the main process for long‐term atmospheric carbon sequestration in vegetation. However, our understanding of the process of wood growth and its response to environmental drivers is limited. Current dynamic global vegetation models (DGVMs) are mainly photosynthesis‐driven and thus do not explicitly include a direct environmental effect on tree growth. However, physiological evidence suggests that, to realistically model vegetation carbon allocation under increased climatic stressors, it is crucial to treat growth responses independently from photosynthesis. A plausible growth response function suitable for global simulations in DGVMs has been lacking. Here, we present the first soil water‐growth response function and parameter range for deciduous and evergreen conifers. The response curve was calibrated against European larch and Norway spruce in a dry temperate forest in the Swiss Alps. We present a new data‐driven approach based on a combination of tree ring width (TRW) records, growing season length and simulated subdaily soil hydrology to parameterize ring width increment simulations. We found that a simple linear response function, with an intercept at zero moisture stress, used in growth simulations reproduced 62.3% and 59.4% of observed TRW variability for larch and spruce respectively and, importantly, the response function slope was much steeper than literature values for soil moisture effects on photosynthesis and stomatal conductance. Specifically, we found stem growth stops at soil moisture potentials of ?0.47 MPa for larch and ?0.66 MPa for spruce, whereas photosynthesis in trees continues down to ?1.2 MPa or lower, depending on species and measurement method. These results are strong evidence that the response functions of source and sink processes are indeed very different in trees, and need to be considered separately to correctly assess vegetation responses to environmental change. The results provide a parameterization for the explicit representation of growth responses to soil water in vegetation models.     

格氏栲天然林林窗植物物种多样性与系统发育多样性

林窗环境异质性导致群落物种多样性与系统发育多样性(phylogenetic diversity, PD)存在差异, 研究不同大小的林窗中群落的物种多样性与系统发育多样性有助于揭示林下生物多样性的形成及维持机制。本文以格氏栲(Castanopsis kawakamii)天然林为研究对象, 通过Pearson相关性分析与广义线性模型探讨了林窗内物种多样性与系统发育多样性间的相互关系及其环境影响因素。结果表明: (1)大林窗(面积 > 200 m²)植物种类及多度均高于中林窗(50 m² ≤ 面积 < 100 m²)、小林窗(30 m² ≤ 面积 < 50 m²)和非林窗(面积 = 100 m²)。大林窗群落系统发育结构趋于发散, 中、小林窗和非林窗群落系统发育结构受到生境过滤和竞争排斥综合作用。(2)群落系统发育多样性指数与物种丰富度(species richness, SR)、Margalef丰富度指数和Shannon-Wiener指数均呈显著正相关, 这与林窗内稀有种种类组成多于优势种有关。(3)林窗面积对物种多样性存在显著正效应; 土壤全氮含量对系统发育多样性和系统发育结构存在显著正效应。林窗形成提高了格氏栲天然林群落物种多样性和系统发育多样性, 林窗面积与土壤全氮共同驱动了格氏栲天然林林窗物种多样性和系统发育多样性的变化。     

Deep Convolution Neural Network Based System for Early Diagnosis of Alzheimer's Disease

ObjectivesAlzheimer's Disease (AD) is the most general type of dementia. In all leading countries, it is one of the primary reasons of death in senior citizens. Currently, it is diagnosed by calculating the MSME score and by the manual study of MRI Scan. Also, different machine learning methods are utilized for automatic diagnosis but existing has some limitations in terms of accuracy. So, main objective of this paper to include a preprocessing method before CNN model to increase the accuracy of classification.Materials and methodIn this paper, we present a deep learning-based approach for detection of Alzheimer's Disease from ADNI database of Alzheimer's disease patients, the dataset contains fMRI and PET images of Alzheimer's patients along with normal person's image. We have applied 3D to 2D conversion and resizing of images before applying VGG-16 architecture of Convolution neural network for feature extraction. Finally, for classification SVM, Linear Discriminate, K means clustering, and Decision tree classifiers are used.ResultsThe experimental result shows that the average accuracy of 99.95% is achieved for the classification of the fMRI dataset, while the average accuracy of 73.46% is achieved with the PET dataset. On comparing results on the basis of accuracy, specificity, sensitivity and on some other parameters we found that these results are better than existing methods.Conclusionsthis paper, suggested a unique way to increase the performance of CNN models by applying some preprocessing on image dataset before sending to CNN architecture for feature extraction. We applied this method on ADNI database and on comparing the accuracies with other similar approaches it shows better results.     

Vegetation and Soil Development in Compost‐Amended Iron Oxide Precipitates at a 50‐Year‐Old Acid Mine Drainage Barrens

Acid mine drainage (AMD) barrens result from destruction of vegetation within AMD flow paths. When exposed to air, soluble iron in AMD undergoes oxidation and hydrolysis to form ferric iron (oxyhydr)oxides which accumulate on soil surfaces. A restoration experiment was conducted at a 50‐year‐old AMD barrens created by discharge from an abandoned underground coal mine. The objective was to determine whether vegetation could be established by altering rather than removing surface layers of acidic precipitates at a site representative of other mining‐degraded areas. Three zones in the barrens were identified based on moisture content, pH (2.7–3.3), and thickness of precipitates (0–35 cm). Our hypothesis was that application of the same reclamation method to all zones would fail to sustain >70% vegetative cover in each zone after four growing seasons. The method consisted of applying 11 t/ha lime and 27 or 54 t/ha compost before rototilling (top 15 cm) and mulching with oat straw containing viable seeds for a nurse crop. Lime‐only plots were included for comparison, and all amended plots were sown with a mine reclamation seed mix. Oats, sown species, and indigenous species dominated cover in the first, second, and fourth growing seasons, respectively. In the fourth year following reclamation, compost‐amended plots had >70% cover and improved soil properties in all three zones, providing evidence to reject our hypothesis. Vegetative restoration of AMD barrens did not require removal of highly acidic precipitates, since they could be transformed at low‐cost into a medium that supports indigenous plants.     

Molecular divergence in tropical tree populations occupying environmental mosaics

Unveiling the genetic basis of local adaptation to environmental variation is a major goal in molecular ecology. In rugged landscapes characterized by environmental mosaics, living populations and communities can experience steep ecological gradients over very short geographical distances. In lowland tropical forests, interspecific divergence in edaphic specialization (for seasonally flooded bottomlands and seasonally dry terra firme soils) has been proven by ecological studies on adaptive traits. Some species are nevertheless capable of covering the entire span of the gradient; intraspecific variation for adaptation to contrasting conditions may explain the distribution of such ecological generalists. We investigated whether local divergence happens at small spatial scales in two stands of Eperua falcata (Fabaceae), a widespread tree species of the Guiana Shield. We investigated Single Nucleotide Polymorphisms (SNP) and sequence divergence as well as spatial genetic structure (SGS) at four genes putatively involved in stress response and three genes with unknown function. Significant genetic differentiation was observed among sub‐populations within stands, and eight SNP loci showed patterns compatible with disruptive selection. SGS analysis showed genetic turnover along the gradients at three loci, and at least one haplotype was found to be in repulsion with one habitat. Taken together, these results suggest genetic differentiation at small spatial scale in spite of gene flow. We hypothesize that heterogeneous environments may cause molecular divergence, possibly associated to local adaptation in E. falcata.