棉田多虫复合危害互作效应及“两红”复合防治指标应用研究

针对长江中游棉区棉花多虫复合危害问题,设计了６种害虫４种组合的复合危害系统．运用通径分析方法,定量描述了多种害虫对棉花的侵害及其间的互作效应．通过多种害虫对棉花产量和品级的直接影响效应和间接影响效应分析,认为当多虫复合危害时,害虫间的相互作用就其对作物的侵害而言,表现为增强效应和减弱效应．在此基础上,研究并制定了“两红”（棉红铃虫和朱砂叶螨）复合危害动态防治指标,经验证、示范及推广应用,取得了显著的经济、生态和社会效益．     

通用选择指数的通径分析化模型

通过对各种选择指数的通径分析化模型进行系统化的研究,建立了通用选择指数的通径分析化模型,并给出了各种选择指数及其通径分析化的统一表示形式．用该模型可以充分利用各种信息来源,特别是在对各种信息来源需作各种约束时,为选种和评定优种提供了行之有效的计算方法和统一程序,避免了在实际应用中因计算体系不同而导致的混乱．     

污染预测的非线性参数模型

本文探索了Cr~（3 ）,Cr~（6 ）溶液浓度与绿豆幼苗根过氧化物酶活性之间的相关关系,建立了线性与非线性的数学模型,经相关指数,剩余标准差检验,找出了拟合度比较高的非线性参数模型．本文的结果为利用过氧化物酶活性增长率作为水环境受重金属污染的生物监测指标提供了理论依据．     

城郊景观动态模型研究：以沈阳市东陵区为例

基于渗透理论、马尔柯夫过程理论,采用中性模型方法,建立了３个不同的城郊景观动态模型,模型中分别介入不同自然因子和决策因子．利用模型对研究区景观进行了动态变化模拟．对模拟结果进行了评价,评价方法与指标包括：１）多分辨率拟合分析;２）最近邻概率;３）斑块大小和数目．结果发现,综合介入决策因素和自然因子的模型具有最好的效果．     

矮生菜豆豆荚产量构成因素的通径分析

以8个矮生菜豆品种为材料,研究了单株豆荚产量与其主要构成因素的关系,结果表明,单株豆荚产量及作为豆荚产量构成因素的主茎高、分枝数、花序数、豆荚数、豆荚长和豆荚重在品种间均存在极显著的差异;但这些产量构成因素与单株豆荚产量之间,无论是表型相关系数还是遗传相关系数或是环境相关系数均未达到显著水平。通径分析结果显示,一个与单株豆荚产量关系密切的性状。其对单株豆荚产量的效应总是由于存在一个或多个负向的间接通径系数而被削弱。从而掩盖了该性状对单株豆荚产量的遗传效应,从表型和遗传通径系数看,对单株产量最为重要的是单株结荚数及单株花序数。单株分枝数、主茎高和豆荚重其次。豆荚长则较为次要。根据通径分析结果,就矮生菜豆丰产性育种中各有关性状的选择进行了讨论。     

喀斯特峰丛洼地植被演替对土壤微生物生物量碳、氮及酶活性的影响

采用经典统计分析与通径分析,研究了桂西北喀斯特峰丛洼地4种植被演替阶段(草丛、灌木林、次生林、原生林)表层(0—15 cm)土壤微生物生物量和土壤酶活性的变化特征,探讨了其与土壤理化性质之间的关系。结果表明:土壤微生物生物量和土壤酶活性随植被正向演替的变化规律并不完全一致。土壤微生物生物量碳、微生物生物量氮和碱性磷酸酶活性整体表现为随植被正向演替而增加。而土壤蔗糖酶活性表现为:次生林草丛≈灌木林原生林,脲酶活性表现为:草丛≈次生林≈灌木林原生林。通径分析结果表明,土壤微生物生物量的直接影响因素和主要影响因素为土壤有机碳;蔗糖酶活性的直接影响因素为土壤有机碳和土壤微生物生物量碳,而从总效应来看,各因素对蔗糖酶活性的影响均较小;脲酶和碱性磷酸酶活性的直接影响因素和主要影响因素均为全氮,但全氮对脲酶活性表现为强烈的负效应,而对碱性磷酸酶活性表现为强烈的正效应。此外,土壤微生物生物量碳、氮及蔗糖酶、脲酶和碱性磷酸酶活性的剩余通径系数均较大,说明存在其它未被考虑因素对其具有影响。     

通径分析的矩阵算法

本文根据通径分析的原理,定义了4个特别阵和一个3值常数;在此基础上,提出了利用矩阵进行通径分析的方法,并给出了应用实例．     

高粱-苏丹草杂交种产草量与品质性状的回归分析

通过对7个高粱-苏丹草杂交种产草量与品质性状的相关和回归分析,结果表明：前一茬株高与后一茬产草量呈显著负相关,刈割3次的产草量间呈显著正相关,且均与总产草量呈极显著正相关;粗蛋白质与粗纤维含量呈显著负相关。粗脂肪与粗灰分含量呈显著正相关;产草量高低与品质性状无显著相关关系。通径分析结果表明：第1茬分蘖及3次产草量与总产量的直接通径系数都为正值,其中第2次刈割的产草量的直接通径系数最大,为0．5334^**。总产草量依各产草量性状的最优回归方程为：y^=-0．0522 0．0096x9 1．0006x13 1．0002x14 0．9989x15。     

云南松苗木生长与生物量的相关性及通径分析

为了解云南松苗木生长对生物量的影响程度和相对重要性,以采自云南省昆明市宜良县禄丰村林场种子培育的2年生云南松苗木为材料,对苗高、地径、主根长、侧根长、针叶长和侧根数6个生长性状及各组分生物量进行测定,运用相关性分析和逐步回归分析剔除了对生物量影响不大的性状,在此基础上采用通径分析方法研究了云南松苗木生长性状指标与生物量指标间的关系。结果表明：云南松苗木生长性状和生物量之间均呈现极显著的正相关关系。各生长性状对生物量均有直接或间接的正向效应,直接影响云南松苗木生物量的优势因素为地径和苗高,针叶长、侧根长和主根长对生物量的影响也起正向效应。各生长性状对生物量的直接效应均较大,而通过其他性状的间接效应均较小,其中侧根长通过其他性状对生物量产生的间接影响最大。依据相关性分析和通径分析,建立了云南松各组分生物量与生长性状之间的数学模型,可用于云南松苗期生物量的估测。     

基于解释结构模型的煤电一体化开发生态环境累积效应识别

为了对煤电一体化开发的生态环境累积效应进行识别与分析,从煤电一体化开发的主要开采建设活动、土壤环境、水资源、大气环境、生物、景观、生态7方面共筛选出29个生态环境因子,综合文献调查、专家调查问卷与访谈等方法,分析了各生态环境因子之间的相互作用关系.运用解释结构模型(ISM)分析了各因子间的关联关系与层次结构,揭示了不同开发建设活动产生累积效应的途径.结合具体的评价与管理需求,提出了煤电一体化开发生态环境累积效应研究与评估整体框架.研究结果可以为煤电一体化开发的规划与管理提供一定的理论基础与技术手段.     

Modeling residual force enhancement with generic cross-bridge models

The interaction of actin and myosin through cross-bridges explains much of muscle behavior. However, some properties of muscle, such as residual force enhancement, cannot be explained by current cross-bridge models. There is ongoing debate whether conceptual cross-bridge models, as pioneered by Huxley (A.F. Huxley, Muscle structure and theories of contraction, Prog. Biophys. Biophys. Chem. 7 (1957) 255) could, if suitably modified, fit experimental data showing residual force enhancement. Here we prove that there are only two ways to explain residual force enhancement with these ‘traditional’ cross-bridge models: the first requires cross-bridges to become stuck on actin (the stuck cross-bridge model) while the second requires that cross-bridges that are pulled off beyond a critical strain enter a ‘new’ unbound state that leads to a new force-producing cycle (the multi-cycle model). Stuck cross-bridge models cannot fit the velocity and stretch amplitude dependence of residual force enhancement, while the multi-cycle models can. The results of this theoretical analysis demonstrate that current kinetic models of cross-bridge action cannot explain the experimentally observed residual force enhancement. Either cross-bridges in the force-enhanced state follow a different kinetic cycle than cross-bridges in a ‘normal’ force state, or the assumptions underlying traditional cross-bridge models must be violated during experiments that show residual force enhancement.     

Phylogeny versus body size as determinants of food web structure

Food webs are the complex networks of trophic interactions that stoke the metabolic fires of life. To understand what structures these interactions in natural communities, ecologists have developed simple models to capture their main architectural features. However, apparently realistic food webs can be generated by models invoking either predator-prey body-size hierarchies or evolutionary constraints as structuring mechanisms. As a result, this approach has not conclusively revealed which factors are the most important. Here we cut to the heart of this debate by directly comparing the influence of phylogeny and body size on food web architecture. Using data from 13 food webs compiled by direct observation, we confirm the importance of both factors. Nevertheless, phylogeny dominates in most networks. Moreover, path analysis reveals that the size-independent direct effect of phylogeny on trophic structure typically outweighs the indirect effect that could be captured by considering body size alone. Furthermore, the phylogenetic signal is asymmetric: closely related species overlap in their set of consumers far more than in their set of resources. This is at odds with several food web models, which take only the view-point of consumers when assigning interactions. The echo of evolutionary history clearly resonates through current food webs, with implications for our theoretical models and conservation priorities.     

SEQUENTIAL MONADIC DESIGNS: SOME THEORETICAL CONSIDERATIONS

When choosing a design for estimating product differences we must consider both the appropriate model for the analysis of results and the economic aspects of the test. If no residual effects are expected the SMD design provides an efficient way of estimating the product difference. If the residual effect is known to be present it is better to consider an alternative design. The choice of an alternative will depend on both theoretical (desired precision of estimates) and practical (cost of conducting a study) considerations. In this paper, I review both the theoretical underpinnings of SMD and some of its possible alternatives.     

Which coordinate system for modelling path integration?

Path integration is a navigation strategy widely observed in nature where an animal maintains a running estimate, called the home vector, of its location during an excursion. Evidence suggests it is both ancient and ubiquitous in nature, and has been studied for over a century. In that time, canonical and neural network models have flourished, based on a wide range of assumptions, justifications and supporting data. Despite the importance of the phenomenon, consensus and unifying principles appear lacking. A fundamental issue is the neural representation of space needed for biological path integration. This paper presents a scheme to classify path integration systems on the basis of the way the home vector records and updates the spatial relationship between the animal and its home location. Four extended classes of coordinate systems are used to unify and review both canonical and neural network models of path integration, from the arthropod and mammalian literature. This scheme demonstrates analytical equivalence between models which may otherwise appear unrelated, and distinguishes between models which may superficially appear similar. A thorough analysis is carried out of the equational forms of important facets of path integration including updating, steering, searching and systematic errors, using each of the four coordinate systems. The type of available directional cue, namely allothetic or idiothetic, is also considered. It is shown that on balance, the class of home vectors which includes the geocentric Cartesian coordinate system, appears to be the most robust for biological systems. A key conclusion is that deducing computational structure from behavioural data alone will be difficult or impossible, at least in the absence of an analysis of random errors. Consequently it is likely that further theoretical insights into path integration will require an in-depth study of the effect of noise on the four classes of home vectors.     

Evolution in heterogeneous environments and the potential of maintenance of genetic variation in traits of adaptive significance

The maintenance of genetic variation in traits of adaptive significance has been a major dilemma of evolutionary biology. Considering the pattern of increased genetic variation associated with environmental clines and heterogeneous environments, selection in heterogeneous environments has been proposed to facilitate the maintenance of genetic variation. Some models examining whether genetic variation can be maintained, in heterogeneous environments are reviewed. Genetic mechanisms that constrain evolution in quantitative genetic traits indicate that genetic variation can be maintained but when is not clear. Furthermore, no comprehensive models have been developed, likely due to the genetic and environmental complexity of this issue. Therefore, I have suggested two empirical approaches to provide insight for future theoretical and empirical research. Traditional path analysis has been a very powerful approach for understanding phenotypic selection. However, it requires substantial information on the biology of the study system to construct a causal model and alternatives. Exploratory path analysis is a data driven approach that uses the statistical relationships in the data to construct a set of models. For example, it can be used for understanding phenotypic selection in different environments, where there is no prior information to develop path models in the different environments. Data from Brassica rapa grown in different nutrients indicated that selection changed in the different environments. Experimental evolutionary studies will provide direct tests as to when genetic variation is maintained.     

Sampling Animal Movement Paths Causes Turn Autocorrelation

Animal movement models allow ecologists to study processes that operate over a wide range of scales. In order to study them, continuous movements of animals are translated into discrete data points, and then modelled as discrete models. This discretization can bias the representation of the movement path. This paper shows that discretizing correlated random movement paths creates a biased path by creating correlations between successive turning angles. The discretization also biases statistical tests for correlated random walks (CRW) and causes an overestimate in distances travelled; a correction is given for these biases. This effect suggests that there is a natural scale to CRWs, but that distance-discretized CRWs are in a sense, scale invariant. Perhaps a new null model for continuous movement paths is needed. Authors need to be aware of the biases caused by discretizing correlated random walks, and deal with them appropriately.     

A novel approach for assessing factors affecting biodiversity based on networks analysis

We propose in this work a novel approach aiming at assessing cause and effect relationships between variables that can affect target biodiversity issues. These cause–effect relations are used to build a network whose nodes represent variables linked by directed arcs. The arcs have associated a value that represents trends of cause–effect relations. An important novelty of this approach is the use of product and addition operations between trends of cause–effect relations for assessing factors that can affect target variables. For the analysis of the network we use the concept of paths. Paths are defined as sequences of cause–effect relations from source variables to target variables. For example, the path from population increment that causes effects on the increment of transport routes, which in turn causes effects on the loss of vegetation cover. This approach was applied to the assessment of vegetation cover in the Morelos State, México during the period 2000–2010. The results show a promising practical alternative to assess the potential effects on biodiversity issues based on the analysis of the paths represented in the network.     

Effects of Genetic and Environmental Factors on Trait Network Predictions From Quantitative Trait Locus Data

The use of high-throughput genomic techniques to map gene expression quantitative trait loci has spurred the development of path analysis approaches for predicting functional networks linking genes and natural trait variation. The goal of this study was to test whether potentially confounding factors, including effects of common environment and genes not included in path models, affect predictions of cause–effect relationships among traits generated by QTL path analyses. Structural equation modeling (SEM) was used to test simple QTL-trait networks under different regulatory scenarios involving direct and indirect effects. SEM identified the correct models under simple scenarios, but when common-environment effects were simulated in conjunction with direct QTL effects on traits, they were poorly distinguished from indirect effects, leading to false support for indirect models. Application of SEM to loblolly pine QTL data provided support for biologically plausible a priori hypotheses of QTL mechanisms affecting height and diameter growth. However, some biologically implausible models were also well supported. The results emphasize the need to include any available functional information, including predictions for genetic and environmental correlations, to develop plausible models if biologically useful trait network predictions are to be made.     

Finding the way with a noisy brain

Successful navigation is fundamental to the survival of nearly every animal on earth, and achieved by nervous systems of vastly different sizes and characteristics. Yet surprisingly little is known of the detailed neural circuitry from any species which can accurately represent space for navigation. Path integration is one of the oldest and most ubiquitous navigation strategies in the animal kingdom. Despite a plethora of computational models, from equational to neural network form, there is currently no consensus, even in principle, of how this important phenomenon occurs neurally. Recently, all path integration models were examined according to a novel, unifying classification system. Here we combine this theoretical framework with recent insights from directed walk theory, and develop an intuitive yet mathematically rigorous proof that only one class of neural representation of space can tolerate noise during path integration. This result suggests many existing models of path integration are not biologically plausible due to their intolerance to noise. This surprising result imposes significant computational limitations on the neurobiological spatial representation of all successfully navigating animals, irrespective of species. Indeed, noise-tolerance may be an important functional constraint on the evolution of neuroarchitectural plans in the animal kingdom.