生态阈值:概念、方法与研究展望

生态阈值概念是20世纪70年代提出的,主要指生态系统的几个稳态之间突然改变的点或区域。在阐明生态系统结构与功能的关系、构建区域可持续发展范式以及服务于生态系统管理和生态红线的划定中,生态阈值的检测和量化有着重要的理论和实践意义。该文首先梳理了前人关于生态阈值的概念、类型的一些提法,从预警研究角度提出可以从两个层次理解生态阈值概念:生态阈值点是系统从量变到质变的转折点,类似于红色界限;而生态阈值带可以理解为量变过程中不同稳态之间的转换区域,类似于黄色与橙色预警边界带。黄色生态阈值表示生态系统可通过自身的调节能力重新达到稳定状态;橙色生态阈值表示需要排除干扰因子使得生态系统重新达到平衡;而红色生态阈值为关键阈值点,超过此阈值,生态系统将发生不可逆的退化甚至崩溃。该文还总结了目前确定生态阈值的主要方法,主要是基于野外观测数据的统计分析与模型模拟方法。最后,基于生态系统服务、生物多样性保护与生态系统管理等几个当今生态学热点研究领域,简单总结归纳了生态阈值的研究现状,并提出生态阈值未来的3个研究难点和方向:1)开展针对生态阈值检测和量化的研究;2)关注生态阈值的尺度效应并加强野外观测;3)发挥生态阈值的预警作用,指导"生态红线"的划定和生态系统管理。     

生态系统重大突变检测研究进展北大核心CSCD

当一个存在多稳态的生态系统临近突变阈值点时,外界条件即使发生一个微小变化,也会引发生态系统的剧烈响应,使之进入结构和功能截然不同的另一稳定状态,这种现象称为重大突变(critical transition)。重大突变所导致的稳态转换总是伴随着生态系统服务的急剧变化,可能对人类可持续发展产生重大影响。预测生态系统突变的发生非常困难,但科学家在此领域的大量研究结果表明,通过监测一些通用指标可以判断生态系统是否不断临近重大突变阈值点,进而可以进行生态系统重大突变预警。该文对近年来生态系统重大突变检测领域所取得的成果进行总结与归纳,论述了生态系统重大突变的产生机制及其后果,介绍了生态系统突变预警信号提取的理论基础,从时间和空间两个维度总结了近年来生态系统重大突变预警信号的提取方法,概述了当前研究面临的挑战,指出生态系统突变预警信号的检测应充分利用时空动态数据,并且联合多个指标,从多个角度进行综合预警,此外,还应重视生态系统结构与重大突变之间的关系,增强生态系统突变预警能力。     

随机森林算法基本思想及其在生态学中的应用——以云南松分布模拟为例

通常来讲,生态学者对于解释生态关系、描述格局和过程、进行空间或时间预测比较感兴趣。这些工作可以通过模拟输出值(响应)与一些特征值(即解释变量)的关系来实现。然而,生态数据模拟遇到了挑战,这是因为响应变量和预测变量可能是连续变量或离散变量。需要解释的生态关系通常是非线性的,并且解释变量之间具有复杂的相互作用关系。响应变量和解释变量存在缺失值并不是不常有的现象,奇异值也经常出现在生态数据中。此外,生态学者通常希望生态模型即要易于建立又易要于解释。通常是利用多种统计方法来分析处理各种各样情景中出现的独特的生态问题,这些模型包括(多元)逻辑回归、线性模型、生存模型、方差分析等等。随机森林是一个可以处理所有这些问题的有效方法。随机森林可以用来做分类、聚类、回归和生存分析、评估变量的重要性、检测数据中的奇异值、对缺失数据进行插补等。鉴于随机森林本身在算法上的优势,将就随机森林在生态学中的应用进行总结,对建模过程进行概述,并以云南松分布模拟研究为例,对其主要功能特点进行案例展示。通过对随机森林的一般术语、概念和建模思想进行介绍,有利于读者掌握本方法的应用本质,可以预见随机森林在生态学研究中将得到更多的应用和发展。     

Review on detection of critical transition in ecosystems

当一个存在多稳态的生态系统临近突变阈值点时, 外界条件即使发生一个微小变化, 也会引发生态系统的剧烈响应, 使之进入结构和功能截然不同的另一稳定状态, 这种现象称为重大突变(critical transition)。重大突变所导致的稳态转换总是伴随着生态系统服务的急剧变化, 可能对人类可持续发展产生重大影响。预测生态系统突变的发生非常困难, 但科学家在此领域的大量研究结果表明, 通过监测一些通用指标可以判断生态系统是否不断临近重大突变阈值点, 进而可以进行生态系统重大突变预警。该文对近年来生态系统重大突变检测领域所取得的成果进行总结与归纳, 论述了生态系统重大突变的产生机制及其后果, 介绍了生态系统突变预警信号提取的理论基础, 从时间和空间两个维度总结了近年来生态系统重大突变预警信号的提取方法, 概述了当前研究面临的挑战, 指出生态系统突变预警信号的检测应充分利用时空动态数据, 并且联合多个指标, 从多个角度进行综合预警, 此外, 还应重视生态系统结构与重大突变之间的关系, 增强生态系统突变预警能力。     

基于R的结构方程模型在生态学中的应用

结构方程模型已经成为当前生态数据分析的主要方法之一。与其他多变量统计方法不同,结构方程模型的建模过程由理论假设驱动,且可以同时量化多个变量间的直接和间接因果关系。然而,由于结构方程模型引入国内生态学领域的时间相对较短,研究者经常在实际应用中遇到各种问题,各种使用错误也屡见不鲜。对此,本文系统阐述了结构方程模型的建模原理、建模流程、模型评价、模型修正等方面内容,并且结合具体研究案例介绍了结构方程模型分析的两个主流R包—lavaan和piecewiseSEM。其中,lavaan可以分析纳入了潜变量的结构方程模型,piecewiseSEM则可以解决各观测数据不独立以及响应变量残差不满足多元正态分布等问题。本文将有助于研究者准确理解结构方程模型并能扩大其在生态学中的应用。     

土壤动物粒径谱研究进展

群落结构如何响应环境变化是生态学研究长期关注的核心问题之一。粒径谱由个体大小和多度构建而来,与营养级转换速率相关、反映生态系统过程动态以及表征生态系统稳定性,可以将其视为一个综合的功能多样性指标用于预测和表征群落的组成以及生态系统功能如何响应环境压力。粒径谱研究最初始于水生生态系统,近年来被引入到土壤动物群落生态学的研究中。简要回顾粒径谱的概念由来及理论基础,分析比较了当前粒径谱研究中的4种易混淆类型,介绍了常用的两类土壤动物粒径谱构建方法及其生态学意义,梳理了土壤动物粒径谱对环境梯度响应与生态化学计量学相结合的研究进展,并指出了应用粒径谱研究土壤动物群落的难点及限制条件。未来,在基础理论研究方面,土壤动物粒径谱应关注个体大小与营养级位置及能量利用关系;在应用方面,土壤动物粒径谱可结合传统的分类方法广泛应用于指示环境污染、生态恢复、保育生物以及土地利用变化等。     

景观生态学:海洋生态系统研究的一个新视角

全球海洋生态系统作为异质性的复杂巨系统是一类景观生态系统 ,具有明显的等级结构 ,因此 ,景观生态学的原理和方法完全可以应用到海洋生态学的研究中来。生态系统的尺度限制了海洋生态学向更加宏观的方向进一步发展 ,在景观的水平上 ,运用景观生态学的理论和方法可以更好地在多个尺度上开展深入广泛的研究。本文不仅讨论了海洋景观的空间异质性 ,而且就海洋景观生态学的若干研究方向进行了探讨。     

毛乌素沙化景观内斑块间的多种边界

1　引　　言时空异质性是自然系统的普遍特征 ,陆地景观镶嵌体由许多不同的具有内部相对同质性的斑块组成 .因此 ,当考虑整个景观时 ,相邻斑块之间的边界就成为一个不容忽视的景观组分[13 ] .长期以来 ,与边界有关的生态学问题在基础和应用生态学研究的不同领域中备受关注[5,9,10 ,15] .Ｗｉｅｎｓ等[16]提出一个研究景观生态系统空间斑块性的概念框架———边界动态 ,通过对景观镶嵌体中相邻斑块间的边界的研究 ,来揭示生态系统功能的空间结构 .为了增强对形成和维持生态学边界的各种过程的理解 ,首先应确定边界的空间位置[6] .因此许多确…     

定量遥感在生态学研究中的基础应用

生态学问题 ,特别是近来兴起的全球变化问题 ,是存在于不同时空尺度的生物与环境互作的格局和动态变化 ,对它的研究需要较大时空尺度的数据支撑 ,因此不同时空分辩率的遥感影象图就成为了这一重要的数据源。从遥感的功能出发 ,介绍了遥感应用于植被覆盖分类、生态系统参数提取及生态系统模型等方面的基础研究情况 ,试图为生态学研究提供应用遥感的思路 ,为进一步应用遥感解决生态学问题提供基础。     

生态系统服务研究：进展、局限和基本范式

 生态系统服务是生态学研究的热点问题。该文首先从概念、分类、价值评估理论、评估案例等方面总结了目前国内外生态系统服务的研究现状。然后评述了目前生态系统服务研究存在的局限性,包括：评估结果的准确性问题、生态资产与生态系统服务的混淆、物理量评估方法的不确定性、价值量评估的不确定性以及生态系统功能与服务的复杂性。针对这些研究的局限,文章提出了生态系统服务研究的基本范式,包括：严格区分生态资产和生态系统服务、生态系统服务评估必须基于生态观测或生态模型以及区域生态系统服务评估应区分不同样元。最后,文章展望了今后生态系统服务研究的课题：生态系统服务与生态系统结构及生态过程的关联性和复杂关系的研究,人类干扰下生态系统服务的响应与反馈研究,生态系统服务变化对人类福利的影响研究,政策机制对生态系统服务的影响研究以及不同生态系统类型的各种生态服务价值研究等方面。     

Constraint line methods and the applications in ecology北大核心CSCD

With increasing data availability in the big data era, many traditional statistical analyses based on the mean or median are insufficient or inappropriate to elucidate the complex patterns of variation. This is particularly the case when multiple factors are involved and the bivariate scatter occurs as scatter clouds. In such circumstances, constraint line (or envelope) method could be an alternative and effective tool to extract the data boundaries, thus improves our understanding of the complex relationships between limiting factor and response factor. Here, we synthesize the major findings and achievements in the field of applying the constraint line method in ecology. Specifically, we first describe the history and development of the constraint line method. We then discuss the techniques to establish the constraint lines with examples, and discuss the applications and implications of the constraint lines in species distribution, population performance, and optimization problem. We suggest simultaneously application of both constraint lines and regression techniques to the same datasets to achieve a comprehensive understanding of ecological process and underlying mechanisms. Such combined methods should be used with special attention to the role of spatial heterogeneity and scale dependency. We also discuss in detail the potential applicability of the constraint line method in studying the linkages between ecosystem services, and land system design.     

Constraint line methods and the applications in ecology

With increasing data availability in the big data era, many traditional statistical analyses based on the mean or median are insufficient or inappropriate to elucidate the complex patterns of variation. This is particularly the case when multiple factors are involved and the bivariate scatter occurs as scatter clouds. In such circumstances, constraint line (or envelope) method could be an alternative and effective tool to extract the data boundaries, thus improves our understanding of the complex relationships between limiting factor and response factor. Here, we synthesize the major findings and achievements in the field of applying the constraint line method in ecology. Specifically, we first describe the history and development of the constraint line method. We then discuss the techniques to establish the constraint lines with examples, and discuss the applications and implications of the constraint lines in species distribution, population performance, and optimization problem. We suggest simultaneously application of both constraint lines and regression techniques to the same datasets to achieve a comprehensive understanding of ecological process and underlying mechanisms. Such combined methods should be used with special attention to the role of spatial heterogeneity and scale dependency. We also discuss in detail the potential applicability of the constraint line method in studying the linkages between ecosystem services, and land system design.     

Calibration of multivariate scatter plots for exploratory analysis of relations within and between sets of variables in genomic research

The scatter plot is a well known and easily applicable graphical tool to explore relationships between two quantitative variables. For the exploration of relations between multiple variables, generalisations of the scatter plot are useful. We present an overview of multivariate scatter plots focussing on the following situations. Firstly, we look at a scatter plot for portraying relations between quantitative variables within one data matrix. Secondly, we discuss a similar plot for the case of qualitative variables. Thirdly, we describe scatter plots for the relationships between two sets of variables where we focus on correlations. Finally, we treat plots of the relationships between multiple response and predictor variables, focussing on the matrix of regression coefficients. We will present both known and new results, where an important original contribution concerns a procedure for the inclusion of scales for the variables in multivariate scatter plots. We provide software for drawing such scales. We illustrate the construction and interpretation of the plots by means of examples on data collected in a genomic research program on taste in tomato.     

The mode of action of heparan and dermatan sulfates in the regulation of hepatocyte growth factor/scatter factor.

Hepatocyte growth factor/scatter factor, in addition to binding to its specific signal-transducing receptor, Met, also interacts with both heparan and dermatan sulfates with high affinity. We have investigated the comparative role of these two glycosaminoglycans in the activation of Met by hepatocyte growth factor/scatter factor. Using glycosaminoglycan-deficient CHO pgsA-745 cells we have shown that growth factor activity is critically dependent upon glycosaminoglycans, and that heparan sulfate and dermatan sulfate are equally potent as co-receptors. Cross-linked 1:1 conjugates of growth factor and either heparan or dermatan sulfate do not dimerize under physiological conditions and are biologically active. This implies that a ternary signaling complex with Met forms in vivo. Native Met isolated from CHO pgsA-745 cells shows only very weak intrinsic affinity for heparin in vitro. Also, a heparin-derived hexasaccharide, which is the minimal size for high affinity binding to the growth factor alone, is sufficient to induce biological activity. Together these observations imply that the role of these glycosaminoglycan may be primarily to effect a conformational change in hepatocyte growth factor/scatter factor, rather than to induce a necessary growth factor dimerization, or to stabilize a ternary complex by additionally interacting with Met.     

Reinforcing the LINC complex connection to actin filaments: the role of FHOD1 in TAN line formation and nuclear movement

Positioning the nucleus is critical for many cellular processes including cell division, migration and differentiation. The linker of nucleoskeleton and cytoskeleton (LINC) complex spans the inner and outer nuclear membranes and has emerged as a major factor in connecting the nucleus to the cytoskeleton for movement and positioning. Recently, we discovered that the diaphanous formin family member FHOD1 interacts with the LINC complex component nesprin-2 giant (nesprin-2G) and that this interaction plays essential roles in the formation of transmembrane actin-dependent nuclear (TAN) lines and nuclear movement during cell polarization in fibroblasts. We found that FHOD1 strengthens the connection between nesprin-2G and rearward moving dorsal actin cables by providing a second site of interaction between nesprin-2G and the actin cable. These results indicate that the LINC complex connection to the actin cytoskeleton can be enhanced by cytoplasmic factors and suggest a new model for TAN line formation. We discuss how the nesprin-2G-FHOD1 interaction may be regulated and its possible functional significance for development and disease.     

Automated quantitative analysis of epithelial cell scatter

Epithelial cell scatter is a well-known in vitro model for the study of epithelial-mesenchymal transition (EMT). Scatter recapitulates many of the events that occur during EMT, including the dissociation of multicellular structures and increased cell motility. Because it has been implicated in tumor invasion and metastasis, much effort has been made to identify the molecular signals that regulate EMT. To better understand the quantitative contributions of these signals, we have developed metrics that quantitatively describe multiple aspects of cell scatter. One metric (cluster size) quantifies the disruption of intercellular adhesions while a second metric (nearest-neighbor distance) quantifies cell dispersion. We demonstrate that these metrics delineate the effects of individual cues and detect synergies between them. Specifically, we find epidermal growth factor (EGF), cholera toxin (CT) and insulin to synergistically reduce cluster sizes and increase nearest-neighbor distances. To facilitate the rapid measurement of our metrics from live-cell images, we have also developed automated techniques to identify cell nuclei and cell clusters in fluorescence images. Taken together, these studies provide broadly applicable quantitative image analysis techniques and insight into the control of epithelial cell scatter, both of which will contribute to the understanding of EMT and metastasis.Key words: cell scatter, EMT, automated image analysis     

Lab and field experiments: Are they the same animal?

To advance our understanding of biological processes we often plan our experiments based on published data. This can be confusing though, as data from experiments performed in a laboratory environment are sometimes different from, or completely opposite to, findings from similar experiments performed in the “real world”. In this mini-review, we discuss instances where results from laboratory experiments differ as a result of laboratory housing conditions, and where they differ from results gathered in the field environment. Experiments involving endocrinology and behavior appear to be particularly susceptible to influence from the environment in which they are performed.As such, we have attempted to promote discussion of the influence of housing environment on the reproductive axis, circadian biology and behavior, immune function, stress biology, neuroplasticity and photoperiodism. For example, why should a rodent species be diurnal in one housing environment yet nocturnal in another? Are data that are gathered from experiments in the laboratory applicable to the field environment, and vice-versa? We hope not only to highlight the need for experiments in both lab and field when looking at complex biological systems, but also to promote frank discussion of discordant data. Perhaps, just as study of individual variation has been gaining momentum in recent years, data from variation between experimental arenas can provide us with novel lines of research.     

Data mining tools for biological sequences

We describe a methodology, as well as some related data mining tools, for analyzing sequence data. The methodology comprises three steps: (a) generating candidate features from the sequences, (b) selecting relevant features from the candidates, and (c) integrating the selected features to build a system to recognize specific properties in sequence data. We also give relevant techniques for each of these three steps. For generating candidate features, we present various types of features based on the idea of k-grams. For selecting relevant features, we discuss signal-to-noise, t-statistics, and entropy measures, as well as a correlation-based feature selection method. For integrating selected features, we use machine learning methods, including C4.5, SVM, and Naive Bayes. We illustrate this methodology on the problem of recognizing translation initiation sites. We discuss how to generate and select features that are useful for understanding the distinction between ATG sites that are translation initiation sites and those that are not. We also discuss how to use such features to build reliable systems for recognizing translation initiation sites in DNA sequences.     

Single molecule techniques in DNA repair: A primer

A powerful new approach has become much more widespread and offers insights into aspects of DNA repair unattainable with billions of molecules. Single molecule techniques can be used to image, manipulate or characterize the action of a single repair protein on a single strand of DNA. This allows search mechanisms to be probed, and the effects of force to be understood. These physical aspects can dominate a biochemical reaction, where at the ensemble level their nuances are obscured. In this paper we discuss some of the many technical advances that permit study at the single molecule level. We focus on DNA repair to which these techniques are actively being applied. DNA repair is also a process that encompasses so much of what single molecule studies benefit – searching for targets, complex formation, sequential biochemical reactions and substrate hand-off to name just a few. We discuss how single molecule biophysics is poised to transform our understanding of biological systems, in particular DNA repair.