群落结构复杂性的测度方法研究进展

该文对群落结构复杂性的测度方法的研究进展状况进行了综述。根据测度方法建立的方法基础,将现有的方法分成3类:基于多样性的复杂性测度、基于计算复杂性的测度和基于几何学特征的复杂性测度。对每类测度方法进行了介绍,对其优缺点进行了评述。同时提出了未来研究中应给予重视的问题。结果表明,现有群落结构复杂性的测度方法普遍存在区分能力差的问题,对于基于多样性的结构复杂性测度,目前还缺乏确定各测度属性权重的客观方法;现有的一些基于计算复杂性的结构测度与多样性指标关系过于密切,还不完善,同时其生态学的意义还不明确,而另一些计算复杂性指标还缺乏实际检验。今后,如何建立既具有区分力、又与多样性在概念和数值上都有一定区别的群落结构的计算复杂性的测度方法、如何科学合理地确定复杂性测度中的属性权重以及如何建立结构复杂性的测度和功能过程之间的联系等都是需要深入和系统研究的。由于方法的相似性,有关群落结构复杂性的测度方法也可以应用到其它尺度上的结构复杂性的研究中。     

结构稳定性: 概念、方法和应用

群落内物种间相互作用的结构是高度组织化的。群落结构对多物种共存的影响机制是群落生态学的核心科学问题之一。目前生态学界在这一问题上存在多种不同的观点。一个可能的原因是, 由于环境因子的复杂性, 大部分研究忽略了环境因子对群落结构和物种共存的重要影响。在这一背景下, 近期发展起来的结构稳定性理论系统地联系了群落结构、环境因子和物种共存, 并在此基础上建立了一个和经验数据紧密结合的理论框架。本文首先简要回顾了当前关于群落结构研究的争鸣, 然后介绍了结构稳定性的理论框架和计算方法, 最后详细介绍了结构稳定性理论在不同生态群落和不同生态学问题中的应用。在全球气候变化的背景下, 结构稳定性理论提供了一种新的视角来理解群落层面的生物多样性维持机制。     

土地生态系统的复杂性研究

运用复杂科学理论,阐述了土地生态系统的复杂性特征,包括多层次性,高维性,子系统关联的复杂性,结构与功能的不确定性,开放性,动态性,自适应性和自组织性等复杂性特征,并进一步探讨了分形,混沌及人工神经网络在土地生态系统复杂性特征研究中的应用。     

生态复杂性研究——综述与展望

简要介绍了生态复杂性研究的最新进展与动态,生态复杂性研究的背景及若干重要的概念与方法,生态复杂性指生态系统内不同层次上的结构与功能的多样性,自组织及有序性,生态复杂性研究的显著特征是：它应用复杂的理论,方法和观点来研究生态与进化问题,其研究方法主要有元胞机法和遗传算法,认为生态系统是一个适应复杂系统,处于混沌的边缘或临界态,内部作用是生态系统复杂化,有序化及自组织的主要推动力。     

基于Perl语言的序列同源性分析过程自动化的实现

Perl语言已经成为用于生物信息学应用程序开发最流行的语言。为了简化本地的序列同源性分析过程,作者基于Perl语言设计开发了序列同源性分析的自动化程序,该程序具有无需编译、可移植性好、简单易用等优点,大大提高了工作效率,充分体现了Perl语言在生物信息学研究中所具备的优势。     

R语言在生物信息实践中的应用

R语言是一种用来进行数据探索、统计分析、作图的计算机语言。与现有的计算机语言相比,它在计算和数据处理上具有强大的优势。同时,R语言应用简单、操作便捷,极大程度上降低了非计算机专业人员对于计算机语言掌握的难度。随着新一代测序技术和计算机技术的进步,生物信息学产生了大量新技术,新方法,为识别人类复杂疾病的治病靶点,阐明复杂疾病的发病机制,开发新的治疗药物成为可能。本文通过具体的案例分析阐明R语言在生物信息中的重要应用,为生物信息专业的研究者和师生提供有益的借鉴。     

绘画语态的形式探究

从绘画自身的本质特征为着重点,将具象绘画语言划分为形体、结构、光影、空间、色调、调性、情绪气氛、厚薄干湿等基本语汇,以造型语言、色彩语言、结构语言三个体系为框架,揭示绘画构成的一般规律。     

共词分析国内外生物信息学领域研究态势

生物信息学作为自然科学领域中多学科交叉的新兴学科,其发展研究得到了众多学者的关注。为了解生物信息学在国内外的研究态势,以CNKI中文数据库和Web of science外文数据库中生物信息学领域期刊论文为研究对象,利用R语言编程工具,文献计量和共词分析归纳了国内外生物信息学领域的研究现状、热点及趋势。结果表明:国内外生物信息学研究均处于高速发展期,文献量呈逐年增长趋势,研究领域也在不断拓宽;国内外研究热点均聚焦在基因挖掘、蛋白质结构与功能预测、miRNA分析等,但国内偏向于理论研究,而国际更注重其在疾病治疗、药物设计等方面的应用研究。     

基于广义可加模型的昆虫种群动态非线性分析及R语言实现

昆虫种群受到气候、天敌和土壤等多种生态因子的综合作用,其动态具有复杂性、不确定性和非线性等特征。广义加性模型(generalized additive models,GAM)就是适用于响应变量与解释变量之间的关系是非线性或非单调的数据分析。本文以1973—1990年稻纵卷叶螟种群数量与降雨持续天数和降雨量的相关性分析为例,介绍了广义可加模型的应用及其R语言实现步骤,为研究昆虫种群动态及其驱动因子提供了有效的分析工具。     

从生态学语言向景观生态规划设计语言的转化途径

本研究旨在探索景观规划设计领域中的生态知识（生态学语言）与生态实践（景观生态规划设计语言）之间的转化关系,希望通过一套逻辑严密、步骤清晰、简单有效的转化途径推进这一学界持续讨论的问题。研究首先通过相关综述研究了二者之间的关系,提出景观生态规划设计三层次研究体系,从而确立了应用基础研究在转化过程中的桥梁地位。然后通过剖析基于岛屿生物地理学原理开展的自然保护区规划的经典方法,发现从生态学基础研究通往生态规划设计应用的桥梁是“空间导则与空间格局”的提炼,基于此提出了T-P-C的转化途径。最后,通过T-P-C途径的指导提出了具有普适性的5步骤转化方法。研究希望通过这5步骤的转化方法,引导更多生态学基础知识能够言之有理、行之有效地转化到具体的景观规划设计实践之中。     

Multi-dimensional linguistic complexity

Linguistic complexity is a simple and elegant way of calculating complexity of strings of data. It is based on the concept that the greater the vocabulary one uses, the more complex the data. Until now, it has been used only on one-dimensional data, such as DNA and protein sequences and various human language texts. The basic definition can be extended to higher dimensions, thus allowing a practical and simple calculation of linguistic complexity of images, 3D objects and other multi-dimensional data. A simple extension of linguistic complexity is introduced, followed by 2D presentations and a discussion of parametric considerations. An example of linguistic complexity calculations, demonstrating its image processing and medical diagnostic power is presented. The subjects of this paper are patent application pending.     

Linguistic Magic Bullets in the Making of a Modernist Anthropology

This article engages current debates about concepts of culture in U.S. anthropology by examining how assumptions about language shape them. Characterizing linguistic patterns as particularly inaccessible to conscious introspection, Franz Boas suggested that culture is similarly automatic and unconscious—except for anthropologists. He used this notion in attempting to position the discipline as the obligatory passage point for academic and public debate about difference. Unfortunately, this mode of inserting linguistics in the discipline, which has long outlived Boas, reifies language ideologies by promoting simplistic models that belie the cultural complexity of human communication. By pointing to the way that recent work in linguistic anthropology has questioned key assumptions that shaped Boas's concept of culture, the article urges other anthropologists to stop asking their linguistic colleagues for magic bullets and to appreciate the critical role that examining linguistic ideologies and practices can play in discussions of the politics of culture. [Keywords: Franz Boas, culture concept, linguistic anthropology, language ideologies, scientific authority]     

Prediction of psychosis across protocols and risk cohorts using automated language analysis

Language and speech are the primary source of data for psychiatrists to diagnose and treat mental disorders. In psychosis, the very structure of language can be disturbed, including semantic coherence (e.g., derailment and tangentiality) and syntactic complexity (e.g., concreteness). Subtle disturbances in language are evident in schizophrenia even prior to first psychosis onset, during prodromal stages. Using computer‐based natural language processing analyses, we previously showed that, among English‐speaking clinical (e.g., ultra) high‐risk youths, baseline reduction in semantic coherence (the flow of meaning in speech) and in syntactic complexity could predict subsequent psychosis onset with high accuracy. Herein, we aimed to cross‐validate these automated linguistic analytic methods in a second larger risk cohort, also English‐speaking, and to discriminate speech in psychosis from normal speech. We identified an automated machine‐learning speech classifier – comprising decreased semantic coherence, greater variance in that coherence, and reduced usage of possessive pronouns – that had an 83% accuracy in predicting psychosis onset (intra‐protocol), a cross‐validated accuracy of 79% of psychosis onset prediction in the original risk cohort (cross‐protocol), and a 72% accuracy in discriminating the speech of recent‐onset psychosis patients from that of healthy individuals. The classifier was highly correlated with previously identified manual linguistic predictors. Our findings support the utility and validity of automated natural language processing methods to characterize disturbances in semantics and syntax across stages of psychotic disorder. The next steps will be to apply these methods in larger risk cohorts to further test reproducibility, also in languages other than English, and identify sources of variability. This technology has the potential to improve prediction of psychosis outcome among at‐risk youths and identify linguistic targets for remediation and preventive intervention. More broadly, automated linguistic analysis can be a powerful tool for diagnosis and treatment across neuropsychiatry.     

Comment: Got Language? Law, Property, and the Anthropological Imagination

This comment reflects on the legal (specifically, proprietary) tropes of linguistics, and the linguistic tropes of legal anthropology.It suggests analogies between discussions around "language rights" in contemporary political struggles, and discussions around the delineation of objects and subjects in anthropological theory. Such analogies may help side-step the relativism-universalism impasse that has beset the critique of rights and the critique of the objectification of language. [Keywords: language, law, anthropological theory, value]     

Social complexity and linguistic diversity in the Austronesian and Bantu population expansions

Reconstructing the rise and fall of social complexity in human societies through time is fundamental for understanding some of the most important transformations in human history. Phylogenetic methods based on language diversity provide a means to reconstruct pre-historic events and model the transition rates of cultural change through time. We model and compare the evolution of social complexity in Austronesian (n = 88) and Bantu (n = 89) societies, two of the world's largest language families with societies representing a wide spectrum of social complexity. Our results show that in both language families, social complexity tends to build and decline in an incremental fashion, while the Austronesian phylogeny provides evidence for additional severe demographic bottlenecks. We suggest that the greater linguistic diversity of the Austronesian language family than Bantu likely follows the different biogeographic structure of the two regions. Cultural evolution in both the Bantu and Austronesian cases was not a simple linear process, but more of a wave-like process closely tied to the demography of expanding populations and the spatial structure of the colonized regions.     

Language dynamics in finite populations

Any mechanism of language acquisition can only learn a restricted set of grammars. The human brain contains a mechanism for language acquisition which can learn a restricted set of grammars. The theory of this restricted set is universal grammar (UG). UG has to be sufficiently specific to induce linguistic coherence in a population. This phenomenon is known as "coherence threshold". Previously, we have calculated the coherence threshold for deterministic dynamics and infinitely large populations. Here, we extend the framework to stochastic processes and finite populations. If there is selection for communicative function (selective language dynamics), then the analytic results for infinite populations are excellent approximations for finite populations; as expected, finite populations need a slightly higher accuracy of language acquisition to maintain coherence. If there is no selection for communicative function (neutral language dynamics), then linguistic coherence is only possible for finite populations.     

A formal test of linguistic and genetic coevolution in native Central and South America

This paper investigates a mechanism of linguistic and genetic coevolution in Native Central and South America. This mechanism proposes that a process of population fissions, expansions into new territories, and isolation of ancestral and descendant groups will produce congruent language and gene trees. To evaluate this population fissions mechanism, we collected published mtDNA sequences for 1,381 individuals from 17 Native Central and South American populations. We then tested the hypothesis that three well-known language classifications also represented the genetic structure of these populations. We rejected the hypothesis for each language classification. Our tests revealed linguistic and genetic correspondence in several shallow branches common to each classification, but no linguistic and genetic correspondence in the deeper branches contained in two of the language classifications. We discuss the possible causes for the lack of congruence between linguistic and genetic structure in the region, and describe alternative mechanisms of linguistic and genetic correspondence and their predictions.     

Chaos and language

Human language is a complex communication system with unlimited expressibility. Children spontaneously develop a native language by exposure to linguistic data from their speech community. Over historical time, languages change dramatically and unpredictably by accumulation of small changes and by interaction with other languages. We have previously developed a mathematical model for the acquisition and evolution of language in heterogeneous populations of speakers. This model is based on game dynamical equations with learning. Here, we show that simple examples of such equations can display complex limit cycles and chaos. Hence, language dynamical equations mimic complicated and unpredictable changes of languages over time. In terms of evolutionary game theory, we note that imperfect learning can induce chaotic switching among strict Nash equilibria.     

A Bayesian phylogenetic approach to estimating the stability of linguistic features and the genetic biasing of tone

Language is a hallmark of our species and understanding linguistic diversity is an area of major interest. Genetic factors influencing the cultural transmission of language provide a powerful and elegant explanation for aspects of the present day linguistic diversity and a window into the emergence and evolution of language. In particular, it has recently been proposed that linguistic tone—the usage of voice pitch to convey lexical and grammatical meaning—is biased by two genes involved in brain growth and development, ASPM and Microcephalin. This hypothesis predicts that tone is a stable characteristic of language because of its ‘genetic anchoring’. The present paper tests this prediction using a Bayesian phylogenetic framework applied to a large set of linguistic features and language families, using multiple software implementations, data codings, stability estimations, linguistic classifications and outgroup choices. The results of these different methods and datasets show a large agreement, suggesting that this approach produces reliable estimates of the stability of linguistic data. Moreover, linguistic tone is found to be stable across methods and datasets, providing suggestive support for the hypothesis of genetic influences on its distribution.