生境片断化对植物种群遗传结构的影响及植物遗传多样性保护

生境片断化是指大而连续的生境变成空间隔离的小种群的现象。生境片断化对植物种群遗传效应包括生境片断化过程中的取样效应及其后的小种群效应（遗传漂变、近交等）。理论研究表明,生境片断化后,植物种群的遗传变异程度将降低,而残留的小种群间的遗传分化程度将升高。然而对一些植物的研究表明,生境片断化对植物种群的遗传效应要受其他一些因素的影响,如世代长度、片断化时间、片断种群的大小、基因流的改变等。最后,针对生境     

景观生态学:两栖动物生态学研究的新途径

全球两栖动物正以远超过自然灭绝的高速率灭绝,这与生境丧失和景观破碎化有着直接关系。生境丧失导致两栖动物的生存空间减少,使局部种群消失,而景观破碎化则导致两栖动物种群之间的隔离度增加,不利于动物的繁殖和扩散。但两者往往是同时出现,相互作用。复合种群、景观连接度、景观遗传学及景观模型模拟等理论和方法的发展,为在生境丧失与破碎化景观下两栖动物的种群结构、组成和动态变化研究提供了理论基础和技术方法。同时景观生态学中特别重视研究的尺度,生境破碎化是发生在景观尺度下的生境变化过程,因此对生境破碎化的影响应该从现有的主要集中在斑块尺度和斑块-景观尺度转变到景观尺度上来。     

不仅仅是遗传多样性:植物保护遗传学进展

保护遗传学研究的是影响物种灭绝的遗传因素以及濒危物种的遗传管理, 以降低物种的灭绝风险。本文从遗传多样性本身及其对生态系统的影响两个方面介绍了植物保护遗传学的最新进展。根据遗传标记的功能, 保护遗传学研究可分为选择中性遗传变异研究和适应性遗传变异两个方面。对于目前主要采用的选择中性遗传标记研究, 本文着重介绍了以下方面的最新进展: (1)利用遗传标记进行个体、物种或遗传单元的鉴定, 从而有效地设计保护策略, 避免在迁地保护中混淆物种, 提高保护效率; (2)应重视由于物种自身生殖、扩散等原因造成的隐性瓶颈效应。由于选择中性遗传标记并不能准确反映物种的适应性遗传基础, 从适应性遗传变异角度研究濒危物种的进化潜力已成为保护遗传学的研究前沿。大部分相关研究还集中在利用基因组扫描检测受选择的位点, 而对功能基因的适应性研究还比较少。景观遗传学旨在解释景观和生境影响下的种群间基因流和遗传多样性格局, 这方面研究将会促进我们更多了解种群基因流的地理限制因子和不同景观基质下的种群遗传差异。遗传多样性作为物种的一种属性亦可在一定程度上反馈, 并影响生态系统。这提示我们不仅仅是濒危物种, 常见物种的遗传多样性及其保护亦很重要。最后, 我们从4个方面对保护遗传学研究进行了展望, 包括应加强将生态系统各环节联系起来研究遗传多样性, 在技术手段上利用多态性更丰富的分子标记, 同时强调了对常见物种保护遗传学研究的重要性, 并初步分析了我国保护遗传学研究与国际水平的差距, 建议加强种群遗传学和进化生物学基础理论的学习。     

生境破碎化对动物种群存活的影响

生境破碎是生物多样性下降的主要原因之一。通常以岛屿生物地理学、异质种群生物学和景观生态学的理论来解释不同空间尺度中生境破碎化的生态学效应。生境破碎化引起面积效应、隔离效应和边缘效应。这些效应通过影响动物种群的绝灭阈值、分布和多度、种间关系以及生态系统过程,最终影响动物种群的存活。野外研究表明,破碎化对动物的影响,因物种、生境类型和地理区域不同而有所变化,因此,预测物种在破碎生境中的存活比较困难。研究热点集中于：确定生境面积损失和生境斑块的空间格局对破碎景观中物种绝灭的相对影响,破碎景观中物种的适宜生境比例和绝灭阈值,异质种群动态以及生态系统的生态过程。随着3S技术的发展,生境破碎化模型趋于复杂,而发展有效的模型和验证模型将成为一项富有挑战性的任务。     

鸟类栖息地片段化研究的理论基础

栖息地片段化是导致许多森林鸟类种群下降的主要原因之一,而对栖息地片段化的形成及其影响的研究已是成为鸟类生态学的研究热点之一。介绍了鸟类栖息地片段化研究的理论基础,即岛屿生物地理学理论、景观生态学理论以及集合种群理论等,并阐述了鸟类栖息地片段化研究范式转变的原因。     

生境破碎化对生物多样性的影响研究综述

生境破碎化与生物多样性的关系是理论生态学的研究热点。本文在综述国内外相关研究内容的基础上,阐述了生境破碎化的概念内涵与度量,介绍了生境破碎化研究的主要理论基础、研究内容与研究进展;总结了目前生境破碎化研究存在度量破碎景观格局的方法尚未统一、研究方法有待精确以及生境破碎化与生物多样性的阈值效应尚未发现等问题。今后,半干旱地区生境破碎化对生物多样性影响的研究需要加强,空间分析理论和方法将会在生境破碎化对生物多样性影响的研究中得到更多应用。     

基于边界特征的山地森林景观碎裂化研究

景观边界特征与景观碎裂化过程之间的相互关系研究已经引起了广泛的关注。利用 1 987年和 1 997年两个时段遥感景观资料 ,编制了卧龙自然保护区的景观类型图 ,以验证景观碎裂化过程导致边界数量 ,特别是短边界数量增加这一假说。研究工作包括 4个方面的内容 :(1 )利用每年的 2 0个 2 0 0× 2 0 0像元的正方形样地 ,分析工作区内景观边界的一般特征 ;(2 )对每个样地边界数量的长度谱分布特征进行拟合 ,确定能够反映边界属性特征的拟合方程参数 ;(3 )将边界属性特征参数与样地的景观碎裂化指数进行比较研究 ,建立景观边界特征与景观碎裂化程度之间的量化关系模型 ;(4 )利用每个时段的 1 0个验证样地来检验关系模型的有效性。研究结果表明 ,所有样地边界数量的长度谱分布特征可以使用对数方程 y=bln(x) +c进行拟合。景观的碎裂化过程将同时导致拟合方程参数 b的绝对值和 c值线性增加 ,景观碎裂化水平的增加将导致景观边界数量和短边界数量均呈指数增长方式。从研究的结果中还可以推断出 ,生境碎裂化水平对于生物多样性的影响也将呈现出明显的放大效应。     

山地景观遗传学研究文献综述

山地生态系统是生物多样性分布与保护的热点。山地景观遗传学（Mountain Landscape Genetics）研究在山地景观尺度上野生生物的种群遗传格局及其驱动机制和影响因素,是景观遗传学（Landscape Genetics）的重要分支。山地景观遗传学研究对于深入理解物种的空间遗传结构、形成过程、物种形成与分化机制具有重要意义与价值,同时可以为珍稀濒危物种和山地生物多样性的有效保护与管理提供科学指导。为了更好地掌握目前山地景观遗传学的发展趋势与重点研究问题,为未来生物多样性与山地生态系统的保护管理提供科学参考,基于对Web of Science核心数据库和中国知网数据库的系统检索,全面汇总分析了1999-2020年山地景观遗传学领域发表的192篇英文文献与31篇中文文献。结果显示,该领域自2008年起迅速发展,截至2020年共有46个国家的研究机构发表了山地景观遗传相关研究,研究热点地区包括北美洲的落基山脉、内华达山脉、阿巴拉契亚山脉,欧洲的阿尔卑斯山脉、比利牛斯山脉,以及亚洲的喜马拉雅-横断山脉。研究对象类群涵盖真菌、植物、节肢动物、脊椎动物,其中脊椎动物是研究发表最多的类群,占发表文献总数的62.0%;脊椎动物中,又以对哺乳类（占脊椎动物发表文献总数的52.9%）与两栖类（23.5%）的研究最多。目前主要的研究方向包括：（1）识别山地景观中的基因流路径或阻碍;（2）量化山地景观特征对种群遗传结构时空变化的影响。中国是发表山地景观遗传学文章数量最多的亚洲国家,近十年来相关研究发展迅速,研究类群以植物（占在中国发表文献总数的62.3%）与脊椎动物（35.8%）为主,对脊椎动物的研究中以两栖动物为最多（占所有脊椎动物发文数量的52.6%）,研究区域主要集中在喜马拉雅-横断山脉与秦岭。本文进一步对目前山地景观遗传学研究中存在的空缺及未来重点关注问题提出建议。     

植物景观遗传学研究进展

植物景观遗传学是新兴的景观遗传学交叉学科的一个重要研究方向。目前植物景观遗传学的研究虽落后于动物,但其在生物多样性保护方面具有的巨大潜力不可忽视。从景观特征对遗传结构、环境因素对适应性遗传变异影响两个方面,系统综述了近十年来国际上植物景观遗传学的研究焦点和研究进展,比较了植物景观遗传学与动物景观遗传学研究在研究设计和研究方法上的异同,并基于将来植物景观遗传学由对空间遗传结构的描述发展为对景观遗传效应的量化分析及预测的发展框架,具体针对目前景观特征与遗传结构研究设计的系统性差、遗传结构与景观格局在时间上的误配、适应性位点与环境变量的模糊匹配、中性遗传变异与适应性遗传变异研究的分隔、景观与遗传关系分析方法的局限等五个方面提出了研究对策。     

黄河三角洲地区人类活动对景观结构的影响分析

景观生态学是研究由不同生态系统所组成的景观的空间结构、相互作用、功能及动态变化。研究人类活动的区域差异对景观结构的影响,探讨人类活动的强弱对生物生境和资源分布格局的干扰,成为景观生态学研究的一个重要方面。本文以我国黄河三角洲地区东营市为研究区域,通过选取景观多样性、优势度、景观破碎度和景观分离度作为评价指标,分析了该区人类活动和景观结构之间的关系。研究表明,人类活动对景观具有显著的影响,主要表现在     

Putting the "landscape" in landscape genetics

Landscape genetics has emerged as a new research area that integrates population genetics, landscape ecology and spatial statistics. Researchers in this field can combine the high resolution of genetic markers with spatial data and a variety of statistical methods to evaluate the role that landscape variables play in shaping genetic diversity and population structure. While interest in this research area is growing rapidly, our ability to fully utilize landscape data, test explicit hypotheses and truly integrate these diverse disciplines has lagged behind. Part of the current challenge in the development of the field of landscape genetics is bridging the communication and knowledge gap between these highly specific and technical disciplines. The goal of this review is to help bridge this gap by exposing geneticists to terminology, sampling methods and analysis techniques widely used in landscape ecology and spatial statistics but rarely addressed in the genetics literature. We offer a definition for the term "landscape genetics", provide an overview of the landscape genetics literature, give guidelines for appropriate sampling design and useful analysis techniques, and discuss future directions in the field. We hope, this review will stimulate increased dialog and enhance interdisciplinary collaborations advancing this exciting new field.     

EcoGenetics: An R package for the management and exploratory analysis of spatial data in landscape genetics

The integration of ecology and genetics has become established in recent decades, in hand with the development of new technologies, whose implementation is allowing an improvement of the tools used for data analysis. In a landscape genetics context, integrative management of population information from different sources can make spatial studies involving phenotypic, genotypic and environmental data simpler, more accessible and faster. Tools for exploratory analysis of autocorrelation can help to uncover the spatial genetic structure of populations and generate appropriate hypotheses in searching for possible causes and consequences of their spatial processes. This study presents EcoGenetics, an R package with tools for multisource management and exploratory analysis in landscape genetics.     

Representing genetic variation as continuous surfaces: an approach for identifying spatial dependency in landscape genetic studies

Landscape genetics, an emerging field integrating landscape ecology and population genetics, has great potential to influence our understanding of habitat connectivity and distribution of organisms. Whereas typical population genetics studies summarize gene flow as pairwise measures between sampling localities, landscape characteristics that influence population genetic connectivity are often continuously distributed in space. Thus, there are currently gaps in both the ability to analyze genotypic data in a continuous spatial context and our knowledge of expected of landscape genetic structure under varying conditions. We present a framework for generating continuous “genetic surfaces”, evaluate their statistical properties, and quantify statistical behavior of landscape genetic structure in a simple landscape. We simulated microsatellite genotypes under varying parameters (time since vicariance, migration, effective population size) and used ancestry (q) values from STRUCTURE to interpolate a genetic surface. Using a spatially adjusted Pearson's correlation coefficient to test the significance of landscape variable(s) on genetic structure we were able to detect landscape genetic structure on a contemporary time scale (≥5 generations post vicariance, migration probability ≤0.10) even when population differentiation was minimal (F_ST≥0.00015). We show that genetic variation can be significantly correlated with geographic distance even when genetic structure is due to landscape variable(s), demonstrating the importance of testing landscape influence on genetic structure. Finally, we apply genetic surfacing to analyze an empirical dataset of black bears from northern Idaho USA. We find black bear genetic variation is a function of distance (autocorrelation) and habitat patch (spatial dependency), consistent with previous results indicating genetic variation was influenced by landscape by resistance. These results suggest genetic surfaces can be used to test competing hypotheses of the influence of landscape characteristics on genetic structure without delineation of categorical groups.     

Simulation modelling in landscape genetics: on the need to go further

With the emergence of landscape genetics, the basic assumptions and predictions of classical population genetic theories are being re‐evaluated to account for more complex spatial and temporal dynamics. Within the last decade, there has been an exponential increase in such landscape genetic studies ( Holderegger & Wagner 2006 ; Storfer et al. 2010 ), and both methodology and underlying concepts of the field are under rapid and constant development. A number of major innovations and a high level of originality are required to fully merge existing population genetic theory with landscape ecology and to develop novel statistical approaches for measuring and predicting genetic patterns. The importance of simulation studies for this specific research has been emphasized in a number of recent articles (e.g., Balkenhol et al. 2009a ; Epperson et al. 2010 ). Indeed, many of the major questions in landscape genetics require the development and application of sophisticated simulation tools to explore gene flow, genetic drift, mutation and natural selection in landscapes with a wide range of spatial and temporal complexities. In this issue, Jaquiéry et al. (2011) provide an excellent example of such a simulation study for landscape genetics. Using a metapopulation simulation design and a novel ‘scale of phenomena’ approach, Jaquiéry et al. (2011) demonstrate the utility and limitations of genetic distances for inferring landscape effects on effective dispersal.     

Landscape and ecological genomics

Landscape genomics is the modern version of landscape genetics, a discipline that arose approximately 10 years ago as a combination of population genetics, landscape ecology, and spatial statistics. It studies the effects of landscape variables on gene flow and other microevolutionary processes that determine genetic connectivity and variations in populations. In contrast to population genetics, it operates at the level of individual specimens rather than at the level of population samples. Another important difference between landscape genetics and genomics and population genetics is that, in the former, the analysis of gene flow and local adaptations takes quantitative account of landforms and features of the matrix, i.e., hostile spaces that separate species habitats. Landscape genomics is a part of population ecogenomics, which, along with community genomics, is a major part of ecological genomics. One of the principal purposes of landscape genomics is the identification and differentiation of various genome-wide and locus-specific effects. The approaches and computation tools developed for combined analysis of genomic and landscape variables make it possible to detect adaptation-related genome fragments, which facilitates the planning of conservation efforts and the prediction of species’ fate in response to expected changes in the environment.     

Habitat geometry, population viscosity and the rate of genetic drift

In all natural populations, individuals located close to one another tend to interact more than those further apart. The extent of population viscosity can have important implications for ecological and evolutionary processes. Here we develop a spatially explicit population model to examine how the rate of genetic drift depends upon both spatial population structure and habitat geometry. The results show that the time to fixation for a new and selectively neutral mutation is dramatically increased in viscous populations. Furthermore, in viscous populations the time to fixation depends critically on habitat geometry. Fixation time for populations of identical size increases markedly as landscape width decreases and length increases. We suggest that similar effects will also be important in metapopulations, with the spatial arrangement of subpopulations and their connectivity likely to determine the rate of drift. We argue that the recent increases in computer power should facilitate major advances in our understanding of evolutionary landscape ecology over the next few years, and suggest that the time is ripe for a unification of spatial population dynamics theory, landscape ecology and population genetics.     

Landscape genetics of plants

Landscape genetics is the amalgamation of landscape ecology and population genetics to help with understanding microevolutionary processes such as gene flow and adaptation. In this review, we examine why landscape genetics of plants lags behind that of animals, both in number of studies and consideration of landscape elements. The classical landscape distance/resistance approach to study gene flow is challenging in plants, whereas boundary detection and the assessment of contemporary gene flow are more feasible. By contrast, the new field of landscape genetics of adaptive genetic variation, establishing the relationship between adaptive genomic regions and environmental factors in natural populations, is prominent in plant studies. Landscape genetics is ideally suited to study processes such as migration and adaptation under global change.     

A conceptual framework for the spatial analysis of landscape genetic data

Understanding how landscape heterogeneity constrains gene flow and the spread of adaptive genetic variation is important for biological conservation given current global change. However, the integration of population genetics, landscape ecology and spatial statistics remains an interdisciplinary challenge at the levels of concepts and methods. We present a conceptual framework to relate the spatial distribution of genetic variation to the processes of gene flow and adaptation as regulated by spatial heterogeneity of the environment, while explicitly considering the spatial and temporal dynamics of landscapes, organisms and their genes. When selecting the appropriate analytical methods, it is necessary to consider the effects of multiple processes and the nature of population genetic data. Our framework relates key landscape genetics questions to four levels of analysis: (i) node-based methods, which model the spatial distribution of alleles at sampling locations (nodes) from local site characteristics; these methods are suitable for modeling adaptive genetic variation while accounting for the presence of spatial autocorrelation. (ii) Link-based methods, which model the probability of gene flow between two patches (link) and relate neutral molecular marker data to landscape heterogeneity; these methods are suitable for modeling neutral genetic variation but are subject to inferential problems, which may be alleviated by reducing links based on a network model of the population. (iii) Neighborhood-based methods, which model the connectivity of a focal patch with all other patches in its local neighborhood; these methods provide a link to metapopulation theory and landscape connectivity modeling and may allow the integration of node- and link-based information, but applications in landscape genetics are still limited. (iv) Boundary-based methods, which delineate genetically homogeneous populations and infer the location of genetic boundaries; these methods are suitable for testing for barrier effects of landscape features in a hypothesis-testing framework. We conclude that the power to detect the effect of landscape heterogeneity on the spatial distribution of genetic variation can be increased by explicit consideration of underlying assumptions and choice of an appropriate analytical approach depending on the research question.     

Thirty‐five years of spatial autocorrelation analysis in population genetics: an essay in honour of Robert Sokal (1926–2012)

We could not start this review, literally from the beginning, without expressing our sadness over the passing of Professor Robert R. Sokal. We are sure, nevertheless, that the importance of his scientific achievements will ensure he is long remembered. In this modest tribute to Professor Sokal, we highlight his contributions to the field of population genetics and spatial statistical methods. Specifically, we discuss how two papers, co‐authored with Professor N. L. Oden and published in the pages of the Biological Journal of the Linnean Society in 1978, revolutionized the field of analytical population genetics. In these papers, Sokal and Oden created an elegant framework for inferring evolutionary processes (e.g. isolation‐by‐distance, demic diffusion, selection gradients, genetic drift) from the spatial autocorrelation analysis of genetic variation patterns. We also highlight the pivotal importance of Sokal's work to the development of emerging fields (e.g. landscape and conservation genetics). We hope this virtual issue containing the papers that Professor Sokal published in BJLS, and later, related papers by other researchers, will help to remember his work and maintain his legacy of spatial analysis in genetics, ecology, and evolutionary biology. © 2012 The Linnean Society of London, Biological Journal of the Linnean Society, 2012, ??, ??–??.     

Navigating the pitfalls and promise of landscape genetics

The field of landscape genetics has been evolving rapidly since its emergence in the early 2000s. New applications, techniques and criticisms of techniques appear like clockwork with each new journal issue. The developments are an encouraging, and at times bewildering, sign of progress in an exciting new field of study. However, we suggest that the rapid expansion of landscape genetics has belied important flaws in the development of the field, and we add an air of caution to this breakneck pace of expansion. Specifically, landscape genetic studies often lose sight of the fundamental principles and complex consequences of gene flow, instead favouring simplistic interpretations and broad inferences not necessarily warranted by the data. Here, we describe common pitfalls that characterize such studies, and provide practical guidance to improve landscape genetic investigation, with careful consideration of inferential limits, scale, replication, and the ecological and evolutionary context of spatial genetic patterns. Ultimately, the utility of landscape genetics will depend on translating the relationship between gene flow and landscape features into an understanding of long‐term population outcomes. We hope the perspective presented here will steer landscape genetics down a more scientifically sound and productive path, garnering a field that is as informative in the future as it is popular now.