中国濒危兽类保护遗传学研究进展与展望

我国是全球生物多样性大国,拥有包括大熊猫、金丝猴、华南虎、麋鹿、白鱀豚等特有物种和旗舰物种在内的丰富兽类资源。近几十年来,土地利用模式转变、盗猎、环境污染、气候变化等因素使许多兽类物种面临生存威胁,导致物种遗传多样性丧失。而遗传多样性是生物多样性的基本组成部分,决定了物种和种群能否长期生存。保护遗传学作为保护生物学的一大分支学科,旨在通过遗传学分析探明种群遗传变异和物种濒危的遗传学机制。近40年来,随着研究手段和技术的不断发展,我国兽类保护遗传学在遗传多样性和近交水平评估、景观遗传学、生态遗传学和圈养种群遗传管理等方面都取得了重要成果。然而,未来人类社会发展可能为濒危兽类带来的威胁依然存在,高通量测序等新技术的进一步发展则能够帮助我们更加深入地了解濒危物种和种群遗传适应与濒危机制,从而实现对濒危兽类的有效管理与保护。     

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。     

植物保护遗传学研究进展

保护遗传学是过用遗传学的原理和研究手段,以生物多样性尤其是遗传多样性的研究和保护为核心的一门新兴学科,近几十年来,遗传学研究在生物多样性保护的理论和实践中发挥着越来越重要的作用。本文简要回顾了保护遗传学的发展历史,研究方向和涉及的概念,着重介绍了植物保护遗传学研究所取得的一些进展,包括植物系统发育重建和保护单元的确定,遗传多样性与物种和群体适应性之间的关系,群体遗传结构与保护策略的制定以及植物遗传资源的鉴定和利用等方面的内容,并强调保护遗传学研究是未来生物多样性和保护生物学研究中一个亟待加强的研究领域。     

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。     

基于焦点物种的北京市生物保护安全格局规划

快速城市化进程中,城市及周边地区生物栖息地的丧失和破碎化对生物多样性保护构成严重威胁。如何在景观尺度上判别对于生物保护具有重要意义的栖息地及其空间格局成为了重要问题。选取快速城市化的典型地区——北京市作为研究对象,将焦点物种和景观安全格局方法相结合,对北京市生物保护安全格局进行判别和规划。根据焦点物种的定义和选取标准,将在栖息地类型、生物学特征等各方面具有代表性的大白鹭(Casmerodius albus)、绿头鸭(Anas platyrhynchos)、环颈雉(Phasianus colchicus)选作北京市的焦点物种,应用最小阻力模型和GIS空间分析技术,对焦点物种的栖息地适宜性进行分析,并建立物种运动的等阻力面,根据阻力面的空间特征对北京市生物保护安全格局进行规划。结果显示:规划后的安全格局的斑块数量、分离度、邻近距离显著下降,平均斑块面积和最大斑块指数显著上升;该格局用约60%的土地,保护了北京市主要生境类型及关键性空间格局,可有效缓解栖息地丧失和破碎化的问题,从而达到保护整体生物多样性的目的。这一研究方法和成果可为北京市生物多样性保护和城乡生态建设提供决策依据,也对同类研究具有借鉴价值。     

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。     

景观生态学与生物多样性保护

景观生态学的发展为生物多样性保护提供了新理论,方法和技术手段,从景观多样性与遗传多样性,物种多样性,生态系统多样性各层次生物多样性之间的相互关系及生物多样性保护的景观规划等方面评述近年来景观生态学应用于生物多样性保护的主要内容及研究进展,阐述了生物多样性动态及反馈,生物多样性保护的地理途径（ＧＡＰ分析）,景观生态安全格局,区域和大陆尺度的生态网络等一些新的概念和方法。     

基于物种分布的北京平原生态网络构建

高强度的城市化活动导致了生物栖息地破碎化、退化和消失,是生物多样性减少的主要原因。建立城市地区生态网络是保护生物多样性的重要途径。因其他物种数据可获得性差,以观测的典型鸟类群落为指示物种,探讨构建生态网络,可为城市生物多样性保护提供新思路。以北京市平原区为研究范围,重点基于86种鸟类分布大数据,通过Maxent模型掩膜生成栖息地源地并进行分级,在GIS技术的支撑下,以土地利用数据建立鸟类活动阻力面,采用最小累积阻力模型算法,模拟并形成了平原地区分级的生物多样性保护网络。研究结果表明,河湖湿地和城市公园组成了北京平原地区生态网络的优势景观类型,占平原区生态空间的81%。基于景观类型大小与物种数量的线性关系筛选出分级生物栖息地,其中一级生物栖息地58个,二级生物栖息地146个,通过模型模拟形成了平原地区生物多样性保护的一二级生态网络,共948条网络,长3760km。筛选出重要生态节点12处,关键生态廊道6条,是保护平原地区生物多样性的重要生态设施。该生态网络的实施对于提升首都平原区的生物多样性具有重要价值,研究结果可为国土生态空间优化提供重要科学依据和参考。     

栖息地片断化对动物种群间基因流的影响及其测定方法

栖息地片断化指在人为活动和自然干扰下,大面积连续分布的自然栖息地被其它非适宜栖息地分隔成许多面积较小的斑块(岛屿)的过程。栖息地片断化是导致生物多样性丧失和物种绝灭的主要因素,也是威胁自然界生物生存的重要因素之一。栖息地片断化将影响基因在种群间和种群内的运动(基因流),开展栖息地片断化研究对保持物种间遗传多样性具有重要意义。本文介绍了片断化程度和基因流的测定方法。回顾了国内外的研究现状,并探讨了片断化对种群间基因流的影响和对濒危物种保护的意义。     

生物多样性保护的景观规划途径

景观规划设计在生物多样性保护中起着决定性的作用。基于不同的保护哲学,生物多样性保护的景观规划途径主要可分为两种：一是以物种为核心的景观规划途径,另一种是以景观元素为核心和出发点的规划途径。前者首先确定物种,然后根据物种的生态特性来设计景观格局,后者则以各种尺度的景观元素作为保护对象,根据其空间位置和关系设计景观格局。多种空间战略被认为有利于生物多样性的保护,包括保护核心栖息地、建立缓冲区、构筑廊道、增加景观异质性和引入或恢复栖息地。落实这些空间战略必须首先回答选择什么和在什么地方设计上述景观元素的问题。对此,目前尚没有很好的答案。传统的生物保护战略被动地强调现存濒危物种和景观元素的保护,如果将物种运动和生态过程作为一个能动的景观控制过程来对待,我们将会有一种全新的景观规划途径。其中有三个方面的概念对这种新的景观规划途径有启发意义：即景观的空间构型对生态过程的作用,生物进化空间轨迹与景观格局设计及景观阻力与潜在的生态基础设施的设计。景观生态安全格局正是在这些方向上的一个新的探索。     

基于景观遗传学的滇金丝猴栖息地连接度分析

结合景观遗传学,应用最小费用距离模型对物种栖息地进行连接度分析,能够为生物多样性保护和自然保护区管理提供更加真实准确及可实践操作的指导。选取滇金丝猴这一珍稀濒危物种,结合景观遗传学,应用最小费用距离模型对其栖息地进行了连接度和潜在扩散廊道分析。并且通过连接度的分析和制图绘制出了更为准确的种群间潜在扩散廊道,确定了受人工障碍影响的廊道及敏感区域。结果表明,研究区内的5个亚群中,仅S3亚群内的5个猴群保持着较好的连接度,总体来说,各亚群内的连接度相对于各亚群间连接度保持的较好。除S3亚群中猴群间的潜在扩散廊道较为理想外,其余种群间的潜在扩散廊道均受人工斑块的影响,多数廊道被人工障碍阻断,或面临即将被阻断的情况,对于滇金丝猴的扩散交流影响较大。敏感区域多集中在中南部的3个亚群间,这些敏感区域应作为景观恢复及保护区规划的重要优先区域。     

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.     

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.     

A landscape genetics approach for quantifying the relative influence of historic and contemporary habitat heterogeneity on the genetic connectivity of a rainforest bird

Landscape genetics is an important framework for investigating the influence of spatial pattern on ecological process. Nevertheless, the standard analytic frameworks in landscape genetics have difficulty evaluating hypotheses about spatial processes in dynamic landscapes. We use a predictive hypothesis-driven approach to quantify the relative contribution of historic and contemporary processes to genetic connectivity. By confronting genetic data with models of historic and contemporary landscapes, we identify dispersal processes operating in naturally heterogeneous and human-altered systems. We demonstrate the approach using a case study of microsatellite polymorphism and indirect estimates of gene flow for a rainforest bird, the logrunner ( Orthonyx temminckii ). Of particular interest was how much information in the genetic data was attributable to processes occurring in the reconstructed historic landscape and contemporary human-modified landscape. A linear mixed model was used to estimate appropriate sampling variance from nonindependent data and information-theoretic model selection provided strength of evidence for alternative hypotheses. The contemporary landscape explained slightly more information in the genetic differentiation data than the historic landscape, and there was considerable evidence for a temporal shift in dispersal pattern. In contrast, migration rates estimated from genealogical information were primarily influenced by contemporary landscape change. We discovered that landscape heterogeneity facilitated gene flow before European settlement, but contemporary deforestation is rapidly becoming the most important barrier to logrunner dispersal.     

Choosing appropriate genetic markers and analytical methods for testing landscape genetic hypotheses

Landscape genetics and phylogeography both examine population‐level microevolutionary processes, such as population structure and gene flow, in the context of environmental and geographic variation. They differ in terms of the spatial and temporal scales they typically investigate, meaning that different genetic markers and analytical methods are better suited for testing the different hypotheses typically posed by each discipline. In a recent comment, Bohonak & Vandergast (2011) argue that I overlooked the value of mtDNA for landscape genetics in an article I published last year in Molecular Ecology (Wang 2010) and that a gap between landscape genetics and phylogeography, which I outlined, does not exist. Here, I clarify several points in my original article and summarize the commonly held viewpoint that different genetic markers are appropriate for drawing inferences at different temporal scales.     

Mapping the evolutionary twilight zone: molecular markers, populations and geography

Since evolutionary processes, such as dispersal, adaptation and drift, occur in a geographical context, at multiple hierarchical levels, biogeography provides a central and important unifying framework for understanding the patterns of distribution of life on Earth. However, the advent of molecular markers has allowed a clearer evaluation of the relationships between microevolutionary processes and patterns of genetic divergence among populations in geographical space, triggering the rapid development of many research programmes. Here we provide an overview of the interpretation of patterns of genetic diversity in geographical and ecological space, using both implicit and explicit spatial approaches. We discuss the actual or potential interaction of phylogeography, molecular ecology, ecological genetics, geographical genetics, landscape genetics and conservation genetics with biogeography, identifying their respective roles and their ability to deal with ecological and evolutionary processes at different levels of the biological hierarchy. We also discuss how each of these research programmes can improve strategies for biodiversity conservation. A unification of these research programmes is needed to better achieve their goals, and to do this it is important to develop cross‐disciplinary communication and collaborations among geneticists, ecologists, biogeographers and spatial statisticians.     

Isolation by distance,resistance and/or clusters? Lessons learned from a forest‐dwelling carnivore inhabiting a heterogeneous landscape

Landscape genetics provides a valuable framework to understand how landscape features influence gene flow and to disentangle the factors that lead to discrete and/or clinal population structure. Here, we attempt to differentiate between these processes in a forest‐dwelling small carnivore [European pine marten (Martes martes)]. Specifically, we used complementary analytical approaches to quantify the spatially explicit genetic structure and diversity and analyse patterns of gene flow for 140 individuals genotyped at 15 microsatellite loci. We first used spatially explicit and nonspatial Bayesian clustering algorithms to partition the sample into discrete clusters and evaluate hypotheses of ‘isolation by barriers’ (IBB). We further characterized the relationships between genetic distance and geographical (‘isolation by distance’, IBD) and ecological distances (‘isolation by resistance’, IBR) obtained from optimized landscape models. Using a reciprocal causal modelling approach, we competed the IBD, IBR and IBB hypotheses with each other to unravel factors driving population genetic structure. Additionally, we further assessed spatially explicit indices of genetic diversity using sGD across potentially overlapping genetic neighbourhoods that matched the inferred population structure. Our results revealed a complex spatial genetic cline that appears to be driven jointly by IBD and partial barriers to gene flow (IBB) associated with poor habitat and interspecific competition. Habitat loss and fragmentation, in synergy with past overharvesting and possible interspecific competition with sympatric stone marten (Martes foina), are likely the main factors responsible for the spatial genetic structure we observed. These results emphasize the need for a more thorough evaluation of discrete and clinal hypotheses governing gene flow in landscape genetic studies, and the potential influence of different limiting factors affecting genetic structure at different spatial scales.     

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.     

Applying landscape genetics to the microbial world

Landscape genetics, which explicitly quantifies landscape effects on gene flow and adaptation, has largely focused on macroorganisms, with little attention given to microorganisms. This is despite overwhelming evidence that microorganisms exhibit spatial genetic structuring in relation to environmental variables. The increasing accessibility of genomic data has opened up the opportunity for landscape genetics to embrace the world of microorganisms, which may be thought of as ‘the invisible regulators’ of the macroecological world. Recent developments in bioinformatics and increased data accessibility have accelerated our ability to identify microbial taxa and characterize their genetic diversity. However, the influence of the landscape matrix and dynamic environmental factors on microorganism genetic dispersal and adaptation has been little explored. Also, because many microorganisms coinhabit or codisperse with macroorganisms, landscape genomic approaches may improve insights into how micro‐ and macroorganisms reciprocally interact to create spatial genetic structure. Conducting landscape genetic analyses on microorganisms requires that we accommodate shifts in spatial and temporal scales, presenting new conceptual and methodological challenges not yet explored in ‘macro’‐landscape genetics. We argue that there is much value to be gained for microbial ecologists from embracing landscape genetic approaches. We provide a case for integrating landscape genetic methods into microecological studies and discuss specific considerations associated with the novel challenges this brings. We anticipate that microorganism landscape genetic studies will provide new insights into both micro‐ and macroecological processes and expand our knowledge of species’ distributions, adaptive mechanisms and species’ interactions in changing environments.