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。     

景观遗传学：概念与方法

全球变化下的物种栖息地丧失和破碎化给生物多样性保护带来了新的问题和挑战,生物多样性保护必须由单纯的物种保护上升到栖息地景观的保护。景观遗传学是定量确定栖息地景观特征对种群遗传结构影响的一门交叉学科,在生物保护及自然保护区管理方面有巨大的潜力。从生物多样性保护的角度评述了景观结构与遗传多样性的关系,介绍了景观遗传学的基本概念,研究尺度和方法,并对景观遗传学当前的研究焦点及面临的挑战做了总结。     

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。     

Life-history stage and the population genetics of the tiger mosquito Aedes albopictus at a fine spatial scale

As a widespread vector of disease with an expanding range, the mosquito Aedes albopictus Skuse (Diptera: Culicidae) is a high priority for research and management. A. albopictus has a complex life history with aquatic egg, larval and pupal stages, and a terrestrial adult stage. This requires targeted management strategies for each life stage, coordinated across time and space. Population genetics can aid in A. albopictus control by evaluating patterns of genetic diversity and dispersal. However, how life stage impacts population genetic characteristics is unknown. We examined whether patterns of A. albopictus genetic diversity and differentiation changed with life stage at a spatial scale relevant to management efforts. We first conducted a literature review of field-caught A. albopictus population genetic papers and identified 101 peer-reviewed publications, none of which compared results between life stages. Our study uniquely examines population genomic patterns of egg and adult A. albopictus at five sites in Wake County, North Carolina, USA, using 8425 single nucleotide polymorphisms. We found that the level of genetic diversity and connectivity between sites varied between adults and eggs. This warrants further study and is critical for research aimed at informing local management.     

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.     

Contribution of genetics to ecological restoration

Ecological restoration of degraded ecosystems has emerged as a critical tool in the fight to reverse and ameliorate the current loss of biodiversity and ecosystem services. Approaches derived from different genetic disciplines are extending the theoretical and applied frameworks on which ecological restoration is based. We performed a search of scientific articles and identified 160 articles that employed a genetic approach within a restoration context to shed light on the links between genetics and restoration. These articles were then classified on whether they examined association between genetics and fitness or the application of genetics in demographic studies, and on the way the studies informed restoration practice. Although genetic research in restoration is rapidly growing, we found that studies could make better use of the extensive toolbox developed by applied fields in genetics. Overall, 41% of reviewed studies used genetic information to evaluate or monitor restoration, and 59% provided genetic information to guide prerestoration decision‐making processes. Reviewed studies suggest that restoration practitioners often overlook the importance of including genetic aspects within their restoration goals. Even though there is a genetic basis influencing the provision of ecosystem services, few studies explored this relationship. We provide a view of research gaps, future directions and challenges in the genetics of restoration.     

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.     

Expert-based versus habitat-suitability models to develop resistance surfaces in landscape genetics

Landscape genetics aims to investigate functional connectivity among wild populations by evaluating the impact of landscape features on gene flow. Genetic distances among populations or individuals are generally better explained by least-cost path (LCP) distances derived from resistance surfaces than by simple Euclidean distances. Resistance surfaces reflect the cost for an organism to move through particular landscape elements. However, determining the effects of landscape types on movements is challenging. Because of a general lack of empirical data on movements, resistance surfaces mostly rely on expert knowledge. Habitat-suitability models potentially provide a more objective method to estimate resistance surfaces than expert opinions, but they have rarely been applied in landscape genetics so far. We compared LCP distances based on expert knowledge with LCP distances derived from habitat-suitability models to evaluate their performance in landscape genetics. We related all LCP distances to genetic distances in linear mixed effect models on an empirical data set of wolves (Canis lupus) from Italy. All LCP distances showed highly significant (P ≤ 0.0001) standardized β coefficients and R ² values, but LCPs from habitat-suitability models generally showed higher values than those resulting from expert knowledge. Moreover, all LCP distances better explained genetic distances than Euclidean distances, irrespective of the approaches used. Considering our results, we encourage researchers in landscape genetics to use resistance surfaces based on habitat suitability which performed better than expert-based LCPs in explaining patterns of gene flow and functional connectivity.     

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.     

Simulating natural selection in landscape genetics

Linking landscape effects to key evolutionary processes through individual organism movement and natural selection is essential to provide a foundation for evolutionary landscape genetics. Of particular importance is determining how spatially-explicit, individual-based models differ from classic population genetics and evolutionary ecology models based on ideal panmictic populations in an allopatric setting in their predictions of population structure and frequency of fixation of adaptive alleles. We explore initial applications of a spatially-explicit, individual-based evolutionary landscape genetics program that incorporates all factors--mutation, gene flow, genetic drift and selection--that affect the frequency of an allele in a population. We incorporate natural selection by imposing differential survival rates defined by local relative fitness values on a landscape. Selection coefficients thus can vary not only for genotypes, but also in space as functions of local environmental variability. This simulator enables coupling of gene flow (governed by resistance surfaces), with natural selection (governed by selection surfaces). We validate the individual-based simulations under Wright-Fisher assumptions. We show that under isolation-by-distance processes, there are deviations in the rate of change and equilibrium values of allele frequency. The program provides a valuable tool (cdpop v1.0; http://cel.dbs.umt.edu/software/CDPOP/) for the study of evolutionary landscape genetics that allows explicit evaluation of the interactions between gene flow and selection in complex landscapes.     

Statistical approaches in landscape genetics: an evaluation of methods for linking landscape and genetic data

The goal of landscape genetics is to detect and explain landscape effects on genetic diversity and structure. Despite the increasing popularity of landscape genetic approaches, the statistical methods for linking genetic and landscape data remain largely untested. This lack of method evaluation makes it difficult to compare studies utilizing different statistics, and compromises the future development and application of the field. To investigate the suitability and comparability of various statistical approaches used in landscape genetics, we simulated data sets corresponding to five landscape-genetic scenarios. We then analyzed these data with eleven methods, and compared the methods based on their statistical power, type-1 error rates, and their overall ability to lead researchers to accurate conclusions about landscape-genetic relationships. Results suggest that some of the most commonly applied techniques (e.g. Mantel and partial Mantel tests) have high type-1 error rates, and that multivariate, non-linear methods are better suited for landscape genetic data analysis. Furthermore, different methods generally show only moderate levels of agreement. Thus, analyzing a data set with only one method could yield method-dependent results, potentially leading to erroneous conclusions. Based on these findings, we give recommendations for choosing optimal combinations of statistical methods, and identify future research needs for landscape genetic data analyses.     

A meta‐analysis of isolation by distance: relic or reference standard for landscape genetics?

Isolation by distance (IBD) has been a common measure of genetic structure among populations and is based on Euclidean distances among populations. Whereas IBD does not incorporate geographic complexity (e.g. dispersal barriers, corridors) that may better predict genetic structure, a new approach (landscape genetics) joins landscape ecology with population genetics to better model genetic structure. Should IBD be set aside or should it persist as the most simple model in landscape genetics? We evaluated the status of IBD by collecting and analyzing results of 240 IBD data sets among diverse taxa and study systems. IBD typically represented a low proportion of variance in genetic structure (mean r²=0.22) in part because many studies included relatively few populations (mean=11). The number of populations studied (N) was asymptotically related to IBD significance; a study with 9 populations has only 50% probability of significance, while one with >23 populations will have 90% probability of significance. Surprisingly, ectothermic animals were significantly (p=0.0018) more likely to have significant IBD than endotherms, which suggests a metabolic basis underlying gene flow rates. We also observed marginally significant effects on IBD significance for a) taxa in general and b) dispersal modes within actively‐dispersing endotherms. Other factors analyzed (genetic markers, genetic distances, habitats, active or passive dispersal, plant growth form) did not significantly affect IBD, likely related to typical N. For multiple reasons we conclude that IBD should continue as the simplest reference standard against which all other, more complex models should be compared in landscape genetics research.     

基于文献计量法的中国景观连接度应用研究进展

景观连接度反映了景观对物种或某种生态过程在生境斑块之间运动的促进或阻碍作用,对维持景观格局及生态过程的完整性和连续性具有重要指示意义。在当前全球环境变化,自然生境丧失和破碎化不可避免的情况下,景观连接度的评价、模拟与应用已成为景观生态学、保护生物学等相关学科的研究热点。基于CNKI和Web of Science数据库,检索1999—2015年间国内发表的景观连接度期刊论文,通过文献计量法,分别从年发文量、来源期刊、研究机构、研究区域、研究目的、景观类型、景观尺度以及研究方法等多个视角分析国内该领域应用研究进展以及发展趋势。经检索共得到文献240篇,其中英文43篇。分析发现,近20年国内该领域年发文量总体呈显著增长趋势;《生态学报》、《生态学杂志》和Urban and Landscape Planning为主要刊载期刊;研究力量集中分布在北京、南京等东部18所科研机构,文献量占到43.75%;研究目标区域也以东部为主(52.2%),而对中西部生态敏感脆弱区研究较少;研究目的以景观规划为主,但针对物种保护的研究较弱,仅占11.25%,且主要关注大熊猫等被公众熟知的珍稀濒危物种;城市、城乡等人工景观是常见的景观研究类型,相比之下,自然景观所占比例较低;研究的空间尺度集中在5000 km2的中小尺度域,占72.5%,景观粒度以30 m的中等分辨率居多。识别出14种具体度量方法,常用的包括最小费用模型、基于图论原理的功能连接度指数和网络结构指数,以及景观格局指数等;关注功能性连接和多方法的综合应用已成为当前研究的趋势;度量方法的一些参数,如最小费用模型阻力赋值或距离阈值选择主要依据专家经验或已有文献,而针对目标物种采用实证研究的则较少。最后,对国内当前该领域的研究现状和不足展开讨论并展望了未来发展,以促进我国景观连接度后续研究。     

Small geographic range but not panmictic: how forests structure the endangered Point Arena mountain beaver (Aplodontia rufa nigra)

The landscape genetics framework is typically applied to broad regions that occupy only small portions of a species’ range. Rarely is the entire range of a taxon the subject of study. We examined the landscape genetic structure of the endangered Point Arena mountain beaver (Aplodontia rufa nigra), whose isolated geographic range is found in a restricted (85 km²) but heterogenous region in California. Based on its diminutive range we may predict widespread gene flow and a relatively weak role for landscape variation in defining genetic structure. We used skin, bone, tissue and noninvasively collected hair samples to describe genetic substructure and model gene flow. We examined spatial partitioning of multilocus DNA genotypes and mitochondrial DNA haplotypes. We identified 3 groups from microsatellite data, all of which had low estimates of effective population size consistent with significant tests for historical bottlenecks. We used least-cost-path analysis with the microsatellites to examine how vegetation type affects gene flow in a landscape genetics framework. Gene flow was best predicted by a model with “Forest” as the most permeable, followed by “Riparian”. Agricultural lands demonstrated the highest resistance. MtDNA data revealed only two haplotypes: one was represented in all 57 individuals that occurred north of the east–west flowing Garcia River. South of the river, however, both haplotypes occurred, often at the same site suggesting that the river may have affected historical patterns of genetic divergence.     

Landscape genetics of a specialized grasshopper inhabiting highly fragmented habitats: a role for spatial scale

Aim The study of geographical discontinuities in the distribution of genetic variability in natural populations is a central topic in both evolutionary and conservation research. In this study, we aimed to analyse (1) the factors associated with genetic diversity at the landscape spatial scale in the highly specialized grasshopper Mioscirtus wagneri and (2) to identify the relative contribution of alternative factors to the observed patterns of genetic structure in this species. Location La Mancha region, Central Spain. Methods We sampled 28 populations of the grasshopper M. wagneri and genotyped 648 individuals at seven microsatellite loci. We employed a causal modelling approach to identify the most influential variables associated with genetic differentiation within a multiple hypothesis‐testing framework. Results We found that genetic diversity differs among populations located in different river basins and decreases with population isolation. Causal modelling analyses showed variability in the relative influence of the studied landscape features across different spatial scales. When a highly isolated population is considered, the analyses suggested that geographical distance is the only factor explaining the genetic differentiation between populations. When that population is excluded, the causal modelling analysis revealed that elevation and river basins are also relevant factors contributing to explaining genetic differentiation between the studied populations. Main conclusions These results indicate that the spatial scale considered and the inclusion of outlier populations may have important consequences on the inferred contribution of alternative landscape factors on the patterns of genetic differentiation even when all populations are expected to similarly respond to landscape structure. Thus, a multiscale perspective should also be incorporated into the landscape genetics framework to avoid biased conclusions derived from the spatial scale analysed and/or the geographical distribution of the studied populations.     

Landscape genetic analyses reveal cryptic population structure and putative selection gradients in a large-scale estuarine environment

Disentangling the relative contributions of selective and neutral processes underlying phenotypic and genetic variation under natural, environmental conditions remains a central challenge in evolutionary ecology. However, much of the variation that could be informative in this area of research is likely to be cryptic in nature; thus, the identification of wild populations suitable for study may be problematic. We use a landscape genetics approach to identify such populations of three-spined stickleback inhabiting the Saint Lawrence River estuary. We sampled 1865 adult fish over multiple years. Individuals were genotyped for nine microsatellite loci, and georeferenced multilocus data were used to infer population groupings, as well as locations of genetic discontinuities, under a Bayesian model framework ( geneland ). We modelled environmental data using nonparametric multiple regression to explain genetic differentiation as a function of spatio-ecological effects. Additionally, we used genotype data to estimate dispersal and gene flow to parameterize a simple model predicting adaptive vs. plastic divergence between demes. We demonstrate a bipartite division of the genetic landscape into freshwater and maritime zones, independent of geographical distance. Moreover, we show that the greatest proportion of genetic variation (31.5%) is explained by environmental differences. However, the potential for either adaptive or plastic divergence between demes is highly dependent upon the strength of migration and selection. Consequently, we highlight the utility of landscape genetics as a tool for hypothesis generation and experimental design, to identify focal populations and putative selection gradients, in order to distinguish between phenotypic plasticity and local adaptation.     

电路理论在植物景观遗传学研究中的应用潜力分析

景观遗传学的迅速发展为研究异质性系统中的进化生物学问题提供了新颖的理论和方法。本文以电路理论(circuit theory)在植物景观遗传研究中的应用为主题,系统解析理论框架,明确其核心概念和生态学过程间的映射关系,从研究主题、方法和模型验证3方面综述近十年的相关文献,并在此基础上归纳了该理论的优势和局限性。其优势主要为:(1)可对多条潜在传播路径进行通盘考察并予以比较,有助于鉴别影响连接度的关键区域或廊道;(2)对哈迪-温伯格平衡假设和栅格大小不敏感,保证了模型的适应性;(3)模型要求物种对生境无明显经验积累,特别适合分布区形状不规则,以及连续广泛分布的物种,与许多植物的生物学特点相契合。该理论要求物种具有随机漫游和传播各向同性2个前提条件,限制了生物媒介类植物的适用度。在传播过程具有方向偏好的生态系统中,其应用也需慎重考虑。结合未来发展趋势,本文认为电路理论在植物景观遗传学研究中具有很好的潜力,但还需要依据具体的研究问题、物种习性、空间尺度和系统性质来进行科学的应用。     

Regression commonality analyses on hierarchical genetic distances

Landscape genetics is emerging as an important way of supporting decision‐making in landscape management, in response to the deterioration of matrix permeability due to habitat loss and fragmentation. In line with unremitting methodological developments in landscape genetics, a new analytical procedure was recently proposed as a way of evaluating the effects of landscape gradients on genetic structures. This procedure is based on the computation of inter‐individual hierarchical genetic distances (HGD), a metric of genetic differentiation taking into account the hierarchical structure in populations as inferred from clustering algorithms. HGD can be used as dependent variables in multivariate regressions to assess the effects of various landscape predictors on spatial patterns of genetic differentiation. However, multicollinearity may obscure the interpretation of multivariate regressions. We illustrate how regression commonality analyses (CA), a detailed variance partitioning procedure that can be used to deal with multicollinearity issues, can thoroughly improve our understanding of landscape connectivity when HGD are used as a dependent variable, with the red deer Cervus elaphus as an empirical example. Using logistic regression commonality analyses on HGD, we showed that semi‐natural open areas, transportation infrastructures and, to a lesser extent, urban areas and rivers, were associated with an increase in hierarchical genetic differentiation in red deer. Regressions based on HGD provided detailed results that could not have been obtained with regressions based on standard genetic distances, with notably additional insights as to the possible influence of linear features such as roads and highways on landscape connectivity. Furthermore, CA helped identify synergistic associations among variables as well as suppressors, thus resolving inconsistencies among hierarchical levels and revealing spurious correlations that may have gone unnoticed in the course of classical regression analyses. We thus recommend the use of regression commonality analysis on hierarchical genetic distances as a promising statistical tool for landscape geneticists.     

Contribution of A.S. Serebrovski? to population genetics]

The article commemorates Professor Alexander Sergeevich Serebrovsky (1892-1948), a prominent Russian geneticist, the founder of the Department of Genetics of Moscow University. A thorough analysis of his publications written in the 20s and 30s demonstrates that A.S. Serebrovsky: 1) developed the principles of such original fields of population genetics as gene geography and the theory of gene pools; 2) specified the significance of random genetic drift for neutral evolution in small populations; 3) showed that subdivision of an isolated population is essential for long-term conservation of its genetic composition. A.S. Serebrovsky's contribution into population genetics is viewed as an achievement of the Russian scientific school, always distinguished by historic and geographical approach to natural phenomena and processes.     

Landscape genetic inferences vary with sampling scenario for a pond‐breeding amphibian

A critical decision in landscape genetic studies is whether to use individuals or populations as the sampling unit. This decision affects the time and cost of sampling and may affect ecological inference. We analyzed 334 Columbia spotted frogs at 8 microsatellite loci across 40 sites in northern Idaho to determine how inferences from landscape genetic analyses would vary with sampling design. At all sites, we compared a proportion available sampling scheme (PASS), in which all samples were used, to resampled datasets of 2–11 individuals. Additionally, we compared a population sampling scheme (PSS) to an individual sampling scheme (ISS) at 18 sites with sufficient sample size. We applied an information theoretic approach with both restricted maximum likelihood and maximum likelihood estimation to evaluate competing landscape resistance hypotheses. We found that PSS supported low‐density forest when restricted maximum likelihood was used, but a combination model of most variables when maximum likelihood was used. We also saw variations when AIC was used compared to BIC. ISS supported this model as well as additional models when testing hypotheses of land cover types that create the greatest resistance to gene flow for Columbia spotted frogs. Increased sampling density and study extent, seen by comparing PSS to PASS, showed a change in model support. As number of individuals increased, model support converged at 7–9 individuals for ISS to PSS. ISS may be useful to increase study extent and sampling density, but may lack power to provide strong support for the correct model with microsatellite datasets. Our results highlight the importance of additional research on sampling design effects on landscape genetics inference.