Putting the genes into community genetics

As part of the long‐term fusion of evolutionary biology and ecology (Ford, 1964), the field of community genetics has made tremendous progress in describing the impacts of plant genetic variation on community and ecosystem processes. In the “genes‐to‐ecosystems” framework (Whitham et al., 2003), genetically based traits of plant species have ecological consequences, but previous studies have not identified specific plant genes responsible for community phenotypes. The study by Barker et al. (2019) in this issue of Molecular Ecology uses an impressive common garden experiment of trembling aspen (Figure 1) to test for the genetic basis of tree traits that shape the insect community composition. Using a Genome‐Wide Association Study (GWAS), they found that genomic regions associated with phytochemical traits best explain variation in herbivore community composition, and identified specific genes associated with different types of leaf‐modifying herbivores and ants. This is one of the first studies to identify candidate genes underlying the heritable plant traits that explain patterns of insect biodiversity.     

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。     

Sample design effects in landscape genetics

An important research gap in landscape genetics is the impact of different field sampling designs on the ability to detect the effects of landscape pattern on gene flow. We evaluated how five different sampling regimes (random, linear, systematic, cluster, and single study site) affected the probability of correctly identifying the generating landscape process of population structure. Sampling regimes were chosen to represent a suite of designs common in field studies. We used genetic data generated from a spatially-explicit, individual-based program and simulated gene flow in a continuous population across a landscape with gradual spatial changes in resistance to movement. Additionally, we evaluated the sampling regimes using realistic and obtainable number of loci (10 and 20), number of alleles per locus (5 and 10), number of individuals sampled (10–300), and generational time after the landscape was introduced (20 and 400). For a simulated continuously distributed species, we found that random, linear, and systematic sampling regimes performed well with high sample sizes (>200), levels of polymorphism (10 alleles per locus), and number of molecular markers (20). The cluster and single study site sampling regimes were not able to correctly identify the generating process under any conditions and thus, are not advisable strategies for scenarios similar to our simulations. Our research emphasizes the importance of sampling data at ecologically appropriate spatial and temporal scales and suggests careful consideration for sampling near landscape components that are likely to most influence the genetic structure of the species. In addition, simulating sampling designs a priori could help guide filed data collection efforts     

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.     

Perspectives and challenges in landscape genetics

A recent workshop held at the University of Grenoble gathered the leading experts in the field of landscape genetics and spatial statistics. Landscape genetics was only recently defined as an independent research field. It aims to understand the processes of gene flow and local adaptation by studying the interactions between genetic and spatial or environmental variation. This workshop discussed the perspectives and challenges of combining emerging molecular, spatial and statistical tools to unravel how landscape and environmental variables affect genetic variation.     

On applications of landscape genetics

Recent assertions in the literature (e.g., Keller et al. 2015) suggest that landscape genetic research has been infrequently applied by practitioners. We were interested to test this assertion, which is difficult to assess, since applications may not be detectable through searches of peer-reviewed literature. Producing publications may not be a goal of practitioners. We developed a method to search the internet for evidence of research applications and evaluated 25 different research fields in the natural sciences. We found that fields with more publications also had more applications, but the field of landscape genetics was less applied than expected based on the number of peer-reviewed publications—only about 4 % of landscape genetics articles were applied. In fact, all research fields in genetics or evolutionary biology were under-applied compared to ‘whole organism’, ecological research fields. This result suggests the lack of applications in landscape genetics may be due to a systemic under-application of genetics research, perhaps related to a lack of understanding of genetics by practitioners. We did find some evidence of landscape genetic applications however, which we sorted into 5 categories: (1) identification of evolutionarily significant units for conservation, (2) managing pathogens and invasive species, (3) natural heritage systems planning, (4) assessing population status, and (5) restoration of populations.     

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.     

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.     

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。     

Quantifying the lag time to detect barriers in landscape genetics

Understanding how spatial genetic patterns respond to landscape change is crucial for advancing the emerging field of landscape genetics. We quantified the number of generations for new landscape barrier signatures to become detectable and for old signatures to disappear after barrier removal. We used spatially explicit, individual‐based simulations to examine the ability of an individual‐based statistic [Mantel’s r using the proportion of shared alleles’ statistic (Dps)] and population‐based statistic (F_ST) to detect barriers. We simulated a range of movement strategies including nearest neighbour dispersal, long‐distance dispersal and panmixia. The lag time for the signal of a new barrier to become established is short using Mantel’s r (1–15 generations). F_ST required approximately 200 generations to reach 50% of its equilibrium maximum, although G’_ST performed much like Mantel’s r. In strong contrast, F_ST and Mantel’s r perform similarly following the removal of a barrier formerly dividing a population. Also, given neighbour mating and very short‐distance dispersal strategies, historical discontinuities from more than 100 generations ago might still be detectable with either method. This suggests that historical events and landscapes could have long‐term effects that confound inferences about the impacts of current landscape features on gene flow for species with very little long‐distance dispersal. Nonetheless, populations of organisms with relatively large dispersal distances will lose the signal of a former barrier within less than 15 generations, suggesting that individual‐based landscape genetic approaches can improve our ability to measure effects of existing landscape features on genetic structure and connectivity.     

Isolation-by-distance in landscapes: considerations for landscape genetics

In landscape genetics, isolation-by-distance (IBD) is regarded as a baseline pattern that is obtained without additional effects of landscape elements on gene flow. However, the configuration of suitable habitat patches determines deme topology, which in turn should affect rates of gene flow. IBD patterns can be characterized either by monotonically increasing pairwise genetic differentiation (for example, F_ST) with increasing interdeme geographic distance (case-I pattern) or by monotonically increasing pairwise genetic differentiation up to a certain geographical distance beyond which no correlation is detectable anymore (case-IV pattern). We investigated if landscape configuration influenced the rate at which a case-IV pattern changed to a case-I pattern. We also determined at what interdeme distance the highest correlation was measured between genetic differentiation and geographic distance and whether this distance corresponded to the maximum migration distance. We set up a population genetic simulation study and assessed the development of IBD patterns for several habitat configurations and maximum migration distances. We show that the rate and likelihood of the transition of case-IV to case-I F_ST–distance relationships was strongly influenced by habitat configuration and maximum migration distance. We also found that the maximum correlation between genetic differentiation and geographic distance was not related to the maximum migration distance and was measured across all deme pairs in a case-I pattern and, for a case-IV pattern, at the distance where the F_ST–distance curve flattens out. We argue that in landscape genetics, separate analyses should be performed to either assess IBD or the landscape effects on gene flow.     

Recognizing the temporal distinctions between landscape genetics and phylogeography

The steadily advancing fields of landscape genetics and phylogeography share many goals. However, there are some very distinct differences between these two disciplines, including the kinds of data and analyses commonly used, the timescale over which these data are informative, and the hypotheses the data are used to examine. Recently, a number of studies appear to have confused or synonymized phylogeography and landscape genetics. The difference is not merely semantic; understanding the distinctions between these fields is important for ensuring that researchers are aware of the temporal scale over which their data are informative.     

Traits-based approaches support the conservation relevance of landscape genetics

Calls for evaluating general principles in landscape genetics reflect a broader recognition that multispecies inference is a promising strategy for supporting conservation actions across wide-ranging taxonomies and geographies. Formal evaluation of frameworks for multispecies inference is critical to identify opportunities for generalization and to avoid misguided extrapolation that results in ineffective conservation and management efforts. Traits-based approaches are now widely recognized as useful in addressing knowledge gaps where species-specific data may not be available or feasible to obtain. Here we present a case for the application of traits-based approaches in landscape genetics to improve conservation application. We discuss the fundamental theoretical framework and growing empirical evidence supporting the utility of traits-based approaches in landscape genetics, and we highlight an example of the implementation of traits to predict landscape genetic relationships for a range of aquatic taxa native to the southwestern United States. Finally, we discuss opportunities, challenges, and future directions of using traits to characterize landscape genetic relationships. Ultimately, traits-based approaches can help address growing calls for the development and testing of general principles in landscape genetics in order to improve application to conservation challenges.     

A spatial statistical model for landscape genetics

Landscape genetics is a new discipline that aims to provide information on how landscape and environmental features influence population genetic structure. The first key step of landscape genetics is the spatial detection and location of genetic discontinuities between populations. However, efficient methods for achieving this task are lacking. In this article, we first clarify what is conceptually involved in the spatial modeling of genetic data. Then we describe a Bayesian model implemented in a Markov chain Monte Carlo scheme that allows inference of the location of such genetic discontinuities from individual geo-referenced multilocus genotypes, without a priori knowledge on populational units and limits. In this method, the global set of sampled individuals is modeled as a spatial mixture of panmictic populations, and the spatial organization of populations is modeled through the colored Voronoi tessellation. In addition to spatially locating genetic discontinuities, the method quantifies the amount of spatial dependence in the data set, estimates the number of populations in the studied area, assigns individuals to their population of origin, and detects individual migrants between populations, while taking into account uncertainty on the location of sampled individuals. The performance of the method is evaluated through the analysis of simulated data sets. Results show good performances for standard data sets (e.g., 100 individuals genotyped at 10 loci with 10 alleles per locus), with high but also low levels of population differentiation (e.g., F_ST < 0.05). The method is then applied to a set of 88 individuals of wolverines (Gulo gulo) sampled in the northwestern United States and genotyped at 10 microsatellites.     

Adaptive landscape genetics: pitfalls and benefits

Landscape genetics offers a promising framework for assessing the interactions between the environment and adaptive genetic variation in natural populations. A recent workshop held at the University of Neuchatel brought together leading experts in this field to address current insights and future research directions in adaptive landscape genetics. Considerable amounts of genetic and/or environmental data can now be collected, but the forthcoming challenge is to do more with such manna. This requires a markedly better understanding of the genetic variation that is adaptive and prompts for advances in information management together with the development of a balance between theory and data. Moreover, showing the links between landscapes and adaptive genetic variation will ultimately move the field beyond association studies.     

Geneland: a computer package for landscape genetics

Geneland is a computer package that allows to make use of georeferenced individual multilocus genotypes for the inference of the number of populations and of the spatial location of genetic discontinuities between those populations. Main assumptions of the method are: (i) the number of populations is unknown and all values are considered a priori equally likely, (ii) populations are spread over areas given by a union of some polygons of unknown location in the spatial domain, (iii) Hardy–Weinberg equilibrium is assumed within each population and (iv) allele frequencies in each population are unknown and treated as random variable either following the so‐called Dirichlet model or Falush model. Different algorithms implemented in Geneland to perform inferences are first briefly presented. Then major running steps and outputs (i.e. histogram of number of populations and map of posterior probabilities of population membership) are illustrated from the analysis of a simulated data set, which was also produced by Geneland.     

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.