Biodiversity inventories can now be built by collecting and sequencing DNA from the environment, which is not only easier, faster and cheaper than direct observation, but also much more comprehensive and systematic. This gives in particular unprecedented access to little-known microbial diversity. Tapping these data to answer community ecology questions, however, can prove a daunting task, as classical statistical approaches often fall short of the size and complexity of molecular datasets. To uncover the spatial structure of soil biodiversity over 12 ha of primary tropical forest in French Guiana, we borrowed a probabilistic model from text analysis. After demonstrating the performance of the method on simulated data, we used it to capture the co-occurrence and covariance patterns of more than 25,000 taxa of bacteria, protists, fungi and metazoans across 1,131 soil samples, collected every 10 m – a dataset that led to a previous publication in Mol. Ecol. (Zinger et al. 2019). We find that, even though the forest plot is at first sight rather uniform, bacteria, protists and fungi are all clearly structured into three assemblages matching the environmental heterogeneity of the plot, whereas metazoans are unstructured at that scale. We then work though the practical problems ecologists may encounter using this approach, such as whether to use presence-absence or read-count data, how to choose the number of assemblages and how to assess the robustness of the results. Finally, we discuss the potential use of related methods in community ecology and biogeography, and argue that probabilistic models are a way forward for analyzing the ever-expanding amount of data generated by the field.
Full article: Sommeria-Klein G, Zinger L, Coissac E, et al. (2020). Latent Dirichlet Allocation reveals spatial and taxonomic structure in a DNA-based census of soil biodiversity from a tropical forest. Molecular Ecology Resour. 20:371–386. https://doi.org/10.1111/1755-0998.13109
References: Zinger, L., Taberlet, P., et al. (2019). Body size determines soil community assembly in a tropical forest. Molecular Ecology, 28(3), 528–543. https://doi.org/10.1111/mec.14919
Genetic variation in natural systems is complex and affected by a variety of processes, and this reality has contributed to the growing popularity of simulation-based approaches that can help researchers understand the processes acting in their systems. Despite the flexibility of simulation-based approaches, simulations of natural selection across a heterogeneous landscape have typically been limited to one or two loci (e.g. Landguth, Cushman, & Johnson, 2012). In a recent issue of Molecular Ecology Resources, Landguth et al. introduce an approach to model multilocus selection in a spatially-explicit, individual-based framework, implemented in the programs CDPOP and CDMetaPOP. Read the interview with lead author Erin Landguth below to learn about the challenges in developing this program, the potential of this approach to help understand complex genotype-environment associations, and the benefits of working with strong multidisciplinary team! Read the full article here.
What led to your interest in this topic / what was the motivation for this study?
Over the last two decades, there has been an exponential increase in landscape genetic studies, and still, the methodology and underlying theory of the field are under rapid and constant development. Furthermore, interest in simulating multilocus selection, including the ability to model more complex and realistic multivariate environmental scenarios, has been driven by the growing number of empirical genomic data sets derived from next-generation sequencing. We believe many of the major questions in landscape genetics require the development and application of sophisticated simulation tools to explore the interaction of gene flow, genetic drift, mutation, and natural selection in landscapes with a wide range of spatial and temporal complexities. Our interests lie in developing such tools and providing more flexible models that are linked to theory, and that better represent complex genetic variation in real systems. For example, adaptive traits often have a complex genetic basis that interacts with selection strength, gene flow, drift, and mutation rate in a multivariate environmental context; and this module provides the ability to simulate these processes across many adaptive and neutral loci in a landscape genetic context.
What difficulties did you run into along the way?
When developing new modules for existing software packages, my first and primary goal is to validate these modules to theory where possible. This can take some time and many decisions, questions, and trial and errors come up along the way through this very important validation process. For multilocus selection, our validation process was to match simulation output with the theoretical expected change in allele frequencies for selection models developed by Sewall Wright in 1935. If the module is placed in the wrong location in the simulation workflow (i.e., timing) or if all of the Wright-Fisher assumptions are not matched exactly, then the simulation output will not match theoretical expectations. However, once all of these pieces are lined up, there is definitely a eureka moment, and I am then confident in the module’s performance for more complex scenarios where we will not be able to evaluate against theoretical expectations.
What is the biggest or most surprising innovation highlighted in this study?
Multivariate environmental selection can produce complex landscape genetic patterns, even when only a few adaptive loci are involved. The relatively simple “complex” example simulated in the paper illustrates how complicated the underlying relationships can be between allele frequencies and environmental conditions. Simulating these complex relationships will be essential for testing genotype-environment association methods in a more rigorous fashion than has been seen so far. Additionally, the ability to simulate realistic landscape genetic scenarios that reflect the environmental complexity of actual landscapes will be important for validating findings from empirical data sets.
Moving forward, what are the next steps in this area of research?
Epigenetics! We of course have a number of applications in progress for this current module, but we have already started beta testing our next module for simulating epigenetic processes in landscape genetics.
What would your message be for students about to start developing or using novel techniques in Molecular Ecology?
Starting a simulation study in landscape genetics for the first time can be daunting and intimidating. Fear not, we say! As with all software packages, there will be a learning curve, but if you persevere and get past the first few hurdles (e.g., learning the ins and outs of file formats, running the program in a potentially unfamiliar programming interface), the door will be opened to unlimited questions that can be addressed with simulations in your system. Additionally, just like any other field study or experiment, simulation modeling is most informative when coupled with specific questions and hypotheses and well-thought-out study designs.
What have you learned about methods and resources development over the course of this project?
As we begin to add more complex modules to these simulation platforms, I am increasingly relying on multidisciplinary approaches and teams. For example, development of this current module required Brenna Forester for her expertise in landscape ecology and genotype-by-environment concepts, as well as Andrew Eckert, with his in-depth knowledge of population genetics theory, particularly the history of additive vs. multiplicative models for fitness.
Describe the significance of this research for the general scientific community in one sentence.
We have implemented a new module into the landscape genetic simulation programs CDPOP and CDMetaPOP that allows realistic multivariate environmental gradients to drive selection in a multilocus, individual-based, landscape genetic framework.
Describe the significance of this research for your scientific community in one sentence.
This new simulation module provides a valuable addition to the study of landscape genetics, allowing for explicit evaluation of the contributions and interactions between demography, gene flow, and selection-driven processes across multilocus genetic architectures and complex, multivariate environmental and landscape conditions.
Landguth EL, Forester BR, Eckert AJ, et al. (2020). Modelling multilocus selection in an individual-based, spatially-explicit landscape genetics framework. Molecular Ecology Resources, 20, 605–615. https://doi.org/10.1111/1755-0998.13121
The editorial board of the journal Molecular Ecology is seeking nominations for the Harry Smith Prize, which recognizes the best paper published in Molecular Ecology in the previous year by graduate students or early career scholars with no more than five years of postdoctoral or fellowship experience. The prize comes with a cash award of US$1000 and an announcement in the journal and in the Molecular Ecologist. The winner will also be asked to join a junior editorial board for the journal to offer advice on changing research needs and potentially serve as a guest editor. The winner of this annual prize is selected by the junior editorial board.
The prize is named after Professor Harry Smith FRS, who founded the journal and served as both its Chief and Managing Editor during the journal’s critical early years. He continued as the journal’s Managing Editor until 2008, and he went out of his way to encourage early career scholars. In addition to his editorial work, Harry was one of the world’s foremost researchers in photomorphogenesis, where he determined how plants respond to shading, leading to concepts such as “neighbour detection” and “shade avoidance,” which are fundamental to understanding plant responses to crowding and competition. More broadly his research provided an early example of how molecular data could inform ecology, and in 2008 he was awarded the Molecular Ecology Prize that recognized both his scientific and editorial contributions to the field.
Please send a PDF of the paper you are nominating, with a short supporting statement (no more than 250 words; longer submissions will not be accepted) directly to Dr. Janna Willoughby (firstname.lastname@example.org) by 31 May 2020. Self-nominations are accepted.
Phylogeographic studies have long focused on striking biogeographic barriers, and comparative phylogeography often looks for shared divergence across such barriers as evidence of shared responses to similar environments across taxa. However, in addition to such barriers, geographic distances and local adaptation to environmental heterogeneity may shape genetic divergence. In their recent Molecular Ecology paper, Myers and colleagues collect genomic data from 13 co-distributed species of snakes from Southwestern North America and evaluate the relative importance of biogeographic barriers, geographic distance, and environmental heterogeneity in structuring genetic divergence. Much of the previous phylogeographic work in this region has focused on divergence across a prominent biogeographic barrier: the Cochise Filter Barrier (CFB), which separates the Sonoran and Chihuahuan Deserts, and divergence across this barrier has been suggested to be an important factor driving divergence in snakes from the region. Though they expected to find a prominent role of this barrier, instead, Myers and colleagues find strong support for geographic distance and environmental heterogeneity as important factors structuring genetic divergence, but less support for biogeographic barriers. Further, they find that different variables contribute most to divergence across the 13 taxa studied, highlighting the importance of species-specific responses to environmental variation. Read the full article here, and read below for a behind-the-scenes interview with lead author Edward Myers.
What led to your interest in this topic / what was the motivation for this study? As a research team we have a general interest in what factors are promoting population genetic differentiation and whether codistributed species have similar evolutionary histories in response to shared environmental changes over time. Specifically, in this system where there is a well known biogeographic barrier (Cochise Filter Barrier; CFB), we were interested in whether entire assemblages of taxa show similar population structure. Initially the motivation for this study was to assess the degree of co-divergence across the CFB, however, as we analyzed these data it became clear that we needed to incorporate spatial and environmental data to understand population divergence. This study also allowed me spend a significant amount of time in the field collecting tissue samples from snakes!
What difficulties did you run into along the way? One of the biggest difficulties with this study was handling and analyzing all the generated data. We had almost 400 samples sequenced for radseq, so processing and analyzing these data took a significant amount of computational time. Also, one difficulty was the logistics of collecting fresh tissue samples for all of these species across the southwestern US and northern Mexico, but issues like this are easily over come by collaborating.
What is the biggest or most surprising finding from this study? The biggest surprise from this study is that patterns of isolation-by-distance and isolation-by-environment are more important in explaining population genetic differentiation than a commonly cited biogeographic barrier. This result really stresses the importance of incorporating spatial analyses when analyzing phylogeographic data because aspatial analyses may result in spurious results of population structure and mislead our ideas of what is driving population divergence and speciation.
Moving forward, what are the next steps for this research? Moving forward I plan to generate whole genome sequence data for species within this system to understand what loci may be under selection in response to environmental heterogeneity. Given the strong signature of IBE I expect to find patterns of strong selection along transects of temperature and precipitation across the Sonoran and Chihuahuan Deserts. Further, I am interested in how other regions globally that have been cited as important biogeographic barriers in phylogeographic studies might also be strongly influenced by patterns of IBD and IBE, and not vicariant barriers.
What would your message be for students about to start their first research projects in this topic? There is so much great work published in the field of landscape genetics and comparative phylogeography and I would suggest that students start by combing through that work first. But as general advice I would suggest that students really explore their data in a meaningful way and spend some time thinking about what factors could be responsible for similar patterns observed in a genomic data set (e.g., IBD vs vicariance or selection vs historical demography).
What have you learned about science over the course of this project? I have really learned that genomic data should be carefully analyzed as to not be influenced by preconceived ideas of the system that you might be working within. Also, I think that this is becoming more and more true, but you have to collaborate in order to do great science.
Describe the significance of this research for the general scientific community in one sentence. This work demonstrates that codistributed species do not have shared evolutionary histories, and that they do not respond to the same landscape and shared environment in similar ways.
Describe the significance of this research for your scientific community in one sentence. Our work shows that simple patterns of isolation-by-distance and isolation-by-environment have contributed to population genetic differentiation more so than commonly cited biogeographic barriers.
Full article: Myers EA, Xue AT, Gehara M, et al.Environmental heterogeneity and not vicariant biogeographic barriers generate community‐wide population structure in desert‐adapted snakes. Mol Ecol. 2019;28:4535–4548. https://doi.org/10.1111/mec.15182
Much research in community genetics attempts to understand how genetic variation influences community composition, but the majority of studies have been done at the level of the genotype. In their new Molecular Ecology paper, Barker and colleagues use genome-wide association mapping in aspen (Populustremuloides) to identify specific genes that may influence variation in tree traits or in insect communities. They uncover 49 SNPs that are significantly associated with tree traits or insect community composition. Notably, insects with closer associations with host plants have more genetic correlations than less closely associated insects. Barker and colleagues find a SNP associated with insect community diversity and the abundance of interacting species, providing a link between genetic variation in aspen and insect community composition. Finally, they find that tree traits explain some of the significant relationships between SNPs and insect community composition, suggesting a mechanism by which these genes may influence community composition. Read the full article here, and get a behind-the-scenes interview with lead author Hilary Barker below.
What led to your interest in this topic / what was the motivation for this study? For some time, we have been interested in extended phenotypes – the idea that the genes of an organism not only shape the immediate traits of that organism, but also extensions of these traits, such as the community of insects living on a tree. Yet, until our study, most of the previous research had been largely focused on differences across genotypes of ‘host’ organisms (e.g., aspen, cottonwoods, evening primrose), rather the underlying genes. Thus, there were a lot of unknowns yet to be discovered. For instance, would the genetic effects be large enough to detect and identify? Would more underlying tree genes be found for insects that are more closely associated with the tree (i.e., leaf gallers) rather than free feeding insects? Would there be an overlap between genes associated with insect communities and genes associated with particular tree traits?
What difficulties did you run into along the way? I think the largest challenge of conducting a Genome-Wide Association study on a common garden of trees is the planting and maintenance of this small forest. We had 1824 trees that needed planting, phenotyping, and care. This work was most intense in the first four years of the study to ensure that each tree survived a summer drought and multiple harsh winters. The next most challenging hurdle was conducting the insect surveys. These surveys involved a large team effort and happened during some of the hottest days of the summer.
What is the biggest or most surprising finding from this study? The most exciting finding from this study was the identification of an aspen gene (early nodulin-like [ENODL] transmembrane protein, Potra001060g09097) that underlies insect community composition; both diversity and the abundance of key insect species (aphids and ants). While we do not yet know the mechanism by which this gene influences insect communities, we do know that this protein is involved in the transportation of carbohydrates. Thus, it’s possible that this gene directly influences aphids and ants via their interactions with carbohydrate-rich honeydew, and/or indirectly influence insects via numerous tree traits, including both growth (size) and defense. To our knowledge, this is the first identification of allelic variation in a plant gene that is associated with a complex insect community trait (i.e., insect community composition).
Moving forward, what are the next steps for this research? The next step of this research is to explore how the genetic underpinnings of these aspen traits and associated insect communities may vary across different environmental gradients and with tree ontogeny. Previous research has shown that aspen growth and defense traits vary with tree age, and these traits play a significant role in determining which insects will feed upon the foliage. Thus, the genetic contributions of insect community composition may vary substantially for more mature trees. The Lindroth lab is currently working on an expanded version of this study with more detailed traits and mature (reproductive) trees. In addition, gene expression will vary with different environmental conditions, which will likely also modify which genes are most important in shaping insect communities.
What would your message be for students about to start their first research projects in this topic? To complete a large community genomics study such as this, you will need a few key things. First, you will need a lot of help. Start recruiting anyone and everyone in sight. Mentoring undergraduates will be essential and ensuring that you can effectively asses the learning of your mentees and volunteers is critical (e.g., can they correctly identify X insect? Can they successfully complete X protocol in the lab?). Second, get organized. Project management platforms can be really helpful (e.g., Asana, MS Teams, etc.) to keep track of tasks. Third, refine your R markdown scripts. You will generate more data than you know what to do with, and thus creating R scripts to clean up, organize, and analyze your data will be a top priority. Also, if you can get a digital microscope (e.g., Dino Lite), then the tedious task of keying out insect specimens will be much easier and less cumbersome! I highly recommend it.
What have you learned about science over the course of this project? In terms of a genome-wide association study, it is best to have as large a sample size as possible (more genotypes and genetic variation). You do not want to invest a lot of resources into a study that has low statistical power for association testing. Also, phenotype as many traits as you can. At the onset, it is impossible to know which genes, if any, will be associated with which traits. Thus, you could end up with a lot of investment while identifying a small number of associated genes, or potentially no genes at all.
Describe the significance of this research for the general scientific community in one sentence. Our findings show that specific genes in a host organism can shape the composition of associated communities.
Describe the significance of this research for your scientific community in one sentence. Complex extended phenotypes such as community composition have an identifiable genetic basis, and thus we can use this information to test and study the extent and limitations of community evolution.
Full article: Barker HL, Riehl JF, Bernhardsson C, et al. Linking plant genes to insect communities: Identifying the genetic bases of plant traits and community composition. Mol Ecol. 2019;28:4404–4421. https://doi.org/10.1111/mec.15158
Whilst urbanisation poses a major threat to many species, there is growing evidence to suggest that some species, labelled ‘urban adapters’, are thriving within the urban landscape. Urban landscapes differ drastically from native habitats, where urban adapters are often exposed to a more diverse range of novel food items compared to their rural counterparts, which frequently includes human subsidised resources. Diet is one of the most important factors influencing the gut microbiome, an extremely influential symbiotic community that plays a critical role in many processes affecting host health and fitness, including metabolism, nutrition, immunology and development. Here, using populations of the eastern water dragon in Queensland, Australia, we explore the link between urbanisation, diet and gut microbial changes. We show that city dragons exhibit a more diverse gut microbiome than their rural counterparts, and display microbial signatures of a diet that is richer in plant-material and higher in fat. Elevated levels of the Nitrogen-15 isotope in the blood of city dragons also suggests their diet may be richer in protein. These results highlight that urbanisation can have pronounced effects on the gut microbial communities of wild animals, but we do not yet know the possible repercussions of these microbial changes.
Full article: Littleford‐Colquhoun BL, Weyrich LS, Kent N, Frere CH. City life alters the gut microbiome and stable isotope profiling of the eastern water dragon (Intellagama lesueurii). Mol Ecol. 2019;28:4592–4607. https://doi.org/10.1111/mec.15240
In this study, we wanted to know how geography and ecology predicted population genetic structure among 58 populations of the gall wasp Belonocnema treatae, which exhibits regional specialization on three host plant species across the U.S. Gulf Coast. We combined range-wide sampling with a genotype-by-sequencing approach for 40,699 SNPs across 1,217 individuals. Disentangling the processes underlying geographic and environmental patterns of biodiversity is challenging, as such patterns emerge from eco‐evolutionary processes confounded by spatial autocorrelation among sample units. We evaluated this question using a hierarchical Bayesian model (ENTROPY) to assign individuals to genetic clusters and estimate admixture proportions. Using distance-based Moran’s eigenvector mapping, we generated regression variables that represent varying degrees of spatial autocorrelation in genetic variation among sample sites. These spatial variables, along with host association, were incorporated in distance-based redundancy analysis (dbRDA) to partition the relative contributions of host plant and spatial autocorrelation. This novel approach of combining ENTROPY results with dbRDA to analyze SNP data unveiled a complex mosaic of diversification within and among insect populations forming discrete host associated lineages coupled with geographic variation. This demonstrates that geography and ecology play significant roles in explaining patterns of genomic variation in B. treatae – an emerging model of ecological speciation.
Full article: Driscoe AL, Nice CC, Busbee RW,Hood GR, Egan SP, Ott JR. Host plant associations and geography interact to shape diversification in a specialist insect herbivore. Mol Ecol. 2019;28:4197–4211. https://doi.org/10.1111/mec.15220