Interview with the authors: Strong divergent selection at multiple loci in two closely related species of ragworts adapted to high and low elevations on Mount Etna

Non-model organisms provide an interesting avenue to explore evolution in real time in natural populations.Here, we speak to Edgar Wong of Department of Plant Sciences, University of Oxford, UK about his Molecular Ecology article, which investigated speciation in two closely related Senecio species, S. aethnensis and S. chrysanthemifolius, which grow at high and low elevations, respectively, on Mount Etna, Sicily and form a hybrid zone at intermediate elevations.  Wong and his co-authors found an extremely strong selection (up to 0.78) against hybrids in the system. This estimate is one of the highest reported in literature, and much higher than the one reported in the same system in the past. Read on to get a behind-the-scenes view of this study.

1. What led to your interest in this topic / what was the motivation for this study?

Speciation and hybridisation have always been interesting topics to me. In the case of Senecio on Mount Etna, they have an especially fascinating story: first, Mount Etna is a relatively young mountain (less than half a million years old), and previous research hypothesized that the formation of the mountain led to the divergence of the two species, Senecio aethnensis and S. chrysanthemifolius. These species are thought to be a rare example of clear-cut, recent speciation subject to divergent selection – the formation of new species driven by adaptation to distinct conditions – high- and low-elevations in our study. Second, botanists around 300 years ago brought some live Senecio specimens of the plants from Mount Etna back to the UK, and led to hybrid speciation of S. squalidus that has since spread all over the UK (although crossing experiments using plants from Mount Etna suggested hybrid breakdown). A lot is still unknown about the plants both on Mount Etna and in the UK. Hence, I was intrigued to find out unknown aspects in the system and focused on the species on Mount Etna.

2. What difficulties did you run into along the way?

One big difficulty was that Asteraceae (which Senecio belongs to) is notorious for being hard to extract clean DNA. It was a struggle to extract good-quality DNA for this study, which was resolved in the end. Also, we only had a draft genome for the hybrid species, S. squalidus, which limited the scope of analyses we could carry out. Luckily, we managed to find some interesting, highly differentiated genes that might be underlying speciation and adaptation.

3. What is the biggest or most surprising innovation highlighted in this study?

The most surprising finding in our study is that we estimated an extremely strong selection (up to 0.78) against hybrids in the system. This estimate is one of the highest reported in literature, and much higher than the one reported in the same system in the past. Such strong selection was surprising to us because hybrids between the two species are (apparently) happily growing at intermediate elevations between the typical habitats of ‘pure’ S. aethnensis and S. chrysanthemifolius. We think this strong cumulative selection on multiple loci works together with intrinsic incompatibility to maintain the phenotypic and genotypic divergence between the two target species.

4. Moving forward, what are the next steps in this area of research?

In the future, we hope to identify the environmental and ecological selective forces that had shaped this system. We also hope to characterise the genetic aspect of the species by improving the genome assembly and study more in detail the intrinsic incompatibility between the two target species (such as hybrid breakdown). With more data on both extrinsic and intrinsic processes, we can integrate these findings to get a more comprehensive picture of reproduction isolation in this system.

5. What would your message be for students about to start developing or using novel techniques in Molecular Ecology?

I would say to spend enough time understanding the experimental techniques and different types of data analyses (and the theories behind them). Most importantly, make sure that the type of data you generate are suitable for answering your research questions. As a graduate student myself, I would also suggest not to rush your work and not get transfixed on certain issues/ problems along the way – taking a step back and asking for advice and opinions from other researchers are always helpful in getting another perspective, which often helps to find a solution.

6. What have you learned about methods and resources development over the course of this project?

The type of data I used and subsequent data analyses were all new to me when I started the project, so there is no doubt I learnt a great deal about handling new types of data and how to analyse it. Another thing I have learnt is that there are always newer or ‘better’ technologies and methods that give you more data and/ or data with higher accuracy. It is inevitable that sometimes you would be worried whether what you have is not good enough. However, I have come to realise that more isn’t always better and there will always be more advanced methods; the most important thing is to use what you have and try to answer your research questions.

7. Describe the significance of this research for the general scientific community in one sentence.

Non-model organisms inform us a lot about evolutionary processes such as hybridisation, adaptation and speciation.

8. Describe the significance of this research for your scientific community in one sentence.

Strong multifarious selection could be crucial in maintaining species divergence despite on-going gene flow.

Summary from the authors: Interdependent sensory systems regulate larval settlement in a marine sponge

In the ocean, pelagic larvae that settle onto the seafloor and metamorphose into an adult directly regulate the ecology and evolution of all benthic communities. To settle, larvae of most species need to encounter specific biochemical cues that indicate an optimal environment, and many also prefer to settle in the dark. It appears likely, then, that larval responses to light and to biochemical cues are closely linked, but exactly how this happens at a molecular level is largely unexplored.

We explored how changes in gene expression regulate larval settlement in a marine sponge. We find that these larvae naturally settle at twilight, and that this is directly related to the expression of receptors and signalling pathway components. Further, we find that constant light prevents larval settlement via blocking the ability of larvae to respond to biochemical cues. Our data provide the first suggestions of candidate genes and molecular pathways that may regulate the way in which light can directly affect larval settlement. Our findings in a sponge, one of the earliest branching extant animal lineages, raises the possibility that larval responses to light and to biochemical cues might be a mechanism regulating settlement across the animal kingdom.

(Left) Scanning electron micrograph of an Amphimedon queenslandica larva. A ring of very long cilia, which are associated with photosensory pigment cells, are clearly visible at the posterior end of the larva. Photo credit: Sally Leys. (Right) Tahsha Say in the field on Heron Island Reef flat, Great Barrier Reef, Australia.

Full article: Say, TE, Degnan, SM. Molecular and behavioural evidence that interdependent photo ‐ and chemosensory systems regulate larval settlement in a marine sponge. Mol Ecol. 2020; 29: 247– 261. https://doi.org/10.1111/mec.15318

This summary was written by the study’s first author,TE Say.

Victoria Sork awarded the 2020 Molecular Ecology Prize

The Molecular Ecology Prize Committee is pleased to announce that the 2020 Molecular Ecology prize has been awarded to Dr. Victoria Sork, Distinguished Professor in Ecology and Evolutionary Biology, Dean of Life Sciences, and Director of the Mildred E. Mathias Botanical Garden at University of California Los Angeles. Throughout her career, Dr. Sork has made substantial and diverse scientific contributions to the field of molecular ecology – from working to build the foundation of landscape genetics, to pioneering the use of molecular markers in tracking plant dispersal, to unraveling the genomic and epi-genomic basis of climate adaptation in non-model organisms. With well over 100 publications, she has proven herself to be a preeminent scholar in her field for decades, while serving as a role model and mentor for many early career scientists, and as a continual advocate for increasing diversity and inclusion in STEM.

Dr. Sork joins the previous winners of the Molecular Ecology Prize: Godfrey Hewitt, John Avise, Pierre Taberlet, Harry Smith, Terry Burke, Josephine Pemberton, Deborah Charlesworth, Craig Moritz, Laurent Excoffier, Johanna Schmitt, Fred Allendorf, Louis Bernatchez, Nancy Moran, Robin Waples, and Scott Edwards.

Summary from the authors: A metagenomic assessment of microbial eukaryotic diversity in the global ocean

Marine microbial eukaryotes are key components of planktonic ecosystems in all ocean biomes. They are, along with cyanobacteria, responsible for nearly half of the global primary production, and play important roles in food-web dynamics as grazers and parasites, carbon export to the deep ocean, and nutrient remineralization. Currently, one of the most common approaches to survey their diversity is sequencing marker genes amplified from genomic DNA extracted from microbial assemblages. However, this approach requires a PCR step, which is known to introduce biases in microbial diversity estimates. One alternative to overcome this issue involves exploiting the taxonomic information contained in metagenomes, which use massive shotgun sequencing of the same DNA extracts with the goal of assessing the putative functions of environmental microbes.

In this study we investigated the potential of metagenomics to provide taxonomic reports of marine microbial eukaryotes. The overall diversity reported by this approach was similar to that obtained by amplicon sequencing, although the latter performed poorly for some taxonomic groups. We then studied the diversity of picoeukaryotes and nanoeukaryotes using 91 metagenomes from surface down to bathypelagic layers in different oceans, unveiling a clear separation of taxonomic groups between size fractions and depth layers.

Overall, this study shows metagenomics as an excellent resource for taxonomic exploration of marine microbial eukaryotes.

Summary of the relevance of main eukaryotic taxonomic groups within two size fractions of marine plankton (picoeukaryotes [0.2-3 µm] and nanoeukaryotes [3-20µm]) and in two different layers of the global ocean (photic [0-200 m] and aphotic [200-4000m]) as seen by metagenomics. The median of the relative abundance was calculated for each taxonomic group with samples from the 4 categories (pico-photic, pico-aphotic, nano-photic, nano-aphotic) and dots represent these median values transformed to a 0-100 scale. Dots are then colored based on the category where the taxonomic group is most relevant.

This summary was written by the study’s first author, Aleix Obiol.

Full article:
Obiol, A., Giner, C. R., Sánchez, P., Duarte, C. M., Acinas, S. G., & Massana, R. (2020). A metagenomic assessment of microbial eukaryotic diversity in the global ocean. Molecular Ecology Resources. https://doi.org/10.1111/1755-0998.13147

Interview with the authors: Which software is best to use for de novo assembly?

Reduced representation sequencing (e.g. RAD and GBS) is becoming ever more popular, but for species which lack a reference genome, little work has been done to assess which software may be best suited to building de novo assemblies from this data. Here, we speak to Melanie LaCava of the University of Wyoming about her recent Molecular Ecology Resources article, which explores the accuracy of de novo assemblies built by various software programs using DNA generated from double-digest libraries. Melanie and her co-authors found highly variable degrees of accuracy of assemblies built by six different software programs, and discuss which programs are best suited to this application. They also highlight the importance of optimising parameter settings within any given software. Read on to get a behind-the-scenes view of this study.

The completeness of assemblies in simulations of unmutated genomes (a, d), in simulations of an equal number of SNPs and indels (b, e), and simulations of 1–5 base pair indels (c, f). Values are reported for five assemblers: CDHIT (green), STACkS (blue), STACkS2 (purple), VelVeT (pink) and VSeARCH (orange). The hue of each color corresponds to the percent match parameter setting used in the assembly. For more information on this figure go to Figure 1’s caption here.

What led to your interest in this topic / what was the motivation for this study? 

This study began as a research project in a graduate-level course on computational biology at the University of Wyoming led by the senior author on the paper, Alex Buerkle. Dr. Buerkle initiated the project and worked with the rest of the coauthors to pursue this de novo assembly software comparison. As reduced representation genotyping-by-sequencing has become more popular, new and repurposed software programs have been applied to each step in the bioinformatics pipeline. When a reference genome is unavailable for a study species, de novo assembly is essential, yet we recognized a gap in the evaluation of software used for this important step.

What difficulties did you run into along the way? 

Technology and software associated with genotyping-by-sequencing and de novo genome assembly are rapidly changing. During the course of our project, some of the software programs we tested were significantly updated, so we chose to rerun our analyses using the new software versions to ensure we were providing up-to-date information in our manuscript.

What is the biggest or most surprising finding from this study? 

We were surprised to find such a substantial difference in performance among these assembly programs. We were especially surprised at the variation in performance among software for our first simulation where no mutations were introduced. In this scenario, we made many identical copies of genome fragments and then performed de novo assembly using each software program. Without any mutations introduced, the job is basically to generate a list of unique sequences – it should be very straightforward. In some cases, however, these genome fragments were broken into shorter sequences and rearranged beyond recognition, leading to incorrect reconstruction of the simple, unmutated data.

Moving forward, what are the next steps for this research?

For our study, we selected a sample of assemblers from peer-reviewed literature that use different assembly algorithms, are freely available, and have updated user resources available online. However, this was not a comprehensive evaluation of all software capable of de novo assembly. Therefore, the evaluation of other programs would be valuable. Additionally, as new software programs are introduced or existing programs are updated, continued efforts to evaluate de novo assembly performance is warranted.

What would your message be for students about to start their first research projects in this topic? 

Reduced representation genotyping-by-sequencing is becoming less expensive and more accessible, making it a viable option for more research projects. While it is exciting to apply these emerging technologies and methods, it is important to recognize that approaches to filter and analyze these large datasets are still in development. Doing your background research to ensure you are applying the best available tools and using the most appropriate methods for your study is essential to doing good research in this field and in any field of research.

What have you learned about science over the course of this project? 

Doing this study has reaffirmed the importance of simulations to test how software works. Testing analyses on simulated data and altering parameters of the simulation or analysis can provide immense insight into how the software works and how variation in real data may affect software performance. Larger simulation projects like our study can provide information that many people can use, but I also find it incredibly helpful to run a simulated dataset through an analysis before analyzing my own data to ensure I understand what the software is doing. Taking advantage of simulated datasets available in vignettes for software is a great tool to get acquainted with the analyses you plan to do.

Describe the significance of this research for the general scientific community in one sentence.

Our study demonstrates the importance of ensuring that software you use is really doing what you think it is supposed to do; and simulations can help evaluate software performance.

Describe the significance of this research for your scientific community in one sentence.

Researchers who need to perform de novo assembly of reduced representation genotyping-by-sequencing data can use our study as a guide for which software to use and the importance of different parameter settings for assembly.

LaCava, M. E., Aikens, E. O., Megna, L. C., Randolph, G., Hubbard, C., & Buerkle, C. A. (2019). Accuracy of de novo assembly of DNA sequences from double‐digest libraries varies substantially among software. Molecular ecology resources. https://doi.org/10.1111/1755-0998.13108

Interview with the authors: How does invasiveness evolve? A look at feral pigs

Understanding how and why some species readily invade new habitats is an interesting view into the myriad ways species evolve. Limiting the expansion of such introduced species can be important for managing ecosystems, particularly when the invasive species is as ecologically destructive and economically costly as the feral swine in the US south. In a paper published recently in Molecular Ecology, researchers led by Dr. Tim Smyser investigated the origins of the invasive feral swine populations to determine how much the expanding footprint of this species was a due of recently escaped domesticated pigs. Surprisingly, they found that the expanding range was largely attributable to range expansion by the established invasive swine population. Read on to for more details from Dr. Smyser into this very interesting work!

Invasive feral swine originated from a combination of European feral pigs and domesticated stock. Photo by Dr. Mirte Bosse, dvdwphotography.

What led to your interest in this topic / what was the motivation for this study? 

Invasive feral swine have expanded rapidly throughout the United States over the past 30 years. The impetus for the study was to identify the drivers for that expansion, to ask: where are new feral swine populations coming from? Prior to our work, there was a hypothesis that domestic pigs had sufficient phenotypic plasticity that they would revert to a wild phenotype, resembling a wild boar, if living in the wild. Under this hypothesis, any pig farm could have served as a viable source population for invasive feral swine. With this study, we revealed that there is very little direct contribution to invasive feral swine populations from domestic pigs, potbellied pigs, or wild boar. Rather, the rapid expansion observed over the past 30 years has been driven by incremental range expansion of established invasive feral swine, which overwhelmingly represent animals of mixed European wild boar-heritage domestic breed ancestry, and long-distance translocation of feral swine from established populations to uninvaded habitats.

What difficulties did you run into along the way? 

The challenges were largely computational. We had amassed over 9,000 genotypes by the time we compiled the reference set and generated genotypes from invasive feral swine genotypes for this study. Such a large dataset required that we do everything we could to optimize runtime efficiency. Even with these efforts, the analysis still took about 4 months of runtime while using 30 CPUs with 60 threads.

What is the biggest or most surprising innovation highlighted in this study? 

I would say the most surprising result was the very high proportion of invasive feral swine that had a significant ancestry association to European wild boar. The historical record suggests wild boar releases have been far more limited than the potential for domestic pig releases, yet 97% of feral swine had significant European wild boar ancestry. This might suggest hybrid wild boar-domestic pig ancestry is biologically important for feral swine to establish self-sustaining populations and become invasive.

Moving forward, what are the next steps in this area of research?

Descending from this work, our next steps are multifaceted. With this analysis, we have identified the drivers of range expansion at a broad-scale with ancestry results pointing to the expansion of established populations. We are now interested in adding a fine-scale understanding of expansion to identify the specific sources of newly emergent populations and map the patterns of feral swine expansion. Also, this analysis has provided an understanding of the ancestral composition of invasive feral swine. Given the hybrid origin of these animals, we will identify elements of the genomes from their ancestral groups, that is heritage breeds of pig and European wild boar, that have been selectively retained in feral swine. By describing selective sweeps relative to ancestral groups, this analysis will allow us to describe the evolution of invasiveness among feral swine.   

What would your message be for students about to start developing or using novel techniques in Molecular Ecology?

The field of Molecular Ecology is changing so quickly that it is hard as a scientist to keep up, from both a computational/statistical standpoint and with all the new molecular techniques and analyses that allow us to dive deeper into the genome than we had previously imagined. My recommendation for students would be to not let the lack of a specific skill deter you from asking interesting questions – take the time to develop the needed skill sets or develop collaborations to facilitate your learning or use of those skills. Also, keep asking questions – don’t be content with the answers we are able to resolve today.

What have you learned about methods and resources development over the course of this project? 

Reflecting back on my answer immediately above, when I started asking the question of what are the drivers of invasive feral swine range expansion, I did not have the data or the skills to meaningfully address that question. Through the development of a great team of collaborators and independent learning, I was able to assemble the needed skills and then the data to pose this question and reveal interesting results. Through this project, I learned about the statistical tools used in the analyses, developed the coding skills necessary to execute those analyses, and identified strategies to maximize computational efficiency as was needed for working with such a large dataset.

Describe the significance of this research for the general scientific community in one sentence.

We have demonstrated that the recent and rapid expansion of feral swine, an ecologically destructive and economically costly invasive species distributed throughout much of the US and the world, has been facilitated by movement (in many cases anthropogenic movement) from established populations to uninvaded habitats as opposed to novel introductions of either domestic pigs or wild boar.

Describe the significance of this research for your scientific community in one sentence.

In identifying the admixed origins of invasive feral swine, descending from heritage domestic pig breeds and European wild boar ancestry, we can begin to gain an understanding of the evolution of invasiveness for this species and invasive species more broadly. 

Feral Swine are not native to the U.S. They are the result of recent and historical (1500’s Spanish explorers) releases of domestic swine and Eurasian boar. USDA APHIS photo Laurie Paulik.

Smyser TJ, Tabak MA, Slootmaker C, Robeson MS, Miller RS, Bosse M, Megens H-H, Groenen MAM, Rezende Paiva S, Assis de Faria D, Blackburn HD, Schmidt BS, Piaggio AJ. 2020. Mixed ancestry from wild and domestic lineages contributes to the rapid expansion of invasive feral swine. Molecular Ecology. https://doi.org/10.1111/mec.15392

Interview with the authors: can we identify the acting selective regime in evolution experiments?

Rapid adaptation to novel conditions is an exciting and growing area in evolutionary research due, at least in part, to our desire to understand the effects of climate change, introduced species, and other conservation-related concerns. However, our ability to detect this evolution is fraught with both biological realities and technical difficulties. A recent paper by Drs. Pfenninger and Foucault, published in Molecular Ecology, illustrate how deep resequencing of replicated experimental populations can fail to provide evolutionary insights, even with extreme selective pressures, due to adaptation to unintentional environmental conditions that overwhelm the genomic signals of the intended selection. This rapid adaptation, in this case to captivity, is an interesting phenomenon that is almost certain to alter other experimental systems, including those that take place in the field. In addition to more details on this fascinating study, the interview with Dr. Pfenninger below also provides an interesting view into technical issues the research team faced: a short time after the initial publication of their manuscript, they discovered a bug in allele frequency calling software that they used! 

Swarming flight of Chironomus midges over a small puddle. Photo credit: Markus Pfenninger.

What led to your interest in this topic / what was the motivation for this study? 

I wanted to know whether rapid adaptation of a natural population to an environmental stressor, in this case temperature, is possible and, if so, by which processes in detail. Apart from this being a fundamental question in population genetics, it is a crucial issue for biodiversity in the ongoing global change.

This is the official and completely true answer – but, to be completely honest, not the entire story: I wanted to see, analyse and prove evolution by natural selection hands-on. Because it’s one thing to teach something gleaned from literature and another to have seen it with your own eyes.

What difficulties did you run into along the way? 

There were actually quite a few: finding a suitable PhD student, technical difficulties with the experimental facilities that almost killed the long term experiment after some months, to mention only the most important ones.

And finally, of course, the almost detrimental issue with a bugged software tool: A few days after the official publication in January, a student reanalysing the data from a different angle, stumbled over unexplainable inconsistencies between the raw data and the allele-frequencies inferred from them. You can imagine the shock it gave me!

When I looked into the problem, I quickly found out that the allele frequencies had little to do with the raw data for most, but perfidiously not all positions in the genome, in particular not the first few on the first scaffold – that’s why the error escaped my attention during a cursory check. The allele frequencies were extracted by a software tool which indeed produced consistently wrong results – a task in principle so simple that systematic checking would have required to write a second tool that exactly did what the first should have done in the first place.

I immediately contacted the authors of the tool and they promptly confirmed that the version we used contained this bug. They did nothing wrong, though. Once they discovered the bug a few months ago, they had promptly updated the tool and documented the error in the release notes. In fact, it appears that the wrong version was on the server for a few days only. Unfortunately it was exactly during the time when we downloaded it – and who looks into the release notes after a tool seemingly did without a hitch what it was supposed to?

I had no choice but to contact the editorial office of Molecular Ecology, informing Genevieve Horn that parts of the publication were flawed and should probably be retracted. At the same time, I started reanalysing the complete data set with a correct version of the tool. Fortunately, after a hard week of number crunching, it turned out that the wrong values were highly correlated in terms of location and allele frequencies to the true values so that some numerical values, but none of the study’s conclusions, needed to be revised. The journal agreed that in this case, a correction article would be sufficient and here it is.

I have to say that everyone, from the software authors to the editor in chief, I have dealt with in this affair has responded greatly and I want to express my deep gratitude here. Given this experience with Molecular Ecology, I can only encourage everyone to address such unfortunate as perhaps unavoidable mistakes immediately and openly.

What is the biggest or most surprising innovation highlighted in this study? 

The rather unsettling major result of the study was the realisation that it is nearly impossible to experimentally manipulate the selection regime of a natural population in a targeted, predictable manner. I think, however, that such “failures” finally advance science by showing which approaches are worth pursuing and which not. Besides this more philosophical aspect showed the study the impressive power of rapid polygenic adaptation.

Moving forward, what are the next steps in this area of research?

I am currently moving into analysing population genomic time series from the field to get an idea on the selective forces acting on natural populations.

What would your message be for students about to start developing or using novel techniques in Molecular Ecology?

Have a good plan, be ready to revise it once the plan meets reality and be prepared for setbacks, remain critical about your results and incorporate appropriate controls. But perhaps most importantly, always take your time to think what you are currently doing and what should be the next steps.

What have you learned about methods and resources development over the course of this project? 

Obviously to even more thoroughly back-check every single analysis. Beyond this, I realised the value and potential of population genomic time series analysis.

Describe the significance of this research for the general scientific community in one sentence.

An evolutionary experiment tells you something about the experiment – not necessarily about nature.

Describe the significance of this research for your scientific community in one sentence.

The acting selective regime in evolutionary experiments is difficult to predict and to manipulate – but perhaps it may be inferred from the results.

Pfenninger M and Foucault Q. 2020. Genomic processes underlying rapid adaptation of a natural Chironomus ripariuspopulation to unintendedly applied experimental selection pressures Molecular Ecology 59:536-548. https://doi.org/10.1111/mec.15347

Summary from the authors: Latent Dirichlet Allocation reveals spatial and taxonomic structure in a DNA-based census of soil biodiversity from a tropical forest

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.

Left: Primary tropical forest understory on the plot where the data were collected, Nouragues ecological research station, French Guiana. Right: Spatial distribution of assemblages of co-occurring soil taxa (OTUs), obtained by Latent Dirichlet Allocation from OTU presence-absence information only, over a 12-ha plot of plateau forest sampled every 10 m (top); and two main axes of environmental variation over the same forest plot, derived from Airborne Laser Scanning (bottom). Bacteria, protists and fungi exhibit a spatial pattern matching the environmental heterogeneity of the plot: the blue, green and red assemblages match the terra firme, hydromorphic and exposed rock parts of the plot, respectively. In contrast, metazoans such as annelids can be shown to be spatially unstructured at that scale. Sampled locations are indicated by dark dots, and values have been interpolated between samples using kriging.

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

Summary from the authors: inbreeding and management in captive populations

Pacific salmon hatcheries aim to supplement declining wild populations and support commercial and recreational fisheries. However, there are also risks associated with hatcheries because the captive and wild environments are inherently different. It is important to understand these risks in order to maximize the success of hatcheries. Inbreeding, which occurs when related individuals interbreed, is one risk that may inadvertently be higher in hatcheries due to space limitations and other factors. Inbred fish may have reduced fitness and survival compared to non-inbred fish. We quantified inbreeding and its effect on key fitness traits across four generations in two hatchery populations of adult Chinook salmon that were derived from the same source. We utilized recent advancements in DNA sequencing technology, which provide much more precise estimates of inbreeding and its potential effects on fitness. Our results indicate that inbreeding may not be severe in salmon hatcheries, even small ones, provided that appropriate management practices are followed. However, we documented an influence of inbreeding on the phenology of adult spawners, which could have biological implications for individual fitness and population productivity. Our findings provide a better understanding of changes that may occur in hatchery salmon and will further inform research on “best” hatchery practices to minimize potential risks. 

Article: Waters CD, Hard JJ, Fast DE, Knudsen CM, Bosch WJ, Naish KA. 2020. Genomic and phenotypic effects of inbreeding across two different hatchery management regimes in Chinook salmon. Molecular Ecology https://doi.org/10.1111/mec.15356.

Interview with the authors: Modelling multilocus selection in an individual‐based, spatially‐explicit landscape genetics framework

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.

Dr. Erin Landguth coding in CDPOP.

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. 

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Outcome for simulation of a complex landscape and three loci. The three selection landscapes (Figure 1 of Landguth et al., 2020) are superimposed with lighter‐white areas referring to areas where all three landscapes have values of 1 and darker areas mean all three landscapes have values of −1. The copies (either 2, 1, or 0) of the first allele for each of the three loci are plotted, where darker green genotypes have more copies of these alleles (e.g., 2, 2, 2 corresponds to 2 copies of the first allele for the first, second and third loci, respectively). The first locus is associated with the categorical landscape (X1‐Figure 1a of Landguth et al., 2020). The second locus is associated with the gradient landscape (X2‐Figure 1b of Landguth et al., 2020). The third locus is associated with the habitat fragmented landscape (X3‐Figure 1c of Landguth et al., 2020).

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.

Dr. Brenna Forester, post-doctoral researcher at Colorado State University and recently awarded David H. Smith Conservation Research Fellow, helped integrate key genotype-by-environment concepts into the new module.

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.

References

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

Landguth, E. L., Cushman, S. A., & Johnson, N. A. (2012). Simulating natural selection in landscape genetics. Molecular Ecology Resources, 12, 363– 368. https://doi.org/10.1111/j.1755-0998.2011.03075.x

Wright, S. (1935). Evolution in populations in approximate equilibrium. Journal of Genetics, 30, 257– 266. https://doi.org/10.1007/BF02982240