Category Archives: Interviews

Interview with the authors: does indoor spraying alter the genetic diversity of malaria-causing parasites and what does this mean for long-term control?

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In a recent paper in Molecular Ecology, Argyropoulos and Ruybal-Pesántez et al. (2021) investigated the effects of indoor spraying on Plasmodium falciparum, the human malaria-causing protist. They find that 3 consecutive years of indoor spraying reduced transmission and prevalence of malaria by 90% and 35%, respectively, in the high malaria transmission site they surveyed. Despite these large reductions, a change in genetic diversity in P. falciparum that would indicate a large reduction in population size was not detected, illustrating the incredible resiliency of this parasite. Based on these data, the authors suggest that limiting malaria transmission in high transmission areas will require continued indoor spraying or other interventions such as mass drug administration. See the full article and interview with first authors Argyropoulos and Ruybal-Pesántez below for more details of this exciting work.

Images of the local terrain in Bongo District, Ghana taken at the same location at the end of the wet season in October (top) and at the end of the dry season in June (bottom). Photo Credit: Kathryn Tiedje

What led to your interest in this topic / what was the motivation for this study? Global efforts over the past 20 years have significantly reduced malaria mortality and morbidity around the world, but malaria transmission remains high in many countries in sub-Saharan Africa. A major challenge is the fact that most Plasmodium falciparum infections are asymptomatic creating a persistent parasite reservoir that continually fuels transmission to mosquitos. Our group has a long-standing collaboration with colleagues at the Navrongo Health Research Centre and Noguchi Memorial Institute of Medical Research in Ghana, and the University of Chicago in the US, to conduct longitudinal field-based epidemiological studies of the P. falciparumreservoir in Bongo District, Ghana (Tiedje et al., 2017). Our motivation for this study was to understand P. falciparum transmission dynamics in the context of the roll-out of a malaria control intervention by combining population genetics with more traditional epidemiological and entomological parameters. Our previous research in Bongo District established there was high levels of P. falciparum genetic diversity with no population structure (Ruybal‐Pesántez et al., 2017). We were therefore interested in exploring whether the addition of a short-term indoor residual spraying (IRS) programme against a background of widespread long-lasting insecticidal nets (LLINs) would bottleneck this P. falciparum population in Bongo and lead to reductions in diversity and changes in population structure. 

What difficulties did you run into along the way? One of the major technical limitations in P. falciparum genotyping is phasing multi-genome infections to assign multilocus haplotypes. Eighty per cent of the population of all ages where we work in Ghana have multiple diverse parasite genomes. This is  also a problem for whole genome sequencing of isolates. To get around this problem, we focus on genotyping monoclonal infections using panels of multi-allelic microsatellite markers or biallelic SNPs. In high-transmission settings like our study site in Ghana microsatellite genotyping of P. falciparum provides increased power of inference and higher resolution than biallelic SNPs (Anderson et al., 2000; Ellegren, 2004; Selkoe and Toonen, 2006).

What is the biggest or most surprising innovation highlighted in this study? In our paper, we find that despite the addition of three-rounds of IRS against a background of LLINs between 2013 – 2015, it did not lead to a population bottleneck or dramatic change in parasite genetic diversity. This was striking because IRS did achieve a >90% reduction in local malaria transmission intensity and 37.5% fewer malaria infections in the community. The potential for rebound of P. falciparum transmission is therefore highly likely if these control programmes are not implemented long-term. 

Moving forward, what are the next steps in this area of research? Population genomic approaches are increasingly being applied to enhance our understanding of epidemiology, transmission dynamics, and public health strategies for a variety of pathogens. In the malaria field, the potential of genomic data to guide control and elimination strategies has been recognized but is still in early stages with respect to its translation into general practice. In our paper, we highlight that genomic surveillance is pivotal to assess progress towards achieving the World Health Organisation Global Technical Strategy for Malaria 2016-2030 targets. Along with our collaborators in Ghana, we have conducted follow-up surveys in our study site to track the long-term implications of this IRS intervention, as well as other interventions that have been rolled out across Bongo District since 2015. We are also applying phylodynamic approaches to characterize variant antigen genes to further explore the impact of interventions on P. falciparum adaptation and fitness, as alternate but complementary surveillance metrics in this high-transmission setting. 

Dionne Argyropoulos, co-first author on this paper, is investigating the neutral and adaptive genetic diversity of P. falciparum in these follow-up surveys and in the context of other control interventions as part of her PhD research. Shazia Ruybal-Pesántez, co-first author on this paper, is now currently applying a suite of genomic epidemiology approaches to better understand residual and resurgent malaria transmission dynamics in the Asia-Pacific and Americas regions as part of her post-doctoral research.

What have you learned about methods and resources development over the course of this project? Firstly, it is important that you understand the basic principles of the concepts that you are using. It may seem rudimentary, but these principles will ensure that you are answering the scientific question that you are interested in and are maintaining scientific integrity throughout the research process. Asking for help or support from others in your field is also useful to bounce ideas and enhance your understanding of your research findings. The most exciting part of Molecular Ecology is how we utilise the insights molecular techniques to answer big picture questions. Our study integrated population genetics and genomic surveillance to address key research questions about malaria transmission and control interventions. To do this, we used existing molecular techniques (i.e., microsatellites) in new ways (i.e., to evaluate IRS over time). We also believe that it is important to not be afraid to apply novel techniques to new research questions, such as using bioinformatic tools and various packages in R.

What would your message be for students about to start developing or using novel techniques in Molecular Ecology? This project was unique as it involved field sample collection and processing, parasite genotyping, data generation and for the analysis required combining traditional epidemiological methods with population genetics and genomics approaches. When working with large sample sets and datasets, it is critical to pay attention to detail during data generation, curation and downstream analyses. Developing and strengthening coding skills was instrumental in enabling us to execute the necessary analyses of these data. We found R to be an incredibly useful resource to document our analyses and facilitate discussion and interpretation of the data with colleagues, while ensuring reproducibility of our work. We used several well-established R packages for data management and the population genetics analyses. Overall, this multidisciplinary project would not have been possible without being part of a multi-disciplinary team with a wealth of knowledge and the strong collaborations with experienced researchers in Ghana. 

Describe the significance of this research for the general scientific community in one sentence. We show how parasite genetics can be harnessed to better understand the efficacy of malaria control interventions, particularly by identifying key factors leading to parasite resilience that may not be reflected in other commonly used evaluation metrics. 

Describe the significance of this research for your scientific community in one sentence. Short-term indoor residual spraying with insecticides did not cause a dramatic change on the genetic diversity of P. falciparum in Bongo District, Ghana, therefore long-term strategies are necessary to genetically bottleneck the parasite population. 

Argyropoulos DC*, Ruybal-Pesántez S*, Deed SL, Oduro AR, Dadzie SK, Apparu MA, Asoala V, Pascual M, Koram KA, Day KP, Tredje KE. THe impact of indoor residual spraying on Plasmodium falciparum microsatellite variation in an area of high seasonal malaria transmission in Ghana, West Africa. Molecular Ecology. https://doi.org/10.1111/mec.16029. (*joint lead authors)

Joint lead authors Dionne Argyropoulos (left) and Shazia Ruybal-Pésantez (right). Photo Credits: The Stockholm International Youth Science Seminar, Unga Forskare; http://www.ungaforskare.se (left) and The Walter and Eliza Hall Institute of Medical Research; www.wehi.edu.au (right). 

Interview with the authors: genomic and phenotypic divergence between populations in translocated species

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In a recent issue of Molecular Ecology, Taylor et al. explore how between population translocations of a small and endangered freshwater fish may break the long-term evolutionary boundaries between populations in this species. In this study, the researchers used a combination of genomic and phenotypic data to show that translocation efforts, which were necessary for meeting species conservation goals, could alter some important genetic and morphological differences between populations. To read the complete story, see the full article now available online as well as the interview with the authors below.

The Bluemask Darter (Etheostoma akatulo) is an endangered freshwater fish restricted to four tributaries of the Caney Fork River in central Tennessee, USA. Photo credit: Todd Amacker (Tennessee Valley Authority).

What led to your interest in this topic / what was the motivation for this study? Some excellent work with microsatellites had previously identified three populations of Bluemask Darters across their small range (Robinson et al. 2013, Cons. Gen.). One population, larger and more genetically diverse than the others, was in the Collins River, in the western portion of the range. A second population was in Rocky River, more central. A third population was in Cane Creek and the Caney Fork to the east. There was also a population in the Calfkiller River, which has been extirpated for several decades. In this context, captive-reared Bluemask Darter progeny from the Collins River population were being introduced to the Calfkiller River. But the location of the Calfkiller, near the center of the range, gave an important quirk to the system. If the three populations were not equally distinct, then Calfkiller River might be better suited with individuals from Rocky River, Cane Creek, or Caney Fork, rather than the western Collins River. In other words, the geography of the system meant that we needed to know the phylogenetic or hierarchical structure of population structure to know what boundaries might be lurking between Collins River and an introduced population in the Calfkiller River.

What difficulties did you run into along the way? One challenge in our project was navigating the connection between our scientific discoveries and the underlying goals of conservation. Our analyses were focused on the quantitative aspects of Bluemask Darters phylogenetics. However, at the end of the day, we are talking about an endangered species, incredibly imperiled, with a tiny range and an uncertain future. No quantitative value can give us strict guidance about the normative problems of conservation. So a challenge was to unpack, as best as we could, how our conclusions about the phylogenetics, population structure, and demography of this species could ultimately help us conserve the multiple diverging lineages of Bluemask Darters. The reviewers and editors from Molecular Ecology helped us refine our logic and our language, and the final result is a paper that acknowledges the complexities and competing concerns of translocation in a system like this.

What is the biggest or most surprising innovation highlighted in this study? One of the most significant findings of this study was the discovery of two divergent clades of Bluemask Darters — precisely the boundary being broken by current conservation management decisions that move fish between clades! One clade includes western individuals and the other includes eastern individuals. To top it off, we had the unique opportunity to use historic morphological data from across the range, including the Calfkiller River site where the fish had been extirpated, and which was now being restored with fish originating from the western population. The consistent result was that eastern sites harbor a distinct population from western sites, and that the Calfkiller River was associated with the eastern population. It is now apparent that translocated individuals should be from a source consistent with the clade that previously occupied the Calfkiller River, and from a source that will not artificially perturb existing evolutionary boundaries. In our study, there are additional complicating factors — the ideal eastern translocation sources are low abundance and not as genetically diverse. So our study was also a new opportunity to address how we might balance multiple concerns, with genetic details, while addressing a complicated conservation issue.

Moving forward, what are the next steps in this area of research? In our paper, we discuss how there are juvenile Bluemask Darters that drift into the reservoir at the center of the range and may not be able to migrate upstream to appropriate habitats needed as adults. These young fish are from the Rocky River, and are part of the appropriate clade for restocking the Calfkiller River. However, the success of this strategy would depend on the population dynamics of young fish in the reservoir. Jeff Simmons, co-author on this paper, and colleagues will be pushing forward with the critical next steps. There will be studies of the density and abundance of juvenile fish in the reservoir, including whether juveniles recruit into a breeding population or simply perish before maturity. There is also ongoing monitoring of the translocated fish in the Calfkiller, and across the species range. All of this work is being combined with habitat quality monitoring aimed at unraveling the location, frequency, and cause(s) of water quality issues that are harming darters in this system. All together, we’re continuing to build a picture of how best to conserve the distinct lineages of Bluemask Darters. 

What have you learned about methods and resources development over the course of this project? Making this project successful meant combining dozens of different analyses — assembling, aligning, and filtering sequences, phylogenetics, population structure, genetic differentiation statistics, demographic simulations, to name a few — each of which have their own traps and idiosyncrasies. Getting these methods working required, first, well, getting everything to run, and then getting everything to run correctly. As useful as online documentation is, I learned there is no substitute for learning with colleagues who are engaging in similar research. Shout out especially to Dan MacGuigan, Daemin Kim, and Ava Ghezelayagh, all students with Tom Near. My conversations with these and other colleagues were critical for avoiding analytical pitfalls. These conversations also spurred ideas about new analyses and perspectives that will continue moving phylogenetic and population genetic work forward. 

What would your message be for students about to start developing or using novel techniques in Molecular Ecology? It’s been said before, but it really was important to have reproducible code for this project. Working with next-generation sequence data meant an enormous number of different files and analysis packages. Being able to switch between versions (like with git), automate programs (like with bash scripts), and manage software environments (like with conda) saved us hundreds of hours. At the end, you can neatly package everything up; all of our data and code, for example, is now stored on a dryad repository that could basically reproduce our paper from scratch in just a few commands. Even after publication, sharing code has also meant starting new conversations with other scientists about best practices, alternate methods, and new ideas for genetic analyses.

Describe the significance of this research for the general scientific community in one sentence. Our study uses genetic and morphological data to unravel how translocation strategies for an endangered freshwater fish might balance the competing conservation concerns of phylogenetic divergence, genetic diversity, and population demography.

Describe the significance of this research for your scientific community in one sentence. Our study identifies two distinct clades of endangered Bluemask Darters across their small range, where current management decisions are translocating individuals across those diverging lineages.

Taylor LU, Benavides E, Simmons JW, Near TJ. 2021. Genomic and phenotypic divergence informs translocation strategies for an endangered freshwater fish. Molecular Ecology. https://onlinelibrary.wiley.com/doi/10.1111/mec.15947.

Interview with the authors: Molecular dating for phylogenies containing a mix of populations and species by using Bayesian and RelTime approaches

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Written by Beatriz Mello and Sudhir Kumar

The work presents the most extensive evaluation to date of relaxed-clock methods’ performance to infer molecular times for datasets that contain a mixture of population and species divergences. Such datasets are commonly used in phylogeography, phylodynamics, and species delimitation studies. A wide range of biological scenarios was explored, which allowed us to compare and contrast the accuracies and precisions of divergence times for a Bayesian (BEAST) and a non-Bayesian (RelTime in MEGA)  method. Results showed that both RelTime and BEAST generally perform well and that RelTime presents a reliable and computationally efficient alternative to speed up molecular dating.

Read the full text here.

Lead author Beatriz Mello.

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

Our interest in this topic was driven by a major dilemma faced by researchers when analyzing data containing molecular sequences from closely related individuals and individuals from distinct species. This is because the Bayesian framework requires a tree prior to model the inference of divergence times. There is a myriad of tree priors available, but most importantly, they either model divergence between species or intra-species divergences. Thus, the adopted tree prior will be suboptimal to describe the evolutionary process for datasets with mixed sampling. So, our question was, although misspecified, would the use of the same tree prior produce good time estimates? Also, no one has previously examined how well non-Bayesian methods perform for such datasets, as they do not require specification of priors.

What difficulties did you run into along the way? 

One of the major difficulties we faced was the computational burden of Bayesian analysis. We all know that molecular dating using Bayesian methods can be time-consuming. However, they can become onerous in computer simulation studies because many datasets need to be analyzed. Each Bayesian analysis took several hours to complete, and we had to conduct thousands of Bayesian analyses. This was not an issue with the RelTime method, which finished computing in minutes. 

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

Our biggest finding is that, although the tree prior will frequently be an erroneous description of biological evolution, the accuracy of time estimates is not greatly impacted for most choices of the tree prior. This is good news to researchers working with phylogenies containing a mix of population and species. On top of that, RelTime is much faster than the Bayesian approach and produces similar results. This finding is important since the amount of sequence data is increasingly growing. A fast and accurate method allows hypotheses testing to be done using different assumptions and data subsets, improving the scientific rigor and reproducibility by others.

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

For Bayesian methods, it will be useful to develop faster approaches. However, the excellent performance of the RelTime approach that does not require prior specification is very encouraging. Evolutionary simulations employing even more diverse biological conditions and tree topologies, especially involving many sequences, will be a very useful next step, which may only be feasible with RelTime and other fast methods.

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

Our main message for students is to realize that no method is almighty. For those aspiring to develop new methods, it is our first step to apply different methods to a diversity of datasets and examine how the results differ, why they differ, and whether we can solve the problem discovered. It is again important for those applying new methods to use different methods and scrutinize differences in results. It is not a good idea to assume that a popular protocol is better than others by default; we need to keep an open mind and make decisions with evidence.

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

All of us learned quite a lot about the multispecies coalescent approach by analyzing simulated data because we know the correct result. The lesson was that some methods require many assumptions and that sometimes even small changes can have a big impact, resulting in distinct evolutionary inferences. So, we need to be very careful and explore a wide range of biological assumptions. Also, there is a strong need for more realistic simulation studies.

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

Researchers will now be able to decide which methods and approaches to apply in their particular dataset using results from this study.

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

The accuracy and precision of divergence time estimation for datasets that contain both intra- and interspecies molecular sequences is tested for slow (Bayesian) and fast (RelTime) molecular dating approaches.

References

Mello B, Tao Q, Barba-Montoya J, Kumar S. Molecular dating for phylogenies containing a mix of populations and species by using Bayesian and RelTime approaches. Mol Ecol Resour. 2021;21:122–136. https://doi.org/10.1111/1755-0998.13249

Interview with the authors: Museum epigenomics: Characterizing cytosine methylation in historic museum specimens

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Recent work has shown that it may be possible to characterize epigenetic markers from museum specimens, suggesting yet another potential contribution of collections-based research. In their recent Molecular Ecology Resources paper, Rubi et al. used ddRAD and bisulphite treatment to characterize cytosine methylation in deer mice (Peromyscus spp.). They characterized methylation in specimens from 1940, 2003, and 2013-2016. While they were able to characterize patterns in all specimens, older specimens had reduced methylation estimates, less data, and more interindividual variation in data yield than did new specimens. Rubi et al. demonstrate the promise of museum epigenetics while highlighting technical challenges that researchers should consider. Read the interview with lead author Dr. Tricia Rubi below to get a behind-the-scenes look at the research behind the paper.

Read the full paper here.

A Peromyscus maniculatus skull collected in 2002 and housed in the University of Michigan Museum of Zoology collection. Photo Credit: Dr. Tricia Rubi

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

When I wrote the original proposal for this work, the earliest papers had just been published in the field of ancient epigenomics (epigenetic studies using paleontological or archaeological specimens). My proposal centered around museum specimens, and I realized that no work had been done looking at epigenetic effects in more recent historic specimens (decades to centuries old), which comprise the bulk of museum collections. The recent field of museum genomics has already opened up a range of new directions for research using collections; I believe that museum epigenomics could be a similar frontier in collections-based research. In particular, epigenomic studies using museum collections could allow us to characterize change over time, which may help clarify the role of epigenetic effects in ecological and evolutionary processes.

What difficulties did you run into along the way? 

As is the case when developing any novel protocol, we encountered a variety of challenges and dead ends. However, we found that the main challenge for DNA methylation work using museum specimens was actually the same as the main challenge for regular genetic work using museum specimens: recovering usable amounts of DNA in the initial DNA extraction. DNA quantity and quality seemed to be a better predictor of success than specimen age; our oldest specimens (~76 years old) with higher DNA concentrations yielded a similar amount of methylation data relative to much “younger” specimens. The upside is that this challenge is already a familiar one to researchers conducting museum genomics work. Our data suggests that historic DNA samples that have been successfully used for genomic analyses are probably also well suited for methylation analyses.

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

I think the main takeaway from this study is that DNA methylation analyses using historic collections is feasible, even for lower quality specimens such as traditional bone preparations that are several decades old. Our oldest specimens in this study were dried skulls collected in 1940; while those specimens showed considerable variation in the amount of recoverable DNA, the specimens that yielded higher DNA concentrations performed well in our analyses.

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

There is plenty of work to be done! In this paper we highlight future directions for both developing methodology and applying museum epigenomics to ecological and evolutionary questions. Increasing the number of sequenced methylation markers or refining protocols for targeted sequencing are some obvious first steps in improving methods. Museum epigenomics approaches could be used to tackle a variety of questions in ecological and evolutionary epigenomics. In particular, epigenomic studies using museum specimens could be used to infer gene expression in past populations, or to directly measure how epigenetic markers change over time. 

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

Developing or refining novel techniques is an important and potentially rewarding process, but it requires enormous patience, as well as correctly managed expectations about the outcomes of the work. Researchers should be prepared for slower progress and a higher failure rate. Even when protocols do work, it may be more difficult to test broader ecological hypotheses due to unforeseen problems or non-optimal results. However, the upside is that projects using novel approaches can provide an important contribution to the field regardless of the specific outcomes of the work. My advice would be to design projects with several contingency plans to ensure that publishable data can be produced, and to factor in extra time for troubleshooting each step of the novel protocols.

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

Natural history specimens retain patterns of in vivo DNA methylation, the best studied epigenetic marker; museum epigenomics may be the next frontier in collections-based research.

References

Rubi TL, Knowles LL, Dantzer B. Museum epigenomics: Characterizing cytosine methylation in historic museum specimens. Mol Ecol Resour. 2020;20:1161–1170.

Interview with the authors: Evaluation of model fit of inferred admixture proportions

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Admixture models are widely-used in population genetics, but they make several simplifying assumptions, which, if violated, could result in misleading estimates of individual ancestry proportions. In a recent paper published in Molecular Ecology Resources, Garcia-Erill and Albrechtsen introduce evalAdmix, a program for detecting poor fit of admixture models to empirical data. evalAdmix uses the correlations of the residual differences between true and predicted genotypes to detect poor fit; when the assumptions of the model are not violated, the residuals of a pair of individuals should be uncorrelated. In simulation studies and analyses of empirical datasets, evalAdmix was useful in identifying model violations due to gene flow from unsampled ghost populations, continuous variation, population bottlenecks, and an incorrect assumed number of ancestral populations. Read the full article here, and read below for an exclusive interview with lead author Genís Garcia-Erill.

Full text: Garcia-Erill G. and Albrechtsen A. Evaluation of model fit of inferred admixture proportions. Mol Ecol Resour. 2020;20:936–949. https://doi.org/10.1111/1755-0998.13171.

Admixture model and evaluation with our method applied to worldwide human genetic variation. A. Admixture proportions inferred with ADMIXTURE assuming K=5 for all human populations from the 1000Genomes project. B. Evaluation of admixture model with the correlation of residuals performed with evalAdmix. Positive correlations are indicative of a bad model fit. The correlation of residuals shows that modelling with an ancestral population for each of the 5 major continental groups leads to a bad fit within most populations, and furthermore it gives additional information. For example we can see that the populations more genetically distant from the rest with which they are grouped, like Luhya in Webuye, Kenya (LWK) or Finish in Finland (FIN), have higher correlations of residuals, or it indicates the presence of substructure in some populations like the Gujarati Indians in Houston, TX (GIH).

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

The admixture model is one of the most used methods in population genetics, but it has already been known for some time that there are many potential issues with it. Specifically a recent study described very nicely different scenarios that can lead to wrong conclusions when applying the admixture model (Lawson et al. 2018). For example, they showed how multiple scenarios can lead to the same admixture results, and they also presented a method, badMixture, that can distinguish between those scenarios and evaluate model fit. However badMixture is quite difficult to apply, so we thought it would be interesting to develop an alternative method that could help in guiding the interpretation of admixture model results.

What difficulties did you run into along the way?

My background is in Biology and I had limited experience in computer science and statistics when I started with this project, so most of the difficulties were related to my learning how to work in these two disciplines. The method itself was relatively straightforward, but in order for it to work properly we needed to find a way to correct the bias caused by the frequency estimation. The frequency correction is only a small part of the main article, but it was where we put most of the work during the development of the method; that ended up as a few pages full of equations in the supplementary material. Another aspect where I had to put considerable effort was in making the implementation, since again I did not have much experience in developing software that would (hopefully) be used by other people. That made me consider things I would not usually think about.

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

I think the method itself is the main result of the study. As I said there is already a method to evaluate the admixture model fit, badMixture. However that method is rarely used, because it requires performing additional analyses with CHROMOPAINTER and also requires having data with good enough quality to at least call genotypes. The method we present is more generally accessible since it is based on information unique to the admixture model itself, meaning one can directly apply it to any data set to which the admixture model has been applied. So it provides what we think is a simple way, both in the application and in the interpretation, to evaluate the admixture model results.

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

There are several directions in which this work could be expanded. Something we already spent some time on is trying to develop a more firm theoretical foundation for the correlation of residuals as a measure of model fit, for example expressing it in terms of individual-specific Fst and the distance between the populations from which they are sampled, in a framework similar to that in Ochoa and Storey (2018). In the end we could not figure out the math and left it as a short mention in the discussion, but that would be something very nice to do. We also could not find a good way to use the residuals to develop some sort of measure of model fit at a purely individual level (instead of depending on the relationship between pairs of individuals, as it does right now), and that would also be very nice to do. Moreover, individual frequencies can also be calculated using principal component analyses, so this method could be expanded to work as an evaluation of a PCA as a description of population structure. Finally what we are looking forward to the most is to see how the method is applied to different datasets and how that helps gain new scientific insights.

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

I am myself a student who has very recently started developing and using novel techniques in Molecular Ecology, so I am not sure if I have enough experience and perspective to give any useful advice. But based on my limited experience, I would say that it is important not to be afraid to jump into new areas or fields where we feel like we might have too limited experience, and that often what at first seems very difficult will become more and more accessible and doable as we work on it.

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

I started working on this study during my Master studies, so it has been one of my first research experiences. Basically all I know about method development I learned during the course of this project, from the more practical skills related to developing and implementing a method to how to explain it, and make it accessible to the community that might be interested in using it. I realized that this can actually be very important, since it will affect how many people end up using it. Also, as a user of bioinformatics methods I really appreciate when I use a new method if it is easy to use and does not create too many problems.

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

It is important to consider the assumptions of the methods we use, since relevant violations of the assumptions might result in misleading or even meaningless results.    

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

It makes it possible and easy to evaluate the model fit of the admixture model at the individual-level in almost any context in which the admixture model is currently used, so it can be applied before concluding a population is a mixture of others, or it can help to choose a meaningful number of ancestral populations.

References

Lawson, D. J., Van Dorp L., and Falush, D.. A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots. Nature Communications 2018;9.1: 1-11.

Ochoa, A. and Storey, J. D. FST and kinship for arbitrary population structures I: Generalized definitions. BioRxiv 2016: 083915.

Interview with the authors: Applying genomic data in wildlife monitoring

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Massive parallel sequencing has led to an explosion of sequence data in recent years. However, the methods used to obtain such data are usually high-cost and time-intensive, and often require high-quality samples. This creates limits as to whether and how well such data can be used by researchers working in applied conservation science. Here, we speak to Alina von Thaden about her recent study in Molecular Ecology Resources. Using European wildcats as a case study, Alina and co-authors present a relatively low-cost and time-efficient workflow for the development and optimisation of microfluidic SNP panels, which can be used to obtain SNP data from minimally invasive samples. Beyond outlining the workflow and its applications, they go so far as to estimate the costs of their pipeline, providing valuable practical information for conservation scientists. Read on for an in-depth view of this study.

Monitoring elusive European wildcats (Felis silvestris) is heavily reliant on noninvasively collected DNA samples. Photo credit: Annsophie Schmidt.

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

We are mainly working on genetic monitoring of large carnivores and most of our research is based on noninvasively collected wildlife samples such as hairs, faeces and saliva traces. The field demands for very fast and reliable genetic analyses of samples with degraded DNA. And since funding is generally sparse in applied conservation, our methods need to be cost-effective and suitable for high-throughput approaches.

Genomic tools, on the other hand, usually involve large amounts of data, complex bioinformatic pipelines and typically rely on samples with high-quality DNA. We have been looking into ways to combine the advantages of genomics with the challenges of conservation monitoring. For some years now, we have been working with microfluidic arrays combined with reduced SNP panels and wanted to share our experiences with other labs interested in applying them.

What difficulties did you run into along the way? 

Setting-up and optimizing methodological resources comes along with several challenges – but there is a lot to learn! Most important to me was to remain skeptical about the results and to constantly validate them through analyzing the data from several perspectives and with different software. The validation of the technology also took a lot of extra lab hours, but we are confident that the workflow and guidelines that we present now will save others a lot of hands-on time and costs when optimizing SNP panels for degraded samples.

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

First of all, after years of developing the framework, we applied it to a new SNP panel designed for dog-wolf hybridization assessment (to be published) and found that the lab work for generating a new ready-to-use marker panel took us only a few weeks. To see the approach being proved effective was great and encouraged us to share it with the community.

Secondly, a large proportion of noninvasively collected samples could be run without or with only very few genotyping errors as compared to more traditional microsatellite-based genotyping (see also von Thaden et al. 2017). This has direct implications for genotyping costs and thus promotes the broader establishment of a genomic technology in applied conservation.

Alina von Thaden collecting reference samples of European wildcat (Felis silvestris) for testing a newly developed SNP panel. Photo credit: Annsophie Schmidt.

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

One of our next steps is to apply the technology to historical samples from museum collections. Additionally, we are going to implement the SNP panel from our current paper in routine genetic monitoring of European wildcats in Germany.

We currently develop other reduced SNP panels for a variety of endangered species in our lab, such as dormice and European bison. Besides neutral variation, we also aim to integrate functional markers, such as SNPs associated with disease susceptibility.

Further, we will test alternative platforms that will allow generating larger SNP sets for degraded samples. Ultimately, our long-term goal is the effective implementation of an “applied genomic wildlife monitoring” approach.

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

Get in contact with other groups working in this area! Sharing ideas and experience really helps to shape your project and refine the aims of your research. Most people are very cooperative and happy to contribute or answer questions.

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

Perseverance and tenacity. When exploring new directions the research journey may well become bumpy and lead you somewhere else than you initially expected. But it’s worth it – keep your goal in mind and be ready to rethink your strategy.

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

Bridging the gap between genomics and applied conservation is a key prerequisite for effective wildlife management, especially in the light of rapid biodiversity declines.

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

We demonstrate how reduced SNP panels can be efficiently developed and optimized for genotyping based on degraded wildlife samples.

References

von Thaden, A., Cocchiararo, B., Jarausch, A., Jüngling, H., Karamanlidis, A. A., Tiesmeyer, A. … Muñoz-Fuentes, V. (2017). Assessing SNP genotyping of noninvasively collected wildlife samples using microfluidic arrays. Scientific Reports, 7, 83. https://doi.org/10.1038/s41598-017-10647-w

Full paper

von Thaden, A., Nowak, C., Tiesmeyer, A., Reiners, T. E., Alves, P. C., Lyons, L. A., … & Hegyeli, Z. (2020). Applying genomic data in wildlife monitoring: Development guidelines for genotyping degraded samples with reduced single nucleotide polymorphism (SNP) panels. Molecular Ecology Resources. https://doi.org/10.1111/1755-0998.13136

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

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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

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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?

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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.

– Male Chironomus riparius on a heatmap illustrating the recombination landscape across the genome. Photo credit: Markus Pfenninger.

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

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

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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. 

A picture containing building Description automatically generated
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