We interviewed graduate student Alexandre Rêgo and Professor Zach Gompert from Utah State University about their work on evolutionary rescue in seed beetles where they explore how demographic history affects parallel evolution at the genetic level. Their results have important implications for or understanding of repeatability and predictability of evolution. Read the full text below:

Alexandre Rêgo, Frank J. Messina, and Zachariah Gompert. (2019) Dynamics of genomic change during evolutionary rescue in the seed beetle Callosobruchus maculatus. https://onlinelibrary.wiley.com/doi/abs/10.1111/mec.15085?af=R

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

(AR and ZG) We were interested in evolutionary rescue, that is in cases where population decline and extinction in a marginal or stressful environment is halted and reversed by adaptive evolution. We were especially interested in what patterns of change and natural selection look like across the genome during rescue. How many genes are involved? How much do gene/allele frequencies change? And how fast? We turned to an evolve-and-resequence experiment with Callosobruchus maculatus seed beetles for this. Past work has shown that these beetles can barely survive on lentils (survival rates are ~1%), but that they sometimes can rapidly adapt and persist on this novel host (survival rates can climb to 80 or 90% in fewer than 20 generations).

What difficulties did you run into along the way?

(AR and ZG) Extinction. We were drawn to this system because of the potential for adaptation or extinction, and because of the related extraordinary pace and degree of adaptation when it occurs. We wanted to measure selection and genome-wide evolutionary change during evolutionary rescue, but (for better and worse) only one of 10 replicate lines was rescued. This limited our ability to assess parallelism during rescue, but also highlighted how real the possibility of extinction (in the absence of rapid adaptation) is in this system.

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

(AR and ZG) We were most shocked by just how rapid evolutionary changes were at the molecular level. We saw numerous cases where allele frequencies at multiple (albeit not always independent) genetic loci shifted by 20-40% or more in a single generation. This is really much more extreme than rates of change often considered.

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

(AR and ZG) We are taking our work on C. maculatus in two directions. First, we have finished creating and sequencing crosses between the lentil adapted and source populations to identify genetic variants associated with specific fitness components (e.g., development time and adult size) on lentil. This will make a nice comparison with the selection scans. Second, we are now working with additional populations, some of which do a bit better on lentil, to examine consistency in genomic change across lines in lentil adaptation and to figure out whether hybridization facilitates adaptation to this marginal host.

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

(AR) Attention to detail is critical. Be as careful as possible in how you plan and organize your data on analyses. Population genomic analyses, especially with approximate Bayesian computation, generate an inordinate amount of output. And you will almost certainly want to re-run things with slightly different parameters, etc., so carefully documenting everything is critical.

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

(AR) There are many things that must be done in order to complete a study, each requiring a different set of skills. As a graduate student, it can be daunting to acquire and become proficient at so many things. However, at the end of this study, I can look back and see that I have made progress as a scientists on many fronts. What I’ve learned from this project has provided me a solid foundation for my current studies on which I can further improve.

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

(AR and ZG) Very rapid adaptation is possible when species or populations find themselves in harsh environments, sometimes this is enough to prevent extinction, and sometimes it isn’t.

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

(AR and ZG) Population genomic patterns associated with evolutionary rescue differ in subtle and not so subtle ways from patterns observed in other situations involving less extreme or softer selection, and thus warrant more study and consideration.

We interviewed Sarah Knutie (an Assistant Professor at the University of Connecticut) about the research she led examining how humans can shape the microbiota of Darwin’s finches in unexpected ways. Read the full text below:

Knutie, SA, Chaves, JA, Gotanda, KM. Human activity can influence the gut microbiota of Darwin’s finches in the Galapagos Islands. Mol Ecol. 2019; 28: 2441– 2450. https://doi.org/10.1111/mec.15088

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

The inspiration for this project came from our observations of Darwin’s finches trying to steal food from our plates and my co-authors’ recent paper (De Leon et al. 2018) showing that the diet of finches differed in urban and non-urban areas. Since diet can influence the gut microbiota of the host, Kiyoko and I decided to return to the same field site and look at how the gut microbiota of Darwin’s finches differ among sites with varying exposure to human activity. However, at the time, Kiyoko and I were both post-docs without funding for our idea, so we decided to crowdfund the project (Kiyoko was more successful than me!). With help from our crowdfunding campaign and our own out-of-pocket money, we were able to head to the Galapagos for a few weeks to collect the fecal samples for our study.

De León, L. F., Sharpe, D. M., Gotanda, K. M., Raeymaekers, J. A., Chaves, J. A., Hendry, A. P., & Podos, J. (2018). Urbanization erodes niche segregation in Darwin’s finches. Evolutionary Applications.

What difficulties did you run into along the way? 

We decided to do all of the bacterial DNA extractions in the Galapagos since we were unsure whether we would be able to export our samples immediately after the field season. However, equipment and lab space can be hard to come by in the Galapagos. Therefore, we created a clean extraction space in our apartment with whatever equipment I could buy on the cheap and transport from the US to our Galapagos “lab”. Specifically, I brought a sous vide for the heat step and rigged a vintage vortex for the agitation step of the extraction. Was that just an #OverlyHonestMethods moment?

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

Even though De Leon et al. (2018) found that the diet of Darwin’s finches differed in response to urbanization, I was not confident that we would find parallel differences in the gut microbiota. Although the field sites vary in their exposure to human activity, they are geographically quite close to each other. Therefore, a part of me thought that our samples sizes would not be large enough to detect an effect of site on the gut microbiota. Fortunately, my gut instinct was wrong and our study demonstrated that human activity can impact the gut microbiota and body mass of finches, even among adjacent sites.

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

My lab is currently looking at whether the gut microbiota of urban and non-urban Darwin’s finches influences their immune response to the invasive parasitic nest fly Philornis downsi. Since the gut microbiota can affect the development and maintenance of the immune system and the gut microbiota of finches is affected by human activity, it is possible that urban finches defend themselves differently against the invasive parasite than non-urban birds.

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

Start by being present. My favorite project ideas are inspired by observations in the field.  Once you are inspired, be a sponge. Read everything that you can about your topic and if you have the opportunity, talk with experts in the field. Then, create a solid study design using the scientific method; think through all possible outcomes and draw graphs of your predicted results. Although establishing patterns (like in the published paper) is very important to understand the underlying function of the microbiota, try to determine causation with an experiment, if possible. If applicable, when you start your DNA extractions, especially if you are working with birds, talk to experts in the field. Many colleagues and myself have resolved issues with the extraction protocols and sequencing and might be able to help.  

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

I have learned to find collaborators who I enjoy working with on projects. Science is so much more fun when you can interact with brilliant and kind people.

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

Human presence in the Galapagos Islands is affecting the gut microbiota and body mass of Darwin’s finches.

Morphological evidence has long supported that simultaneous hermaphrodites invest more into the male sexual function at larger group sizes. In their recent work, Steven Ramm and colleagues use transcriptomic data link this morphological response to gene expression. Learn more about their study below, and in the full paper.

Ramm SA, Lengerer B, Arbore R, et al. Sex allocation plasticity on a transcriptome scale: Socially sensitive gene expression in a simultaneous hermaphrodite. Mol Ecol. 2019;28:2321–2341. https://doi.org/10.1111/ mec.15077

What led to your interest in this topic / what was the motivation for this study? 
We’ve long since known about the ability of many simultaneous hermaphrodites to adjust their sex allocation at a morphological level, fine tuning their investment into their male and female sex functions according to cues in their social environment so as to maximise their total fitness returns. More specifically, at larger group sizes, individuals have to compete more to gain fertilisations and it therefore pays to shift investment from their female to their male sex function. The exciting prospect with this study was to be able to link this morphological response to the underlying plasticity in gene expression in organs such as the testis and ovary.

What difficulties did you run into along the way? 
One difficulty in switching from a morphological to a transcriptomic level of analysis was simply the sheer amount of data that we generated. We were measuring gene expression in tens of thousands of transcripts and found thousands of differentially expressed candidates that differed in expression according to the social environment, making it initially difficult to decide how best to focus our follow-up studies of the transcriptomic data.

What is the biggest or most surprising finding from this study? 
For me, one of the biggest surprises was that such a large proportion of the M. lignano genome is differentially regulated in its expression according to the social environment. There are different ways we measured that, but at least 10% of the transcriptome showed evidence for variable expression depending on something as simple as the number of other flatworms they regularly encountered. That’s of course both a blessing and a curse, since we’ve still got a big task ahead figuring out the functional roles of all those genes, and in particular the key gene expression changes within the subset of differentially expressed transcripts that really drive the plasticity.

Moving forward, what are the next steps for this research? 
One thing we’ve already been following up on in some detail is that alongside many transcripts which we expected to be plastically expressed in the testis and ovary (since these are the key organs for sex allocation), we found an additional large class of genes that were also highly plastic in their expression and are predominately or exclusively expressed in the tail of the flatworms. We’ve now found that many of these are expressed in the prostate gland cells responsible for seminal fluid production, another key component of male allocation, opening up the possibility of studying their functional and adaptive significance for sperm competition and sexual conflict.  

What would your message be for students about to start their first research projects in this topic? 
Be realistic about what that project can achieve. The ability to measure gene expression on a transcriptome scale has been a huge boon for the field, and opens up many exciting possibilities. But because we can now measure everything at once, there’s always a risk of drowning in data. If, for example, we would now follow up on all the plastically expressed genes we found in our study, that could easily fill several PhD projects. Clear questions and good experimental design become even more important as technology advances, not less.

What have you learned about science over the course of this project? 
The importance of collaboration. I’ve benefited from working with a great team of people spread across four different countries, which allowed us to combine several different techniques (morphological assays, RNA-Seq, in situ hybridisation, RNAi) in a single study. 

Describe the significance of this research for the general scientific community in one sentence.
Our project has begun to show how the dynamic allocation of resources to producing either sperm or eggs in hermaphroditic organisms occurs at the underlying level of the genes responsible for spermatogenesis and oogenesis, respectively. 

Describe the significance of this research for your scientific community in one sentence.
I hope our research can move sex allocation research forward on a couple of fronts: primarily, it offers a first – though still far from complete – glimpse into the mechanisms of phenotypic plasticity in simultaneous hermaphrodites; and second, it provides the starting point for deciphering the functions of a vast swathe of genes now implicated as being of adaptive significance for either the male or female sex function.

In this exciting research, Jack Sullivan and Megan Smith and colleagues use machine learning techniques to create a powerful predictive framework for phylogeographic studies. Learn about their experiences building this novel research approach!

What led to your interest in this topic / what was the motivation for this study? 
We’ve been interested in the question of whether or not we can predict phylogeographic patterns for some time. Initially, we attempted to predict whether or not unstudied species harbored cryptic diversity using climate and taxonomic information (https://royalsocietypublishing.org/doi/pdf/10.1098/rspb.2016.1529). We used taxa that were known to either harbor or lack cryptic diversity to train a Random Forest classifier, and then made predictions about unstudied taxa. We found that we could predict the presence or absence of cryptic diversity (with low error rates when based on cross-validation!) We also saw that taxonomy was a powerful predictor of cryptic diversity, and we began to wonder why. In this study, we evaluate whether life history traits can explain this result.

What difficulties did you run into along the way? 
When trying to use life history traits to make predictions across taxonomic levels, the most difficult problem is finding appropriate traits. Many traits, while likely very informative for specific taxa, are difficult to score across taxonomic groups. Our dataset included mammals, plants, arthropods, gastropods, amphibians, and birds. The biggest difficulty was finding life history traits that we could score across all of these groups and that we hypothesized would be meaningful predictors of phylogeographic patterns.

What is the biggest or most surprising finding from this study? 
Life history traits are great predictors of phylogeographic patterns. In one of the systems we studied, these traits can even replace taxonomy as a predictor, suggesting that taxonomy was serving as a proxy for these traits. We find that traits related to reproduction (e.g. reproductive mode, clutch size) and trophic level are particularly informative in our predictive framework.

Moving forward, what are the next steps for this research? 
There is a wealth of data on phylogeographic patterns available, but most studies have focused on one or a few species. The framework developed in Espíndola et al. (2016) and expanded upon here provides a mechanism for integrating these studies into a predictive framework. As data continue to become available, our approach will allow policymakers and scientists alike to make predictions about what patterns are expected in unstudied species. Further, this approach can provide insight into which life history traits drive differences in species responses to historic events, and this may allow us to begin to understand why species respond to similar events in idiosyncratic ways.

What would your message be for students about to start their first research projects in this topic? 
Think early and often about how your work can be integrated into the field in a broader way. Particularly as molecular data become easier to collect, more and more single species studies accumulate. By looking at these studies in a new light and integrating across studies, we can learn a lot about communities and overarching patterns.

What have you learned about science over the course of this project? 
Over the course of this project, I’ve learned to look at data in many different ways. Our initial work on this topic suggested that taxonomy was the most important predictor of phylogeographic patterns. While true, this told us little about the biology of the taxa we were studying. By delving deeper and adding life history traits to our study we were able to draw biologically meaningful conclusions about why species responded differently to geologic and climatic events.

Describe the significance of this research for the general scientific community in one sentence.
By using machine learning, we can integrate genomic, ecological, and trait data to make predictions about how species have responded to historic events, and to understand which factors lead to idiosyncratic responses.

Describe the significance of this research for your scientific community in one sentence.
Using publicly available data and machine learning techniques, we can make predictions about phylogeographic patterns across broad taxonomic groups, and we can draw conclusions about how life history traits influence these patterns.