Thank you for finding me! I am fascinated by Evolution and currently working as a Postdoc at the University of Bern in Switzerland.

Why are living organisms so incredibly diverse? – My science is driven by the desire to find answers to this fundamental question. Defined broadly, my approach is to integrate population genomics within an ecological and phenotypic framework to shed light on the processes influencing adaptive organismal (population) diversification. However, my research is not contingent upon a specific type of approach, but rather follows my instinct for interesting questions. My main empirical study system is the recent adaptive radiation of threespine stickleback fish, although I have also been working with other organisms (cichlids, Daphnia, icefish, and lampreys). You can find my published work here.

Current projects involve the study of stickleback populations inhabiting distinct environments, such as lakes differing mainly in abiotic factors (e.g., Haenel et al. 2019), or lakes differing in a single biotic factor – that is, the presence vs. absence of a competing fish species. I am describing how the genome (e.g., Miller, Roesti, Schluter 2019), as well as traits (forthcoming), diverge due to this species interaction. It happens to be that most of my current empirical work uses allopatric populations, which stands in contrast to the majority of my previous work, where the emphasis was on population divergence in the presence of gene flow (e.g., Roesti et al. 2012, Roesti et al. 2014, Roesti et al. 2015). I plan to again focus on the latter in the near future.

More recently, I’ve really started to enjoy conducting large field experiments that allow me to test specific predictions prompted by observations in wild populations. One of the questions I am currently answering experimentally is whether a simple biotic change to the environment of an organism has resulted in substantial reproductive isolation (speciation) as a by-product.

Results from my empirical research stimulate and inform my theoretical work. I’m using simulations to better understand the genomic footprints of selection (e.g., Roesti et al. 2014). I have longstanding interest in gaining an evolutionary understanding of intra-genomic variation in the rate of crossover (recombination), and how this variation influences the genome during adaptive diversification (e.g., Roesti et al. 2012, Roesti et al. 2013, Haenel et al. 2018, Roesti 2018). Simulations further help me explore how crossover rate variation in context of polygenic adaptation influences evolutionary genomic methodologies and inferences, such as the search for targets of selection through genome scans (Roesti et al. 2012, Berner & Roesti 2017, Roesti 2018).

If you like to get further information on my past or ongoing work, do not hesitate to contact me!

SNIPPETS OF MY RESEARCH

Left: I nversion polymorphisms  differentiate lake and stream stickleback fish. We demonstrate experimentally the absence of recombination between the two inversion variants. However, comparing the genome between carriers of the different inversion variants suggests  rare recombination between the inversion variants in the center of the inversion polymorphisms  in nature (  Roesti et al. 2015   ).   Right:  Are adaptive loci clustered within a genome?  In this paper, I summarize  ideas of why and how adaptive loci should become clustered  when local adaptation occurs in the face of maladaptive gene flow. I argue that although many of these ideas are theoretically convincing, we have a poor understanding about where in a genome the adaptive loci are actually located. The pattern of ‘clustered loci’ is thus likely to be explained by  detection biases of popular empirical approaches , such as  population genome scans  and  QTL mapping . I use hundreds of previously published QTLs from threespine stickleback fish to demonstrate this bias empirically, and then suggest promising future avenues of how to get around these problems  (   Roesti 2018   )

Left: Inversion polymorphisms differentiate lake and stream stickleback fish. We demonstrate experimentally the absence of recombination between the two inversion variants. However, comparing the genome between carriers of the different inversion variants suggests rare recombination between the inversion variants in the center of the inversion polymorphisms in nature (Roesti et al. 2015).

Right: Are adaptive loci clustered within a genome? In this paper, I summarize ideas of why and how adaptive loci should become clustered when local adaptation occurs in the face of maladaptive gene flow. I argue that although many of these ideas are theoretically convincing, we have a poor understanding about where in a genome the adaptive loci are actually located. The pattern of ‘clustered loci’ is thus likely to be explained by detection biases of popular empirical approaches, such as population genome scans and QTL mapping. I use hundreds of previously published QTLs from threespine stickleback fish to demonstrate this bias empirically, and then suggest promising future avenues of how to get around these problems (Roesti 2018)

Broad-scale genome divergence is similar across many organisms  and is explained by the interaction of polygenic adaptive divergence, gene flow, and recombination rate variation. Tailored to empirical observations, we use  simulations  to quantify different  genetic mechanisms leading to elevated population divergence in regions of low recombination , including  genetic hitchhiking  and  gene flow barriers . We then predict the predictions from these models by comparing allopatric and parapatric populations of stickleback residing in different, or similar, environments  (   Berner & Roesti 2017   ).

Broad-scale genome divergence is similar across many organisms and is explained by the interaction of polygenic adaptive divergence, gene flow, and recombination rate variation. Tailored to empirical observations, we use simulations to quantify different genetic mechanisms leading to elevated population divergence in regions of low recombination, including genetic hitchhiking and gene flow barriers. We then predict the predictions from these models by comparing allopatric and parapatric populations of stickleback residing in different, or similar, environments (Berner & Roesti 2017).

Evidence for  genome-wide adaptation  to a seemingly  simple biotic change  to the environment of an organism: In many postglacial lakes in Western Canada, threespine stickleback and prickly sculpin co-occur and interact (food competition and opportunistic predation). In some lakes, however, there are only stickleback, but no sculpin. We find evidence for strong, parallel selection in the genome of stickleback as a consequence of sculpin presence/absence. The  extent of phenotypic and genomic adaptive divergence are positively associated   (   Miller, Roesti, Schluter 2019   ) . More work on this species interaction is forthcoming.

Evidence for genome-wide adaptation to a seemingly simple biotic change to the environment of an organism: In many postglacial lakes in Western Canada, threespine stickleback and prickly sculpin co-occur and interact (food competition and opportunistic predation). In some lakes, however, there are only stickleback, but no sculpin. We find evidence for strong, parallel selection in the genome of stickleback as a consequence of sculpin presence/absence. The extent of phenotypic and genomic adaptive divergence are positively associated (Miller, Roesti, Schluter 2019). More work on this species interaction is forthcoming.

Left:  Parallel adaptation from shared genetic variation , such as from  pre-existing or introgressed variation , produces a distinct  genetic signature  within a genome. We predict this signature using simulation models, and then confirm the prediction by genome-wide and targeted sequencing of natural stickleback populations  (   Roesti et al. 2014   ).   Right:  Chromosome-wide variation in recombination rate  shapes differentiation and diversity within the stickleback genome.  Recombination rate is consistently elevated towards the tips of chromosomes, and reduced in the center of chromosomes – a pattern that appears to be unrelated to the position of the centromeres , but instead, is related to functional constraints during meiosis. We further find that recombination rate variation influences nucleotide composition within a genome  (   Roesti et al. 2013   ).

Left: Parallel adaptation from shared genetic variation, such as from pre-existing or introgressed variation, produces a distinct genetic signature within a genome. We predict this signature using simulation models, and then confirm the prediction by genome-wide and targeted sequencing of natural stickleback populations (Roesti et al. 2014).

Right: Chromosome-wide variation in recombination rate shapes differentiation and diversity within the stickleback genome. Recombination rate is consistently elevated towards the tips of chromosomes, and reduced in the center of chromosomes – a pattern that appears to be unrelated to the position of the centromeres, but instead, is related to functional constraints during meiosis. We further find that recombination rate variation influences nucleotide composition within a genome (Roesti et al. 2013).

Lake-stream divergence of stickleback in lateral plating and the associated  molecular signatures   (   Roesti et al. 2015   ) .

Lake-stream divergence of stickleback in lateral plating and the associated molecular signatures (Roesti et al. 2015).

Phenotypic adaptation  of stickleback into lake and stream habitats, and associated  genetic and genome-wide signatures of selection . We detect the  constraint of gene flow  to genome-wide differentiation between diversifying populations, and find that the degree of  phenotypic and genome-wide differentiation correlate  positively. This study provided one of the first genome-wide demonstrations of how recombination rate variation shapes genomic differentiation during diversification  (   Roesti et al. 2012   ).

Phenotypic adaptation of stickleback into lake and stream habitats, and associated genetic and genome-wide signatures of selection. We detect the constraint of gene flow to genome-wide differentiation between diversifying populations, and find that the degree of phenotypic and genome-wide differentiation correlate positively. This study provided one of the first genome-wide demonstrations of how recombination rate variation shapes genomic differentiation during diversification (Roesti et al. 2012).

Left: Evidence for  strong genome-wide differentiation between distinct lamprey ecotypes  that were previously thought to be the product of phenotypic plasticity  (   Mateus et al. 2013   ) .  Right:  Phylogenomics  applied to Patagonotothen icefish species reveals  incomplete species boundaries  in this adaptive radiation  (   Ceballos 2019   ).

Left: Evidence for strong genome-wide differentiation between distinct lamprey ecotypes that were previously thought to be the product of phenotypic plasticity (Mateus et al. 2013).

Right: Phylogenomics applied to Patagonotothen icefish species reveals incomplete species boundaries in this adaptive radiation (Ceballos 2019).