Integrating Pool-seq uncertainties into demographic inference

Next-generation sequencing of pooled samples (Pool-seq) is a popular method to assess genome-wide diversity patterns in natural and experimental populations. However, Pool-seq is associated with specific sources of noise, such as unequal individual contributions. Consequently, using Pool-seq for the reconstruction of evolutionary history has remained underexplored. Here we describe a novel Approximate Bayesian Computation (ABC) method to infer demographic history, explicitly modelling Pool-seq sources of error. By jointly modelling Pool-seq data, demographic history and the effects of selection due to barrier loci, we obtain estimates of demographic history parameters accounting for technical errors associated with Pool-seq. Our ABC approach is computationally efficient as it relies on simulating subsets of loci (rather than the whole-genome) and on using relative summary statistics and relative model parameters. Our simulation study results indicate Pool-seq data allows distinction between general scenarios of ecotype formation (single versus parallel origin) and to infer relevant demographic parameters (e.g. effective sizes and split times). We exemplify the application of our method to Pool-seq data from the rocky-shore gastropod Littorina saxatilis, sampled on a narrow geographical scale at two Swedish locations where two ecotypes (Wave and Crab) are found. Our model choice and parameter estimates show that ecotypes formed before colonization of the two locations (i.e. single origin) and are maintained despite gene flow. These results indicate that demographic modelling and inference can be successful based on pool-sequencing using ABC, contributing to the development of suitable null models that allow for a better understanding of the genetic basis of divergent adaptation.

Phylogeography within the Peromyscus maniculatus species group: Understanding past distribution of genetic diversity and areas of refugia in western North America

The effects of anthropogenic climate change on biodiversity have been recognized on every continent, ocean, and across different taxonomic groups. Here, we study the range dynamics and demography of a cosmopolitan species: the deer mouse, Peromyscus maniculatus. We generated a multilocus SNP dataset using the ddRADseq protocol for 218 individuals across the geographic range within three western North American lineages of this species group. We evaluated population structure using several methods and explored the correlation between geographic and genetic distances. We modeled the demographic history using a site frequency spectrum approach and used a machine learning algorithm to infer current and past (Last Glacial Maximum; LGM) environmental suitability. Lastly, we explored the origin of population expansion for the identified lineages. The genome-wide SNP dataset was able to identify-three regionally distinct groups- 1) P. m. gambelii (southern California); 2) P. keeni (Pacific Northwest); 3) P. m. sonoriensis (a broad population spanning the Pacific Northwest through central California and across the Rocky Mountains into the Great Plains). Demographic analysis indicated the splits between the three populations occurred within the last 500 thousand years, with one very recent (late Holocene) split. Ecological niche models for each of these lineages predicted suitable environment present throughout their known ranges for current conditions, and a severe reduction of northern habitat in the past. The deer mouse has responded to past climate changes by expanding its range during interglacial periods and contracting its range during glacial periods leading to strong population differentiation. But lower magnitude climate change or other processes within the Holocene interglacial period led to population differentiation as well, which is likely still ongoing today given the substantial anthropogenic climate change and other landscape transformations caused by humans during the Anthropocene. By understanding the historical processes that led to the contemporary geographic distribution of biodiversity, we can determine the relative importance of different factors that shape biodiversity, now and into the future.

Towards a unified framework to study causality in Earth-life systems

There is considerable interest in better understanding how earth processes shape the generation and distribution of life on Earth. This question, at its heart, is one of causation. In this article I propose that at a regional level, earth processes can be thought of as behaving somewhat deterministically and may have an organized effect on the diversification and distribution of species. However, the study of how landscape features shape biology is challenged by pseudocongruent or collinear variables. I demonstrate that causal structures can be used to depict the cause–effect relationships between earth processes and biological patterns using recent examples from the literature about speciation and species richness in montane settings. This application shows that causal diagrams can be used to better decipher the details of causal relationships by motivating new hypotheses. Additionally, the abstraction of this knowledge into structural equation metamodels can be used to formulate theory about relationships within Earth–life systems more broadly. Causal structures are a natural point of collaboration between biologists and Earth scientists, and their use can mitigate against the risk of misassigning causality within studies. My goal is that by applying causal theory through application of causal structures, we can build a systems‐level understanding of what landscape features or earth processes most shape the distribution and diversification of species, what types of organisms are most affected, and why.