Flea Genetic Diversity in Gunnison's Prairie Dog Colonies and Its Implications for Flea Transmitted Diseases

Understanding disease-causing organisms from a broader ecological perspective has proven a valuable tool for understanding the causes of disease outbreaks in various organisms. Several insect species act as both parasites and pathogen carriers, making them important players in the spread of diseases in human and wildlife communities. This study aimed to determine what could be used to predict the distribution of flea genetic diversity parasitizing Gunnison’s prairie dogs (Cynomys gunnisoni) as a foundation for understanding the potential influence and implications this may have for transmission of disease causing microbes such as Rickettsia, Bartonella, and Yersinia pestis. A much higher level of flea genetic diversity was found in the colonies compared to what has been observed for fleas parasitizing black-tailed prairie dogs (Cynomys ludovicanus). Although none of the factors tested (location of colony relative to others, prairie dog genetic diversity, or number of mammals species) were able to predict the genetic diversity of fleas observed across colonies, potential implications for the spread of disease causing microbes are still considered, with recommendations for further research. The present study emphasizes the need to collect further data on mammals that frequently interact with Gunnison’s prairie dogs, as well as abiotic factors such as climate and temperature, both of which could be used to further investigate the survival and transmission of pathogens in this system

Predicting Evolution and Inferring Its Consequences

This dissertation concerns the roles of genetic and environmental factors in producing trait variation in evolving populations, with an emphasis on the creation and use of statistical tools that facilitate predictions. The research concerns evolution across a variety of spatial and temporal scales and environmental conditions. In each study I employ statistical approaches to make predictions about how observed trait variation is derived from variation due to the environment, or genetics, or the interaction between the two. The first chapter investigates the evolution of species' performance curves through the construction of a Bayesian model that facilitates comparisons among groups. The model is used to investigate how performance curves have evolved among taxa in the genus Lasthenia, and how variation in their performance curves predict where they occur in nature with respect to fine-scale hydrological gradients. I find evidence that the microhabitats taxa occupy along fine-scale hydrological gradients is best predicted by their overall productivity rather than the conditions that optimize their performance. The second chapter concerns predictability of evolution during colonization. Using flour beetle microcosm experiments, this work demonstrates that the genetic and phenotypic predictability of evolution during colonization decays over time, highlighting the relative contributions of stochastic and deterministic forces that shape variation in dispersal, fecundity, and body size. The last chapter addresses key challenges in predicting phenotypes from genetic sequence data. I develop a novel approach to representing DNA sequences, and demonstrate its value to capture multiple types of genetic variation, which I then show can be effectively used as input for models predicting phenotype. Collectively, the three studies provide insights into the complex and interacting roles of genetic and environmental variation in generating traits, and the development and use of statistical methods to make predictions that are important to our understanding of evolutionary processes

Genetic diversity and thermal performance in invasive and native populations of African fig flies

During biological invasions, invasive populations can suffer losses of genetic diversity that are predicted to negatively impact their fitness/performance. Despite examples of invasive populations harboring lower diversity than conspecific populations in their native range, few studies have linked this lower diversity to a decrease in fitness. Using genome sequences, we show that invasive populations of the African fig fly, Zaprionus indianus, have less genetic diversity than conspecific populations in their native range and that diversity is proportionally lower in regions of the genome experiencing low recombination rates. This result suggests that selection may have played a role in lowering diversity in the invasive populations. We next use interspecific comparisons to show that genetic diversity remains relatively high in invasive populations of Z. indianus when compared to other closely related species. By comparing genetic diversity in orthologous gene regions, we also show that the genome-wide landscape of genetic diversity differs between invasive and native populations of Z. indianus, indicating that invasion not only affects amounts of genetic diversity, but also how that diversity is distributed across the genome. Finally, we use parameter estimates from thermal performance curves measured for 13 species of Zaprionus to show that Z. indianus has the broadest thermal niche of measured species, and that performance does not differ between invasive and native populations. These results illustrate how aspects of genetic diversity in invasive species can be decoupled from measures of fitness, and that a broad thermal niche may have helped facilitate Z. indianus's range expansion.Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: Dimensions of Biodiversity award number 1737752Data used to generate genome annotations was generated by extracting whole RNA from groups of ~5 adult flies (24 hours after eclosion). Transcripts were assembed using Trinity (Grabherr et al. 2011; Hass et al. 2013) and annotations were generated using the MAKER pipeline (v3.01.02; Holt and Yandell 2011; Campbell et al. 2014). Data on thermal performance we generated in the lab under controlled conditions. All scripts used to fit thermal performance curves are given in this Dryad deposit. Software available for the method used are available at github.com/silastittes/performr

Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than threefold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed the best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone

rdmc: An Open Source R Package Implementing Convergent Adaptation Models of Lee and Coop (2017)

The availability of whole genome sequencing data from multiple related populations creates opportunities to test sophisticated population genetic models of convergent adaptation. Recent work by Lee and Coop (2017) developed models to infer modes of convergent adaption at local genomic scales, providing a rich framework for assessing how selection has acted across multiple populations at the tested locus. Here I present, rdmc, an R package that builds on the existing software implementation of Lee and Coop (2017) that prioritizes ease of use, portability, and scalability. I demonstrate installation and comprehensive overview of the package’s current utilities

Posterior trees

Each line of the file contains a posterior phylogeny generated using BALi-Phy (Suchard and Redelings 2006

Name key

Name key matching growth chamber taxa and tree tip label

Lasthenia depths

Field measures for average depth (cm) of taxon occurring within vernal pools

Growth chamber data

Raw growth chamber data for fourteen Lasthenia taxa analyzed in manuscript. Response variable: Inflor_biomass. Treatment levels, D (drought = 1), MD (minor drought = 2), B (benign = 3), MF (minor flood = 4), F (flood = 5). See paper and GitHub repository for details on how this file was filtered and parsed for downstream analyses

Data from: Grow where you thrive, or where only you can survive? An analysis of performance curve evolution in a clade with diverse habitat affinities

Performance curves are valuable tools for quantifying the fundamental niches of organisms and testing hypotheses about evolution, life history trade-offs, and the drivers of variation in species' distribution patterns. Here, we present a novel Bayesian method for characterizing performance curves that facilitates comparisons among species. We then use this model to quantify and compare the hydrological performance curves of 14 different taxa in the genus Lasthenia, an ecologically diverse clade of plants that collectively occupy a variety of habitats with unique hydrological features, including seasonally flooded wetlands called vernal pools. We conducted a growth chamber experiment to measure each taxon's fitness across five hydrological treatments that ranged from severe drought to extended flooding, and identified differences in hydrological performance curves that explain their associations with vernal pool and terrestrial habitats. Our analysis revealed that the distribution of vernal pool taxa in the field do not reflect their optimal hydrological environments: all taxa, regardless of habitat affinity, have highest fitness under similar hydrological conditions of saturated soil without submergence. We also found that a taxon's relative position across flood gradients within vernal pools is best predicted by the height of its performance curve. These results demonstrate the utility of our approach for generating insights into when and how performance curves evolve among taxa as they diversify into distinct environments. To facilitate its use, the modeling framework has been developed into an R package (https://github.com/silastittes/performr)