Biology subjects

Heath, K.

Publications and source records attributed to Heath, K..

3 recordsLinked to original sources

bioRxiv2018

Differential effects of conspecific and heterospecific density on the development of Aedes aegypti and Aedes albopictus larvae

O_LIBetween-species competition shapes the distribution and abundance of populations. Aedes aegypti and Ae. albopictus are vectors of pathogens such as dengue and are known to compete at the larval stage.\nC_LIO_LIThe outcome of this inter-species competition has been found to be context dependent, with the strength and direction changing with resource availability and type. We were motivated by this uncertainty, and aimed to elucidate the magnitude and mechanism of competition.\nC_LIO_LIWe manipulated the larval density of mixed and single species cohorts of larvae, measuring the effects on survivorship and development time. Unlike other related studies, we adjusted the feeding regime so that the per-capita resource availability was kept constant across all density treatments, at a level sufficient for successful development. This ensured that each larvae at least had the opportunity to gain the requisite resources for pupation.\nC_LIO_LIOur analysis found that Ae. aegypti suffered notably less mortality due to intra- and interspecific competition. For both species, intra- and interspecific competition led to the survival of faster developing individuals, with the exception that slower developing Ae. albopictus larvae survived when exposed a combination of both high con- and heterospecific densities.\nC_LIO_LIThese results show that the competition between Ae. aegypti and Ae. albopictus can still occur even when resources are theoretically adequate for development. This suggests that larvae can alter resource seeking and consumption parameters when exposed to high densities of conspecifics and heterospecifics, leading to contest competition. Evidence for resource-independent mechanisms of competition such as crowding are also found, as is evidence for the importance of demographic stochasticity in population processes.\nC_LI

ecology↗

bioRxiv2017

xenoGI: reconstructing the history of genomic island insertions in clades of closely related bacteria

BackgroundGenomic islands play an important role in microbial genome evolution, providing a mechanism for strains to adapt to new ecological conditions. A variety of computational methods, both genome-composition based and comparative have been developed to identify them. Some of these methods are explicitly designed to work in single strains, while others make use of multiple strains. In general, existing methods do not identify islands in the context of the phylogeny in which they evolved. Even multiple strain approaches are best suited to identifying genomic islands that are present in one strain but absent in others. They do not automatically recognize islands which are shared between some strains in the clade or determine the branch on which these islands inserted within the phylogenetic tree.\n\nResultsWe have developed a software package, xenoGI, that identifies genomic islands and maps their origin within a clade of closely related bacteria, determining which branch they inserted on. It takes as input a set of sequenced genomes and a tree specifying their phylogenetic relationships. Making heavy use of synteny information, the package builds gene families in a species-tree-aware way, and then attempts to combine into islands those families whose members are adjacent and whose most recent common ancestor is shared. The package provides a variety of text-based analysis functions, as well as the ability to export genomic islands into formats suitable for viewing in a genome browser. We demonstrate the capabilities of the package with several examples from enteric bacteria, including an examination of the evolution of the acid fitness island in the genus Escherichia. In addition we use output from simulations and a set of known genomic islands from the literature to show that xenoGI can accurately identify genomic islands and place them on a phylogenetic tree.\n\nConclusionsxenoGI is an effective tool for studying the history of genomic island insertions in a clade of microbes. It identifies genomic islands, and determines which branch they inserted on within the phylogenetic tree for the clade. Such information is valuable because it helps us understand the adaptive path that has produced living species. Given the large and growing number of sequenced microbial genomes, this sort of analysis will become increasingly useful in the future.

bioinformatics↗

bioRxiv2017

Geographically structured genetic variation in the Medicago lupulina-Ensifer mutualism

Mutualisms are interspecific interactions affecting the ecology and evolution of species. Patterns of geographic variation in interacting species may play an important role in understanding how variation is maintained in mutualisms, particularly in introduced ranges. One agriculturally and ecologically important mutualism is the partnership between legume plants and rhizobia. Through characterizing and comparing the population genomic structure of the legume Medicago lupulina and two rhizobial species (Ensifer medicae and E. meliloti), we explored the spatial scale of population differentiation between interacting partners in their introduced range in North America. We found high proportions of E. meliloti in southeastern populations and high proportions of E. medicae in northwestern populations. Medicago lupulina and the Ensifer genus showed similar patterns of spatial genetic structure (isolation by distance). However, we detected no evidence of isolation by distance or population structure within either species of bacteria. Genome-wide nucleotide diversity within each of the two Ensifer species was low, suggesting limited introduction of strains, founder events, or severe bottlenecks. Our results suggest that there is potential for geographically structured coevolution between M. lupulina and the Ensifer genus, but not between M. lupulina and either Ensifer species.

evolutionary biology↗