Biology subjects

Bewick, S.

Publications and source records attributed to Bewick, S..

3 recordsLinked to original sources

bioRxiv2025

Host and Microbe Scale Processes Shape Spatial Variation in Aphaenogaster (Hymenoptera: Formicidae) Genetics and Their Microbiota

Like all ecological communities, host-associated (HA) microbiota are shaped by environmental selection and dispersal limitation. However, unlike communities of free-living organisms, communities of HA microbes experience selection and dispersal at two separate scales - the scale of the microbes and the scale of their hosts. Thus, HA microbes must tolerate not only the environment created by their host (microbe-scale environment), but also, the environment in which their host resides (host-scale environment). Likewise, HA microbes can disperse between hosts through either horizontal or vertical transmission (microbe-scale dispersal) but can also disperse between locations through host movement (host-scale dispersal). In this paper, we examine how multiscale environmental selection and dispersal limitation shape the genetics and HA microbiota of ants in the Aphaenogaster fulva-rudis-texana (Hymenoptera: Formicidae) complex. We begin by showing how spatial variation in Aphaenogaster genetics is shaped by host-scale environmental selection and dispersal limitation. We then show how this allows both host- and microbe-scale environmental selection to govern spatial variation in Aphaenogaster microbiota. Finally, we discuss the possibility that microbe-scale dispersal limitation also impacts spatial variation in Aphaenogaster microbiota and that this, in turn, may contribute to spatial variation in Aphaenogaster genetics. Ultimately, our results help to shed light on the myriad of interacting factors governing spatial variation in HA microbiota, including the potential for complex, bidirectional interactions between host- and microbe-scale processes.

ecology↗

bioRxiv2023

A Conceptual Framework for Host-Associated Microbiomes of Hybrid Organisms

Hybridization between organisms from evolutionarily distinct lineages can have profound consequences on organism ecology, with cascading effects on fitness and evolution. Most studies of hybrid organisms have focused on organismal traits, for example various aspects of morphology and physiology. However, with the recent emergence of holobiont theory, there has been growing interest in understanding how hybridization impacts and is impacted by host-associated microbiomes. Better understanding of the interplay between host hybridization and host-associated microbiomes has the potential to provide insight into both the roles of host-associated microbiomes as dictators of host performance as well as the fundamental rules governing host-associated microbiome assembly. Unfortunately, there is a current lack of frameworks for understanding the structure of host-associated microbiomes of hybrid organisms. In this paper, we develop four conceptual models describing possible relationships between the host-associated microbiomes of hybrids and their progenitor or parent taxa. We then integrate these models into a quantitative 4H index and present a new R package for calculation, visualization, and analysis of this index. Finally, we demonstrate how the 4H index can be used to compare hybrid microbiomes across disparate plant and animal systems.

ecology↗

bioRxiv2019

Implicit versus explicit control strategies in models for vector-borne disease epidemiology

Throughout the vector-borne disease modeling literature, there exist two general frameworks for incorporating vector management strategies (e.g. area-wide adulticide spraying and larval source reduction campaigns) into vector population models, namely, the \"implicit\" and \"explicit\" control frameworks. The more simplistic \"implicit\" framework facilitates derivation of mathematically rigorous results on disease suppression and optimal control, but the biological connection of these results to realworld \"explicit\" control actions that could guide specific management actions is vague at best. Here, we formally define the biological and mathematical relationships between implicit and explicit control, and we provide detailed mathematical expressions relating the strength of implicit control to management-relevant properties of explicit control for four common intervention strategies. These expressions allow optimal control and sensitivity analysis results in existing implicit control studies to be interpreted in terms of real world actions. Our work reveals a previously unknown fact: implicit control is a meaningful approximation of explicit control only when resonance-like synergistic effects between multiple controls have a negligible effect on average population reduction. When non-negligible synergy exists, implicit control results, despite their mathematical tidiness, fail to provide accurate predictions regarding vector control and disease spread. The methodology we establish can be applied to study the interaction of phenological effects with control strategies, and we present a new technique for finding impulse control strategies that optimally reduce a vector population in the presence of seasonally oscillating model parameters. Collectively, these elements build an effective bridge between analytically interesting and mathematically tractable implicit control and the challenging, action-oriented explicit control.

ecology↗