Biology subjects

Boots, M.

Publications and source records attributed to Boots, M..

3 recordsLinked to original sources

bioRxiv2018

Industrial bees: when agricultural intensification doesn’t impact local disease prevalence

O_LIAlthough it is generally thought that the intensification of farming will result in higher disease prevalences there is little specific modelling testing this idea. We build multi-colony models to inform how apicultural intensification is predicted to impact honeybee pathogen epidemiology at the apiary scale.\nC_LIO_LICounter to the prevailing view, our models predict that intensification, captured though increased population sizes, changes in population network structure, and increased between-colony transmission, is likely to have little effect on disease prevalence within an apiary.\nC_LIO_LIThe greatest impacts of intensification are found for diseases with relatively low R0 (basic reproduction number), however, such diseases cause little overall disease prevalence and therefore the impacts of intensification are minor. Furthermore, the smallest impacts of intensification are found for diseases with high R0 values, which we argue are typical of important honeybee diseases.\nC_LIO_LIPolicy Implications: Our findings highlight a lack of support for the hypothesis that current and ongoing intensification leads to notably higher disease prevalences. More broadly, our work demonstrates the need for informative models of agricultural systems and management practices in order to understand the implications of management changes on diseases.\nC_LI

epidemiology↗

bioRxiv2018

When parasites are selected to kill the young

The impact of infectious disease is often very different in juveniles and adults, but theory has focused on the drivers of stage-dependent defense in hosts rather than the potential for stage-dependent virulence evolution. Stage-structure has the potential to be important to the evolution of pathogens because it exposes parasites to heterogeneous environments in terms of both host characteristics and transmission routes. We develop a stage-structured (juvenile-adult) epidemiological model and examine the evolutionary outcomes of stage-specific virulence under the classic assumption of a transmission-virulence trade-off. We show that selection on virulence against adults remains consistent with the classic theory. However, the evolution of juvenile virulence is sensitive to both demography and transmission pathway with higher virulence against juveniles being favored either when the transmission pathway is assortative (juveniles preferentially interact together) and the juvenile stage is short, or in contrast when the transmission pathway is disassortative and the juvenile stage is long. These results highlight the potentially profound effects of host stage-structure on determining parasite virulence in nature. This new perspective may have broad implications for both understanding and managing disease severity. Impact summaryUnderstanding the evolution of parasite virulence remains one of the most important questions in evolutionary ecology. Virulence is often very different in young and old hosts, but previous theory has presumed that these differences are attributed to adaptation in host defense rather than parasite adaptation. However, stage-structure within host populations can expose parasites to heterogeneous environments, which may lead to differential selection on parasite virulence (stage-specific virulence). Surprisingly, no study has investigated the effects of hosts stage-structure on the evolution of stage-specific virulence. We present a theoretical analysis to examine when selection can favor higher virulence against juveniles (juvenile-virulence) versus adults (adult-virulence). Our key result is that higher juvenile-virulence is selected for either when the transmission is assortative within age classes and maturation is slow, or when the transmission is disassortative (occurring predominantly between-classes) and maturation is relatively fast. These at first sight contrasting outcomes can be understood as adaptation to the exploitation of the more available host stage. Although the data on assortativity in infectious disease systems is limited, empirical studies for the virulence of Great Island Virus in guillemots (Uria aalge) and for salmon louse in pink salmon (Oncorhynchus gorbuscha) are consistent with our predictions. Our work provides testable predictions for stage-specific virulence and presents a novel mechanism that may explain variation in virulence in nature. There are also management implications for conservation, public health, vaccination programs, and farming to understanding the drivers of stage dependent virulence.

ecology↗

bioRxiv2017

Consensus and conflict among ecological forecasts of Zika virus outbreaks in the United States

Ecologists are increasingly involved in the pandemic prediction process. In the course of the Zika outbreak in the Americas, several ecological models were developed to forecast the potential global distribution of the disease. Conflicting results produced by alternative methods are unresolved, hindering the development of appropriate public health forecasts. We compare ecological niche models and experimentally-driven mechanistic forecasts for Zika transmission in the continental United States, a region of high model conflict. We use generic and uninformed stochastic county-level simulations to demonstrate the downstream epidemiological consequences of conflict among ecological models, and show how assumptions and parameterization in the ecological and epidemiological models propagate uncertainty and produce downstream model conflict. We conclude by proposing a basic consensus method that could resolve conflicting models of potential outbreak geography and seasonality. Our results illustrate the unacceptable and often undocumented margin of uncertainty that could emerge from using any one of these predictions without reservation or qualification. In the short term, ecologists face the task of developing better post hoc consensus that accurately forecasts spatial patterns of Zika virus outbreaks. Ultimately, methods are needed that bridge the gap between ecological and epidemiological approaches to predicting transmission and realistically capture both outbreak size and geography.

ecology↗