In this new post Amy Sweeny, postdoctoral research at University of Edinburgh, presents her last work ‘Spatiotemporal variation in drivers of parasitism in a wild wood mouse population.’ She discusses the importance of recognising the forces behind parasitism, the difficulties behind field work and her career path on disease ecology.
About the paper
In natural populations, parasite infection is ubiquitous. However, some individuals within a population become much more infected than others and it is not always clear why this happens. We don’t know, for example, if certain individuals’ characteristics like their age, sex, or body condition are likely to make them prone to infection. In addition, it might be that some individuals live in a particularly ‘parasite-y’ area. A major problem that has limited our ability to answering these questions, is that these different factors are not independent of each other. For example, the relationships between host age or sex and parasite infection, are not always consistent and can actually change over time and between different populations. However, because data collection in the wild can be difficult and expensive to conduct, we often only have data for short periods of time or for a single location, and therefore don’t know if the factors that contribute to infection are consistent in time and space. Answering these questions is important for determining when and where parasites are likely to spread and cause negative impacts on host populations.
This paper reports findings from a large project from my PhD in which we used a long-term, multi-site study of wild wood mouse populations to investigate both environmental, and host characteristics which determine the intensity of infection with a common gut nematode. Importantly, we monitored wood mice and their parasite community over 6 years and from 5 different woodland populations, we were also able to see if the factors determining infection varied over space and time. We found that season, as well as host body condition and sex of the wood mice, were the most important in determining who was parasitised. However, the relationship between each factor and parasite intensity varied in the strength, and even in direction across the years. These results highlight how dynamic relationships between the environment, hosts, and parasites are in the wild. Overall, we highlight the value of long-term monitoring over large spatiotemporal scales for understanding epidemiology in natural populations.
Our paper has relevance to other research in disease ecology,in that it uses mark and recapture sampling from several temporal and spatial duplicates to understand infectious disease dynamics in a wild population. Our investigation addresses a common question about data sampling that many researchers typically grapple with – how to design a field study to collect appropriate data across duplicates to increase power. Since the first year of my PhD, I have puzzled over how disease ecologists can maximise inference from replicates of datasets, especially when biological processes can vary over time and space. What excited me about this paper was using large, longitudinal dataset that was replicated across both space and time, and then being able to grapple a statistical approach to understand both biological and sampling variation in the factors that determine parasitism of a well-known and important parasite in this study population.
About the research
Understanding levels of spatiotemporal variation in host exposure and susceptibility to infection, is crucial for predicting how wildlife populations might respond to environmental change, particularly as global and environmental change accelerates. This is something I think about a lot in my research. For example, it is becoming important to understand how changing resource landscapes or access to food can alter host behaviour and condition, and subsequently impact disease transmission. Although, it was beyond the scope of this study to identify the exact processes underlying the spatiotemporal variation that we detected, we can learn a lot from future investigation of new hypotheses arising from these results, while accounting for spatiotemporal variation in long-term datasets.
This study also serves as a powerful example of the inherent difficulties in both gathering and collating data, for synthesising across replicates. The data for this paper was collected through six years of fieldwork including carefully designed experiments as part of a large research program run by Dr Amy Pedersen and Professor Andy Fenton. Planning, setting up, and running the experiments for 8 months of the year was a massive effort carried out by a large team of field technicians, PhD students, Post-docs, field assistants, and PIs. When I started my PhD, I was very interested in using the full range of field data for considering questions of an ecological scale in the well-studied system. However, even for data collected in a similar manner and in the same system, my analysis involved a significant investment of time to collate and curate the dataset for this analysis, as well as significant time working with my co-authors to develop a set of questions and statistical models that we were really excited to apply to this dataset. Above all, the need for researchers to synthesise information across large numbers of datasets from many study systems is increasing, and so this analysis taught me several skills and lessons that are helpful going forward.
About the author
I studied Ecology, Evolution and Global Health as an undergraduate at Princeton University. This background fuelled my enthusiasm for understanding infectious disease dynamics from a socioecological context. During this time, I conducted my undergraduate thesis research with Professor Andrea Graham in wildlife disease ecology, and I have been investigating questions in this discipline across a range of systems and questions since this experience. I completed my PhD at the University of Edinburgh with the Pedersen group and am currently a Postdoctoral Research Associate at the same university, now working with a team investigating gut ecosystem dynamics as part of the Soay sheep project. In this role, I have been investigating the function of the gut microbiome in host ecology and evolution. More broadly, I am interested in coinfection dynamics and how they influence disease transmission, and in leveraging longitudinal data across wild systems to generate hypotheses for dynamic disease processes. For more about my work find me at @arsweeny or amysweeny.me!
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