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This repository release is to provide a DOI for the R code underlying a forthcoming publication in Ecosphere: Title: Parasite spatial distribution shapes parasite aggregation on host populations, but not at high parasite density Authors: Chris Wojan, Allison Shaw, Meggan Craft 

Abstract Spatial aggregation of environmental or trophically transmitted parasites has the potential to influence host–parasite interactions. The distribution of parasites on hosts is one result of those interactions, and the role of spatial aggregation is unclear. We use a spatially explicit agent‐based model to determine how spatial aggregation of parasites influences the distribution of parasite burdens across a range of parasite densities and host recovery rates. Our model simulates the random movement of hosts across landscapes with varying spatial configurations of areas occupied by environmental parasites, allowing hosts to acquire parasites they encounter and subsequently lose them. When parasites are more spatially aggregated in the environment, the aggregation of parasite burdens on hosts is higher (i.e., more hosts with few parasites, fewer hosts with many parasites), but the effect is less pronounced at high parasite density and fast host recovery rates. In addition, the correlation between individual hosts' final parasite burdens and their cumulative parasite burdens (including lost parasites) is greater at higher levels of spatial parasite aggregation. Our work suggests that fine‐scale spatial patterns of parasites can play a strong role in shaping how hosts are parasitized, particularly when parasite density is low‐to‐moderate and recovery rates are slow. 

Social and spatial structures of host populations play important roles in pathogen transmission. For environmentally transmitted pathogens, the host space use interacts with both the host social structure and the pathogen’s environmental persistence (which determines the time-lag across which two hosts can transmit). Together, these factors shape the epidemiological dynamics of environmentally transmitted pathogens. While the importance of both social and spatial structures and environmental pathogen persistence has long been recognized in epidemiology, they are often considered separately. A better understanding of how these factors interact to determine disease dynamics is required for developing robust surveillance and management strategies. Here, we use a simple agent-based model where we vary host mobility (spatial), host gregariousness (social) and pathogen decay (environmental persistence), each from low to high levels to uncover how they affect epidemiological dynamics. By comparing epidemic peak, time to epidemic peak and final epidemic size, we show that longer infectious periods, higher group mobility, larger group size and longer pathogen persistence lead to larger, faster growing outbreaks, and explore how these processes interact to determine epidemiological outcomes such as the epidemic peak and the final epidemic size. We identify general principles that can be used for planning surveillance and control for wildlife host–pathogen systems with environmental transmission across a range of spatial behaviour, social structure and pathogen decay rates. This article is part of the theme issue ‘The spatial–social interface: a theoretical and empirical integration’. 

Theory is a critical component of the biological research process, and complements observational and experimental approaches. However, most biologists receive little training on how to frame a theoretical question and, thus, how to evaluate when theory has successfully answered the research question. Here, we develop a guide with six verbal framings for theoretical models in biology. These correspond to different personas one might adopt as a theorist: ‘Advocate’, ‘Explainer’, ‘Instigator’, ‘Mediator’, ‘Semantician' and ‘Tinkerer’. These personas are drawn from combinations of two starting points (pattern or mechanism) and three foci (novelty, robustness or conflict). We illustrate each of these framings with examples of specific theoretical questions, by drawing on recent theoretical papers in the fields of ecology and evolutionary biology. We show how the same research topic can be approached from slightly different perspectives, using different framings. We show how clarifying a model’s framing can debunk common misconceptions of theory: that simplifying assumptions are bad, more detail is always better, models show anything you want and modelling requires substantial maths knowledge. Finally, we provide a roadmap that researchers new to theoretical research can use to identify a framing to serve as a blueprint for their own theoretical research projects.