![]() ![]() We found out-degree and betweenness potential super-spreaders to be critical for disease transmission across multiple communities. Using this regional network, we model three major types of potential super-spreaders of communicable disease: in-degree, out-degree, and betweenness potential super-spreaders. Analysis of WTD bachelor groups and random network modeling demonstrated that scraping networks depict real social networks, allowing detection of direct and indirect contacts, which could spread diseases. We combined local network communities to form a large region-wide social network consisting of 96 male WTD. In this study, we used network analysis from scrape visitation data to identify potential super-spreaders among multiple communities of a rural WTD herd. Super-spreaders are infected individuals that infect more contacts than other infectious individuals within a population. In this paper, we speculate that “super-spreaders” mediate disease transmission via direct social interactions and indirectly via body fluids exchanged at scrape sites. Currently, it is not well understood how WTD are spreading these diseases. White-tailed deer (Odocoileus virginianus, WTD) spread communicable diseases such the zoonotic coronavirus SARS-CoV-2, which is a major public health concern, and chronic wasting disease (CWD), a fatal, highly contagious prion disease occurring in cervids. Taken together, our results indicated partial spatiotemporal segregation of migratory phenotypes that may generate assortative mating and promote population differentiation. Analysis of arrival and departure dates indicated opportunity for co-occurrence at the site visited by all phenotypes but showed Lake Huron migrants arrived approximately 2 weeks before Lake St. Clair migrants, and, to a lesser extent, annual Lake St. ![]() Clair migrants whereas the other site was visited by Lake Huron migrants, intermittent Lake St. A direct test for differences in space use revealed one site was almost exclusively visited by Lake St. Social network analyses indicated lake sturgeon generally co-occurred with individuals of the same migratory phenotype more often than with different migratory phenotypes. Clair River each year (annual migrants) or intermittently (intermittent migrants). Clair migrants were further distinguished by whether they migrated into the St. Acoustic telemetry over 9 years monitored use of two major spawning sites by lake sturgeon that moved north to overwinter in Lake Huron or south to overwinter in Lake St. Clair River of North America’s Laurentian Great Lakes but differ in how often they migrate into the river and in which direction they move after spawning. In this study, the potential for spatiotemporal segregation was tested among three migratory phenotypes of lake sturgeon (Acipenser fulvescens) that spawn in the St. Migratory diversity can promote population differentiation if sympatric phenotypes become temporally, spatially, or behaviorally segregated during breeding. Note that, while our proposed methodology was developed with VIAPPL in mind, its potential usage extends to any type of social interaction data. ![]() We find that ingroup favouritism and reciprocity are present in social interactions observed on this platform, and that these behaviours strengthen over time. We specifically consider experimental data collected via the novel Virtual Interaction APPLication (VIAPPL). Moreover, we provide network visualisations that identify the extent of ingroup favouritism and reciprocity as well as particular individuals whose behaviour differs markedly from the norm. ![]() This allows us to discover, for example, the extent to which the structure of social interactions differs from that of random interactions. Therefore, we propose an approach that compares the output of a fitted (linear) model from the observed interaction data to that generated by an assumed agent-based null model. We consider the analysis of temporal data arising from online interactive social experiments, which is complicated by the fact that classical independence assumptions about the observations are not satisfied. ![]()
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