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Hilker, F. M.

Publications and source records attributed to Hilker, F. M..

3 recordsLinked to original sources

Selfish grower behavior can group-optimally eradicate plant diseases caused by coinfection

Game-theoretic behavioral and epidemiological models suggest that it is impossible to eradicate a disease through voluntary control actions when individuals behave according to their own interests. The simple reason is that free-riding behavior, which is best for self-interest, leads to a control coverage on the group level that is insufficient to eradicate the disease. Here we show that, for diseases that are caused by coinfection, selfinterest can actually result in the socially optimal outcome of disease eradication. Our result challenges the conventional wisdom that selfish behavior undermines the group interest; it resolves a social dilemma in the absence of any cooperation, institutional arrangements, or social norms. Our model is motivated by coinfecting plant viruses, common among agricultural crops and natural plants, and the behavioral dynamics of growers to adopt protective action (biological or chemical control). The epidemiological scenario, in which the disease is eradicated by self-interest, is characterized by a positive feedback process in which coinfection enhances infectivity. Similar feedback structures exist for a range of typical epidemiological processes that facilitate disease persistence if prevalence is sufficiently large. The underlying mechanism may therefore be applicable to other diseases.

ecology↗

Dispersal between interconnected patches can reduce the total population size

Human activities increasingly result in a fragmentation of natural ecosystems. However, the ecological consequences of fragmentation remain poorly understood. While some studies report that fragmentation may enhance population growth, others suggest the opposite pattern. Here we investigated how habitat connectivity affects the population size of a single species when habitat patches differ in quality. We combined dispersal experiments, in which bacterial populations of Escherichia coli were repeatedly transferred between two qualitatively different environments, with a process-based mathematical model. Both experiments and model consistently revealed that increased dispersal between patches reduced the total population size, thus demonstrating a detrimental effect of habitat connectivity on population size. This observation could be explained with a net loss of individuals upon migration from a productive to an overcrowded patch. Our findings suggest that conservation measures, which promote movement between fragmented habitats, such as dispersal corridors or stepping stones, are potentially detrimental for some species.

ecology↗

Co-infections by non-interacting pathogens are not independent and require new tests of interaction

If pathogen species, strains or clones do not interact, intuition suggests the proportion of co-infected hosts should be the product of the individual prevalences. Independence consequently underpins the wide range of methods for detecting pathogen interactions from cross-sectional survey data. However, the very simplest of epidemiological models challenge the underlying assumption of statistical independence. Even if pathogens do not interact, death of co-infected hosts causes net prevalences of individual pathogens to decrease simultaneously. The induced positive correlation between prevalences means the proportion of co-infected hosts is expected to be higher than multiplication would suggest. By modeling the dynamics of multiple non-interacting pathogens, we develop a pair of novel tests of interaction that properly account for non-independence. Our tests allow us to reinterpret data from previous studies including pathogens of humans, plants, and animals. Our work demonstrates how methods to identify interactions between pathogens can be updated using simple epidemic models.

epidemiology↗