Low probability of a dilution effect for Lyme borreliosis in Belgian forests

Abstract

An increasing number of studies have investigated the consequences of biodiversity loss for the occurrence of vector-borne diseases such as Lyme borreliosis, the most common tick-borne disease in the northern hemisphere. As host species differ in their ability to transmit the Lyme borreliosis bacteria Borrelia burgdorferi s.l. to ticks, increased host diversity can decrease disease prevalence by increasing the proportion of dilution hosts, host species that transmit pathogens less efficiently. Previous research shows that Lyme borreliosis risk differs between forest types and suggests that a higher diversity of host species might dilute the contribution of small rodents to infect ticks with B. afzelii, a common Borrelia genospecies. However, empirical evidence for a dilution effect in Europe is largely lacking. We tested the dilution effect hypothesis in 19 Belgian forest stands of different forest types along a diversity gradient. We used empirical data and a Bayesian belief network to investigate the impact of the proportion of dilution hosts on the density of ticks infected with B. afzelii, and identified the key drivers determining the density of infected ticks, which is a measure of human infection risk. Densities of ticks and B. afzelii infection prevalence differed between forest types, but the model indicated that the density of infected ticks is hardly affected by dilution. The most important variables explaining variability in disease risk were related to the density of ticks. Combining empirical data with a model-based approach supported decision making to reduce tick-borne disease risk. We found a low probability of a dilution effect for Lyme borreliosis in a north-western European context. We emphasize that under these circumstances, Lyme borreliosis prevention should rather aim at reducing tick-human contact rate instead of attempting to increase the proportion of dilution hosts.

Publication
Ticks and Tick-borne Diseases
Date
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