Beech tree masting explains the inter-annual variation in the fall and spring peaks ofIxodes ricinusticks with different time lags

Abstract

Background: The tick Ixodes ricinus is an important vector of tick-borne diseases including Lyme borreliosis. In continental Europe, the nymphal stage of I. ricinus often has a bimodal phenology with a large spring/early summer peak and a smaller fall peak. While there is consensus about the origin of the spring nymphal peak, there are two alternative hypotheses for the fall nymphal peak, direct development versus delayed diapause. These two hypotheses make different predictions about the time lags of the correlations between the spring peak, the fall peak, and seed production (masting) by deciduous trees. Methods: To determine which hypothesis is most important for explaining the fall peak, we used data from a long-term surveillance study (15 years) on the density of I. ricinus nymphal ticks at 4 different elevation sites in an area of Switzerland that is endemic for Lyme borreliosis, and long-term data on the mast of the European beech tree from the literature. Results: I. ricinus nymphs had a bimodal phenology at the three lower elevation sites, but a unimodal phenology at the top elevation site. At the lower elevation sites, the density of nymphs (DON) in the fall was strongly correlated with the DON in the spring of the following year. The inter-annual variation in the densities of I. ricinus nymphs in the fall and spring were best explained by a 1-year versus a 2-year time lag with the beech tree masting index. Fall nymphs had higher fat content and are younger than spring nymphs. All of these observations are consistent with the direct development hypothesis for the fall peak of I. ricinus nymphs at our study site. Our study provides new insight into the complex bimodal phenology of this important disease vector. Conclusions: Public health officials in Europe should be aware that following a strong mast year, the DON will increase 1 year later in the fall and 2 years later in the spring and summer. Population ecology studies of I. ricinus should consider that the spring and fall peak in the same calendar year represent different generations of ticks.

How do host population dynamics impact Lyme disease risk dynamics in theoretical models?

“…In studies on vector-borne diseases, research has aimed to understand the impact of the traits (e.g. reservoir competency, mean population density) of different species and species communities on disease risk [10][11][12][13][14][15][16][17][18][19][20]. Lyme disease exemplifies this research.…”

“…Rodents are known to serve as important reservoir hosts for B. burgdorferi [10,16,21,22], and populations of these species often undergo extreme fluctuations in density driven by resources [10,20,21], predators [10,22], and environmental effects [10,23]. Other host species undergo population dynamics and many species are subject to changing populations and extinctions as a result of human caused disturbances [10,23].…”

“…However, the impact of host and community dynamics on Lyme disease risk is fairly uncertain considering the widely implicated potential of these dynamics to have a significant impact on how Borrelia burgdorferi flows through wildlife communities. Rodents are known to serve as important reservoir hosts for B. burgdorferi, and populations of these species often undergo extreme fluctuations in density driven by resources [10,20,21], predators [10,22], and environmental effects [10,23].…”

“…To investigate the impact of host dynamics on Lyme disease risk, we have created a mathematical model which simulates the flow of pathogen between a stage-structured compartment model of ticks and a small community of tick hosts. To investigate the impact of host dynamics on the model, densities of the primary host, mice, are varied on an annual basis, as commonly observed in natural mouse populations [10,[20][21][22]. By changing the mean density and variation from the mean in a collection of simulations, we will use our model to explore the impact that simple host dynamics have on disease risk measures of the model for a range of population dynamics.…”