A modified matrix model to describe the seasonal population ecology of the European tick Ixodes ricinus

Abstract

Summary1. The sheep tick Ixodes ricinus is the most multicompetent vector in Europe, which is responsible for significant diseases of humans and livestock throughout the northern hemisphere. Modelling the tick's complex seasonal dynamics, upon which pathogen transmission potential depends, underpins the analysis of tick-borne disease risk and potential tick control. 2. We use laboratory-and field-derived empirical data to construct a population model for I. ricinus. The model is a substantially modified stage-classified Leslie matrix and includes functions for temperature-dependent development, density-dependent mortality and saturation deficit-meditated probability of questing. 3. The model was fitted to field data from three UK sites and successfully simulated seasonal patterns at a fourth site. After modification of a single parameter, the model also replicated divergent seasonal patterns in central Spain, but any biological factors underlying this geographical heterogeneity have not yet been identified. The model's applicability to wide geographical areas is thus constrained, but in ways that highlight gaps in our knowledge of tick biology. 4. Sensitivity analysis indicated that the model was generally robust, particularly to changes in density-independent mortality values, but was most sensitive to changes in parameters related to density-dependent mortality. 5. Synthesis and applications. Vector population models allow investigation into the effects of individual environmental factors on population dynamics in ways not easily possible by experimental manipulation of in situ populations. Our model can be used to evaluate public health risk, tick management strategies and potential effects of future environmental change.

Meteorological Influences on the Seasonality of Lyme Disease in the United States

“…Interstadial development rates in ixodid ticks typically increase with increasing temperature, [6][7][8]46,47 and the timing of nymphal host seeking is correlated with ambient temperatures. 6,43,[48][49][50] The negative relationship between cumulative GDD through Week 20 and the week of the year when the Lyme disease season begins, indicating that higher cumulative GDD at Week 20 are associated with an earlier start to the season (or the positive relationship between the number of weeks to reach 300 GDD and the week when the Lyme disease season begins), is likely due in part to this positive effect of temperature on the activity of newly emerged nymphs in the spring. Cumulative GDD were a better predictor of seasonal timing than weekly minimum, mean, or maximum temperatures.…”

“…9,52,[55][56][57] In this case larval development and nymphal emergence would likely be subject to cumulative spring temperatures. 8,49 Tick development, survival, and activity levels are all potentially sensitive to moisture levels in addition to temperature. In the overall best-fit model (and three of the four regional models) a higher mean saturation deficit in the 5 weeks before the beginning of the Lyme disease season was associated with a delayed start to the season.…”

Modelling the seasonality of Lyme disease risk and the potential impacts of a warming climate within the heterogeneous landscapes of Scotland

“…Existing mechanistic models for studying the effects of temperature on Lyme disease risk are increasing in number (e.g. [16][17][18][19][20]). However, existing models do not incorporate spatial heterogeneity or focus on issues related only to the dynamics of vector populations, ignoring key components, such as host/pathogen distribution and habitat suitability, which in reality are distributed unevenly and can change rapidly over time.…”

The wild life of tick-borne pathogens

“…However, the low survival rates between the different life stages of I. ricinus imply that the majority of larvae and nymphs do not survive to become adults 22 . Indeed, the few studies that have tried to estimate the chance that a tick finds a host estimated this chance to be very low 22,280 . This suggests that the majority of larvae and nymphs never find a host, which would mean that every added host will just feed the ticks that otherwise would not have found a host 158 .…”

“…contact rate 280 . In my study sites (Chapter 4-7) the number of larvae counted per 10 m drag was positively correlated with the number of nymphs counted on the same drag (generalized linear mixed model with a negative binomial distribution and a random intercept per month nested in site: β = 0.2, p < 0.001), suggesting that α l and α n could be correlated as well.…”