Impact of climate trends on tick-borne pathogen transmission

Abstract

Recent advances in climate research together with a better understanding of tick-pathogen interactions, the distribution of ticks and the diagnosis of tick-borne pathogens raise questions about the impact of environmental factors on tick abundance and spread and the prevalence and transmission of tick-borne pathogens. While undoubtedly climate plays a role in the changes in distribution and seasonal abundance of ticks, it is always difficult to disentangle factors impacting on the abundance of tick hosts from those exerted by human habits. All together, climate, host abundance, and social factors may explain the upsurge of epidemics transmitted by ticks to humans. Herein we focused on tick-borne pathogens that affect humans with epidemic potential. Borrelia burgdorferi s.l. (Lyme disease), Anaplasma phagocytophilum (human granulocytic anaplasmosis), and tick-borne encephalitis virus (tick-borne encephalitis) are transmitted by Ixodes spp. Crimean-Congo hemorrhagic fever virus (Crimean-Congo hemorrhagic fever) is transmitted by Hyalomma spp. In this review, we discussed how vector tick species occupy the habitat as a function of different climatic factors, and how these factors impact on tick survival and seasonality. How molecular events at the tick-pathogen interface impact on pathogen transmission is also discussed. Results from statistically and biologically derived models are compared to show that while statistical models are able to outline basic information about tick distributions, biologically derived models are necessary to evaluate pathogen transmission rates and understand the effect of climatic variables and host abundance patterns on pathogen transmission. The results of these studies could be used to build early alert systems able to identify the main factors driving the subtle changes in tick distribution and seasonality and the prevalence of tick-borne pathogens.

Keywords: Anaplasma; Borrelia; climate; genetics; model; tick; virus.

Figure 1

Predicted climate suitability for the tick I. ricinus in the western Palearctic. (A) Predicted climate suitability (0–100) was evaluated by a model trained with more than 4,000 tick occurrence points using MaxEnt as modeling software (Phillips et al., 2006). The map is based on previous developments by Estrada-Peña et al. (2006). The ramp of colors shows the probability to find permanent tick populations as driven only by climate conditions, including a set of remotely sensed monthly average temperature and vegetation stress (NDVI, a proxy for tick water stress) from 2000 to 2010. (B) Changes in climate suitability for I. ricinus in the period 2000–2010 (from 0, the minimum, to 1, the maximum) based on the same model. Results are based on modeling climate suitability for ticks separately for each year and then evaluating the suitability index trend along years 2000–2010. Both maps (A,B) do not represent tick abundance but the appropriateness of the climate for the development of the tick (A) and how this factor evolved in time (B).

Figure 2

Compared output between a statistical and a process-driven model of H. marginatum in the Mediterranean basin. (A) The statistical model was trained with records of tick occurrence in the region and displays the probability to find permanent tick populations (in the range 0–1) as reported by Estrada-Peña and Venzal (2007). The model is based only on climate features found at the sites where the tick has been recorded. (B) The process-driven model uses the same set of climate explanatory variables (average monthly temperature and water deficit) and represents the same probability based on tick development and mortality rates over a period of 1 year (Estrada-Peña et al., 2011b). The process-driven model predicted a larger range northern to the Mediterranean area, sites which were regarded as unsuitable by the statistical model, and reported no suitability in large areas of the Sahara desert.