Impact of life stage-dependent dispersal on the colonization dynamics of host patches by ticks and tick-borne infectious agents

Abstract

Background: When colonization and gene flow depend on host-mediated dispersal, a key factor affecting vector dispersal potential is the time spent on the host for the blood meal, a characteristic that can vary strongly among life history stages. Using a 2-patch vector-pathogen population model and seabird ticks as biological examples, we explore how vector colonization rates and the spread of infectious agents may be shaped by life stage-dependent dispersal. We contrast hard (Ixodidae) and soft (Argasidae) tick systems, which differ strongly in blood- feeding traits.

Results: We find that vector life history characteristics (i.e. length of blood meal) and demographic constraints (Allee effects) condition the colonization potential of ticks; hard ticks, which take a single, long blood meal per life stage, should have much higher colonization rates than soft ticks, which take repeated short meals. Moreover, this dispersal potential has direct consequences for the spread of vector-borne infectious agents, in particular when transmission is transovarial.

Conclusions: These results have clear implications for predicting the dynamics of vector and disease spread in the context of large-scale environmental change. The findings highlight the need to include life-stage dispersal in models that aim to predict species and disease distributions, and provide testable predictions related to the population genetic structure of vectors and pathogens along expansion fronts.

Keywords: Allee effect; Borrelia burgdorferi; Climate change; Ixodes uriae; Lyme disease; Ornithodoros maritimus; Parasite spread; Range expansion; Vertical transmission.

Fig. 1

The known worldwide distribution of hard (black dots) and soft (grey dots) ticks of seabirds. Only ticks from the Ixodes and Ornithodoros (Carios) genera are represented. Arrows are illustrative and represent the potential colonization areas for hard (black arrow) and soft (grey arrow) seabird ticks, respectively. Data from Dietrich et al. [65]

Fig. 2

Schematic representation of the vector-borne infection model. Susceptible vectors from any stage (L_S, Np_S, A_S) can become infected at rate λ _v when they feed on infected hosts (H_I). Susceptible hosts can become infected at rate λ _h following a feeding event by an infected vector. Contact rate is set to 1 year^-1 to respect the fact that ticks feed once per year. Equations are found in text

Fig. 3

Density of a hard and b soft ticks in each life stage in the receiving patch. Default parameter values are found in Table 1. m_L = m_N = m_A = 0.01 year^-1 for hard ticks, and the migration rate for soft ticks is adjusted so that an equivalent number of larval ticks disperse

Fig. 4

Strength of the Allee effect and total vector population density in the receiving patch for a hard and b soft ticks (a = 1, 2, 8 and 40; higher values indicate a stronger Allee effect). The arrows are for illustrative purposes. They represent the inflexion points for the short (I₂) and long-term (I₁) dynamics. On the right panel (b), the inflexion point (I₁), represents the threshold above which the population growth becomes positive. Default parameter values are found in Table 1. m_L = 0.017 year^-1 for the soft and m_L = m_N = m_A = 0.01 year^-1 for hard ticks

Fig. 5

Infection spread in the new patch through stage-dependent and bidirectional dispersal prevalence for low (solid lines, θ = 0.001) and high vertical (transovarial) transmission rates (θ = 0.1, dashed lines), following arrival of susceptible and infected ticks from patch 1. Default parameters are found in Table 1. Only 1 bloodmeal per stage (c = 1) is considered. The polyphagous nature of soft ticks (c > 1) partially compensates for the lag in local density, but the infection prevalence in soft ticks, still does not reach that of hard ticks (see Additional file 2: Figure S2, for details)

Fig. 6

Results of the sensitivity analysis (SA) for the vector population size in the newly colonized patch after 30 years (a) and the prevalence of the infection in this patch after 30 years (b), using the PRCC index which indicates the quantitative impact and direction of the relationship between a parameter and the model output. The SA is performed for 150 runs of the LHS matrix. Parameter used for the models are found in Additional file 2: Table S1