Dispersal in heterogeneous environments drives population dynamics and control of tsetse flies

Abstract

Spatio-temporally heterogeneous environments may lead to unexpected population dynamics. Knowledge is needed on local properties favouring population resilience at large scale. For pathogen vectors, such as tsetse flies transmitting human and animal African trypanosomosis, this is crucial to target management strategies. We developed a mechanistic spatio-temporal model of the age-structured population dynamics of tsetse flies, parametrized with field and laboratory data. It accounts for density- and temperature-dependence. The studied environment is heterogeneous, fragmented and dispersal is suitability-driven. We confirmed that temperature and adult mortality have a strong impact on tsetse populations. When homogeneously increasing adult mortality, control was less effective and induced faster population recovery in the coldest and temperature-stable locations, creating refuges. To optimally select locations to control, we assessed the potential impact of treating them and their contribution to the whole population. This heterogeneous control induced a similar population decrease, with more dispersed individuals. Control efficacy was no longer related to temperature. Dispersal was responsible for refuges at the interface between controlled and uncontrolled zones, where resurgence after control was very high. The early identification of refuges, which could jeopardize control efforts, is crucial. We recommend baseline data collection to characterize the ecosystem before implementing any measures.

Keywords: disease vector; experimental and field data; mechanistic modelling; mortality scenario; spatio-temporal dynamics.

Figure 1.

Within-cell model diagram of tsetse fly population dynamics (time unit: day). Transitions between stages except from pupa (P) to nulliparous female (N) trigger the birth of a new pupa P₁. Transitions occur at development rate δ_S for stage S ∈ {P, N, F_x} (parity x ∈ {1, 2, 3, 4+}) according to temperature θ_t,c at time t in cell c, giving rise daily to a minimum jump of l states from each state i of stage S, with (1−q)S_t,c,i individuals going from state $S_{t,c,i}$ to state $S_{t+1,c,i+l}$ and $q{S}_{t,c,i}$ individuals going to $S_{t+1,c,i+l+1}$. If i + l > n_S (respectively, i + l + l > n_S, with n_S the maximum number of states in stage S ∈ {P, N, F_x}), then selected individuals go to the next stage. Equations are in the electronic supplementary material, S4.1. (Online version in colour.)

Figure 2.

Mortality increase and tsetse fly population size. (a) Relative increase in mortality needed to reduce the female population size to 2% (circle) or 5% (triangle) of its initial size after 1 year of control, when a fraction of cells was targeted. (b) Contribution of cells to the total population size ((i): no control, (ii): homogeneous control, (iii): heterogeneous control targeting 47% of the cells). (c) Local control efficacy ((i)–(ii): same as in b), the darkest being the most effective (lighter colour: uncontrolled cells). (Online version in colour.)

Figure 3.

Local population resurgence 1 year after the end of a homogeneous control. (a) Local growth rate, (b–d) variations of the local growth rate with carrying capacity, mean annual temperature and annual standard deviation of temperature. Colours: control efficacy (blue (bottom colour): most effective). (Online version in colour.)

Figure 4.

Local population resurgence 1 year after the end of a heterogeneous control (47% of controlled cells). (a) Local growth rate, (b–d) variations of the local growth rate with carrying capacity, mean annual temperature and annual standard deviation of temperature. Colours: control score (the highest: still targeted when the proportion of controlled cells is reduced). Cyan: uncontrolled cells. (Online version in colour.)