Fine-scale modelling finds that breeding site fragmentation can reduce mosquito population persistence

McCormack, Clare P; Ghani, Azra C; Ferguson, Neil M.

doi:10.1038/s42003-019-0525-0

Cited by 15 publications

(21 citation statements)

References 66 publications

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“…The theoretical approximation of the conditions for Wolbachia -based mosquito replacement [ 47 ] and simulation modeling of this and other mosquito control strategies [ 10 , 55 – 58 ] have all been developed assuming isotropic dispersal (invariable based on direction) in a 2-dimensional landscape. The approximation of the dispersal kernel for high-rise uniform landscapes could be achieved by considering the releases of mosquitoes from multiple floors rather than from the ground level only.…”

Section: Discussionmentioning

confidence: 99%

Using spatial genetics to quantify mosquito dispersal for control programs

et al. 2020

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Background Hundreds of millions of people get a mosquito-borne disease every year and nearly one million die. Transmission of these infections is primarily tackled through the control of mosquito vectors. The accurate quantification of mosquito dispersal is critical for the design and optimization of vector control programs, yet the measurement of dispersal using traditional mark-release-recapture (MRR) methods is logistically challenging and often unrepresentative of an insect’s true behavior. Using Aedes aegypti (a major arboviral vector) as a model and two study sites in Singapore, we show how mosquito dispersal can be characterized by the spatial analyses of genetic relatedness among individuals sampled over a short time span without interruption of their natural behaviors. Results Using simple oviposition traps, we captured adult female Ae. aegypti across high-rise apartment blocks and genotyped them using genome-wide SNP markers. We developed a methodology that produces a dispersal kernel for distance which results from one generation of successful breeding (effective dispersal), using the distance separating full siblings and 2nd- and 3rd-degree relatives (close kin). The estimated dispersal distance kernel was exponential (Laplacian), with a mean dispersal distance (and dispersal kernel spread σ) of 45.2 m (95% CI 39.7–51.3 m), and 10% probability of a dispersal > 100 m (95% CI 92–117 m). Our genetically derived estimates matched the parametrized dispersal kernels from previous MRR experiments. If few close kin are captured, a conventional genetic isolation-by-distance analysis can be used, as it can produce σ estimates congruent with the close-kin method if effective population density is accurately estimated. Genetic patch size, estimated by spatial autocorrelation analysis, reflects the spatial extent of the dispersal kernel “tail” that influences, for example, the critical radii of release zones and the speed of Wolbachia spread in mosquito replacement programs. Conclusions We demonstrate that spatial genetics can provide a robust characterization of mosquito dispersal. With the decreasing cost of next-generation sequencing, the production of spatial genetic data is increasingly accessible. Given the challenges of conventional MRR methods, and the importance of quantified dispersal in operational vector control decisions, we recommend genetic-based dispersal characterization as the more desirable means of parameterization.

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Section: Discussionmentioning

confidence: 99%

Using spatial genetics to quantify mosquito dispersal for control programs

et al. 2020

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“…The above mentioned theoretical approximation of the conditions for Wolbachia initiation and spread [61] assumes isotropic dispersal in a 2-dimensional habitat. Optimal release strategy for the Sterile Insect Technique programs [62], different suppression and replacement strategies [63,64], the effect of larval habitat fragmentation on population crash [65], and confinement and reversibility conditions for threshold-dependent gene drive systems [10] have all been simulated in the spatially explicit models of mosquito populations that applied the exponential dispersal kernel in a 2-dimensional landscape. The approximation of the parametrized dispersal kernel for high-rise landscapes could be achieved by considering the releases of mosquitoes from multiple floors rather than from the ground level only.…”

Section: Discussionmentioning

confidence: 99%

Using spatial genetics to quantify mosquito dispersal for control programs

Filipović

Hapuarachchi

Tien

et al. 2020

Preprint

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Background: Hundreds of millions of people get a mosquito-borne disease every year, of which nearly one million die. Mosquito-borne diseases are primarily controlled and mitigated through the control of mosquito vectors. Accurately quantified mosquito dispersal in a given landscape is critical for the design and optimization of the control programs, yet the field experiments that measure dispersal of mosquitoes recaptured at certain distances from the release point (markrelease-recapture MRR studies) are challenging for such small insects and often unrepresentative of the insect's true field behavior. Using Singapore as a study site, we show how mosquito dispersal patterns can be characterized from the spatial analyses of genetic relatedness among individuals sampled over a short time span without interruption of their natural behaviors. Methods and Findings:We captured ovipositing females of Aedes aegypti, a major arboviral disease vector, across floors of high-rise apartment blocks and genotyped them using thousands of genome-wide SNP markers. We developed a methodology that produces a dispersal kernel for distance that results from one generation of successful breeding (effective dispersal), using the distances separating full siblings, 2 nd and 3 rd degree relatives (close kin). In Singapore, the estimated dispersal distance kernel was exponential (Laplacian), giving the mean effective dispersal distance (and dispersal kernel spread σ) of 45.2 m (95%CI: 39.7-51.3 m), and 10% probability of dispersal >100 m (95%CI: 92-117 m). Our genetic-based estimates matched the parametrized dispersal kernels from the previously reported MRR experiments. If few close-kin are captured, a conventional genetic isolation-by-distance analysis can be used, and we show that it can produce σ estimates congruent with the close-kin method, conditioned on the accurate estimation of effective population density. We also show that genetic patch size, estimated with the spatial autocorrelation analysis, reflects the spatial extent of the dispersal kernel 'tail' that influences e.g. predictions of critical radii of release zones and Wolbachia wave speed in mosquito replacement programs. Conclusions:We demonstrate that spatial genetics (the newly developed close-kin analysis, and conventional IBD and spatial autocorrelation analyses) can provide a detailed and robust characterization of mosquito dispersal that can guide operational vector control decisions. With the decreasing cost of next generation sequencing, acquisition of spatial genetic data will become increasingly accessible, and given the complexities and criticisms of conventional MRR methods, but the central role of dispersal measures in vector control programs, we recommend genetic-based dispersal characterization as the more desirable means of parameterization.

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“…Limitations in the winged phase were not reported in any study. Finally, we consider the limitation term in the aquatic phase (larvae and pupae), where it is effective [ 16 ].…”

Section: Methods and Modellingmentioning

confidence: 99%

“…We assume that all eggs hatch and the corresponding mortality rate coefficient is equal to zero. The mortality rate coefficient of the aquatic phase is defined by the larvae’s coefficient, resulting in the parameter approximately equal to 0.025 (1/day) [ 16 ].…”

Section: Methods and Modellingmentioning

confidence: 99%

“…The most common one uses ordinary differential equations (ODEs) following the seminal work by Focks et al [ 13 , 14 ]. The importance of temperature and precipitation on mosquito population patterns is investigated in [ 15 , 16 ]. Authors study the vectorial transmission of diseases using ODEs based on about eight parameters for each spatial location.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and simulation of the spatial population dynamics of the Aedes aegypti mosquito with an insecticide application

2020

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Background The Aedes aegypti mosquito is the primary vector for several diseases. Its control requires a better understanding of the mosquitoes’ live cycle, including the spatial dynamics. Several models address this issue. However, they rely on many hard to measure parameters. This work presents a model describing the spatial population dynamics of Aedes aegypti mosquitoes using partial differential equations (PDEs) relying on a few parameters. Methods We show how to estimate model parameter values from the experimental data found in the literature using concepts from dynamical systems, genetic algorithm optimization and partial differential equations. We show that our model reproduces some analytical formulas relating the carrying capacity coefficient to experimentally measurable quantities as the maximum number of mobile female mosquitoes, the maximum number of eggs, or the maximum number of larvae. As an application of the presented methodology, we replicate one field experiment numerically and investigate the effect of different frequencies in the insecticide application in the urban environment. Results The numerical results suggest that the insecticide application has a limited impact on the mosquitoes population and that the optimal application frequency is close to one week. Conclusions Models based on partial differential equations provide an efficient tool for simulating mosquitoes’ spatial population dynamics. The reduced model can reproduce such dynamics on a sufficiently large scale.

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Fine-scale modelling finds that breeding site fragmentation can reduce mosquito population persistence

Cited by 15 publications

References 66 publications

Using spatial genetics to quantify mosquito dispersal for control programs

Using spatial genetics to quantify mosquito dispersal for control programs

Using spatial genetics to quantify mosquito dispersal for control programs

Modeling and simulation of the spatial population dynamics of the Aedes aegypti mosquito with an insecticide application

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