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Capture-Recapture (CR) statistical models based on noninvasive genetic tags: application to disease vector mosquitoes

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Date Limite Candidature : jeudi 3 décembre 2020

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General information

Reference : UMR5175-ROGPRA-001
Workplace : MONTPELLIER
Date of publication : Thursday, November 12, 2020
Scientific Responsible name : Roger PRADEL & Rémi CHOQUET
Type of Contract : PhD Student contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 1 January 2021
Proportion of work : Full time
Remuneration : 2 135,00 € gross monthly

Description of the thesis topic

Mosquitoes transmit to humans some of the deadliest parasites like those responsible of diseases such as malaria or yellow fever. Detailed knowledge of mosquito dispersal and population dynamics would allow more precise evaluations of the efficacy of different disease control strategies, as well as more efficient targeting of the states and habitats favourable to mosquito development. These processes, however, are poorly known due to the inappropriateness of methods and tools suitable for their study in natural mosquito populations. This is principally due to the fact that it is not possible to follow-up individual mosquitoes in nature.

Yet, the follow-up of individual CR histories is necessary to understand the demographic processes underlying mosquito larval and adult survival under different environmental conditions, as well as their dispersal in nature. Novel methods are under development within the framework of the ANR project MoVe=>ADAPT to identify individual mosquitoes based on noninvasive genetic fingerprinting. Identification of genetic tags, however, is not without error. Several variables such as population size, kinship, number and degree of polymorphism of genetic loci, all affect the likelihood with which one can associate a tag to a specific individual (i.e. the probability of identity). Some statistical methods and computational tools have been developed to calculate the probability of identity (1,2,7). Moreover, other sources of error are generated by sampling bias.

In order to study the population dynamics of natural mosquito populations and in particular their survival, one has to sample regularly---say, daily---to follow individual histories through time. Under these circumstances, one can apply the Capture-Recapture methodological framework. The underlying principle of CR methods is that individuals are identified without error, but statistical methods to handle data subject to identification errors have been developed recently (3,4). These methods, however, are restrained to closed populations. Mathematical models allowing survival estimation under these circumstances are just receiving attention and still have practical constraints (5,6). These models rely on bayesian analysis and only specific kinds of errors are presently allowed for.

The PhD projet will focus on developing statistical tools to handle genetic and sampling errors in CR models in order to estimate the individual and environmental factors affecting mosquito survival. A trade-off should be sought among the required precision in individual identity assignment and the cost due to the specificities of the genetic methods employed, as a function of the quality and quantity of genetic loci used to tag individuals.

The PhD candidate will also contribute to the data collection and analysis of experimental studies in mesocosms (i.e. greenhouses or other large enclosures where mosquitoes are released) and of field studies in Africa (Senegal, Gabon) or French Oversea Departments (French Guyana, La Réunion island). These studies will focus on mosquito larvae growing in contrasting habitats such as urban polluted water collections vs natural rural breeding sites, freshwater *vs* brackish water, in order to measure the cost of adaptation to the environmental stressors occurring in these alternative biotopes (e.g. among others, osmotic and oxidative stress, paucity of nutrients, competition/predation.)

Work Context

The thesis will be supervised by Roger PRADEL, Research Director at CNRS, and Rémi CHOQUET, Research Engineer at CNRS, respectively members of the “Human-Animal Interactions” team and of the “Evolutionary Ecology and Epidemiology” team of Center for Evolutionary and Functional Ecology. They are both capture-recapture model specialists and software authors in the area. Carlo COSTANTINI, Research Director at IRD, member of Centre for Research on the Ecology and Evolution
of DiseaSes and of the “Evolution des Systèmes Vectoriels” team of the UMR MIVEGEC, will supervise the application of theoretical models to laboratory and field environments. The supervisor has several decades of experience in the evolutionary ecology of vector systems in Africa.
The PhD salary (employer CNRS) will be covered by the ANR project MoVe=>ADAPT, and the thesis work will be in accordance with the project objectives and research program.
Desk space will be provided at CEFE for the PhD fellow. S/he will have access to the laboratories and to the IRD Vectopôle located at the IRD regional delegation in Occitanie. S/he can also gain access to the computing ressources of the MBB platform, CEFE cluster, and IRD bioinformatics service.
The experimental work will be conducted at the Vectopôle, in mesocosms identified as things progress (Montpellier greenhouses network, climatic chambers of Polo GGB), and in study fields in Africa (Senegal, Gabon) or in French Guyana and la Réunion in collaboration with the local partners of UMR MIVEGEC.

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