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Simulating population genetics of pathogen vectors in changing landscapes: guidelines and application with Triatoma brasiliensis.

Rebaudo F, Costa J, Almeida CE, Silvain JF, Harry M, Dangles O - PLoS Negl Trop Dis (2014)

Bottom Line: We then applied our model with Triatoma brasiliensis, originally restricted to sylvatic habitats and now found in peridomestic and domestic habitats, posing as the most important Trypanosoma cruzi vector in Northeastern Brazil.We focused on the effects of vector migration rate, maximum dispersal distance and attraction by domestic habitat on T. brasiliensis population dynamics and spatial genetic structure.Our hope is that our study may provide a testable and applicable modeling framework to a broad community of epidemiologists for formulating scenarios of landscape change consequences on vector dynamics, with potential implications for their surveillance and control.

View Article: PubMed Central - PubMed

Affiliation: BEI-UR072, IRD, Gif-sur-Yvette, France; LEGS-UPR9034, CNRS-UPSud11, Gif-sur-Yvette, France.

ABSTRACT

Background: Understanding the mechanisms that influence the population dynamics and spatial genetic structure of the vectors of pathogens infecting humans is a central issue in tropical epidemiology. In view of the rapid changes in the features of landscape pathogen vectors live in, this issue requires new methods that consider both natural and human systems and their interactions. In this context, individual-based model (IBM) simulations represent powerful yet poorly developed approaches to explore the response of pathogen vectors in heterogeneous social-ecological systems, especially when field experiments cannot be performed.

Methodology/principal findings: We first present guidelines for the use of a spatially explicit IBM, to simulate population genetics of pathogen vectors in changing landscapes. We then applied our model with Triatoma brasiliensis, originally restricted to sylvatic habitats and now found in peridomestic and domestic habitats, posing as the most important Trypanosoma cruzi vector in Northeastern Brazil. We focused on the effects of vector migration rate, maximum dispersal distance and attraction by domestic habitat on T. brasiliensis population dynamics and spatial genetic structure. Optimized for T. brasiliensis using field data pairwise fixation index (FST) from microsatellite loci, our simulations confirmed the importance of these three variables to understand vector genetic structure at the landscape level. We then ran prospective scenarios accounting for land-use change (deforestation and urbanization), which revealed that human-induced land-use change favored higher genetic diversity among sampling points.

Conclusions/significance: Our work shows that mechanistic models may be useful tools to link observed patterns with processes involved in the population genetics of tropical pathogen vectors in heterogeneous social-ecological landscapes. Our hope is that our study may provide a testable and applicable modeling framework to a broad community of epidemiologists for formulating scenarios of landscape change consequences on vector dynamics, with potential implications for their surveillance and control.

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Validation of the model initialization using the response of FST to migration rate.FSTs between sampling points (from left to right for each migration rate: AB, AC, AD, AE, BC, BD, BE, CD, CE, DE), are represented as a function of migration rate from 0.1 to 1. Simulated results are represented using boxplots of 30 repetitions, for all values of dispersal distance (d ranging from 1 to 5 by 1), i.e. 150 pairwise FST values per boxplot. The theoretical expectation is represented by a solid grey line (FST ≈ 1/(4Nm+1) with N = 50 and m ranging continuously from 0 to 1).
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pntd-0003068-g002: Validation of the model initialization using the response of FST to migration rate.FSTs between sampling points (from left to right for each migration rate: AB, AC, AD, AE, BC, BD, BE, CD, CE, DE), are represented as a function of migration rate from 0.1 to 1. Simulated results are represented using boxplots of 30 repetitions, for all values of dispersal distance (d ranging from 1 to 5 by 1), i.e. 150 pairwise FST values per boxplot. The theoretical expectation is represented by a solid grey line (FST ≈ 1/(4Nm+1) with N = 50 and m ranging continuously from 0 to 1).

Mentions: Validation is an important process in model development, and consist in the demonstration that the model meets performance standards under specific conditions [33], [34]. To validate SimAdapt initialization for T. brasiliensis, we calculated FST using Arlequin (based on genotypes from the 7 microsatellite loci and assuming no selection for habitat type) and compared its values with FST from theoretical expectation in an island model [35]. In an island model, the theoretical response of FST to migrant number fits a curve of the form FST ≈ 1/(4Nm+1), where N is the effective population size and m is the migration rate between populations. Our case slightly differed from the island model as it included a finite number of populations and dispersal mechanisms partially driven by the habitat types. We consequently expected different FST values than those predicted by theory [36], [37]. We found a significant relationship between FST and Nm using a nonlinear least squares model for migration rate from 0.1 to 1 (FST  =  1/(5.85Nm+1.24), p<0.05, see Figure 2). This result confirmed that, within the range of parameters' space, our simulation model behaved according to theoretical expectations thereby validating its use for T. brasiliensis.


Simulating population genetics of pathogen vectors in changing landscapes: guidelines and application with Triatoma brasiliensis.

Rebaudo F, Costa J, Almeida CE, Silvain JF, Harry M, Dangles O - PLoS Negl Trop Dis (2014)

Validation of the model initialization using the response of FST to migration rate.FSTs between sampling points (from left to right for each migration rate: AB, AC, AD, AE, BC, BD, BE, CD, CE, DE), are represented as a function of migration rate from 0.1 to 1. Simulated results are represented using boxplots of 30 repetitions, for all values of dispersal distance (d ranging from 1 to 5 by 1), i.e. 150 pairwise FST values per boxplot. The theoretical expectation is represented by a solid grey line (FST ≈ 1/(4Nm+1) with N = 50 and m ranging continuously from 0 to 1).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4125301&req=5

pntd-0003068-g002: Validation of the model initialization using the response of FST to migration rate.FSTs between sampling points (from left to right for each migration rate: AB, AC, AD, AE, BC, BD, BE, CD, CE, DE), are represented as a function of migration rate from 0.1 to 1. Simulated results are represented using boxplots of 30 repetitions, for all values of dispersal distance (d ranging from 1 to 5 by 1), i.e. 150 pairwise FST values per boxplot. The theoretical expectation is represented by a solid grey line (FST ≈ 1/(4Nm+1) with N = 50 and m ranging continuously from 0 to 1).
Mentions: Validation is an important process in model development, and consist in the demonstration that the model meets performance standards under specific conditions [33], [34]. To validate SimAdapt initialization for T. brasiliensis, we calculated FST using Arlequin (based on genotypes from the 7 microsatellite loci and assuming no selection for habitat type) and compared its values with FST from theoretical expectation in an island model [35]. In an island model, the theoretical response of FST to migrant number fits a curve of the form FST ≈ 1/(4Nm+1), where N is the effective population size and m is the migration rate between populations. Our case slightly differed from the island model as it included a finite number of populations and dispersal mechanisms partially driven by the habitat types. We consequently expected different FST values than those predicted by theory [36], [37]. We found a significant relationship between FST and Nm using a nonlinear least squares model for migration rate from 0.1 to 1 (FST  =  1/(5.85Nm+1.24), p<0.05, see Figure 2). This result confirmed that, within the range of parameters' space, our simulation model behaved according to theoretical expectations thereby validating its use for T. brasiliensis.

Bottom Line: We then applied our model with Triatoma brasiliensis, originally restricted to sylvatic habitats and now found in peridomestic and domestic habitats, posing as the most important Trypanosoma cruzi vector in Northeastern Brazil.We focused on the effects of vector migration rate, maximum dispersal distance and attraction by domestic habitat on T. brasiliensis population dynamics and spatial genetic structure.Our hope is that our study may provide a testable and applicable modeling framework to a broad community of epidemiologists for formulating scenarios of landscape change consequences on vector dynamics, with potential implications for their surveillance and control.

View Article: PubMed Central - PubMed

Affiliation: BEI-UR072, IRD, Gif-sur-Yvette, France; LEGS-UPR9034, CNRS-UPSud11, Gif-sur-Yvette, France.

ABSTRACT

Background: Understanding the mechanisms that influence the population dynamics and spatial genetic structure of the vectors of pathogens infecting humans is a central issue in tropical epidemiology. In view of the rapid changes in the features of landscape pathogen vectors live in, this issue requires new methods that consider both natural and human systems and their interactions. In this context, individual-based model (IBM) simulations represent powerful yet poorly developed approaches to explore the response of pathogen vectors in heterogeneous social-ecological systems, especially when field experiments cannot be performed.

Methodology/principal findings: We first present guidelines for the use of a spatially explicit IBM, to simulate population genetics of pathogen vectors in changing landscapes. We then applied our model with Triatoma brasiliensis, originally restricted to sylvatic habitats and now found in peridomestic and domestic habitats, posing as the most important Trypanosoma cruzi vector in Northeastern Brazil. We focused on the effects of vector migration rate, maximum dispersal distance and attraction by domestic habitat on T. brasiliensis population dynamics and spatial genetic structure. Optimized for T. brasiliensis using field data pairwise fixation index (FST) from microsatellite loci, our simulations confirmed the importance of these three variables to understand vector genetic structure at the landscape level. We then ran prospective scenarios accounting for land-use change (deforestation and urbanization), which revealed that human-induced land-use change favored higher genetic diversity among sampling points.

Conclusions/significance: Our work shows that mechanistic models may be useful tools to link observed patterns with processes involved in the population genetics of tropical pathogen vectors in heterogeneous social-ecological landscapes. Our hope is that our study may provide a testable and applicable modeling framework to a broad community of epidemiologists for formulating scenarios of landscape change consequences on vector dynamics, with potential implications for their surveillance and control.

Show MeSH