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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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Goodness of fit between observed and simulated FST for T. brasiliensis in Northeastern Brazil.The red from blue color gradient represents an Akima interpolation of the least-square optimization between observed and simulated FST for different values of vector migration rate, dispersal distance and domestic habitat attraction. FST were computed using Arlequin over the 10 couples of sampled points. Sets of simulations were repeated 30 times for each value of migration rate (m index ranging from 0.1 to 1 by 0.1), dispersal distance (d ranging from 2 to 10 km by 2 km) and domestic habitat attraction (l index ranging from 0 to 10 by 2). The plot is represented using mean values with a gradient from blue (high value, i.e., poor fit), to red (low value, i.e., good fit).
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pntd-0003068-g003: Goodness of fit between observed and simulated FST for T. brasiliensis in Northeastern Brazil.The red from blue color gradient represents an Akima interpolation of the least-square optimization between observed and simulated FST for different values of vector migration rate, dispersal distance and domestic habitat attraction. FST were computed using Arlequin over the 10 couples of sampled points. Sets of simulations were repeated 30 times for each value of migration rate (m index ranging from 0.1 to 1 by 0.1), dispersal distance (d ranging from 2 to 10 km by 2 km) and domestic habitat attraction (l index ranging from 0 to 10 by 2). The plot is represented using mean values with a gradient from blue (high value, i.e., poor fit), to red (low value, i.e., good fit).

Mentions: We performed forward-time simulations of the potential effects on T. brasiliensis population genetics of landscape change (urbanization) within the next 50 years. Our simplified scenario of urbanization consisted in the progression of peridomestic and domestic habitats over sylvatic habitats (1 km per year to mimic worst case scenario of deforestation, see [44], [45] for a discussion).The prospective scenarios included selection considering one locus under selection per habitat type (domestic, peridomestic and sylvatic), with two alleles (“generalist” and “specialized”, see [9]). As selection coefficient and degree of dominance (s and h, see [46]) were unknown and because our field sampling did not allow to infer such parameters (no time series of alleles frequencies, see [47]), the degree of dominance was represented to reproduce co-dominance (h = 0.5), and the selection coefficient to reproduce strong selection (s = 0.2) (see [46]). T. brasiliensis dispersal variables were set up using results from the optimization procedure (m = 0.6; d = 3; l = 2; see Figure 3). The simulations were run over 100 triatomine generations, with one generation equivalent to approximately 6 months ([13]). Individuals were sampled every generation at all sampling points, and FST computed over time using Arlecore (see Table 1 for parameterization). Four scenarios were compared using two factors (land-use change and selection) and two states (with/without) using two-way analysis of variance of FST after 100 generations.


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)

Goodness of fit between observed and simulated FST for T. brasiliensis in Northeastern Brazil.The red from blue color gradient represents an Akima interpolation of the least-square optimization between observed and simulated FST for different values of vector migration rate, dispersal distance and domestic habitat attraction. FST were computed using Arlequin over the 10 couples of sampled points. Sets of simulations were repeated 30 times for each value of migration rate (m index ranging from 0.1 to 1 by 0.1), dispersal distance (d ranging from 2 to 10 km by 2 km) and domestic habitat attraction (l index ranging from 0 to 10 by 2). The plot is represented using mean values with a gradient from blue (high value, i.e., poor fit), to red (low value, i.e., good fit).
© Copyright Policy
Related In: Results  -  Collection

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

pntd-0003068-g003: Goodness of fit between observed and simulated FST for T. brasiliensis in Northeastern Brazil.The red from blue color gradient represents an Akima interpolation of the least-square optimization between observed and simulated FST for different values of vector migration rate, dispersal distance and domestic habitat attraction. FST were computed using Arlequin over the 10 couples of sampled points. Sets of simulations were repeated 30 times for each value of migration rate (m index ranging from 0.1 to 1 by 0.1), dispersal distance (d ranging from 2 to 10 km by 2 km) and domestic habitat attraction (l index ranging from 0 to 10 by 2). The plot is represented using mean values with a gradient from blue (high value, i.e., poor fit), to red (low value, i.e., good fit).
Mentions: We performed forward-time simulations of the potential effects on T. brasiliensis population genetics of landscape change (urbanization) within the next 50 years. Our simplified scenario of urbanization consisted in the progression of peridomestic and domestic habitats over sylvatic habitats (1 km per year to mimic worst case scenario of deforestation, see [44], [45] for a discussion).The prospective scenarios included selection considering one locus under selection per habitat type (domestic, peridomestic and sylvatic), with two alleles (“generalist” and “specialized”, see [9]). As selection coefficient and degree of dominance (s and h, see [46]) were unknown and because our field sampling did not allow to infer such parameters (no time series of alleles frequencies, see [47]), the degree of dominance was represented to reproduce co-dominance (h = 0.5), and the selection coefficient to reproduce strong selection (s = 0.2) (see [46]). T. brasiliensis dispersal variables were set up using results from the optimization procedure (m = 0.6; d = 3; l = 2; see Figure 3). The simulations were run over 100 triatomine generations, with one generation equivalent to approximately 6 months ([13]). Individuals were sampled every generation at all sampling points, and FST computed over time using Arlecore (see Table 1 for parameterization). Four scenarios were compared using two factors (land-use change and selection) and two states (with/without) using two-way analysis of variance of FST after 100 generations.

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