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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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Study landscape (A) and matrix representation of the landscape in simAdapt (B).The different habitat types, roads and lakes are represented by different colors. The black triangles represent the location of sample points from the field data in the Caicó municipality in Northeastern Brazil.
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pntd-0003068-g001: Study landscape (A) and matrix representation of the landscape in simAdapt (B).The different habitat types, roads and lakes are represented by different colors. The black triangles represent the location of sample points from the field data in the Caicó municipality in Northeastern Brazil.

Mentions: The first step to perform simulations in SimAdapt is to build a digital representation of the study landscape. To achieve this goal, the landscape is categorized into habitat types. Optionally, as SimAdapt selection submodel can be set to consider that being adapted to a given habitat is considered advantageous while the individual is in this habitat, but disadvantageous in others habitats (see [23] for a discussion on alternative selection models), the categorization of habitat type can reflect a selective pressure resulting in a differential reproductive fitness of individuals. Once categorized into habitat types, the landscape is converted into a matrix representation. In this matrix, habitat types are converted into a discrete variable made of grid cells (raster representation, see Figure 1 in the study case below) unlike in a geographic information system (GIS), where delimited areas (here habitat types) are typically drawn continuously (vectorial representation). The tessellation defines the resolution of the landscape which should be defined according to the ecological characteristics of the vector model, in particular its dispersal capabilities. At this point, each cell of the grid corresponding to a habitat type can be parameterized with a carrying capacity and a resistance to vectors' emigration. The carrying capacity defines the maximum number of individuals that can be located into a cell, and the resistance for migration defines the permeability of the landscape (sensu[24]) ranging from 0 (i.e., no resistance) to 100 (i.e., impermeable barrier) − see [1], [25] for reviews on the effects of landscape resistance on infectious diseases and vectors. Technically, any GIS software provides the necessary features to define areas (habitat types), as well as additional layers for carrying capacity and resistance for migration. This information can be exported into SimAdapt as three different “ascii” files at a chosen resolution. Alternatively, the map can be built entirely from the graphical user interface (see Appendix S1 in [9]).


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)

Study landscape (A) and matrix representation of the landscape in simAdapt (B).The different habitat types, roads and lakes are represented by different colors. The black triangles represent the location of sample points from the field data in the Caicó municipality in Northeastern Brazil.
© Copyright Policy
Related In: Results  -  Collection

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

pntd-0003068-g001: Study landscape (A) and matrix representation of the landscape in simAdapt (B).The different habitat types, roads and lakes are represented by different colors. The black triangles represent the location of sample points from the field data in the Caicó municipality in Northeastern Brazil.
Mentions: The first step to perform simulations in SimAdapt is to build a digital representation of the study landscape. To achieve this goal, the landscape is categorized into habitat types. Optionally, as SimAdapt selection submodel can be set to consider that being adapted to a given habitat is considered advantageous while the individual is in this habitat, but disadvantageous in others habitats (see [23] for a discussion on alternative selection models), the categorization of habitat type can reflect a selective pressure resulting in a differential reproductive fitness of individuals. Once categorized into habitat types, the landscape is converted into a matrix representation. In this matrix, habitat types are converted into a discrete variable made of grid cells (raster representation, see Figure 1 in the study case below) unlike in a geographic information system (GIS), where delimited areas (here habitat types) are typically drawn continuously (vectorial representation). The tessellation defines the resolution of the landscape which should be defined according to the ecological characteristics of the vector model, in particular its dispersal capabilities. At this point, each cell of the grid corresponding to a habitat type can be parameterized with a carrying capacity and a resistance to vectors' emigration. The carrying capacity defines the maximum number of individuals that can be located into a cell, and the resistance for migration defines the permeability of the landscape (sensu[24]) ranging from 0 (i.e., no resistance) to 100 (i.e., impermeable barrier) − see [1], [25] for reviews on the effects of landscape resistance on infectious diseases and vectors. Technically, any GIS software provides the necessary features to define areas (habitat types), as well as additional layers for carrying capacity and resistance for migration. This information can be exported into SimAdapt as three different “ascii” files at a chosen resolution. Alternatively, the map can be built entirely from the graphical user interface (see Appendix S1 in [9]).

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