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Am. J. Trop. Med. Hyg., 76(5), 2007, pp. 930-937
Copyright © 2007 by The American Society of Tropical Medicine and Hygiene

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ASSESSMENT OF TRIATOMA DIMIDIATA DISPERSAL IN THE YUCATAN PENINSULA OF MEXICO BY MORPHOMETRY AND MICROSATELLITE MARKERS

ERIC DUMONTEIL*, FRÉDÉRIC TRIPET, MARIA JESUS RAMIREZ-SIERRA, VINCENT PAYET, GREGORY LANZARO, AND FRÉDÉRIC MENU
Laboratorio de Parasitología, Centro de Investigaciones Regionales Dr. Hideyo Noguchi, Universidad Autónoma de Yucatán, Mérida, Mexico; Center for Applied Entomology and Parasitology, School of Life Sciences, Keele University, Newcastle, United Kingdom; Department of Entomology, University of California, Davis, California; Laboratoire de Biométrie et Biologie Evolutive, Unité Mixte de Recherches, Centre National de la Recherche Scientifique 5558, Université de Lyon 1, Villeurbanne, France


ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
In the Yucatán Peninsula of Mexico, the main vector of Chagas disease is Triatoma dimidiata. Field studies suggest that natural transmission occurs through transient and seasonal invasion of houses by sylvatic/peridomestic triatomines, rather than through persistent domiciliated bug populations. We investigated the genetic structure of T. dimidiata populations, using morphometry and microsatellite markers, to assess dispersal of individuals in this triatomine species and to understand the dynamics of domestic infestation. We observed low phenotypic and genetic differentiation among populations from different villages, with an FST of only 0.0553, which suggested a weak but significant population structure at this level. Similarly low but significant differences were observed among populations from the same village but different biotopes (sylvatic, peridomestic, and domestic), with FST values ranging from 0.0096 to 0.0455. These data suggested elevated dispersal of bugs between biotopes (Nm = 5–25), which was confirmed by likelihood and Bayesian assignment tests. A proportion of bugs collected within domiciles were significantly assigned to peridomestic and sylvatic areas. This study showed that T. dimidiata has important dispersal capabilities that can explain the seasonal pattern of domicile infestation by peridomestic and sylvatic bugs. Therefore, dispersal should be taken into account in the design of effective vector control strategies.


INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Chagas disease, or American trypanosomiasis, is caused by the protozoan parasite Trypanosoma cruzi, and represents a major public health problem in Latin America, particularly in Mexico.1 Because of the limited efficacy of current therapeutic treatment and the lack of an effective vaccine, control of Chagas disease remains based on vector control using insecticides2 and on blood donor screening.3 Vector control programs in South America have focused on the interruption of natural transmission by attacking domiciliated vector populations using pyrethroide insecticides,2 and these have been enormously successful. The interruption of natural transmission is thus underway in the southern cone countries (Argentina, Brazil, Bolivia, and Chile).4

In the Yucatán Peninsula of Mexico, the main vector of Chagas disease is Triatoma dimidiata, an apparently ubiquitous species of triatomine found in a variety of environments.5 A thorough knowledge of key aspects of T. dimidiata biology and ecology is required for the implementation of sucessful and sustainable vector control programs. To this end, we previously characterized the geographic distribution of this vector6 and developed predictive models of vector-borne Chagas disease transmission risk to humans in the region.7 Also, our field data suggested that natural transmission occurs by transient and seasonal invasion of houses by sylvatic/peridomestic triatomines, rather than through established resident domiciliated bug populations.6 This transmission pattern implies high risk of re-infestation by non-domiciliated bugs after conventional indoor insecticide spraying. This was confirmed in a pilot study in which we documented the re-infestation of houses within only 3–4 months after insecticide application.8 Dispersal of T. dimidiata was later observed in northern Guatemala.9 Using mathematical modeling, we also provided theoretical evidence suggesting a major role of dispersal/immigration of bugs to explain seasonal infestation of domiciles by T. dimidiata in the Yucatan Peninsula (Gourbiere S and others, unpublished data).

Our previous work suggests that it is important to have a good understanding of T. dimidiata dispersal to design appropriate vector control strategies in the region. Recent studies on T. dimidata from different regions, using morphometry, chemical and random amplified polymorphic DNA (RAPD) molecular markers,10–15 suggested only weak genetic structuring among populations and that this species has strong dispersal capabilities. However, although these techniques may be sufficient to detect clearly defined subpopulations separated by large distances (more than 120 km), they may have limited resolution to detect population structure over smaller distances and between biotopes.

Alternatively, microsatellite markers have been extensively used for populations studies of many different species over a wide range of geographic scales.16–18 Several microsatellite loci have been identified in Rhodnius pallescens,19 T. infestans,20,21 and T. dimidiata,22 making these markers an attractive molecular tool for the analysis of triatomine population structure and dispersal. We report a study of the genetic structure of T. dimidiata populations in the Yucatan Peninsula of Mexico, using morphometry and microsatellite markers, to assess dispersal of this triatomine species.


MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Field work. Triatomine bugs were collected from 18 sites separated by as little as 1 km, and up to 280 km, corresponding to 14 villages and 3 biotopes (Figure 1Go). Different subsets of these collections were used for analysis at different geo-graphic scales as indicated. Domestic bugs, defined as those collected from indoor areas, were captured by manual search of domiciles by research personnel and/or inhabitants of the premises. Peridomestic bugs were defined as those collected in the yard surrounding houses, generally enclosed by piled-stone walls, and sylvatic bugs were defined as those collected in the bush/forest at a minimum of 500 meters from the nearest house in the village. For peridomestic and sylvatic collections, we used mouse traps8,23 in addition to manual collections. A total of 436 bugs were collected with sample sizes ranging from 11 to 36 bugs per site. Bugs were stored in individual tubes and kept at –20°C until analyzed.


Figure 1
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  		    FIGURE 1. Map of the study area in Mexico. Collection sites and villages are indicated. Hunucma N = Hunucma Norte; Hunucma S = Hunucma Sur. Domestic bugs were collected at all 14 villages and in Dzidzilche and Tetiz, where peridomestic and sylvatic bugs were also collected.

 
Morphometric measurements and analysis. A total of six head measurements as defined by Borges and others24 were taken with a stereoscopic microscope using an ocular micrometer. All measurements were taken by the same investigator. For wing measurements, left wings were mounted on glass cover slips and digital photographs were taken. Distances between eight wing landmarks15 were determined by analyzing digital images in a geographic information system environment (ArcView 3.2; Environmental Systems Research Institute, Redlands, CA) based on their coordinates. One-way analysis of variance and Guillaumin profiles of morphometric measurements from bugs from each collection site were performed for initial comparisons between different subpopulations. An analysis of principal components from the variance-covariance matrix of log-transformed measurements was then performed as described.24 The variance explained by each principal component was approximately 65%, 14%, and 6% for the first three principal components, respectively. Discriminant analysis using these principal components as new variables was then used to identify potential differences between collection sites. Separate analyses were performed that included bugs from different collection sites so that potential morphometric differences could be explored at different geographic scales, ranging from between villages far apart to between biotopes within a single village. Depending on group size, only the first 3–5 principal components were used to ensure that the number of individuals per group was at least double that of these variables, which allowed 85–93% of the variance to be taken into account. Results are presented as canonical maps of the first and second canonical discriminant factors, and statistical significance of potential differences between populations was assessed by Wilks’ Lambda.13,25 Probabilities of bug assignment to distinct collection sites were also calculated from the discriminant scores to identify potential migrants from one population to another. Agreement between the assignments and the field collection sites was assessed by Kappa statistics.26 All univariate and multivariate statistical analysis were performed with JMP 5.0 software (SAS Institute, Cary, NC).

Insect genotyping and analysis. DNA was extracted from 1–3 legs of the collected bugs using standard phenol-chloroform extraction and precipitation. A total of eight microsatellite loci were amplified by polymerase chain reaction (PCR) from 50–100 ng of DNA, using the primers and PCR conditions described by Anderson and others,22 and PCR products were analyzed on an ABI310 automatic sequencer using GenScan software (Applied Biosystems, Foster City, CA).

All genotype data were analyzed using Arlequin version 2.001 software.27 We first assessed compliance with Hardy-Weinberg equilibrium for all populations and loci after Bonferoni correction. Population genetic structure was evaluated at two levels, between villages and between biotopes within villages, respectively, using F statistics.28 We calculated FST, a measure of divergence among such populations, FIS, a measure of imbreeding relative to an S population and its allele frequency, and Nm, the number of migrants per generation between subpopulations.16,29 A Mantel test on linearized FSTs and log geographic distances was used to evaluate isolation by distance. As with morphometric data analysis, we further analyzed genotype data with assignment tests to detect potential migrants between populations and habitats. We used the likelihood method implemented in Arlequin version 2.001,27 as well as a Bayesian approach implemented in Immanc 1.5.30 Both methods assume that populations are in Hardy-Weinberg equilibrium and that loci are in linkage equilibrium. The approach implemented in Arlequin version 2.001 uses allelic frequencies in each population to calculate the likelihood that a given genotype belongs to a certain population. If an individual genotype is more likely to belong to a population other than that from where it was sampled, the individual is considered a migrant. The Bayesian approach implemented in Immanc 1.5 uses a Bayesian derivation to calculate the probability density of population allele frequencies from sample population frequencies.31 The probability that a given genotype belongs to one or the other population is calculated using a Monte Carlo re-sampling procedure simulating a large number of random multilocus genotypes based on allele frequencies directly estimated from the reference population samples. We used the program to calculate the probability of an individual’s genotype being a current migrant or the progeny of a migrant mated with an individual from the local population. We used 10,000 replications for each analysis as recommended by the authors and a significance value of 0.05.30 As for morphometric analysis, the agreement between bug collection sites and genotype assignments was evaluated by Kappa statistics.26


RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Population structure between villages. We initially evaluated T. dimidiata population structure across the Yucatan Peninsula by comparing bugs collected from different villages. Univariate analysis of head and wing measurements and Guillaumin profiles of 336 domestic bugs from a subset of 10 villages showed significant differences between bugs from the various collection sites for some morphometric variables. Discriminant analysis showed marked sexual dimorphism with females being significantly larger than males (Wilks’ Lambda = 0.671, P < 0.0001) and bugs from different sex were thus analyzed separately. Discriminant analysis was also able to detect significant differences between bugs from the 10 collection sites (Wilks’ Lambda = 0.400, P < 0.0001 for males and Wilks’ Lambda = 0.331, P < 0.001 for females), although there was considerable overlap of the respective canonical maps (Figure 2AGo). There was a significant correlation between the first canonical factor centroids (an indicator of size) and the longitude of the villages (Figure 2BGo, r2 = 0.42, P = 0.002), which suggested a significant gradient in size from west to east and a significant phenotypic structure across the peninsula. A similar level of phenotypic differentiation was observed using wing measurements. Combining head and wing measurements did not improve the level of differentiation detected and provided similar results.


Figure 2
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  		    FIGURE 2. Discriminant analysis of morphometric data of populations from different villages in Mexico. Analysis of head measurements of bugs from the indicated villages are shown. A, Canonical maps indicating significant differences between male populations (Wilks’ Lambda = 0.400, P < 0.0001). Only a subset of five villages is shown for clarity. B, Correlation between the centroid of the domestic populations for 10 villages and the village’s longitude (r2 = 0.42, P = 0.002 for male and female). Open circles = females, black circles = males.

 
Population genetic structure was also assessed using microsatellite markers, and 295 bugs from 12 collection sites were genotyped. Of the eight microsatellite loci included in our study, two did not give reproducible amplifications (Tdms1 and Tdms19), and one was monomorphic (Tdms3); these were thus discarded from the study. One additional locus gave a disproportionate excess of homozygotes (Tdms16), which suggested a high frequency of null alleles, and was also removed from the analysis. Thus, four loci were included in our final analysis to assess population structure (Tdms4, Tdms9, Tdms11, and Tdms22). These microsatellite loci were highly polymorphic, with a minimum of 4 and up to 21 alleles per locus (Table 1Go). A large number of alleles had not been described previously in T. dimidiata populations.22 After Bonferonni correction, Hardy-Weinberg expectations were significantly rejected in only 2 of 48 cases of populations and loci.


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TABLE 1
Heterozygosity and Hardy-Weinberg expectations for four microsatellite loci*
 
The FST estimates were computed for a subset of domestic populations from nine villages (Table 2Go). These indicated a rather low but significant level of genetic differentiation across the Yucatan peninsula. The maximum FST was 0.0553, between Tres Reyes and Hunucma Norte, which are separated by a distance of 250 km. However, there was no evidence of isolation by distance between populations (P = 0.402, by Mantel test on linearized FSTs and log geographic distance), and comparable FST values were obtained between very close villages (e.g., 0.0367 between Hunucma Norte and Sur, separated by only a few kilometers). Microsatellite markers were thus able to detect a finer genetic structure than morphometric analysis. The inbreeding coefficient Fis indicated low inbreeding in all collection sites (average Fis = 0.040), except in Tixcacalcupul, where it reached 0.104 (Table 1Go). Overall, both morphometric and microsatellite data suggested important dispersal capabilities of T. dimidiata over large geographic distances (up to 280 km), which resulted in detectable but weak genetic structure and population differentiation in the Yucatan Peninsula.


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TABLE 2
FST statistics between pairs of domestic populations from nine villages in Mexico*
 
Population structure between biotopes. We then conducted an analysis to determine if we could detect substructuring between domestic, peridomestic, and sylvatic populations. Morphometric analysis of male bugs indicated significant morphologic differences between bugs from the different biotopes from Tetiz (Wilks’ Lambda = 0.401, P = 0.015), but not from Dzidzilche (Wilks’ Lambda = 0.686, P = 0.27) (Figure 3Go), and analysis of female bugs indicated a significant difference between biotopes in Dzidzilche but not in Tetiz (Wilks’ Lambda = 0.557, P = 0.006 and Wilks’ Lambda = 0.812, P = 0.66, respectively). These data suggested a weak level of phenotypic differentiation between biotopes in both villages. We also looked at the assignment of bugs to sub-populations/biotopes based on the canonical scores of the discriminant analysis. For both Dzidzilche and Tetiz, when we used either head or wing measurements, only 43–82% of bugs could be correctly assigned to the biotope where they had been collected (Table 3Go), which resulted in a low agreement between assignments and collection sites (Kappa statistics = 0.370 ± 0.075 and 0.349 ± 0.085 for Dzildzilche and Tetiz, respectively). This analysis confirmed the weak or borderline level of population differentiation between the respective biotopes (Table 3Go). Conversely, the important proportion of missassigned bugs suggested significant dispersal between biotopes. Approximately 27–36% of bugs collected in the domiciles seemed to originate from the peridomicile, and 7–24% were assigned to the sylvatic biotope (Table 3Go). Furthermore, several peridomestic and even sylvatic bugs appeared to come from the domiciles, which suggested dispersal in multiple directions.


Figure 3
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  		    FIGURE 3. Discriminant analysis of head morphometric data of populations in Mexico from different biotopes. Analysis of head measurement of male bugs from the indicated biotopes are shown for Tetiz (A) and Dzidzilche (B). Significant differences (Wilks’ Lambda = 0.401, P = 0.015) were observed in Tetiz, but not in Dzidzilche (Wilks’ Lambda = 0.686, P = 0.27).

 

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TABLE 3
Biotope assignment based on discriminant analysis of morphometric data*
 
As before, population substructuring was also assessed using microsatellite markers. There seemed to be some differences in allele frequencies between subpopulations, and a few private alleles were detected in populations from each biotope (Figure 4Go). The FST statistics for Dzidzilche and Tetiz are shown in Table 4Go. In Dzidzilche, there was no significant genetic differentiation between peridomestic and domestic populations, which suggested a high number of migrants per generation (Nm = 25.8). In contrast, bug populations from domestic and peridomestic habitats were significantly differentiated from those in the sylvatic habitat, and the estimate of migrants between those biotopes was comparatively lower (Nm = 5–6). However, this differentiation was not observed in Tetiz where domestic bugs were not significantly differentiated from sylvatic ones (Table 4Go).


Figure 4
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  		    FIGURE 4. Allele frequencies of populations from different biotopes in Dzidzilche, Mexico. Allele frequencies for the indicated four microsatellite loci are shown for bugs from domestic, peridomestic, and sylvatic collection sites. The presence of some alleles is restricted to a particular biotope (private alleles).

 

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TABLE 4
FST statistics between biotopes in Dzidzilche and Tetiz, Mexico*
 
We further analyzed dispersal between sub-populations from Dzidzilche using genotype assignment tests. The likelihood method resulted in assignment plots showing V-shaped distributions of bugs for all biotopes pairs, which is characteristic of poorly differentiated populations. Nonetheless, 70–78% of the bugs collected in Dzidzilche could be correctly assigned to their corresponding biotopes, which indicated good agreement between assignments and actual collection sites (Table 5Go, Kappa statistic = 0.609 ± 0.073). Bayesian assignments resulted in a somewhat improved accuracy, with 77–95% of the bugs correctly assigned (Table 5Go, Kappa statistic = 0.796 ± 0.057). In spite of good agreement obtained between genotype assignments and field collection sites, approximately 10–22% of the bugs collected in the domiciles seem to be immigrants from the peridomicile and sylvatic area. Also, dispersal in multiple directions seems to occur because several peridomestic and sylvatic bugs were assigned to the domiciles (Table 5Go), as observed with morphometric analysis.


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TABLE 5
Biotope assignment based on genotypes in Dzidzilche*
 

DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
We investigated the genetic structure of T. dimidiata as an indicator of dispersal because understanding dispersal is critical for the design and implementation of effective vector control strategies. Morphometric analysis has been widely used to evaluate triatomine population structure because of its ease of use.32,33 However, it may lack the power to distinguish between closely related subpopulations. Furthermore, morphometric studies essentially describe phenotypic variation, which does not necessarily imply that the same pattern of variation would be observed at the genetic level. Microsatellites have been extensively and successfully used for population genetics studies of a wide variety of species, and over a variety of geographic scales,34 but to our knowledge, this is the first study in triatomines, even though various microsatellite loci have been described in several triatomine species.19–22

Our results indicate that there was an overall good agreement between morphometric and molecular data, although the latter seemed more sensitive to population structure, particularly at the level of biotopes from single villages. At the level of the Yucatan Peninsula, both methods indicated a weak but significant differentiation of T. dimidiata populations. Morphometric analysis was able to detect a west-east gradient in bug size, and genotypic data indicated some degree of genetic differentiation shown by low but significant FSTs between populations from different villages, but no significant isolation by distance. These results are in good agreement with previous population genetic studies of T. dimidiata that showed a weak population structure over large distances. Previous morphometric analysis showed significant differences between populations from central Mexico and Guatemala,13,15,25 and populations from the Peten region of Northern Guatemala were very similar to those from Yucatan state in Mexico.15 Similarly, molecular markers such as RAPDs enabled detection of significant genetic differentiation between populations from different regions in Guatemala, with FSTs up to 0.175.14

Population substructure is more difficult to assess when we focus on a smaller geographic area such as single villages. Using morphometric analysis, we were able to detect some significant differences between bugs from domestic, peridomestic, and sylvatic biotopes in Tetiz and Dzidzilche, depending on the sex of the analyzed bugs. Conversely, microsatellite analysis indicated some population substructure in Dzidzilche, even though FSTs remained very low, but not in Tetiz. A likely explanation for these discrepancies is that we may be within the threshold of detection of phenotypic and genotypic differentiation at this level. Thus, T. dimidiata seemed to have a high dispersal capacity, as suggested by the fairly high migration rates we observed between biotopes (5–25 migrants/generation). Again, this is in general agreement with other studies of this species using RAPD markers, which reported rates of 2–311 to 9.7 migrants/generation10 between biotopes and adjacent villages in Colombia and Guatemala, respectively. However, because the accuracy of migration rate estimations depends on the extent of population differentiation, some uncertainty regarding dispersal ability of T. dimidiata may remain because of the low level of differentiation detected in our study. In that respect, it is unfortunate that only four loci provided reliable genotypic information. To overcome this limitation, new microsatellite markers for T. dimidiata have recently been identified (Krische MA and others, unpublished data), and future studies including these new markers and a greater number of collection sites may enable us to refine our observations. Taken together, these data strongly suggest that T. dimidiata has high dispersal capabilities and a large panmictic unit, which contrasts with other triatomine species such as T. infestans or T. rubrovaria or T. braziliensis, which show much stronger population structure.35–38

Genotype assignment tests also appear to provide a clearer picture that conventional FSTs. It has been suggested that these tests, and particularly those based on Bayesian statistics,39 can still identify immigrants even when there is very low genetic differentiation between populations and no detectable isolation by distance.30,40 It is important to note that some of the loci used in this study were very highly polymorphic. Highly polymorphic microsatellite loci tend to underestimate levels of genetic divergence as estimate by FST (probably due to size homoplasy), thus diminishing their power to discriminate among samples.41 The use of assignment tests, such as those used in this study, avoid this problem. Because of the low statistical significance of morphometric differentiation between populations from distinct biotopes that we observed, it is likely that immigration is overestimated by this method. However, likelihood and Bayesian assignment tests provided an identification of immigrants and non-immigrants that is much more statistically robust.42 Of particular interest is the observation that 10–22% of the bugs collected in the domiciles were immigrants from the peridomicile and sylvatic areas. It is also important to note that some domestic bugs emigrate to the peridomicile and sylvatic areas as well, resulting in dispersal in multiple directions.

Finally, these results are in agreement with our hypothesis of seasonal infestation of houses by flying adults and limited colonization of domiciles (Gourbiere S and others, unpublished data).6,43 These data explain the short-term efficacy of insecticide spraying for the control of domestic infestation by T. dimidiata in the Yucatan Peninsula.8 It is thus crucial to take this particular behavior of T. dimidiata into account for effective control, and our results point towards potential alternative strategies aimed primarily at reducing bug dispersal into the domiciles with physical barriers such as mosquito screens. In addition, we observed an important role for the peridomicile as a transit area between sylvatic and domestic biotopes. This is in agreement with previous observations that peridomicile infestation and colonization is critical for reinfestation of domiciles after control of several triatomine species.44,45 Accordingly, reducing the suitability of the peridomicile for triatomine infestation by removing potential bug refuges (cleaning and possibly insecticide spraying) may also improve T. dimidiata control. Lastly, the apparent elevated dispersal of T. dimidiata over large distances, likely involving a combination of diffusion-like local dispersal and passive human- or animal-mediated dispersal over larger distances, stresses the need for integrated control measures over large geographic areas to prevent rapid reinfestation. Alternatively, this high dispersal may represent a key advantage for the introduction of paratransgenic control of T. cruzi transmission46 because it would facilitate the spread of T. cruzi resistance in natural T. dimidiata populations. This species thus represents an excellent model for further exploration of this novel control strategy. Testing some of these alternative strategies in field studies would provide confirmation of their usefulness for T. dimidiata control.

Received June 10, 2006. Accepted for publication January 18, 2007.

Acknowledgments: We thank M. Mestres-Tarres and J. F. Cornu for help with the morphometric measurements.

Financial support: This study was supported by the UNDP/World Bank/World Health Organization Special Program for Research and Training in Tropical Diseases (TDR), Project #A02424, and grant #20020404 from SISIERRA/CONACYT, Mexico, to Eric Dumonteil, and by the Région Rhône-Alpes, grant n° 03 00 57 3401 to Vincent Payet and Frédéric Menu.

* Addresss correspondence to Eric Dumonteil, Laboratorio de Parasitología, Centro de Investigaciones Regionales Dr. Hideyo Noguchi, Universidad Autónoma de Yucatán, Ave. Itzaes #490 x 59, 97000 Mérida, Yucatán, Mexico. E-mail: oliver{at}uady.mx Back

Authors’ addresses: Eric Dumonteil and Maria Jesus Ramirez-Sierra, Laboratorio de Parasitología, Centro de Investigaciones Regionales Dr. Hideyo Noguchi, Universidad Autónoma de Yucatán, Ave. Itzaes #490 x 50, 97000, Mérida, Yucatán, Mexico, Telephone: 52-999-924-5910, Fax: 52-999-923-6120, E-mail: oliver{at}uady.mx. Frédéric Tripet, Center for Applied Entomology and Parasitology, School of Life Sciences, Keele University, Newcastle ST5 5BG, United Kingdom. Vincent Payet and Frédéric Menu, Laboratoire de Biométrie et Biologie Evolutive, Unité Mixte de Recherches 5558, Centre National de la Recherche Scientifique, Université de Lyon, Villeurbanne, F-69622, France. Gregory Lanzaro, Department of Entomology, University of California, Davis, CA 95616.

Reprint requests: Eric Dumonteil, Laboratorio de Parasitología, Centro de Investigaciones Regionales Dr. Hideyo Noguchi, Universidad Autónoma de Yucatán, Ave. Itzaes #490 x 50, 97000, Mérida, Yucatán, Mexico.


REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Dumonteil E, 1999. Update on Chagas’ disease in Mexico. Salud Publica Mex 41: 322–327.[Web of Science][Medline]
  2. Silveira A, Vinhaes M, 1999. Elimination of vector-borne transmission of Chagas disease. Mem Inst Oswaldo Cruz 94: 405–411.[Web of Science][Medline]
  3. Schmuñis GA, 1999. Prevention of transfusional Trypanosoma cruzi infection in Latin America. Mem Inst Oswaldo Cruz 94: 93–101.[Web of Science][Medline]
  4. World Health Organization, 2002. Control of Chagas disease. World Health Organ Tech Rep Ser 905: 1–109.
  5. Zeledón R, 1981. El Triatoma dimidiata (Latreille, 1811) y su relación con la enfermedad de Chagas. San José, Costa Rica: Universidad Estatal a Distancia.
  6. Dumonteil E, Gourbiere S, Barrera-Perez M, Rodriguez-Felix E, Ruiz-Piña H, Baños-Lopez O, Ramirez-Sierra MJ, Menu F, Rabinovich JE, 2002. Geographic distribution of Triatoma dimidiata and transmission dynamics of Trypanosoma cruzi in the Yucatan peninsula of Mexico. Am J Trop Med Hyg 67: 176–183.[Abstract]
  7. Dumonteil E, Gourbiere S, 2004. Prediction of Triatoma dimidiata vector abundance and infection rate: a risk map for Trypanosoma cruzi natural transmission in the Yucatan peninsula of Mexico. Am J Trop Med Hyg 70: 514–519.[Abstract/Free Full Text]
  8. Dumonteil E, Ruiz-Piña H, Rodriguez-Felix E, Barrera-Perez M, Ramirez-Sierra MJ, Rabinovich JE, Menu F, 2004. Reinfestation of houses after intra-domicile insecticide application in the Yucatán peninsula, Mexico. Mem Inst Oswaldo Cruz 99: 253–256.[Web of Science][Medline]
  9. Monroy MC, Bustamante DM, Rodas AG, Enriquez ME, Rosales RG, 2003. Habitats, dispersion and invasion of sylvatic Triatoma dimidiata (Hemiptera: Reduviidae: Triatominae) in Peten, Guatemala. J Med Entomol 40: 800–806.[Web of Science][Medline]
  10. Dorn PL, Melgar S, Rouzier V, Gutierrez A, Combe C, Rosales R, Rodas A, Kott S, Salvia D, Monroy CM, 2003. The Chagas vector, Triatoma dimidiata (Hemiptera: Reduviidae), is panmictic within and among adjacent villages in Guatemala. J Med Entomol 40: 436–440.[Web of Science][Medline]
  11. Ramirez CJ, Jaramillo CA, del Pilar Delgado M, Pinto NA, Aguilera G, Guhl F, 2005. Genetic structure of sylvatic, peridomestic and domestic populations of Triatoma dimidiata (Hemiptera: Reduviidae) from an endemic zone of Boyaca, Colombia. Acta Trop 93: 23–29.[Web of Science][Medline]
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