Unveiling the ecology and spatial dynamics of Trypanosoma cruzi, its DTUs and Triatoma vitticeps in the Atlantic Forest of south-eastern Espírito Santo State, Brazil

TL;DR

This study explores how environmental factors influence the spread of Trypanosoma cruzi and its vector, Triatoma vitticeps, in Brazil's Atlantic Forest.

Contribution

The study identifies wind speed as a novel abiotic factor influencing long-distance transmission of T. cruzi via infected triatomines.

Findings

T. vitticeps is most prevalent in high-altitude, humid regions with dense vegetation in Espírito Santo.

Wind speed, mammal richness, and temperature are key factors influencing T. vitticeps infection and dispersal.

Decision tree analysis correctly identified 84% of infected triatomines, highlighting the predictive potential of machine learning in this context.

Abstract

The transmission dynamics of Trypanosoma cruzi in natural environments exhibit considerable variation at the micro-locality scale. However, the specific biotic and abiotic factors driving this heterogeneity remain largely unidentified. The Atlantic Forest of the state of Espírito Santo (ES) presents a unique transmission network of T. cruzi, in which Triatoma vitticeps represents the absolute majority among existing triatomines, with high infection rates and diversity of genotypes, frequently invading homes. No infection was found in peridomestic mammals. This study aimed to elucidate the spatial and environmental distribution patterns of T. vitticeps and its infection by T. cruzi DTUs throughout Espírito Santo, quantifying the influence of abiotic variables on both vector occurrence and infection dynamics. Species Distribution Modeling (SDM) of T. cruzi genotypes in T. vitticeps collected in the Atlantic Forest of Espírito Santo was performed using the ModleR package, in the R programming language, with climate and landscape variables (~1km²) selected by Spearman’s correlation [-0.7 ≤ ρ ≤ 0.7]. True Skill Statistic (≥ 0.7) was used to evaluate model performance. Decision tree to classify T. vitticeps infection by T. cruzi was created using machine learning algorithms in WEKA 3.8.6 software. The SDMs of T. vitticeps and its infection demonstrated: i. Central and South mesoregions presented better environmental conditions for their occurrence; ii. association with mountainous regions with high altitudes, humid and superhumid, with vegetation density and vigor and high values of topographic diversity; iii. Schoener similarity suggests Z3 is mixed, dominated by TcIV and TcIII in Central–South, with TcIII influence Northwest and North Coast; iv. Infection was explained by wind speed, mammal richness, and temperature, with the decision tree identifying 84% of positives and 29% of negatives. T. vitticeps may originate in high-altitude regions and disperse via wind to lowlands, promoting domiciliary invasion and supporting previously hypothesized long-distance transmission of T. cruzi.

The spatial distribution of Trypanosoma cruzi Discrete Typing Units (DTUs) has yet to be clearly associated with specific biomes or host habitats. Niche modeling, used in this study as species distribution modeling (SDM), provided a valuable approach for assessing environmental suitability for the transmission of the main T. cruzi genotypes, emerging as a potential predictive tool for identifying areas at risk of transmission in natural environments. This study demonstrates that wind speed functions as an abiotic variable influencing the dispersal capacity of an infected triatomine species (Triatoma vitticeps), thereby establishing a novel long-distance transmission mechanism for T. cruzi. Spatiotemporal modeling of the parasite–host interaction relies on an empirical environmental assessment of abiotic factors. The findings presented here underscore wind speed as a key abiotic determinant of T. vitticeps movement when infected, highlighting its role in enabling long-range T. cruzi transmission. In the Atlantic Forest biome of Espírito Santo, particularly within the Central and Southern mesoregions, optimal conditions for such dispersal are associated with the interplay of wind speed, altitude, vegetation density, relative humidity, and topographic diversity. Decision tree analysis further revealed that species richness constitutes a critical biotic variable associated with the infection of triatomines.

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