Distributional potential of the Triatoma brasiliensis species complex at present and under scenarios of future climate conditions

Abstract

BackgroundThe Triatoma brasiliensis complex is a monophyletic group, comprising three species, one of which includes two subspecific taxa, distributed across 12 Brazilian states, in the caatinga and cerrado biomes. Members of the complex are diverse in terms of epidemiological importance, morphology, biology, ecology, and genetics. Triatoma b. brasiliensis is the most disease-relevant member of the complex in terms of epidemiology, extensive distribution, broad feeding preferences, broad ecological distribution, and high rates of infection with Trypanosoma cruzi; consequently, it is considered the principal vector of Chagas disease in northeastern Brazil.MethodsWe used ecological niche models to estimate potential distributions of all members of the complex, and evaluated the potential for suitable adjacent areas to be colonized; we also present first evaluations of potential for climate change-mediated distributional shifts. Models were developed using the GARP and Maxent algorithms.ResultsModels for three members of the complex (T. b. brasiliensis, N = 332; T. b. macromelasoma, N = 35; and T. juazeirensis, N = 78) had significant distributional predictivity; however, models for T. sherlocki and T. melanica, both with very small sample sizes (N = 7), did not yield predictions that performed better than random. Model projections onto future-climate scenarios indicated little broad-scale potential for change in the potential distribution of the complex through 2050.ConclusionsThis study suggests that T. b. brasiliensis is the member of the complex with the greatest distributional potential to colonize new areas: overall; however, the distribution of the complex appears relatively stable. These analyses offer key information to guide proactive monitoring and remediation activities to reduce risk of Chagas disease transmission.

The impact of climate change on the geographical distribution of two vectors of Chagas disease: implications for the force of infection

“…However, recent studies suggest a shift (rather than expansion) in the geographical distribution of species and vector-borne diseases in a global warming context [85][86][87], which is consistent with our results for both R. prolixus and T. infestans as well as other triatomine studies [37,38]. In accounting for the fact that insects adapted to higher latitudes may present a broader thermal-tolerance range than those adapted to lower latitudes, some authors have proposed that tropical organisms will be more sensitive to temperature changes [88] and consequently, the impact of climate change will be different across latitudes and species [89].…”

Utilizing ecological niche modelling to predict habitat suitability of eastern equine encephalitis in Florida

“…These bioclimatic variables were calculated to match parameters utilized in previous Maxent studiesit was suggested by the Maxent modelling package (Phillips, Anderson, and Schapire 2006) that bioclimatic variables identified by WorldClim (worldclim.org) be utilized as climate variables for modelling purposes. These variables were utilized in other Maxent studies, either all 19 or a selected group determined by methods such as a principal component analysis (Belkhiria et al, 2018;Costa et al 2014;González, Paz, and Ferro 2014;Gwitira et al 2015;Rose and Wall 2011;Signorini et al 2014).…”

“…Studies employing ecological niche modelling in spatial epidemiology routinely use the Maxent software package, exposing a presence-only-based species distribution model (Phillips, Anderson, and Schapire 2006), to predict potential distribution for a multitude of diseases, including flu outbreak risk in California, US. (Belkhiria et al, 2018), Chagas disease in Brazil (Costa et al 2014), and malaria in Zimbabwe (Gwitira et al 2015). Maxent is a software package which operationalizes a modelling approach often used for estimating habitat suitability, species distribution and the probability of occurrence given presence-only data and environmental (climatic) variables in coordinate space (Elith et al 2011;Phillips, Anderson, and Schapire 2006;Merow, Smith, and Silander 2013).…”

Influence of human population density on spatial distribution patterns of environmental suitability for triatomine vectors of Chagas disease

“…Following other authors [17][18][19][20][21], we have modeled the niche of each one of out 14 triatomine species using species distribution models (SDMs) built upon climatic variables related to temperature (in ºC) and precipitation (in mm). So, we used the 19 bioclimatic variables of the World Clim (www.worldclim.org; [37]), with resolution of 2.5 minutes (≈ 5km × 5km in equatorial regions).…”