Rabinovich et al 2015 models the climatic niche distribution of two Chagas disease vectors, Rhodnius prolixus and Triatoma infestans, in Venezuala and Argentina, respectively. ENM were used to determine the current distribution and potential future distribution under climate change scenario using WorldClim environmental data set, determine which variables are significant drivers of triatomine distribution and evaluate potential disease risk shifts in areas known to have high disease prevalence. The authors used presence-only data of the two triatomine species generated from range maps (which may have contained pseudo-absences). To prevent over-fitting of the niche models, 5% of the data were randomly selected for presence points of each species from the complete distribution range. MaxEnt was used to predict the climatic niche for both species under current and future conditions and partial area under the curve (pAUC) was use to evaluate the goodness-of-fit for the models predictions. An interesting follow up would be to understand why AUC has been criticized and pAUC considered favorable (since this was the first time I had heard of pAUC). To evaluate transmission risks to humans and climate suitability, two approaches were used: 1) directly relating suitability and the rate of acquiring the disease (force of infection, FOI) for specific regions in the countries and 2) relating suitability with household vector density and then vector density with FOI. The two approaches likely come with a slew of assumptions such as population demographics associated with infection risk from rural villages and also rely heavily on health data that may be biased or erroneous (since this is considered a neglected disease). However, there probably is no alternative. Results suggest that climate change will have a different impact on each triatomine distribution and transmission risk, which depends on the biology of the triatomine, although a general decrease in disease risk associated with 2050 climate conditions was the take home message. Although the papers methods and approach were useful, it seemed too general to make any solid conclusions from and instead of answering questions, it creates more regarding which (environmental, climatic, microhabitat, etc.) factors are actually important in the distribution of triatomines and how we can gather that data and how use that data for future projects.