Disentangling the contributions of biotic and abiotic predictors in the niche and the species distribution model of Trypanosoma cruzi, etiological agent of Chagas disease

• Published in: Acta tropica 2023-02, Vol.238, p.106757-106757, Article 106757
• Main Authors: Rengifo-Correa, Laura, González-Salazar, Constantino, Stephens, Christopher R.
• Format: Article
• Language: English
• Subjects: Chagas disease; Data mining; Niche models; Predictor contributions; Species distribution models; Transmission cycle
• ISSN: 0001-706X; 1873-6254
• DOI: 10.1016/j.actatropica.2022.106757

•Our risk model directly predicts the presence of Trypanosoma cruzi as opposed to indirectly through a model for its vectors.•We generate ecological niche and Species Distribution models (SDMs) of T. cruzi using a spatial data mining technique.•Combining biotic and abiotic predictors lead to more predictive and ecologically plausible SDMs.•Biotic factors are more important than abiotic factors for the presence of T. cruzi.•The confirmed hosts of T. cruzi emerge as the most predictive biotic niche factors. The potential benefits of incorporating biotic, as well as abiotic, predictors in niche and species distribution models (SDMs), as well as how to achieve this, is still debated, with their interpretability and explanatory potential being particularly questioned. It is therefore important to stress test modelling methodologies that include biotic factors against use cases where there is ample knowledge of the potential biotic component of the niche. Relatively well studied and important vector-borne diseases offer just such an opportunity, where knowledge of the agents involved in the transmission cycle –vectors and hosts– can serve to calibrate and test the niche model and corresponding SDM. Here, we study the contributions of biotic –14 vectors, 459 potential hosts– and abiotic –258 climatic categories– predictors to the explanatory and predictive features of the niche and corresponding SDM for the etiological agent of Chagas disease, Trypanosoma cruzi, in Mexico. Using an established spatial data mining technique, we generate biotic, abiotic and biotic+abiotic niche and SDM models. We test our models by comparing predictions of the most important probable hosts of Chagas disease with a previously published list of confirmed hosts. We quantify, compare, and contrast the individual and total contributions of predictors to the niche and distribution of Chagas disease in Mexico. We assess the relative predictive potential of these variables to model performance, showing that models that include relevant biotic niche variables lead to more predictive, more ecologically realistic SDMs. Our research illustrates a useful general procedure for identifying and ranking potential biotic interactions and for assessing the relative importance of biotic and abiotic predictors. We conclude that the inclusion of both abiotic and biotic predictors in SDMs not only provides more predictive and accurate models but also models that are more understandable and explainable fr