Machine-learning model led design to experimentally test species thermal limits: The case of kissing bugs (Triatominae)

Abstract

Species Distribution Modelling (SDM) determines habitat suitability of a species across geographic areas using macro-climatic variables; however, micro-habitats can buffer or exacerbate the influence of macro-climatic variables, requiring links between physiology and species persistence. Experimental approaches linking species physiology to micro-climate are complex, time consuming and expensive. E.g., what combination of exposure time and temperature is important for a species thermal tolerance is difficult to judge a priori. We tackled this problem using an active learning approach that utilized machine learning methods to guide thermal tolerance experimental design for three kissing-bug species: Triatoma infestans, Rhodnius prolixus, and Panstrongylus megistus (Hemiptera: Reduviidae: Triatominae), vectors of the parasite causing Chagas disease. As with other pathogen vectors, triatomines are well known to utilize micro-habitats and the associated shift in microclimate to enhance survival. Using a limited literature-collected dataset, our approach showed that temperature followed by exposure time were the strongest predictors of mortality; species played a minor role, and life stage was the least important. Further, we identified complex but biologically plausible nonlinear interactions between temperature and exposure time in shaping mortality, together setting the potential thermal limits of triatomines. The results from this data led to the design of new experiments with laboratory results that produced novel insights of the effects of temperature and exposure for the triatomines. These results, in turn, can be used to better model micro-climatic envelope for the species. Here we demonstrate the power of an active learning approach to explore experimental space to design laboratory studies testing species thermal limits. Our analytical pipeline can be easily adapted to other systems and we provide code to allow practitioners to perform similar analyses. Not only does our approach have the potential to save time and money: it can also increase our understanding of the links between species physiology and climate, a topic of increasing ecological importance.

Positive associations matter: Microbial relationships drive tick microbiome composition

“…While our study provides insight into the drivers of I. scapularis microbiome composition, our findings are necessarily associative and should help guide future more mechanistic studies (Rabinovich et al, 2021). These studies could include the controlled examination of host-seeking behaviour under varying environmental conditions using ticks that have been infected with pathogens of interest.…”

“…More broadly, mechanistic models have shown that non‐linear (i.e., threshold) effects of temperature are common in vector‐pathogen systems, but there have been few field studies that have investigated these effects (Mordecai et al, 2019; Rabinovich et al, 2021). Moreover, the statistical models used to investigate these nonlinear responses often struggle to separate temperature effects from covarying environmental factors and biotic factors (Mordecai et al, 2019).…”

“…The MrIML approach also provides extensive autotuning capabilities to optimize algorithm performance as well as 10‐fold cross‐validation to guard against model overfitting. Moreover, multi‐algorithm performance in prediction across ASVs can be easily compared and the model with highest performance further interrogated (Fountain‐Jones et al, 2021; Rabinovich et al, 2021). We compared the predictive performance of three different algorithms; generalized linear models (GLMs, logistic regression), random forests (RF) and extreme gradient boosting (XGB).…”

“…The MrIML approach also provides extensive autotuning capabilities to optimize algorithm performance as well as 10-fold cross-validation to guard against model overfitting. Moreover, multi-algorithm performance in prediction across ASVs can be easily compared and the model with highest performance further interrogated (Fountain-Jones et al, 2021;Rabinovich et al, 2021).…”

Precision Prediction for Dengue Fever in Singapore: A Machine Learning Approach Incorporating Meteorological Data

“…This approach imposes constraints on the types of data that can be effectively utilized for etiological inference [ 9 ]. In contrast, machine learning offers a promising avenue for addressing nonlinearity and interactions among variables [ 10 ], while also adeptly handling multi-dimensional datasets. Furthermore, the current prediction models for DF primarily rely on historical dengue case data.…”