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

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.

Impact of alkaloids in food consumption, metabolism and survival in a blood-sucking insect.

The sense of taste provides information about the "good" or "bad" quality of a food source, which may be potentially nutritious or toxic. Most alkaloids taste bitter to humans, and because bitter taste is synonymous of noxious food, they are generally rejected. This response may be due to an innate low palatability or due to a malaise that occurs after food ingestion, which could even lead to death. We investigated in the kissing bug Rhodnius prolixus, whether alkaloids such as quinine, caffeine and theophylline, are merely distasteful, or if anti-appetitive responses are caused by a post-ingestion physiological effect, or both of these options. Although anti-appetitive responses were observed for the three alkaloids, only caffeine and theophylline affect metabolic and respiratory parameters that reflected an underlying physiological stress following their ingestion. Furthermore, caffeine caused the highest mortality. In contrast, quinine appears to be a merely unpalatable compound. The sense of taste helps insects to avoid making wrong feeding decisions, such as the intake of bitter/toxic foods, and thus avoid potentially harmful effects on health, a mechanism preserved in obligate hematophagous insects.