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: Tr

SUBMITTER: Rabinovich JE 

PROVIDER: S-EPMC7971882 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature