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

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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. Experimenta...

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Autores principales: Rabinovich, Jorge Eduardo, Álvarez Costa, Agustín, Muñoz, Ignacio, Schilman, Pablo E., Fountain Jones, Nicholas
Formato: Articulo
Lenguaje:Inglés
Publicado: 2021
Materias:
Ciencias Naturales
Machine learning
Hemiptera
Artificial intelligence
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/124633
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-124633
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Naturales
Machine learning
Hemiptera
Artificial intelligence
spellingShingle Ciencias Naturales
Machine learning
Hemiptera
Artificial intelligence
Rabinovich, Jorge Eduardo
Álvarez Costa, Agustín
Muñoz, Ignacio
Schilman, Pablo E.
Fountain Jones, Nicholas
Machine-learning model led design to experimentally test species thermal limits: The case of kissing bugs (Triatominae)
topic_facet Ciencias Naturales
Machine learning
Hemiptera
Artificial intelligence
description 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.
format Articulo
Articulo
author Rabinovich, Jorge Eduardo
Álvarez Costa, Agustín
Muñoz, Ignacio
Schilman, Pablo E.
Fountain Jones, Nicholas
author_facet Rabinovich, Jorge Eduardo
Álvarez Costa, Agustín
Muñoz, Ignacio
Schilman, Pablo E.
Fountain Jones, Nicholas
author_sort Rabinovich, Jorge Eduardo
title Machine-learning model led design to experimentally test species thermal limits: The case of kissing bugs (Triatominae)
title_short Machine-learning model led design to experimentally test species thermal limits: The case of kissing bugs (Triatominae)
title_full Machine-learning model led design to experimentally test species thermal limits: The case of kissing bugs (Triatominae)
title_fullStr Machine-learning model led design to experimentally test species thermal limits: The case of kissing bugs (Triatominae)
title_full_unstemmed Machine-learning model led design to experimentally test species thermal limits: The case of kissing bugs (Triatominae)
title_sort machine-learning model led design to experimentally test species thermal limits: the case of kissing bugs (triatominae)
publishDate 2021
url http://sedici.unlp.edu.ar/handle/10915/124633
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