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dc.date.accessioned 2021-09-10T19:09:18Z
dc.date.available 2021-09-10T19:09:18Z
dc.date.issued 2021-03-08
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/124633
dc.description.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. en
dc.language en es
dc.subject Machine learning es
dc.subject Hemiptera es
dc.subject Artificial intelligence es
dc.title Machine-learning model led design to experimentally test species thermal limits: The case of kissing bugs (Triatominae) en
dc.type Articulo es
sedici.identifier.other https://doi.org/10.1371/journal.pntd.0008822 es
sedici.identifier.issn 1935-2727 es
sedici.creator.person Rabinovich, Jorge Eduardo es
sedici.creator.person Álvarez Costa, Agustín es
sedici.creator.person Muñoz, Ignacio es
sedici.creator.person Schilman, Pablo E. es
sedici.creator.person Fountain Jones, Nicholas es
sedici.subject.materias Ciencias Naturales es
sedici.description.fulltext true es
mods.originInfo.place Centro de Estudios Parasitológicos y de Vectores es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution 4.0 International (CC BY 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by/4.0/
sedici.description.peerReview peer-review es
sedici.relation.journalTitle PLOS Neglected Tropical Diseases es
sedici.relation.journalVolumeAndIssue vol. 2021 es
sedici.relation.isRelatedWith http://sedici.unlp.edu.ar/handle/10915/124630 es


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Creative Commons Attribution 4.0 International (CC BY 4.0) Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International (CC BY 4.0)