Published August 14, 2020 | Version v1
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Dataset and scripts from: Predicting temperature mortality and selection in natural Drosophila populations

  • 1. Pontifical Catholic University of Chile
  • 2. MTA Centre for Ecological Research
  • 3. Autonomous University of Barcelona

Description

The study develops and validates a theoretical model to predict thermal mortality under natural conditions, based on measurements of mortality performed in the laboratory at multiple constant temperatures. The theoretical model first fits a thermal tolerance landscape, which describes how survival probability is affected by both temperature and exposure time, to the empirical measurements of mortality obtained in the laboratory under controlled conditions. Then, employing a numerical approximation to the analytical solution based on differential calculus, it combines this tolerance landscape with ambient temperature records in natural settings to predict the survival probability curve under these thermal conditions. These predictions were validated by contrasting predicted and observed mortality curves in 11 Drosophila species under three different warming rates, reported in the literature, which were virtually indistinguishable. Having validated the model, the study then examines how mortality should be affected by climate change in a natural population of Drosophila subobscura from Santiago, Chile, employing temperature records for this location during 1984 - 1991 and 2014 - 2018. The cumulative mortality predicted from temperature records closely resemble the periods of population collapse recorded for this population during the Austral summer and, according to the model, warming temperatures in the past 30 years may have advanced this period by almost a month. This methodology is highly general and can in principle be employed to predict temperature mortality in small ectotherms under any varying thermal regime.  

Notes

Additional information is available in the attached Readme.txt

Funding provided by: Fondo Nacional de Desarrollo Científico y Tecnológico
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100002850
Award Number: 1170017

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Dataset_Brncic.txt

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