Extreme events, notably summer heatwaves, are exacerbated by global climate change. The susceptibility of a species to heatwaves likely depends on several variables, including the duration and intensity of thermal exposure. While heat survival time models (thermal tolerance landscapes) have a century-long history, their core assumptions still need to be tested under dynamic heatwave regimes. This study first examines whether the model based on log-linear regression of lethality buildup rates in response to constant temperatures can fairly predict the survival probability of a mussel population observed under heatwave regimes. Therefore, we conducted a protocol consisting of an indoor experiment of heat selection under constant temperatures, an outdoor experiment under dynamic heatwave regimes, and a Monte-Carlo simulation framed around the mathematically formulated assumptions. Second, we provide a Markov-Chain Monte-Carlo Approximate-Bayesian algorithm to separately predict posterior parameter distributions using the observed heatwave survival data for each experiment. We indicate that the new approach can inform regarding populations’ heat lethality buildup and sensitivity parameters and, therefore, it is applicable to test hypotheses about acclimation or adaptation effect on the parameters defining the population survival trajectory and to provide local prediction of future heatwave selection pressures on natural and aqua-cultured populations.

Primary Presenter: Jahangir Vajedsamiei, GEOMAR (jvajedsamiei@geomar.de)

Authors:

Niklas Warlo, GEOMAR  ()

Frank Melzner, GEOMAR  ()