Dataset and scripts from: Predicting organismal response to marine heatwaves using dynamic thermal tolerance landscape models

Marine heatwaves (MHWs) can cause thermal stress in marine organisms, experienced as extreme ‘pulses’ against the gradual trend of anthropogenic warming. When thermal stress exceeds organismal capacity to maintain homeostasis, organism survival becomes time-limited and can result in mass mortality events. Current methods of detecting and categorizing MHWs rely on statistical analysis of historic climatology, and do not consider biological effects as a basis of MHW severity. The reemergence of ectotherm thermal tolerance landscape models provides a physiological framework for assessing the lethal effects of MHWs by accounting for both the magnitude and duration of extreme heat events. Here, we used a simulation approach to understand the effects of a suite of MHW profiles on organism survival probability across 1) three thermal tolerance adaptive strategies, 2) interannual temperature variation, and 3) seasonal timing of MHWs. We identified survival isoclines across MHW magnitude and duration where acute (short duration-high magnitude) and chronic (long duration-low magnitude) events had equivalent lethal effects on marine organisms. While most research attention has focused on chronic MHW events, we show similar lethal effects can be experienced by more common but neglected acute marine heat spikes. Critically, a statistical definition of MHWs does not accurately categorize biological mortality. By letting organism responses define the extremeness of a MHW event, we can build a mechanistic understanding of MHW effects from a physiological basis. Organism responses can then be transferred across scales of ecological organization and better predict marine ecosystem shifts to MHWs. 

Methods
The marine heatwave dataset used in this study was simulated using the included code. While the entirity of each script can be run from start to finish, we have silenced strategic chunks in each .rmd document and added data read-in lines to speed up script execution. Running silenced chunks of each .rmd file can result in long computation times. Detailed information about data and script files can be found in the README.md document.

Creation of MHW profiles: We simulated MHW profiles that covered a range of magnitudes (0-8 °C, 0-20 °C in extended analysis script, 0.5 °C interval) and durations (1 hour - 30 days, 1 day interval). We accomplished this by first simulating the MHW component of a temperature time series in the shape of a triangle - that is, a ramp up and ramp down period of equal lengths and no plateau period at the maximum magnitude. We next simulated a climatological time series over the year 2022 using a sine wave with a mean of 12°C and a range of 14°C, thus giving a minimum temperature of -2°C and a maximum temperature of 26°C. The maximum temperature of this sine wave occurred on 17th, and we added the MHW triangle on top of the sine wave so that the MHW maxima occurred directly over the climatological sine wave maxima. This gave us 527 unique MHW profiles of varying magnitude and duration. 

Assessment of MHW strength using statistical methods: Once we simulated the MHWs, we used the statistical MHW categorization techniques developed by Hobday et al. 2018 and implemented into the heatwaveR package by Schlegel and Smit 2018. We slightly altered heatwaveR functions to allow for hourly durations. This method relies on historical (30 year) variability at a site to determine the 90th percentile of temperatures, which it then uses as a threshold to delineate MHWs from non-MHWs and assign categorizations based on the magnitude exceedance of this threshold. Since we simulated our temperature data, we set three different 90th percentile values of 0.25, 0.75, and 1.5°C above climatology, from which we built three additional thresholds delineating events that were 2x, 3x, and 4x the difference between the 90th percentile threshold and climatology. For each interannual variability manipulation, we assess MHW category based on the Hobday 2018 framework for each of the 527 MHW profiles. 

Simulation of three organism's thermal death time curves: We simulated three thermal death time curves that intersect at three different time and temperature magnitudes and thus give three different adaptive strategies - an acute tolerator, with a high CTmax and high z, a chronic tolerator, with a low CTmax and low z, and a mixed strategy underperforming species with low CTmax and a medium level of z. These parameters are derived from the shape of thermal death time curves, a method of assessing thermal tolerance changes with time that is highly conserved across taxa (Rezende et al. 2014). These TDT curves intersect at values that should result in changes in ranking of the thermal performance of the organisms throughout our MHW simulations. The parameter values of each TDT curve approximates different marine bivalve species' TDT curves. 

Assessment of MHW strength using physiological dynamic tolerance models: We 'exposed' each of the three hypothetical organisms to each MHW profile and calculated survival probability using dynamic tolerance models formalized by Rezende et al. 2020. We extracted the final survival estimate from each organism at the end of each MHW profile. 

Assessment of the role of MHW timing on survival: As a final manipulation, we simulated heatwaves of 24 day duration but ranging in magnitude from 0-8°C across ten timepoints, starting from being centered exactly over the climatological maxima (sine wave maxima) to 90 days after this summer maxima. We repeated step 4 on these MHW profiles to extract final survival estimates of the MHW for one species, the acute tolerator.