Juvenile Tree Mortality from Extreme Heat and Drought

Tree loss is increasing rapidly due to drought- and heat-related mortality and intensifying fire activity. Consequently, the fate of many forests depends on the ability of juvenile trees to withstand heightened climate and disturbance anomalies. Extreme climatic events, such as droughts and heatwaves, are increasing in frequency and severity, and trees in mountainous regions must contend with these landscape-level climate episodes. Recent research focuses on how mortality of individual tree species may be driven by drought and heatwaves, but how juvenile mortality under these conditions would vary among species spanning an elevational gradient—given concurrent variation in climate, ecohydrology, and physiology–remains unclear. We address this knowledge gap by implementing a growth chamber study, imposing extreme drought with and without a compounding heatwave, for juveniles of five species that span a forested life zones in the Southwestern United States. Overall, the length of a progressive drought required to trigger mortality differed by up to 20 weeks among species. Inclusion of a heatwave hastened mean time to mortality for all species by about 1 week. Lower-elevation species that grow in warmer ambient conditions died earlier (Pinus ponderosa in 10 weeks, Pinus edulis in 14 weeks) than did higher-elevation species from cooler ambient conditions (Picea engelmannii and Pseudotsuga menziesii in 19 weeks, and Pinus flexilis in 30 weeks). When exposed to a heatwave in conjunction with drought, mortality advanced significantly only for species from cooler ambient conditions (Pinus flexilis: 2.7 weeks earlier; Pseudotsuga menziesii: 2.0 weeks earlier). Cooler ambient temperatures may have buffered against moisture loss during drought, resulting in longer survival of higher-elevation species despite expected drought tolerance of lower-elevation species due to tree physiology. Our study suggests that droughts will play a leading role in juvenile tree mortality and will most directly impact species at warmer climate thresholds, with heatwaves in tandem with drought potentially exacerbating mortality especially of high elevation species. These responses are relevant for assessing the potential success of both natural and managed reforestation, as differential juvenile survival following episodic extreme events will determine future landscape-scale vegetation trajectories under changing climate.

Code Purpose and Order
How to Use HeatwaveProject: README and Metadata There are 2 .csv files in the "README" folder. One is titled "Metadata_CollectedValues.csv" and the other "Metadata_CalculatedValues.csv". Both files contain a descriptoin of each column name found in the data folders of this project. The CollectedValues.csv file describes data that was collected directly (data found in the "data_raw", "data_clean", and "data_QAQC" folders). The CalculatedValues.csv file describes data that was calculated from the collected data and used in statistical analysis (found in the "data_analysis" folder). Scripts folder enter the "scripts" folder. The order of the folders (1-5) is the order you should run the code. You may skip folder 0_colors unless there are new photos to extract pixel data from (will need a supercomputer). enter a subfolder in the "scripts" folder (e.g. 1_clean). You will see a second level of ordering in the script naming (1_clean_1, 1_clean_2), followed by a 1-word description of what the script does. Run these scripts in order. If a script has the same level of ordering, it doesn't matter which one is run first. each script will contain more detailed notes about each line of code to re-perform data analysis and statistics only, you may skip folders 0_colors, 1_clean, and 2_QAQC (these scripts are to put the data in a usable format and quality check the data). You can start directly with folders 3_analysis, 4_stats, and 5_viz. the "old" folder contains exploratory code, not used for analysis Data folders the "data_raw" folder contains .csv files which were directly entered during data collection. It contains unaltered data. the "data_clean" folder contains .csv files which are in a format useful for analysis in R. It contains unaltered data. the "data_QAQC" folder contains .csv files which were quality checked for data errors. The data in these files were altered if an error was found. This data was used for final analysis the "data_analysis" folder contains .csv files with outputs from analysis and new columns with summary statistics. It is a repository for statistical data output. Photo folders the folders "Phase1_Photos" and "Phase1_Photos_Background" are to house .jpg files to be used for scripts contained in the folder "scrips/0_colors". Photos are not backed up on github due to space limitations. The data extracted from these photos are housed in the data folders, so it shouldn't be necessary to re-process these photos. Graphs folder The "graphs" folder is a repository for graphing outputs from R, produced using scrips in the "scripts/5_viz" folder.

Disclaimer
This software has been approved for release by the Climate Adaptation Science Centers. No warranty, expressed or implied, is made as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. Furthermore, the software is released on condition that the Climate Adaptation Science Centers shall not be held liable for any damages resulting from its authorized or unauthorized use.

Acknowledgements
This data release was supported by Grant Award No. G20AC00257 from the U.S. Geological Survey National Climate Adaptation Science Center.