DOI: 10.5061/dryad.m905qfv97
title: Mapping Tree Species Drought Sensitivity Under Climate Change

description: Forests cover approximately 30% of Earth's land surface, absorb more
 carbon than all other terrestrial ecosystems, and provide trillions of
 dollars’ worth of ecosystem services (Food and Agriculture Organization of
 the United Nations, 2005). However, climate change-induced droughts pose a
 significant threat to these vital ecosystems. As climate change
 intensifies, it is critical for our planning and management that we
 understand how and where trees will be the most threatened. Previous
 research has examined the effects of these droughts on forests at a global
 scale, but these large-scale analyses are not particularly helpful for
 land managers who often focus on specific regions and only a limited
 number of species. Our project addresses this gap by assessing
 species-specific sensitivity to increasingly severe and frequent droughts,
 considering the variations within their ranges. This localized information
 is crucial for land managers to develop targeted conservation strategies.
 By analyzing species-specific data, we demonstrate that the impacts of
 drier conditions are not uniform across or within species. Our findings
 suggest that effective management strategies must adopt a multifaceted and
 area-specific approach. To make our findings easily usable, we developed
 an interactive dashboard for land managers and the public. Here, users can
 find species-specific sensitivity maps that highlight the areas of
 greatest concern within manageable spaces, providing a valuable tool for
 informed decision-making. Our project contributes to the understanding of
 the potential future drought impacts on forests and emphasizes the need
 for targeted conservation efforts to mitigate the consequences of climate
 change on these essential ecosystems.
description: No data was actively collected to complete this project.
 Processed data is not available as it is being used in ongoing research,
 please contact the client Dr. Joan Dudney for more information
 (dudney@bren.ucsb.edu). Raw data was accessed from the following, publicly
 available sources: <strong>Tree ring
 data</strong> - International Tree Ring Data Bank <strong>Date of Access: </strong>2020-07-05 <strong>Version:</strong> ITRDB v.7.22 Climate data - Terra Climate <strong>Date of Access:</strong> 2024-04-02 <strong>Version: </strong>Annual data (1958-present) Data pre-processing and the developed workflow were conducted using the R programming language. 
description: # Mapping Tree Species Drought Sensitivity Under Climate Change
 [https://doi.org/10.5061/dryad.m905qfv97](https://doi.org/10.5061/dryad.m905qfv97) ## General Information 1. **Title:** Mapping Tree Species Drought Sensitivity Under Climate Change 2. **Author Information:** Briana Barajas, Fletcher McConnell, Rosemary Juarez, Vanessa Salgado 3. **Principal Investigator/Client:** Dr. Joan Dudney * **Institution:** Bren School of Environmental Science & Management, University of California, Santa Barbara * **Contact:** [dudney@bren.ucsb.edu](mailto:dudney@bren.ucsb.edu) 4. **Data Sources & Retrieval:** No data was actively collected to complete this project. Processed data is not available as it is being used in ongoing research, please contact the client Dr. Joan Dudney for more information. Raw data was accessed from the following, publicly available sources: * **Tree ring data** - [International Tree Ring Data Bank](https://www.ncei.noaa.gov/products/paleoclimatology/tree-ring) * **Date of Access:** 2020-07-05 * **Version:** ITRDB v.7.22 * **Climate data** - [Terra Climate](https://www.climatologylab.org/terraclimate.html) * **Date of Access:** 2024-04-02 * **Version:** Annual data (1958-present) ## Sharing & Access Information 1. **License/data use agreement:** Public domain [CCO1.0](https://creativecommons.org/publicdomain/zero/1.0/) 2. **Ancillary data sets:** This workflow builds off the existing framework developed by our client Dr. Joan Dudney, and her colleagues Dr. Robert Heilmayr, and Dr. Frances C Moore. Original code scripts and available at the following repository: * **GitHub Repository** - [Treeconomics](https://github.com/rheilmayr/Treeconomics) 3. **Raw data derivation:** Raw data was accessed from the following public sources: * **Tree ring data** - [International Tree Ring Data Bank](https://www.ncei.noaa.gov/products/paleoclimatology/tree-ring) * **Date of Access:** 2020-07-05 * **Version:** ITRDB v.7.22 * **Climate data** - [Terra Climate](https://www.climatologylab.org/terraclimate.html) * **Date of Access:** 2024-04-02 * **Version:** Annual data (1958-present) 4. **Processed data description:** The following files are currently not available to the public, but key in executing the species sensitivity workflow. For questions regarding reproducibility or access to this data, please contact Dr. Joan Dudney for more information. | File Name | Description | | :--------------------------- | :--------------------------------------------------------------------------------- | | TerraClimate19611990\_def.nc | Raster of historic climatic water deficit (CWD), monthly averaged across 1961-1990 | | TerraClimate19611990\_pet.nc | Raster of historic potential transpiration (PET), average across 1961-1990 | | itrdbsites\_pet.csv | Monthly potential transpiration (PET) for sites | | itrdbsites\_def.csv | Climatic water deficit (CWD) for sites | | site\_summary.csv | Attributes for sites within the International Tree Ring Data Bank (ITRDB) | | merged\_ranges\_dissolve.shp | Tree species range map shapefile | | cmip5\_cwdaet\_start.Rdat | Climatic water deficit predictions using CMIP5 | | cmip5\_cwdaet\_end.Rdat | Climatic water deficit predictions using CMIP5 | | rwi\_long.csv | De-trended tree ring data from the ITRDB | 5\. **Recommended citation:** Barajas, B., Juarez, R., McConnell, F., & Salgado, V. (2024). Mapping Global Tree Vulnerability Under Climate Change, *Master of Environmental Data Science*(2024). Retrieved from: [https://github.com/ClimaTree](https://github.com/ClimaTree) ## Data & File Overview 1. **File List** | File Name | Description | | :---------------------------- | :------------------------------------------------------------------------------------------------------ | | 1\_climate\_niche.R | Extract and standardize climate data across species ranges | | 2\_plot\_level\_regressions.R | Estimate site-level sensitivity to variation in climatic water deficit | | 3\_run\_regressions.R | Estimate species sensitivity fluctuation under historic climate | | 4\_sens\_predictions.R | Predict species growth given estimates for future variations in climatic water deficit through 2100\*\* | | 5\_mapping.R | Map species sensitivity and growth through 2100\*\* | | 6\_mapping\_levels.R | Interactively map species CWD sensitivity, as categorical sensitivity level | | main.R | Consolidation of R-scripts 3-5; create sensitivity maps without creation of intermediate data steps | | combined\_predictions.csv | Final results, sensitivity values for 26 tree species | | species\_metadata.csv | Metadata for tree species included in the analysis | 2. **Relationship between files:** * \**Predicted species growth (ΔRWI) is calculated in the scripts but not included in the final data. High climate variable correlation along with data processing limitations has resulted in select species having abnormal ranges for predicted growth. More processing is required to produce Δaccurate RWI values.  * The scripts with numerical indicators (1-5) must be run sequentially in order to reproduce the final results. The output for each script becomes the input for the following step. * The main.R file sources script 3-5 to generate species sensitivity maps in a single step, without the production of any intermediate steps. Running individual scripts instead of main.R will produce the same results, along with additional with additional intermediate data. ## Methodological Information 1. **Methods for Data Processing:** The starting data, including processed climate and ring width data, were provided by the principal investigator. Therefore the data was pre-processed before being entered into the sensitivity workflow. The sensitivity estimates can be produced by running scripts in the following order: * 1_climate_niche.R * 2_plot_level_regressions.R * 3_run_regressions.R * 4_sens_predictions.R * 5_mapping.R Scripts 1-4 calculate sensitivity across individual species ranges, while 5 plots this data as a map. This order is required, as the outputs from the previous scripts often become inputs. For more information, each script begins with a list of required inputs, and final outputs. For a more rapid approach, main.R can be used instead. This file sources scripts 3-5 to rapidly produce maps without intermediate steps. This workflow was used on a select number of tree species to produce the final combined_predictions.csv, which contains sensitivity to climatic water deficit for 26 tree species. 2. Software-specific information needed to interpret data: * **Package versions:** [ClimaTree GitHub Repository](https://github.com/ClimaTree/climatree-mapping-repo/blob/main/renv/renv.lock) * R version: 4.4.2 3. **Quality-assurance:** Tree species with insufficient data, including several species with only a single species sampled, were removed to minimize bias in sensitivity estimates. The pre-processed data has no missing values, and the intermediate steps were rigorously checked to ensure no data gaps were being created. Additional checks such as mapping variables to ensure proper scaling are available in the scripts and commented out to improve run times. A final manual check of sensitivity estimates was conducted to ensure there were no notable outliers or inaccuracies. 4. **Data processing support:** * Principal investigator, Dr. Joan Dudney ([dudney@bren.ucsb.edu](mailto:dudney@bren.ucsb.edu)) * External advisor, Dr. Robert Heilmayr ([rheilmayr@bren.ucsb.edu](mailto:rheilmayr@bren.ucsb.edu)) ## Data-Specific Information 1. combined_predictions.csv * **Number of variables:** 4 * **Number of rows:** 37627 * **Variable name/descriptions:** * **cwd_send:** Sensitivity to climatic water deficit (CWD) * **sp_code:** 4-letter species code * **Longitude** * **Latitude** * **Missing data code:** No missing values 2. species_metadata.csv  * **Number of variables:** 3 * **Number of rows:** 26 * **Variable name/description:** * **sp_code:** 4-letter species code * **scientific_name** * **common_name** * **Missing data code:** No missing values