Mapping Tree Species Drought Sensitivity Under Climate Change
Data files
May 30, 2024 version files 1.45 MB
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combined_predictions.csv
1.44 MB
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README.md
9.30 KB
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species_metadata.csv
1.13 KB
Jun 21, 2024 version files 1.45 MB
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combined_predictions.csv
1.44 MB
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README.md
9.24 KB
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species_metadata.csv
1.13 KB
Abstract
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.
https://doi.org/10.5061/dryad.m905qfv97
General Information
- Title: Mapping Tree Species Drought Sensitivity Under Climate Change
- Author Information: Briana Barajas, Fletcher McConnell, Rosemary Juarez, Vanessa Salgado
- Principal Investigator/Client: Dr. Joan Dudney
- Institution: Bren School of Environmental Science & Management, University of California, Santa Barbara
- Contact: dudney@bren.ucsb.edu
- 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
- Date of Access: 2020-07-05
- Version: ITRDB v.7.22
- Climate data - Terra Climate
- Date of Access: 2024-04-02
- Version: Annual data (1958-present)
- Tree ring data - International Tree Ring Data Bank
Sharing & Access Information
- License/data use agreement: Public domain CCO1.0
- 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
- Raw data derivation: Raw data was accessed from the following public sources:
- Tree ring data - International Tree Ring Data Bank
- Date of Access: 2020-07-05
- Version: ITRDB v.7.22
- Climate data - Terra Climate
- Date of Access: 2024-04-02
- Version: Annual data (1958-present)
- Tree ring data - International Tree Ring Data Bank
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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
Data & File Overview
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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 -
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
- 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.
- Software-specific information needed to interpret data:
- Package versions: ClimaTree GitHub Repository
- R version: 4.4.2
- 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.
- Data processing support:
- Principal investigator, Dr. Joan Dudney (dudney@bren.ucsb.edu)
- External advisor, Dr. Robert Heilmayr (rheilmayr@bren.ucsb.edu)
Data-Specific Information
- 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
- 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
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:
-
Tree ring data - International Tree Ring Data Bank
- Date of Access: 2020-07-05
- Version: ITRDB v.7.22
- Climate data - Terra Climate
- Date of Access: 2024-04-02
- Version: Annual data (1958-present)
Data pre-processing and the developed workflow were conducted using the R programming language.