DOI: 10.5061/dryad.1ns1rn91g
title: Climate change is an important predictor of extinction risk on macroevolutionary timescales

description: Anthropogenic climate change is increasing rapidly and already impacting
 biodiversity. Despite the importance for future projections, understanding
 of the underlying mechanisms by which climate mediates extinction remains
 limited. We present an integrated approach examining the role of intrinsic
 traits vs. extrinsic climate change in mediating extinction risk for
 marine invertebrates over the past 485 million years. We found that a
 combination of physiological traits and the magnitude of climate change
 are necessary to explain marine invertebrate extinction patterns. Our
 results suggest that taxa previously identified as extinction resistant
 may still succumb to extinction if the magnitude of climate change is
 great enough.
description: This README file was generated on [25/02/2024] by [Cooper Malanoski].
 GENERAL INFORMATION 1. Title of Dataset: Climate change is an important
 predictor of extinction risk on macroevolutionary timescales 2. Author
 Information A. Principal Investigator Contact Information Name: [Cooper
 Malanoski] Institution: [Oxford University] Address: [Department of Earth
 Sciences, Oxford University, South Parks Road, Oxford, OX1 3AN, UK.]
 Email: [] B. Associate or
 Co-investigator Contact Information Name: [Dr. Erin Saupe] Institution:
 [Oxford University] Address: [1Department of Earth Sciences, Oxford
 University, South Parks Road, Oxford, OX1 3AN, UK.] Email:
 [] 3. Date of data collection: [NA] 4.
 Geographic location of data collection: [NA] 5. Information about funding
 sources: [National science research council (NERC), Award: NE/V011405/1
 Leverhulme Prize Chinese Academy of Sciences Visiting Professorship for
 Senior International Scientists, Award: 2021FSE0001] SHARING/ACCESS
 INFORMATION 1. Licenses/restrictions placed on the data: [Copyright © 2024
 the authors, some rights reserved; exclusive licensee American Association
 for the Advancement of Science. No claim to original US government works.
 
 reuse] 2. Links to publications that cite or use the data: Malanoski et
 al. (2024). [Climate change is an important predictor of extinction risk
 on macroevolutionary timescales]. [Science]. 3. Links to other publicly
 accessible locations of the data: [NA] 4. Links/relationships to ancillary
 data sets: [Monarrez et al. (2021) was used to source the
 Generic_bodysize_data_monarrezetal2021.csv file] 5. Was data derived from
 another source? [Yes] A. If yes, list source(s): [Monarrez et al. (2021)
 was used to source the Generic_bodysize_data_monarrezetal2021.csv file] 6.
 Recommended citation for this dataset: Malanoski et al. (2024). Data from:
 Climate change is an important predictor of extinction risk on
 macroevolutionary timescales. Dryad Digital Repository.
 [doi:10.5061/dryad.1ns1rn91g] DATA & FILE OVERVIEW 1. File List: A)
 pbdbdata_code.Rmd B) Generic_bodysize_data_monarrezetal2021.csv C)
 raw_extracted_climatemodeldata.csv D) Geographic_range_code.Rmd E)
 Climate_based_variable_code.Rmd F) figures_code.Rmd G)
 intrinsic_and_extrinsic_variables.csv 2. Relationship between files: The
 utility of each dataset and code file is detailed below. 3. Additional
 related data collected that was not included in the current data package:
 [NA] 4. Are there multiple versions of the dataset? [No] A. If yes, name
 of file(s) that was updated: [NA] i. Why was the file updated? [NA] ii.
 When was the file updated? [NA] DATA-SPECIFIC INFORMATION
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: Generic_bodysize_data_monarrezetal2021.csv
 includes the genera, log body volume and log body size estimates provided
 in Monarrez et al. (2021). For our analyses we use logvol, but logsize is
 retained for future studies. We removed bony fish from the original
 dataset, and the reasoning is provided in the Supplementary methods and
 materials. We join the log volume with our data based on the genus level.
 Higher taxonomic ranking revisions which Monarrez et al. (2021) revised
 were applied to our data using code found in Geographic_range_code.Rmd. 1.
 Number of variables: 7 2. Number of cases/rows: 9,461 3. Variable List: *
 genus: all invertebrate genera with body size information in Monarrez et
 al. (2021), except for Bony fish genera. * class: Linnean Class * logsize:
 log body size data from Monarrez et al. (2021) calculated from the
 treatise images in mm-squared. * logvol: log body volume data from
 Monarrez et al. (2021) calculated from the treatise images in mm-cubed. *
 phylum: Linnean Phylum * order: Linnean Order * family: Linnean Family 4.
 Missing data: Some Na's are present if a higher taxonomic ranking was
 not available for a genus. 5. Specialized formats or other abbreviations
 used: None
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: raw_extracted_climatemodeldata.csv 1.
 Number of variables: 12 2. Number of cases/rows: 462,855 3. Variable List:
 * collection_no: Collection number of occurrence in the PBDB * stage:
 Geologic stage * Age: Age in millions of years before present * phylum:
 Linnean phylum * class: Linnean class * order: linnean order * family:
 linnean family * genus: linnean genus * paleolng: Paleolatitude
 coordinates * paleolat: Paleolongitude coordinates * Localized
 temperature: The temperature extracted for each occurrence in degrees
 Celsius * Localized change in temperature: Change in temperature between
 stages for each occurrence in degrees Celsius. 4. Missing data codes: Some
 Na's are present if a higher taxonomic ranking was not available for
 a genus. 5. Specialized formats or other abbreviations used: None
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: intrinsic_and_extrinsic_variables.csv
 includes the climate model data for each occurrence in the PBDB data that
 can be sourced from pbdbdata_code.Rmd. This includes the localized
 temperatures and climate change estimates necessary to carry out future
 studies, and all analyses in Malanoski et al. (2024) 1. Number of
 variables: 17 2. Number of cases/rows: 22,222 3. Variable List: * ext:
 Binary extinction variable based on range through methods. A value of 0
 indicates that the genus survived into subsequent stages and 1 indicates
 that the genus went extinct and is absent from subsequent stages. * Genus:
 Linnean genus * Stage: Geologic stage * Phylum: Linnean phylum * Class:
 Linnean class * Order: Linnean order * Family: Linnean family *
 Realized_thermal_niche_breadth: Realized thermal niche breadth calculated
 as the difference between the maximum and minimum occuppied temperatures
 for each genus. This variable is based on the median of all subsampled
 ranges for a genus. * Absolute_realized_thermal_preference: Realized
 thermal preference is calculated as the absolute value of the deviation in
 median occuppied temperature for a genus from the median for all
 occurrences within a stage. This variable is based on the median of all
 subsampled preferences for a genus. * Geographic_range_size: Geographic
 range size is calculated using the log convex hull area (km-squared). This
 variable is based on the median of all subsampled areas for a genus. *
 Body_size: Body size is calculated as the log body volume (mm-cubed) for
 each genus, derived from Monarrez et al. (2021). *
 Absolute_temperature_change: Change in temperature or climate change is
 calculated as the absolute change in temperature from stage n to n+1. This
 variable is based on the median of all subsampled ranges for a genus. *
 Realized_thermal_niche_breadth_std: Standardized realized thermal niche
 breadth * Realized_thermal_preference_std: Standardized realized thermal
 preference * Geographic_range_size_std: Standardized geographic range size
 * Body_size_std: Standardized body size * Absolute_temperature_change_std:
 Standardized absolute temperature change 4. Missing data codes: NA (data
 not applicable). Higher taxonomic levels may contain NA values if there
 are none applicable for a genus. 5. Specialized formats or other
 abbreviations used:
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: pbdbdata_code.Rmd pbdbdata_code.Rmd is
 based on Kocsis et al. (2019) it can be used to download a dataset from
 the Paleobiology database (PBDB), and process and clean the data using the
 methods used in this manuscript. The code filters out occurrences which
 cannot be assigned to a stage and assigns up to date stages, filters out
 taxa which are not included in Generic_bodysize_data_monarrezetal2021.csv,
 and vets the occurrences for spatial duplicates and data without
 coordinates.
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: Geographic_range_code.Rmd and
 Climate_based_variable_code.Rmd Geographic_range_code.Rmd and
 Climate_based_variable_code.Rmd are used to calculate geographic range
 size, absolute realized thermal preference, realized thermal niche
 breadth, and absolute change in occupied temperature. These R-markdown
 files rely on the raw_extracted_climatemodeldata.csv file and We provide
 code to calculate these variables for both jackknife and bootstrap
 subsampling methods. The geographic range code is adapted from Casey et
 al. (2021). The output from these files is provided as
 intrinsic_and_extrinsic_variables.csv and is used as the input for our
 statistical models, which can be made using figures_code.Rmd.
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: figures_code.Rmd figures_code.Rmd can be
 used and modified to reproduce the main text and supplementary tables and
 figures. The code initially runs all model combinations for our 5
 predictors using a generalized linear mixed effect model. Then we use the
 output from the best model total.glmer2 to make the marginal effects plots
 seen in figure 2, the supplementary conditional mode plots, and the AIC
 tables seen in the supplemental materials and methods. Lastly, we provide
 the code to produce figure 1.
 \#########################################################################