Code and data from simulations that apply multiple regression analysis models to biased occurrence data to detect thermophilization.

READ ME Description of this repository This repository houses the code and data for simulations that apply multiple regression analysis models to biased occurrence data to detect thermophilization. Explanation of each file SimulationCode.R This R code simulates the application of a multiple regression analysis model to biased occurrence data to detect thermophilization. Note: To save the running time, we used a parallel computation approach (run time of approximately 30 minutes). Since seven CPUs were used, an equal or greater number of CPUs would be required to reproduce the same results. 01_GeneratedDistributionData.csv Simulation-generated distribution data of fictitious biota species. The column names are explained below. Column Names Explanation IndID Unique individual identification number SpeciesID Unique identification number for the species to witch the individual belongs. Step Steps in which the individual exists. LTI Local Temperature Index (LTI) of the location where the individual occurred. SpeciesLTICenter Central value of the species-specific LTI at the time of its Step Prob.BiasToWarm Value of weighting sampled when Bias to Warm is present. Prob.BiasToCold Value of weighting sampled when Bias to Cold is present. 02_ExtractedBiasedOccurrenceData.csv The result of extracting 2,000 biased occurrences data ofrom the Distribution data. Column Names Explanation IndID Unique identification number of the extracted individual. SpeciesID Unique identification number for the species to witch the individual belongs. Step Steps in which the individual is extracted LTI Local Temperature Index (LTI) of the location where the individual occurred. EstSTI Species Temperature Index (STI) of the record species calculated on the basis of the occurrence data. BiasType The type of bias iter The number of iteration Reference This simulation code uses the following packages. {tidyverse} package, Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” _Journal of Open Source Software_, *4*(43), 1686. doi:10.21105/joss.01686 <https://doi.org/10.21105/joss.01686>. {broom} package, Robinson D, Hayes A, Couch S (2024). broom: Convert Statistical Objects into Tidy Tibbles. R package version 1.0.7, https://github.com/tidymodels/broom, {rlist} package. Ren K (2021). _rlist: A Toolbox for Non-Tabular Data Manipulation_. R package version 0.4.6.2, <https://CRAN.R-project.org/package=rlist>. {data.table} package Barrett T, Dowle M, Srinivasan A, Gorecki J, Chirico M, Hocking T (2024). _data.table: Extension of `data.frame`_. R package version 1.15.4, <https://CRAN.R-project.org/package=data.table>. {snowfall} package Knaus J (2023). _snowfall: Easier Cluster Computing (Based on 'snow')_. R package version 1.84-6.3, <https://CRAN.R-project.org/package=snowfall>. {magrittr} package Bache S, Wickham H (2022). _magrittr: A Forward-Pipe Operator for R_. R package version 2.0.3, <https://CRAN.R-project.org/package=magrittr>. {ggpmisc} package Aphalo P (2024). _ggpmisc: Miscellaneous Extensions to 'ggplot2'_. R package version 0.5.6, <https://CRAN.R-project.org/package=ggpmisc>. {effsize} package Torchiano M (2020). _effsize: Efficient Effect Size Computation_. doi:10.5281/zenodo.1480624 <https://doi.org/10.5281/zenodo.1480624>, R package version 0.8.1, <https://CRAN.R-project.org/package=effsize>. {conflicted] package Wickham H (2023). _conflicted: An Alternative Conflict Resolution Strategy_. R package version 1.2.0, <https://CRAN.R-project.org/package=conflicted>.