DOI: 10.5281/zenodo.13431532
title: Code and data from simulations that apply multiple regression analysis models to biased occurrence data to detect thermophilization.

description: 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>.