Data and code from: A bias-robust framework for quantifying community responses to the climate change using the occurrence data

This repository houses the simulation code and data for \"A Bias-robust Framework for Quantifying Community Responses to the Climate Change Using the Occurrence Data.\" There are two simulation codes. In the first simulation (SimulationCode.R), distribution data for a pseudo-biological community—whose range shifts due to climate warming—is generated, and numerous rounds of biased sampling are conducted from that distribution. The CCDM (Community Change Detection Model) is then applied to the resulting biased occurrence data to evaluate the rate of thermophilization. In the second simulation (SimulationCode_Sensitivity_Analysis.R), sampling and species distribution generation are carried out under different bias conditions to assess the robustness of CCDM across various scenarios.
, SimulationCode.R

Generation of true community data: We constructed a fictional community of 100 species that shifted to cooler regions without delay. The optimal LTI for each species at Year 50—the midpoint of the simulation—was randomly assigned from a uniform distribution ranging from −10 °C to 10 °C. The optimal LTI changes from year to year, whereas the STI—also referred to as the climatic niche—remains constant over time. Warming caused a 0.01 °C annual decrease in each species' optimal LTI. Because the LTI is a representative temperature index for a site, it remains constant even under warming conditions. Therefore, decreasing the optimal LTI by 0.01 °C/year represents range shifts at a rate of 0.01 °C/year to colder regions, not a change in climatic niches. Each year, 100,000 individuals were generated and randomly assigned to one of the 100 species. The LTI of each individual's location was drawn from a normal distribution with the mean equal to that species' optimal LTI for th..., # Dryad dataset

Dataset DOI: [10.5061/dryad.ksn02v7hj](10.5061/dryad.ksn02v7hj)

## 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.

For detailed methods and the mechanism of Community Change Detection Model (CCDM), please refer to the original paper ([https://onlinelibrary.wiley.com/doi/10.1111/geb.70223](https://onlinelibrary.wiley.com/doi/10.1111/geb.70223)).

## Description of the data and file structure

### File: SimulationCode.R

**Description:** R code to conduct the simulation. In the simulation, a fictional species community is generated. And 2 type x 1,000 iteration times samplings are conducted. The samplings include the biases from spatiotemporal variation of observation effort and truncation effect. This code generates Figure_3.eps and Figure_3.tiff.

### File: SimulationCode(Sensitivity_Analysis).R

**Description:** R code to con..., ,