Homogeneous selection and stochasticity overrule heterogeneous selection across biotic taxa and ecosystems

Deterministic factors including homogeneous and heterogeneous selection and stochastic factors jointly shape ecological communities. However, a quantitative synthesis of the factors underlying the balance among different assembly processes is lacking. Here, we synthesized data from 149 datasets covering major biotic groups and ecosystem types globally. We used a null model approach based on Raup-Crick dissimilarities and Bayesian meta-regression to analyze the data. We found that communities were more under homogeneous selection than heterogeneous selection across biotic taxa and ecosystems. Environment selected species homogeneously more often at small scales while heterogeneously more often at large scales. Stochasticity also showed scale-dependence as stochastic community assembly increased with study scale. Homogeneous and heterogeneous selection were strongest at high latitudes while stochastic factors were strongest in tropics. Marine systems had the highest degree of homogeneous ..., This data has been collected from  Graco-Roza et al., 2022 (https://doi.org/10.1111/geb.13513). The datasets were used to estimate Raup-Crick and NST values for pairwise comparison and later modelled to investigate the main drivers of stochasticity and deterministic assembly processes on pairs of communities., , # Homogeneous selection and stochasticity overrule heterogeneous selection across biotic taxa and ecosystems

[https://doi.org/10.5061/dryad.4j0zpc8ms](https://doi.org/10.5061/dryad.4j0zpc8ms)

This dataset contains community data from 148 datasets used in the paper by Graco-Roza et al., 2022. The community data includes environmental variables and coordinates for multiple taxonomic groups.

## Description of the Data and File Structure

### Folder Structure

To ensure everything runs smoothly, create the following folders in your project directory:

* `raw_data`: To include the raw dataset files.
* `model`: To include the BRMS model files (e.g., .rds files).
* `results_RC`: To include the results of the Raup-Crick analysis.
* `results_NST`: To include the results of the NST analysis.
* `table`: To include the `input_between.xlsx` which contains the METADATA for the datasets compiled from Graco-Roza et al., (2022), and to save the files generated during the `compile_results.R` script.
*...

确定性因素（包括均质选择与异质选择）与随机性因素共同塑造生态群落。然而，目前仍缺乏对不同群落构建过程间平衡机制的定量综合研究。本研究整合了全球范围内涵盖主要生物类群与生态系统类型的149个数据集。我们采用基于Raup-Crick dissimilarities（Raup-Crick差异度）的零模型方法与贝叶斯元回归对数据进行分析。结果表明，在各类生物类群与生态系统中，群落受均质选择的影响强于异质选择。环境在小尺度上更倾向于对物种进行均质选择，而在大尺度上则更常表现为异质选择。随机性同样具有尺度依赖性：随着研究尺度增大，群落构建的随机性增强。均质选择与异质选择在高纬度地区作用最强，而随机性因素在热带地区影响最显著。海洋生态系统的均质选择程度最高……该数据来源于Graco-Roza等人2022年的研究（https://doi.org/10.1111/geb.13513）。这些数据集用于估算Raup-Crick值与NST值以进行成对比较，并后续建模以探究群落对间随机性与确定性构建过程的主要驱动因素。

# 均质选择、异质选择与随机性主导全球生物类群及生态系统中的异质选择

[https://doi.org/10.5061/dryad.4j0zpc8ms](https://doi.org/10.5061/dryad.4j0zpc8ms)

本数据集包含Graco-Roza等人2022年研究中使用的148个数据集的群落数据，涵盖多个分类群的环境变量与坐标信息。

## Description of the Data and File Structure

### Folder Structure

为确保流程顺畅，请在项目目录中创建以下文件夹：

* `raw_data`：存放原始数据集文件。
* `model`：存放BRMS模型文件（如.rds文件）。
* `results_RC`：存放Raup-Crick分析结果。
* `results_NST`：存放NST分析结果。
* `table`：存放`input_between.xlsx`文件（含Graco-Roza等人2022年整合数据集的元数据）及`compile_results.R`脚本生成的文件。
...