Dark diversity in temperate forests of northeastern China: drivers and implications

Understanding the species composition of a community, including those species present and those absent but potentially able to occur, is vital for assessing biodiversity changes and informing conservation planning. Typically, studies focus on observed taxonomic diversity but ignore undetected species expected to be present based on co-occurrence patterns—referred to as dark diversity. Dark diversity serves as a sensitive indicator of biodiversity change, often responding earlier than observed diversity. However, its underlying drivers, especially environmental and anthropogenic factors, remain poorly understood in forest ecosystems. In this study, we quantified both dark and observed diversity and applied logistic regression to identify traits influencing species’ likelihood of belonging to dark diversity at the species level. Variance partitioning and spatial autoregressive models were used to disentangle the effects of environmental and human drivers at the plot level. Our results rev..., , , # Dark diversity in temperate forests of northeastern China: drivers and implications

[https://doi.org/10.5061/dryad.3n5tb2rvc](https://doi.org/10.5061/dryad.3n5tb2rvc)

## Description of the data and file structure

This dataset contains information from 456 0.1-hectare forest plots sampled in northeastern China. It includes measures of biodiversity, climate, soil, topography, and human influence for each plot. This repository contains the code (`code.R`) for the analysis focusing exclusively on the plot-level drivers (i.e., what factors influence DD and OD values per plot). It employs a multi-method statistical approach to partition the variance explained by different groups of drivers and to model their effects while accounting for spatial autocorrelation.\
Core Analyses:

1. Variance Partitioning: Separates the influences of environmental variables, spatial structure (PCNM vectors), and human factors on DD and OD.
2. Partial RDAs: Tests the unique statistical significance of each v...,