DOI: 10.5061/dryad.3n5tb2rvc
title: Dark diversity in temperate forests of northeastern China: drivers and implications

description: 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 revealed that key traits such as mycorrhizal type,
 specific leaf area and tree height determine a species’ likelihood of
 belonging to dark diversity, and that dark and observed diversity respond
 differently to environmental and anthropogenic factors. Dark diversity was
 mainly influenced by the interaction of environmental factors and human
 impacts, with annual mean temperature being the strongest environmental
 driver. Observed diversity, in contrast, was most influenced by annual
 mean precipitation. Dark diversity increased with mean annual temperature,
 declined under the combined influence of human footprint and
 precipitation, and increased with the interaction between human footprint
 and precipitation seasonality. Observed diversity was positively affected
 by precipitation and altitude, and negatively influenced by human
 footprint-temperature and altitude-roadless area interactions. Our
 findings highlight the importance of addressing climate and human
 pressures in conservation planning, as dark diversity signals hidden
 constraints on species establishment and persistence.
description: # 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 variable group. 3. Random Forest Models: Ranks the importance of individual environmental predictors. 4. Spatial (SAR) Models: Models key interaction effects between climate and human factors while accounting for spatial autocorrelation. Requirements: * Data: A dataframe named data_VPA_RF_SAR must be pre-loaded. * Libraries: Requires the vegan, randomForest, and spdep packages. - Spatial Settings: The SAR model uses a spatial weights matrix based on the 4 nearest neighbors (k=4). ## Files and variables #### File: data_VPA_RF_SAR.csv **Description:**  ##### Variables * plot: A unique identifier for each of the 456 sampled forest plots in the study area. Categorical (Plot ID) * DD: Dark Diversity: The pool of species absent from a local site but present in the surrounding region and potentially able to inhabit the local site conditions. Number of species (count) * OD: Observed Diversity: The number of species actually recorded and observed within the forest plot. Number of species (count) * longitude: The geographic longitude coordinate of the plot's center. Decimal degrees (WGS 84) * latitude: The geographic latitude coordinate of the plot's center. Decimal degrees (WGS 84) * BIO1: Annual Mean Temperature: The average annual temperature at the plot location. Degrees Celsius (°C) * BIO12: Annual Mean Precipitation: The total annual precipitation at the plot location. Millimeters (mm) * BIO15: Precipitation Seasonality: The coefficient of variation (CV) of monthly precipitation totals. Measures how precipitation fluctuates throughout the year. Unitless (Coefficient of Variation) * altitude: The elevation of the ground surface at the plot's center above sea level. Meters (m) * slope: The incline of the terrain at the plot, likely representing the angle of the surface from horizontal. Degrees (°)  * moisture: Soil Volumetric Water Content: A measure of the amount of water present in the soil sample from the plot.  (-33 to -1500 kPa, cm m^-1^) * C_N: Carbon to Nitrogen Ratio: A key soil property indicating the ratio of organic carbon mass to nitrogen mass in the soil. Ratio (mass carbon / mass nitrogen) * pH: Soil pH: A measure of the acidity or alkalinity of the soil sample. pH scale (0 - 14) * HF: Human Footprint: A cumulative measure of direct human pressure on the environment (e.g., from infrastructure, land use, population density). Index (typically 0-50 or 0-100, where 0 = no pressure) * HC: Human Footprint Change: The change in the Human Footprint index over a specific time period. Likely indicates intensification or reduction of human pressure. Change in Index value over time (e.g., ΔHF/decade) * RA: Roadless Area: An indicator of the plot's proximity to or location within areas devoid of roads. (km^2^) * PA: Protected Area: An indicator of whether the plot is located within a legally designated protected area. (km^2^) * PCNM_X7: PCNM Vector 7: A spatial eigenvector from a Principal Coordinates of Neighbourhood Matrix (PCNM) analysis, representing a specific scale of spatial patterning. Unitless (Eigenvector score) * PCNM_X14: PCNM Vector 14: A spatial eigenvector from a PCNM analysis, representing a specific scale of spatial patterning. Unitless (Eigenvector score) * PCNM_X115: PCNM Vector 115: A spatial eigenvector from a PCNM analysis, representing a specific (often very fine) scale of spatial patterning. Unitless (Eigenvector score)