DOI: 10.5061/dryad.dr7sqvb4n
title: Tree diversity across multiple scales and environmental heterogeneity promote ecosystem multifunctionality in a large temperate forest region

description: Aim: Biodiversity across different scales provides multidimensional
 insurance for ecosystem functioning. Although the positive effects of
 local scale (α-diversity) biodiversity on ecosystem multifunctionality are
 widely accepted, species turnover across communities (β-diversity) which
 is often an important driver of ecosystem functioning did not receive the
 same attention. This study broadens the understanding of how multiple
 attributes of biodiversity maintain ecosystem multifunctionality from
 local to regional scales, across diverse environmental gradients.
 Location: North-eastern China. Time period: 2017. Major taxa studied:
 Woody plants. Methods: We estimate ecosystem multifunctionality using both
 averaging and modified multiple thresholds (50%, 70% and 90%) approaches.
 Multiple dimensions of biodiversity across varying spatial scales were
 measured within the framework of Hill‒Chao numbers. Linear and nonlinear
 models were used to evaluate the optimal patterns of multifunctionality
 and biodiversity along the latitude. Using variance decomposition,
 structural equation modeling and linear mixed models, we explored how
 multiple attributes of tree diversity at varying spatial scales affect
 multifunctionality, and how these relationships are modulated by
 environmental drivers. Results: Our results show that multifunctionality
 decreased with increasing latitude, mirroring the pattern of tree
 diversity along latitudinal gradients. Phylogenetic β-diversity and
 species α-diversity emerged as crucial diversity indices for sustaining
 multifunctionality in these temperate forests. Soil and climatic
 conditions had either direct effects on multifunctionality, or indirect
 ones mediated by tree diversity. Environmental heterogeneity played a
 pivotal role in maintaining high levels of multifunctionality, exerting
 influence both directly and indirectly via phylogenetic β-diversity. Main
 conclusions: This study underscores the positive effects of biodiversity
 on multifunctionality across multiple dimensions. Based on our findings,
 we conclude that any design of a forested landscape that is aimed at
 maximizing multifunctionality should consider maintaining high local
 diversity as well as forest community heterogeneity at varying scales.
description: This data package contains data from the publication: **Tree diversity
 across multiple scales and environmental heterogeneity promote ecosystem
 multifunctionality in a large temperate forest region** The data contains
 2 files (one *.csv format and one *.Rmd format) that were used in the
 analyses of the above-mentioned publication. A detailed description of
 each data file is given below. \####################################
 **1_data_for_BEMF.csv** the scaled (Z-score) forest community data at the
 regional scale. Each row represents a forest community, and each column
 represents a variable, which includes 1\)region: the geographical
 locations of forest communities (one of the eight mountain areas)
 2\)TD_alpha: species α-diversity; 3\)TD_beta: species β-diversity;
 4\)FD_alpha: functional α-diversity; 5\)FD_beta: functional β-diversity;
 6\)PD_alpha: phylogenetic α-diversity; 7\)PD_beta: phylogenetic
 β-diversity; 8\)longitude; 9\)latitude; 10\)env.het: environmental
 heterogeneity; 11\)rEMF: average multifunctionality index of regional
 forest community; 12\)rEMF_T0.5: 50% threshold level multifunctionality
 index of regional forest communities; 13\)rEMF_T0.7: 70% threshold level
 multifunctionality index of regional forest communities; 14\)rEMF_T0.9:
 90% threshold level multifunctionality index of regional forest
 communities; 15\)climate1: the first principal component (PC1) of six
 climate variables (annual mean temperature, annual precipitation,
 precipitation of driest month, precipitation seasonality, precipitation of
 driest quarter); 16\)soil1: the first principal component (PC1) of four
 soil variables (soil sand content, cation exchange capacity at pH=7, soil
 clay content, soil silt content). \####################################
 **2_Code_for_main_analysis.Rmd** R code for the analyses of the above
 mentioned publication. \####################################