Tree diversity across multiple scales and environmental heterogeneity promote ecosystem multifunctionality in a large temperate forest region
Data files
Dec 21, 2023 version files 88.69 KB
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1_data_for_BEMF.csv
67.31 KB
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2_Code_for_main_analysis.Rmd
19.42 KB
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README.md
1.97 KB
Mar 14, 2024 version files 84.58 KB
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1_data_for_BEMF_R1.xlsx
72.98 KB
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2_Code_for_main_analysis_R1.Rmd
9.71 KB
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README.md
1.90 KB
Abstract
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.
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.
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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).
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2_Code_for_main_analysis.Rmd
R code for the analyses of the above mentioned publication.
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