DOI: 10.5061/dryad.cnp5hqc90
title: Data from: Sampling origins and directions affect the minimum sampling area in forest plots

description: The minimum sampling area (or minimum area), representing the smallest
 space that reflects the species composition and characteristics
 of a plant community, is estimated using species-area relationships (SARs)
 for designing and managing biodiversity conservation. However, the
 sampling design’s effect on the determination of the minimum area has
 rarely been systematically evaluated. In this study, we used tree census
 data from three forest dynamic plots of 25–60 ha in different climatic
 zones in China to calculate the minimum areas of woody plants in the plots
 and analyze the effects of species richness and topographic heterogeneity
 on the areas by changing sampling origins and directions. Our findings
 reveal that the estimated size of the minimum areas varied significantly
 with sampling origins and directions, with a difference of approximately
 1.5–2 times in forest plots. Topographic heterogeneity affected the
 minimum area through changes in species composition, while species
 richness had only a weak impact. These results suggest the importance of
 considering the sampling origin and direction design when utilizing SARs
 to estimate the minimum area and species diversity in plant communities,
 which contributes to a better understanding of vegetation characteristics
 and the minimum area required for censuses in heterogeneous habitats.
description: All woody plant individuals
 with a DBH &gt; 1 cm
 in the plots were mapped, tagged, and
 identified.
description: \#Data and code from: Sampling origins and directions affect the minimum
 sampling area in forest plots. CITATION: Fan, F., Zhao, L., Ma, T., Xiong,
 X., Zhang, Y., Shen, X., & Li, S. (2022). Community composition and
 structure in a 25.2 hm~2 subalpine dark coniferous forest dynamics plot in
 Wanglang,Sichuan,China. Chinese Journal of Plant Ecology, 46(9),
 1005-1017. Qin, Y., Zhang, J., Liu, J., Liu, M., Wan, D., Wu, H., . . .
 Jiang, M. (2018). Community composition and spatial structure in the
 Badagongshan 25 ha Forest Dynamics Plot in Hunan Province. Biodiversity
 Science, 26(9), 1016-1022. Xu, H., Li, Y., Lin, M., Wu, J., Luo, T., Zhou,
 Z., . . . Liu, S. (2015). Community characteristics of a 60 ha dynamics
 plot in the tropical montane rain forest in Jianfengling, Hainan Island.
 Biodiversity Science, 23(2), 192-201. AUTHORS' INFORMATION: Chenqi,
 He Institute of Ecology, Peking University, Beijing 100871, China Email:
 chuckiey_email@stu.pku.edu.cn Fan, Fan Institute of Ecology, Peking
 University, Beijing 100871, China Email: fanf@pku.edu.cn Xiujuan Qiao Key
 Laboratory of Aquatic Botany and Watershed Ecology, Chinese Academy of
 Sciences, Wuhan, 430074, China Email: xjqiao@wbgcas.cn Zhang Zhou Research
 Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou
 510520, China Email: zhouzhang@caf.ac.cn Han Xu Research Institute of
 Tropical Forestry, Chinese Academy of Forestry, Guangzhou 510520, China
 Email: hanxu81@gmail.com Sheng, Li School of Life Sciences, Peking
 University, Beijing 100871, China Email: shengli@pku.edu.cn Shaopeng Wang
 Institute of Ecology, Peking University, Beijing 100871, China Email:
 shaopeng.wang@pku.edu.cn Zhiyao Tang Institute of Ecology, Peking
 University, Beijing 100871, China Email: zytang@urban.pku.edu.cn Jingyun
 Fang Institute of Ecology, Peking University, Beijing 100871, China Email:
 jyfang@urban.pku.edu.cn BRIEF SUMMARY: This study uses tree census data of
 three forest dynamic plots of 25–60 ha in different climatic zones in
 China to calculate the minimum areas of woody plants in the plots and
 analyze the effects of species richness and topographic heterogeneity on
 the areas by changing sampling origins and directions. RESPONSIBLE AUTHORS
 FOR CODE: Chenqi He RESPONSIBLE AUTHORS FOR COLLECTING DATA: Fan Fan,
 Xiujuan Qiao, Zhang Zhou, Han Xu, Sheng Li. LOADED PACKAGES: All data and
 code are analyzed with R version 4.0.5. dplyr, tidyr,scales packages: data
 clean up. sars,nlme,piecewiseSEM packages: data analysis. ggplot2, cowplot
 packages: draw figures. rstudioapi packages: data save. LIST OF ALL
 FOLDERS AND FILES: fig2: figure2.R: R scripts to draw the species-area
 relationships and fitted power-law models for woody plants in three forest
 dynamic plots. figure2.RData: Data to draw the species-area relationships
 in three forest dynamic plots including three datasets of mean
 (BDGS_average, JFL_average, and WL_average). Variables in the datasets of
 mean: Area, sampling area (m2). Mean, mean species richness in the
 sampling area. X2.5., species richness at 2.5 quantiles. X25., species
 richness at 25 quantiles. X75., species richness at 75 quantiles. X97.5.,
 species richness at 97.5 quantiles. fig3: figure3.R: R scripts to draw the
 frequency distribution of estimated parameters c and z values based on
 power-law SAR models sampled from different origins and directions.
 figure3.RData: model dataset of species-area relationships in three forest
 dynamic plots (czvalues_BDGS, czvalues_JFL, and czvalues_WL) and power-law
 model fitting results (BDGS_fit, JFL_fit and WL_fit). Variables in the
 model dataset: c, z, power-law model parameters. R2, AICc, BIC,
 determination coefficient, Akaike information criterion, and bayesian
 information criterion of power-law model to evaluate the model fittness.
 TSR, total species richness in the forest dynamic plots. 50%-90%, species
 survey targets. beta, beta-diversity in the plots. alpha, alpha-diversity
 in the plots. group_beta, quantile beta-diversity into five groups.
 group_alpha, quantile alpha-diversity into five groups. fig4: figure4.R: R
 scripts to draw the frequency distribution of the minimum sampling area of
 the woody plant community in three forest plots, under the 90% woody plant
 species survey target.. figure4.RData: model dataset of species-area
 relationships in three forest dynamic plots (czvalues_BDGS, czvalues_JFL,
 and czvalues_WL) and power-law model fitting results (BDGS_fit, JFL_fit
 and WL_fit). Variables in the model dataset: c, z, power-law model
 parameters. R2, AICc, BIC, determination coefficient, Akaike information
 criterion, and bayesian information criterion of power-law model to
 evaluate the model fittness. TSR, total species richness in the forest
 dynamic plots. 50%-90%, species survey targets. beta, beta-diversity in
 the plots. alpha, alpha-diversity in the plots. group_beta, quantile
 beta-diversity into five groups. group_alpha, quantile alpha-diversity
 into five groups. fig5: figure5.R: R scripts to draw the structural
 equation models which indicated topographic heterogeneity affects minimum
 sampling area through alpha-diversity and beta-diversity. figure5.RData:
 model dataset of species-area relationships in three forest dynamic plots
 (czvalues_BDGS, czvalues_JFL, and czvalues_WL) and topographic
 heterogeneous (sd_topo_BDGS, sd_topo_JFL, and sd_topo_WL). Variables in
 the model dataset: c, z, power-law model parameters. R2, AICc, BIC,
 determination coefficient, Akaike information criterion, and bayesian
 information criterion of power-law model to evaluate the model fittness.
 TSR, total species richness in the forest dynamic plots. 50%-90%, species
 survey targets. beta, beta-diversity in the plots. alpha, alpha-diversity
 in the plots. group_beta, quantile beta-diversity into five groups.
 group_alpha, quantile alpha-diversity into five groups. Variables in the
 topographic heterogeneous: sd_elev, standard deviation of elevation.
 sd_slope, standard deviation of slope. sd_aspect, standard deviation of
 aspect. group_elev, group_slope, group_aspect, quantile these topographic
 heterogeneous into five groups. figS1: figureS1.R: R scripts to draw the
 fitness of power-law model to species-area relationships. figureS1.RData:
 model dataset of species-area relationships (czvalues_BDGS, czvalues_JFL,
 and czvalues_WL), datasets of mean (BDGS_average, JFL_average, and
 WL_average), and different results of species-area curves (BDGS_SAR,
 JFL_SAR, and WL_SAR). Variables in the model dataset: c, z, power-law
 model parameters. R2, AICc, BIC, determination coefficient, Akaike
 information criterion, and bayesian information criterion of power-law
 model to evaluate the model fittness. TSR, total species richness in the
 forest dynamic plots. 50%-90%, species survey targets. beta,
 beta-diversity in the plots. alpha, alpha-diversity in the plots.
 group_beta, quantile beta-diversity into five groups. group_alpha,
 quantile alpha-diversity into five groups. Variables in the datasets of
 mean: Area, sampling area (m2). Mean, mean species richness in the
 sampling area. X2.5., species richness at 2.5 quantiles. X25., species
 richness at 25 quantiles. X75., species richness at 75 quantiles. X97.5.,
 species richness at 97.5 quantiles. Variables in the different results of
 species-area curves: Area, sampling area (m2). Richness, species richness
 in the sampling area. figS2: figureS2.R: R scripts to draw the topographic
 heterogeneity and their Pearson coefficients.. figureS2.RData: data of
 topographic heterogeneous (sd_topo_BDGS, sd_topo_JFL, and sd_topo_WL).
 Variables in the topographic heterogeneous: sd_elev, standard deviation of
 elevation. sd_slope, standard deviation of slope. sd_aspect, standard
 deviation of aspect. figS3: figureS3D.R, figureS3E.R, figureS3F.R: R
 scripts to draw the spatial pattern of alpha diversity and relationships
 with minimum sampling area in three forest dynamic plots. figureS3D.RData,
 figureS3E.RData, figureS3F.RData: model dataset of species-area
 relationships (czvalues_BDGS, czvalues_JFL, and czvalues_WL). Variables in
 the model dataset: c, z, power-law model parameters. R2, AICc, BIC,
 determination coefficient, Akaike information criterion, and bayesian
 information criterion of power-law model to evaluate the model fittness.
 TSR, total species richness in the forest dynamic plots. 50%-90%, species
 survey targets. beta, beta-diversity in the plots. alpha, alpha-diversity
 in the plots. group_beta, quantile beta-diversity into five groups.
 group_alpha, quantile alpha-diversity into five groups. figS4:
 figureS4D.R, figureS4E.R, figureS4F.R: R scripts to draw the spatial
 pattern of beta diversity and relationships with minimum sampling area in
 three forest dynamic plots. figureS4D.RData, figureS4E.RData,
 figureS4F.RData: model dataset of species-area relationships
 (czvalues_BDGS, czvalues_JFL, and czvalues_WL). Variables in the model
 dataset: c, z, power-law model parameters. R2, AICc, BIC, determination
 coefficient, Akaike information criterion, and bayesian information
 criterion of power-law model to evaluate the model fittness. TSR, total
 species richness in the forest dynamic plots. 50%-90%, species survey
 targets. beta, beta-diversity in the plots. alpha, alpha-diversity in the
 plots. group_beta, quantile beta-diversity into five groups. group_alpha,
 quantile alpha-diversity into five groups. figS6: figureS6A.R,
 figureS6B.R, figureS6CF.R: R scripts to draw the relationships between
 topographic heterogeneity and minimum sampling area in three forest
 dynamic plots. figureS6A.RData, figureS6B.RData, figureS6C.RData: model
 dataset of species-area relationships (czvalues_BDGS, czvalues_JFL, and
 czvalues_WL), and topographic heterogeneous (sd_topo_BDGS, sd_topo_JFL,
 and sd_topo_WL). Variables in the model dataset: c, z, power-law model
 parameters. R2, AICc, BIC, determination coefficient, Akaike information
 criterion, and bayesian information criterion of power-law model to
 evaluate the model fittness. TSR, total species richness in the forest
 dynamic plots. 50%-90%, species survey targets. beta, beta-diversity in
 the plots. alpha, alpha-diversity in the plots. group_beta, quantile
 beta-diversity into five groups. group_alpha, quantile alpha-diversity
 into five groups. Variables in the topographic heterogeneous: sd_elev,
 standard deviation of elevation. sd_slope, standard deviation of slope.
 sd_aspect, standard deviation of aspect. group_elev, group_slope,
 group_aspect, quantile these topographic heterogeneous into five groups.
 figS7: figureS7.R: R scripts to draw the frequency distribution of minimum
 sampling area under different woody plant species survey targets.
 figureS7.RData: model dataset of species-area relationships
 (czvalues_BDGS, czvalues_JFL, and czvalues_WL) and power-law model fitting
 results (BDGS_fit, JFL_fit and WL_fit). Variables in the model dataset: c,
 z, power-law model parameters. R2, AICc, BIC, determination coefficient,
 Akaike information criterion, and bayesian information criterion of
 power-law model to evaluate the model fittness. TSR, total species
 richness in the forest dynamic plots. 50%-90%, species survey targets.
 beta, beta-diversity in the plots. alpha, alpha-diversity in the plots.
 group_beta, quantile beta-diversity into five groups. group_alpha,
 quantile alpha-diversity into five groups. Relationships and workflow of
 those files and scripts: Parallel for each figure. Each script run
 sequentially.