Data from: Sampling origins and directions affect the minimum sampling area in forest plots
Data files
Jan 15, 2024 version files 10.51 MB
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Data_and_code.rar
10.50 MB
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README.md
11.98 KB
Abstract
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.
README
#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.
Methods
All woody plant individuals with a DBH > 1 cm in the plots were mapped, tagged, and identified.