Data from: Cavities and the demographic performance of tropical rainforest trees
Data files
Feb 11, 2025 version files 1.74 MB
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all.rgr.cav.df_2.csv
939.41 KB
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all.surv.cav.df_2.csv
770.05 KB
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forFig2.csv
460 B
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README.md
4.51 KB
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rgr.names.rev4.csv
2.10 KB
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surv.names.rev4.csv
952 B
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xtbg.sp.trait.csv
20.36 KB
Abstract
In tropical forests, trees often have damage in the form of visible cavities. However, the impacts of these cavities on tropical tree growth and survival are unknown, despite potential implications for the global carbon cycle. Here, we integrate 10 years of forest dynamics data with a survey of cavity presence on 25,450 rainforest trees (> 5cm in diameter) in the 20 ha Xishuangbanna plot in southern China. We found that cavities negatively impacted tree growth, but not survival, with growth of smaller trees more negatively affected by cavities. Variation in the impact of cavities was not explained by functional traits related to species life history strategy (specific leaf area, wood density, seed mass, leaf %N, leaf %P). These results suggest that cavities may affect both the compositional and carbon dynamics of tropical forests, but further research is needed to determine what drives variation among tree species in cavity impact.
https://doi.org/10.5061/dryad.dz08kps7h
Description of the data and file structure
The data original field data from the Xishuangbanna forest plot contains diameter (DBH), presence of cavities, growth, survival, traits, and quadrat number. Additional files from model output to produce a figure and for joining model parameter outputs with trait data are provided. R code used to generate the models and figures is provided.
Files and variables
File: forFig2.csv
Description: Model output used for making figure 2.
Variables
- param: the model parameter. The parameter names match the models shown in the manuscript.
- mean: the mean of the posterior probability distribution for the parameter.
- sd: the standard deviation of the posterior probability distribution for the parameter.
- 2.50%: the 2.5% quantile of the posterior probability distribution for the parameter.
- 97.50%: the 97.5% quantile of the posterior probability distribution for the parameter.
File: all.rgr.cav.df_2.csv
Description: the data put into the RGR model (see RGR_rev4.R for code). NA values in the file indicate that the individual
Variables
- tag: tree tag number
- spp: tree species identity as an integer
- cavity: tree cavity presence (2) or absence (1)
- dbh: diameter at breast height log transformed
- rgr: diameter relative growth rate
- quad: the 20x20m quadrat the tree is located in.
File: rgr.names.rev4.csv
Description: A vector of species names in the order for the RGR model. These names are joined with model output for trait correlations.
Variables
- Species names from the plot using 6 letter codes.
File: surv.names.rev4.csv
Description: A vector of species names in the order for the Survival model. These names are joined with model output for trait correlations.
Variables
- Species names from the plot using 6 letter codes.
File: all.surv.cav.df_2.csv
Description: the data put into the RGR model (see Surv_rev4.R for code)
Variables
- tag: tree tag number
- spp: tree species identity as an integer
- cavity: tree cavity presence (2) or absence (1)
- dbh: diameter at breast height log transformed
- survive: did the tree survival (1) or not (0)
- quad: the 20x20m quadrat the tree is located in
File: xtbg.sp.trait.csv
Description: The trait data used in this study to correlate with model posterior probability distributions. NA values indicate data for that trait for that species were not available.
Variables
- sp: species codes in 6 letter format
- thick: leaf thickness (mm)
- sla: specific leaf area (cm^2/g)
- densitymean: wood density (g/cm^3)
- seedmass: seed mass (mg)
- leaf.n: leaf %N
- leaf.p: leaf %P
Code/software
The R statistical computing environment is needed to run the data using the associated Zenodo .R scripts. This study used R v4.4.1.
- RGR_rev4.r: the R script for running the growth models and writing out the output. Equation 1 in the manuscript.
- Surv_rev4.r: the R script for running the growth models and writing out the output. Equation 2 in the manuscript.
- correlations_r4.R: the R script for running correlations between trait data and draws from the posterior distributions of the model parameters. Functional Traits and Demographic Rate Model Parameters section of the Methods in the manuscript. This code uses the output from the models and the xtbg.sp.trait.csv file.
- fig2.R: code for making Figure 2 in the manuscript. This code uses model output that is stored in the forFig2.csv file.
- Fig.3_rev4.r: code for making Figure 3 in the manuscript, which are growth model parameter - trait correlations. This code is used with files output from the correlations_r4.R code.
- Fig.4_rev4.r: code for making Figure 4 in the manuscript, which are survival model parameter - trait correlations. This code is used with files output from the correlations_r4.R code.
- Fig.sup_rev4.r: code for making Figures S1-S4 in the manuscript, which are species level parameter posterior distributions.
- rgr.ppc_figure.R: code for making the posterior predictive check figure in the manuscript (Figure S5). This uses output from the RGR_rev4.r code.
- surv.ppc_figure.R: code for making the posterior predictive check figure in the manuscript (Figure S6). This uses output from the Surv_rev4.r code.