This is supplementary data for the paper entitled “Breakdown of the growth–mortality trade-off along a soil phosphorus gradient in diverse neotropical forest”. The files hold (i) the R codes for growth and mortality analyses using data of long-term vegetation dynamics (hierarchical Bayesian analysis), (ii) example dataset, and (iii) the specie-specific estimation of beta values (species-specific responses to size or environmental factors; see the original article for more information). In this article, we applied the R code to the data of plot-network in Panama (Condit et al. 2019; Turner & Condit 2022).
 
Condit, R., Pérez, R., Aguilar, S. & Lao, S. (2019). Census data from 65 tree plots in Panama, 1994-2015. Dryad, Dataset
Turner, B.L. & Condit, R. (2022). Soil chemistry and dry season intensity, Panama Canal Area. Dryad, Dataset
 
 
#1. R codes######################################
The R codes define the “genMCMC” function to estimate species-specific relationship of growth or mortality with size and environmental factors based on data of long-term vegetation dynamics, and the effects of species environmental association (distribution in relation to soil P, shade, and moisture gradients) on growth or mortality responses to size and environmental factors.
 
genMCMC = function(data, data_association, xName, yName, sName, AName, Period,
                    numSavedSteps = 10000, thinSteps = 10, saveName = NULL,
                    runjagsMethod = runjagsMethodDefault,
                    nChains = nChainsDefault) {…}
 
The function requires several data as follows.

1. data: This is a dataframe including columns of y values (e.g., growth or status) and x values (e.g., size and environmental factors) with species name and census intervals (only for mortality analysis) at the first level of the Bayesian model. See “ExampleDataForMortalityAnalysis.csv” for an example dataset.
2. data_association: This is a dataframe including columns of species name and its environmental associations (e.g., P associations), which are used at the second level of the Bayesian model. See “ExampleDataAssociationForMortalityAnalysis.csv” for an example dataset.
3. runjagsMethod and nChains: These data will be defined automatically by the R code.
4. saveName: The name of the file including the result of MCMC. “filename.csv”
5. Also, the function requires to specify the column names in the dataframe (data).

Examples.
yName = "growth rate" or “status” (1, alive; 0, dead)
xName = c("ShadingIndex","dbh", "Resin.P", "Water deficit");
sName = "Species"
AName = c("Phosphorus_association", "Shade_association","Moisture_association", "PS", "PM", "SM", "PSM")
Period = “Period” (census intervals in year)
 
#2. Example data set ######################################
Two csv files include “ExampleDataForMortalityAnalysis.csv” and “ExampleDataAssociationForMortalityAnalysis.csv”.
 
Columns in “ExampleDataForMortalityAnalysis.csv”
DataID: IDs for each individual.
Status: Individual status at the second census (after the census interval); 1, alive; 0, dead.
Deficit: The dry season moisture deficit, which represents the intensity of the dry season (between December and April) as the minimum annual value of cumulative daily precipitation minus evapotranspiration, varied from –392 to –580 mm. A more negative moisture deficit indicates a longer, drier, dry season.
Resin.P: Concentrations of readily available phosphorus (mg kg^–1), which was determined by extraction with anion-exchange membranes (i.e., resin phosphate).
ShadingIndex: Index for light availability, which was calculated as the sum of the distance-weighted basal areas of trees larger than and within 10 m of each focal tree.
Dbh: Diameter at breast height (mm).
Species: Species code. See Condit et al. 2019 and Turner and Condit 2022 for the more information.
Period: Census intervals (year).
Plot: Plot name.
 
Columns in “ExampleDataAssociationForMortalityAnalysis.csv”
DataID: IDs for each species.
Species: Species code. See Condit et al. 2019 and Turner and Condit 2022 for the more information.
Phosphorus_association: Species-specific environmental  association in relation to soil phosphorus.
Shade_association: Species-specific environmental  association in relation to shade.
Moisture_association: Species-specific environmental  association in relation to moisture availability.
PS: Interaction between phosphorus association × shade association
PM: Interaction between phosphorus association × moisture association
SM: Interaction between shade association × moisture association
PSM: Interaction between phosphorus association × shade association× moisture association
 
#3. Specie-specific estimation of beta values ######################################
The csv files include the information on species characteristics and the estimation of beta values. See original article for more information.
 
Species: Species code. See Condit et al. 2019 and Turner and Condit 2022 for the more information.
Phosphorus_association: Species-specific environmental  association in relation to soil phosphorus.
Shade_association: Species-specific environmental  association in relation to shade.
Moisture_association: Species-specific environmental  association in relation to moisture availability.
beta0_median: Species-specific estimation of intrinsic growth or mortality rate.
beta1_median: Species-specific estimation of growth or mortality responses to shade.
beta2_median: Species-specific estimation of growth or mortality responses to size.
beta3_median: Species-specific estimation of growth or mortality responses to soil phosphorus availability
beta4_median: Species-specific estimation of growth or mortality responses to moisture availability