Data from: Identifying seed families with high mixture performance in a subtropical forest biodiversity experiment
Data files
Oct 25, 2024 version files 552.96 KB
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Data_1_for_Figure_2.xlsx
520.51 KB
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Data_2_for_Figure_3.xlsx
16.74 KB
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Data_3_for_Figure_4.xlsx
14.51 KB
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README.md
1.20 KB
Abstract
This dataset is from the manuscript Identifying tree genotypes for high-performance mixed forests. We investigated whether tree genetic identity in eight test species affected individual growth (i.e., cumulative biomass) over time in experimental forest stands, across mixtures providing a gradient of species richness. First, we used the ten-year (2012–2021) biomass data from the center of the plots in the experiment, where 3,106 trees had been measured continuously over time, to test the relative contributions of variation in species and genetic identity to variation in tree growth over time. (Data 1 for Figure 2). Second, using biomass data for the two years 2012 and 2021 from all 13,519 trees surviving until 2021, we tested how genetic identity affected tree growth across the tree species richness gradient (Data 2 for Figure 3). Third, using data of the 13,519 trees from the two years 2012 and 2021 again, we tested if the relative contribution of genetic identity increased with species richness, as predicted because of reduced intraspecific competition (Data 3 for Figure 4).
README: Data from: Identifying seed families with high mixture performance in a subtropical forest biodiversity experiment
https://doi.org/10.5061/dryad.pnvx0k6w4
This dataset is from the figures of the manuscript "Identifying tree genotypes for high-performance mixed forests".
Description of the data and file structure
File 1: Data 1 for Figure 2 (one meta data and one data were included)
File 2: Data 2 for Figure 3 (one meta data and one data were included)
File 3: Data 3 for Figure 4 (one meta data and three data sheets included)
File 4: Code for all the figures and tables_Tang.R. the R script of the data analyses
Definitions of all variables, abbreviations, missing data codes, and units were included in the metadata of each file.
Note: the cells that contain "NA" in File 3 represents the F value and p value of residual which were not able to be calculated by linear model.
Links to other publicly accessible locations of the data
There is no other publicly accessible location of this data.
Code/Software
The data were analyzed by R 4.0.5, and the R script is available in Code for all the figures and tables_Tang.R.
Methods
First, we tested how genetic identity and its interaction with species richness affected tree cumulative aboveground biomass within each species separately. We used the “aov” function in R to perform mixed-model ANOVAs. Seed-family identity, species richness, the interaction of seed-family identity and species richness, plot, and the interaction of seed-family identity and plot were fitted sequentially in type-I ANOVA for each species. Species richness was log2-transformed to linearize relationships. To determine whether different seed families of each species differentially responded to species richness, we plotted the reaction norm of tree cumulative aboveground biomass in 2021 along the species richness gradient for each of the 8 species. We manually calculated the F value and corresponding P value of the interaction effects based on the mean squares and degrees of freedom results from ANOVAs: the F value of seed-family identity was obtained by dividing the mean square of seed-family identity by the mean square of the residual; the F value of species richness by dividing the mean square of species richness by the mean square of plots; and the F value for the interaction of seed-family identity and species richness by dividing the mean square of the interaction of seed-family identity and species richness by the mean square of the interaction of seed-family identity and plot.
Second, to assess how the expression of genetic identity changed with species richness, we used the Intraclass Correlation Coefficient (ICC) to quantify the contribution of seed-family identity to tree cumulative aboveground biomass for a given species in each plot. ICC was calculated by dividing the variance component of seed-family identity by the sum of the variance component to seed-family identity and the residual. To obtain broad-sense heritability estimates, it would be possible to divide ICC values by the relatedness among seed-family members (from 1 for fully inbred seed families to 0.5 for perfect full-sibs to 0.25 for perfect half-sibs ). Here, we used ICC instead of heritability because the exact relationships among individuals within a seed family (which may be anything between full- to half-sibs) could not be confirmed. “Seed-family identity” was used as the random factor and the fixed factor was kept as “1” in linear mixed models (LMMs) to calculate the variance component of the seed-family identity and the residual
Third, we tested if the contribution of species identity, genetic identity and species richness to tree cumulative aboveground biomass would change with increasing stand age. We used the “aov” function in R to fit sequentially species identity, seed-family identity within species and species richness, the interaction of species richness and species identity and the interaction of species richness and seed-family identity within species (type-I ANOVA) to tree cumulative aboveground biomass increments for each year from 2013 to 2021. Species richness was log2-transformed to linearize relationships. The sums of squares of the different terms were used to calculate the percentage of variance explained by each term each year. We used the function “lm” in R to detect the effects of stand age on these percentages of explained variation (sum-of-squares %).