Commit aa205e1c by Kunstler Georges

### output for all new ecocode

parent 5be64c3e
 ... @@ -123,11 +123,13 @@ The effect of a focal species' trait value, $$t_f$$, on its ... @@ -123,11 +123,13 @@ The effect of a focal species' trait value, $$t_f$$, on its maximum growth was include as: maximum growth was include as: \label{Gmax} \label{Gmax} \log{G_{\textrm{max} \, f,p,s}} = m_{0} + m_1 \, t_f + \varepsilon_{G_{\textrm{max}}, f} + \varepsilon_{G_{\textrm{max}}, p} + \varepsilon_{G_{\textrm{max}}, s}. \log{G_{\textrm{max} \, f,p,s}} = m_{0} + m_1 \, t_f + m_2 \, MAT + m_2 \, MAP + \varepsilon_{G_{\textrm{max}}, f} + \varepsilon_{G_{\textrm{max}}, p} + \varepsilon_{G_{\textrm{max}}, s}. Here $$m_0$$ is the average maximum growth, $$m_1$$ gives the effect of Here $$m_0$$ is the average maximum growth, $$m_1$$ gives the effect of the focal species trait, and $$\varepsilon_{G_{\textrm{max}}, f}$$, the focal species trait, $m_2$ and $m_3$ of mean annual temperature $MAT$ and sum of annual precipitation $MAP$ respectively, and $$\varepsilon_{G_{\textrm{max}}, f}$$, $$\varepsilon_{G_{\textrm{max}}, p}$$, $$\varepsilon_{G_{\textrm{max}}, s}$$ $$\varepsilon_{G_{\textrm{max}}, p}$$, $$\varepsilon_{G_{\textrm{max}}, s}$$ are normally distributed random effect for species $$f$$, plot or are normally distributed random effect for species $$f$$, plot or quadrat $$p$$ (see below), and data set $$s$$ {[}where quadrat $$p$$ (see below), and data set $$s$$ {[}where ... @@ -182,6 +184,31 @@ where: ... @@ -182,6 +184,31 @@ where: {[}$\varepsilon_{\alpha_s,s} \sim \mathcal{N} (0,\sigma_{\alpha_s})${]}. {[}$\varepsilon_{\alpha_s,s} \sim \mathcal{N} (0,\sigma_{\alpha_s})${]}. \end{itemize} \end{itemize} To explore whether the trait similarity effect $\alpha_s$ was driven only by different competitive effect between conspecific and heterospecific, we also explored an alternative model where the traits similarity was computed only for heterospecific and distinct trait independent parameter $alpha_0$ were estimated for intraspecific and interspecific competition. Intra-specific competition was modelled as: \alpha_{f,f} = \alpha_{0,intra,f} + \alpha_t \, t_f + \alpha_e \, t_f where $$\alpha_{0,intra,f}$$ is the intraspecific trait independent competition for the focal species $$f$$ (with the same random structure as above), and the other parameters have the same definition as in the previous model. And inter-specific competition was modelled as: \alpha_{f,c} = \alpha_{0,inter,f} + \alpha_t \, t_f + \alpha_e \, t_c + \alpha_s \, \vert t_c - t_f \vert where $$\alpha_{0,inter,f}$$ is the interspecific trait independent competition for the focal species $$f$$ (with the same random structure as above), and the other parameters have the same definition as in the previous model. Eqs. \ref{logG1}-\ref{alpha} were then fitted to empirical estimates of Eqs. \ref{logG1}-\ref{alpha} were then fitted to empirical estimates of growth based on change in diameter between census $t$ growth based on change in diameter between census $t$ and $t+1$ (respectively at year $y_t$ and $y_{t+1}$), given by and $t+1$ (respectively at year $y_t$ and $y_{t+1}$), given by ... @@ -204,7 +231,11 @@ enabled us to explore variation among biomes. Because some biomes had ... @@ -204,7 +231,11 @@ enabled us to explore variation among biomes. Because some biomes had few observations, we merged those with biomes with similar climates. Tundra was few observations, we merged those with biomes with similar climates. Tundra was merged with taiga, tropical rainforest and tropical seasonal forest were merged with taiga, tropical rainforest and tropical seasonal forest were merged into tropical forest, and deserts were not included in this final merged into tropical forest, and deserts were not included in this final analysis as too few plots were available. analysis as too few plots were available. To evaluate if our results were robust to the random effect structure we also explored a model with a single effect constant across biomes but with a random effect for the parameters with both the data set and a local ecoregion using the K{\"o}ppen-Geiger ecoregion\citep{Kriticos-2012}. \section{Data}\label{data} \section{Data}\label{data} ... ...
 ... @@ -388,7 +388,7 @@ $\alpha_s$ are fitted from data using maximum likelihood method. ... @@ -388,7 +388,7 @@ $\alpha_s$ are fitted from data using maximum likelihood method. \begin{figure}[htbp] \begin{figure}[htbp] \centering \centering \includegraphics{../../figs/figres12.pdf} \includegraphics{../../figs/figres12_TP.pdf} \caption{\textbf{Global trait effects and trait-independent effects on \caption{\textbf{Global trait effects and trait-independent effects on maximum growth and competition and their variation among biomes.} maximum growth and competition and their variation among biomes.} Standardized regression coefficients for growth models, fitted Standardized regression coefficients for growth models, fitted ... ...
 ... @@ -29,6 +29,24 @@ a <- fun.hexbin.with.smooth.ggplot(data.BA.G$MAT, data.BA.G$BA.G, ... @@ -29,6 +29,24 @@ a <- fun.hexbin.with.smooth.ggplot(data.BA.G$MAT, data.BA.G$BA.G, b <- fun.hexbin.with.smooth.ggplot(data.BA.G$MAP, data.BA.G$BA.G, b <- fun.hexbin.with.smooth.ggplot(data.BA.G$MAP, data.BA.G$BA.G, 'MAP', 'BA.G') 'MAP', 'BA.G') library(ggplot2) library(ggplot2) levels(data.glob\$set) <- c("Sweden", "New Zealand", "USA", "Canada", "Australia", "France", "Switzerland", "Spain", "Panama", "French Guiana", "Japan", "Taiwan", "Puerto Rico", "Central African Republic") pdf('figs/boxplot_batot_set.pdf') p0 = ggplot(data.glob, aes(y = BATOT)) + geom_boxplot(aes(x = factor(set)), outlier.size = 0) p1 = p0 + coord_cartesian(ylim = c(0, 125)) +ylab(expression('Total basal area' (m^2/ha))) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +xlab('data') p1 dev.off() pdf('figs/boxplot_batot_biomes.pdf') p0 = ggplot(data.glob, aes(y = BATOT)) + geom_boxplot(aes(x = factor(biomes)), outlier.size = 0) p1 = p0 + coord_cartesian(ylim = c(0, 125)) +ylab(expression('Total basal area' (m^2/ha))) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +xlab('Biomes') p1 dev.off() multiplot(a, b,cols=2) multiplot(a, b,cols=2) dev.off() dev.off() ... ...