Small change :)

supplementary_materials/Supplementary_materials.Rmd

@@ -168,7 +168,7 @@ kableExtra::kbl(
 
 We inferred a high IV even in the absence of intrinsic IV (Table \@ref(tab:EstimateTable)). 
 
-```{r PlotIntrinsicIV, fig.cap="Plot of the real values - points - and estimated mean - bold lines - and 95 % confidence interval - thin lines - of $Y$ versus $X_1$. The dashed lines correspond to the 95 % interval due to intrinsic IV."}
+```{r PlotIntrinsicIV, fig.cap="Plot of the real values - points - and estimated mean - bold lines - and 95\\% confidence interval - thin lines - of $Y$ versus $X_1$. The dashed lines correspond to the 95\\% interval due to intrinsic IV."}
 
 knitr::include_graphics(here::here("outputs", "theoretical_model", "figures", "Partial_knowledge_model_genetic_var.png"))
 ```
@@ -259,7 +259,7 @@ knitr::include_graphics(here::here("outputs", "clonal_analysis", "figures", "Tra
 knitr::include_graphics(here::here("outputs", "clonal_analysis", "figures", "Trace_block_random_effects.png"))
 ```
 
-```{r PlotsRanef, fig.cap="Mean values and 95% confidence interval of the temporal, genetic and spatial and random effects."}
+```{r PlotsRanef, fig.cap="Mean values and 95\\% confidence interval of the temporal, genetic and spatial and random effects."}
 
 knitr::include_graphics(here::here("outputs", "clonal_analysis", "figures", "Ranef_values.png"))
 ```
@@ -387,7 +387,7 @@ For the BCI site, we obtained a dataset with `r nrow(BCI_no_mean)` growth estima
 
 ## Estimation of IV
 
-We evaluate the convergence of the model using trace and density plots of the posterior estimates.
+We evaluated the convergence of the model using trace and density plots of the posterior estimates.
 
 ### Paracou
 
@@ -431,6 +431,10 @@ knitr::include_graphics(here::here("outputs", "tropical_analysis", "figures", "M
 knitr::include_graphics(here::here("outputs", "tropical_analysis", "figures", "MCMC_density_BCI.png"))
 ```
 
+## Spatial analyses
+
+"Most abundant species" are species with more than 3,000 individuals.
+
 ## Spatial autocorrelation in undisturbed plots in the Paracou site
 
 One of our main hypotheses was that since many environmental variables are spatially structured and that tree growth is largely influenced by environmental variables, tree growth should be spatially structured. Our analysis using Moran’s I test showed that tree growth was indeed spatially structured at our study sites. The Paracou dataset offers the opportunity to test this hypothesis further. Indeed, some plots were disturbed in the early eighties, creating artificial gaps, whereas others were not disturbed. As the creation of gaps results in a strong spatial structure of the light available under the canopy, we hypothesized that growth should be less structured in plots that were not disturbed.