Improved species matching, corr prop. sp. with traits

01_Extract_data_Project_31.Rmd

@@ -16,9 +16,10 @@ always_allow_html: yes
         
 **Timestamp:** `r date()`  
 **Drafted:** Francesco Maria Sabatini  
-**Version:** 1.2  
+**Version:** 1.3  
 
 *Changes to version 1.2* - Corrected final selection of plots in header. Only plots with CWM info are retained, but no minimum trait coverage threshold is used.  
+*Changes to version 1.3* - Resolved species list from xylem_data using TNRS to improve matching. Account for unresolved species when calculating the proportion of species with traits used for calculating CWMs. Relative covers are not normalized to 100% by plot x layer.   
 <br>
 This report documents the data extraction for **sPlot project proposal #31** - *The adaptive value of xylem physiology within and across global ecoregions* as requested by Daniel Laughlin and Jesse Robert Fleri 
 
@@ -59,6 +60,52 @@ Import data on xylem traits, provided by Jesse Robert Fleri on October 26th, 202
 load("xylem_data.RData")
 ```
 
+Create list of species to submit to [TNRS](https://tnrs.biendata.org/) for taxonomic standardization
+```{r}
+xylem_data %>% 
+  dplyr::select(Species) %>% 
+  mutate(Species=factor(Species)) %>% 
+  write_delim(file="Species_list.txt", delim="\t")
+```
+Reimport resolved species names
+```{r}
+xylem_resolved <- read_csv("tnrs_result.csv")
+```
+
+```{r, echo=F}
+knitr::kable(xylem_resolved, 
+             caption="Resolved names from TNRS") %>%
+    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
+                  full_width = F, position = "center")%>%
+    scroll_box(width = "1000px", height = "600px")
+```
+Associate resolved names to xylem data names to improve matching with sPlot's DT2
+```{r, warning=F, message=F}
+xylem_data <- xylem_data %>% 
+  left_join(xylem_resolved %>% 
+              dplyr::select(Name_submitted, Accepted_species, Taxonomic_status) %>% 
+              mutate_all(~as.character(.)), 
+            by=c("Species"="Name_submitted")) %>% 
+  mutate(Coalesced_Species=coalesce(Accepted_species, Species)) %>% 
+  ungroup()
+```
+There are `r xylem_data %>% filter(Species != Coalesced_Species) %>% nrow()` which changed names after taxonomic resolution.
+<br>
+Attach synonyms to the bottom of xylem data to maximize matching with sPlot. After standardization, some species have multiple entries for traits. Calculate species level means
+```{r, warning=F, message=F}
+xylem_data <- xylem_data %>% 
+  dplyr::select(Coalesced_Species, Group, P50, Ks) %>% 
+  rename(Species=Coalesced_Species) %>% 
+  bind_rows(xylem_data %>% 
+              filter(Species != Coalesced_Species) %>% 
+              dplyr::select(Species:Ks)) %>% 
+  distinct(Species, .keep_all=T) %>% 
+  group_by(Species) %>% 
+  summarize_at(.vars=vars(P50, Ks), 
+               .funs=list(~mean(., na.rm=T))) %>% 
+  ungroup()
+```
+
 # 1 Extract plots from sPlot based on species with xylem traits  
 Extract all plots containing at least one species in the xylem list.
 ```{r, message=F, results=F, warning=F}
@@ -150,15 +197,16 @@ combine.cover <- function(x){
 }
 
 DT.xylem <- DT.xylem %>%
-  dplyr::select(PlotObservationID, Species,Layer, Relative_cover) %>%
-  # normalize relative cover to 1 for each plot x layer combination
-  left_join({.} %>% 
-              group_by(PlotObservationID, Layer) %>% 
-              summarize(Tot.Cover=sum(Relative_cover), .groups="drop"), 
-            by=c("PlotObservationID", "Layer")) %>% 
-  mutate(Relative_cover=Relative_cover/Tot.Cover) %>% 
-  group_by(PlotObservationID, Species) %>%
-  # merge layers together
+  dplyr::select(PlotObservationID, Species, Species_original, Layer, Relative_cover) %>%
+#  # normalize relative cover to 1 for each plot x layer combination
+#  # Commented in version 1.3
+#  left_join({.} %>% 
+#              group_by(PlotObservationID, Layer) %>% 
+#              summarize(Tot.Cover=sum(Relative_cover), .groups="drop"), 
+#            by=c("PlotObservationID", "Layer")) %>% 
+#  mutate(Relative_cover=Relative_cover/Tot.Cover) %>% 
+  group_by(PlotObservationID, Species, Species_original) %>%
+#  # merge layers together
   summarize(Relative_cover=combine.cover(Relative_cover), .groups="drop") %>%
   ungroup() %>% 
   # normalize relative cover to 1 after merging layers together
@@ -186,7 +234,7 @@ Calculate CWM and trait coverage for each trait and each plot. ~~Select plots ha
 # Merge species data table with traits
 CWM.xylem0 <- DT.xylem %>%
   as_tibble() %>%
-  dplyr::select(PlotObservationID, Species, Relative_cover) %>%
+  dplyr::select(PlotObservationID, Species, Species_original, Relative_cover) %>%
   left_join(xylem_data %>%
               dplyr::select(Species, P50, Ks), 
             by="Species")