1.1. Shapefile

Let’s assume we are interested in having a floristic knowledge of thewestern part of the Mediterranean basin. For this purpose, we can simplyuse a shape file of the region of interest and feed it to theGIFT_checklists() function.

We do provide a shape file of this region in theGIFT Rpackage, which you can access using thedata("western_mediterranean") command.

data("western_mediterranean")world<-ne_coastline(scale ="medium",returnclass ="sf")world_countries<-ne_countries(scale ="medium",returnclass ="sf")# Fixing polygons crossing datelineworld<-st_wrap_dateline(world)world_countries<-st_wrap_dateline(world_countries)# Eckert IV projectioneckertIV<-"+proj=eck4 +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs"ggplot(world)+geom_sf(color ="gray50")+geom_sf(data = western_mediterranean,fill ="darkblue",color ="black",alpha =0.5,size =1)+labs(title ="Western Mediterranean basin")+lims(x =c(-20,20),y =c(24,48))+theme_void()

Please note that shapes used in GIFT are unprojected(Geographic CoordinateSystem WGS84), and that all shapefilesprovided should be in this CRS.You can check the coordinatereference system of a sf object by usingsf::st_crs().

1.2. Main arguments

Now that we have a shape for the region of interest, let’s callGIFT_checklists(). This wrapper function has manyarguments, which we detail in this subsection.
First, the taxonomicgroup of interest. We may be interested in a particular group of plants,say only Angiosperms. In this case, we would set thetaxon_name argument like thistaxon_name = "Angiospermae". If we are interested in aparticular family of plants, let’s say orchids, thentaxon_name = "Orchidaceae".
To see all the options forthetaxon_name argument, you can run theGIFT_taxonomy() function and look at thetaxon_name column of its output.
Along with this firstargument comescomplete_taxon. This argument, set to TRUEby default, determines whether only regions represented by checklists inGIFT that completely coverthe taxon of interest shouldbe retrieved. Figure 1 illustrates the principle.

In Figure 1, we want to retrieve checklists of Angiosperms. Inthe first available region, region A, only one checklist is of interest.This checklist is then always retrieved. In region B, there is only onechecklist of orchids, which is only a subset of Angiosperms. Ifcomplete_taxon is set toTRUE, then thischecklist won’t be retrieved, otherwise yes. Finally, in region C, thereis a checklist for vascular plants and one for orchids. In both cases,the checklist of vascular plants will be retrieved after filtering outthe non-angiosperm species. The checklist of Orchids is also retrievedin both cases because it is not the only one available and because itcan complete the floristic knowledge for Angiosperms in this region.

The following arguments ofGIFT_checklists()refer to the floristic status of plant species. For example, we may beinterested only in endemic or naturalized species. The default value isto get all native species.
Similarly, two arguments are needed inthe function. First,floristic_group defines the group ofinterest. Second,complete_floristic indicates whether ornot to retrieve incomplete regions with respect to the selectedfloristic group. The logic is detailed in Figure 2 and is similar to thecomplete_taxon argument shown above

The next set of arguments relate to the spatial match between thedesired area and the GIFT database.

The main argument in this regard, when providing a shapefile or a setof coordinates, is theoverlap argument. This argument cantake 4 options, each of which produces different result, as shown inFigure 3.

In Figure3, the GIFT polygons shown in orange either intersect, fallinside or outside the provided shape file. The overlap argument beloweach GIFT polygon illustrates in which situation a given GIFT polygonwill or will not be retrieved.

Another important spatial feature we provide is the possibility toremove overlapping polygons. In fact, for many regions of the world,there are several polygons in the GIFT database that cover them. Ifoverlapping polygons are not an issue for your case study, you cansimply setremove_overlap to FALSE (top right part ofFigure 4). However, if you want to have only one polygon per region, youcan setremove_overlap toTRUE. In this case,theGIFT_checklists() will either retrieve the smaller orthe larger polygon. This depends on the values set for thearea_threshold_mainland argument as shown in Figure 4.

area_threshold_mainland takes a value in\(km^2\). If the area of the smaller polygonis less than the threshold, then the larger overlapping polygon isretrieved (lower left part in Figure 4). If the smaller polygon exceedsthe threshold, then it is retrieved (lower right part of Figure 4).There is a similar argument for islands,area_threshold_island, which is set to 0\(km^2\) by default. This way the smallerislands are always retrieved by default.

Note also thatpolygons are considered to overlap if they exceed a certain percentageof overlap. This percentage can be modified using theoverlap_threshold argument (Figure 5). This argument is setby default to 10%.

1.3. GIFT_checklists()

Now that we have covered the main arguments ofGIFT_checklists(), we can retrieve plant checklists for theMediterranean region.GIFT_checklists() returns a list withtwo elements. First the metadata of the checklists matching thedifferent criteria, named$lists. The second element is adata.frame of all the checklists with the speciescomposition per checklist ($checklists).
If you onlywant to retrieve the metadata, you can set thelist_set_only argument toTRUE.

ex_meta<-GIFT_checklists(taxon_name ="Angiospermae",shp = western_mediterranean,overlap ="centroid_inside",list_set_only =TRUE)

And to retrieve the species composition:

medit<-GIFT_checklists(taxon_name ="Angiospermae",complete_taxon =TRUE,floristic_group ="native",complete_floristic =TRUE,geo_type ="All",shp = western_mediterranean,overlap ="centroid_inside",remove_overlap =FALSE,taxonomic_group =TRUE)# this argument adds two# columns to the checklist: plant family and taxonomic group of each species

We can now have an estimation on the number of checklists with nativeAngiosperm species in the western part of the Mediterranean basin, aswell as of the number of species.

# Number of references coveredlength(unique(medit[[2]]$ref_ID))#   22 references# Number of checklists covered (one reference can have several lists inside)length(unique(medit[[2]]$list_ID))#   115 checklists# Number of specieslength(unique(medit[[2]]$work_species))#   12840 plant species

You can now apply different values for the arguments detailedabove. As you can see, the number of checklists retrieved decreases asyou become stricter on some criteria. For example, when removingoverlapping regions:

medit_no_overlap<-GIFT_checklists(shp = western_mediterranean,overlap ="centroid_inside",taxon_name ="Angiospermae",remove_overlap =TRUE)# Number of references coveredlength(unique(medit[[2]]$ref_ID))# 23 referenceslength(unique(medit_no_overlap[[2]]$ref_ID))# 22 references

Note that the function not only works with a shape file but canaccept a set of coordinates. The example below illustrates a case whereyou want to retrieve GIFT checklists that intersect the coordinates ofGöttingen.

custom_point<-cbind(9.9,51)# coordinates of Göttingengot<-GIFT_checklists(coordinates = custom_point,overlap ="extent_intersect",taxon_name ="Angiospermae",remove_overlap =TRUE,list_set_only =TRUE)

To cite properly the references retrieved, you can run the functionGIFT_references() and look for the columnref_long. The columngeo_entity_ref associateseach reference to a name.

1.4. Species richness map

Once we have downloaded a set of checklists, it is possible to mapthe species richness of the taxonomic group of interest. To do this, weuse a combination of two functions:GIFT_richness() whichreturns either species richness or trait coverage per polygon, andGIFT_shapes() which returns the shapefile of a list of GIFTpolygons.
The next two chunks illustrate this for the Angiospermsin the World and in the Western part of the Mediterranean basin.

gift_shapes<-GIFT_shapes()# retrieves all shapefiles by default

angio_rich<-GIFT_richness(taxon_name ="Angiospermae")rich_map<- dplyr::left_join(gift_shapes, angio_rich,by ="entity_ID")%>%  dplyr::filter(stats::complete.cases(total))ggplot(world)+geom_sf(color ="gray50")+geom_sf(data = rich_map,aes(fill = total+1))+scale_fill_viridis_c("Species number\n(log-transformed)",trans ="log10",labels = scales::number_format(accuracy =1))+labs(title ="Angiosperms",subtitle ="Projection EckertIV")+coord_sf(crs = eckertIV)+theme_void()

By customizing the code above, you can also produce a nicermap:

Below is theR code to produce the above map ifinterested.

# Background boxxmin<-st_bbox(world)[["xmin"]]; xmax<-st_bbox(world)[["xmax"]]ymin<-st_bbox(world)[["ymin"]]; ymax<-st_bbox(world)[["ymax"]]bb<- sf::st_union(sf::st_make_grid(st_bbox(c(xmin = xmin,xmax = xmax,ymax = ymax,ymin = ymin),crs =st_crs(4326)),n =100))# Equator lineequator<-st_linestring(matrix(c(-180,0,180,0),ncol =2,byrow =TRUE))equator<-st_sfc(equator,crs =st_crs(world))# Color code from Barthlott 2007hexcode_barthlott2007<-c("#fbf9ed","#f3efcc","#f6e39e","#cbe784","#65c66a","#0e8d4a","#4a6fbf","#b877c2","#f24dae","#ed1c24")ggplot(world)+geom_sf(data = bb,fill ="aliceblue")+geom_sf(data = equator,color ="gray50",linetype ="dashed",linewidth =0.1)+geom_sf(data = world_countries,fill ="antiquewhite1",color =NA)+geom_sf(color ="gray50",linewidth =0.1)+geom_sf(data = bb,fill =NA)+geom_sf(data = rich_map,aes(fill =ifelse(rich_map$entity_class%in%c("Island/Mainland","Mainland","Island Group","Island Part"),                            total+1,NA)),size =0.1)+geom_point(data = rich_map,aes(color =ifelse(rich_map$entity_class%in%c("Island"),                                total+1,NA),geometry = geometry),stat ="sf_coordinates",size =1,stroke =0.5)+scale_color_gradientn("Species number",trans ="log10",limits =c(1,40000),colours = hexcode_barthlott2007,breaks =c(1,10,100,1000,10000,40000),labels =c(1,10,100,1000,10000,40000),na.value ="transparent")+scale_fill_gradientn("Species number",trans ="log10",limits =c(1,40000),colours = hexcode_barthlott2007,breaks =c(1,10,100,1000,10000,40000),labels =c(1,10,100,1000,10000,40000),na.value ="transparent")+labs(title ="Angiosperms",subtitle ="Projection EckertIV")+coord_sf(crs = eckertIV)+theme_void()

We can also produce maps of richness at intermediate scales. Here isthe code and the map of Angiosperms in the Western Mediterraneanbasin.

med_shape<- gift_shapes[which(gift_shapes$entity_ID%in%unique(medit[[2]]$entity_ID)), ]med_rich<- angio_rich[which(angio_rich$entity_ID%in%unique(medit[[2]]$entity_ID)), ]med_map<- dplyr::left_join(med_shape, med_rich,by ="entity_ID")%>%  dplyr::filter(stats::complete.cases(total))ggplot(world)+geom_sf(color ="gray50")+geom_sf(data = western_mediterranean,fill ="darkblue",color ="black",alpha =0.1,size =1)+geom_sf(data = med_map,aes(fill = total))+scale_fill_viridis_c("Species number")+labs(title ="Angiosperms in the Western Mediterranean basin")+lims(x =c(-20,20),y =c(24,48))+theme_void()

2.1. Available species

To know what plant species are available, you can first run thefunctionGIFT_species().

all_sp<-GIFT_species()

364571 species are currently available in the database. This numbermay increase with new releases of the database. See thededicatedsection in the advanced vignettes for more details.

2.2. Species names and taxonomic harmonization

Since GIFT is a collection of checklists in which authors use theirown taxonomic knowledge to describe species, there is a step oftaxonomic harmonization when including checklists in the database. Themost commonly used backbone is theWorld Checklists of Vascular Plants(WCVP).
Both original and harmonized names are stored in thedatabase and you can use theGIFT_species_lookup() functionto look up the differences for particular species. For example, the woodanemoneAnemonoides nemorosa.

anemone_lookup<-GIFT_species_lookup(genus ="Anemonoides",epithet ="nemorosa")kable(anemone_lookup,"html")%>%kable_styling(full_width =FALSE)

Looking at the output table, you can see the original species namesand their identification numbers (name_ID)before taxonomic harmonization. The species names andIDsafter taxonomic harmonization are the last columnson the right starting with the prefixwork_.

2.3. Species distribution

Now that we have a focal species and its harmonized name, we canretrieve its distribution usingGIFT_species_distribution().
Note that here we set theaggregation argument toTRUE in order to haveonly one floristic status per polygon. See the function’s help page formore details.

anemone_distr<-GIFT_species_distribution(genus ="Anemonoides",epithet ="nemorosa",aggregation =TRUE)anemone_statuses<- anemone_distr%>%mutate(native =ifelse(native==1,"native","non-native"),naturalized =ifelse(naturalized==1,"naturalized","non-naturalized"),endemic_list =ifelse(endemic_list==1,"endemic_list","non-endemic_list"))%>%  dplyr::select(entity_ID, native, naturalized, endemic_list)table(anemone_statuses$endemic_list)

## ## non-endemic_list ##               46

This species is not listed as endemic in any of the GIFT polygons.Let’s check the places where it is listed as native or naturalized.

table(paste(anemone_statuses$native, anemone_statuses$naturalized,sep ="_"))

## ##                      NA_NA                  native_NA ##                          6                         12 ##     native_non-naturalized              non-native_NA ##                         99                          1 ##     non-native_naturalized non-native_non-naturalized ##                          4                          2

Looking at the different combinations of statuses, we can distinguishseveral situations: in 13 polygons, there is no status available. Thespecies is listed as native and non-naturalized (or naturalized statusis missing) in 99+12=111 polygons. It is naturalized and non native in 5polygons.
More surprising are the cases where the species isnon-native and non-naturalized, which happens in 2 polygons. Thisparticular combination can occur in unstable cases where the species isin the process of becoming naturalized.

Now that we know in which polygons the species occurs and with whatstatus, we can map its distribution using the GIFT shapes we retrievedearlier withGIFT_shapes().

# We rename the statuses based on the distinct combinationsanemone_statuses<- anemone_statuses%>%mutate(Status =case_when(    native=="native"& naturalized=="non-naturalized"~"native",    native=="native"&is.na(naturalized)~"native",    native=="non-native"&is.na(naturalized)~"non-native",    native=="non-native"& naturalized=="naturalized"~"naturalized",    native=="non-native"& naturalized=="non-naturalized"~"non-native",is.na(native)&is.na(naturalized)~"unknown"  ))# Merge with the shapesanemone_shape<- gift_shapes[which(gift_shapes$entity_ID%in%unique(anemone_distr$entity_ID)), ]anemone_map<- dplyr::left_join(anemone_shape, anemone_statuses,by ="entity_ID")# Area of distribution with floristic statusggplot(world)+geom_sf(color ="gray70")+geom_sf(data = anemone_map,color ="black",aes(fill =as.factor(Status)))+scale_fill_brewer("Status",palette ="Set2")+labs(title =expression(paste("Distribution map of ",italic("Anemonoides nemorosa"))),subtitle ="Unprojected (GCS: WGS84)")+lims(x =c(-65,170),y =c(-45,70))+theme_void()

By customizing the code above, you can also produce a nicermap:

Below is theR code to produce the above map ifinterested.

anemone_map_plot_bg_parts<-ggplot(world)+geom_sf(data = bb,fill ="aliceblue",color =NA)+geom_sf(data = equator,color ="gray50",linetype ="dashed",linewidth =0.1)+geom_sf(data = world_countries,fill ="antiquewhite1",color =NA)+geom_sf(color ="gray50",linewidth =0.1)+geom_sf(data = bb,fill =NA)+geom_sf(data = anemone_map,color ="black",aes(fill =as.factor(Status)))+scale_fill_manual("Status",values =c("native"="#2c7bb6","naturalized"="#d7191c","non-native"="#fdae61","unknown"="#abd9e9"))+labs(title =expression(paste("b) Distribution map of ",italic("Anemonoides nemorosa"))))+theme_void()+theme(axis.title =element_blank(),axis.text =element_blank(),axis.ticks =element_blank())(anemone_map_plot_bg_parts+lims(x =c(-69,61),y =c(37,70))+# Europe & Newfoundlandtheme(panel.border =element_rect(fill =NA,linewidth =1))+theme(legend.position ="bottom")|    anemone_map_plot_bg_parts+lims(x =c(165,178),y =c(-47,-35))+# New Zealandlabs(title ="")+guides(fill ="none")+theme(panel.border =element_rect(fill =NA,linewidth =1)))

3.1. Metadata

There are many functional traits available in GIFT. Each ofthese traits has an identification number calledtrait_ID.Since the two functions for retrieving trait values,GIFT_traits() andGIFT_traits_raw(), rely onthese IDs, the first step is to call the functionGIFT_traits_meta() to know what the ID of the desired traitis.
For example, let’s say we want to retrieve the maximumvegetative heights of plant species.

trait_meta<-GIFT_traits_meta()trait_meta[which(trait_meta$Trait2=="Plant_height_max"), ]

##    Lvl1   Category Lvl2       Trait1  Lvl3           Trait2 Units    type## 12    1 Morphology  1.6 Plant height 1.6.2 Plant_height_max     m numeric##    comment count## 12    <NA> 70367

We can see that the ID of this trait is1.6.2. Nowthat we have the ID, we can useGIFT_traits() to retrievethe growth form values for different plant species.

3.2. Trait values

height<-GIFT_traits(trait_IDs =c("1.6.2"),agreement =0.66,bias_ref =FALSE,bias_deriv =FALSE)height_raw<-GIFT_traits_raw(trait_IDs =c("1.6.2"))# Raw valuesas.numeric(height_raw[which(height_raw$work_species=="Fagus sylvatica"),"trait_value"])# Aggregated valueas.numeric(height[which(height$work_species=="Fagus sylvatica"),"trait_value_1.6.2"])

references<-GIFT_references(GIFT_version ="beta")unique(unlist(strsplit(height$references_1.6.2,",")))references<- references[which(references$ref_ID%in%unique(unlist(strsplit(height$references_1.6.2,",")))), ]references[1:2, ]

trait_tax<-GIFT_traits_tax(trait_IDs =c("1.1.1","1.2.1","1.4.1"),bias_ref =FALSE,bias_deriv =FALSE)trait_tax[1:3, ]

3.3. Trait coverage

We can also retrieve trait coverage information for polygons, usingthe same function as for species richnessGIFT_coverage().
In combination with the previously loaded shapes, we can also mapthe trait coverage.

angio_height<-GIFT_coverage(what ="trait_coverage",taxon_name ="Angiospermae",trait_ID ="1.6.2")angio_height_shape<- gift_shapes[which(gift_shapes$entity_ID%in%unique(angio_height$entity_ID)), ]angio_height_map<- dplyr::left_join(  angio_height_shape, angio_height,by ="entity_ID")angio_height_map<- angio_height_map[complete.cases(angio_height_map$native), ]ggplot(world)+geom_sf(color ="gray50")+geom_sf(data = angio_height_map[complete.cases(angio_height_map$native), ],aes(fill = native))+scale_fill_viridis_c("Coverage (%)")+labs(title ="Coverage for maximal vegetative height of Angiosperms",subtitle ="Projection EckertIV")+coord_sf(crs = eckertIV)+theme_void()

By customizing the code above, you can also produce a nicermap:

Below is theR code to produce the above map ifinterested.

ggplot(world)+geom_sf(data = bb,fill ="aliceblue")+geom_sf(data = equator,color ="gray50",linetype ="dashed",linewidth =0.1)+geom_sf(data = world_countries,fill ="antiquewhite1",color =NA)+geom_sf(color ="gray50",linewidth =0.1)+geom_sf(data = bb,fill =NA)+geom_sf(data = angio_height_map,aes(fill =ifelse(angio_height_map$entity_class%in%c("Island/Mainland","Mainland","Island Group","Island Part"),100*native,NA)),size =0.1)+geom_point(data = angio_height_map,aes(color =ifelse(angio_height_map$entity_class%in%c("Island"),100*native,NA),geometry = geometry),stat ="sf_coordinates",size =1,stroke =0.5)+scale_color_gradientn("Coverage (%)",colours =rev(RColorBrewer::brewer.pal(9,name ="PuBuGn")),limits =c(0,100),na.value ="transparent")+scale_fill_gradientn("Coverage (%)",colours =rev(RColorBrewer::brewer.pal(9,name ="PuBuGn")),limits =c(0,100),na.value ="transparent")+labs(title ="Coverage for maximal vegetative height of Angiosperms",subtitle ="Projection EckertIV")+coord_sf(crs = eckertIV)+theme_void()

4.1. Metadata

We here illustrate how to retrieve environmental variables,summarized at the polygon level, for a subset of polygons.
We hereretrieve environmental variables for the polygons falling into thewestern Mediterranean basin, retrieved in the section 1.
To knowwhat variables are available, you can run these two metadata functions:GIFT_env_meta_misc() andGIFT_env_meta_raster(). They respectively give access tothe list of miscellaneous variables and raster layers available in theGIFT database.
The references to cite when using environmentalvariables are also available through these functions (columnref_long of the outputs).

misc_env<-GIFT_env_meta_misc()raster_env<-GIFT_env_meta_raster()

4.2. Environmental values

Let’s say we want to retrieve the perimeter and biome of each polygonas well as elevation and mean temperature. For these two raster layers,we need to define summary statistics, since the polygons are usuallylarger than the raster resolution.
There are many summarystatistics available, you can check the help page ofGIFT_env() to see them all. Let’s get the mean and medianof the elevation and the maximal value of the average temperature.

med_env<-GIFT_env(entity_ID =unique(medit[[2]]$entity_ID),miscellaneous =c("perimeter","biome"),rasterlayer =c("mn30_grd","wc2.0_bio_30s_01"),sumstat =list(c("mean","med"),"max"))med_env[1, ]

We see here that the region of El Hierro has an average altitude of579 meters above sea level and an average annual temperature of 21.6degrees Celsius.

4.3. Map

Using the previously loaded shapes, we can also map a specificenvironmental variable for all GIFT polygons.

world_temp<-GIFT_env(entity_ID =unique(angio_rich$entity_ID),rasterlayer =c("wc2.0_bio_30s_01"),sumstat =c("mean"))temp_shape<- gift_shapes[which(gift_shapes$entity_ID%in%unique(angio_rich$entity_ID)), ]temp_map<- dplyr::left_join(temp_shape, world_temp,by ="entity_ID")ggplot(world)+geom_sf(color ="gray50")+geom_sf(data = temp_map,aes(fill = mean_wc2.0_bio_30s_01))+scale_fill_viridis_c("Celsius degrees")+labs(title ="Average temperature",subtitle ="Projection EckertIV")+coord_sf(crs = eckertIV)+theme_void()

By customizing the code above, you can also produce a nicermap:

Below is theR code to produce the above map ifinterested.

ggplot(world)+geom_sf(data = bb,fill ="aliceblue")+geom_sf(data = equator,color ="gray50",linetype ="dashed",linewidth =0.1)+geom_sf(data = world_countries,fill ="antiquewhite1",color =NA)+geom_sf(color ="gray50",linewidth =0.1)+geom_sf(data = bb,fill =NA)+geom_sf(data = temp_map,aes(fill =ifelse(temp_map$entity_class%in%c("Island/Mainland","Mainland","Island Group","Island Part"),                            mean_wc2.0_bio_30s_01,NA)),size =0.1)+geom_point(data = temp_map,aes(color =ifelse(temp_map$entity_class%in%c("Island"),                                mean_wc2.0_bio_30s_01,NA),geometry = geometry),stat ="sf_coordinates",size =1,stroke =0.5)+scale_color_gradientn("°C",colours = RColorBrewer::brewer.pal(9,name ="Reds"),limits =c(-20,30),na.value ="transparent")+scale_fill_gradientn("°C",colours = RColorBrewer::brewer.pal(9,name ="Reds"),limits =c(-20,30),na.value ="transparent")+labs(title ="Average temperature",subtitle ="Projection EckertIV")+coord_sf(crs = eckertIV)+theme_void()

# Retrieving phylogeny, taxonomy and species from GIFTphy<-GIFT_phylogeny(clade ="Tracheophyta",GIFT_version ="beta")tax<-GIFT_taxonomy(GIFT_version ="beta")gift_sp<-GIFT_species(GIFT_version ="beta")gf<-GIFT_traits(trait_IDs ="1.2.1",agreement =0.66,bias_ref =FALSE,bias_deriv =FALSE,GIFT_version ="beta")

# Replacing space with _ for the species namesgf$work_species<-gsub(" ","_", gf$work_species,fixed =TRUE)

# Retrieving family of each speciessp_fam<-GIFT_taxgroup(work_ID =unique(gift_sp$work_ID),taxon_lvl ="family",GIFT_version ="beta")sp_genus_fam<-data.frame(work_ID =unique(gift_sp$work_ID),work_species =unique(gift_sp$work_species),family = sp_fam)sp_genus_fam<-left_join(sp_genus_fam,                          gift_sp[,c("work_ID","work_genus")],by ="work_ID")colnames(sp_genus_fam)[colnames(sp_genus_fam)=="work_genus"]<-"genus"# Problem with hybrid species on the tip labels of the phylo treephy$tip.label[substring(phy$tip.label,1,2)=="x_"]<-substring(phy$tip.label[substring(phy$tip.label,1,2)=="x_"],3,nchar(phy$tip.label[substring(phy$tip.label,1,2)=="×_"]))phy$tip.label[substring(phy$tip.label,1,2)=="×_"]<-substring(phy$tip.label[substring(phy$tip.label,1,2)=="×_"],3,nchar(phy$tip.label[substring(phy$tip.label,1,2)=="×_"]))

sp_genus_fam<-left_join(sp_genus_fam,                          gf[,c("work_ID","trait_value_1.2.1")],by ="work_ID")genus_gf<- sp_genus_fam%>%group_by(genus)%>%mutate(prop_gf =round(100*sum(is.na(trait_value_1.2.1))/n(),2))%>%ungroup()%>%  dplyr::select(-work_ID,-work_species,-family,-trait_value_1.2.1)%>%distinct(.keep_all =TRUE)fam_gf<- sp_genus_fam%>%group_by(family)%>%mutate(prop_gf =round(100*sum(is.na(trait_value_1.2.1))/n(),2))%>%ungroup()%>%  dplyr::select(-work_ID,-work_species,-genus,-trait_value_1.2.1)%>%distinct(.keep_all =TRUE)sp_genus_fam$species<-gsub("([[:punct:]])|\\s+","_",                             sp_genus_fam$work_species)# Keeping one species per genus onlyone_sp_per_gen<-data.frame()for(iin1:n_distinct(sp_genus_fam$genus)){# loop over genera# Focal genus  focal_gen<-unique(sp_genus_fam$genus)[i]# All species in that genus  gen_sp_i<- sp_genus_fam[which(sp_genus_fam$genus== focal_gen),"species"]# Species from the genus available in the phylogeny  gen_sp_i<- gen_sp_i[gen_sp_i%in% phy$tip.label]# Taking the first one (if at least one is available)  gen_sp_i<- gen_sp_i[1]  one_sp_per_gen<-rbind(one_sp_per_gen,data.frame(species = gen_sp_i,genus = focal_gen))}# Adding the trait coverage per genusone_sp_per_gen<-left_join(one_sp_per_gen, genus_gf,by ="genus")# Adding the trait coverage per familyone_sp_per_gen<-left_join(one_sp_per_gen,                            sp_genus_fam[!duplicated(sp_genus_fam$genus),c("genus","family")],by ="genus")colnames(one_sp_per_gen)[colnames(one_sp_per_gen)=="prop_gf"]<-"prop_gf_gen"one_sp_per_gen<-left_join(one_sp_per_gen, fam_gf,by ="family")colnames(one_sp_per_gen)[colnames(one_sp_per_gen)=="prop_gf"]<-"prop_gf_fam"

phy_gen<- ape::keep.tip(phy = phy,tip = one_sp_per_gen[complete.cases(one_sp_per_gen$species),"species"])

library("BiocManager")install("ggtree")library("ggtree")library("tidytree")install("ggtreeExtra")library("ggtreeExtra")

ggtree(phy_gen,color ="grey70",layout ="circular")%<+% one_sp_per_gen+geom_fruit(geom = geom_tile,mapping =aes(fill = prop_gf_gen),width =50,offset =0.1)+geom_fruit(geom = geom_tile,mapping =aes(color = prop_gf_fam,fill = prop_gf_fam),width =50,offset =0.1,show.legend =FALSE)+scale_color_viridis_c()+scale_fill_viridis_c("Growth form availability per genus (%)")+theme(legend.position ="bottom")