# HG changeset patch # User ecology # Date 1711029901 0 # Node ID 5cde56683579d65d1cee5d7bdde09ec016002bce planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/Ecoregionalization_workflow commit 5d48df67919fbc9d77b98a8243d438c397f61a0e diff -r 000000000000 -r 5cde56683579 GeoNN.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/GeoNN.R Thu Mar 21 14:05:01 2024 +0000 @@ -0,0 +1,77 @@ +#Date : 09/02/2024 +#Author : Seguineau Pauline + +#Load libraries +library(tidyr) +library(dplyr) +library(sf) + +#load arguments +args = commandArgs(trailingOnly=TRUE) +if (length(args)==0) +{ + stop("This tool needs at least one argument") +}else{ + enviro <- args[1] + envlong <- as.numeric(args[2]) + envlat <- as.numeric(args[3]) + occu <- args[4] + occulat <- as.numeric(args[5]) + occulong <- as.numeric(args[6]) +} + +env = read.table(enviro, header = TRUE, sep="\t") +occ = read.table(occu, header = TRUE, sep = "\t") + +cols_env = c(names(env[envlong]),names(env[envlat])) +cols_occ = c(names(occ[occulong]),names(occ[occulat])) + +###calculate distances### +#transform tables into sf object + +env_sf <- st_as_sf(env, coords = cols_env, crs = "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs") +occ_sf <- st_as_sf(occ, coords = cols_occ, crs = "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs") + +#Find the indices of env_sf entities closest to each point in occ_sf. + +nearest_indices <- st_nearest_feature(occ_sf, env_sf) + +nearest_points <- env[nearest_indices, ] + +# Calculate distances between env_sf and occ_sf points +distances <- st_distance(env_sf, occ_sf) + +#Extract the corresponding distances between occ and env + +nearest_distances <- numeric(length(nearest_indices)) + +for (i in 1:length(nearest_indices)) { + nearest_distances[i] <- st_distance(env_sf[nearest_indices[i],], occ_sf[i,]) +} + +#assemble occurrences and environmental parameters in the same file + +nearest_points <- nearest_points[, !names(nearest_points) %in% cols_env] #remove lat and long from env to clean data +new_occ = cbind(occ, nearest_points) + +#Save the file + +write.table(new_occ, file = "occurrence_env.tsv",sep ="\t",quote = F, row.names = F,col.names = T) + +#create an information file with the distances between the points of the two files + +distance_info <- data.frame( + occ_geometry = occ_sf$geometry, + env_geometry = env_sf$geometry[nearest_indices], + distance = nearest_distances +) + +colnames(distance_info)[1] <- "occ_geometry" +colnames(distance_info)[2] <- "env_geometry" +colnames(distance_info)[3] <- "Distances (meters)" +#save the information file + +write.table(distance_info, file = "infos_file.tsv",sep ="\t",quote = F, row.names = F,col.names = T) + + + diff -r 000000000000 -r 5cde56683579 GeoNearestNeighbor.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/GeoNearestNeighbor.xml Thu Mar 21 14:05:01 2024 +0000 @@ -0,0 +1,135 @@ + + Merge two data tables by finding the closest points based on latitude and longitude coordinates (WGS84 projection). + + r-base + r-tidyr + r-sf + r-dplyr + + + +
+ + + +
+
+ + + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + +
diff -r 000000000000 -r 5cde56683579 brt.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/brt.R Thu Mar 21 14:05:01 2024 +0000 @@ -0,0 +1,121 @@ +#16/02/2023 +## Analyse BRT data + +### Clean environment +rm(list = ls(all.names = TRUE)) +options(warn=-1) + +### load packages + +library(dismo, warn.conflicts = FALSE) +library(gbm, warn.conflicts = FALSE) +library(ggplot2, warn.conflicts = FALSE) + + +#load arguments +args = commandArgs(trailingOnly=TRUE) +if (length(args)==0) +{ + stop("This tool needs at least one argument") +}else{ + enviro <- args[1] + species_files <- args[2] + abio_para <- args[3] + dec_env <- args[8] + dec_species <- args[9] +} + +### load data + +env = read.table(enviro, dec = dec_env, header = TRUE, sep="\t", na.strings = "-9999") +pred_vars = strsplit(abio_para, ",")[[1]] +data_files = strsplit(species_files,",") + +pred.vars <- character(length(pred_vars)) + +for (i in seq_along(pred_vars)) { + pred_var_col <- as.numeric(pred_vars[i]) + pred.vars[i] <- names(env)[pred_var_col]} + +#environemental parameters +#Carbo,Grav,Maxbearing,Maxmagnit,Meancurmag,Meansal,Meantheta,Mud,Prof,Rugosity,Sand,Seaice_prod,Sili,Slope,Standcurmag,Standsal,Standtheta + +#Load functions + +make.brt <- function(spe,data,pred.vars,env,nb_file){ + brt_step <- gbm.step(data= data, gbm.x = pred.vars, gbm.y = spe, family = "bernoulli", tree.complexity = 2, learning.rate = 0.0001,max.trees = 10000,plot.main = F) + #plot + if (is.null(brt_step)==FALSE){ + pdf(file = paste("BRT-",spe,".pdf")) + gbm.plot(brt_step, write.title = T,show.contrib = T, y.label = "fitted function",plot.layout = c(3,3)) + dev.off() + #total deviance explained as (Leathwick et al., 2006) + total_deviance <- brt_step$self.statistics$mean.null + cross_validated_residual_deviance <- brt_step$cv.statistics$deviance.mean + total_deviance_explained <- (total_deviance - cross_validated_residual_deviance)/total_deviance + #Validation file + valid = cbind(spe,brt_step$cv.statistics$discrimination.mean,brt_step$gbm.call$tree.complexity,total_deviance_explained) + write.table(valid, paste(nb_file,"_brts_validation_ceamarc.tsv",sep=""), quote=FALSE, dec=".",sep="\t" ,row.names=F, col.names=F,append = T)} + + return(brt_step) + } + +make.prediction.brt <- function(brt_step){ + #predictions + preds <- predict.gbm(brt_step,env,n.trees=brt_step$gbm.call$best.trees, type="response") + preds <- as.data.frame(cbind(env$lat,env$long,preds)) + colnames(preds) <- c("lat","long","Prediction.index") + #carto + ggplot()+ + geom_raster(data = preds , aes(x = long, y = lat, fill = Prediction.index))+ + geom_raster(data = preds , aes(x = long, y = lat, alpha = Prediction.index))+ + scale_alpha(range = c(0,1), guide = "none")+ + scale_fill_viridis_c( + alpha = 1, + begin = 0, + end = 1, + direction = -1, + option = "D", + values = NULL, + space = "Lab", + na.value = "grey50", + guide = "colourbar", + aesthetics = "fill")+ + xlab("Longitude") + ylab("Latitude")+ ggtitle(paste(spe,"Plot of BRT predictions"))+ + theme(plot.title = element_text(size = 10))+ + theme(axis.title.y = element_text(size = 10))+ + theme(axis.title.x = element_text(size = 10))+ + theme(axis.text.y = element_text(size = 10))+ + theme(axis.text.x = element_text(size = 10))+ + theme(legend.text = element_text(size = 10))+ + theme(legend.title = element_text(size = 10))+ + coord_quickmap() + output_directory <- ggsave(paste("BRT-",spe,"_pred_plot.png")) + + #Write prediction in a file + preds <- cbind(preds,spe) + write.table(preds, paste(nb_file,"_brts_pred_ceamarc.tsv",sep=""), quote=FALSE, dec=".", row.names=F, col.names=T,append = T,sep="\t") +} + +#### RUN BRT #### +nb_file = 0 + +for (file in data_files[[1]]) { + species_data <- read.table(file, dec = dec_species, sep = "\t", header = TRUE, na.strings = "NA", colClasses = "numeric") + nb_file = nb_file + 1 + `%!in%` <- Negate(`%in%`) + sp = list() + for (n in names(species_data)) { + if (n %!in% names(env) && n != 'station'){ + sp = cbind(sp,n) + } + } + for (spe in sp){ + try(make.prediction.brt(make.brt(spe,species_data,pred.vars,env,nb_file))) + } +} + +cat("Here is the list of your abiotic parameters:\n") +cat(paste(pred.vars, collapse = ", "), "\n") + + diff -r 000000000000 -r 5cde56683579 cluster_ceamarc.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/cluster_ceamarc.R Thu Mar 21 14:05:01 2024 +0000 @@ -0,0 +1,52 @@ +##13/04/2023 +##Seguineau Pauline +### Clustering with Clara algorithm + +#load library +library(cluster) +library(dplyr) +library(tidyverse) + +#load arguments +args = commandArgs(trailingOnly=TRUE) +if (length(args)==0) +{ + stop("This tool needs at least one argument") +}else{ + data <- args[1] + enviro <- args[2] + data.bio <- args[3] + k <- as.numeric(args[4]) + metric <- args[5] + sample <- as.numeric(args[6]) +} + +#load data +env.data <- read.table(enviro, header=TRUE, sep="\t",dec = ".", na.strings = "-9999") +data.bio <- read.table(data.bio, header=TRUE, sep="\t") +test3 <- read.table(data, header = TRUE, sep="\t") + +###################################################################################################### +#Make clustering + +k <- k #number of clusters +test5 <- clara(test3, k, metric = metric, samples = sample, sampsize = min(nrow(test3), (nrow(data.bio)/nrow(test3))+2*k)) + +####################################################################################################### +#save results + +png("sih.png") +plot(silhouette(test5)) +dev.off() + +clus <- cbind(data.bio[1:nrow(test3), 1:2],test5$clustering) +names(clus) <- c("lat", "long", "cluster") +clus <- cbind(clus,test3,env.data[,3:19]) + +write.table(clus[1:3], file = "points_clus.tsv", quote = FALSE, sep="\t", row.names = FALSE) +write.table(clus, file = "clus.tsv", quote = FALSE, sep="\t", row.names = FALSE) + + + + + diff -r 000000000000 -r 5cde56683579 crea_carte_G.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/crea_carte_G.R Thu Mar 21 14:05:01 2024 +0000 @@ -0,0 +1,38 @@ +#Author : Seguineau Pauline + + +#Create a map from cluster + +library(sf) +library(tmap) +library(dplyr) + +args = commandArgs(trailingOnly=TRUE) +if (length(args)==0) +{ + stop("This tool needs at least one argument") +}else{ + data <- args[1] +} + +clus <- read.table(data, sep="\t" ,header=TRUE, na.strings = "na") + +#tmap method + +sf_data <- st_as_sf(clus, coords = c("long", "lat"), crs =4326) + +grouped_data <- sf_data %>% + group_by(cluster) %>% + summarize() + +map <- tm_shape(grouped_data) + + tm_dots(col = "cluster", palette = "Accent", n=256, size = 0.1, title = "ecoregions")+ + tm_scale_bar(position = c("right","top"))+ + tm_compass(position = c("right","top"))+ + tm_layout(frame = FALSE,legend.position = c("left","bottom"))+ + tm_xlab("Longitude")+ + tm_ylab("Latitude")+ + tm_grid(alpha = 0.2) + +#Save the map +tmap_save(map, "ecoregions.png") diff -r 000000000000 -r 5cde56683579 nb_clust_G.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/nb_clust_G.R Thu Mar 21 14:05:01 2024 +0000 @@ -0,0 +1,73 @@ +# Script to determine the optimal number of clusters thanks to the optimization of the SIH index and to produce the files needed in the next step of clustering + +#load packages +library(cluster) +library(dplyr) +library(tidyverse) + +#load arguments +args = commandArgs(trailingOnly=TRUE) +if (length(args)==0) +{ + stop("This tool needs at least one argument") +}else{ + enviro <- args[1] + taxa_list <- args[2] + preds <- args[3] + max_k <- as.numeric(args[4]) + metric <- args[5] + sample <- as.numeric(args[6]) +} + +#load data + +env.data <- read.table(enviro, sep="\t", header = TRUE, dec = ".", na.strings = "-9999") + +##List of modelled taxa used for clustering +tv <- read.table(taxa_list, dec=".", sep=" ", header=F, na.strings = "NA") +names(tv) <- c("a") + +################Grouping of taxa if multiple prediction files entered ################ + +data_split = str_split(preds,",") +data.bio = NULL + +for (i in 1:length(data_split[[1]])) { +data.bio1 <- read.table(data_split[[1]][i], dec=".", sep="\t", header=T, na.strings = "NA") +data.bio <- rbind(data.bio,data.bio1) +remove(data.bio1) +} + +names(data.bio) <- c("lat", "long", "pred", "taxon") + +#keep selected taxa +data.bio <- data.bio[which(data.bio$taxon %in% tv$a),] + +write.table(data.bio,file="data_bio.tsv",sep="\t",quote=F,row.names=F) + +#format data + +test3 <- matrix(data.bio$pred , nrow = nrow(env.data), ncol = nrow(data.bio)/nrow(env.data)) +test3 <- data.frame(test3) +names(test3) <- unique(data.bio$taxon) + +write.table(test3, file="data_to_clus.tsv", sep="\t",quote=F,row.names=F) + +#Max number of clusters to test +max_k <- max_k + +# Initialization of vectors to store SIH indices +sih_values <- rep(0, max_k) + +# Calculation of the SIH index for each number of clusters +for (k in 2:max_k) { + # Clara execution + clara_res <- clara(test3, k, metric =metric, samples = sample, sampsize = min(nrow(test3), (nrow(data.bio)/nrow(test3))+2*k)) + # Calculation of the SIH index + sih_values[k] <- clara_res$silinfo$avg.width +} + +# Plot SIH Index Chart by Number of Clusters +png("Indices_SIH.png") +plot(2:max_k, sih_values[2:max_k], type = "b", xlab = "Number of clusters", ylab = "SIH index") +dev.off() diff -r 000000000000 -r 5cde56683579 recup_liste_taxon.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/recup_liste_taxon.R Thu Mar 21 14:05:01 2024 +0000 @@ -0,0 +1,93 @@ +#This script allows us to create a file telling us for each taxon if we obtained a BRT model. As well as the list of taxa. + +#load packages +library(dplyr, warn.conflicts = FALSE) +library(taxonomyCleanr, warn.conflicts = FALSE) +library(stringr, warn.conflicts = FALSE) + +#load arguments +args = commandArgs(trailingOnly=TRUE) + +if (length(args)==0){ + stop("This tool needs at least one argument") +}else{ + data <- args[1] + preds <- args[2] + enviro <- args[3] +} + +env = read.table(enviro, sep="\t", dec=".", header=T, na.strings = "-9999") +occurrence_files = strsplit(data,",") +preds_files = strsplit(preds,",") + +#########functions########## + +`%!in%` <- Negate(`%in%`) + +have_model = data.frame() +pres = 0 + +have.model <- function(taxon_phylum,noms_sp,comptage_sp,brt_phylum){ + for (tax in taxon_phylum) { + if (tax %in% names(noms_sp)){ + pres = sum(comptage_sp[tax]) + } + if (tax %in% brt_phylum$spe ) { + brt = c(tax,"Yes", pres) + have_model = rbind(have_model,brt, make.row.names = F)} + else { + brt = c(tax,"No", pres) + have_model = rbind(have_model,brt, make.row.names = F)} + } + colnames(have_model) = c("Taxa","Model","Occurences") + return(have_model)} + +##########Execution######## +brt = NULL +for (j in 1:length(preds_files[[1]])){ + brt <- rbind(brt,read.table(preds_files[[1]][j], sep="\t", header = TRUE, na.strings = "na")) +} + +for (i in 1:length(occurrence_files[[1]])) { + occurrence <- NULL + cmpt <- NULL + taxon <- list() + + occurrence <- read.table(occurrence_files[[1]][i], sep = "\t", header = TRUE, na.strings = "na") + + taxon_names <- names(occurrence) + new_taxon <- taxon_names[!(taxon_names %in% names(env)) & taxon_names != "station"] + taxon <- c(taxon, new_taxon) + + cmpt <- occurrence[, new_taxon] + cmpt <- as.data.frame(cmpt) + + have_model <- have.model(taxon, occurrence, cmpt, brt) +} + +#Taxa for which a model was obtained +have_model2 = subset(have_model, have_model$`Model` != "No") +have_model3 = subset(have_model, have_model$`Model` != "No") + +#Obtain a list of taxa (cleaned) that have obtained a BRT model (file that can be submitted to the match taxa tool of the WoRMS database to obtain their classification and be able to sort duplicates between taxonomic ranks) + +have_model2$Taxa <- as.character(trim_taxa(have_model2$Taxa)) + +#Second clean-up (elimination of all taxa ending in sp1./sp2 etc which represents a duplicate) + +have_model2 <- have_model2 %>% filter(!str_ends(Taxa, "sp.1|sp[0-9]")) +have_model3 <- have_model3 %>% filter(!str_ends(Taxa, "sp.1|sp[0-9]")) +have_model <- have_model %>% filter(!str_ends(Taxa, "sp.1|sp[0-9]")) + +#extraction of the have_model object +write.table(have_model,file = "have_model.tsv", sep="\t", quote = F, row.names = F) + +#getting list of taxa for next step if not using worms +list_taxon = have_model3$Taxa +write.table(list_taxon, file= "list_taxa.txt", quote = F, row.names = F, col.names = F) + +#getting the final list to submit to worms +liste_taxon = have_model2$Taxa +write.table(liste_taxon,file = "list_taxa_clean.txt", quote = F, row.names = F, col.names = F) + + diff -r 000000000000 -r 5cde56683579 test-data/1_brts_pred_ceamarc.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/1_brts_pred_ceamarc.tsv Thu Mar 21 14:05:01 2024 +0000 @@ -0,0 +1,32 @@ +lat long Prediction.index spe +-65.57 139.22 0.841140843659849 Actiniaria +-65.57 139.22 0.84803516905332 Actiniaria +-65.57 139.23 0.845987230372577 Actiniaria +-65.57 139.24 0.845807234659773 Actiniaria +-65.57 139.24 0.845815355113451 Actiniaria +-65.57 139.25 0.838300654503413 Actiniaria +-65.57 139.26 0.836294936412005 Actiniaria +-65.57 139.26 0.836091676730536 Actiniaria +-65.57 139.27 0.835788235641246 Actiniaria +-65.57 139.28 0.83554848351174 Actiniaria +-65.57 139.28 0.842919257873871 Actiniaria +-65.57 139.29 0.843113627396164 Actiniaria +-65.57 139.3 0.837674310999705 Actiniaria +-65.57 139.3 0.78950055854148 Actiniaria +-65.57 139.31 0.789217507689643 Actiniaria +lat long Prediction.index spe +-65.57 139.22 0.247351772498557 Thouarella_vulpicauda +-65.57 139.22 0.247098939858693 Thouarella_vulpicauda +-65.57 139.23 0.246144373571671 Thouarella_vulpicauda +-65.57 139.24 0.245908115738853 Thouarella_vulpicauda +-65.57 139.24 0.245908115738853 Thouarella_vulpicauda +-65.57 139.25 0.245908115738853 Thouarella_vulpicauda +-65.57 139.26 0.292387985632338 Thouarella_vulpicauda +-65.57 139.26 0.24323878088903 Thouarella_vulpicauda +-65.57 139.27 0.233523508895553 Thouarella_vulpicauda +-65.57 139.28 0.222381660561301 Thouarella_vulpicauda +-65.57 139.28 0.22225741167332 Thouarella_vulpicauda +-65.57 139.29 0.215965799041526 Thouarella_vulpicauda +-65.57 139.3 0.224167139202414 Thouarella_vulpicauda +-65.57 139.3 0.251901045968841 Thouarella_vulpicauda +-65.57 139.31 0.251653126333346 Thouarella_vulpicauda diff -r 000000000000 -r 5cde56683579 test-data/Data.bio_table.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Data.bio_table.tsv Thu Mar 21 14:05:01 2024 +0000 @@ -0,0 +1,31 @@ +lat long pred taxon +-65.57 139.22 0.841140843659849 Actiniaria +-65.57 139.22 0.84803516905332 Actiniaria +-65.57 139.23 0.845987230372577 Actiniaria +-65.57 139.24 0.845807234659773 Actiniaria +-65.57 139.24 0.845815355113451 Actiniaria +-65.57 139.25 0.838300654503413 Actiniaria +-65.57 139.26 0.836294936412005 Actiniaria +-65.57 139.26 0.836091676730536 Actiniaria +-65.57 139.27 0.835788235641246 Actiniaria +-65.57 139.28 0.83554848351174 Actiniaria +-65.57 139.28 0.842919257873871 Actiniaria +-65.57 139.29 0.843113627396164 Actiniaria +-65.57 139.3 0.837674310999705 Actiniaria +-65.57 139.3 0.78950055854148 Actiniaria +-65.57 139.31 0.789217507689643 Actiniaria +-65.57 139.22 0.247351772498557 Thouarella_vulpicauda +-65.57 139.22 0.247098939858693 Thouarella_vulpicauda +-65.57 139.23 0.246144373571671 Thouarella_vulpicauda +-65.57 139.24 0.245908115738853 Thouarella_vulpicauda +-65.57 139.24 0.245908115738853 Thouarella_vulpicauda +-65.57 139.25 0.245908115738853 Thouarella_vulpicauda +-65.57 139.26 0.292387985632338 Thouarella_vulpicauda +-65.57 139.26 0.24323878088903 Thouarella_vulpicauda +-65.57 139.27 0.233523508895553 Thouarella_vulpicauda +-65.57 139.28 0.222381660561301 Thouarella_vulpicauda +-65.57 139.28 0.22225741167332 Thouarella_vulpicauda +-65.57 139.29 0.215965799041526 Thouarella_vulpicauda +-65.57 139.3 0.224167139202414 Thouarella_vulpicauda +-65.57 139.3 0.251901045968841 Thouarella_vulpicauda +-65.57 139.31 0.251653126333346 Thouarella_vulpicauda diff -r 000000000000 -r 5cde56683579 test-data/Data_to_cluster.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Data_to_cluster.tsv Thu Mar 21 14:05:01 2024 +0000 @@ -0,0 +1,16 @@ +Actiniaria Thouarella_vulpicauda +0.841140843659849 0.247351772498557 +0.84803516905332 0.247098939858693 +0.845987230372577 0.246144373571671 +0.845807234659773 0.245908115738853 +0.845815355113451 0.245908115738853 +0.838300654503413 0.245908115738853 +0.836294936412005 0.292387985632338 +0.836091676730536 0.24323878088903 +0.835788235641246 0.233523508895553 +0.83554848351174 0.222381660561301 +0.842919257873871 0.22225741167332 +0.843113627396164 0.215965799041526 +0.837674310999705 0.224167139202414 +0.78950055854148 0.251901045968841 +0.789217507689643 0.251653126333346 diff -r 000000000000 -r 5cde56683579 test-data/List_of_taxa.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/List_of_taxa.txt Thu Mar 21 14:05:01 2024 +0000 @@ -0,0 +1,2 @@ +Actiniaria +Thouarella_vulpicauda diff -r 000000000000 -r 5cde56683579 test-data/List_of_taxa_clean.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/List_of_taxa_clean.txt Thu Mar 21 14:05:01 2024 +0000 @@ -0,0 +1,2 @@ +Actiniaria +Thouarella vulpicauda diff -r 000000000000 -r 5cde56683579 test-data/SIH_index_plot.png Binary file test-data/SIH_index_plot.png has changed diff -r 000000000000 -r 5cde56683579 test-data/Summary_of_taxa_model.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/Summary_of_taxa_model.tsv Thu Mar 21 14:05:01 2024 +0000 @@ -0,0 +1,8 @@ +Taxa Model Occurences +Acanthogorgiidae No 0 +Actiniaria Yes 50 +Ainigmaptilon_edisto No 2 +Alcyonacea No 43 +Anthozoa No 62 +Thouarella_variabilis No 3 +Thouarella_vulpicauda Yes 24 diff -r 000000000000 -r 5cde56683579 test-data/ceamarc_env.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/ceamarc_env.tsv Thu Mar 21 14:05:01 2024 +0000 @@ -0,0 +1,16 @@ +long lat Carbo Grav Maxbearing Maxmagnit Meancurmag Meansal Meantheta Mud Prof Rugosity Sand Seaice_prod Sili Slope Standcurmag Standsal Standtheta +139.22 -65.57 0.88 28.59 3.67 0.03 0.03 34.62 -0.13 22.72 -441 -9999 55.76 0.24 3.27 0.28 0.01 0.01 0.18 +139.22 -65.57 0.88 28.61 3.64 0.02 0.03 34.62 -0.13 22.48 -439 -9999 55.74 0.24 3.29 0.27 0.01 0.01 0.18 +139.23 -65.57 0.92 28.62 3.59 0.02 0.03 34.62 -0.14 22.25 -438 -9999 56.28 0.25 3.32 0.22 0.01 0.01 0.19 +139.24 -65.57 0.92 28.63 3.51 0.01 0.03 34.62 -0.14 21.95 -436 -9999 56.57 0.26 3.3 0.08 0.01 0.01 0.19 +139.24 -65.57 0.92 28.64 3.35 0.01 0.03 34.62 -0.14 21.7 -437 -9999 56.58 0.26 3.28 0.05 0.01 0.01 0.19 +139.25 -65.57 0.93 28.65 3 0.0096293305978179 0.03 34.62 -0.15 21.44 -436 -9999 56.63 0.26 3.26 0.29 0.01 0.01 0.19 +139.26 -65.57 0.93 28.63 2.49 0.00871255807578564 0.03 34.62 -0.15 21.11 -432 -9999 56.67 0.26 3.23 0.43 0.01 0.01 0.19 +139.26 -65.57 0.93 28.64 2.01 0.01 0.03 34.62 -0.16 20.83 -429 -9999 56.71 0.26 3.21 0.37 0.01 0.01 0.19 +139.27 -65.57 0.94 28.65 1.71 0.01 0.03 34.62 -0.16 20.55 -427 -9999 56.75 0.26 3.19 0.32 0.01 0.01 0.2 +139.28 -65.57 0.94 28.66 1.54 0.01 0.03 34.62 -0.16 20.21 -424 -9999 56.8 0.26 3.17 0.28 0.01 0.01 0.2 +139.28 -65.57 0.94 28.67 1.44 0.02 0.03 34.62 -0.17 19.74 -422 -9999 56.84 0.26 3.14 0.26 0.01 0.01 0.2 +139.29 -65.57 0.94 28.68 1.74 0.01 0.03 34.62 -0.17 20.86 -421 -9999 56.87 0.26 3.13 0.22 0.01 0.01 0.2 +139.3 -65.57 0.95 28.7 3.46 0.01 0.03 34.62 -0.17 21.3 -420 -9999 56.91 0.26 3.11 0.32 0.01 0.01 0.2 +139.3 -65.57 0.95 28.71 3.91 0.03 0.03 34.62 -0.18 21.01 -414 -9999 57.18 0.26 3.09 0.59 0.01 0.01 0.21 +139.31 -65.57 0.96 28.72 4.03 0.05 0.03 34.62 -0.18 20.76 -406 -9999 57.54 0.26 3.07 0.53 0.01 0.01 0.21 diff -r 000000000000 -r 5cde56683579 test-data/cnidaria_filtered.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cnidaria_filtered.tsv Thu Mar 21 14:05:01 2024 +0000 @@ -0,0 +1,64 @@ +station lat long Carbo Grav Maxbearing Maxmagnit Meancurmag Meansal Meantheta Mud Prof Rugosity Sand Seaice_prod Sili Slope Standcurmag Standsal Standtheta Acanthogorgiidae Actiniaria Ainigmaptilon_edisto Alcyonacea Anthozoa Thouarella_variabilis Thouarella_vulpicauda +1 1 -65,99 142,33 5,51 12,59 2,03 0,16 0,07 34,6 -1,47 29,8 -233 1 56,16 0,11 3,94 0,05 0,03 0,03 0,24 0 1 0 0 1 0 0 +2 10 -66,33 141,3 3,17 5,31 5,2 0,18 0,08 34,61 -1,35 21,5 -215 1 67,39 0,17 5,8 0,24 0,03 0,02 0,28 0 1 0 1 1 0 0 +3 11 -66,56 141,29 3,1 3,28 4,93 0,2 0,08 34,65 -1,73 34,89 -192 1 73,13 0,87 4,48 0,71 0,03 0,04 0,24 0 0 0 1 1 0 0 +4 12 -66,55 140,82 2,78 3,08 3,74 0,34 0,1 34,59 -1,19 25,83 -217 1 68,37 2,62 4,05 2,57 0,06 0,02 0,24 0 1 0 1 1 1 0 +5 13 -66,15 140,65 2,06 7,62 5,36 0,21 0,09 34,56 -1,04 13,18 -217 1 71,68 0,07 2,89 0,37 0,04 0,02 0,26 0 1 0 0 1 0 0 +6 14 -66,33 140,67 2,96 3,12 2,42 0,16 0,08 34,54 -1,02 12,15 -171 1 73,16 0,78 1,17 0,08 0,03 0,01 0,22 0 1 0 0 1 0 0 +7 15 -66,38 139,8 1,06 1,65 1,87 0,14 0,05 34,64 -1,53 69,08 -855 1 48,44 1,43 13,55 2,94 0,03 0,01 0,06 0 1 0 1 1 0 0 +8 16 -66,34 139,99 1,34 2,78 1,61 0,15 0,05 34,6 -1,12 41,06 -538 1 54,72 1,23 5,97 3,7 0,03 0,00839 0,08 0 1 0 0 1 0 0 +9 17 -66,17 139,96 1,71 8,13 4,39 0,31 0,12 34,54 -0,96 17,16 -151 1 61,42 0,54 1,99 0,25 0,05 0,02 0,3 0 1 0 1 1 1 0 +10 18 -66,16 139,65 1,49 7,75 2,24 0,15 0,06 34,59 -1,01 32,71 -432 1 51,4 0,56 3,35 0,42 0,03 0,01 0,26 0 1 0 1 1 0 1 +11 19 -66,15 139,31 1,18 8,58 5,7 0,12 0,04 34,61 -1,36 47,4 -674 1 46,41 0,34 9,92 0,31 0,02 0,00722 0,09 0 1 0 0 1 0 0 +12 2 -65,99 141,32 3,3 14,21 5,01 0,16 0,07 34,6 -1,26 14,05 -235 1 67,07 -0,01 2,09 0,13 0,03 0,03 0,32 0 1 1 1 1 0 1 +13 20 -66 139,99 1,95 20,59 4,98 0,19 0,09 34,56 -1,05 13,25 -192 1 63,46 0,27 1,92 0,21 0,03 0,03 0,26 0 1 0 0 1 0 0 +14 21 -66 139,64 2,26 18,21 2,85 0,16 0,07 34,58 -1,04 44,81 -277 1 57,14 0,76 5,88 1,66 0,03 0,01 0,26 0 1 0 0 1 0 0 +15 22 -66 139,31 1,39 19,2 4,87 0,1 0,04 34,6 -1,05 37,84 -476 1 49,4 0,65 7,09 0,25 0,02 0,00884 0,15 0 1 0 1 1 0 0 +16 26 -66,52 140,02 1,51 1,47 4,4 0,12 0,05 34,6 -1,11 49,11 -247 1 49,52 2,38 10,29 3,16 0,02 0,00984 0,16 0 1 0 0 1 0 0 +17 27 -66,02 142,74 2,61 11,58 5,42 0,13 0,05 34,64 -1,73 25,07 -440 1 46,97 0,24 2,19 0,23 0,02 0,03 0,17 0 1 0 1 1 0 1 +18 28 -65,99 143,02 2,15 11,5 5,37 0,14 0,05 34,64 -1,77 39,29 -467 1 38,66 0,24 2,07 0,32 0,02 0,03 0,12 0 1 0 1 1 0 1 +19 29 -66,02 143,29 1,77 9,98 5,41 0,14 0,05 34,64 -1,78 62,2 -470 1 38,73 0,33 4,81 0,19 0,02 0,03 0,1 0 1 0 1 1 0 1 +20 3 -65,99 141,98 5,27 14,32 5,31 0,01 0,06 34,6 -1,4 22,84 -245 1 61,04 -0,00886 3,93 0,15 0,03 0,03 0,25 0 1 0 0 1 0 0 +21 30 -66 143,68 2,06 9,47 5,42 0,15 0,06 34,61 -1,71 54,73 -429 1 41,27 0,39 1,03 0,22 0,03 0,03 0,19 0 1 0 0 1 0 0 +22 31 -66,55 144,99 1,05 19,55 5,28 0,12 0,03 34,62 -1,73 35,57 -437 1 39,47 5,14 1,77 0,45 0,02 0,03 0,16 0 0 0 1 1 0 1 +23 34 -66,33 144,34 1,26 6,31 4,92 0,13 0,04 34,63 -1,77 44,96 -455 1 40,49 1,87 2,96 0,39 0,02 0,03 0,12 0 1 0 1 1 0 1 +24 35 -66,33 144,01 1,2 4,9 2,11 0,12 0,04 34,64 -1,78 49,09 -511 1 39,41 1,54 4,15 0,32 0,02 0,02 0,1 0 1 0 1 1 0 0 +25 36 -66,32 143,65 1,12 4,29 2,16 0,12 0,04 34,64 -1,79 53,85 -565 1 38,83 1,26 8,68 0,38 0,02 0,02 0,13 0 1 0 1 1 0 1 +26 37 -66,55 143,31 1,34 4,32 2,02 0,12 0,04 34,66 -1,82 78,15 -820 1 47,92 2,76 13,57 0,26 0,02 0,03 0,11 0 1 0 0 1 0 0 +27 38 -66,33 143,31 0,82 3,84 2,2 0,1 0,04 34,65 -1,8 84,13 -703 1 41,01 0,98 16,47 0,25 0,02 0,03 0,12 0 1 0 1 1 1 0 +28 39 -66,56 143,02 1,67 4,11 1,5 0,13 0,05 34,65 -1,8 47,63 -862 1 56,71 2,22 7,3 0,18 0,02 0,02 0,1 0 1 0 0 1 0 0 +29 40 -66,65 142,98 1,94 3,93 5,42 0,13 0,04 34,63 -1,75 41,4 -598 1 58,72 3,57 5,71 1,57 0,02 0,03 0,12 0 0 0 1 1 0 1 +30 41 -66,76 142,65 2,67 2,67 0,14 0,15 0,04 34,65 -1,76 38,32 -598 1 62,33 4,43 6,01 4,29 0,02 0,04 0,16 0 0 0 1 1 0 1 +31 42 -66,87 142,66 2,92 1,71 5,49 0,13 0,04 34,63 -1,72 47,93 -391 1 62,77 8,27 6,89 1,19 0,02 0,05 0,2 0 0 0 1 1 0 1 +32 43 -66,74 143,33 2 3,28 4,67 0,19 0,06 34,61 -1,74 62,82 -332 1 54,92 7,42 9,35 3,78 0,03 0,05 0,16 0 1 0 1 1 0 0 +33 45 -66,75 144 2,19 3,12 5,5 0,16 0,04 34,61 -1,8 42,36 -661 1 43,64 7,49 5,95 4,51 0,03 0,02 0,07 0 1 0 1 1 0 1 +34 46 -66,87 144,1 2,59 1,31 5,89 0,17 0,05 34,59 -1,78 38,87 -561 1 43,42 10,54 4,09 1,16 0,03 0,04 0,14 0 0 0 1 1 0 1 +35 48 -66,93 144,65 1,59 0,58 5,75 0,21 0,06 34,6 -1,83 73,19 -407 1 40,23 11,89 10,52 5,23 0,03 0,03 0,09 0 1 0 1 1 0 0 +36 5 -66,32 142,29 3,03 4,93 5,05 0,2 0,1 34,63 -1,61 39,53 -216 1 61,67 0,27 2,63 0,11 0,04 0,02 0,17 0 0 0 1 1 0 1 +37 50 -66,75 145,27 1,02 5,5 5,49 0,13 0,04 34,64 -1,77 52,4 -596 1 39,8 9,53 4,66 0,23 0,02 0,03 0,1 0 1 0 1 1 0 1 +38 51 -66,74 145,48 0,95 6,47 5,5 0,2 0,05 34,62 -1,78 49,32 -535 1 40,91 10,66 3,37 0,55 0,03 0,04 0,11 0 1 0 0 1 0 0 +39 52 -66,55 145,31 1,04 19,26 5,26 0,09 0,03 34,59 -1,67 36,48 -409 1 42,62 6,4 1,12 0,09 0,01 0,03 0,22 0 1 0 0 1 0 0 +40 53 -66,33 144,66 1,24 6,73 5,36 0,13 0,04 34,61 -1,71 62,77 -420 1 42,45 2,27 3,99 0,17 0,02 0,04 0,18 0 1 0 0 1 0 0 +41 54 -65,91 144,02 3,17 12,05 5,16 0,15 0,06 34,57 -0,92 31,38 -375 1 46,27 0,41 2,1 2,62 0,02 0,02 0,4 0 1 1 1 1 0 1 +42 55 -66,33 145,01 1,12 8,85 5,25 0,12 0,04 34,56 -1,68 40,75 -387 1 44,3 3,06 2,2 0,58 0,02 0,03 0,26 0 1 0 1 1 0 1 +43 56 -66,56 144,67 1,3 16,82 2,34 0,13 0,04 34,65 -1,79 45,94 -582 1 39,22 4,26 3,93 0,38 0,02 0,03 0,1 0 1 0 0 1 0 0 +44 57 -66,74 145 1,18 5,21 2,42 0,16 0,05 34,65 -1,8 53,42 -646 1 38,62 7,59 7,28 0,68 0,03 0,03 0,09 0 1 0 0 1 0 0 +45 58 -66,75 144,67 1,28 4,2 2,34 0,18 0,05 34,65 -1,81 79,28 -837 1 34,69 7,08 12,46 0,61 0,03 0,03 0,08 0 1 0 1 1 0 1 +46 6 -66,32 142,66 1,83 4,69 1,69 0,1 0,08 34,65 -1,74 18,2 -384 1 58,78 0,43 2,05 0,09 0,03 0,02 0,15 0 1 0 1 1 0 0 +47 60 -66,56 143,93 1,51 7,74 2,05 0,14 0,05 34,65 -1,82 47,04 -799 1 38,88 3,65 7,36 0,94 0,03 0,03 0,09 0 1 0 1 1 0 0 +48 61 -66,33 142,97 1,15 4,14 5,31 0,13 0,05 34,64 -1,74 52,04 -644 1 49,19 0,67 9,1 0,82 0,02 0,03 0,16 0 1 0 1 1 0 0 +49 62 -66,15 143,32 1,06 10,88 2,17 0,12 0,04 34,64 -1,76 59,46 -545 1 37,97 0,52 5,25 0,23 0,02 0,02 0,14 0 1 0 1 1 0 0 +50 63 -65,85 142,98 3,71 20,11 5,24 0,27 0,11 34,61 -1,22 24,73 -423 1 41,04 0,19 1,03 0,56 0,05 0,02 0,44 0 0 0 1 1 0 1 +51 65 -65,81 143 3,89 21,43 5,23 0,39 0,13 34,61 -0,67 25,78 -777 1 41,24 0,18 1,56 7,69 0,06 0,02 0,33 0 1 0 1 1 0 1 +52 66 -65,75 143,04 NA NA 4,48 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0 0 0 1 1 0 0 +53 7 -66,55 142,64 2,12 4,31 1,77 0,18 0,07 34,66 -1,75 15,34 -255 1 65,49 1,46 1,38 6,15 0,03 0,03 0,18 0 0 0 0 0 0 0 +54 70 -66,42 140,52 2,3 2,63 5,35 0,08 0,03 34,64 -1,49 20,33 -1109 1 67,5 1,56 3,67 5,9 0,01 0,01 0,05 0 1 0 0 1 0 0 +55 71 -66,39 140,48 2,22 2,24 5,27 0,06 0,02 34,62 -1,36 19,67 -768 1 67,65 1,36 3,1 7,04 0,01 0,01 0,04 0 1 0 1 1 0 1 +56 72 -66,34 140,48 2,18 2,6 2,34 0,09 0,04 34,58 -1,14 17,49 -407 1 67,61 1,01 2,1 2,74 0,01 0,01 0,11 0 1 0 1 1 0 0 +57 79 -65,7 140,56 1,83 25,43 4,77 0,34 0,16 34,6 -0,94 3,7 -506 1 66,84 0,09 1,91 5,63 0,06 0,02 0,38 0 1 0 1 1 0 1 +58 8 -66,56 142,33 3,36 3,2 1,96 0,16 0,06 34,68 -1,85 67,67 -374 1 60,65 1,04 11,68 1,03 0,03 0,03 0,1 0 1 0 1 1 0 1 +59 81 -65,65 140,44 1,42 28,49 4,93 0,3 0,15 34,62 -0,81 3,75 -1193 1 66,73 0,13 1,29 5,59 0,05 0,02 0,32 0 0 0 1 1 0 0 +60 84 -65,45 139,37 0,98 28,81 1,16 0,06 0,03 34,62 -0,21 17,56 -395 NA 58,04 0,28 2,92 0,29 0,01 0,01 0,22 0 0 0 1 1 0 0 +61 86 -65,47 139,35 0,96 28,78 2,13 0,00947 0,03 34,62 -0,2 18,67 -398 NA 57,91 0,27 2,96 0,26 0,01 0,01 0,22 0 1 0 1 1 0 0 +62 87 -65,49 139,33 0,95 28,75 4,01 0,05 0,03 34,62 -0,19 20,03 -400 NA 57,77 0,27 3,02 0,42 0,01 0,01 0,21 0 1 0 0 1 0 0 +63 9 -66,55 141,99 3,19 3,31 5,38 0,17 0,05 34,68 -1,85 40,51 -357 1 65,13 0,81 6,85 3,19 0,03 0,03 0,1 0 0 0 1 1 0 1 diff -r 000000000000 -r 5cde56683579 test-data/ecoregions.png Binary file test-data/ecoregions.png has changed diff -r 000000000000 -r 5cde56683579 test-data/fish_wide.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/fish_wide.tsv Thu Mar 21 14:05:01 2024 +0000 @@ -0,0 +1,20 @@ +lat long rajidae_bathyraja_sp. myctophidae_electrona_antarctica myctophidae_protomyctophum_bolini +-67.22 139.96 4 1 0 +-65.46 139.31 0 4 0 +-65.82 142.96 0 0 1 +-65.77 142.92 0 1 1 +-65.64 140.45 0 2 0 +-65.74 142.86 0 1 0 +-65.85 144.04 0 2 0 +-65.44 139.32 0 0 0 +-65.71 140.6 0 0 0 +-66.75 143.95 0 0 0 +-66.41 140.51 0 0 0 +-66.34 140.03 0 0 0 +-66.17 139.35 0 0 0 +-66.33 143.36 0 0 0 +-66.39 140.43 0 0 0 +-66.75 144.96 0 0 0 +-66.34 143.04 0 0 0 +-67.05 145.15 0 0 0 + diff -r 000000000000 -r 5cde56683579 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