Mercurial > repos > ecology > stoc_trend_indic
changeset 0:916b49d725ba draft
"planemo upload for repository https://github.com/Alanamosse/Galaxy-E/tree/stoctool/tools/stoc commit f82f897ab22464de40c878e17616333855814e25"
| author | ecology |
|---|---|
| date | Thu, 02 Apr 2020 03:35:29 -0400 |
| parents | |
| children | 5c244408661f |
| files | FunctExeTemporalAnalysisofIndicatorTrait.r FunctTrendSTOCGalaxy.r coordCarreSTOCfaux.tabular species_indicateur_fonctionnel.tabular stoceps_macros.xml tabSpecies.csv temp_analysis_indic.xml |
| diffstat | 7 files changed, 4942 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/FunctExeTemporalAnalysisofIndicatorTrait.r Thu Apr 02 03:35:29 2020 -0400 @@ -0,0 +1,328 @@ +#!/usr/bin/env Rscript + +######################################################################################################################################################################## +############## FUNCTION TO CALCULATE AND PLOT VARIATION IN TIME OF INDICATOR OR MEAN TRAIT VALUE OF COMMUNITIES function:csi_cti_ctri ############################## +######################################################################################################################################################################## + +#### Based on Romain Lorrillière R script +#### Modified by Alan Amosse and Benjamin Yguel for integrating within Galaxy-E +### made with R version 3.5.1 + + +suppressMessages(library(RODBC)) ##Version: 1.3-15 +suppressMessages(library(reshape)) ##Version: 0.8.8 +suppressMessages(library(data.table)) ##Version: 1.12.0 +suppressMessages(library(rgdal)) ##Version: 1.3-4 +suppressMessages(library(lubridate)) ##Version: 1.7.4 +#suppressMessages(library(RPostgreSQL)) ##Version: 0.6-2 +suppressMessages(library(doBy)) ##Version: 4.6-2 +suppressMessages(library(arm)) ##Version: 1.10-1 +suppressMessages(library(ggplot2)) ##Version: 3.1.0 +suppressMessages(library(scales)) ##Version: 1.0.0 +suppressMessages(library(mgcv)) ##Version: 1.8-24 +#suppressMessages(library(visreg)) ##Version: 2.5-0 +suppressMessages(library(plyr)) ##Version: 1.8.4 +#suppressMessages(library(lme4)) ##Version: 1.1-18-1 +#suppressMessages(library(lmerTest)) ##Version: 3.1-0 +suppressMessages(library(glmmTMB)) ###Version: 0.2.3 + + +########### +#delcaration des arguments et variables/ declaring some variables and load arguments + +args = commandArgs(trailingOnly=TRUE) +#for( i in 1:length(args)){print(args[i])} +if (length(args)<12) { + stop("At least 12 arguments must be supplied :\n- An input dataset filtered (.tabular). May come from the filter rare species tool.\n- A species detail table (.tabular)\n- A species ssi/sti table.\n- A table with plots coordinates.\n- table with csi calculated before 2001.\n\n", call.=FALSE) #si pas d'arguments -> affiche erreur et quitte / if no args -> error and exit1 +} else { + Datafiltered<-args[1] ###### Nom du fichier avec extension ".typedefichier", peut provenir de la fonction "FiltreEspeceRare" / file name without the file type ".filetype", may result from the function "FiltreEspeceRare" + tabSpecies<-args[2] ###### Nom du fichier avec extension ".typedefichier", fichier mis à disposition dans Galaxy-E avec specialisation à l'habitat des especes et si espece considérée comme indicatrice / file name without the file type ".filetype", file available in Galaxy-E containing habitat specialization for each species and whether or not they are considered as indicator + tabtrait<-args[3] ##### Nom du fichier avec extension ".typedefichier", fichier mis à disposition dans Galaxy-E avec degre de specialisation de l espece et affinite thermique /file name without the file type ".filetype", file available in Galaxy-E containing specilalization degree as well as thermic preferences + coordCarre<-args[4] #### Nom du fichier avec extension ".typedefichier", fichier mis à disposition dans Galaxy-E avec les coordonnees gps des carres /file name without the file type ".filetype", file available in Galaxy-E containing gps coordinates of the plots + Var <- args[5] #### Nom du trait dans fichier de traits "nomdutrait" exemple: "ssi" pour l'indice de specialisation par sps / Name of the trait in the file containing trait data + indicator <- args[6] #### Nom de l'indicateur ou du trait par communauté ex pour ssi c'est csi calculé au niveau communauté / Name of the indicator or the trait per community ex: for the ssi, it is the csi measured at the community level + methode <- args[7] #### Methode d'analyse de l'evolution du trait ou de l'indicateur, lmer pour modèloe mixte seul ou gam pour generalized additive model / name of the models used to analyze evolution of mean trait or indicator + dd <- args[8] ##### Nom du fichier si déjà un fichier avec trait moyen par communauté, avec une colonne annee appelé "year" et une colonne plot appelé "carre" correspondant à l'echelle des communautés etudiées / name of the file if a file with the mean trait value per community is already prepared with one column named "year" for the year, one column named "carre" for the plots (the scale of the community measurment) + id<-args[9]#Id name for output res repo + plot_smooth<-args[10]#TRUE or FALSE + ic<-args[11]#TRUE or FALSE + source(args[12]) +} + +#Import des données / Import data +tabCLEAN <- read.table(Datafiltered,sep="\t",dec=".",header=TRUE) #### charge le fichier de données d abondance / load abundance of species +tabsp <- read.table(tabSpecies,sep="\t",dec=".",header=TRUE) #### charge le fichier de donnees sur nom latin, vernaculaire et abbreviation, espece indicatrice ou non / load the file with information on species specialization and if species are indicators + +vars_tabCLEAN<-c("carre","annee","espece","abond") +err_msg_tabCLEAN<-"The input dataset filtered doesn't have the right format. It need to have the following 4 variables :\n- carre\n- annee\n- espece\n- abond\n" + +vars_tabsp<-c("espece","nom","nomscientific","indicateur","specialisation") +err_msg_tabsp<-"\nThe species dataset filtered doesn't have the right format. It need to have the following 4 variables :\n- espece\n- nom\n- nomscientific\n- indicateur\n- specialisation\n" + +check_file(tabCLEAN,err_msg_tabCLEAN,vars_tabCLEAN,4) +check_file(tabsp,err_msg_tabsp,vars_tabsp,5) + + +if(!dd==""){ + vars_dd<-c("carre","year","longitude_grid_wgs84","latitude_grid_wgs84","indic") #### si vous avez déjà votre tableau d'analyse indic correspond au trait moyen par communauté ou au calcul de l'indicateur + err_msg_dd<-"\nThe dataset for analysis doesn't have the right format. It need to have the following 5 variables :\n- carre\n- year\n- longitude_grid_wgs84\n- latitude_grid_wgs84\n- indic\n" + check_file(dd,err_msg_dd,vars_dd,5) + dd <- read.table(dd,sep="\t",dec=".",header=TRUE) #### charge le fichier pour analyse si déjà construit (voir ci dessus pour les détails ) / load the required file for the analysis if already prepared (see above for details) +}else{ + dd<-NULL +} + +spTrait=read.table(tabtrait,sep="\t",dec=".",header=TRUE) ############# species_indicateur_fonctionnel.csv pour le STOC sinon fichier avec traits pour calcul du trait moyen par communauté / file with the trait for the community weighted mean calculation +coordCarre=read.table(coordCarre,sep="\t",dec=".",header=TRUE) ######## carre.csv charge les coordonnées des carrés qui sont utilisés comme covariable / load the gps coordinates of the plots, is used as covariable in the models + +dir.create(paste("Output/",sep=""),recursive=TRUE,showWarnings=FALSE)##### Creation du dossier de sortie +#cat(paste("Create Output/","\n",sep="")) + +############################# The function + + +csi_cti_ctri <- function(tabCLEAN=tabCLEAN,coordCarre=coordCarre,spTrait=spTrait,dd=NULL,ic=TRUE, Var="ssi",indicator="csi", methode="gam", ####### Var= nom du trait dans le fichier de trait (pour le calcul du csi, le trait est ssi par exemple); Indicator= nom de l'indicateur ou du trait moyen par communauté ; methode: choisir modele "gam" ou "glmmtmb" ; ic pour calcul de l'interval de confiance plus rapide sans mais moins fiable / Var=name of the trait in the trait file; Indicator= name of the indicator or of the community weighted mean trait used in graphical output and output files; methode is the statistical model use for the analysis "gam" or "glmmtmb" ; ic is for the calculation of confidence interval faster without but less reliable + firstYear=NULL,lastYear=NULL,altitude=800,departement=NULL,onf=TRUE,distance_contact=NULL, #### altitude, departement onf, distance de contact = Argument non utilise, se trouvait dans requete sql / altitude, departement onf, distance de contact = not use anymore was in a postgres request + spExcluPassage1=c("MOTFLA","SAXRUB","ANTPRA","OENOEN","PHYTRO"),# (Prince et al. 2013 Env. Sc. and Pol.) + "OENOEN","PHYTRO" avis d'expert F. Jiguet, #### Argument non utilise, se trouvait dans requete sql / not use anymore was in a postgres request + seuilAbondance=.99,plot_smooth=TRUE, ###### init_1989 si TRUE, option que pour csi et besoin du fichier des csi calculés sur les données avant 2001 (pas forcement fiable car protocole un peu different) / init_1989 if TRUE, only working for csi, and use calculation of csi based on data before 2001 (protocol was bit different, not totally reliable) + champSp = "code_sp", sp=NULL,champsHabitat=FALSE, #### Argument non utilise, se trouvait dans requete sql / not use anymore was in a postgres request + anglais=FALSE,seuilSignif=0.05,##### #### anglais=FALSE Argument non utilise, se trouvait dans requete sql / not use anymore was in a postgres request + couleur="#4444c3", + titreY=indicator,titreX="Années",titre=indicator, + savePostgres=FALSE,output=FALSE, ##### OPTION "output" pour afficher le resultat dans R / OPTION "output" is only to show the result in the R window + operateur=c("Lorrilliere Romain","lorrilliere@mnhn.fr"), encodingSave="ISO-8859-1",fileName="dataCSI",id="France"){ ####### nom des fichiers de sorties et de l'operateur / name of the output files and of the operator + + + start <- Sys.time() + dateExport <- format(start,"%Y-%m-%d") + + if(is.null(firstYear)) firstYear <- 2001 + if(is.null(lastYear)) lastYear <- 9999 + if(is.null(altitude)) altitude <- 10000 + + ############################################# calcul de l'indicateur ou du trait moyen pondéré par communauté / Calculation of the weighted mean trait or of the indicator per community i.e. calculation of the csi, cti, ctri (corresponds to Community weighted mean or CWM for traits) + if (is.null(dd)){ + colnames(spTrait)[colnames(spTrait) == Var] <- "trait" + spTrait$trait <- spTrait$trait + ###browser() + tabCLEAN$trait <- spTrait$trait[match(tabCLEAN$espece,spTrait$pk_species)] ### recupere donnee du trait par espece calcule / retrieve trait data for each species + tabCLEAN=na.omit(tabCLEAN) ##### pour faire les moyennes pondérées sur les espèces avec des données de trait (donc pas de prise en compte des sps sans traits dans l'abondance totale par carré) + traitcarre <- aggregate(trait*abond~annee+carre,tabCLEAN,sum) ### somme des traits par annee et carre pondere par les abondances / sum of the trait per year and per plots weighted by abundances + abcarre <- aggregate(abond~annee+carre,tabCLEAN,sum) ### somme des abondances totales par annee et carre / sum of total abundance per year and plots + indic <- traitcarre[,3]/abcarre[,3] #### le trait moyen par carre = indicateur par carre et annee / mean trait per plots = indicator per year and plots + dd <- data.frame(indic,traitcarre$carre,traitcarre$annee) + names(dd)[2] <- "carre" + names(dd)[3] <- "year" + dd$longitude_grid_wgs84 <- coordCarre$longitude_grid_wgs84[match(dd$carre,coordCarre$pk_carre)] #### recupere coordonnées gps / retrieve gps coordinates + dd$latitude_grid_wgs84 <- coordCarre$latitude_grid_wgs84[match(dd$carre,coordCarre$pk_carre)] #### recupere coordonnées gps / retrieve gps coordinates + dd$id_plot <- dd$carre ### id_plot nom données aux carrés dans le script /id_plot is use as the name of the plot in the following script + }else{ + colnames(dd)[colnames == "indicator"] <- "indic" + } + + ############################################ fin du calcul de l'indicateur ou du trait moyen pondéré par communauté / end of the calculation of the indicator or the mean trait value per community + annee <- sort(unique(dd$year)) + nban <- length(annee) + pasdetemps <-nban-1 + + if(methode == "gam") { + cat("Methode: gam\n") + ## Utilisation des modèles GAMM pour obtenir les tendances d evolution par an du csi cti ou ctri !!!! Marche pas si peu de données !!!! / Use of GAMM model for the estimation of the annual variations of the csi cti or ctri !!! does not work with few data !!! + cat("\nEstimation de la variation annuelle ",indicator,"~ factor(year)+s(longitude_grid_wgs84,latitude_grid_wgs84,bs='sos'),random=reStruct(object = ~ 1| id_plot,correlation=corAR1(form=~year)\n",sep="") + gammf <- gamm(indic ~ factor(year)+s(longitude_grid_wgs84,latitude_grid_wgs84,bs="sos"), data=dd,random=reStruct(object = ~ 1| id_plot, pdClass="pdDiag"),correlation=corAR1(form=~year)) #### spline sur les coordonnées, effet aleatoire sur les carres, methode autoregressive sur l'année N-1 / spline on the gps coordinates, random effect on the plots, autoregressive method on the year-1 +# gammf <- gamm(indic ~ factor(year)+s(longitude_grid_wgs84,latitude_grid_wgs84,bs="sos"), data=dd,random=reStruct(object = ~ 1| id_plot, pdClass="pdDiag"),correlation=corAR1(form=~year)) #### spline sur les coordonnées, effet aleatoire sur les carres, methode autoregressive sur l'année N-1 / spline on the gps coordinates, random effect on the plots, autoregressive method on the year-1 + sgammf<-summary(gammf$gam) + coefdata=coefficients(gammf$gam) ### recupere les coefficient de regression de la variable "annee" / retrieve the regression coefficient of the variable "year" + coefannee <- c(0,sgammf$p.coeff[2:nban]) ### meme chose que au dessus / same as before + erreuran <- c(0,sgammf$se[2:nban])### recupere les erreurs standard des coefficient de regression de la variable "annee" / retrieve the standard errors of the regression coefficient of the variable "year" + pval <- c(1,sgammf$p.pv[2:nban])### recupere les p value de la variable "annee" / retrieve the p value of the variable year + + + ## calcul des intervalles de confiance / confidence interval calculation + if(ic) { + # gammf.sim <- sim(gammf) ###################### VERSION ROMAIN mais fct sim() ne marche pas avec GAMM / old version using function sim() but did not work with Gamm models + # ic_inf_sim <- c(0,tail(apply(coef(gammf.sim), 2, quantile,.025),pasdetemps)) + # ic_sup_sim <- c(0,tail(apply(coef(gammf.sim), 2, quantile,.975),pasdetemps)) + icalpha05 <- as.data.frame(confint(gammf$gam))[2:nban,1:2] ########## VERSION BENJ + ic_inf_sim <- icalpha05[,1] + ic_inf_sim <- c("NA",ic_inf_sim[1:nban-1]) + ic_sup_sim <- icalpha05[,2] + ic_sup_sim <- c("NA",ic_sup_sim[1:nban-1]) + } else{ + ic_inf_sim <- "not assessed" + ic_sup_sim <- "not assessed" + } + + tabfgamm <- data.frame(model = "gamm factor(year) plot",annee,coef=coefannee,se = erreuran,pval,signif=pval<seuilSignif,Lower_ci=ic_inf_sim,upper_ci=ic_sup_sim,indicator=indicator) #### recupère les resultats des modèles avec interval de confiance / retrieve results of the models used with confidence interval + write.table(tabfgamm,paste("Output/",indicator,"_gammParannee_",id,".tabular",sep=""),row.names=FALSE,sep="\t") + gg <- ggplot(data=tabfgamm,aes(x=annee,y=coef)) + gg= gg + geom_line(size=1.5,colour=couleur)+ geom_point(size=3,colour=couleur) + geom_point(size=1.5,colour="white") + + if (ic){ + gg <- gg + geom_errorbar(aes(ymin=coef-se, ymax=coef+se), width=0,colour=couleur,alpha=0.5) + gg <- gg + geom_ribbon(aes(ymin=coef-se, ymax=coef+se),fill = couleur,alpha=.2) + } + gg <- gg + labs(y=indicator,x="annee")+scale_x_continuous(breaks=pretty_breaks()) + ggsave(paste("Output/fig",indicator,"_carre_",id,".png",sep=""),gg) + + + if(plot_smooth) { #### Representation graphique de l'evolution annuelle des indicateurs / Graphical representation of the annual evolution of the indicators + cat("\nGam pour la figure ",indicator,"~s(year),random=reStruct(object = ~ 1| id_plot,correlation=corAR1(form=~year)\n",sep="") + ## create a sequence of temperature that spans your temperature #####not use anymore + ## http://zevross.com/blog/2014/09/15/recreate-the-gam-partial-regression-smooth-plots-from-r-package-mgcv-with-a-little-style/ #### method for the plot + ####dd$yearf=factor(dd$year) PAS BON j'ai modifié la ligne suivante en mettant s(year) et plus s(yearsf) + gammgg <- gamm(indic ~ s(year), data=dd,random=reStruct(object = ~ 1| id_plot, pdClass="pdDiag"),correlation=corAR1(form=~year)) #### spline sur l'année, effet aleatoire des carres sur ordonnée à l'origine, methode autoregressive sur l'année N-1 / spline on the year, random effect of the plots on the intercept, autoregressive method on the year-1 + maxyear<-max(dd$year) + minyear<-min(dd$year) + year.seq<-sort(unique(c(minyear:maxyear,(seq(minyear, maxyear,length=1000))))) + year.seq<-data.frame(year=year.seq) + # predict only the temperature term (the sum of the ########### ???? not use anymore + # term predictions and the intercept gives you the overall########### ???? not use anymore + # prediction)########### ???? not use anymore + preds<-predict(gammgg$gam, newdata=year.seq, type="terms", se.fit=TRUE) #### Utilise model pour predire les valeurs de indic sur sequence d'années defini au dessus \ Use of the model to predict value of the indicator in the year sequence define above + # set up the temperature, the fit and the upper and lower########### ???? not use anymore + # confidence interval########### ???? not use anymore + year <-year.seq$year + realYear <- sort(unique(dd$year)) + fit<-as.vector(preds$fit) + init <- fit[1] + fit.up95 <- fit-1.96*as.vector(preds$se.fit) + fit.low95 <- fit+1.96*as.vector(preds$se.fit) + # ggGamData <- data.frame(year=year, csi=fit,ic_low95 = fit.low95, ic_up95 = fit.up95) + fit <- fit - init ### Réechelonne les predictions du modèle sur la 1ère valeure de la prediction ? ne sait pas pourquoi + fit.up95 <- fit.up95 - init ### Réechelonne IC superieur sur la 1ère valeure de la prediction ? ne sait pas pourquoi + fit.low95 <- fit.low95 - init ### Réechelonne IC inferieur sur la 1ère valeure de la prediction ? ne sait pas pourquoi + ggGamData <- data.frame(year=year, indic=fit,ic_low95 = fit.low95, ic_up95 = fit.up95) ####### Recupère les resultats des modèles / retrieve the results of the models + + + ## The ggplot: + gg <- ggplot(data=ggGamData,aes(x=year,y=indic))+ geom_line(size=1,colour=couleur) + if (ic) { + gg <- gg + geom_ribbon(aes(ymin=ic_low95, ymax=ic_up95),fill = couleur,alpha=.2) + } + gg <- gg + geom_point(data = subset(ggGamData,year %in% realYear),size=3,colour=couleur) + geom_point(data = subset(ggGamData,year %in% realYear),size=1.5,colour="white") + gg <- gg + labs(y=titreY,x=titreX,title=titre)+scale_x_continuous(breaks=pretty_breaks()) + ggsave(paste("Output/fig",indicator,"_plot",id,".png",sep=""),gg) + #cat("\n--> Output/fig",indicator,"_plot",id,".png\n",sep="") + + tabPredict <- subset(ggGamData,year %in% realYear)########### Tableau des resultats pour ne prendre que les valeurs d'IC pour l'année pas entre les années (spline sur annee) !!!plus utilisé!! / Table of the results not taking confidence interval between year but at each year (because of the spline of year) + colnames(tabPredict)[1:2] <- c("annee",paste(indicator,"_predict",sep="")) + tabgamm <- merge(tabfgamm,tabPredict,by="annee") #### Desactivation car merge sortie de modèles différents (le modèle dont on tire les coef de regression pour année, avec spli ne sur les coordonnées geo vs celui pour faire la figure avec splin sur année uniquement) / not use anymore (as before) because use the results of the restricted model with the spline on the year while the better analysis is on full model with the spline on gps coordinates + ##tabgamm <- tabfgamm #### remplace la ligne au dessus / replace the line above + write.table(tabPredict,paste("Output/",indicator,"_gammsmooth",id,".tabular",sep=""),row.names=FALSE,sep="\t") + }else { + tabgamm = tabfgamm + } + ###### CROCHET deplacer apres le dernier ggplot ici mais dans version Romain CROCHET placé AVANT le dernier ggplot qui n'était pas dans la condition if(init_1989) + cat("\nEstimation de la tendence ",indicator,"~ year+s(longitude_grid_wgs84,latitude_grid_wgs84,bs='sos'),random=reStruct(object = ~ 1| id_plot,correlation=corAR1(form=~year)\n") + gammc <- gamm(indic~year+s(longitude_grid_wgs84,latitude_grid_wgs84,bs="sos"), data=dd,random=reStruct(object = ~ 1| id_plot, pdClass="pdDiag"),correlation=corAR1(form=~year))### spline sur les coordonnées, effet aleatoire des carres sur ordonnée à l'origine, methode autoregressive sur l'année N-1 / spline on the gps coordinates, random effect of the plots on intercept, autoregressive method on the year-1 + sgammc=summary(gammc$gam) + coefannee <- sgammc$p.coeff[2] #### coefficient de regression de la variable année / regression coefficient of the variable "year" + ## erreur standard / standard error + erreuran <- sgammc$se[2] + ## p value + pval <- sgammc$p.pv[2] + + #### Calcul des intervalles de confiances / calculation of the confidence intervals + if(ic) { + # gammc.sim <- sim(gammc)###################### VERSION ROMAIN mais fct sim() marche pas avec Gamm / old version using function sim() but did not work with Gamm models + # ic_inf_sim <- c(0,tail(apply(coef(gammc.sim), 2, quantile,.025),pasdetemps)) + # ic_sup_sim <- c(0,tail(apply(coef(gammc.sim), 2, quantile,.975),pasdetemps)) + icalpha052 <- as.data.frame(confint(gammc$gam))[2,1:2] ########## VERSION BENJ + ic_inf_sim2 <- icalpha052[,1] + ic_sup_sim2 <- icalpha052[,2] + } else{ + ic_inf_sim2 <- "not assessed" + ic_sup_sim2 <- "not assessed" + } + tabcgamm <- data.frame(model = "gamm numeric(year) plot",annee = NA,coef = coefannee,se = erreuran,pval,signif = pval<seuilSignif, indicator= indicator , Lower_ci = as.factor(ic_inf_sim2), upper_ci = as.factor(ic_sup_sim2), csi_predict =NA ,ic_low95 =NA,ic_up95=NA)#### recupère les resultats des modèles avec interval de confiance / retrieve results of the models used with confidence interval + ########### MODIF tabcgamm en remplacant ic_low95 ic_up95 par Lower_ci et upper_ci pour coller avec les sorties des modèles "pour les stats" et non celui utilisé pour le graphe uniquement, et rajout des colonnes spécifique a model ggsmooth pour les garder +#### ai rajouté aussi as.factor(ic) car ne savait pas pourquoi mais tabfgamm sont en facteur et besoin de la meme class pour rbind() ci dessous + tabcgamm <- tabcgamm[,colnames(tabgamm)] ## + tabgamm <- tabgamm[,colnames(tabcgamm)] #### recupère que les colonnes de tabcgamm donc perds les infos du modèle du ggsmooth si pas declarer dans le tableau tabcgamm (maintenant c fait) + tabgamm <- rbind(tabgamm,tabcgamm) + write.table(tabgamm,paste("Output/",indicator,"_gammCOMPLET_",id,".tabular",sep=""),row.names=FALSE,sep="\t") + #cat("\n --> Output/",indicator,"_gammPlot_",id,".tabular\n",sep="") + } + + + if (methode == "glmmtmb") { + #cat("Method : lmer \n") + cat("Method : glmmTMB \n") + ################### + ### Utilisation des modèles mixtes pour obtenir les tendances d evolution par an du csi cti ou ctri / Use of mixte model for the estimation of the annual variations of the csi cti or ctri + #cat("\nEstimation de la variation annuelle lmer(",indicator,"~ factor(year)+(1|id_plot)\n",sep="") + cat("\nEstimation de la variation annuelle glmmTMB(",indicator,"~ factor(year)+(1|id_plot)\n",sep="") + #md.f <- lmer(indic~ factor(year)+(1|id_plot),data=dd) ##### effet aleatoire liés aux carrés sur l'ordonnée à l'origine / random effects of plots on intercept + md.f <- glmmTMB(indic~ factor(year)+(1|id_plot),data=dd) + smd.f <- summary(md.f) + # coefdata.f <- as.data.frame(smd.f$coefficients) ### version pour sortie lmer() + coefdata.f <- as.data.frame(smd.f$coefficients$cond[-1,]) + coefdata.f <- data.frame(model="Annual fluctuation", variable = rownames(coefdata.f),coefdata.f) + # ggdata <<- data.frame(year=c(as.numeric(substr(coefdata.f$variable[-1],13,16))),##### version pour sortie lmer() + ggdata <- data.frame(year=c(as.numeric(substr(coefdata.f$variable,13,17))), + estimate=c(coefdata.f$Estimate), + se=c(coefdata.f$Std..Error)) ##################### resultat du modèle / results of the models + #ggdata$estimate <- ggdata$estimate + #ggdata$se.supR <- ggdata$estimate + ggdata$se ############################################################################## METHODE ROMAIN + #ggdata$se.infR <- ggdata$estimate - ggdata$se + # ggdata$estimate2 <- c(coefdata.f$Estimate[1],coefdata.f$Estimate[1] + coefdata.f$Estimate[-1]) + # ggdata$se.sup2 <- ggdata$estimate2 + ggdata$se + # ggdata$se.inf2 <- ggdata$estimate2 - ggdata$se + #prof <- profile(md.f) #### Nouvel interval de confiance avec utilisation du logarithme des ecarts types / logarithms of standard deviations are used, while varianceProf converts from the standard-deviation to the variance scale + MODconfint <- confint (md.f) #### plus rapide de ne pas passer par la fonction profile et pas indispensable fonctionne aussi directement sur modele mixte md.f / more rapid using both function profile and confint but works also directly on output of the model + se.sup <- MODconfint[2:nban,2]#### [2:nban+2,2] version pour sortie lmer() + se.inf <- MODconfint[2:nban,1]#### [2:nban+2,2] version pour sortie lmer() + if (ic) { + ggdata$se.sup <- se.sup + ggdata$se.inf <- se.inf + } else{ + ggdata$se.sup <- "not assessed" + ggdata$se.inf <- "not assessed" + } + coefdata.f$se.inf <- ggdata$se.inf + coefdata.f$se.sup <- ggdata$se.sup + #gg <<- ggplot(ggdata,aes(x=year,y=estimate))+ geom_ribbon(ymin=ggdata$se.infR,ymax=ggdata$se.supR,alpha=.25)+geom_errorbar(ymin=ggdata$se.infR,ymax=ggdata$se.supR,width=0,alpha=.25)+ geom_point() + geom_line() + ylim(min(ggdata$se.infR),max(ggdata$se.supR)) + labs(x="Years",y=paste(indic," variation",sep="")) ##### AVEC INTERVAL ROMAIN + gg <- ggplot(ggdata,aes(x=year,y=estimate))+ geom_point() + geom_line() + labs(x="Years",y=paste(indicator," variation",sep="")) #####+ ylim(min(ggdata$se.inf),max(ggdata$se.sup)) + gg <- gg + geom_line(size=1.5,colour=couleur)+ geom_point(size=3,colour=couleur) + geom_point(size=1.5,colour="white") + if (ic) { + gg <- gg + geom_ribbon(aes(ymin=ggdata$se.inf,ymax=ggdata$se.sup),alpha=.25,fill = couleur) + geom_errorbar(ymin=ggdata$se.inf,ymax=ggdata$se.sup,width=0,alpha=.25) + } + ggfile <- paste("Output/",indicator,"_glmmTMB_",id,".png",sep="") + ggsave(ggfile,gg) + + ############ Estimation de la tendance sur la periode étudiée / Trends estimation on the time period studied + #cat("\nEstimation de la tendance lmer(",indicator,"~ year+(1|id_plot)\n",sep="") + cat("\nEstimation de la tendance glmmTMB(",indicator,"~ year+(1|id_plot)\n",sep="") + #md.c <- lmer(indic~ year+(1|id_plot),data=dd)##### effet aleatoire liés aux carrés sur l'ordonnée à l'origine / random effects of plots on intercept ### version lmer + md.c <- glmmTMB(indic~ year+(1|id_plot),data=dd) + smd.c<-summary(md.c) + # coefdata.c <- as.data.frame(smd.c$coefficients) #### pour la version lmer + coefdata.c <- as.data.frame(smd.c$coefficients$cond)[2,] + #profc=profile(md.c) ######### Ajout des intervalles de confiances / addition of the confidence intervals + if (ic) { + MODconfint=confint(md.c) ### plus rapide de ne pas passer par profile + se.inf=MODconfint[2,1]### [4,1] pour la version lmer + se.sup=MODconfint[2,2]### [4,2] pour la version lmer + } else{ + se.inf <- "not assessed" + se.sup <- "not assessed" + } + coefdata.c <- data.frame(model = "Linear trend", variable = rownames(coefdata.c),coefdata.c,se.inf,se.sup) + coefdata <- rbind(coefdata.c,coefdata.f) + write.table(coefdata,paste("Output/GlmmTMB_coefficient_",indicator,id,".tabular",sep=""),row.names=FALSE,sep="\t") + write.table(ggdata,paste("Output/ggdata_",indicator,id,".tabular",sep=""),row.names=FALSE,sep="\t") + smd.file <- paste("Output/summary_lmer_",indicator,"_",id,".txt",sep="") + ##################### + } +} + + + + + +################## +### Do your analysis + +csi_cti_ctri(tabCLEAN=tabCLEAN,coordCarre=coordCarre,spTrait=spTrait,dd=NULL,Var=Var,indicator=indicator,ic=ic,plot_smooth = plot_smooth,methode=methode)#,init_1989 = FALSE) ##### exemple pour l'indicateur csi sans csi déjà calculé donc à partir du ssi avec interval de confiance et utilisant modele mixte / example for the csi index which is not already calculated from the ssi with confidence interval using the mixte model +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/FunctTrendSTOCGalaxy.r Thu Apr 02 03:35:29 2020 -0400 @@ -0,0 +1,776 @@ +#!/usr/bin/env Rscript + + +################################################################################################################################## +############## FUNCTION TO CALCULATE AND PLOT EVOLUTION OF SPECIES POPULATION function:main.glm ############################## +################################################################################################################################## + +#### Based on Romain Lorrillière R script +#### Modified by Alan Amosse and Benjamin Yguel for integrating within Galaxy-E + +##### workes with the R version 3.5.1 (2018-07-02) +##### Package used with the version: +#suppressMessages(library(lme4)) version 1.1.18.1 +#suppressMessages(library(ggplot2)) version 3.0.0 +#suppressMessages(library(speedglm)) version 0.3.2 +#suppressMessages(library(arm)) version 1.10.1 +#suppressMessages(library(reshape)) version 0.8.8 +#suppressMessages(library(data.table)) version 1.12.0 +#suppressMessages(library(reshape2)) version 1.4.3 + + + +######################################### debut de la fonction makeTableAnalyse / stard of the function makeTableAnalyse +## mise en colonne des especes et rajout de zero mais sur la base des carrés selectionné sans l'import / Species are placed in separated columns and addition of zero on plots where at least one selected species is present + +makeTableAnalyse <- function(data) { + tab <- reshape(data + ,v.names="abond" + ,idvar=c("carre","annee") + ,timevar="espece" + ,direction="wide") + tab[is.na(tab)] <- 0 ###### remplace les na par des 0 / replace NAs by 0 + + colnames(tab) <- sub("abond.","",colnames(tab))### remplace le premier pattern "abond." par le second "" / replace the column names "abond." by "" + return(tab) +} + +######################################### fin de la fonction makeTableAnalyse / end of the function makeTableAnalyse + + + + + +############################################# les fonctions qui filtrent les données pas suffisantes pour analyses fiables / The filtering functions removing species with not enough data to perform accurate analyses + +filter_absent_species<-function(tab){ +##################### Filtre les espèces jamais présentes (abondance=0) / Filter of species with 0 abundance +################################################################################# PARTIE POTENTIELLEMENT ISOLABLE ET INSERABLE AVANT LA BOUCLE = permet de gagner du temps sur la boucle car supprime sps pas vu, donc pas repris par la boucle + + ## Fait la somme des abondances totales par espèce / calculate the sum of all abundance per species + if(ncol(tab)==3) { + tabSum <- sum(tab[,3])## cas d'une seule especes (problème de format et manip un peu differente) / when selecting only one species, use a different method + names(tabSum) <- colnames(tab)[3] + } else { ## cas de plusieurs espèce/ when selecting more than one species + tabSum <- colSums(tab[,-(1:2)]) + } + ## colNull= espece(s) toujours absente /species with 0 total abundance + colNull <- names(which(tabSum==0)) + ## colconserve= espece(s) au moins presente 1 fois/ species at least with 1 presence + colConserve <- names(which(tabSum>0)) + ## Affichage des espèces rejetees / show species eliminated for the analyses + if(length(colNull)>0){ + cat("\n",length(colNull)," Species removed from the analysis, abundance is always 0.\n\n",sep="") #Espèces enlevées de l'analyse car abondance toujours égale a 0\n\n",sep="") + #tabNull <- data.frame(Code_espece = colNull, nom_espece = tabsp[colNull,"nom"]) + #cat("\n\n",sep="") + tab <- tab[,c("carre","annee",colConserve)] + } +################################################################################ FIN DE LA PARTIE ISOLABLE + return(tab) +} + + + + +###################### Filtre les especes trop rare pour avoir des analyses robustes i.e. espèce non presente la 1ère année, avec plus de 3 ans consecutif sans données et moins de 3 ans consécutif avec données +###################### Filter too rare species for accurate analysis i.e. species absent the first year, with more than 3 consecutive years with 0 abundance, or with less than 3 consecutive years with presence + +### +filter_rare_species<-function(tab){ + exclude_threshold <- NULL + ## calcul et filtre pour chaque (colonne) espece / measure and filter for each species + for(i in 3:ncol(tab)) { + ## v =abondance par annee / v= abundance per year + v <- tapply(tab[,i],tab$annee,sum) #################### + ## v0 =presence(1) abscence(0) per year + v0 <- ifelse(v>0,1,0) ##### + tx <- paste(v0,collapse="") #### colle les 0 et 1 / stick the 0 and 1 + + p <- unlist(strsplit(tx,"0"))#### Enleve les 0, ce qui séparent les sequences de "1", les sequences de "1" = nbre d'années consécutives avec data / remove 0, splitting sequences of "1" which correspond to consecutve year with data (e.g. 111 = 3 years consecutive years with data) + p <- p[p!=""] #### ne garde pas les partie sans 1 ou 0 dans les sequences + ## gsSup0 = plus grande serie temporelle de presence =calcul du nbre de 1 consécutif max / calcul of the biggest temporal series which corresponds to the maximum number of consecutive "1" + gsSup0 <- max(nchar(p))#### + ## gsInf0 plus grande serie temporelle d'absccence ou sans données = enlève les 1 séparant sequence de 0 qui correspondent au nbre d'année consecutive sans données / calcul of the biggest temporal series without data which corresponds to max numbzer fo consecutive "0" + gsInf0 <- max(nchar(unlist(strsplit(tx,"1")))) #### + ## y0is0 absence la premiere annee + y0is0 <- v0[1]==0 #### True ou false pour presence de "0"(=pas de données) dans la 1ère année / look if the first year of the time sequence analyzed has no data + ## seuil d'exclusion / exclusion threshold + exclude_threshold <- c(exclude_threshold,as.vector(ifelse( y0is0 | gsInf0 > 3 | gsSup0 < 3 ,"exclu","bon"))) ############## exclu sps absente la 1ère année, avec plus de 3 ans consécutifs sans données, et avec moins de 3 années consécutives sans données / indicate if the max consecutive year with data and without data, as well as whether the first year of the time sequence analyzed has data + } + names(exclude_threshold) <- colnames(tab)[3:ncol(tab)] + + ## colonnes conservees avec assez de données / Column with enough data + colConserve <- names(exclude_threshold)[exclude_threshold=="bon"] + + + ## colonnes supprimees / Column that will conserved + colSupr <- names(exclude_threshold)[exclude_threshold=="exclu"] + tabCLEAN <- tab[,c("carre","annee",colConserve)] #### Garde les sps à conserver / select only species with enough data + lfiltre <- list(tabCLEAN=tabCLEAN,colConserve=colConserve,colSupr=colSupr) + +################################################################################# + + ## colConserve espece conservees / extract species that will be kept to print them + colConserve <- lfiltre$colConserve + ## colsupr espece trop rare et donc supprimée de l'analyse / extract species that will be deleted to print them + colSupr <- lfiltre$colSupr + ## affichage des especes retirer de l'analyse / print species that will be deleted + if(length(colSupr)>0){ + cat("\n",length(colSupr)," Rare species removed from the analysis.\n\n",sep="") + #tabSupr <- subset(tabsp,espece %in% colSupr ,select=c("espece","nom")) + #tabSupr <- tabSupr[order(tabSupr$espece),] + #cat("\n\n",sep="") + + } + if(length(colConserve)==0) { + mess <- "No species available to calculate abundance variation in this dataset." + stop(mess) + } + + tabCLEAN <- lfiltre$tabCLEAN + + #### MARCHE PAS NE SAIT PAS PQUOI + tabCLEAN <- melt(tabCLEAN, id.vars=c("carre", "annee")) #### remet le format de base :le nom d'espèce et abondance dans des colonnes séparées / back to the first format of the file: species name and abundance in separated column + + colnames(tabCLEAN)[3:4] <- c("espece","abond") + tabCLEAN$annee <- as.numeric(as.character(tabCLEAN$annee)) +################################################################################ + return(tabCLEAN) +} + +####################################################################################################################### fin des 2 fonctions de filtre des données / end of the two function to filter the data + + + + + + + + + + + + + + + + + + + + + + + + + + +############################################################################################ debut de la Function main.glm / start of the function main.glm + +main.glm <- function(id="france",donneesAll=dataCLEAN,assessIC= TRUE,tabsp=tabsp,annees=annees,figure=TRUE,description=TRUE,tendanceSurFigure=TRUE, ###### declaration des arguments listSp=sp était avant declaré avant la fonction mais il me semble que ca marche aussi comme cela + seuilOccu=14,seuilAbond=NA) { + + + + filesaveAn <- paste("Output/",id,"/variationsAnnuellesEspece_",id,".tabular", ##### Nom du dossier ET fichier de sortie des resultats par année / name of the output file with results for each years + sep = "") + filesaveTrend <- paste("Output/",id,"/tendanceGlobalEspece_",id,".tabular", ##### Nom du dossier ET fichier de sortie des resultats pour la période "annee" complete / name of the output file with the results for the period + sep = "") + fileSaveGLMs <- paste("Output/",id,"/listGLM_",id,sep = "") ##### Nom du dossier ET fichier de sortie des modèles lineaire generalisés / name of the output file of the generlized linear models + + + + + seuilSignif <- 0.05 ## seuil de significativite / significancy threshold + + + rownames(tabsp) <- tabsp$espece ## change nom des lignes de tabsp (table de reference des especes) + + + ##vpan vecteur des panels de la figure ###### POUR FAIRE LES GRAPHIQUES + vpan <- c("Variation abondance") + if(description) vpan <- c(vpan,"Occurrences","Abondances brutes") + + + ## specifications des variables temporelles necesaires pour les analyses / specification of temporal variable necessary for the analyses + annee <- sort(unique(donneesAll$annee)) + nbans <- length(annee) + pasdetemps <- nbans-1 + firstY <- min(annee) + lastY <- max(annee) + + + + + + ## Ordre de traitement des especes ### order of species to be analyzed + spOrdre <- aggregate(abond~espece,data=donneesAll,sum) #### calcul les sommes des abondances pour ordonner / calculate the sum for the ordination + spOrdre <- merge(spOrdre,tabsp,by="espece") #### rajoute la colonne avec les abondances totales par espece / add a new column with the sum + + spOrdre <- spOrdre[order(as.numeric(spOrdre$indicateur),spOrdre$abond,decreasing = TRUE),] #### mets les especes plus abondantes en premiers (plus long pour faire tourner le modèle) / order the species by abundance, the most abundant species being the less fast analysis + + + listSp <- spOrdre$espece + i <- 0 + nbSp <- length(listSp) + # browser() + ## analyse par espece +### browser() + ## affichage des especes conservees pour l'analyse ### PAS SUR QUE CE SOIT ENCORE UTILE + cat("\n",nbSp," Espèces conservées pour l'analyse\n\n",sep="") + rownames(tabsp) <- tabsp$espece + print(tabsp[,1:2]) + #tabCons <- data.frame(Code_espece = listSp, nom_espece = tabsp[as.character(listSp),"nom"]) + #print(tabCons) + cat("\n\n",sep="") + flush.console() + + + ## initialisation de la liste de sauvegarde + + +##browser() + + for (sp in listSp) { ######## Boucle pour analyse par espèce / loop for the analysis by species + + + i <- i + 1 + + d <- subset(donneesAll,espece==sp) ## d data pour l'espece en court / cut the data keeping only the i species + + #nomSp <- as.character(tabsp[sp,"nom"]) ## info sp + nomSp <- tabsp$nom[which(tabsp$espece==sp)] ## info sp + cat("\n(",i,"/",nbSp,") ",sp," | ", nomSp,"\n",sep="") + flush.console() + + #indic <- tabsp[sp,"indicateur"] ## indic :espece utilisee pour le calcul des indicateurs par groupe de specialisation / list the species used as species indicators by trophic specialization + indic <- tabsp$indicateur[which(tabsp$espece==sp)] ## indic :espece utilisee pour le calcul des indicateurs par groupe de specialisation / list the species used as species indicators by trophic specialization + nb_carre = tapply(rep(1,nrow(d)),d$annee,sum) ## nb_carre nombre de carre suivie par annee / number of plots per year + + nb_carre_presence = tapply(ifelse(d$abond>0,1,0),d$annee,sum) ## nb_carre_presence nombre de carre de presence par annee / number the plots where the species were observed + + tab2 <- data.frame(annee=rep(annee,2),val=c(nb_carre,nb_carre_presence),LL = NA,UL=NA, ## tab2 table de resultat d'analyse / data.frame of the analyses results + catPoint=NA,pval=NA, + courbe=rep(c("carre","presence"),each=length(annee)),panel=vpan[2]) + tab2$catPoint <- ifelse(tab2$val == 0,"0",ifelse(tab2$val < seuilOccu,"infSeuil",NA)) + + abond <- tapply(d$abond,d$annee,sum) ## abond abondance par annee / abundance per year + + tab3 <- data.frame(annee=annee,val=abond,LL = NA,UL=NA,catPoint=NA,pval=NA,courbe=vpan[3],panel=vpan[3]) ## table pour la figure / data.frame made to realize the graphical outputs + tab3$catPoint <- ifelse(tab3$val == 0,"0",ifelse(tab3$val < seuilAbond,"infSeuil",NA)) + + ## GLM pour calcul des tendances annuelles de l'evolution des populations / GLM to measure annual tendency of population evolution + formule <- as.formula("abond~as.factor(carre)+as.factor(annee)") #### specification du modèle = log lineaire / specifying the model = log linear + if(assessIC) {##### OPTION A RENTRER AU DEBUT PEUT ËTRE A METTRE DANS LES ARGUMENTS SI LAISSE LE CHOIX SINON L ARG PAR DEFAUT LORS DE LA DECLARATION DE LA FONCTION + glm1 <- glm(formule,data=d,family=quasipoisson) ##### fit model lineaire general avec intervalle de confiance disponible / fit linear and generalized model with confidence intervalle available + } else { + glm1 <- try(speedglm(formule,data=d,family=quasipoisson())) ##### fit modele lineaire et generaux pour les gros jeux de données / fit of linear and generalized model for large-medium dataset + if(class(glm1)[1]=="try-error") + glm1 <- glm(formule,data=d,family=quasipoisson) ##### comprends pas mais je pense que c'est speedglm qui marche pas avec toutes les données + } + sglm1 <- summary(glm1) #### sortie du modele / output of the model + sglm1 <- coefficients(sglm1) ### coefficient regression de chaque variable avec les résultats des tests statistiques / regression coefficient of each predictive variables with results of the statistical tests + sglm1 <- tail(sglm1,pasdetemps) #### recupére les derniers elements du modèle avec la taille de l'objet "pasdetemps" car le nombre de coef = nbre d'année et pas les coefficient de regression de la variable carre / retrieve only the coefficient regression of the variable year + coefan <- as.numeric(as.character(sglm1[,1]))#### coefficient de regression de la variable année (1 pour chaque année) + + coefannee <- c(1,exp(coefan))## coefannee vecteur des variation d'abondance par annee avec transformation inverse du log :exp() / regression coefficient of the year back transformed from log(abundance) : exp() + + erreuran <- as.numeric(as.character(sglm1[,2])) #### erreur standard sur le coefficient de regression de la variable annee / standard error on the regression coefficient of the year + erreurannee1 <- c(0,erreuran*exp(coefan))## erreur standard par année / the standard error per year ###### LA J AI UN DOUTE NORMALEMENT INTERVAL DE CONF C CI_lower <- coefficients(lin_mod)[2] - 1.96*summary(lin_mod)$coefficients[2,2] + ####CI_upper <- coefficients(lin_mod)[2] + 1.96*summary(lin_mod)$coefficients[2,2] + + pval <- c(1,as.numeric(as.character(sglm1[,4])))###### p value + + ## calcul des intervalle de confiance avec methode de bootstrap pour simuler des coef de regress sur lequel intervalle de conf sont mesurés/ calcul of the confidence interval using bootstrap method to simulate set regression coefficients and s.e.with uncertainty POURQUOI PAS UTILISE confint.glm() ou boot() ou ci.boot() + + if(assessIC) { + glm1.sim <- sim(glm1) + ic_inf_sim <- c(1,exp(tail(apply(coef(glm1.sim), 2, quantile,.025),pasdetemps))) + ic_sup_sim <- c(1,exp(tail(apply(coef(glm1.sim), 2, quantile,.975),pasdetemps))) + } else { + ic_inf_sim <- NA + ic_sup_sim <- NA + + } + + + + tab1 <- data.frame(annee,val=coefannee, ## tab1 table pour la realisation des figures / table for the graphical outputs ### 2EME POUR GRAPH ici ce sont le coef de regress annee en fonction des annéés alors que tab3 c'est les abondance en fct des années et tab2 nombre de carré total et avec presence + LL=ic_inf_sim,UL=ic_sup_sim, + catPoint=ifelse(pval<seuilSignif,"significatif",NA),pval, + courbe=vpan[1], + panel=vpan[1]) + ## netoyage des intervalle de confiance mal estimés et qd donnees pas suffisantes pour calcul d'IC /cleaning of wrong or biaised measures of the confidence interval + if(assessIC) { + tab1$UL <- ifelse( nb_carre_presence==0,NA,tab1$UL) + tab1$UL <- ifelse(tab1$UL == Inf, NA,tab1$UL) + tab1$UL <- ifelse(tab1$UL > 1.000000e+20, NA,tab1$UL) + tab1$UL[1] <- 1 + tab1$val <- ifelse(tab1$val > 1.000000e+20,1.000000e+20,tab1$val) + } + ## indice de surdispersion / overdispersion index + ## browser() + if(assessIC) dispAn <- glm1$deviance/glm1$null.deviance else dispAn <- glm1$deviance/glm1$nulldev + + + ## tabAn table de sauvegarde des resultats par année / table of the results per year ###### reprends bcp de tabl DIFFERENCE AVEC tab2 c les abondances relatives, alors que nb de carre, nb de carre presnce, p val sont aussi ds tab2 + tabAn <- data.frame(id,code_espece=sp, nom_espece = nomSp,indicateur = indic,annee = tab1$annee, + abondance_relative=round(tab1$val,3), + IC_inferieur = round(tab1$LL,3), IC_superieur = round(tab1$UL,3), + erreur_standard = round(erreurannee1,4), + p_value = round(tab1$pval,3),significatif = !is.na(tab1$catPoint), + nb_carre,nb_carre_presence,abondance=abond) + + ## GLM pour calcul des tendance generale sur la periode avec modele log lineaire / GLM to measure the tendency of population evolution on the studied period with log linear model + formule <- as.formula(paste("abond~ as.factor(carre) + annee",sep="")) ### + # browser() + + + if(assessIC) { + md2 <- glm(formule,data=d,family=quasipoisson) } + else { + md2 <- try(speedglm(formule,data=d,family=quasipoisson()),silent=TRUE) + + if(class(md2)[1]=="try-error") + md2 <- glm(formule,data=d,family=quasipoisson) + } + + + smd2 <- summary(md2) #### sortie du modele / output of the model + smd2 <- coefficients(smd2) ### coefficient regression de chaque variable avec les résultats des tests statistiques / regression coefficient of each predictive variables with results of the statistical tests + smd2 <- tail(smd2,1) ### coefficient regression de variable annee avec les résultats des tests statistiques / regression coefficient of the variable year with results of the statistical tests + + + coefan <- as.numeric(as.character(smd2[,1])) ## tendences sur la periode = coefficient regression de variable annee / tendency of population evolution on the studied period = regression coefficient of the variable year + trend <- round(exp(coefan),3) + + pourcentage <- round((exp(coefan*pasdetemps)-1)*100,2) ## pourcentage de variation sur la periode / percentage of population variation on the studied period + pval <- as.numeric(as.character(smd2[,4])) + + erreuran <- as.numeric(as.character(smd2[,2])) #### récuperer l'erreur standard / retrieve the error + ## erreur standard + erreurannee2 <- erreuran*exp(coefan) + + + ## calcul des intervalle de confiance avec methode de bootstrap pour simuler des coef de regress sur lequel intervalle de conf sont mesurés/ calculating the confidence interval based on bootstrap method to simulate set regression coefficients and s.e.with uncertainty + LL <- NA + UL <- NA + if(assessIC) { + md2.sim <- sim(md2) + LL <- round(exp(tail(apply(coef(md2.sim), 2, quantile,.025),1)),3) + UL <- round(exp(tail(apply(coef(md2.sim), 2, quantile,.975),1)),3) + } else { + LL <- NA + UL <- NA + } + + ## tab1t table utile pour la realisation des figures / table used for the figures + tab1t <- data.frame(Est=trend, + LL , UL, + pourcent=pourcentage,signif=pval<seuilSignif,pval) + + + trendsignif <- tab1t$signif + pourcent <- round((exp(coefan*pasdetemps)-1)*100,3) + ## mesure de la surdispersion / overdispersion measurment + + if(assessIC) dispTrend <- md2$deviance/md2$null.deviance else dispTrend <- md2$deviance/md2$nulldev + + + + ## classement en categorie incertain /classifying wrong or not reliable results + # browser() + if(assessIC) { + if(dispTrend > 2 | dispAn > 2 | median( nb_carre_presence)<seuilOccu) catIncert <- "Incertain" else catIncert <-"bon" ##### en fonction de l'indice de surdispersion et presence < à seuil occurence / based on the overdispersion index and the presence on a minimum number of plots + vecLib <- NULL + if(dispTrend > 2 | dispAn > 2 | median( nb_carre_presence)<seuilOccu) { + if(median( nb_carre_presence)<seuilOccu) { + vecLib <- c(vecLib,"espece trop rare") + } + if(dispTrend > 2 | dispAn > 2) { + vecLib <- c(vecLib,"deviance") + } + } + raisonIncert <- paste(vecLib,collapse=" et ") + } else { + catIncert <- NA + raisonIncert <- NA + } + + + + ## affectation des tendence EBCC / retrieve the trend of population evolution on the studied period + catEBCC <- NA + if(assessIC) catEBCC <- affectCatEBCC(trend = as.vector(trend),pVal = pval,ICinf=as.vector(LL),ICsup=as.vector(UL)) else catEBCC <- NA + ## table complete de resultats pour la periode etudiée / complete table with results for the studied period + # browser() + tabTrend <- data.frame( + id,code_espece=sp,nom_espece = nomSp,indicateur = indic, + nombre_annees = pasdetemps,premiere_annee = firstY,derniere_annee = lastY, + tendance = as.vector(trend) , IC_inferieur=as.vector(LL) , IC_superieur = as.vector(UL),pourcentage_variation=as.vector(pourcent), + erreur_standard = as.vector(round(erreurannee2,4)), p_value = round(pval,3), + significatif = trendsignif,categorie_tendance_EBCC=catEBCC,mediane_occurrence=median( nb_carre_presence) , + valide = catIncert,raison_incertitude = raisonIncert) + + + if(assessIC) listGLMsp <- list(list(glm1,glm1.sim,md2,md2.sim)) else listGLMsp <- list(list(glm1,md2)) + names(listGLMsp)[[1]] <-sp + fileSaveGLMsp <- paste(fileSaveGLMs,"_",sp,".Rdata",sep="") + + save(listGLMsp,file=fileSaveGLMsp) + cat("--->",fileSaveGLMsp,"\n") + flush.console() + + if(sp==listSp[1]) { + glmAn <- tabAn + glmTrend <- tabTrend + } else { + glmAn <- rbind(glmAn,tabAn) + glmTrend <- rbind(glmTrend,tabTrend) + } + ## les figures + if(figure) { + ## table complete pour la figure en panel par ggplot2 + ## table pour graphe en panel par ggplot2 + if(description) dgg <- rbind(tab1,tab2,tab3) else dgg <- tab1 + ## les figures + + ggplot.espece(dgg,tab1t,id,serie=NULL,sp,valide=catIncert,nomSp,description,tendanceSurFigure,seuilOccu=14,vpan = vpan,assessIC=assessIC) + + } + + + + + } + + write.table(glmAn,filesaveAn,row.names=FALSE,quote=FALSE,sep="\t",dec=".",fileEncoding="UTF-8") + cat("--->",filesaveAn,"\n") + write.table(glmTrend,filesaveTrend,row.names=FALSE,quote=FALSE,sep="\t",dec=".",fileEncoding="UTF-8") + cat("--->",filesaveTrend,"\n") + + + flush.console() + + + +} +########################################################################################################## Fin de la fonction main.glm / end of the function main.glm + + + + + + + + + + + + + + + +########################################################################################################### fonction appelée par main.glm renvoyant la categorie European Bird Census Council en fonction des resultats des modèles / function called by main.glm to classify results depending on the quality of the data and analyses +## renvoie la categorie EBCC de la tendance en fonction +## trend l'estimateur de la tendance / estimation of the trends +## pVal la p value +## ICinf ICsup l intervalle de confiance a 95 pourcent +affectCatEBCC <- function(trend,pVal,ICinf,ICsup){ + catEBCC <- ifelse(pVal>0.05, + ifelse(ICinf < 0.95 | ICsup > 1.05,"Incertain","Stable"), + ifelse(trend<1, + ifelse(ICsup<0.95,"Fort declin","Declin moderee"), + ifelse(ICinf>1.05,"Forte augmentation","Augmentation modere"))) + return(catEBCC) +} + +############################################################################################################ fin de la fonction renvoyant la categorie EBCC / end of the function main.glm + + + + + + + + +############################################################################################################ fonction graphique appelée par main.glm / function called by main.glm for graphical output +ggplot.espece <- function(dgg,tab1t,id,serie=NULL,sp,valide,nomSp=NULL,description=TRUE, + tendanceSurFigure=TRUE,seuilOccu=14, vpan,assessIC=TRUE) { + + # serie=NULL;nomSp=NULL;description=TRUE;valide=catIncert + # tendanceSurFigure=TRUE;seuilOccu=14 + require(ggplot2) + + figname<- paste("Output/",id,"/",ifelse(valide=="Incertain","Incertain/",""), + sp,"_",id,serie, ".png", + sep = "") + ## coordonnee des ligne horizontal de seuil pour les abondances et les occurences + hline.data1 <- data.frame(z = c(1), panel = c(vpan[1]),couleur = "variation abondance",type="variation abondance") + hline.data2 <- data.frame(z = c(0,seuilOccu), panel = c(vpan[2],vpan[2]),couleur = "seuil",type="seuil") + hline.data3 <- data.frame(z = 0, panel = vpan[3] ,couleur = "seuil",type="seuil") + hline.data <- rbind(hline.data1,hline.data2,hline.data3) + titre <- paste(nomSp)#,"\n",min(annee)," - ",max(annee),sep="") + + ## texte de la tendance / text for the population evolution trend + tab1 <- subset(dgg,panel =="Variation abondance") + pasdetemps <- max(dgg$annee) - min(dgg$annee) + 1 + if(assessIC){ + txtPente1 <- paste(tab1t$Est, + ifelse(tab1t$signif," *","")," [",tab1t$LL," , ",tab1t$UL,"]", + ifelse(tab1t$signif,paste("\n",ifelse(tab1t$pourcent>0,"+ ","- "), + abs(tab1t$pourcent)," % en ",pasdetemps," ans",sep=""),""),sep="") + }else{ + txtPente1 <- ifelse(tab1t$signif,paste("\n",ifelse(tab1t$pourcent>0,"+ ","- "), + abs(tab1t$pourcent)," % en ",pasdetemps," ans",sep=""),"") + + } + ## table du texte de la tendance / table of the text for the population evolution trend + tabTextPent <- data.frame(y=c(max(c(tab1$val,tab1$UL),na.rm=TRUE)*.9), + x=median(tab1$annee), + txt=ifelse(tendanceSurFigure,c(txtPente1),""), + courbe=c(vpan[1]),panel=c(vpan[1])) + ## les couleurs / the colors + vecColPoint <- c("#ffffff","#eeb40f","#ee0f59") + names(vecColPoint) <- c("significatif","infSeuil","0") + vecColCourbe <- c("#3c47e0","#5b754d","#55bb1d","#973ce0") + names(vecColCourbe) <- c(vpan[1],"carre","presence",vpan[3]) + vecColHline <- c("#ffffff","#e76060") + names(vecColHline) <- c("variation abondance","seuil") + + col <- c(vecColPoint,vecColCourbe,vecColHline) + names(col) <- c(names(vecColPoint),names(vecColCourbe),names(vecColHline)) + + ## si description graphique en 3 panels + if(description) { + p <- ggplot(data = dgg, mapping = aes(x = annee, y = val)) + ## Titre, axes ... + p <- p + facet_grid(panel ~ ., scale = "free") + + theme(legend.position="none", + panel.grid.minor=element_blank(), + panel.grid.major.y=element_blank()) + + ylab("") + xlab("Annee")+ ggtitle(titre) + + scale_colour_manual(values=col, name = "" , + breaks = names(col))+ + scale_x_continuous(breaks=min(dgg$annee):max(dgg$annee)) + p <- p + geom_hline(data =hline.data,mapping = aes(yintercept=z, colour = couleur,linetype=type ), + alpha=1,size=1.2) + if(assessIC){ ############# ONLY FOR THE CONFIDENCE INTERVAL + p <- p + geom_ribbon(mapping=aes(ymin=LL,ymax=UL),fill=col[vpan[1]],alpha=.2) + p <- p + geom_pointrange(mapping= aes(y=val,ymin=LL,ymax=UL),fill=col[vpan[1]],alpha=.2) + } + p <- p + geom_line(mapping=aes(colour=courbe),size = 1.5) + p <- p + geom_point(mapping=aes(colour=courbe),size = 3) + p <- p + geom_point(mapping=aes(colour=catPoint,alpha=ifelse(!is.na(catPoint),1,0)),size = 2) + p <- p + geom_text(data=tabTextPent, mapping=aes(x,y,label=txt),parse=FALSE,color=col[vpan[1]],fontface=2, size=4) + ggsave(figname, p,width=16,height=21, units="cm") + print (figname) ##### CAN BE REMOVED IF YOU DO NOT WANT THE GRAPH TO BE PLOTTED + } else { + + p <- ggplot(data = subset(dgg,panel=="Variation abondance"), mapping = aes(x = annee, y = val)) + ## Titre, axes ... + p <- p + facet_grid(panel ~ ., scale = "free") + + theme(legend.position="none", + panel.grid.minor=element_blank(), + panel.grid.major.y=element_blank()) + + ylab("") + xlab("Annee")+ ggtitle(titre) + + scale_colour_manual(values=col, name = "" , + breaks = names(col))+ + scale_x_continuous(breaks=min(dgg$annee):max(dgg$annee)) + p <- p + geom_hline(data =subset(hline.data,panel=="Variation abondance"),mapping = aes(yintercept=z, colour = couleur,linetype=type ), + alpha=1,size=1.2) + + if(assessIC){ ############# ONLY FOR THE CONFIDENCE INTERVAL + p <- p + geom_ribbon(mapping=aes(ymin=LL,ymax=UL),fill=col[vpan[1]],alpha=.2) + p <- p + geom_pointrange(mapping= aes(y=val,ymin=LL,ymax=UL),fill=col[vpan[1]],alpha=.2) + } + p <- p + geom_line(mapping=aes(colour=courbe),size = 1.5) + p <- p + geom_point(mapping=aes(colour=courbe),size = 3) + p <- p + geom_point(mapping=aes(colour=catPoint,alpha=ifelse(!is.na(catPoint),1,0)),size = 2) + p <- p + geom_text(data=tabTextPent, mapping=aes(x,y,label=txt),parse=FALSE,color=col[vpan[1]],fontface=2, size=4) + ggsave(figname, p,width=15,height=9,units="cm") + print (figname) ##### CAN BE REMOVED IF YOU DO NOT WANT THE GRAPH TO BE PLOTTED + } +} +############################################################################################################ fin fonction graphique / end of function for graphical output + + + + +#################################################################################################################### debut de la fonction de moyenne geometrique pondere / start of the geometric weighted mean function +geometriqueWeighted <- function(x,w=1) exp(sum(w*log(x))/sum(w)) +#################################################################################################################### fin de la fonction de moyenne geometrique pondere / end of the geometric weighted mean function + + + +##################################################################################################################### debut de la fonction analyseGroupe / start of the function analyseGroupe +## Analyse par groupe de specialisation a partir des resulats de variation d'abondance par especes / analysis by specialization group based on results of the analysis of population evolution trend +# + + +analyseGroupe <- function(id="france",tabsp=tabsp,donnees=donnees,donneesTrend=donneesTrend,ICfigureGroupeSp=TRUE,powerWeight=2, + correctionAbondanceNull = 0.000001, + groupeNom = c("generaliste","milieux batis","milieux forestiers","milieux agricoles"), + groupeCouleur = c("black","firebrick3","chartreuse4","orange")) { + + + + + + ## donnees tendances globales / results of the global trends + donneesTrend <- subset(donneesTrend, select = c(code_espece,valide,mediane_occurrence)) + + ## table de reference espece / reference table for species + tabsp <- subset(tabsp, select= c(sp,nom,indicateur, specialisation)) + donnees <- merge(donnees,donneesTrend,by="code_espece") + donnees <- merge(donnees,tabsp,by.x="code_espece",by.y="sp") + ## table de correspondance de biais en fonction des medianes des occuerences + + + nameFileSpe <- paste("Output/",id,"/variationsAnnuellesGroupes_",id, ############# Declare le fichier de sortie des variations annuelles par groupe / declare the name of the outputfile for annual population evolution trend by group + ".tabular",sep="" ) + nameFileSpepng <- paste("Output/",id,"/variationsAnnuellesGroupes_",id, ############# Declare le fichier de sortie graphique des variations annuelles par groupe / declare the name of the graphical output file for annual population evolution trend by group + ".png",sep="" ) + + grpe <- donnees$specialisation + + ####### valeur seuil sont obtenues à partir de simulations / threshold values are obtained from simulations + ff <- function(x,y) max(which(y<=x)) ## fonction pour recherche le poid associé à valeur max parmi valeur seuil d'occurence inferieur ou egale à occurence mediane obs / function to retrieve the weight associated with the max occurence threshold equal or smaller than the occurence mediane observed + + IncertW <- ifelse(donnees$valide=="Incertain",tBiais$biais[sapply(as.vector(donnees$mediane_occurrence),ff,y=tBiais$occurrenceMed)],1) ## pr verifier poids de l'espèce dans analyse, récupére seuil occurence minimum pour lequel tendance pas bonne, et compare avec mediane occurence des données / to check the weight of species in the analysis, this retrieve occurence threshold with wich real occurence measured on data are compared in order to verify the accuracy of the trend measurment + ## poids du a la qualite de l'estimation + # erreur_stW <- 1/((donnees$erreur_st+1)^powerWeight) + # erreur_stW <- ifelse( is.na(donnees$IC_superieur),0,erreur_stW) + erreur_stW <- ifelse(is.na(donnees$IC_superieur),0,1)##### si pas d'interval de confiance met 0 et donne un poid de 0 à l'esps (voir ci dessous) / if no confidence interval calculated give a weight of 0 for the sps + ## calcul du poids total de chaque espèce / calcul of the weight of each species + W <- IncertW * erreur_stW + + ## variable de regroupement pour les calculs par groupe de specialisation et par an / variables gathered to identify group for the calculation (per specialization and per year) + grAn <- paste(donnees$specialisation,donnees$annee,sep="_") + ## data frame pour le calcul / dataframe made for the calcul + dd <- data.frame(grAn,annee = donnees$annee, grpe,W,ab=donnees$abondance_relative,ICinf= donnees$IC_inferieur, ICsup= ifelse(is.na(donnees$IC_superieur),10000,donnees$IC_superieur)) + ## table resumer de tous les poids / table to sum up the weights of each species depending on the incertainty in the calcul of the poulation evolution trends + ddd <- data.frame(code_espece = donnees$code_espece,nom_espece = donnees$nom_espece,annee = donnees$annee, + groupe_indicateur = grpe, + poids_erreur_standard = round(erreur_stW,3), poids_incertitude = round(IncertW,3),poids_final = round(W,3), + abondance_relative=donnees$abondance_relative, + IC_inferieur= donnees$IC_inferieur, + IC_superieur= ifelse(is.na(donnees$IC_superieur),10000,donnees$IC_superieur), + valide = donnees$valide, mediane_occurrence = donnees$mediane_occurrence) + + nomFileResum <- paste("Output/",id,"/donneesGroupes_",id, ###### declaration du nom du repertoire et des fichiers de sortie / declaring the name of the output folder and files + ".tabular",sep="" ) + write.table(ddd,nomFileResum,row.names=FALSE,sep="\t",dec=".",fileEncoding="UTF-8") + cat("-->",nomFileResum,"\n") + + ## calcul des moyennes ponderees par groupe par an et pour les abondance et les IC / calcul of weighted means per specialization group and per year for the abundance and confidence interval + for(j in 5:7) dd[,j] <- ifelse(dd[,j]==0,correctionAbondanceNull,dd[,j]) + ag <- apply(dd[,5:7], 2, ######## sur les abondances relatives, les ICinf et ICsup + function(x) { + sapply(split(data.frame(dd[,1:4], x), dd$grAn), ###### fait les moyennes pondérés par groupe grAn / calculate the weighted mean by group grAn + function(y) round(geometriqueWeighted(y[,5], w = y$W),3)) + }) + ## gg <- subset(dd,as.character(dd$grAn)=="milieux forestier_2014") ############################################################# + + ag <- ifelse(is.na(ag),1,ag) + ag <- as.data.frame(ag) + ag$grAn <- rownames(ag) + dbon <- subset(donnees,valide=="bon") + dIncert <- subset(donnees,valide=="Incertain") + ## calcul nombre d'espece "bonne" pour le calcul / calculating the number of species with low level of incertainty, "good" species + bon <- tapply(dbon$nom,dbon$specialisation,FUN=function(X)length(unique(X)) ) + bon <- ifelse(is.na(bon),0,bon) + tbon <- data.frame(groupe=names(bon),bon) + ## calcul nombre d'especes "incertaines" pour le calcul / calculating the number of species with high level of incertainty, "bad" species + Incert <- tapply(dIncert$nom,dIncert$specialisation,FUN=function(X)length(unique(X)) ) + Incert <- ifelse(is.na(Incert),0,Incert) + tIncert <- data.frame(groupe=names(Incert),Incertain=Incert) + + tIncert <- merge(tIncert,tbon,by="groupe") + + ## table de données avec les moyennes ponderees par groupe / table of the data with the weighted mean by group + da <- merge(unique(dd[,1:3]),ag,by="grAn")[,-1] + colnames(da) <- c("annee","groupe","abondance_relative","IC_inferieur","IC_superieur") + + da$annee <- as.numeric(da$annee) + da <- merge(da,tIncert,by="groupe") #### ajoute le nombre d'espece "incertaines" et "bonne" aux resultats / add the number of "good" and "bad" species to the overall resutls + da <- subset(da, groupe != "non") + colnames(da)[6:7] <- c("nombre_especes_incertaines","nombre_espece_bonnes") + a <- data.frame(id,da) + write.table(da,file=nameFileSpe,row.names=FALSE,quote=FALSE,sep="\t",dec=".",fileEncoding="UTF-8") + + cat("-->",nameFileSpe,"\n") + yearsrange <- c(min(da$annee),max(da$annee)) + + ## figure par ggplot2 / plots with ggplot2 + titre <- paste("Variation de l'indicateur groupe de specialisation",sep="") + + vecCouleur <- setNames(groupeCouleur,groupeNom) + #browser() + p <- ggplot(data = da, mapping = aes(x = annee, y = abondance_relative, colour=groupe,fill=groupe)) + p <- p + geom_hline(aes(yintercept = 1), colour="white", alpha=1,size=1.2) + if(ICfigureGroupeSp) + p <- p + geom_ribbon(mapping=aes(ymin=IC_inferieur,ymax=IC_superieur),linetype=2,alpha=.1,size=0.1) + p <- p + geom_line(size=1.5) + p <- p + ylab("") + xlab("Annee")+ ggtitle(titre) + if(!is.null(groupeNom)) p <- p + scale_colour_manual(values=vecCouleur, name = "" )+ + scale_x_continuous(breaks=unique(da$annee)) + if(!is.null(groupeNom)) p <- p + scale_fill_manual(values=vecCouleur, name="") + p <- p + theme(panel.grid.minor=element_blank(), panel.grid.major.y=element_blank()) + ggsave(nameFileSpepng, p,width=17,height=10,units="cm") + + # cat(" <==",nameFileSpepng,"\n") + + ## calul pour chaque groupe une pente de regression d'evolution des abondances sur la periode étudiée / calculating for each group the regression slope for the abundance evolution on the studied period + vecSpe <- unique(da$groupe) + datasum <- data.frame(groupe=NULL,tendance=NULL,pourcentage_variation=NULL) + for(spe in 1:4){ + # print(spe) + subtab <- subset(da,groupe==vecSpe[spe]) + if(nrow(subtab)>1) { + sumlm <- summary(lm(abondance_relative~annee,data=subtab)) ##### recupère les resultats du modèle linéaire / retrieve the results of the linear model + subdatasum <- data.frame(groupe=vecSpe[spe], + tendance=round(sumlm$coefficients[2,1],3), + pourcentage_variation=round(sumlm$coefficients[2,1]*(nrow(subtab)-1)*100,3)) #### assemble les resultats pour en faire une sortie / bring together the results for an output file + datasum <- rbind(datasum,subdatasum) + + } + + } + datasum <- merge(datasum,tIncert,by="groupe") #### + datasum <- data.frame(id,datasum) + #datasum$cat_tendance_EBCC <- affectCatEBCC(trend,pVal,ICinf,ICsup + namefilesum <- paste("Output/",id,"/tendancesGlobalesGroupes_",id, + ".tabular",sep="" ) + write.table(datasum,file=namefilesum,row.names=FALSE,quote=FALSE,sep="\t",dec=".",fileEncoding="UTF-8") + cat("-->",namefilesum,"\n") +} + +################################################################################################################## fin de la fonction analyseGroupe / end of the function analyseGroupe + + + + + + + +################################################################################################################### debut de la fonction check_file / start of the function check_file +# Fonction pour verifier les données d'entrée / General function to check integrity of input file. Will check numbers and contents of variables(colnames). +#return an error message and exit if mismatch detected +#Faut rentrer le nom du jeu de données, le nbre et le nom des variables / Enter dataset name, expected number and names of variables. + an exit error message to guide user. + +check_file<-function(dataset,err_msg,vars,nb_vars){ + if(ncol(dataset)!=nb_vars){ #Verifiction de la présence du bon nb de colonnes, si c'est pas le cas= message d'erreur / checking for right number of columns in the file if not = error message + cat("\nerr nb var\n") + stop(err_msg, call.=FALSE) + } + + for(i in vars){ + if(!(i %in% names(dataset))){ + stop(err_msg,call.=FALSE) + } + } +} + +##################################################################################################################### +
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"930023" 12.42 5.36 +NA NA NA "930027" 32.12 0.84 +NA NA NA "930030" 82.02 -1.56 +NA NA NA "930031" 62.63 3.4 +NA NA NA "930034" 80.2 4.06 +NA NA NA "930035" 88.37 -3.76 +NA NA NA "930038" 12.62 -1.73 +NA NA NA "930042" 17.81 3.63 +NA NA NA "930043" 89.07 -4.33 +NA NA NA "930044" 37.86 5.54 +NA NA NA "930045" 1.16 5.01 +NA NA NA "930049" 24.12 7.61 +NA NA NA "930050" 15.79 -4.68 +NA NA NA "930053" 71.22 -2.75 +NA NA NA "930055" 8.64 7.54 +NA NA NA "930056" 18.67 3.34 +NA NA NA "930058" 17.27 -1.2 +NA NA NA "940008" 30.96 -4.28 +NA NA NA "940025" 81.05 5.73 +NA NA NA "940030" 37.78 1.1 +NA NA NA "950017" 39.84 7.51 +NA NA NA "950035" 24.94 0.9 +NA NA NA "950036" 38.61 -2.17 +NA NA NA "950037" 51.34 7.61 +NA NA NA "950038" 35.88 -2.67 +NA NA NA "950040" 27.25 4.72 +NA NA NA "950079" 32.25 0.81 +NA NA NA "950089" 6.45 -1.78 +NA NA NA "950091" 10.65 4.11 +NA NA NA "950096" 85.65 -0.45 +NA NA NA "950153" 40.63 1.1 +NA NA NA "950182" 41.8 -1.46 +NA NA NA "950183" 28.09 -4.06 +NA NA NA "950184" 56.89 7.24 +NA NA NA "950218" 27.66 3.34 +NA NA NA "950311" 70.72 3.81
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/species_indicateur_fonctionnel.tabular Thu Apr 02 03:35:29 2020 -0400 @@ -0,0 +1,442 @@ +"pk_species" "ssi" "ssi_old" "ssi_2007" "sti" "sti_europe" "thermal_niche_mean" "thermal_niche_range" "thermal_niche_max" "thermal_niche_min" "stri" "exp_stri" "trophic_vegetation" "trophic_invertebrate" "trophic_vertebrate" +"_ANTVIR" "" "" "" "" "15.6536" "" "" "" "" "" "" "" "" "" +"_EMBRUS" "" "" "" "" "6.33549" "" "" "" "" "" "" "" "" "" +"ACCGEN" "1.86088" "" "" "" "11.8008" "" "" "" "" "3" "20.09" "" "" "" +"ACCNIS" "0.614413" "0.787363" "0.620015" "13.8629" "11.9163" "12.3454" "15.773" "20.0974" "4.3244" "3" "20.09" 0 0 100 +"ACRAGR" "" "" "" "" "15.605" "" "" "" "" "" "" "" "" "" +"ACRARU" "" "" "" "" "13.8794" "" "" "" "" "" "" "" "" "" +"ACRDUM" "" "" "" "" "9.51237" "" "" "" "" "" "" "" "" "" +"ACRMEL" "" "" "" "" "16.574" "" "" "" "" "" "" "" "" "" +"ACRPAL" "" "" "" "" "12.3618" "" "" "" "" "" "" "" "" "" +"ACRRIS" "1.70476" "" "" "" "12.2481" "" "" "" "" "2" "7.39" "" "" "" +"ACRSCH" "2.50428" "" "" "" "11.3914" "" "" "" "" "1.95" "7.03" "" "" "" +"ACRSCI" "2.08314" "" "" "" "13.1524" "" "" "" "" "1.95" "7.03" "" "" "" +"ACTHYP" "" "" "" "" "11.2048" "" "" "" "" "" "" "" "" "" +"AEGCAU" "0.585681" "0.62316" "0.438564" "14.4881" "12.6608" "12.9783" "13.3142" "19.328" "6.01376" "1.95" "7.03" 5 95 0 +"AEGFUN" "" "" "" "" "9.10055" "" "" "" "" "" "" "" "" "" +"AEGMON" "" "" "" "" "16.1423" "" "" "" "" "" "" "" "" "" +"AIXGAL" "" "" "" "" "12.0189" "" "" "" "" "" "" "" "" "" +"AIXSPO" "" "" "" "" "12.3348" "" "" "" "" "" "" "" "" "" +"ALAARV" "1.18758" "1.1554" "0.921955" "14.1497" "12.1983" "12.508" "15.4477" "19.7438" "4.29613" "1.25" "3.49" 75 25 0 +"ALCATT" "2.18545" "" "" "" "13.5319" "" "" "" "" "2.81" "16.61" "" "" "" +"ALCTOR" "" "" "" "" "8.98323" "" "" "" "" "" "" "" "" "" +"ALEBAR" "" "" "" "" "17.9565" "" "" "" "" "" "" "" "" "" +"ALECHU" "" "" "" "" "16.2056" "" "" "" "" "" "" "" "" "" +"ALEGRA" "" "" "" "" "13.1661" "" "" "" "" "" "" "" "" "" +"ALERUF" "1.10584" "1.0973" "0.92039" "16.0692" "14.8075" "15.2367" "11.2723" "20.5323" "9.26" "1.1" "3" 90 10 0 +"ALLALL" "" "" "" "" "-8.26474" "" "" "" "" "" "" "" "" "" +"ALOAEG" "" "" "" "" "12.3931" "" "" "" "" "" "" "" "" "" +"ANAACU" "" "" "" "" "8.52735" "" "" "" "" "" "" "" "" "" +"ANACLY" "" "" "" "" "11.7556" "" "" "" "" "" "" "" "" "" +"ANACRE" "" "" "" "" "10.0713" "" "" "" "" "" "" "" "" "" +"ANAPEN" "" "" "" "" "6.68293" "" "" "" "" "" "" "" "" "" +"ANAPLA" "" "" "" "" "11.8111" "" "" "" "" "" "" "" "" "" +"ANAQUE" "" "" "" "" "12.3896" "" "" "" "" "" "" "" "" "" +"ANASTR" "" "" "" "" "12.5159" "" "" "" "" "" "" "" "" "" +"ANSALB" "" "" "" "" "1.40206" "" "" "" "" "" "" "" "" "" +"ANSANS" "" "" "" "" "11.1956" "" "" "" "" "" "" "" "" "" +"ANSBRA" "" "" "" "" "3.35896" "" "" "" "" "" "" "" "" "" +"ANSERY" "" "" "" "" "2.03545" "" "" "" "" "" "" "" "" "" +"ANSFAB" "" "" "" "" "4.29454" "" "" "" "" "" "" "" "" "" +"ANTCAM" "2.42803" "1.99574" "1.50106" "16.3834" "14.429" "15.7224" "11.5149" "21.4068" "9.89193" "1.95" "7.03" 5 95 0 +"ANTCER" "" "" "" "" "3.13445" "" "" "" "" "" "" "" "" "" +"ANTHOD" "" "" "" "" "4.92396" "" "" "" "" "" "" "" "" "" +"ANTPRA" "1.37755" "1.37459" "1.26191" "11.8267" "9.94877" "9.85028" "13.4452" "15.2037" "1.75849" "1.75" "5.75" 25 75 0 +"ANTSPI" "" "" "" "" "11.6825" "" "" "" "" "" "" "" "" "" +"ANTSPISPI" "" "" "" "" "11.6825" "" "" "" "" "" "" "" "" "" +"ANTTRI" "0.729307" "0.910273" "0.705309" "13.2523" "11.2439" "11.6459" "13.9098" "17.9549" "4.04508" "1.95" "7.03" 5 95 0 +"APUAPU" "1.27125" "1.29827" "1.24046" "14.3837" "12.447" "13.2293" "16.075" "21.2854" "5.21042" "2" "7.39" 0 100 0 +"APUCAF" "" "" "" "" "19.268" "" "" "" "" "" "" "" "" "" +"APUMEL" "" "" "" "" "14.7488" "" "" "" "" "" "" "" "" "" +"APUPAL" "" "" "" "" "17.4132" "" "" "" "" "" "" "" "" "" +"AQUCHR" "" "" "" "" "10.7378" "" "" "" "" "" "" "" "" "" +"AQUCLA" "" "" "" "" "12.2126" "" "" "" "" "" "" "" "" "" +"AQUPOM" "" "" "" "" "12.6813" "" "" "" "" "" "" "" "" "" +"ARDCIN" "" "" "" "" "12.6944" "" "" "" "" "" "" "" "" "" +"ARDPUR" "" "" "" "" "15.246" "" "" "" "" "" "" "" "" "" +"ARDRAL" "" "" "" "" "15.9223" "" "" "" "" "" "" "" "" "" +"AREINT" "" "" "" "" "7.73594" "" "" "" "" "" "" "" "" "" +"ASIFLA" "" "" "" "" "9.62002" "" "" "" "" "" "" "" "" "" +"ASIOTU" "" "" "" "" "12.294" "" "" "" "" "" "" "" "" "" +"ATHNOC" "" "" "" "" "14.2083" "" "" "" "" "" "" "" "" "" +"AYTFER" "" "" "" "" "12.4454" "" "" "" "" "" "" "" "" "" +"AYTFUL" "" "" "" "" "10.1386" "" "" "" "" "" "" "" "" "" +"AYTMAR" "" "" "" "" "4.37113" "" "" "" "" "" "" "" "" "" +"AYTNYR" "" "" "" "" "14.3405" "" "" "" "" "" "" "" "" "" +"BOMGAR" "" "" "" "" "5.77321" "" "" "" "" "" "" "" "" "" +"BONBON" "" "" "" "" "9.7371" "" "" "" "" "" "" "" "" "" +"BOTSTE" "" "" "" "" "13.1076" "" "" "" "" "" "" "" "" "" +"BRABER" "" "" "" "" "-4.59769" "" "" "" "" "" "" "" "" "" +"BRACAN" "" "" "" "" "9.54893" "" "" "" "" "" "" "" "" "" +"BRALEU" "" "" "" "" "9.28419" "" "" "" "" "" "" "" "" "" +"BUBBUB" "" "" "" "" "11.8718" "" "" "" "" "" "" "" "" "" +"BUBIBI" "" "" "" "" "18.2107" "" "" "" "" "" "" "" "" "" +"BUCCLA" "" "" "" "" "8.47429" "" "" "" "" "" "" "" "" "" +"BUCISL" "" "" "" "" "3.04567" "" "" "" "" "" "" "" "" "" +"BULBUL" "" "" "" "" "17.7121" "" "" "" "" "" "" "" "" "" +"BUROED" "1.95056" "1.89748" "1.52193" "17.4327" "15.8668" "18.5569" "17.9197" "29.1006" "11.1809" "1.95" "7.03" 5 95 0 +"BUTBUT" "0.574316" "0.494943" "0.585595" "14.6525" "12.6974" "13.0152" "13.1745" "19.4801" "6.30557" "2.9" "18.17" 0 10 90 +"BUTLAG" "" "" "" "" "5.23134" "" "" "" "" "" "" "" "" "" +"BUTRUF" "" "" "" "" "16.2118" "" "" "" "" "" "" "" "" "" +"CALALP" "" "" "" "" "7.06638" "" "" "" "" "" "" "" "" "" +"CALBRA" "" "" "" "" "16.28" "" "" "" "" "" "" "" "" "" +"CALCAL" "" "" "" "" "14.2968" "" "" "" "" "" "" "" "" "" +"CALDIO" "" "" "" "" "18.5569" "" "" "" "" "" "" "" "" "" +"CALLAP" "" "" "" "" "4.06383" "" "" "" "" "" "" "" "" "" +"CALMAR" "" "" "" "" "3.87988" "" "" "" "" "" "" "" "" "" +"CALMEL" "" "" "" "" "-1.22119" "" "" "" "" "" "" "" "" "" +"CALMIN" "" "" "" "" "2.73302" "" "" "" "" "" "" "" "" "" +"CALRUF" "" "" "" "" "17.0539" "" "" "" "" "" "" "" "" "" +"CALTEM" "" "" "" "" "4.17153" "" "" "" "" "" "" "" "" "" +"CAPEUR" "" "" "" "" "13.1103" "" "" "" "" "" "" "" "" "" +"CAPRUF" "" "" "" "" "17.5621" "" "" "" "" "" "" "" "" "" +"CARCAN" "0.678321" "0.69683" "0.597376" "15.0534" "13.1604" "14.2114" "13.2644" "21.4034" "8.13908" "1.05" "2.86" 95 5 0 +"CARCAR" "0.753282" "0.704341" "0.650258" "15.3679" "13.4542" "14.6936" "14.3588" "22.9379" "8.5791" "1.05" "2.86" 95 5 0 +"CARCHL" "0.838069" "0.657207" "0.570617" "15.1894" "12.6006" "13.8886" "15.2923" "21.5397" "6.24732" "1.05" "2.86" 95 5 0 +"CARERY" "" "" "" "" "10.3483" "" "" "" "" "" "" "" "" "" +"CARFLA" "" "" "" "" "8.19231" "" "" "" "" "" "" "" "" "" +"CARHOR" "" "" "" "" "3.08868" "" "" "" "" "" "" "" "" "" +"CARRIS" "" "" "" "" "6.94625" "" "" "" "" "" "" "" "" "" +"CARSPI" "2.62081" "" "" "" "10.2425" "" "" "" "" "1.03" "2.8" "" "" "" +"CEPGRY" "" "" "" "" "7.45566" "" "" "" "" "" "" "" "" "" +"CERBRA" "0.612574" "0.621996" "0.544511" "15.7002" "13.8973" "15.3616" "12.1146" "21.2164" "9.10174" "2" "7.39" 0 100 0 +"CERFAM" "1.95824" "1.89" "1.42419" "13.0651" "11.1195" "11.7709" "12.6035" "17.7345" "5.13102" "1.95" "7.03" 5 95 0 +"CERGAL" "" "" "" "" "18.6085" "" "" "" "" "" "" "" "" "" +"CETCET" "1.84937" "1.35808" "1.14564" "17.3463" "15.9492" "16.9112" "14.7673" "23.3435" "8.57622" "2" "7.39" 0 100 0 +"CHAALE" "" "" "" "" "16.3072" "" "" "" "" "" "" "" "" "" +"CHAASI" "" "" "" "" "16.2645" "" "" "" "" "" "" "" "" "" +"CHADUB" "" "" "" "" "12.7729" "" "" "" "" "" "" "" "" "" +"CHAHIA" "" "" "" "" "8.22146" "" "" "" "" "" "" "" "" "" +"CHALES" "" "" "" "" "14.6817" "" "" "" "" "" "" "" "" "" +"CHAMOR" "" "" "" "" "5.38502" "" "" "" "" "" "" "" "" "" +"CHEDUP" "" "" "" "" "14.9653" "" "" "" "" "" "" "" "" "" +"CHEGRE" "" "" "" "" "14.6349" "" "" "" "" "" "" "" "" "" +"CHLHYB" "" "" "" "" "14.8788" "" "" "" "" "" "" "" "" "" +"CHLLEU" "" "" "" "" "13.1741" "" "" "" "" "" "" "" "" "" +"CHLNIG" "" "" "" "" "13.0082" "" "" "" "" "" "" "" "" "" +"CICCIC" "2.91363" "" "" "" "13.611" "" "" "" "" "2.6" "13.46" "" "" "" +"CICNIG" "" "" "" "" "13.1077" "" "" "" "" "" "" "" "" "" +"CINCIN" "2.72292" "" "" "" "11.015" "" "" "" "" "2" "7.39" "" "" "" +"CIRAER" "2.2666" "2.05704" "2.38313" "15.2728" "13.2259" "13.6722" "15.1964" "22.6326" "7.43621" "3" "20.09" 0 0 100 +"CIRCYA" "1.37981" "1.25317" "1.48217" "12.7212" "10.6284" "11.063" "13.2879" "16.833" "3.54511" "3" "20.09" 0 0 100 +"CIRGAL" "1.60618" "" "" "" "14.6544" "" "" "" "" "3" "20.09" "" "" "" +"CIRMAC" "" "" "" "" "13.8642" "" "" "" "" "" "" "" "" "" +"CIRPYG" "1.41354" "1.68954" "1.5083" "15.7504" "13.6904" "14.3518" "9.32976" "19.6703" "10.3405" "2.6" "13.46" 0 40 60 +"CISJUN" "2.41649" "2.14811" "1.64227" "18.1548" "16.8491" "19.0736" "15.5653" "27.854" "12.2886" "2" "7.39" 0 100 0 +"CLAGLA" "2.07065" "" "" "" "16.706" "" "" "" "" "2" "7.39" "" "" "" +"CLAHYE" "" "" "" "" "3.38535" "" "" "" "" "" "" "" "" "" +"COCCOC" "1.30057" "0.983755" "0.857174" "14.9707" "12.9763" "14.0176" "10.9858" "19.8149" "8.8291" "1.05" "2.86" 95 5 0 +"COLLIV" "2.04338" "2.03723" "2.15525" "14.611" "12.7922" "15.5755" "23.37" "30.228" "6.858" "1.01" "2.75" 99 1 0 +"COLOEN" "0.884448" "1.29082" "0.951158" "14.1809" "12.4358" "13.4029" "11.2216" "19.2938" "8.07224" "1.01" "2.75" 99 1 0 +"COLPAL" "0.343291" "0.299951" "0.210529" "14.1544" "12.2214" "12.989" "16.3147" "21.5235" "5.20879" "1.01" "2.75" 99 1 0 +"COLVIR" "" "" "" "" "14.4899" "" "" "" "" "" "" "" "" "" +"CORCOR" "0.40942" "0.281072" "0.214543" "14.0953" "12.1294" "" "" "" "" "1.51" "4.53" "" "" "" +"CORFRU" "1.10376" "0.845527" "0.879635" "14.7353" "12.7341" "13.4259" "11.0666" "18.7374" "7.67086" "1.63" "5.1" 46 42 11 +"CORGAR" "2.08737" "" "" "" "14.817" "" "" "" "" "2" "7.39" "" "" "" +"SURULU" "" "" "" "" "5.79559" "" "" "" "" "" "" "" "" "" +"CORMON" "0.847356" "0.959838" "0.834487" "14.8876" "12.9582" "13.4372" "13.6661" "20.2449" "6.57877" "1.11" "3.03" 90 9 1 +"CORRAX" "1.40905" "1.22823" "1.03099" "14.0953" "11.7378" "12.3658" "20.2476" "22.6104" "2.36277" "1.75" "5.75" 50 25 25 +"COTCOT" "1.39988" "1.52376" "1.35456" "15.5838" "13.6003" "14.693" "13.7205" "22.1066" "8.3861" "1.22" "3.39" 78 22 0 +"CRECRE" "" "" "" "" "11.9688" "" "" "" "" "" "" "" "" "" +"CUCCAN" "0.388081" "0.43348" "0.436097" "14.1171" "12.1394" "12.714" "16.1486" "20.46" "4.31148" "2" "7.39" 0 100 0 +"CYACYA" "" "" "" "" "17.5572" "" "" "" "" "" "" "" "" "" +"CYGCOL" "" "" "" "" "0.740472" "" "" "" "" "" "" "" "" "" +"CYGCYG" "" "" "" "" "6.26509" "" "" "" "" "" "" "" "" "" +"CYGOLO" "2.56696" "" "" "" "12.19" "" "" "" "" "1" "2.72" "" "" "" +"DELURB" "1.3038" "1.26632" "1.55545" "14.3044" "12.3471" "13.0523" "17.0008" "21.4263" "4.42548" "2" "7.39" 0 100 0 +"DENLEU" "" "" "" "" "11.4359" "" "" "" "" "" "" "" "" "" +"DENMAJ" "0.635651" "0.63788" "0.579273" "14.0998" "12.1154" "12.1895" "15.2036" "19.3768" "4.17318" "1.7" "5.47" 30 70 0 +"DENMED" "2.22312" "1.92" "1.79394" "15.1839" "13.2449" "14.6267" "10.278" "19.7291" "9.4511" "1.7" "5.47" 30 70 0 +"DENMIN" "0.858045" "0.913494" "0.591457" "13.5716" "11.551" "11.9554" "14.8884" "18.9171" "4.02869" "2" "7.39" 0 100 0 +"DENSYR" "" "" "" "" "14.1598" "" "" "" "" "" "" "" "" "" +"DRYMAR" "1.0734" "1.23504" "1.09912" "13.0689" "11.1194" "11.3861" "13.1613" "17.5672" "4.4059" "2" "7.39" 0 100 0 +"EGRALB" "" "" "" "" "14.8916" "" "" "" "" "" "" "" "" "" +"EGRGAR" "2.57997" "" "" "" "15.917" "" "" "" "" "2.5" "12.18" "" "" "" +"ELACAE" "" "" "" "" "17.6926" "" "" "" "" "" "" "" "" "" +"EMBAUR" "" "" "" "" "9.21227" "" "" "" "" "" "" "" "" "" +"EMBCAE" "" "" "" "" "17.9868" "" "" "" "" "" "" "" "" "" +"EMBCIA" "1.91018" "1.47" "0.670555" "16.3343" "14.4204" "15.816" "16.8576" "22.7405" "5.88289" "1.3" "3.67" 70 30 0 +"EMBCIR" "0.714234" "0.585545" "0.473803" "16.7448" "15.0545" "16.5473" "11.8069" "21.45" "9.64315" "1.3" "3.67" 70 30 0 +"EMBCIT" "0.681933" "0.711271" "0.577612" "13.5041" "11.5528" "11.7548" "12.8308" "17.3722" "4.54137" "1.3" "3.67" 70 30 0 +"EMBHOR" "2.16648" "1.32" "0.991336" "14.7907" "12.7263" "13.1475" "12.7177" "19.3929" "6.67514" "1.95" "7.03" 5 95 0 +"EMBLEU" "" "" "" "" "8.24743" "" "" "" "" "" "" "" "" "" +"EMBMEL" "" "" "" "" "16.6374" "" "" "" "" "" "" "" "" "" +"EMBPUS" "" "" "" "" "4.66467" "" "" "" "" "" "" "" "" "" +"EMBSCH" "2.50757" "" "" "" "11.2704" "" "" "" "" "1.3" "3.67" "" "" "" +"EREALP" "" "" "" "" "7.89509" "" "" "" "" "" "" "" "" "" +"ERIRUB" "0.523052" "0.48404" "0.414872" "13.9545" "11.9984" "12.2379" "14.9169" "19.5628" "4.64583" "1.83" "6.23" 17 83 0 +"FALBIA" "" "" "" "" "16.8611" "" "" "" "" "" "" "" "" "" +"FALCHE" "" "" "" "" "14.2762" "" "" "" "" "" "" "" "" "" +"FALCOL" "" "" "" "" "6.82328" "" "" "" "" "" "" "" "" "" +"FALELE" "" "" "" "" "18.7439" "" "" "" "" "" "" "" "" "" +"FALNAU" "" "" "" "" "16.7317" "" "" "" "" "" "" "" "" "" +"FALPER" "1.91272" "" "" "" "12.6432" "" "" "" "" "3" "20.09" "" "" "" +"FALRUS" "" "" "" "" "3.7442" "" "" "" "" "" "" "" "" "" +"FALSUB" "1.25106" "1.5" "1.28034" "14.6875" "12.6788" "13.3098" "13.8093" "20.0283" "6.21897" "2.25" "9.49" 0 75 25 +"FALTIN" "0.632997" "0.680845" "0.656079" "14.4077" "12.4264" "13.7595" "21.4584" "25.6245" "4.16607" "2.85" "17.29" 0 15 85 +"FALVES" "" "" "" "" "14.2518" "" "" "" "" "" "" "" "" "" +"FICALB" "" "" "" "" "13.0604" "" "" "" "" "" "" "" "" "" +"FICHYP" "1.316" "" "" "" "10.4275" "" "" "" "" "2" "7.39" "" "" "" +"FICPAR" "" "" "" "" "11.5986" "" "" "" "" "" "" "" "" "" +"FRAARC" "" "" "" "" "8.0917" "" "" "" "" "" "" "" "" "" +"FRICOE" "0.29307" "0.271686" "0.251446" "14.2614" "12.2952" "13.0696" "16.3341" "21.132" "4.79794" "1.1" "3" 90 10 0 +"FRIMON" "" "" "" "" "7.03053" "" "" "" "" "" "" "" "" "" +"FULATR" "" "" "" "" "12.9625" "" "" "" "" "" "" "" "" "" +"FULCRI" "" "" "" "" "19.6963" "" "" "" "" "" "" "" "" "" +"FULGLA" "" "" "" "" "8.4431" "" "" "" "" "" "" "" "" "" +"GALCHL" "" "" "" "" "13.3437" "" "" "" "" "" "" "" "" "" +"GALCRI" "1.82879" "0.443536" "1.43798" "16.4479" "14.4982" "17.7215" "19.0139" "29.4257" "10.4118" "1.55" "4.71" 45 55 0 +"GALGAL" "" "" "" "" "10.267" "" "" "" "" "" "" "" "" "" +"GALMED" "" "" "" "" "8.19334" "" "" "" "" "" "" "" "" "" +"GALTHE" "" "" "" "" "16.8013" "" "" "" "" "" "" "" "" "" +"GARGLA" "0.449625" "0.699789" "0.439798" "14.4084" "12.4668" "12.9317" "15.4567" "20.6722" "5.21551" "1.72" "5.58" 28 72 0 +"GAVADA" "" "" "" "" "-4.90987" "" "" "" "" "" "" "" "" "" +"GAVARC" "" "" "" "" "6.82986" "" "" "" "" "" "" "" "" "" +"GAVIMM" "" "" "" "" "4.20971" "" "" "" "" "" "" "" "" "" +"GAVSTE" "" "" "" "" "6.05105" "" "" "" "" "" "" "" "" "" +"GELNIL" "" "" "" "" "17.043" "" "" "" "" "" "" "" "" "" +"GLANOR" "" "" "" "" "15.2277" "" "" "" "" "" "" "" "" "" +"GLAPAS" "" "" "" "" "8.63044" "" "" "" "" "" "" "" "" "" +"GLAPRA" "" "" "" "" "17.4814" "" "" "" "" "" "" "" "" "" +"GRUGRU" "" "" "" "" "9.50057" "" "" "" "" "" "" "" "" "" +"GYPBAR" "" "" "" "" "12.793" "" "" "" "" "" "" "" "" "" +"GYPFUL" "" "" "" "" "15.2044" "" "" "" "" "" "" "" "" "" +"HAEOST" "" "" "" "" "10.474" "" "" "" "" "" "" "" "" "" +"HALALB" "" "" "" "" "10.7599" "" "" "" "" "" "" "" "" "" +"HIEFAS" "" "" "" "" "16.8334" "" "" "" "" "" "" "" "" "" +"HIEPEN" "" "" "" "" "15.1247" "" "" "" "" "" "" "" "" "" +"HIMHIM" "" "" "" "" "16.2911" "" "" "" "" "" "" "" "" "" +"HIPCAL" "" "" "" "" "11.8382" "" "" "" "" "" "" "" "" "" +"HIPICT" "1.99649" "" "" "" "11.3917" "" "" "" "" "1.95" "7.03" "" "" "" +"HIPOLI" "" "" "" "" "17.7628" "" "" "" "" "" "" "" "" "" +"HIPPAL" "" "" "" "" "16.7489" "" "" "" "" "" "" "" "" "" +"HIPPOL" "0.536869" "0.7212" "0.395575" "16.4226" "14.8356" "16.1998" "13.4147" "21.5603" "8.14565" "1.95" "7.03" 5 95 0 +"HIRDAU" "" "" "" "" "16.6554" "" "" "" "" "" "" "" "" "" +"HIRRUS" "0.76372" "1.14095" "1.00694" "14.3572" "12.4305" "13.7202" "19.2028" "24.3558" "5.15301" "2" "7.39" 0 100 0 +"HISHIS" "" "" "" "" "3.565" "" "" "" "" "" "" "" "" "" +"HYDPEL" "" "" "" "" "13.9625" "" "" "" "" "" "" "" "" "" +"IXOMIN" "" "" "" "" "14.2935" "" "" "" "" "" "" "" "" "" +"JYNTOR" "0.789751" "1.31465" "0.468726" "13.9297" "11.8813" "12.5214" "14.1635" "19.1389" "4.97541" "2" "7.39" 0 100 0 +"LAGLAG" "" "" "" "" "6.73787" "" "" "" "" "" "" "" "" "" +"LAGMUT" "" "" "" "" "6.02468" "" "" "" "" "" "" "" "" "" +"LANCOL" "1.10226" "1.23797" "0.920833" "14.6483" "12.6239" "13.4682" "12.5961" "19.7362" "7.14011" "2.15" "8.58" 0 85 15 +"LANEXC" "" "" "" "" "11.2044" "" "" "" "" "" "" "" "" "" +"LANMIN" "" "" "" "" "14.8453" "" "" "" "" "" "" "" "" "" +"LANSEN" "1.7427" "" "" "" "15.8754" "" "" "" "" "2.05" "7.77" "" "" "" +"LARARG" "" "" "" "" "9.05834" "" "" "" "" "" "" "" "" "" +"LARARGARG" "" "" "" "" "9.05834" "" "" "" "" "" "" "" "" "" +"LARAUD" "" "" "" "" "18.6663" "" "" "" "" "" "" "" "" "" +"LARCAN" "" "" "" "" "8.57406" "" "" "" "" "" "" "" "" "" +"LARFUS" "" "" "" "" "8.91787" "" "" "" "" "" "" "" "" "" +"LARGEN" "" "" "" "" "17.4584" "" "" "" "" "" "" "" "" "" +"LARGLA" "" "" "" "" "-2.94903" "" "" "" "" "" "" "" "" "" +"LARHYP" "" "" "" "" "-0.69199" "" "" "" "" "" "" "" "" "" +"LARMAR" "" "" "" "" "8.36946" "" "" "" "" "" "" "" "" "" +"LARMEL" "" "" "" "" "13.9748" "" "" "" "" "" "" "" "" "" +"LARMIC" "1.7216" "" "" "" "16.193" "" "" "" "" "2.3" "9.97" "" "" "" +"LARMIN" "" "" "" "" "9.35761" "" "" "" "" "" "" "" "" "" +"LARRID" "" "" "" "" "11.068" "" "" "" "" "" "" "" "" "" +"LIMFAL" "" "" "" "" "4.97336" "" "" "" "" "" "" "" "" "" +"LIMLAP" "" "" "" "" "3.25162" "" "" "" "" "" "" "" "" "" +"LIMLIM" "" "" "" "" "12.0468" "" "" "" "" "" "" "" "" "" +"LOCFLU" "" "" "" "" "12.3187" "" "" "" "" "" "" "" "" "" +"LOCLAN" "" "" "" "" "6.37862" "" "" "" "" "" "" "" "" "" +"LOCLUS" "" "" "" "" "13.5908" "" "" "" "" "" "" "" "" "" +"LOCNAE" "1.18018" "1.24" "0.92612" "13.5811" "11.7684" "" "" "" "" "2" "7.39" "" "" "" +"LOXCUR" "2.22013" "" "" "" "10.4943" "" "" "" "" "1.11" "3.03" "" "" "" +"LOXLEU" "" "" "" "" "6.03978" "" "" "" "" "" "" "" "" "" +"LOXPYT" "" "" "" "" "7.21678" "" "" "" "" "" "" "" "" "" +"LOXSCO" "" "" "" "" "9.23721" "" "" "" "" "" "" "" "" "" +"LULARB" "0.910982" "0.903077" "0.623972" "15.4347" "13.472" "14.5056" "12.4622" "21.2742" "8.81198" "1.5" "4.48" 50 50 0 +"LUSLUS" "" "" "" "" "11.8425" "" "" "" "" "" "" "" "" "" +"LUSMEG" "0.481346" "0.470259" "0.358723" "16.2469" "14.4949" "16.0099" "13.3447" "22.2074" "8.86272" "2" "7.39" 0 100 0 +"LUSSVE" "3.01919" "" "" "" "10.1598" "" "" "" "" "2" "7.39" "" "" "" +"LYMMIN" "" "" "" "" "5.82881" "" "" "" "" "" "" "" "" "" +"MARANG" "" "" "" "" "18.8024" "" "" "" "" "" "" "" "" "" +"MELCAL" "" "" "" "" "16.1242" "" "" "" "" "" "" "" "" "" +"MELFUS" "" "" "" "" "4.81336" "" "" "" "" "" "" "" "" "" +"MELNIG" "" "" "" "" "4.22591" "" "" "" "" "" "" "" "" "" +"MERALB" "" "" "" "" "5.48342" "" "" "" "" "" "" "" "" "" +"MERAPI" "1.60825" "0.861843" "1.02448" "17.3289" "15.3796" "16.4095" "14.3269" "24.0378" "9.71084" "2" "7.39" 0 100 0 +"MERMER" "" "" "" "" "7.91186" "" "" "" "" "" "" "" "" "" +"MERPER" "" "" "" "" "14.7569" "" "" "" "" "" "" "" "" "" +"MERSER" "" "" "" "" "7.37249" "" "" "" "" "" "" "" "" "" +"MILCAL" "1.21116" "1.46435" "1.04335" "16.039" "14.2314" "" "" "" "" "1.28" "3.6" "" "" "" +"MILMIG" "0.789268" "0.753045" "0.840926" "15.5972" "13.4666" "13.9509" "14.8916" "21.7092" "6.81755" "2.83" "16.95" 0 17 83 +"MILMIL" "1.31651" "1.34374" "1.41433" "15.342" "13.544" "" "" "" "" "2.85" "17.29" "" "" "" +"MONNIV" "" "" "" "" "9.80504" "" "" "" "" "" "" "" "" "" +"MONSAX" "" "" "" "" "13.7288" "" "" "" "" "" "" "" "" "" +"MONSOL" "" "" "" "" "16.3664" "" "" "" "" "" "" "" "" "" +"MOTALB" "0.717506" "0.693271" "0.635722" "13.9073" "11.9859" "11.8371" "18.4186" "20.056" "1.63743" "2" "7.39" 0 100 0 +"MOTCIN" "1.60406" "" "" "" "12.9122" "" "" "" "" "2" "7.39" "" "" "" +"MOTCIT" "" "" "" "" "12.5406" "" "" "" "" "" "" "" "" "" +"MOTFLA" "1.86497" "2.09124" "1.6649" "14.2181" "12.137" "12.6236" "17.551" "21.4981" "3.94717" "2" "7.39" 0 100 0 +"MUSSTR" "0.875145" "0.967366" "0.649096" "14.1296" "12.1492" "12.6632" "16.4036" "20.7408" "4.33717" "2" "7.39" 0 100 0 +"NEOPER" "" "" "" "" "15.3774" "" "" "" "" "" "" "" "" "" +"NETRUF" "" "" "" "" "14.5833" "" "" "" "" "" "" "" "" "" +"NUCCAR" "" "" "" "" "10.7296" "" "" "" "" "" "" "" "" "" +"NUMARQ" "1.93931" "" "" "" "10.0493" "" "" "" "" "2" "7.39" "" "" "" +"NUMPHA" "2.19265" "" "" "" "5.88832" "" "" "" "" "2" "7.39" "" "" "" +"NYCNYC" "" "" "" "" "15.1905" "" "" "" "" "" "" "" "" "" +"NYCSCA" "" "" "" "" "2.91235" "" "" "" "" "" "" "" "" "" +"OCECAS" "" "" "" "" "17.4696" "" "" "" "" "" "" "" "" "" +"OCELEU" "" "" "" "" "8.41565" "" "" "" "" "" "" "" "" "" +"OENHIS" "" "" "" "" "16.6059" "" "" "" "" "" "" "" "" "" +"OENISA" "" "" "" "" "15.5466" "" "" "" "" "" "" "" "" "" +"OENLEU" "" "" "" "" "17.0871" "" "" "" "" "" "" "" "" "" +"OENOEN" "1.88809" "1.70359" "1.28301" "13.5562" "11.6202" "11.5893" "18.5619" "19.8588" "1.29683" "1.95" "7.03" 5 95 0 +"OENPLE" "" "" "" "" "15.2638" "" "" "" "" "" "" "" "" "" +"ORIORI" "0.48111" "0.473188" "0.459814" "15.5236" "13.4707" "14.4162" "11.8073" "20.5141" "8.70674" "1.95" "7.03" 5 95 0 +"OTITAR" "" "" "" "" "15.6407" "" "" "" "" "" "" "" "" "" +"OTUSCO" "" "" "" "" "15.4051" "" "" "" "" "" "" "" "" "" +"OXYJAM" "" "" "" "" "11.3382" "" "" "" "" "" "" "" "" "" +"OXYLEU" "" "" "" "" "17.8705" "" "" "" "" "" "" "" "" "" +"PAGEBU" "" "" "" "" "-8.95161" "" "" "" "" "" "" "" "" "" +"PANBIA" "" "" "" "" "13.878" "" "" "" "" "" "" "" "" "" +"PANHAL" "" "" "" "" "8.91938" "" "" "" "" "" "" "" "" "" +"PARATE" "1.56738" "1.38635" "0.976722" "13.7258" "11.9046" "12.3275" "14.2139" "19.8551" "5.64115" "1.6" "4.95" 40 60 0 +"PARCAE" "0.363874" "0.350865" "0.300248" "14.6174" "12.7367" "13.8151" "14.4744" "20.8357" "6.36133" "1.8" "6.05" 20 80 0 +"PARCIN" "" "" "" "" "4.4728" "" "" "" "" "" "" "" "" "" +"PARCRI" "1.54348" "1.61674" "1.27495" "13.6245" "11.7118" "12.1938" "11.9518" "17.9174" "5.96556" "1.6" "4.95" 40 60 0 +"PARCYA" "" "" "" "" "11.7754" "" "" "" "" "" "" "" "" "" +"PARLUG" "" "" "" "" "15.1051" "" "" "" "" "" "" "" "" "" +"PARMAJ" "0.279454" "0.294945" "0.228392" "14.3054" "12.3437" "12.8921" "16.3441" "20.8579" "4.51377" "1.85" "6.36" 15 85 0 +"PARMON" "1.42374" "1.39209" "0.807668" "12.2559" "10.1958" "10.7927" "12.4771" "15.9845" "3.50745" "1.6" "4.95" 40 60 0 +"PARPAL" "1.07803" "0.987562" "0.784673" "14.21" "12.3251" "13.3596" "11.6305" "17.9866" "6.35607" "1.7" "5.47" 30 70 0 +"PASDOM" "1.2793" "1.26364" "1.10447" "14.1404" "12.2628" "13.8934" "24.0132" "27.7104" "3.69728" "1.2" "3.32" 80 20 0 +"PASHIS" "" "" "" "" "17.062" "" "" "" "" "" "" "" "" "" +"PASMON" "1.07451" "1.25134" "0.91032" "15.0374" "13.0709" "12.8182" "15.622" "19.4418" "3.81977" "1.15" "3.16" 85 15 0 +"PELCRI" "" "" "" "" "15.2608" "" "" "" "" "" "" "" "" "" +"PELONO" "" "" "" "" "14.6745" "" "" "" "" "" "" "" "" "" +"PERAPI" "1.22689" "" "" "" "11.9608" "" "" "" "" "2" "7.39" "" "" "" +"PERINF" "" "" "" "" "5.7726" "" "" "" "" "" "" "" "" "" +"PERPER" "2.2038" "2.10562" "1.11597" "14.8069" "12.8464" "13.3899" "10.6253" "18.8805" "8.25517" "1.1" "3" 90 10 0 +"PETPET" "2.43754" "" "" "" "15.8837" "" "" "" "" "1.22" "3.39" "" "" "" +"PHAARI" "" "" "" "" "12.5428" "" "" "" "" "" "" "" "" "" +"PHACAR" "" "" "" "" "12.4695" "" "" "" "" "" "" "" "" "" +"PHACOL" "0.603798" "0.868791" "0.532474" "14.5849" "12.865" "14.1541" "10.8734" "19.3016" "8.42817" "1.25" "3.49" 75 25 0 +"PHAFUL" "" "" "" "" "4.15042" "" "" "" "" "" "" "" "" "" +"PHALOB" "" "" "" "" "4.7092" "" "" "" "" "" "" "" "" "" +"PHAPYG" "" "" "" "" "15.9922" "" "" "" "" "" "" "" "" "" +"PHIPUG" "" "" "" "" "7.9733" "" "" "" "" "" "" "" "" "" +"PHOOCH" "1.06392" "1.11689" "0.958918" "15.1858" "13.2394" "14.4645" "12.9299" "20.7704" "7.84044" "1.85" "6.36" 15 85 0 +"PHOPHO" "0.756574" "0.970977" "0.690626" "13.2935" "11.3098" "11.7703" "15.1255" "18.89" "3.76445" "1.93" "6.89" 7 93 0 +"PHORUB" "" "" "" "" "19.1058" "" "" "" "" "" "" "" "" "" +"PHYBON" "0.961767" "0.858597" "0.629748" "15.7488" "13.8609" "15.5607" "15.7586" "21.5934" "5.83487" "2" "7.39" 0 100 0 +"PHYBOR" "" "" "" "" "5.47238" "" "" "" "" "" "" "" "" "" +"PHYCOL" "0.473271" "0.460005" "0.394842" "13.8845" "11.9466" "11.5805" "14.0988" "17.757" "3.65817" "1.95" "7.03" 5 95 0 +"PHYDES" "" "" "" "" "9.76171" "" "" "" "" "" "" "" "" "" +"PHYINO" "" "" "" "" "3.97172" "" "" "" "" "" "" "" "" "" +"PHYSIB" "1.82163" "1.71953" "1.32234" "13.3549" "11.3756" "12.4653" "10.8992" "17.1979" "6.29872" "1.95" "7.03" 5 95 0 +"PHYTRO" "1.12831" "1.11753" "0.697834" "12.452" "10.4597" "" "" "" "" "1.95" "7.03" "" "" "" +"PICCAN" "2.21313" "" "" "" "12.0606" "" "" "" "" "2" "7.39" "" "" "" +"PICPIC" "0.72715" "0.702208" "0.629173" "14.1716" "12.1558" "12.6757" "18.8408" "22.5157" "3.67492" "1.51" "4.53" 50 49 1 +"PICTRI" "" "" "" "" "7.98197" "" "" "" "" "" "" "" "" "" +"PICVIR" "0.335355" "0.384167" "0.325771" "14.7495" "12.9414" "13.7777" "13.0868" "19.5996" "6.51277" "2" "7.39" 0 100 0 +"PINENU" "" "" "" "" "5.00735" "" "" "" "" "" "" "" "" "" +"PLALEU" "" "" "" "" "15.2992" "" "" "" "" "" "" "" "" "" +"PLEFAL" "" "" "" "" "16.1132" "" "" "" "" "" "" "" "" "" +"PLENIV" "" "" "" "" "4.04482" "" "" "" "" "" "" "" "" "" +"PLUAPR" "" "" "" "" "7.00315" "" "" "" "" "" "" "" "" "" +"PLUSQU" "" "" "" "" "1.01043" "" "" "" "" "" "" "" "" "" +"PODAUR" "" "" "" "" "8.29965" "" "" "" "" "" "" "" "" "" +"PODCRI" "" "" "" "" "12.6861" "" "" "" "" "" "" "" "" "" +"PODGRI" "" "" "" "" "11.9387" "" "" "" "" "" "" "" "" "" +"PODNIG" "" "" "" "" "13.3391" "" "" "" "" "" "" "" "" "" +"POLSTE" "" "" "" "" "0.96746" "" "" "" "" "" "" "" "" "" +"PORPAR" "" "" "" "" "13.5009" "" "" "" "" "" "" "" "" "" +"PORPOR" "" "" "" "" "12.4065" "" "" "" "" "" "" "" "" "" +"PORPUS" "" "" "" "" "15.3497" "" "" "" "" "" "" "" "" "" +"PORRIO" "" "" "" "" "18.071" "" "" "" "" "" "" "" "" "" +"PRUCOL" "" "" "" "" "11.496" "" "" "" "" "" "" "" "" "" +"PRUMOD" "0.421095" "0.495331" "0.287379" "12.8065" "10.9083" "10.9918" "13.9506" "17.5781" "3.62745" "1.5" "4.48" 50 50 0 +"PSIKRA" "" "" "" "" "13.7106" "" "" "" "" "" "" "" "" "" +"PTEALC" "" "" "" "" "17.2732" "" "" "" "" "" "" "" "" "" +"PTEORI" "" "" "" "" "16.8935" "" "" "" "" "" "" "" "" "" +"PTYRUP" "1.65226" "" "" "" "14.4734" "" "" "" "" "2" "7.39" "" "" "" +"PUFASS" "" "" "" "" "17.4834" "" "" "" "" "" "" "" "" "" +"PYRGRA" "" "" "" "" "11.3931" "" "" "" "" "" "" "" "" "" +"PYRPYR" "0.979659" "1.0525" "0.889121" "12.6644" "10.7563" "" "" "" "" "1.1" "3" "" "" "" +"PYRRAX" "" "" "" "" "14.3291" "" "" "" "" "" "" "" "" "" +"RALAQU" "" "" "" "" "13.3215" "" "" "" "" "" "" "" "" "" +"RECAVO" "" "" "" "" "14.5831" "" "" "" "" "" "" "" "" "" +"REGIGN" "1.13798" "1.08052" "0.819508" "15.0695" "13.1999" "14.5515" "12.7657" "20.6917" "7.92593" "2" "7.39" 0 100 0 +"REGREG" "1.66634" "1.45985" "1.34067" "12.6939" "10.8075" "11.1916" "12.4064" "17.0259" "4.61946" "2" "7.39" 0 100 0 +"REMPEN" "" "" "" "" "13.7399" "" "" "" "" "" "" "" "" "" +"RHOGIT" "" "" "" "" "18.3578" "" "" "" "" "" "" "" "" "" +"RIPRIP" "2.08732" "" "" "" "11.9551" "" "" "" "" "2" "7.39" "" "" "" +"RISTRI" "" "" "" "" "7.38737" "" "" "" "" "" "" "" "" "" +"SAXRUB" "1.5989" "1.46251" "1.00506" "13.3062" "11.299" "11.6614" "13.6775" "17.8335" "4.15606" "2" "7.39" 0 100 0 +"SAXTOR" "0.804" "0.775838" "0.688668" "15.772" "13.9903" "15.2056" "13.2555" "21.0812" "7.82576" "2" "7.39" 0 100 0 +"SCORUS" "" "" "" "" "10.6011" "" "" "" "" "" "" "" "" "" +"SERCIT" "" "" "" "" "11.3532" "" "" "" "" "" "" "" "" "" +"SERSER" "0.89938" "0.77666" "0.681981" "15.8333" "13.8814" "15.5488" "13.0175" "22.158" "9.14048" "1" "2.72" 100 0 0 +"SITEUR" "0.958605" "0.922888" "0.778929" "14.7519" "12.7947" "13.0224" "13.2114" "19.6074" "6.39604" "1.63" "5.1" 37 63 0 +"SITNEU" "" "" "" "" "16.4086" "" "" "" "" "" "" "" "" "" +"SITWHI" "" "" "" "" "15.8652" "" "" "" "" "" "" "" "" "" +"SOMMOL" "" "" "" "" "8.16883" "" "" "" "" "" "" "" "" "" +"STEALB" "" "" "" "" "13.6555" "" "" "" "" "" "" "" "" "" +"STEBEN" "" "" "" "" "18.1615" "" "" "" "" "" "" "" "" "" +"STECAS" "" "" "" "" "10.2516" "" "" "" "" "" "" "" "" "" +"STECUS" "" "" "" "" "5.96359" "" "" "" "" "" "" "" "" "" +"STEDOU" "" "" "" "" "12.7248" "" "" "" "" "" "" "" "" "" +"STEFUS" "" "" "" "" "17.3272" "" "" "" "" "" "" "" "" "" +"STEHIR" "" "" "" "" "11.4396" "" "" "" "" "" "" "" "" "" +"STELON" "" "" "" "" "3.57231" "" "" "" "" "" "" "" "" "" +"STEPAR" "" "" "" "" "6.7215" "" "" "" "" "" "" "" "" "" +"STEPOM" "" "" "" "" "0.525384" "" "" "" "" "" "" "" "" "" +"STESAN" "" "" "" "" "12.3093" "" "" "" "" "" "" "" "" "" +"STESKU" "" "" "" "" "5.0753" "" "" "" "" "" "" "" "" "" +"STRALU" "" "" "" "" "12.8739" "" "" "" "" "" "" "" "" "" +"STRDEC" "1.04334" "0.991969" "0.960984" "14.7911" "12.9162" "14.6564" "19.7723" "26.9157" "7.14336" "1" "2.72" 100 0 0 +"STRNEB" "" "" "" "" "6.85586" "" "" "" "" "" "" "" "" "" +"STRSEN" "" "" "" "" "14.8563" "" "" "" "" "" "" "" "" "" +"STRTUR" "0.451923" "0.400338" "0.379551" "15.7236" "13.7662" "15.5491" "21.8016" "29.5579" "7.75632" "1" "2.72" 100 0 0 +"STRURA" "" "" "" "" "9.2298" "" "" "" "" "" "" "" "" "" +"STUROS" "" "" "" "" "15.8791" "" "" "" "" "" "" "" "" "" +"STUUNI" "" "" "" "" "16.5557" "" "" "" "" "" "" "" "" "" +"STUVUL" "0.678736" "0.570593" "0.530414" "13.7636" "11.8028" "12.3572" "14.9439" "19.2662" "4.32237" "1.5" "4.48" 50 50 0 +"SULBAS" "" "" "" "" "8.73326" "" "" "" "" "" "" "" "" "" +"SYLATR" "0.291053" "0.316278" "0.235556" "14.5232" "12.6192" "13.1921" "13.8615" "20.0016" "6.14015" "1.6" "4.95" 40 60 0 +"SYLBOR" "0.679919" "0.693892" "0.449187" "13.299" "11.3163" "12.0225" "13.1888" "17.7172" "4.52839" "1.6" "4.95" 40 60 0 +"SYLCAN" "1.77527" "1.33114" "1.24176" "18.0076" "16.4215" "17.3734" "12.0385" "22.1208" "10.0823" "1.8" "6.05" 20 80 0 +"SYLCOM" "0.624932" "0.653973" "0.556872" "14.5746" "12.6536" "13.3891" "13.5529" "20.3339" "6.78104" "1.6" "4.95" 40 60 0 +"SYLCON" "" "" "" "" "16.9718" "" "" "" "" "" "" "" "" "" +"SYLCUR" "0.91895" "1.01449" "0.773661" "13.6456" "11.6399" "12.3091" "13.3496" "18.5634" "5.21378" "1.8" "6.05" 20 80 0 +"SYLHOR" "2.04495" "1.35" "0.966924" "17.7918" "16.2234" "17.5009" "12.0605" "21.8019" "9.74135" "1.7" "5.47" 30 70 0 +"SYLMEL" "1.61695" "0.755948" "0.596166" "18.4332" "16.9658" "18.3292" "12.0397" "23.0259" "10.9863" "1.7" "5.47" 30 70 0 +"SYLNIS" "" "" "" "" "12.8776" "" "" "" "" "" "" "" "" "" +"SYLRUE" "" "" "" "" "19.113" "" "" "" "" "" "" "" "" "" +"SYLSAR" "" "" "" "" "17.5973" "" "" "" "" "" "" "" "" "" +"SYLUND" "2.66103" "2.06494" "1.46052" "17.1553" "15.8211" "16.7325" "10.1522" "21.0617" "10.9095" "1.9" "6.69" 10 90 0 +"SYRREE" "" "" "" "" "13.4773" "" "" "" "" "" "" "" "" "" +"TACRUF" "" "" "" "" "13.4634" "" "" "" "" "" "" "" "" "" +"TADFER" "" "" "" "" "15.5742" "" "" "" "" "" "" "" "" "" +"TADTAD" "" "" "" "" "12.0434" "" "" "" "" "" "" "" "" "" +"TARCYA" "" "" "" "" "6.56761" "" "" "" "" "" "" "" "" "" +"TETRAX" "" "" "" "" "16.1633" "" "" "" "" "" "" "" "" "" +"TETRIX" "" "" "" "" "8.99648" "" "" "" "" "" "" "" "" "" +"TETURO" "" "" "" "" "8.73708" "" "" "" "" "" "" "" "" "" +"TICMUR" "" "" "" "" "11.0821" "" "" "" "" "" "" "" "" "" +"TRICIN" "" "" "" "" "10.598" "" "" "" "" "" "" "" "" "" +"TRIERY" "" "" "" "" "4.77171" "" "" "" "" "" "" "" "" "" +"TRIGLA" "" "" "" "" "7.67062" "" "" "" "" "" "" "" "" "" +"TRINEB" "" "" "" "" "6.45053" "" "" "" "" "" "" "" "" "" +"TRIOCH" "" "" "" "" "9.87852" "" "" "" "" "" "" "" "" "" +"TRISTA" "" "" "" "" "13.0028" "" "" "" "" "" "" "" "" "" +"TRITOT" "" "" "" "" "11.0359" "" "" "" "" "" "" "" "" "" +"TROTRO" "0.430257" "0.371592" "0.34744" "14.0432" "12.2368" "12.9074" "15.9244" "20.6657" "4.74128" "2" "7.39" 0 100 0 +"TURILI" "" "" "" "" "8.25096" "" "" "" "" "" "" "" "" "" +"TURMER" "0.237414" "0.233557" "0.193482" "14.4463" "12.5843" "13.7146" "16.3924" "21.7949" "5.40248" "1.6" "4.95" 40 60 0 +"TURPHI" "0.45115" "0.401706" "0.375644" "13.3325" "11.3957" "11.6012" "14.214" "17.9786" "3.76458" "1.57" "4.81" 43 57 0 +"TURPIL" "1.49239" "1.38586" "0.892988" "12.1874" "10.0287" "10.5189" "13.5309" "16.2458" "2.71486" "1.6" "4.95" 40 60 0 +"TURRUF" "" "" "" "" "4.03574" "" "" "" "" "" "" "" "" "" +"TURSYL" "" "" "" "" "20.7412" "" "" "" "" "" "" "" "" "" +"TURTOR" "2.24162" "" "" "" "9.03076" "" "" "" "" "1.7" "5.47" "" "" "" +"TURVIS" "0.529272" "0.517882" "0.506435" "13.8021" "11.9147" "12.4433" "14.7843" "19.8083" "5.02401" "1.55" "4.71" 45 55 0 +"TYTALB" "" "" "" "" "13.9906" "" "" "" "" "" "" "" "" "" +"UPUEPO" "0.544659" "0.606751" "0.386247" "16.2542" "14.1794" "15.9169" "15.3301" "24.5791" "9.24903" "2" "7.39" 0 100 0 +"URIAAL" "" "" "" "" "9.58898" "" "" "" "" "" "" "" "" "" +"URILOM" "" "" "" "" "1.03554" "" "" "" "" "" "" "" "" "" +"VANVAN" "2.20961" "2.22758" "2.01969" "13.5008" "11.5326" "11.9405" "14.1509" "18.5567" "4.40578" "1.9" "6.69" 10 90 0 +"XENCIN" "" "" "" "" "10.598" "" "" "" "" "" "" "" "" ""
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/stoceps_macros.xml Thu Apr 02 03:35:29 2020 -0400 @@ -0,0 +1,73 @@ +<macros> + <token name="@VERSION@">0.0.1</token> + <xml name="reshape_requirements"> + <requirements> + <requirement type="package" version="0.8.8">r-reshape</requirement> + <requirement type="package" version="1.12.0">r-data.table</requirement> + </requirements> + </xml> + <xml name="reshape2_requirements"> + <requirements> + <requirement type="package" version="1.4.3">r-reshape2</requirement> + </requirements> + </xml> + <xml name="mainglm_requirements"> + <requirements> + <requirement type="package" version="3.0.0">r-ggplot2</requirement> + <requirement type="package" version="0.3_2">r-speedglm</requirement> + <requirement type="package" version="1.10_1">r-arm</requirement> + <requirement type="package" version="1.12.0">r-data.table</requirement> + <requirement type="package" version="1.4.3">r-reshape2</requirement> + </requirements> + </xml> + <xml name="temp_indic_requirements"> + <requirements> + <requirement type="package" version="1.3_15">r-rodbc</requirement> + <requirement type="package" version="0.8.8">r-reshape</requirement> + <requirement type="package" version="1.12.0">r-data.table</requirement> + <requirement type="package" version="1.4_3">r-rgdal</requirement> + <requirement type="package" version="1.7.4">r-lubridate</requirement> + <requirement type="package" version="4.6_2">r-doby</requirement> + <requirement type="package" version="1.10_1">r-arm</requirement> + <requirement type="package" version="3.1.0">r-ggplot2</requirement> + <requirement type="package" version="1.0.0">r-scales</requirement> + <requirement type="package" version="1.8_24">r-mgcv</requirement> + <requirement type="package" version="1.8.4">r-plyr</requirement> + <requirement type="package" version="0.3_2">r-speedglm</requirement> + <requirement type="package" version="3.1_0">r-lmertest</requirement> + <requirement type="package" version="0.2.3">r-glmmtmb</requirement> + </requirements> + </xml> + <xml name="stoceps_input_filtered"> + <param name="input" type="data" format="tabular" label="Stoc filtered input" help="Input Stoc count file, shaped and filtered with the 'preprocess population data' and 'filter species' tools." /> + </xml> + <xml name="stoceps_advanced_params_select"> + <param name="advanced" type="select" label="Specify advanced parameters"> + <option value="simple" selected="true">No, use program defaults.</option> + <option value="advanced">Yes, see full parameter list.</option> + </param> + <when value="simple"> + </when> + </xml> + <xml name="stoceps_compute_ic"> + <param name="compute_ic" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="yes" label="Compute confidence intervals"/> + </xml> + <xml name="stoceps_filter_glmmtmb"> + <filter> settings['advanced'] == 'advanced'</filter> + <filter> settings['method'] == 'glmmtmb'</filter> + </xml> + <xml name="stoceps_filter_gam"> + <filter> settings['method'] == 'gam'</filter> + </xml> + <xml name="stoceps_bibref"> + <citations> + <citation type="bibtex"> + @unpublished{stocepsromain, + title={Vigie-Nature STOC unpublished scripts}, + author={Lorrilliere, R}, + url={http://www.vigienature.fr/sites/vigienature/files/atoms/files/analysestoceps_0.zip} + } + </citation> + </citations> + </xml> +</macros>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tabSpecies.csv Thu Apr 02 03:35:29 2020 -0400 @@ -0,0 +1,325 @@ +"espece" "nom" "nomscientific" "indicateur" "specialisation" +"ACCGEN" "Autour des palombes" "Accipiter gentilis" FALSE "" +"ACCNIS" "Epervier d'Europe" "Accipiter nisus" FALSE "" +"ACRARU" "Rousserolle turdoïde" "Acrocephalus arundinaceus" FALSE "" +"ACRMEL" "Lusciniole à moustaches" "Acrocephalus melanopogon" FALSE "" +"ACRPAL" "Phragmite aquatique" "Acrocephalus paludicola" FALSE "" +"ACRRIS" "Rousserolle verderolle" "Acrocephalus palustris" FALSE "" +"ACRSCH" "Phragmite des joncs" "Acrocephalus schoenobaenus" FALSE "" +"ACRSCI" "Rousserolle effarvatte" "Acrocephalus scirpaceus" FALSE "" +"ACTHYP" "Chevalier guignette" "Actitis hypoleucos" FALSE "" +"AEGCAU" "Mésange à longue queue" "Aegithalos caudatus" FALSE "" +"AEGFUN" "Chouette de Tengmalm" "Aegolius funereus" FALSE "" +"AIXGAL" "Canard mandarin" "Aix galericulata" FALSE "" +"AIXSPO" "Canard carolin" "Aix sponsa" FALSE "" +"ALAARV" "Alouette des champs" "Alauda arvensis" TRUE "milieux agricoles" +"ALCATT" "Martin-pêcheur d'Europe" "Alcedo atthis" FALSE "" +"ALCTOR" "Pingouin torda" "Alca torda" FALSE "" +"ALEGRA" "Perdrix bartavelle" "Alectoris graeca" FALSE "" +"ALERUF" "Perdrix rouge" "Alectoris rufa" TRUE "milieux agricoles" +"ALLALL" "Mergule nain" "Alle alle" FALSE "" +"ALOAEG" "Ouette d'Egypte" "Alopochen aegyptiaca" FALSE "" +"ANAACU" "Canard pilet" "Anas acuta" FALSE "" +"ANACLY" "Canard souchet" "Anas clypeata" FALSE "" +"ANACRE" "Sarcelle d'hiver" "Anas crecca" FALSE "" +"ANAPEN" "Canard siffleur" "Anas penelope" FALSE "" +"ANAPLA" "Canard colvert" "Anas platyrhynchos" FALSE "" +"ANAQUE" "Sarcelle d'été" "Anas querquedula" FALSE "" +"ANASTR" "Canard chipeau" "Anas strepera" FALSE "" +"ANSANS" "Oie cendrée" "Anser anser" FALSE "" +"ANSIND" "Oie à tête barrée" "Anser indicus" FALSE "" +"ANTCAM" "Pipit rousseline" "Anthus campestris" TRUE "milieux agricoles" +"ANTCER" "Pipit à gorge rousse" "Anthus cervinus" FALSE "" +"ANTPET" "Pipit maritime" "Anthus petrosus" FALSE "" +"ANTPRA" "Pipit farlouse" "Anthus pratensis" TRUE "milieux agricoles" +"ANTRIC" "Pipit de Richard" "Anthus richardi" FALSE "" +"ANTSPI" "Pipit spioncelle" "Anthus spinoletta" FALSE "" +"ANTTRI" "Pipit des arbres" "Anthus trivialis" FALSE "" +"APUAPU" "Martinet noir" "Apus apus" TRUE "milieux bâtis" +"APUMEL" "Martinet à ventre blanc" "Apus melba" FALSE "" +"APUPAL" "Martinet pâle" "Apus pallidus" FALSE "" +"AQUCHR" "Aigle royal" "Aquila chrysaetos" FALSE "" +"ARDCIN" "Héron cendré" "Ardea cinerea" FALSE "" +"ARDPUR" "Héron pourpré" "Ardea purpurea" FALSE "" +"ARDRAL" "Crabier chevelu" "Ardeola ralloides" FALSE "" +"AREINT" "Tournepierre à collier" "Arenaria interpres" FALSE "" +"ASIFLA" "Hibou des marais" "Asio flammeus" FALSE "" +"ASIOTU" "Hibou moyen-duc" "Asio otus" FALSE "" +"ATHNOC" "Chevêche d'Athéna" "Athene noctua" FALSE "" +"AYTAFF" "Fuligule à tête noire" "Aythya affinis" FALSE "" +"AYTFER" "Fuligule milouin" "Aythya ferina" FALSE "" +"AYTFUL" "Fuligule morillon" "Aythya fuligula" FALSE "" +"AYTMAR" "Fuligule milouinan" "Aythya marila" FALSE "" +"BOMGAR" "Jaseur boréal" "Bombycilla garrulus" FALSE "" +"BONBON" "Gélinotte des bois" "Bonasa bonasia" FALSE "" +"BOTSTE" "Butor étoilé" "Botaurus stellaris" FALSE "" +"BRABER" "Bernache cravant" "Branta bernicla" FALSE "" +"BRACAN" "Bernache du Canada" "Branta canadensis" FALSE "" +"BRALEU" "Bernache nonnette" "Branta leucopsis" FALSE "" +"BUBBUB" "Grand-duc d'Europe" "Bubo bubo" FALSE "" +"BUBIBI" "Héron garde-boeufs" "Bubulcus ibis" FALSE "" +"BUROED" "Oedicnème criard" "Burhinus oedicnemus" FALSE "" +"BUTBUT" "Buse variable" "Buteo buteo" TRUE "milieux agricoles" +"CALACU" "Bécasseau à queue pointue" "Calidris acuminata" FALSE "" +"CALALB" "Bécasseau sanderling" "Calidris alba" FALSE "" +"CALALP" "Bécasseau variable" "Calidris alpina" FALSE "" +"CALBRA" "Alouette calandrelle" "Calandrella brachydactyla" FALSE "" +"CALCAN" "Bécasseau maubèche" "Calidris canutus" FALSE "" +"CALMIN" "Bécasseau minute" "Calidris minuta" FALSE "" +"CAPEUR" "Engoulevent d'Europe" "Caprimulgus europaeus" FALSE "" +"CARCAN" "Linotte mélodieuse" "Carduelis cannabina" TRUE "milieux agricoles" +"CARCAR" "Chardonneret élégant" "Carduelis carduelis" TRUE "milieux bâtis" +"CARCHL" "Verdier d'Europe" "Carduelis chloris" TRUE "milieux bâtis" +"CARFLA" "Sizerin flammé" "Carduelis flammea" FALSE "" +"CARHOR" "Sizerin blanchâtre" "Carduelis hornemanni" FALSE "" +"CARRIS" "Linotte à bec jaune" "Carduelis flavirostris" FALSE "" +"CARSPI" "Tarin des aulnes" "Carduelis spinus" FALSE "" +"CERBRA" "Grimpereau des jardins" "Certhia brachydactyla" TRUE "milieux forestiers" +"CERFAM" "Grimpereau des bois" "Certhia familiaris" TRUE "milieux forestiers" +"CETCET" "Bouscarle de Cetti" "Cettia cetti" FALSE "" +"CHAALE" "Gravelot à collier interrompu" "Charadrius alexandrinus" FALSE "" +"CHADUB" "Petit Gravelot" "Charadrius dubius" FALSE "" +"CHAHIA" "Grand Gravelot" "Charadrius hiaticula" FALSE "" +"CHLHYB" "Guifette moustac" "Chlidonias hybrida" FALSE "" +"CHLNIG" "Guifette noire" "Chlidonias niger" FALSE "" +"CICCIC" "Cigogne blanche" "Ciconia ciconia" FALSE "" +"CICNIG" "Cigogne noire" "Ciconia nigra" FALSE "" +"CINCIN" "Cincle plongeur" "Cinclus cinclus" FALSE "" +"CIRAER" "Busard des roseaux" "Circus aeruginosus" FALSE "" +"CIRCYA" "Busard Saint-Martin" "Circus cyaneus" FALSE "" +"CIRGAL" "Circaète Jean-le-blanc" "Circaetus gallicus" FALSE "" +"CIRMAC" "Busard pâle" "Circus macrourus" FALSE "" +"CIRPYG" "Busard cendré" "Circus pygargus" FALSE "" +"CISJUN" "Cisticole des joncs" "Cisticola juncidis" FALSE "" +"CLAGLA" "Coucou geai" "Clamator glandarius" FALSE "" +"COCCOC" "Grosbec casse-noyaux" "Coccothraustes coccothraustes" TRUE "milieux forestiers" +"COLLIV" "Pigeon biset" "Columba livia" FALSE "" +"COLOEN" "Pigeon colombin" "Columba oenas" FALSE "" +"COLPAL" "Pigeon ramier" "Columba palumbus" TRUE "generaliste" +"COLVIR" "Colin de Virginie" "Colinus virginianus" FALSE "" +"CORCOR" "Corneille noire" "Corvus corone" TRUE "generaliste" +"CORFRU" "Corbeau freux" "Corvus frugilegus" TRUE "milieux agricoles" +"CORGAR" "Rollier d'Europe" "Coracias garrulus" FALSE "" +"CORMON" "Choucas des tours" "Corvus monedula" TRUE "milieux bâtis" +"CORRAX" "Grand Corbeau" "Corvus corax" FALSE "" +"COTCOT" "Caille des blés" "Coturnix coturnix" TRUE "milieux agricoles" +"CRECRE" "Râle des genêts" "Crex crex" FALSE "" +"CUCCAN" "Coucou gris" "Cuculus canorus" TRUE "generaliste" +"CYGATR" "Cygne noir" "Cygnus atratus" FALSE "" +"CYGOLO" "Cygne tuberculé" "Cygnus olor" FALSE "" +"DELURB" "Hirondelle de fenêtre" "Delichon urbicum" TRUE "milieux bâtis" +"DENLEU" "Pic à dos blanc" "Dendrocopos leucotos" FALSE "" +"DENMAJ" "Pic épeiche" "Dendrocopos major" TRUE "milieux forestiers" +"DENMED" "Pic mar" "Dendrocopos medius" TRUE "milieux forestiers" +"DENMIN" "Pic épeichette" "Dendrocopos minor" FALSE "" +"DRYMAR" "Pic noir" "Dryocopus martius" TRUE "milieux forestiers" +"EGRALB" "Grande Aigrette" "Ardea alba" FALSE "" +"EGRGAR" "Aigrette garzette" "Egretta garzetta" FALSE "" +"ELACAE" "Elanion blanc" "Elanus caeruleus" FALSE "" +"EMBAUR" "Bruant auréole" "Emberiza aureola" FALSE "" +"EMBCAE" "Bruant cendrillard" "Emberiza caesia" FALSE "" +"EMBCIA" "Bruant fou" "Emberiza cia" FALSE "" +"EMBCIR" "Bruant zizi" "Emberiza cirlus" TRUE "milieux agricoles" +"EMBCIT" "Bruant jaune" "Emberiza citrinella" TRUE "milieux agricoles" +"EMBHOR" "Bruant ortolan" "Emberiza hortulana" FALSE "milieux agricoles" +"EMBLEU" "Bruant à calotte blanche" "Emberiza leucocephalos" FALSE "" +"EMBMEL" "Bruant mélanocéphale" "Emberiza melanocephala" FALSE "" +"EMBSCH" "Bruant des roseaux" "Emberiza schoeniclus" FALSE "" +"EREALP" "Alouette hausse-col" "Eremophila alpestris" FALSE "" +"ERIRUB" "Rougegorge familier" "Erithacus rubecula" TRUE "milieux forestiers" +"EUOMAL" "Capucin bec-de-plomb" "Euodice malabarica" FALSE "" +"FALCOL" "Faucon émerillon" "Falco columbarius" FALSE "" +"FALNAU" "Faucon crécerellette" "Falco naumanni" FALSE "" +"FALPER" "Faucon pèlerin" "Falco peregrinus" FALSE "" +"FALSUB" "Faucon hobereau" "Falco subbuteo" FALSE "" +"FALTIN" "Faucon crécerelle" "Falco tinnunculus" TRUE "milieux agricoles" +"FALVES" "Faucon kobez" "Falco vespertinus" FALSE "" +"FICALB" "Gobemouche à collier" "Ficedula albicollis" FALSE "" +"FICHYP" "Gobemouche noir" "Ficedula hypoleuca" FALSE "" +"FICPAR" "Gobemouche nain" "Ficedula parva" FALSE "" +"FRAARC" "Macareux moine" "Fratercula arctica" FALSE "" +"FRICOE" "Pinson des arbres" "Fringilla coelebs" TRUE "generaliste" +"FRIMON" "Pinson du Nord" "Fringilla montifringilla" FALSE "" +"FULATR" "Foulque macroule" "Fulica atra" FALSE "" +"FULCRI" "Foulque caronculée" "Fulica cristata" FALSE "" +"GALCHL" "Gallinule poule-d'eau" "Gallinula chloropus" FALSE "" +"GALCRI" "Cochevis huppé" "Galerida cristata" TRUE "milieux agricoles" +"GALGAL" "Bécassine des marais" "Gallinago gallinago" FALSE "" +"GALTHE" "Cochevis de Thékla" "Galerida theklae" FALSE "" +"GARGLA" "Geai des chênes" "Garrulus glandarius" TRUE "generaliste" +"GLAPAS" "Chevêchette d'Europe" "Glaucidium passerinum" FALSE "" +"GRUGRU" "Grue cendrée" "Grus grus" FALSE "" +"GYPFUL" "Vautour fauve" "Gyps fulvus" FALSE "" +"HAEOST" "Huîtrier pie" "Haematopus ostralegus" FALSE "" +"HIEFAS" "Aigle de Bonelli" "Aquila fasciata" FALSE "" +"HIEPEN" "Aigle botté" "Hieraaetus pennatus" FALSE "" +"HIMHIM" "Echasse blanche" "Himantopus himantopus" FALSE "" +"HIPICT" "Hypolaïs ictérine" "Hippolais icterina" FALSE "" +"HIPPAL" "Hypolaïs pâle" "Iduna pallida" FALSE "" +"HIPPOL" "Hypolaïs polyglotte" "Hippolais polyglotta" TRUE "generaliste" +"HIRDAU" "Hirondelle rousseline" "Cecropis daurica" FALSE "" +"HIRRUS" "Hirondelle rustique" "Hirundo rustica" TRUE "milieux bâtis" +"IXOMIN" "Blongios nain" "Ixobrychus minutus" FALSE "" +"JYNTOR" "Torcol fourmilier" "Jynx torquilla" FALSE "" +"LAGLAG" "Lagopède des saules" "Lagopus lagopus" FALSE "" +"LAGMUT" "Lagopède alpin" "Lagopus muta" FALSE "" +"LANCOL" "Pie-grièche écorcheur" "Lanius collurio" TRUE "milieux agricoles" +"LANEXC" "Pie-grièche grise" "Lanius excubitor" FALSE "" +"LANMER" "Pie-grièche méridionale" "Lanius meridionalis" FALSE "" +"LANMIN" "Pie-grièche à poitrine rose" "Lanius minor" FALSE "" +"LANSEN" "Pie-grièche à tête rousse" "Lanius senator" FALSE "" +"LARARG" "Goéland argenté" "Larus argentatus" FALSE "" +"LARCAN" "Goéland cendré" "Larus canus" FALSE "" +"LARFUS" "Goéland brun" "Larus fuscus" FALSE "" +"LARGEN" "Goéland railleur" "Chroicocephalus genei" FALSE "" +"LARMAR" "Goéland marin" "Larus marinus" FALSE "" +"LARMEL" "Mouette mélanocéphale" "Ichthyaetus melanocephalus" FALSE "" +"LARMIC" "Goéland leucophée" "Larus michahellis" FALSE "" +"LARRID" "Mouette rieuse" "Chroicocephalus ridibundus" FALSE "" +"LEILUT" "Leïothrix jaune" "Leiothrix lutea" FALSE "" +"LIMLAP" "Barge rousse" "Limosa lapponica" FALSE "" +"LIMLIM" "Barge à queue noire" "Limosa limosa" FALSE "" +"LOCFLU" "Locustelle fluviatile" "Locustella fluviatilis" FALSE "" +"LOCLUS" "Locustelle luscinioïde" "Locustella luscinioides" FALSE "" +"LOCNAE" "Locustelle tachetée" "Locustella naevia" FALSE "" +"LOXCUR" "Bec-croisé des sapins" "Loxia curvirostra" FALSE "" +"LOXLEU" "Bec-croisé bifascié" "Loxia leucoptera" FALSE "" +"LULARB" "Alouette lulu" "Lullula arborea" TRUE "milieux agricoles" +"LUSLUS" "Rossignol progné" "Luscinia luscinia" FALSE "" +"LUSMEG" "Rossignol philomèle" "Luscinia megarhynchos" TRUE "generaliste" +"LUSSVE" "Gorgebleue à miroir" "Luscinia svecica" FALSE "" +"MARANG" "Sarcelle marbrée" "Marmaronetta angustirostris" FALSE "" +"MELCAL" "Alouette calandre" "Melanocorypha calandra" FALSE "" +"MERALB" "Harle piette" "Mergellus albellus" FALSE "" +"MERAPI" "Guêpier d'Europe" "Merops apiaster" FALSE "" +"MERMER" "Harle bièvre" "Mergus merganser" FALSE "" +"MILCAL" "Bruant proyer" "Emberiza calandra" TRUE "milieux agricoles" +"MILMIG" "Milan noir" "Milvus migrans" FALSE "" +"MILMIL" "Milan royal" "Milvus milvus" FALSE "" +"MONNIV" "Niverolle alpine" "Montifringilla nivalis" FALSE "" +"MONSAX" "Monticole de roche" "Monticola saxatilis" FALSE "" +"MONSOL" "Monticole bleu" "Monticola solitarius" FALSE "" +"MOTALB" "Bergeronnette grise" "Motacilla alba" FALSE "" +"MOTCIN" "Bergeronnette des ruisseaux" "Motacilla cinerea" FALSE "" +"MOTFLA" "Bergeronnette printanière" "Motacilla flava" TRUE "milieux agricoles" +"MUSSTR" "Gobemouche gris" "Muscicapa striata" FALSE "" +"NEOPER" "Vautour percnoptère" "Neophron percnopterus" FALSE "" +"NETRUF" "Nette rousse" "Netta rufina" FALSE "" +"NUCCAR" "Cassenoix moucheté" "Nucifraga caryocatactes" FALSE "" +"NUMARQ" "Courlis cendré" "Numenius arquata" FALSE "" +"NUMPHA" "Courlis corlieu" "Numenius phaeopus" FALSE "" +"NYCNYC" "Bihoreau gris" "Nycticorax nycticorax" FALSE "" +"OCELEU" "Océanite culblanc" "Oceanodroma leucorhoa" FALSE "" +"OENHIS" "Traquet oreillard" "Oenanthe hispanica" FALSE "" +"OENOEN" "Traquet motteux" "Oenanthe oenanthe" TRUE "milieux agricoles" +"OENPLE" "Traquet pie" "Oenanthe pleschanka" FALSE "" +"ORIORI" "Loriot d'Europe" "Oriolus oriolus" TRUE "generaliste" +"OTUSCO" "Petit-duc scops" "Otus scops" FALSE "" +"PANBIA" "Panure à moustaches" "Panurus biarmicus" FALSE "" +"PANHAL" "Balbuzard pêcheur" "Pandion haliaetus" FALSE "" +"PARATE" "Mésange noire" "Periparus ater" TRUE "milieux forestiers" +"PARCAE" "Mésange bleue" "Cyanistes caeruleus" TRUE "generaliste" +"PARCRI" "Mésange huppée" "Lophophanes cristatus" TRUE "milieux forestiers" +"PARCYA" "Mésange azurée" "Parus cyanus" FALSE "" +"PARLUG" "Mésange lugubre" "Poecile lugubris" FALSE "" +"PARMAJ" "Mésange charbonnière" "Parus major" TRUE "generaliste" +"PARMON" "Mésange boréale" "Parus montanus" FALSE "" +"PARPAL" "Mésange nonnette" "Poecile palustris" TRUE "milieux forestiers" +"PASDOM" "Moineau domestique" "Passer domesticus" TRUE "milieux bâtis" +"PASHIS" "Moineau espagnol" "Passer hispaniolensis" FALSE "" +"PASITA" "Moineau cisalpin" "Passer italiae" FALSE "" +"PASMON" "Moineau friquet" "Passer montanus" TRUE "milieux bâtis" +"PERAPI" "Bondrée apivore" "Pernis apivorus" FALSE "" +"PERPER" "Perdrix grise" "Perdix perdix" TRUE "milieux agricoles" +"PETPET" "Moineau soulcie" "Petronia petronia" FALSE "" +"PHACAR" "Grand Cormoran" "Phalacrocorax carbo" FALSE "" +"PHACOL" "Faisan de Colchide" "Phasianus colchicus" FALSE "" +"PHIPUG" "Combattant varié" "Calidris pugnax" FALSE "" +"PHOCHI" "Flamant du Chili" "Phoenicopterus chilensis" FALSE "" +"PHOOCH" "Rougequeue noir" "Phoenicurus ochruros" TRUE "milieux bâtis" +"PHOPHO" "Rougequeue à front blanc" "Phoenicurus phoenicurus" TRUE "milieux bâtis" +"PHORUB" "Flamant rose" "Phoenicopterus roseus" FALSE "" +"PHYBON" "Pouillot de Bonelli" "Phylloscopus bonelli" TRUE "milieux forestiers" +"PHYBOR" "Pouillot boréal" "Phylloscopus borealis" FALSE "" +"PHYCOL" "Pouillot véloce" "Phylloscopus collybita" TRUE "milieux forestiers" +"PHYSIB" "Pouillot siffleur" "Phylloscopus sibilatrix" TRUE "milieux forestiers" +"PHYTRO" "Pouillot fitis" "Phylloscopus trochilus" TRUE "milieux forestiers" +"PICCAN" "Pic cendré" "Picus canus" TRUE "milieux forestiers" +"PICPIC" "Pie bavarde" "Pica pica" TRUE "milieux bâtis" +"PICTRI" "Pic tridactyle" "Picoides tridactylus" FALSE "" +"PICVIR" "Pic vert" "Picus viridis" TRUE "generaliste" +"PLALEU" "Spatule blanche" "Platalea leucorodia" FALSE "" +"PLEFAL" "Ibis falcinelle" "Plegadis falcinellus" FALSE "" +"PLUAPR" "Pluvier doré" "Pluvialis apricaria" FALSE "" +"PLUSQU" "Pluvier argenté" "Pluvialis squatarola" FALSE "" +"PODCRI" "Grèbe huppé" "Podiceps cristatus" FALSE "" +"PORPOR" "Marouette ponctuée" "Porzana porzana" FALSE "" +"PRUCOL" "Accenteur alpin" "Prunella collaris" FALSE "" +"PRUMOD" "Accenteur mouchet" "Prunella modularis" TRUE "generaliste" +"PSIKRA" "Perruche à collier" "Psittacula krameri" FALSE "" +"PTYRUP" "Hirondelle de rochers" "Ptyonoprogne rupestris" FALSE "" +"PYRGRA" "Chocard à bec jaune" "Pyrrhocorax graculus" FALSE "" +"PYRPYR" "Bouvreuil pivoine" "Pyrrhula pyrrhula" TRUE "milieux forestiers" +"PYRRAX" "Crave à bec rouge" "Pyrrhocorax pyrrhocorax" FALSE "" +"RALAQU" "Râle d'eau" "Rallus aquaticus" FALSE "" +"RECAVO" "Avocette élégante" "Recurvirostra avosetta" FALSE "" +"REGIGN" "Roitelet à triple bandeau" "Regulus ignicapilla" TRUE "milieux forestiers" +"REGREG" "Roitelet huppé" "Regulus regulus" TRUE "milieux forestiers" +"RIPRIP" "Hirondelle de rivage" "Riparia riparia" FALSE "" +"SAXRUB" "Tarier des prés" "Saxicola rubetra" TRUE "milieux agricoles" +"SAXTOR" "Tarier pâtre" "Saxicola rubicola" TRUE "milieux agricoles" +"SCORUS" "Bécasse des bois" "Scolopax rusticola" FALSE "" +"SERCIT" "Venturon montagnard" "Serinus citrinella" FALSE "" +"SERCOR" "Venturon corse" "Serinus corsicanus" FALSE "" +"SERSER" "Serin cini" "Serinus serinus" TRUE "milieux bâtis" +"SITEUR" "Sittelle torchepot" "Sitta europaea" TRUE "milieux forestiers" +"SITWHI" "Sittelle corse" "Sitta whiteheadi" FALSE "" +"STEALB" "Sterne naine" "Sternula albifrons" FALSE "" +"STEHIR" "Sterne pierregarin" "Sterna hirundo" FALSE "" +"STESAN" "Sterne caugek" "Thalasseus sandvicensis" FALSE "" +"STRALU" "Chouette hulotte" "Strix aluco" FALSE "" +"STRDEC" "Tourterelle turque" "Streptopelia decaocto" TRUE "milieux bâtis" +"STRORI" "Tourterelle orientale" "Streptopelia orientalis" FALSE "" +"STRSEN" "Tourterelle maillée" "Streptopelia senegalensis" FALSE "" +"STRTUR" "Tourterelle des bois" "Streptopelia turtur" FALSE "" +"STUROS" "Étourneau roselin" "Sturnus roseus" FALSE "" +"STUUNI" "Étourneau unicolore" "Sturnus unicolor" FALSE "" +"STUVUL" "Étourneau sansonnet" "Sturnus vulgaris" FALSE "" +"SURULU" "Chouette épervière" "Surnia ulula" FALSE "" +"SYLATR" "Fauvette à tête noire" "Sylvia atricapilla" TRUE "generaliste" +"SYLBOR" "Fauvette des jardins" "Sylvia borin" FALSE "" +"SYLCAN" "Fauvette passerinette" "Sylvia cantillans" FALSE "" +"SYLCOM" "Fauvette grisette" "Sylvia communis" TRUE "milieux agricoles" +"SYLCON" "Fauvette à lunettes" "Sylvia conspicillata" FALSE "" +"SYLCUR" "Fauvette babillarde" "Sylvia curruca" FALSE "" +"SYLHOR" "Fauvette orphée" "Sylvia hortensis" FALSE "" +"SYLMEL" "Fauvette mélanocéphale" "Sylvia melanocephala" TRUE "milieux forestiers" +"SYLNIS" "Fauvette épervière" "Sylvia nisoria" FALSE "" +"SYLSAR" "Fauvette sarde" "Sylvia sarda" FALSE "" +"SYLUND" "Fauvette pitchou" "Sylvia undata" FALSE "" +"SYRREE" "Faisan vénéré" "Syrmaticus reevesii" FALSE "" +"TACRUF" "Grèbe castagneux" "Tachybaptus ruficollis" FALSE "" +"TADFER" "Tadorne casarca" "Tadorna ferruginea" FALSE "" +"TADTAD" "Tadorne de Belon" "Tadorna tadorna" FALSE "" +"TETRAX" "Outarde canepetière" "Tetrax tetrax" FALSE "" +"TETRIX" "Tétras lyre" "Tetrao tetrix" FALSE "" +"TETURO" "Grand Tétras" "Tetrao urogallus" FALSE "" +"THRAET" "Ibis sacré" "Threskiornis aethiopicus" FALSE "" +"TICMUR" "Tichodrome échelette" "Tichodroma muraria" FALSE "" +"TRIERY" "Chevalier arlequin" "Tringa erythropus" FALSE "" +"TRIGLA" "Chevalier sylvain" "Tringa glareola" FALSE "" +"TRINEB" "Chevalier aboyeur" "Tringa nebularia" FALSE "" +"TRIOCH" "Chevalier culblanc" "Tringa ochropus" FALSE "" +"TRITOT" "Chevalier gambette" "Tringa totanus" FALSE "" +"TROTRO" "Troglodyte mignon" "Troglodytes troglodytes" TRUE "milieux forestiers" +"TURILI" "Grive mauvis" "Turdus iliacus" FALSE "" +"TURMER" "Merle noir" "Turdus merula" TRUE "generaliste" +"TURPHI" "Grive musicienne" "Turdus philomelos" TRUE "milieux forestiers" +"TURPIL" "Grive litorne" "Turdus pilaris" FALSE "" +"TURRUF" "Grive à gorge noire ou rousse" "Turdus ruficollis" FALSE "" +"TURTOR" "Merle à plastron" "Turdus torquatus" FALSE "" +"TURVIS" "Grive draine" "Turdus viscivorus" TRUE "milieux forestiers" +"TYTALB" "Effraie des clochers" "Tyto alba" FALSE "" +"UPUEPO" "Huppe fasciée" "Upupa epops" TRUE "milieux agricoles" +"VANVAN" "Vanneau huppé" "Vanellus vanellus" TRUE "milieux agricoles" +"ZOODAU" "Grive dorée" "Zoothera dauma" FALSE ""
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/temp_analysis_indic.xml Thu Apr 02 03:35:29 2020 -0400 @@ -0,0 +1,209 @@ +<tool id="stoceps_trend_indic" name="Temporal trend indicator" version="@VERSION@"> + <description>using GlmmTMB or GAM models</description> + <macros> + <import>stoceps_macros.xml</import> + </macros> + <expand macro="temp_indic_requirements"/> + <command detect_errors="exit_code"><![CDATA[ + Rscript + '$__tool_directory__/FunctExeTemporalAnalysisofIndicatorTrait.r' + '$input' + '$inputtabSpecies' + '$inputspeciesindic' + '$inputcoord' + #if $index=='csi' + 'ssi' + 'csi' + #elif $index=='cti' + 'sti' + 'cti' + #else + 'stri' + 'ctri' + #end if + #if $settings.advanced=='advanced' + $settings.method '' 'idindicatortrait' + $settings.smooth_plot + $settings.compute_ic + #else + 'gam' '' 'idindicatortrait' + 'TRUE' + 'TRUE' + #end if + '$__tool_directory__/FunctTrendSTOCGalaxy.r' + ]]> + </command> + <inputs> + <expand macro="stoceps_input_filtered"/> + <param name="inputcoord" type="data" format="tabular" label="Coordinates of sites" help="Input localization tabular file, with 3 columns (site ID, latitude, longitude)." /> + <param name="inputtabSpecies" type="data" format="tabular" label="Species file" help="Input species tabular file, with 5 columns (species ID, species name, species scientific name, specialization status)." /> + <param name="inputspeciesindic" type="data" format="tabular" label="indicators info file" help="Input indicator info tabular file, with a `species ID` column and at least one index column (named `ssi` or `sti` or `stri`)." /> + <param name="index" type="select" help="Available index" label="Chose the index you want to compute"> + <option selected="true" value="csi">CSI</option> + <option value="cti">CTI</option> + <option value="ctri">CTRI</option> + </param> + <conditional name="settings"> + <expand macro="stoceps_advanced_params_select"/> + <when value="advanced"> + <param name="smooth_plot" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="yes" label="Smooth visualization."/> + <param name="method" type="select" help="Available methods are GlmmTMB and Gam" label="Chose the model method"> + <option selected="true" value="glmmtmb">GlmmTMB</option> + <option value="gam">Gam</option> + </param> + <expand macro="stoceps_compute_ic"/> + </when> + </conditional> + </inputs> + <outputs> + <data name="output_tab_gammcomplet" from_work_dir="Output/csi_gammCOMPLET_France.tabular" format="tabular" label="GAM - Temp trends all data - on ${on_string}"> + <expand macro="stoceps_filter_gam"/> + </data> + <data name="output_tab_gam_annee" from_work_dir="Output/csi_gammParannee_France.tabular" format="tabular" label="GAM - Temp trends per year - on ${on_string}"> + <expand macro="stoceps_filter_gam"/> + </data> + <data name="output_plot_carre" from_work_dir="Output/figcsi_carre_France.png" format="png" label="GAM - Temp trends plot on ${on_string}"> + <expand macro="stoceps_filter_gam"/> + </data> + <data name="output_plot__gam_csiplot" from_work_dir="Output/figcsi_plotFrance.png" format="png" label="GAM - Temp trends plot on ${on_string}"> + <expand macro="stoceps_filter_gam"/> + </data> + <data name="output_tab_gam_smoothed" from_work_dir="Output/csi_gammsmoothFrance.tabular" format="tabular" label="GAM - Temp trends smoothed data on ${on_string}"> + <expand macro="stoceps_filter_gam"/> + </data> + + <data name="output_plot_glmmtb_csiplot" from_work_dir="Output/csi_glmmTMB_France.png" format="png" label="GlmmTMB - Temp trends plot on ${on_string}"> + <expand macro="stoceps_filter_glmmtmb"/> + </data> + <data name="output_tab_glmmtb_annee" from_work_dir="Output/ggdata_csiFrance.tabular" format="tabular" label="GlmmTMB - Temp trends per year - on ${on_string}"> + <expand macro="stoceps_filter_glmmtmb"/> + </data> + <data name="output_tab_glmmtb_gammcomplet" from_work_dir="Output/GlmmTMB_coefficient_csiFrance.tabular" format="tabular" label="GlmmTMB - Temp trends per year - on ${on_string}"> + <expand macro="stoceps_filter_glmmtmb"/> + </data> + </outputs> + <tests> + <test> + <param name="input" value="data_FrenchBBS_squarre_dataSTOCallSp_France_trend_2001_2017_ALAARV-PARCAE_2001_2018.tabular"/> + <param name="inputcoord" value="coordCarreSTOCfaux.tabular"/> + <param name="inputspeciesindic" value="species_indicateur_fonctionnel.tabular"/> + <param name="inputtabSpecies" value="tabSpecies.csv"/> + <param name="advanced" value="advanced"/> + <param name="smooth_plot" value="TRUE"/> + <param name="method" value="gam"/> + <param name="compute_ic" value="FALSE"/> + <output name="output_tab_gammcomplet"> + <assert_contents> + <has_n_lines n="20"/> + <has_size value="3500" delta="100"/> + </assert_contents> + </output> + <output name="output_tab_gam_annee"> + <assert_contents> + <has_n_lines n="19"/> + <has_size value="2400" delta="100"/> + </assert_contents> + </output> + <output name="output_tab_gam_smoothed"> + <assert_contents> + <has_n_lines n="19"/> + <has_size value="1200" delta="50"/> + </assert_contents> + </output> + <output name="output_plot_carre"> + <assert_contents> + <has_text text="PNG"/> + </assert_contents> + </output> + <output name="output_plot__gam_csiplot"> + <assert_contents> + <has_text text="PNG"/> + </assert_contents> + </output> + </test> + <test> + <param name="input" value="Datafilteredfortrendanalysis.tabular"/> + <param name="inputcoord" value="coordCarreSTOCfaux.tabular"/> + <param name="inputspeciesindic" value="species_indicateur_fonctionnel.tabular"/> + <param name="inputtabSpecies" value="tabSpecies.csv"/> + <param name="advanced" value="advanced"/> + <param name="smooth_plot" value="FALSE"/> + <param name="method" value="glmmtmb"/> + <param name="compute_ic" value="TRUE"/> + <output name="output_plot_glmmtb_csiplot"> + <assert_contents> + <has_text text="PNG"/> + </assert_contents> + </output> + <output name="output_tab_glmmtb_annee"> + <assert_contents> + <has_n_lines n="18"/> + <has_size value="1400" delta="50"/> + </assert_contents> + </output> + <output name="output_tab_glmmtb_gammcomplet"> + <assert_contents> + <has_n_lines n="19"/> + <has_size value="2900" delta="100"/> + </assert_contents> + </output> + </test> + </tests> + <help><![CDATA[ +================================================= +STOC Temporal population trend indicator +================================================= + +**What it does** + +Compute and plot time variation of indicator or mean trait values of communities. + +CSI Temporal analysis indicator +This script computes the indicator csi, cti and ctri per year and site, wich also correspond to a community weighted mean (CWM), and analyse its temporal evolution trend and create graphical vizualisation. +As indicator you can thus use also a trait. + +| + +**Input description** + +A tabular file with count data including one column for the abundance, one column indicating the species, one indicating the site and one indicating the year +that could be extracted from the STOC on demand: romain.lorrilliere@mnhn.fr + +IMPORTANT: The tabular file with the count data should not include zero count because this will biais the estimation of the weighted mean. +In the same line, the species trait data should be complete or you should remove the species without trait value from your count data file. + +One tabular species file, with a `species ID` column and species names. + +One tabular file with trait data including at least one column with the species name or ID (the one used in the count data file) and one column with the trait value for each species (named `ssi` or `sti` or `stri`). For the CSI indicator notably (community specialization index), this should be the ssi (species specialization index). + +One tabular file with coordinates in latitude and longitude (one column for each) including also the site ID (the one used in the count data file) + +| + +**Output** + +Gam method :: + + - Two tabulars that details computed indicator per year and globaly and one optional and additional table with the smoothed data. + + - One plot that show trends across years and one optional and additional plot with the smooth method. + +| + +GlmmTMB method :: + + - Two tabulars that details computed indicator per year and globaly. + + - A plot that show trends across years. + + +| + +**Source** + +UnPublished script available at http://www.vigienature.fr/sites/vigienature/files/atoms/files/analysestoceps_0.zip +and the first version written by Romain Lorrilliere + + ]]></help> + <expand macro="stoceps_bibref" /> +</tool>