Reading data
Tous les relevés phyto du CBNA après 2013 (incluant les zones CEPAZ et les autres) sont chargés.
La table des Zones Pastorales avec infos conplémentaires est chargée.
La liste de taxons du CBNA relevés après 2013 avec le statut des espèces, et la liste des espèces de la zp avec leur statuts et autres stats.
library(openxlsx)
# datphyto = read.xlsx("releves_phyto_CEPAZ_2013+.xlsx")
# head(datphyto,3)
library(rgdal)## Loading required package: sp
## rgdal: version: 1.5-12, (SVN revision 1018)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.1.1, released 2020/06/22
## Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 6.3.1, February 10th, 2020, [PJ_VERSION: 631]
## Path to PROJ shared files: /Library/Frameworks/R.framework/Versions/4.0/Resources/library/rgdal/proj
## Linking to sp version:1.4-2
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.datphyto = readOGR(dsn = "releves_phyto_cbna_sup2013_v3.shp", layer = "releves_phyto_cbna_sup2013_v3")## OGR data source with driver: ESRI Shapefile
## Source: "/Users/isabelleboulangeat/Documents/PROJETS/(CEPAZ)/cepaz-git/releves_phyto_cbna_sup2013_v3.shp", layer: "releves_phyto_cbna_sup2013_v3"
## with 355035 features
## It has 52 fields
## Integer64 fields read as strings: rel_pente 2DHA2 2DHAIV 2DHAV 5PRNA1 5PRNA2 6PRPAC 6PRRH 7PR01 7PR04 7PR05 7PR38 7PR74# nb 355 035 releves-especes
head(datphyto@data,3)## numchrono date libsource codeobs1 libobs1 codeinsee
## 1 1540150 20190614 CBN Alpin JCV VILLARET Jean-Charles 38052
## 2 1540150 20190614 CBN Alpin JCV VILLARET Jean-Charles 38052
## 3 1540124 20190614 CBN Alpin JCV VILLARET Jean-Charles 38052
## libcomune codesurfac libsurface rel_surfac libgeologi
## 1 Le Bourg-d'Oisans 2 de 11 \303\240 100 m2 100 <NA>
## 2 Le Bourg-d'Oisans 2 de 11 \303\240 100 m2 100 <NA>
## 3 Le Bourg-d'Oisans 2 de 11 \303\240 100 m2 30 <NA>
## altinf altisup codeexposi libexposit codepente
## 1 860 870 <NA> <NA> 3
## 2 860 870 <NA> <NA> 3
## 3 850 860 <NA> <NA> 4
## libpente rel_pente
## 1 de 27\302\260 \303\240 45\302\260 30
## 2 de 27\302\260 \303\240 45\302\260 30
## 3 de 45\302\260 \303\240 70\302\260 60
## rel_milieu
## 1 Boisement clair de Quercus petraea ou de l'hybride Quercus petraea x pubescens avec Tilia platyphyllos et en sous-\303\251tage avec quelques Acer monspessulanum, \303\240 sous-bois essentiellement domin\303\251 par la liti\303\250re de feuilles mortes avec ...
## 2 Boisement clair de Quercus petraea ou de l'hybride Quercus petraea x pubescens avec Tilia platyphyllos et en sous-\303\251tage avec quelques Acer monspessulanum, \303\240 sous-bois essentiellement domin\303\251 par la liti\303\250re de feuilles mortes avec ...
## 3 Pelouse rupicole x\303\251rophile et acidiphile \303\240 Festuca acuminata avec Brachypodium rupestre et Bromus erectus, \303\251tablie sur des vires et corniches rocheuses fortement inclin\303\251es et localement colonis\303\251es par des massifs de frutic\303\251e arbustive ...
## idreleve_f codestrate libstrate precouvres hauteurstr numtaxon
## 1 7480675 03 S/Arbustive 10 0 13
## 2 7480669 02 Arbustive 20 0 13
## 3 7480184 02 Arbustive 5 0 13
## nom_comple taxref_cd_ nom_comp00 abondance
## 1 Acer monspessulanum L. 79763 Acer monspessulanum L., 1753 +
## 2 Acer monspessulanum L. 79763 Acer monspessulanum L., 1753 1
## 3 Acer monspessulanum L. 79763 Acer monspessulanum L., 1753 1
## code_cotat codestra00 libetatstr
## 1 LC 09 Observation valeur par d\303\251faut (saisie)
## 2 LC 09 Observation valeur par d\303\251faut (saisie)
## 3 LC 09 Observation valeur par d\303\251faut (saisie)
## typezoneca libzonecar codeloc_et longl93fic latl93fich lmprecisio CODE X2DHA2
## 1 P Points 10 940521.1 6439791 5 <NA> 0
## 2 P Points 10 940521.1 6439791 5 <NA> 0
## 3 P Points 10 940542.9 6439745 5 <NA> 0
## X2DHAIV X2DHAV X5PRNA1 X5PRNA2 X6PRPAC X6PRRH X7PR01 X7PR04 X7PR05 X7PR38
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## X7PR74 dep
## 1 0 38
## 2 0 38
## 3 0 38colnames(datphyto@data)## [1] "numchrono" "date" "libsource" "codeobs1" "libobs1"
## [6] "codeinsee" "libcomune" "codesurfac" "libsurface" "rel_surfac"
## [11] "libgeologi" "altinf" "altisup" "codeexposi" "libexposit"
## [16] "codepente" "libpente" "rel_pente" "rel_milieu" "idreleve_f"
## [21] "codestrate" "libstrate" "precouvres" "hauteurstr" "numtaxon"
## [26] "nom_comple" "taxref_cd_" "nom_comp00" "abondance" "code_cotat"
## [31] "codestra00" "libetatstr" "typezoneca" "libzonecar" "codeloc_et"
## [36] "longl93fic" "latl93fich" "lmprecisio" "CODE" "X2DHA2"
## [41] "X2DHAIV" "X2DHAV" "X5PRNA1" "X5PRNA2" "X6PRPAC"
## [46] "X6PRRH" "X7PR01" "X7PR04" "X7PR05" "X7PR38"
## [51] "X7PR74" "dep"head(datphyto@data$CODE,100) # code ZP ou NA## [1] NA NA NA "ZP7308001" NA NA
## [7] NA NA NA NA NA NA
## [13] "ZP0419204" "ZP0417802" NA NA NA NA
## [19] NA NA NA NA NA NA
## [25] NA NA NA "ZP0416706" NA NA
## [31] NA NA "ZP0500502" NA NA NA
## [37] NA NA NA "ZP8415101" "ZP8415101" "ZP8415101"
## [43] "ZP8415101" NA NA NA NA NA
## [49] NA NA NA NA NA NA
## [55] NA NA NA NA NA NA
## [61] NA NA NA NA NA NA
## [67] NA NA NA NA NA NA
## [73] NA NA NA NA NA NA
## [79] NA NA NA NA NA NA
## [85] NA NA NA NA NA NA
## [91] NA NA NA NA NA NA
## [97] NA NA NA NAinsideZP = datphyto@data[which(!is.na(datphyto$CODE)),]
dim(insideZP)## [1] 21237 52# polyZone = readOGR("CEPAZ_Zone_Etude_et_ZP", "CEPAZ_Zones_Pastorales")
# head(polyZone@data,3)
# nrow(polyZone@data[which(polyZone$INSEEDEP %in% c("01", "74", "73", "38", "26", "05", "04")),])
#
tabZP = read.csv2("ZP_table.csv", encoding = "UTF-8")
head(tabZP,3)## X.U.FEFF.CODE INSEEDEP INSEECOM NOM1 NOM2 SURFACE SURF_MNT ETAGE_ALT
## 1 ZP0405001 4 4050 LES MAYOLS MAYOLS 221.82 228.56 PM
## 2 ZP0405002 4 4050 LES JAUMES 403.12 425.98 PM
## 3 ZP0405701 4 4057 CLOT GARCIN 143.60 153.11 PM
## PROP_TYPE1 PROP_REG USAGE MOTIF TRAITE TRANSFORM PHAE MAE SURF_MAE ANNEE_REF
## 1 COM N O N N N N 0 2012
## 2 COM N O N N N N 0 2012
## 3 COM N O N N N N 0 2012
## SOURCE ID_SOURCE AUT_SOURCE PROP_TYPE2 PROP_TYPE3 DERN_USAGE EXPLOIT TYPE1
## 1 CERPAM PRI N NA 1 OA
## 2 CERPAM PRI N NA 1 EQ
## 3 CERPAM PRI N NA 1 OA
## TYPE2 TYPE3 PRINTEMPS ETE AUTOMNE HIVER EF_OV_15J EF_CA_15J EF_VL_15J
## 1 O O O N 250 0 0
## 2 O O O O 0 0 0
## 3 O O O O 300 0 0
## EF_AB_15J EF_EQ_15J CH_MAX_OV CH_MAX_CA CH_MAX_VL CH_MAX_AB CH_MAX_EQ DFCI
## 1 0 0 250 0 0 0 0 N
## 2 0 15 0 0 0 0 15 N
## 3 0 0 300 0 0 0 0 N
## MILIEU ENQUETEUR ORIG_FID
## 1 L DB 0
## 2 L DB 0
## 3 D DB 0colnames(tabZP)[1] = "CODE"
nrow(tabZP)## [1] 5084statuts_all = read.xlsx("taxon_cbna_2013+.xlsx")
head(statuts_all,3)## numtaxon Taxoncbna_nom_complet CD_NOM.(Taxref12)
## 1 17343 Abies alba Miller 79319
## 2 17345 Abies cephalonica Loudon 79325
## 3 17350 Abies nordmanniana (Steven) Spach 79345
## Nom_complet.(Taxref12) CD_REF.(Taxref12) 2DHA2 2DHAIV 2DHAV
## 1 Abies alba Mill., 1768 79319 NA NA NA
## 2 Abies cephalonica Loudon, 1838 79325 NA NA NA
## 3 Abies nordmanniana (Steven) Spach, 1841 79345 NA NA NA
## 5PRNA1 5PRNA2 6PRPACA 6PRRH 7PR01 7PR04 7PR05 7PR38 7PR74 UICN_PACA UICN_RA
## 1 NA NA NA NA NA NA NA NA NA LC LC
## 2 NA NA NA NA NA NA NA NA NA <NA> <NA>
## 3 NA NA NA NA NA NA NA NA NA <NA> <NA>nrow(statuts_all)## [1] 5606statuts_all$UICN = statuts_all$UICN_PACA
statuts_all$UICN[which(is.na(statuts_all$UICN_PACA))] = statuts_all$UICN_RA[which(is.na(statuts_all$UICN_PACA))]
sp_zp = read.xlsx("stat_taxons_zp_statuts.xlsx")
head(sp_zp)## numtaxon nom_cbna
## 1 40496 Acer campestre L., 1753
## 2 10912 Agrimonia eupatoria L.
## 3 15467 Anthoxanthum odoratum L. subsp. odoratum
## 4 6684 Anthyllis vulneraria L.
## 5 4948 Arenaria serpyllifolia L.
## 6 15482 Arrhenatherum elatius (L.) P. Beauv. ex J. & C. Presl subsp. elatius
## cd_nom nom_comple
## 1 79734 Acer campestre L., 1753
## 2 80410 Agrimonia eupatoria L., 1753
## 3 131447 Anthoxanthum odoratum subsp. odoratum L., 1753
## 4 82999 Anthyllis vulneraria L., 1753
## 5 9999993 Zz Attente rattachement
## 6 131693 Arrhenatherum elatius (L.) P.Beauv. ex J. & C.Presl subsp. elatius
## cd_ref nom_valid
## 1 79734 Acer campestre L., 1753
## 2 80410 Agrimonia eupatoria L., 1753
## 3 82922 Anthoxanthum odoratum L., 1753
## 4 82999 Anthyllis vulneraria L., 1753
## 5 9999993 Zz Attente rattachement
## 6 131693 Arrhenatherum elatius (L.) P.Beauv. ex J. & C.Presl subsp. elatius
## CODE dep reg _min _max _count DHA2 DHAIV DHAV PRNA1 PRNA2 PRPAC PRRH
## 1 ZP0123901 01 RA 2014 2014 1 NA NA NA NA NA NA NA
## 2 ZP0123901 01 RA 2014 2014 1 NA NA NA NA NA NA NA
## 3 ZP0123901 01 RA 2014 2014 1 NA NA NA NA NA NA NA
## 4 ZP0123901 01 RA 2014 2014 1 NA NA NA NA NA NA NA
## 5 ZP0123901 01 RA 2014 2014 1 NA NA NA NA NA NA NA
## 6 ZP0123901 01 RA 2014 2014 1 NA NA NA NA NA NA NA
## PR01 PR04 PR05 PR38 PR74 lr_fr lr_paca lr_ra
## 1 NA NA NA NA NA LC <NA> LC
## 2 NA NA NA NA NA LC <NA> LC
## 3 NA NA NA NA NA LC <NA> LC
## 4 NA NA NA NA NA LC <NA> LC
## 5 NA NA NA NA NA <NA> <NA> <NA>
## 6 NA NA NA NA NA LC <NA> <NA>Taille des ZP représentées
library(dplyr)##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
# taille des ZP représentées
tabZP_phyto = merge(tabZP, insideZP, by = "CODE", all=FALSE)
tabZP_phyto$dataset = "phyto"
tabZP$dataset = "all"
selectCol = c("CODE", "dataset", "SURFACE")
rbind(tabZP_phyto[, selectCol], tabZP[, selectCol]) %>%
ggplot(aes(SURFACE, fill=dataset, colour = dataset, stat(density))) +
geom_histogram(alpha=0.2, position = "identity")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
On a une sous-représentation des ZP de petite taille.
Taille des ZP par departement
selectCol = c("CODE", "dep", "SURFACE")
tabZP_phyto[, selectCol] %>%
ggplot(aes(SURFACE, fill=dep, colour = dep, stat(density))) +
geom_histogram(alpha=0.2, position = "identity")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Taille des ZP par milieu
selectCol = c("CODE", "MILIEU", "SURFACE")
tabZP_phyto[, selectCol] %>%
ggplot(aes(SURFACE, fill=MILIEU, colour = MILIEU, stat(density))) +
geom_histogram(alpha=0.2, position = "identity")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Nb de relevés par ZP
# nb ZP où il y a (au moins) un releve phyto
length(unique(insideZP$CODE))## [1] 172length(unique(tabZP$CODE))## [1] 5084# nombre de releve phyto par ZP
zp_releve = insideZP %>% group_by(CODE) %>% summarise(n = length(unique(numchrono)))## `summarise()` ungrouping output (override with `.groups` argument)summary(zp_releve$n)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 2.000 4.529 5.000 69.000zp = readOGR("Extract_raster_zp(20_02_2020)", "CEPAZ_ZP")## OGR data source with driver: ESRI Shapefile
## Source: "/Users/isabelleboulangeat/Documents/PROJETS/(CEPAZ)/cepaz-git/Extract_raster_zp(20_02_2020)", layer: "CEPAZ_ZP"
## with 5084 features
## It has 48 fields
## Integer64 fields read as strings: Join_Count TARGET_FID ORIG_FIDhead(zp@data)## Join_Count TARGET_FID CODE INSEEDEP INSEECOM NOM1 NOM2 SURFACE
## 0 1 0 ZP0405001 04 04050 LES MAYOLS MAYOLS 221.82
## 1 1 1 ZP0405002 04 04050 LES JAUMES <NA> 403.12
## 2 1 2 ZP0405701 04 04057 CLOT GARCIN <NA> 143.60
## 3 1 3 ZP0405702 04 04057 LES GRAVES <NA> 129.17
## 4 1 4 ZP0405703 04 04057 BANE <NA> 65.45
## 5 1 5 ZP0405704 04 04057 ROUAST <NA> 29.38
## SURF_MNT ETAGE_ALT PROP_TYPE1 PROP_REG USAGE MOTIF TRAITE
## 0 228.56 PM COM N O <NA> N
## 1 425.98 PM COM N O <NA> N
## 2 153.11 PM COM N O <NA> N
## 3 135.37 PM COM N O <NA> N
## 4 70.25 PM COM N N probl\303\250mes fonciers N
## 5 30.97 PM COM N O <NA> N
## TRANSFORM PHAE MAE SURF_MAE ANNEE_REF SOURCE ID_SOURCE AUT_SOURCE PROP_TYPE2
## 0 N N N 0 2012 CERPAM <NA> <NA> PRI
## 1 N N N 0 2012 CERPAM <NA> <NA> PRI
## 2 N N N 0 2012 CERPAM <NA> <NA> PRI
## 3 N N N 0 2012 CERPAM <NA> <NA> PRI
## 4 N N N 0 2012 CERPAM <NA> <NA> PRI
## 5 N N N 0 2012 CERPAM <NA> <NA> PRI
## PROP_TYPE3 DERN_USAGE EXPLOIT TYPE1 TYPE2 TYPE3 PRINTEMPS ETE AUTOMNE HIVER
## 0 N <NA> 1 OA <NA> <NA> O O O N
## 1 N <NA> 1 EQ <NA> <NA> O O O O
## 2 N <NA> 1 OA <NA> <NA> O O O O
## 3 N <NA> 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 N 2009 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 N <NA> 1 AB <NA> <NA> O O N N
## EF_OV_15J EF_CA_15J EF_VL_15J EF_AB_15J EF_EQ_15J CH_MAX_OV CH_MAX_CA
## 0 250 0 0 0 0 250 0
## 1 0 0 0 0 15 0 0
## 2 300 0 0 0 0 300 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 25 0 0 0 0 0 0
## CH_MAX_VL CH_MAX_AB CH_MAX_EQ DFCI MILIEU ENQUETEUR ORIG_FID
## 0 0 0 0 N L DB 0
## 1 0 0 15 N L DB 0
## 2 0 0 0 N D DB 0
## 3 0 0 0 N H DB 0
## 4 0 0 0 N L DB 0
## 5 0 0 0 N H DB 0zp_n = merge(zp, zp_releve, by = "CODE")
zp_n$CBNA = ifelse(is.na(zp_n$n), 0, 1)Nb de ZP dans territoire agrement CBNA
CBNA = readOGR(".", "CBNA")## OGR data source with driver: ESRI Shapefile
## Source: "/Users/isabelleboulangeat/Documents/PROJETS/(CEPAZ)/cepaz-git", layer: "CBNA"
## with 1 features
## It has 11 fields
## Integer64 fields read as strings: ID_GEOFLACBNA_proj = spTransform(CBNA, CRS("+init=epsg:4326"))
library(leaflet)
zp_n_proj <- spTransform(zp_n, CRS("+init=epsg:4326")) # Reproject coordinates
qpal <- colorBin(c("red", "blue"), zp_n_proj$CBNA, bins=3)
# qpal <- colorQuantile("Greens", zp_n_proj$n, n = 2, reverse = TRUE)
# leaflet(zp_n_proj) %>%
# addPolygons(stroke = TRUE,opacity = 1,fillOpacity = 0.5, smoothFactor = 0.5, color=NA,fillColor = ~qpal(CBNA),weight = 1,) %>%
# addLegend(values=~CBNA,pal=qpal, labels = c("aucune", "au moins une"), labFormat = "factor", title="observations CBNA") %>%
# addProviderTiles("CartoDB.Positron") %>%
# addPolygons(data = CBNA_proj, fill = F, weight = 2, color = "black")
table(zp_n$CBNA)##
## 0 1
## 4912 172library(rgeos)## rgeos version: 0.5-3, (SVN revision 634)
## GEOS runtime version: 3.8.1-CAPI-1.13.3
## Linking to sp version: 1.4-2
## Polygon checking: TRUEindex = lapply(1:nrow(zp_n_proj), function(x){gWithin(zp_n_proj[x,], CBNA_proj)})
sum(unlist(index))## [1] 3948sum(unlist(index)) / nrow(zp_n_proj)## [1] 0.7765539table(zp_n$CBNA, zp_n$INSEEDEP)##
## 01 04 05 06 13 26 38 73 74 83 84
## 0 7 922 542 220 217 972 291 304 769 369 299
## 1 1 42 17 0 0 36 27 35 13 0 1table(zp_n$CBNA, zp_n$MILIEU)##
## B D H L
## 0 1536 293 1882 1175
## 1 39 25 73 35# > table(zp_n$CBNA, zp_n$INSEEDEP)
#
# 01 04 05 06 13 26 38 73 74 83 84
# 0 7 922 542 220 217 972 291 304 769 369 299
# 1 1 42 17 0 0 36 27 35 13 0 1
# > table(zp_n$CBNA, zp_n$MILIEU)
#
# B D H L
# 0 1536 293 1882 1175
# 1 39 25 73 35Diversité alpha des ZP
# nombre d'especes par ZP, pour releves phyto
zp_espece_phyto = insideZP %>% group_by(numchrono) %>% summarise(sp_richness_phyto = length(unique(numtaxon)))## `summarise()` ungrouping output (override with `.groups` argument)div_alpha_zp = unique(merge(insideZP[, c("CODE", "numchrono")], zp_espece_phyto, by = "numchrono"))
head(div_alpha_zp)## numchrono CODE sp_richness_phyto
## 1 936131 ZP0420911 14
## 15 936132 ZP0420911 17
## 32 936133 ZP0420911 11
## 43 936134 ZP0420911 17
## 60 936135 ZP0420911 12
## 72 936143 ZP0420911 12summary(div_alpha_zp)## numchrono CODE sp_richness_phyto
## Min. : 936131 Length:779 Min. : 1.00
## 1st Qu.: 973367 Class :character 1st Qu.:17.00
## Median :1143734 Mode :character Median :26.00
## Mean :1183099 Mean :26.94
## 3rd Qu.:1360846 3rd Qu.:35.00
## Max. :1552452 Max. :87.00stat_alpha = div_alpha_zp %>% group_by(CODE) %>% summarise(alpha_mean = mean(sp_richness_phyto), nb_rel = length(unique(numchrono)))## `summarise()` ungrouping output (override with `.groups` argument)summary(stat_alpha)## CODE alpha_mean nb_rel
## Length:172 Min. : 3.00 Min. : 1.000
## Class :character 1st Qu.:18.88 1st Qu.: 1.000
## Mode :character Median :26.14 Median : 2.000
## Mean :27.21 Mean : 4.529
## 3rd Qu.:33.54 3rd Qu.: 5.000
## Max. :66.00 Max. :69.000# richesse des releves phyto hors ZP (ou total)
div_alpha_tot = datphyto@data %>% group_by(numchrono) %>% summarise(sp_richness_phyto = length(unique(numtaxon)))## `summarise()` ungrouping output (override with `.groups` argument)summary(div_alpha_tot)## numchrono sp_richness_phyto
## Min. : 930251 Min. : 1.0
## 1st Qu.: 973228 1st Qu.:12.0
## Median :1199246 Median :20.0
## Mean :1211180 Mean :20.9
## 3rd Qu.:1448651 3rd Qu.:28.0
## Max. :1552558 Max. :88.0# detail par milieu et departement (figure)
stat_alpha_details = unique(merge(stat_alpha, tabZP, by = "CODE"))
head(stat_alpha_details)## CODE alpha_mean nb_rel INSEEDEP INSEECOM NOM1
## 1 ZP0400802 64.00 1 4 4008 LES GASTRES
## 2 ZP0401801 42.75 4 4 4018 PIED D ENROUX
## 3 ZP0401804 8.50 4 4 4018 LE PETIT TOURTOUILLE
## 4 ZP0402003 9.20 5 4 4020 LE CHATEAU-VAULX-DESCOURES
## 5 ZP0402004 21.50 2 4 4020 TOURTOUREAU
## 6 ZP0402301 16.50 2 4 4023 LE SEUIL
## NOM2 SURFACE SURF_MNT ETAGE_ALT PROP_TYPE1 PROP_REG USAGE
## 1 179.26 211.72 MM PRI N O
## 2 ENROUX 212.13 219.07 PM PRI N O
## 3 LE PETIT TOURTOUILLE 43.54 44.77 PM PRI N N
## 4 355.52 394.64 MM PRI N O
## 5 112.55 131.94 MM PRI N N
## 6 REBEIE 569.22 636.92 MM COM N O
## MOTIF TRAITE TRANSFORM PHAE MAE SURF_MAE ANNEE_REF SOURCE
## 1 O O N N 0 2012 CERPAM
## 2 N N N N 0 2013 CERPAM
## 3 ARRET ACTIVITE N N N N 0 2013 CERPAM
## 4 N N N N 0 2012 CERPAM
## 5 TROP DE LOUPS!!! N N 0 2012 CERPAM
## 6 N N N N 0 2012 CERPAM
## ID_SOURCE AUT_SOURCE PROP_TYPE2 PROP_TYPE3 DERN_USAGE EXPLOIT TYPE1 TYPE2
## 1 COM N NA 1 VL CL
## 2 N N NA 1 OA N
## 3 N N 2013 1 CL N
## 4 COM N NA 4 OA EQ
## 5 COM N 2008 0
## 6 N N NA 3 AB OA
## TYPE3 PRINTEMPS ETE AUTOMNE HIVER EF_OV_15J EF_CA_15J EF_VL_15J EF_AB_15J
## 1 N O O O O 0 80 10 0
## 2 N N O N N 420 0 0 0
## 3 N O O O O 0 30 0 0
## 4 N O N O N 0 0 0 0
## 5 0 0 0 0
## 6 O N O N 0 0 0 0
## EF_EQ_15J CH_MAX_OV CH_MAX_CA CH_MAX_VL CH_MAX_AB CH_MAX_EQ DFCI MILIEU
## 1 0 0 80 10 0 0 N B
## 2 0 420 0 0 0 0 N B
## 3 0 0 30 0 0 0 N B
## 4 0 2000 0 0 0 5 N L
## 5 0 0 0 0 0 0 N H
## 6 0 500 0 0 60 0 N D
## ENQUETEUR ORIG_FID dataset
## 1 SG 0 all
## 2 PG 0 all
## 3 PG 0 all
## 4 DB 0 all
## 5 DB 0 all
## 6 DB 0 allstat_alpha_details %>% group_by(MILIEU) %>%
ggplot(aes(MILIEU, alpha_mean)) +
geom_boxplot(notch=TRUE) +
stat_boxplot(na.rm=TRUE) +
ylim(0,100) +
facet_wrap(~INSEEDEP)## notch went outside hinges. Try setting notch=FALSE.
## notch went outside hinges. Try setting notch=FALSE.
## notch went outside hinges. Try setting notch=FALSE.
## notch went outside hinges. Try setting notch=FALSE.
## notch went outside hinges. Try setting notch=FALSE.
## notch went outside hinges. Try setting notch=FALSE.
## notch went outside hinges. Try setting notch=FALSE.
## notch went outside hinges. Try setting notch=FALSE.
## notch went outside hinges. Try setting notch=FALSE.
## notch went outside hinges. Try setting notch=FALSE.
## notch went outside hinges. Try setting notch=FALSE.
## notch went outside hinges. Try setting notch=FALSE.
## notch went outside hinges. Try setting notch=FALSE.
## notch went outside hinges. Try setting notch=FALSE.
Statut des espèces des ZP
Nombre et proportion d’espèces liste rouge parmi les espèces relevées Rappel: Eteinte (EX), Eteinte à l’état sauvage (EW), En danger critique (CR), En danger (EN), Vulnérable (VU), Quasi menacée (NT), Préoccupation mineure (LC), Données insuffisantes (DD), Non évaluée (NE).
Evalué avec les données “points” comprenant les relevés phyto et des relevés supplémentaires d’espèces cible.
sp_phyto = unique(datphyto@data$numtaxon)
length(sp_phyto) # 3789 especes## [1] 3789sp_phyto_zp = unique(insideZP$numtaxon)
length(sp_phyto_zp) # 1587 especes## [1] 1587sp_points = unique(statuts_all$numtaxon) # après 2013
length(sp_points) # 5606## [1] 5606sp_zp_2013 = sp_zp[which(sp_zp$`_max`>=2013),]
sp_points_zp = unique(sp_zp_2013$numtaxon)
length(sp_points_zp) # 2146## [1] 2146length(unique(sp_zp_2013$CODE))## [1] 742statuts_all$UICN =as.factor(statuts_all$UICN)
levels(statuts_all$UICN) = c("CR","CR","DD","EN","EW","LC", "NE" ,"NT", "RE", "VU")
table(unique(statuts_all[, c("numtaxon", "UICN")])$UICN)##
## CR DD EN EW LC NE NT RE VU
## 53 433 116 2 2431 186 173 13 164aa = table(unique(statuts_all[, c("numtaxon", "UICN")])$UICN) / length(sp_points)
sum(is.na(unique(statuts_all[, c("numtaxon", "UICN")])$UICN))/ length(sp_points)## [1] 0.3630039aa##
## CR DD EN EW LC NE
## 0.0094541563 0.0772386729 0.0206921156 0.0003567606 0.4336425259 0.0331787371
## NT RE VU
## 0.0308597931 0.0023189440 0.0292543703sp_zp_2013$UICN =as.factor(sp_zp_2013$lr_fr)
table(unique(sp_zp_2013[, c("numtaxon", "UICN")])$UICN)##
## DD EN LC NT VU
## 33 4 1628 28 4zp = table(unique(sp_zp_2013[, c("numtaxon", "UICN")])$UICN) / length(sp_points_zp)
sum(is.na(unique(sp_zp_2013[, c("numtaxon", "UICN")])$UICN))/ length(sp_points_zp)## [1] 0.2176142zp_vec = unclass(zp)
zp##
## DD EN LC NT VU
## 0.015377446 0.001863933 0.758620690 0.013047530 0.001863933library(reshape2)
rbind(unclass(aa),c(CR=0, zp_vec[1:2], EW=0, zp_vec[3], NE=0, zp_vec[4], RE=0, zp_vec[5])) %>% melt() %>%
ggplot(aes(x = Var2, y = value, fill = factor(Var1))) +
geom_bar(stat = "identity", position = position_dodge2()) +
scale_x_discrete(name="statut UICN") +
scale_y_continuous(name = "proportion de taxons") +
labs(fill = "emprise") +
scale_fill_hue(labels = c("CBNA", "ZP"))
On
retrouve 38% de la flore de la zone d’agrément du CBNA échantillonnée
dans les ZP.
Attention pour les statuts il manque 36% de données dans la liste complète et 22% dans les espèces des ZP.
Attention, toute cette évaluation exclue les départements hors zone CBNA (au Sud).