Climate Space
Kristen Noelle Finch
7/28/2017
Details
This is an R Markdown document.
Dissertation Research: The purpose of this analysis is to assess how much of the climate space is captured by my samples.
Climate data from WorldClim. Citation: Fick, S.E. and R.J. Hijmans, 2017. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology.
Cedrela odorata observation data from GBIF Citation: GBIF (2012). Recommended practices for citation of the data published through the GBIF Network. Version 1.0 (Authored by Vishwas Chavan), Copenhagen: Global Biodiversity Information Facility. Pp.12, ISBN: 87-92020-36-4.
Load packages
library(raster)
library(sp)
library(ggplot2)
library(RColorBrewer)Load Climate Data
r <- getData("worldclim",var="bio",res=10)
r <- r[[c(1,12)]]
names(r) <- c("Temp","Prec")Load observations
#gbif data----
gbif<-read.csv("20170726_climate_space.csv",header=1)
names(gbif)## [1] "scientificname" "countrycode" "decimallatitude"
## [4] "decimallongitude" "elevation" "google_ele"
## [7] "year" "basisofrecord" "institutioncode"
## [10] "collectioncode" "catalognumber" "recordnumber"
## [13] "identifiedby" "license" "rightsholder"
## [16] "recordedby" "lastinterpreted"coords<-gbif[c(4,3)]
samps<-gbif[c(-3,-4)]Connect locations to Climate
points <- SpatialPoints(coords, proj4string = r@crs)
values <- extract(r,points) #if this doesn't work, restart R and reload raster package. The error is a conflict with another package.
clim.space<- cbind.data.frame(coordinates(points),values)
clim.space<-cbind.data.frame(samps, clim.space)
summary(clim.space)## scientificname countrycode
## Cedrela odorata L. :2232 MX :1299
## Cedrela dugesii S. Wats. : 118 BR : 264
## Cedrela mexicana M. Roemer : 97 CO : 222
## Cedrela angustifolia Moc. & Sesse ex DC.: 96 PE : 140
## Cedrela occidentalis C. DC. & Rose : 33 EC : 109
## Cedrela longipetiolulata Harms : 8 CR : 102
## (Other) : 36 (Other): 484
## elevation google_ele year
## Min. :-4999.5 Min. :-3779.30 Min. :1829
## 1st Qu.: 34.0 1st Qu.: 82.01 1st Qu.:1980
## Median : 250.0 Median : 294.80 Median :1988
## Mean : 510.8 Mean : 577.64 Mean :1986
## 3rd Qu.: 750.0 3rd Qu.: 843.59 3rd Qu.:1997
## Max. : 9999.0 Max. : 3394.00 Max. :2017
## NA's :880 NA's :331
## basisofrecord institutioncode collectioncode
## HUMAN_OBSERVATION : 69 MO : 506 MO : 505
## LITERATURE : 2 INECOL : 241 XAL : 306
## LIVING_SPECIMEN : 2 ENCB-IPN: 195 ENCB : 212
## OBSERVATION : 2 F : 165 Botany : 205
## PRESERVED_SPECIMEN:2118 IE : 141 MEXU : 165
## UNKNOWN : 427 IBUNAM : 138 (Other):1226
## (Other) :1234 NA's : 1
## catalognumber recordnumber identifiedby
## NO DISPONIBLE: 291 :1551 : 563
## : 52 s.n. : 13 NO DISPONIBLE : 229
## NO APLICA : 33 1 : 8 ND : 121
## 644523 : 4 1054 : 5 T.D. Pennington (K): 68
## 110671 : 3 1740 : 5 T.D. Pennington : 61
## 120632 : 3 14046 : 4 J. Rzedowski : 43
## (Other) :2234 (Other):1034 (Other) :1535
## license
## CC_BY_4_0 : 668
## CC_BY_NC_4_0:1515
## CC0_1_0 : 437
##
##
##
##
## rightsholder
## Comisi<97>n Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO):869
## :754
## Missouri Botanical Garden :505
## The Field Museum of Natural History :163
## The New York Botanical Garden : 95
## Universidad Nacional de Colombia : 36
## (Other) :198
## recordedby lastinterpreted decimallongitude
## : 142 2017-02-17T06:48Z: 937 Min. :-110.95
## NO DISPONIBLE : 59 2017-03-24T12:57Z: 95 1st Qu.: -96.37
## J. Rzedowski : 36 2017-03-26T12:44Z: 93 Median : -88.11
## J. Cuevas S. : 28 2017-03-26T01:36Z: 87 Mean : -82.84
## CJ : 20 2017-03-25T11:14Z: 80 3rd Qu.: -74.14
## H. D<92>az-Barriga: 18 2017-03-26T00:41Z: 65 Max. : 32.57
## (Other) :2317 (Other) :1263
## decimallatitude Temp Prec
## Min. :-35.387 Min. : 78.0 Min. : 232
## 1st Qu.: 1.633 1st Qu.:219.0 1st Qu.:1114
## Median : 16.716 Median :244.0 Median :1506
## Mean : 9.403 Mean :232.5 Mean :1731
## 3rd Qu.: 19.578 3rd Qu.:257.0 3rd Qu.:2201
## Max. : 26.817 Max. :286.0 Max. :7962
## NA's :11 NA's :11#write.csv(clim.space,"20170726_gbif_worldclim.csv",row.names = FALSE)
#removed NA's in EXCEL
clim.space<-read.csv("20170726_gbif_worldclim.csv",header=1)Load my data and connect to climate data
#my data----
ceod.finch<-read.csv("20170615_mapping.csv",header=1)
names(ceod.finch)## [1] "Longitude" "Latitude" "Spp" "Tube" "ID"
## [6] "remove" "N_DATA" "CAvsSA" "Country" "Peru.NvS"
## [11] "NEcvP" "CR" "Elevation" "goog_elev"coords<-ceod.finch[c(1,2)]
samps<-ceod.finch[c(-1,-2)]
points <- SpatialPoints(coords, proj4string = r@crs)
values <- extract(r,points) #if this doesn't work, restart R and reload raster package. The error is a conflict with another package.
ceod.clim<- cbind.data.frame(coordinates(points),values)
ceod.clim<-cbind.data.frame(samps, ceod.clim)
summary(ceod.clim)## Spp Tube ID remove N_DATA CAvsSA
## CEOD:82 Min. : 2.00 ced101 : 1 No:82 Min. :102721 CA:29
## 1st Qu.: 82.75 ced103 : 1 1st Qu.:102721 SA:53
## Median :148.50 ced104 : 1 Median :102721
## Mean :150.23 ced105 : 1 Mean :102721
## 3rd Qu.:227.25 ced109 : 1 3rd Qu.:102721
## Max. :301.00 ced116 : 1 Max. :102721
## (Other):76
## Country Peru.NvS NEcvP CR Elevation
## Peru :24 N:16 E:10 NE: 2 Min. : 8.96
## Ecuador :20 S: 8 W:26 NW: 2 1st Qu.: 126.20
## Nicaragua :14 X:58 X:46 SJ: 3 Median : 347.50
## Costa Rica: 8 X :75 Mean : 549.19
## Panama : 7 3rd Qu.: 840.00
## Bolivia : 5 Max. :2150.00
## (Other) : 4
## goog_elev Longitude Latitude Temp
## Min. : 4.0 Min. :-86.88 Min. :-21.0378 Min. : 89.0
## 1st Qu.: 124.5 1st Qu.:-84.06 1st Qu.: -3.8333 1st Qu.:219.2
## Median : 350.5 Median :-77.66 Median : -0.7167 Median :247.5
## Mean : 601.5 Mean :-78.01 Mean : 0.8934 Mean :235.7
## 3rd Qu.: 961.3 3rd Qu.:-73.52 3rd Qu.: 9.9150 3rd Qu.:259.0
## Max. :2624.0 Max. :-63.37 Max. : 13.5055 Max. :269.0
##
## Prec
## Min. : 749
## 1st Qu.:1763
## Median :2373
## Mean :2418
## 3rd Qu.:2970
## Max. :4315
## Reload data, some analyses done in Excel (could have been done in R[Add columns for group])
clim.comp<-read.csv("20170726_clim.comp.csv",header=1)
summary(clim.comp)## origin ele decimallongitude decimallatitude
## MX :1295 Min. : 0.00 Min. :-110.95 Min. :-35.3869
## BR : 264 1st Qu.: 88.12 1st Qu.: -96.30 1st Qu.: -0.3683
## CO : 220 Median : 300.00 Median : -87.59 Median : 16.0636
## PE : 164 Mean : 582.89 Mean : -82.76 Mean : 9.1224
## EC : 129 3rd Qu.: 847.47 3rd Qu.: -74.14 3rd Qu.: 19.5388
## CR : 110 Max. :3394.00 Max. : 32.57 Max. : 26.8167
## (Other): 509
## Temp Prec cat
## Min. : 78.0 Min. : 232 finch : 33
## 1st Qu.:219.0 1st Qu.:1125 finch.epb: 49
## Median :244.0 Median :1542 gbif :2609
## Mean :232.6 Mean :1752
## 3rd Qu.:257.0 3rd Qu.:2249
## Max. :286.0 Max. :7962
## finch <- clim.comp[ which(clim.comp$cat!='gbif'), ]
mytitle<-expression(paste(italic("Cedrela odorata"), " Climate Space A"))
clim.space.plot<-ggplot(data=clim.comp,aes(Temp,Prec))+geom_hex(bins=50)+theme_bw()+
scale_fill_gradient(low = "grey", high = "black",
name="Sample\nDensity")+
geom_point(data=finch, aes(Temp,Prec, color=cat))+
scale_color_manual(values = c("blue","red"),
labels=c("All", "Candidate"),
name="Sample\nCategory")+
labs(x="Mean Annual Temperature (Cº x 10)",y="Mean Annual Precipitation (mm)")+
theme(panel.grid = element_blank())+
ggtitle(mytitle)
#ggsave("20170726_clim.space.plot.pdf", clim.space.plot,width=7,height=6)
mytitle.2<-expression(paste(italic("Cedrela odorata"), " Climate Space B"))
clim.space.plot.2<-ggplot(data=clim.comp,aes(ele,Prec))+geom_hex(bins=50)+theme_bw()+
scale_fill_gradient(low = "grey", high = "black",
name="Sample\nDensity")+
geom_point(data=finch, aes(ele,Prec, color=cat))+
scale_color_manual(values = c("blue","red"),
labels=c("All", "Candidate"),
name="Sample\nCategory")+
labs(x="Elevation (m)",y="Mean Annual Precipitation (mm)")+
theme(panel.grid = element_blank())+
ggtitle(mytitle.2)
#ggsave("20170726_clim.space.plot.2.pdf", clim.space.plot.2,width=7,height=6)clim.space.plot
clim.space.plot.2
Climate correlations
ec.p.bo<-read.csv("20170725_mapping_clim.csv",header=1)
names(ec.p.bo)## [1] "X" "INDV" "origin" "elev" "nvs" "ec.p.evw"
## [7] "elev.lvh" "long" "lat" "Temp" "Prec" "t.group"
## [13] "p.group"clim<-ec.p.bo[c(4,8,9,10,11)]
library(PerformanceAnalytics)
chart.Correlation(clim, histogram=TRUE, pch=19)