Lain E. Pardo
This code provides a general protocol for exploring camera trap data from Snapshot Safari Project.
This is based on the Snapshot safari project South Africa, but can be applied to any camera based study. This is a sample script using a snapshot report from Mountain Zebra S1. In order to use this script for other sites, you have to adapt certain names in the arguments, but specially in the outputs (e.g. csv tables, figures, etc).
In this exercise we are using several reserves, but can be easily modified for a single reserve, maintaining the same structure. Use the tab bottom (right top of this screen) to get to a particular section of interest, however, some sections depends on previous sections.
This is a work in progress if you want to contribute or find any issues please let us know.
Please give credit to Lain E. Pardo, Snapshot Safari or WildEco Lab at Nelson Mandela University. We are open to collaborations.
library(ggplot2)
library(hrbrthemes)
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v tibble 3.1.4 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 2.0.1 v forcats 0.5.1
## v purrr 0.3.4
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()sp_rec <- read_csv("data_in/+sp_records_only_mammals_updated_temp.csv")## New names:
## * `` -> ...1
## Rows: 19674 Columns: 16
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (11): Reserve.Location, Camera.Site, Common.Name, Scientific.Name, Phot...
## dbl (4): ...1, Number.of.Individuals, Roll, Consensus
## time (1): Photo.Time
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.unique(sp_rec$Common.Name)## [1] "giraffe" "zebraburchells"
## [3] "kudu" "impala"
## [5] "warthog" "hyenaspotted"
## [7] "elephant" "wildebeestblue"
## [9] "aardvarkantbear" "hare"
## [11] "steenbok" "duikercommon"
## [13] "honeybadger" "leopard"
## [15] "baboon" "genetcommonsmallspotted"
## [17] "wilddog" "porcupine"
## [19] "jackalsidestriped" "grysbokcape"
## [21] "rhinoceroswhite" "bushpig"
## [23] "lion" "bushbuck"
## [25] "buffalo" "civet"
## [27] "nyala" "rhinocerosblack"
## [29] "hippopotamus" "waterbuck"
## [31] "springbok" "foxbateared"
## [33] "jackalblackbacked" "aardwolf"
## [35] "gemsbokoryx" "klipspringer"
## [37] "rockhyraxdassie" "hartebeestred"
## [39] "zebramountain" "polecatstriped"
## [41] "hyenabrown" "catafricanwild"
## [43] "reedbuck" "caracal"
## [45] "sable" "monkeyvervet"
## [47] "genetrustyspotted" "blesbok"
## [49] "wildebeestblack" "eland"
## [51] "harescrub" "mongooseyellow"
## [53] "harespring" "reedbuckmountain"
## [55] "bontebok" "rhebokgrey"
## [57] "genetcapelargespotted" "foxcape"
## [59] "meerkatsuricate" "cheetah"
## [61] "catblackfooted" "rodent"
## [63] "rhinoceros" "mongooseslender"
## [65] "serval" "tsessebe"
## [67] "fallowdeer" "weaselstriped"
## [69] "mongoosebanded" "mongoose"
## [71] "antelope" "zebra"
## [73] "mongoosedwarf" "mongoosewhitetailed"
## [75] "jackal" "roan"
## [77] "pangolin" "squirrelground"unique(sp_rec$Camera.Site)## [1] "APN_K011" "APN_K012" "APN_K013" "APN_K014" "APN_K021" "APN_K023"
## [7] "APN_K024" "APN_K034" "APN_K041" "APN_K044" "APN_K051" "APN_K053"
## [13] "APN_K062" "APN_K064" "APN_K073" "APN_K082" "AUG_C02" "AUG_C03"
## [19] "AUG_C11" "AUG_C13" "AUG_C14" "AUG_D01" "AUG_D02" "AUG_D03"
## [25] "AUG_D05" "AUG_D06" "AUG_D10" "AUG_D11" "AUG_D13" "AUG_E05"
## [31] "AUG_E08" "AUG_E09" "AUG_E11" "AUG_E12" "AUG_E13" "AUG_E14"
## [37] "BLO_A04" "BLO_B02" "BLO_B03" "BLO_B04" "BLO_B05" "BLO_B06"
## [43] "BLO_C02" "BLO_C03" "BLO_C04" "BLO_C05" "BLO_C06" "BLO_C07"
## [49] "BLO_D02" "BLO_D03" "BLO_D04" "BLO_D05" "BLO_D06" "BLO_E04"
## [55] "CDB_A05" "CDB_A06" "CDB_B04" "CDB_B06" "CDB_B07" "CDB_C03"
## [61] "CDB_C04" "CDB_C05" "CDB_C06" "CDB_C07" "CDB_D02" "CDB_D03"
## [67] "CDB_D04" "CDB_D05" "CDB_D06" "CDB_E02" "CDB_E03" "CDB_E04"
## [73] "CDB_E05" "CDB_F03" "DHP_A06" "DHP_B06" "DHP_B08" "DHP_C07"
## [79] "DHP_D01" "DHP_D02" "DHP_D03" "DHP_D05" "DHP_E02" "DHP_E03"
## [85] "DHP_E04" "DHP_E05" "DHP_F02" "GOE_A01" "GOE_A03" "GOE_A04"
## [91] "GOE_A05" "GOE_A06" "GOE_A07" "GOE_A08" "GOE_A09" "GOE_A10"
## [97] "GOE_A11" "GOE_A12" "GOE_A13" "GOE_A14" "GOE_A15" "GOE_A16"
## [103] "GOE_A17" "GOE_A18" "GOE_A19" "GOE_A20" "KAR_A01" "KAR_A02"
## [109] "KAR_A03" "KAR_B01" "KAR_B02" "KAR_B03" "KAR_C01" "KAR_C02"
## [115] "KAR_C03" "KAR_C04" "KAR_D01" "KAR_D02" "KAR_D03" "KAR_D04"
## [121] "KAR_E01" "KAR_E02" "KAR_E03" "KAR_F01" "KAR_F02" "KAR_F03"
## [127] "KGA_A01" "KGA_A02" "KGA_A03" "KGA_A04" "KGA_A05" "KGA_A06"
## [133] "KGA_A07" "KGA_A08" "KGA_A09" "KGA_A10" "KGA_A11" "KGA_B01"
## [139] "KGA_B02" "KGA_B03" "KGA_B04" "KGA_B05" "KGA_B06" "KGA_B07"
## [145] "KGA_B08" "KGA_B09" "KHA_A03" "KHA_A04" "KHA_A05" "KHA_B02"
## [151] "KHA_B03" "KHA_B04" "KHA_B05" "KHA_B06" "KHA_C02" "KHA_C03"
## [157] "KHA_C04" "KHA_C05" "KHA_C06" "KHA_D02" "KHA_D03" "KHA_D04"
## [163] "KHA_D05" "KHA_D06" "KHA_E03" "KHA_E04" "KRU_1" "KRU_2"
## [169] "KRU_3" "KRU_4" "KRU_5" "KRU_6" "KRU_7" "KRU_8"
## [175] "KRU_9" "KRU_10" "KRU_11" "KRU_12" "KRU_13" "KRU_14"
## [181] "KRU_15" "KRU_16" "KRU_18" "KRU_19" "KRU_20" "KRU_22"
## [187] "KRU_23" "KRU_24" "KRU_25" "KRU_27" "KRU_28" "KRU_29"
## [193] "KRU_30" "KRU_34" "KRU_35" "KRU_36" "KRU_41" "KRU_42"
## [199] "KRU_43" "KRU_44" "KRU_45" "KRU_46" "KRU_47" "KRU_48"
## [205] "KRU_49" "MAD_A03" "MAD_A04" "MAD_A05" "MAD_A06" "MAD_B03"
## [211] "MAD_B04" "MAD_B05" "MAD_B06" "MAD_C04" "MAD_C05" "MAD_C06"
## [217] "MAD_C07" "MAD_D03" "MAD_D04" "MAD_D05" "MAD_D06" "MAD_D07"
## [223] "MAD_E04" "MAD_E06" "MAD_E07" "MAD_F03" "MAD_F04" "MAD_F05"
## [229] "MAD_G04" "MAD_G05" "MAD_G06" "MAD_G07" "MAD_G08" "MAD_H04"
## [235] "MAD_H05" "MAD_H06" "MAD_H07" "MAD_H08" "MAD_H09" "MAD_I04"
## [241] "MAD_I05" "MAD_I06" "MAD_I07" "MAD_I08" "MAD_I09" "MTZ_B04"
## [247] "MTZ_B05" "MTZ_B08" "MTZ_C06" "MTZ_C07" "MTZ_C08" "MTZ_D03"
## [253] "MTZ_D04" "MTZ_D05" "MTZ_D06" "MTZ_D07" "MTZ_E03" "MTZ_E04"
## [259] "MTZ_E05" "MTZ_E06" "MTZ_F04" "MTZ_F05" "MTZ_G04" "NAM_A01"
## [265] "NAM_A02" "NAM_A03" "NAM_A04" "NAM_A05" "NAM_A06" "NAM_A07"
## [271] "NAM_A08" "NAM_A09" "NAM_A10" "NAM_A11" "NAM_A12" "NAM_A13"
## [277] "NAM_A14" "NAM_A16" "NAM_A17" "NAM_A18" "NAM_A19" "NAM_A20"
## [283] "PLN_A01" "PLN_A02" "PLN_A03" "PLN_B01" "PLN_B02" "PLN_B03"
## [289] "PLN_B04" "PLN_B05" "PLN_C01" "PLN_C02" "PLN_C03" "PLN_C04"
## [295] "PLN_C05" "PLN_C06" "PLN_D01" "PLN_D02" "PLN_D03" "PLN_D04"
## [301] "PLN_D05" "PLN_E02" "PLN_E03" "PLN_E04" "SOM_A2" "SOM_B1"
## [307] "SOM_C1" "SOM_C4" "SOM_C5" "SOM_C6" "SOM_D1" "TAN_A01"
## [313] "TAN_A02" "TAN_A03" "TAN_A04" "TAN_A05" "TAN_A06" "TAN_A07"
## [319] "TAN_A08" "TAN_A09" "TAN_A10" "TAN_A11" "TAN_A12" "TAN_A13"
## [325] "TAN_A14" "TAN_A16" "TAN_A17" "TAN_B01" "TAN_B02" "TSW_KA02"
## [331] "TSW_KA03" "TSW_KA05" "TSW_KA06" "TSW_KA07" "TSW_KB02" "TSW_KB03"
## [337] "TSW_KB04" "TSW_KB05" "TSW_KB06" "TSW_KB07" "TSW_KC02" "TSW_KC03"
## [343] "TSW_KC04" "TSW_KC05" "TSW_KC06" "TSW_KC07" "TSW_KD02" "TSW_KD03"
## [349] "TSW_KD06" "TSW_LB02" "TSW_LB03" "TSW_LC01" "TSW_LC03" "TSW_LD02"
## [355] "TSW_LD03" "TSW_LE01" "TSW_LE02" "TSW_LE03" "VEN_A01" "VEN_A02"
## [361] "VEN_A03" "VEN_A04" "VEN_A05" "VEN_B01" "VEN_B02" "VEN_B04"
## [367] "VEN_B05" "VEN_C01" "VEN_C02" "VEN_C04" "VEN_C05" "VEN_D01"
## [373] "VEN_D02" "VEN_D04" "VEN_E02"data_gral <- sp_rec %>%
group_by(Reserve.Location) %>%
summarise(sites = n_distinct(Camera.Site),
No_spp = n_distinct(Common.Name),
events = n())
data_gral ## # A tibble: 18 x 4
## Reserve.Location sites No_spp events
## <chr> <int> <int> <int>
## 1 APN 16 30 987
## 2 AUG 20 18 284
## 3 BLO 18 30 3626
## 4 CDB 20 25 2108
## 5 DHP 13 12 203
## 6 GOE 19 12 329
## 7 KAR 20 20 674
## 8 KGA 20 24 531
## 9 KHA 20 26 497
## 10 KRU 39 27 1577
## 11 MAD 40 41 2537
## 12 MTZ 18 35 1263
## 13 NAM 19 19 425
## 14 PLN 22 40 920
## 15 SOM 7 30 443
## 16 TAN 18 14 151
## 17 TSW 29 38 2035
## 18 VEN 17 33 1084#write.csv(data_gral, "data_out/gral_sites_spp.csv")sp_allsites <- sp_rec %>%
group_by(Common.Name) %>%
summarise (events = n())
#write.csv(sp_allsites , "data_out/sp_list_events.csv")
#library(forcats)
sp_allsites %>%
mutate(Common.Name = fct_reorder(Common.Name, desc(events))) %>%
ggplot(aes(x = Common.Name, y = events)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90,hjust = 1, vjust = 0.5),
text = element_text(size=8))+ #working
ggtitle("Independent events per spp (no std)")
#ggsave("figures/events_per_sp.jpeg") sp_res <- data_gral %>%
mutate(Reserve.Location = fct_reorder(Reserve.Location, desc(No_spp))) %>%
ggplot(aes(x = Reserve.Location, y = No_spp)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90,hjust = 1, vjust = 0.5),
text = element_text(size=8))+ #working
ggtitle("Number of observed species per reserve")
sp_res
#ggsave("figures/sp_per_reserve.jpeg") Make sure you are using independent records, if not interpret accordingly. Also note that this is not taking into account effort (see 1_SNAPSHOT_source_functions.R)
sp_event <- plyr::ddply(sp_rec, c("Common.Name", "Reserve.Location"), summarise,
events = n())
#plot it
sp_res_ev <- sp_event %>%
mutate(Reserve.Location = fct_reorder(Reserve.Location, desc(events))) %>%
ggplot(aes(x = Reserve.Location, y = events)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90,hjust = 1, vjust = 0.5),
text = element_text(size=3)) +
facet_wrap("Common.Name", scales = "free") +
ggtitle("Number events per species per reserve")
sp_res_ev
#ggsave("figures/events_per_sp_per_res.jpeg") library(ggforce)## Warning: package 'ggforce' was built under R version 4.0.5class(sp_event$Common.Name)## [1] "character"sp_event$Common.Name <- as.factor(sp_event$Common.Name)
sp_res_ev2 <- sp_event %>%
mutate(Reserve.Location = fct_reorder(Reserve.Location, desc(events))) %>%
ggplot(aes(x = Reserve.Location, y = events)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90,hjust = 1, vjust = 0.5),
text = element_text(size=6)) +
facet_wrap_paginate("Common.Name", ncol = 5, nrow = 5, page = 1, scales = "free") +
ggtitle("Number events per species per reserve") #with this method I will need to copy and add "page = 2 for the other page and so on
sp_res_ev2
#ggsave("figures/events_per_sp_per_res_p1.jpeg")
#second page
sp_res_ev3 <- sp_event %>%
mutate(Reserve.Location = fct_reorder(Reserve.Location, desc(events))) %>%
ggplot(aes(x = Reserve.Location, y = events)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90,hjust = 1, vjust = 0.5),
text = element_text(size=6)) +
facet_wrap_paginate("Common.Name", ncol = 5, nrow = 5, page = 2, scales = "free") +
ggtitle("Number events per species per reserve")
sp_res_ev3
#ggsave("figures/events_per_sp_per_res_p2.jpeg")
#3th page
sp_res_ev4 <- sp_event %>%
mutate(Reserve.Location = fct_reorder(Reserve.Location, desc(events))) %>%
ggplot(aes(x = Reserve.Location, y = events)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90,hjust = 1, vjust = 0.5),
text = element_text(size=6)) +
facet_wrap_paginate("Common.Name", ncol = 5, nrow = 5, page = 3, scales = "free") +
ggtitle("Number events per species per reserve")
sp_res_ev4
#ggsave("figures/events_per_sp_per_res_p3.jpeg")
#page 4 not working,..had to set col=3 and nrow=1 and page 26 for the three species left
#four page
#par(mfrow = c(3, 1))
sp_res_ev5 <- sp_event %>%
mutate(Reserve.Location = fct_reorder(Reserve.Location, desc(events))) %>%
ggplot(aes(x = Reserve.Location, y = events)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90,hjust = 1, vjust = 0.5),
text = element_text(size=6)) +
facet_wrap_paginate("Common.Name", ncol = 3, nrow = 1, page = 26, scales = "free") +
ggtitle("Number events per species per reserve")
sp_res_ev5
#this plot is fine but a bit bulky lets try another representation
if you have camera sites instead, just change Reserve.Location for your camera site
det_tab <- tapply(sp_event$Common.Name,
list(sp_event$Common.Name,sp_event$Reserve.Location),
length) #give us only the presence
det_tab <- tapply(sp_rec$Common.Name,
list(sp_rec$Common.Name,sp_rec$Reserve.Location),
length)
write.csv(det_tab, "data_out/sp_events_matrix.csv")
library(plot.matrix)## Warning: package 'plot.matrix' was built under R version 4.0.5par(mar=c(4,8,1,5))
plot(log(det_tab[order(apply(det_tab,1,max,na.rm=T),decreasing = TRUE),]),
col = viridis::viridis(30,option = "D"),
axis.row = list(side=2, las=1, cex.axis=0.7), ylab="", main="") 
base plot, not supernice, let´s try ggplot better
we can get a quick similarity matrix to see general patterns
#first change NA to zeros
det_tab[is.na(det_tab)] <- 0
# heatmap with similarity index
heatm <-heatmap(det_tab) #this includes a , just confirm if values are scaled then... 
#seems to work only with matrix
#ggsave("figures/heat_map_simil.jpeg") #page 4 not working,..try later...***but it is probably clearer to use another heatmap
det_tab.df <- as.data.frame(det_tab)
det_tab.df <- tibble::rownames_to_column(det_tab.df, "Species")
library(hrbrthemes)
library(viridis)## Warning: package 'viridis' was built under R version 4.0.5
## Loading required package: viridisLite
## Warning: package 'viridisLite' was built under R version 4.0.5temp <- det_tab.df %>%
pivot_longer(!Species, names_to = "Reserve", values_to = "events")
range(temp$events)## [1] 0 1362## put correct format
det_tab.df %>%
pivot_longer(!Species, names_to = "Reserve", values_to = "events") %>%
# create a new variable from count to discretized the events, otherwise the colours are meaningless
mutate(countfactor=cut(events,breaks=c(0,20,30,40,50, 150,300,450,600,750,900, max(events,na.rm=T)),
labels=c("0-20", "21-30","31-40","41-50","51-150","151-300","301-450","451-600","602-750",
"750-900",">901"))) %>%
# Viz
ggplot(aes(Reserve, Species, fill = countfactor)) + #, color = terrain.colors(nrow(.))) +
geom_tile() +
#theme(legend.position="none") +
scale_fill_viridis(discrete=TRUE) +
#scale_fill_gradientn(low="white", high="blue") +
#scale_fill_distiller(palette = "BrBg") +
theme_ipsum() +
theme(axis.text.x = element_text(angle = 90,hjust = 1, vjust = 0.5, size=8),
axis.text.y = element_text(size=5),
text = element_text(size=2),
legend.text=element_text(size=7,face="bold")) 
#ggsave("figures/heat_map_cats_events.jpeg") ##change the date format, we need yyyy-mm-dd
sp_rec$Photo.Date <- lubridate::parse_date_time(x = sp_rec$Photo.Date,
order = c("dmY", "Ymd","dmy"))
survey_lenght <- plyr::ddply(sp_rec, c("Reserve.Location", "Camera.Site"), #
summarise,
First.Photo.Date = min(as.Date(Photo.Date)),
Last.Photo.Date = max(as.Date(Photo.Date)),
Cam.Days = Last.Photo.Date-First.Photo.Date)
head(survey_lenght)## Reserve.Location Camera.Site First.Photo.Date Last.Photo.Date Cam.Days
## 1 APN APN_K011 2018-10-25 2018-12-12 48 days
## 2 APN APN_K012 2018-10-25 2018-12-26 62 days
## 3 APN APN_K013 2018-10-23 2019-01-22 91 days
## 4 APN APN_K014 2018-10-26 2018-11-28 33 days
## 5 APN APN_K021 2018-10-23 2019-01-25 94 days
## 6 APN APN_K023 2018-10-23 2019-01-24 93 days#write.csv(survey_lenght,"data_out/survey_lenght_all_updated_.csv")surv_1 <- survey_lenght %>%
ggplot() +
geom_segment(aes(x= First.Photo.Date, xend = Last.Photo.Date,
y = Camera.Site, yend = Camera.Site ))
surv_1
#way too many sites, difficult to see in one chart, but you can customize this
# each of the "groups" represent cameras within a reserve, we can see they were surveyed at
# different timeshead(sp_rec)## # A tibble: 6 x 16
## ...1 Reserve.Location Camera.Site Common.Name Scientific.Name
## <dbl> <chr> <chr> <chr> <chr>
## 1 1121 APN APN_K011 giraffe Giraffa camelopardalis
## 2 1122 APN APN_K011 zebraburchells Equus quagga burchellii
## 3 1123 APN APN_K011 giraffe Giraffa camelopardalis
## 4 1124 APN APN_K011 kudu Tragelaphus strepsiceros
## 5 1125 APN APN_K011 impala Aepyceros melampus
## 6 1126 APN APN_K011 impala Aepyceros melampus
## # ... with 11 more variables: Photo.Date <dttm>, Photo.Time <time>,
## # Number.of.Individuals <dbl>, Photo.ID <chr>, Season <chr>, Roll <dbl>,
## # Zooniverse.Url.1 <chr>, Zooniverse.Url.2 <chr>, Zooniverse.Url.3 <chr>,
## # Consensus <dbl>, DateTimeOriginal <chr>sp_rec$DateTimeOriginal1## Warning: Unknown or uninitialised column: `DateTimeOriginal1`.
## NULLsp_rec$DateTimeOriginal <- as.POSIXct(sp_rec$DateTimeOriginal, format = "%d/%m/%Y %k:%M") # transform character to POSIXct = date-time
sp_rec %>% ggplot() +
geom_point(aes(x = DateTimeOriginal, y = Camera.Site,
col = Common.Name),
alpha = 0.5, show.legend = FALSE) +
scale_x_datetime(date_labels = "%d %b %y", # you can also use other date formatting codes (here I chose %d (day digits) %b (month abbreviation) and %y (year 2 last digits))
date_minor_breaks = "1 week", # you can also choose to change breaks to eg "1 week"
date_breaks = "2 months") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
xlab("Date") + ylab("Camera")
#ggsave("figures/survey_effort_all_points.jpeg")
#a lot of points too but better to see each point (species) and potential gaps
# if you want to put this figures into a report, then tidy it upwe now want to use other covariates from other files, such as the latitute and longitute, or scientific name, as we want to do some spatial explicit maps see source functions to see how to include scientific names into the species records
this contains the scientific name
traits <- read_csv("data_in/+traits_in_sp_records_updated.csv")## Rows: 91 Columns: 14
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (12): Scien.Code, Comm.Code, Snapshot.Name, Common.Name, Scientific.Name...
## dbl (2): id, Mass.kg
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.show all unique entries of the scientific names in spp records that are not in traits
unique(sp_rec$Scientific.Name)[!unique(sp_rec$Scientific.Name) %in% traits$Scientific.Name]## [1] NAunique(traits$Scientific.Name)## [1] "Orycteropus afer" "Proteles cristata"
## [3] "Papio ursinus" "Damaliscus pygargus phillipsi"
## [5] "Damaliscus pygargus pygargus" "Syncerus caffer"
## [7] "Galago moholi" "Tragelaphus scriptus"
## [9] "Potamochoerus larvatus" "Caracal caracal"
## [11] "Felis silvestris" "Felis nigripes"
## [13] "Acinonyx jubatus" "Civettictis civetta"
## [15] "Madoqua kirkii" "Sylvicapra grimmia"
## [17] "Cephalophus natalensis" "Taurotragus oryx"
## [19] "Loxodonta africana" "Dama dama"
## [21] "Otocyon megalotis" "Vulpes chama"
## [23] "Nanger granti" "Eudorcas thomsonii"
## [25] "Oryx gazella" "Genetta tigrina"
## [27] "Genetta genetta" "Genetta maculata"
## [29] "Gerbillurus paeba" "Giraffa camelopardalis"
## [31] "Raphicerus melanotis" "Raphicerus sharpei"
## [33] "Lepus capensis" "Lepus saxatilis"
## [35] "Pedetes capensis" "Alcelaphus buselaphus"
## [37] "Atelerix frontalis" "Hippopotamus amphibius"
## [39] "Mellivora capensis" "Parahyeana brunnea"
## [41] "Crocuta crocuta" "Aepyceros melampus"
## [43] "Canis mesomelas" "Canis adustus"
## [45] "Oreotragus oreotragus" "Tragelaphus strepsiceros"
## [47] "Panthera pardus" "Panthera leo"
## [49] "Suricata suricatta" "Mungos mungo"
## [51] "Helogale parvula" "Herpestes ichneumon"
## [53] "Paracynictis selousi" "Galerella sanguinea"
## [55] "Galerella pulverulentus" "Atilax paludinosus"
## [57] "Ichneumia albicauda" "Cynictis penicillata"
## [59] "Rhynchogale melleri" "Chlorocebus pygerythrus"
## [61] "Tragelaphus angasii" "Ourebia ourebi"
## [63] "Aonyx capensis" "Smutsia temminckii"
## [65] "Ictonyx striatus" "Hystrix africaeaustralis"
## [67] "Pronolagus rupestris" "Bunolagus monticularis"
## [69] "Redunca arundinum" "Redunca fulvorufula"
## [71] "Pelea capreolus" "Diceros bicornis"
## [73] "Ceratotherium simum" "Hippotragus equinus"
## [75] "Procavia capensis" "Hippotragus niger"
## [77] "Leptailurus serval" "Antidorcas marsupialis"
## [79] "Xerus inauris" "Paraxerus cepapi"
## [81] "Raphicerus campestris" "Neotragus moschatus"
## [83] "Damaliscus lunatus" "Phacochoerus africanus"
## [85] "Kobus ellipsiprymnus" "Poecilogale albinucha"
## [87] "Lycaon pictus" "Connochaetes gnou"
## [89] "Connochaetes taurinus" "Equus quagga burchellii"
## [91] "Equus zebra"show all unique entries of the snapshot names in spp records that are not in traits
unique(sp_rec$Common.Name)[!unique(sp_rec$Common.Name) %in% traits$Snapshot.Name]## [1] "rodent" "rhinoceros" "mongoose" "antelope" "zebra"
## [6] "jackal"now with covariates file
covars <- read_csv("data_in/+covariates_sites_fin_updated.csv")## Rows: 377 Columns: 22
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (9): Reserve.Location, Camera.Site, Cam.Brand, Flash, RSA.Vegetation, ...
## dbl (11): ID, Cam.Days, Lat_Y, Long_X, Reserve.Area..km2., Stablishment.Yea...
## date (2): Setup.Date, Last.Photo.Date
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.# show all unique sites in spp records that are not covariates
unique(sp_rec$Camera.Site)[!unique(sp_rec$Camera.Site) %in% covars$Camera.Site]## character(0)# show all unique sites in covariates that are not in sp records
unique(covars$Camera.Site)[!unique(covars$Camera.Site) %in% sp_rec$Camera.Site]## [1] "DHP_E01" "TAN_A15"sp_lat <- sp_rec %>%
group_by(Camera.Site) %>%
summarise(No_spp = n_distinct(Common.Name),
events = n())
sp_lat## # A tibble: 375 x 3
## Camera.Site No_spp events
## <chr> <int> <int>
## 1 APN_K011 8 48
## 2 APN_K012 10 50
## 3 APN_K013 11 82
## 4 APN_K014 6 18
## 5 APN_K021 12 105
## 6 APN_K023 15 111
## 7 APN_K024 12 62
## 8 APN_K034 13 83
## 9 APN_K041 10 80
## 10 APN_K044 8 102
## # ... with 365 more rows#add lat long to data_lat
lat_long <- covars[, c(3, 7:8)]
join <- left_join(sp_lat, lat_long, by = "Camera.Site")
#now plot
e <-ggplot(join, aes(Long_X, Lat_Y)) +
geom_point(aes(size=No_spp, colour=No_spp),alpha=1) +
scale_size_area() +
ylab("Longitude") +
xlab("Latitute") +
guides(color= guide_legend("Species #"), size= guide_legend("Species #")) +
scale_color_gradient(low = "skyblue", high = "blue")
e
#not very informative, as cams overlap, would need to split each reserve, not necesary right now...
#save this to add sites, number sp and number of events to covariates file exploration
#but use this to paste it to covars so we can explore responses of richness and events per site
#that´s done in 3.explo_covars
#firt delet lat/long in join to not repeat it
join <- join[,c(1:3)]
join_cov <- left_join(covars, join, by = "Camera.Site")
#save
#write.csv(join_cov , "data_out/covars_with_richness_events.csv")plot reserve level variable and a single camera coordinate
lat_long2 <- covars[, c(2, 7:8)]
lat_long3 <- lat_long2 %>%
group_by(Reserve.Location) %>%
summarise(lat = Lat_Y[1],
long = Long_X[1])
join2 <- left_join(lat_long3, data_gral, by = "Reserve.Location")
write.csv(join2, "data_out/rich_reserve_lat_long.csv")
(f <-ggplot(join2, aes(long, lat)) + #try adding label = No_spp, but no geom_text
geom_point(aes(size=No_spp, colour=No_spp),alpha=1) +
geom_text(aes(label=Reserve.Location),hjust= -0.25, size = 2.5) + #try geom_text_repel() library(ggrepel)
scale_size_area() +
ylab("Longitude") +
xlab("Latitute") +
guides(color= guide_legend("Species #"), size= guide_legend("Species #")) +
scale_color_gradient(low = "skyblue", high = "blue"))
#ggsave("figures/sp_lat_long.jpeg") #join_cov %>%
ggplot(aes(x= reorder(Reserve.Location, No_spp), y = No_spp, fill=Reserve.Location)) +
geom_boxplot() +
geom_jitter(color="black", size=0.2, alpha=0.5)+
theme(legend.position = "none") +
scale_color_viridis(discrete=TRUE) +
labs(x = "Reserves", y = "Species richness")## Warning: Removed 2 rows containing non-finite values (stat_boxplot).
## Warning: Removed 2 rows containing missing values (geom_point).
library(rgdal)## Loading required package: sp
## Warning: package 'sp' was built under R version 4.0.5
## Please note that rgdal will be retired by the end of 2023,
## plan transition to sf/stars/terra functions using GDAL and PROJ
## at your earliest convenience.
##
## rgdal: version: 1.5-26, (SVN revision 1148)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: C:/Users/admin/Documents/R/win-library/4.0/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: C:/Users/admin/Documents/R/win-library/4.0/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-5
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.
## Overwritten PROJ_LIB was C:/Users/admin/Documents/R/win-library/4.0/rgdal/projlibrary(ggmap)## Warning: package 'ggmap' was built under R version 4.0.3
## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.
## Please cite ggmap if you use it! See citation("ggmap") for details.library(raster)## Warning: package 'raster' was built under R version 4.0.5
##
## Attaching package: 'raster'
## The following object is masked from 'package:dplyr':
##
## select
## The following object is masked from 'package:tidyr':
##
## extractlibrary(hrbrthemes)
library(ggrepel)## Warning: package 'ggrepel' was built under R version 4.0.5#Definitions:
cf is the csv camera file for the site X cf_geo is the Spatial Points Data Frame in WGS84 of cf The used background doesn’t need any API and comes from Stamen map, type=terrain The loading can take few minutes. It is normal cameras object represents the georeferenced cameras points
join2 <- read.csv("data_out/rich_reserve_lat_long.csv")
head(join2)## X Reserve.Location lat long sites No_spp events
## 1 1 APN -24.23569 31.18658 16 30 987
## 2 2 AUG -28.54306 20.02006 20 18 284
## 3 3 BLO -22.96079 29.09420 18 30 3626
## 4 4 CDB -32.17137 24.49324 20 25 2108
## 5 5 DHP -34.39777 20.53871 13 12 203
## 6 6 GOE -29.65453 18.02626 19 12 329#Data loading (cf) and geospatialisation of the camera file (cf_geo)
cf <- join2
cf_geo <- join2
#Georeferencing of cf (Can be useful for other uses)
cf_geo$long <- as.numeric(cf_geo$long)
cf_geo$lat <- as.numeric(cf_geo$lat)
coordinates(cf_geo) <- ~ long + lat
proj4string(cf_geo) <- CRS("+proj=longlat +datum=WGS84")
#Background + ggplot = ggmap
#Extent
#The extent has to be manually set up considering the area by modulating the + and - values
left <- xmin(cf_geo)-0.6
right <- xmax(cf_geo)+0.8
bottom <- ymin(cf_geo)-0.4
top <- ymax(cf_geo)+0.4
#Basemap loading
#these are just different options of maps, let´s use only one
#basemap1 <- get_stamenmap(bbox= c(left=left,right=right,bottom=bottom,top=top),
# zoom=6, maptype = "terrain", crop=T, force=T,
# style = feature:administrative.country|element:labels.text|visibility:off)
#
#basemap2 <- get_stamenmap(bbox= c(left=left,right=right,bottom=bottom,top=top),
# zoom=6, maptype = "terrain", crop=T, force=T,
# style featureType:administrative.country|elementType:labels.text|visibility:off)
basemap5 <- get_stamenmap(bbox= c(left=left,right=right,bottom=bottom,top=top),
zoom=6, maptype = "terrain", crop=T, force=T,
style=c(feature="administrative.country",element="labels",
visibility="off"))## Source : http://tile.stamen.com/terrain/6/35/35.png
## Source : http://tile.stamen.com/terrain/6/36/35.png
## Source : http://tile.stamen.com/terrain/6/37/35.png
## Source : http://tile.stamen.com/terrain/6/35/36.png
## Source : http://tile.stamen.com/terrain/6/36/36.png
## Source : http://tile.stamen.com/terrain/6/37/36.png
## Source : http://tile.stamen.com/terrain/6/35/37.png
## Source : http://tile.stamen.com/terrain/6/36/37.png
## Source : http://tile.stamen.com/terrain/6/37/37.png
## Source : http://tile.stamen.com/terrain/6/35/38.png
## Source : http://tile.stamen.com/terrain/6/36/38.png
## Source : http://tile.stamen.com/terrain/6/37/38.pngggmap(basemap5, legend = "topleft") +
geom_point(aes(x = long, y = lat, colour = No_spp, size = No_spp),data = cf, alpha = 1,) +
geom_text_repel(aes(x =long, y =lat,label = Reserve.Location), hjust= -0.25, size = 3,
fontface = 'bold', color = 'black', data = cf) + #try geom_text_repel() library(ggrepel)
scale_size_area() +
ylab("Longitude") +
xlab("Latitute") +
guides(color= guide_legend("Species #"), size= guide_legend("Species #")) +
#scale_color_gradient(low = "skyblue", high = "blue") +
scale_color_gradient(low = "blue", high = "red") +
#scale_color_distiller(palette = "YlOrRd", direction = 1) +
theme_bw()## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
#ggsave("figures/sp_map_terrain.jpeg") #