This code provides a general protocol to build a tidy biodiversity report from Snapshot Camera Trap records. This consists of a series of general analysis including, species richness, relative abundance index and useful visualizations. If you want only the results in a tidy html, please refer to “3b_clean_biodiv_report” script. Other useful visualizations can be found in 2_species_exploration
For more specific biodiversity analysis use script “biodiversity analysis”. the input data is a species records table either with Zooniverse format or manually created with Digikam.
OBJ: 1. clean table with standard sp names, cols and values 2. calculate independence and create a temp file to get RAIs 3. create plots (richness, RAI, etc)
It is important to familiarized with the customized functions in 1_SNAPSHOT_source_functions, there might be some extra steps that need to be done by researches to accommodate data and also to double check. For example, if the researcher needs very detailed number of individuals and sex they must explore and adapt raw data accordingly. For instance, due to the uncertainty of counting individuals by volunteers there are some strings including a range of values (e.g. 11-51”. Since for this code we need a single number, the formulation takes the minimum number, but it is responsibility of the researcher to check this and adapt the code according to their ecological system and needs.
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). It is important to have a clear idea of the sampling periods…see also 2_species_exploration.
Please give credit to Lain E. Pardo, Snapshot Safari or WildEco Lab at Nelson Mandela University if you use this tool.
This is a work in progress if you want to contribute or find any issues please let us know.
library(readr) #read cols as character ## Warning: package 'readr' was built under R version 4.0.5library(readxl)
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.0.5
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v dplyr 1.0.7
## v tibble 3.1.4 v stringr 1.4.0
## v tidyr 1.1.3 v forcats 0.5.1
## v purrr 0.3.4
## Warning: package 'ggplot2' was built under R version 4.0.5
## Warning: package 'tibble' was built under R version 4.0.5
## Warning: package 'tidyr' was built under R version 4.0.5
## Warning: package 'dplyr' was built under R version 4.0.5
## Warning: package 'forcats' was built under R version 4.0.5
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggplot2)
library(hrbrthemes)
library(lubridate)## Warning: package 'lubridate' was built under R version 4.0.5
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(viridis)## Warning: package 'viridis' was built under R version 4.0.5
## Loading required package: viridisLite
## Warning: package 'viridisLite' was built under R version 4.0.5library(camtrapR) #for camera op matrix needed for other analysis
source("1_SNAPSHOT_source_functions.R") # customized functions are store here, this must be included in the working directorynote: if report comes from DIGIKAM start from : “# For roaming sites and reports coming from DIGIKAM” start from lines xxx
rep_sp1 <- read_csv("data_in/MTZ_S1_full_report_0-50%_agreement_corrected_fin.csv") ## New names:
## * `` -> ...27
## * `` -> ...28
## * `` -> ...29
## * `` -> ...30
## * `` -> ...31
## * ...
## Rows: 5629 Columns: 36
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (11): capture_id, season, site, capture_time_local, zooniverse_url_0, z...
## dbl (14): roll, capture, subject_id, question__standing, question__resting,...
## lgl (10): ...27, ...28, ...29, ...30, ...31, ...32, ...33, ...34, ...35, ...36
## date (1): capture_date_local
##
## 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.#check names quickly
unique(rep_sp1$question__species)## [1] "zebramountain" "ostrich"
## [3] "jackalblackbacked" "baboon"
## [5] "birdother" "springbok"
## [7] "eland" "hare"
## [9] "lionfemale" "lionmale"
## [11] "porcupine" "hartebeestred"
## [13] "gemsbokoryx" "buffalo"
## [15] "tortoise" "kudu"
## [17] "secretarybird" "cheetah"
## [19] "hyenabrown" "genetcommonsmallspotted"
## [21] "wildebeestblack" "birdsofprey"
## [23] "blesbok" "rhinocerosblack"
## [25] "aardwolf" "monkeyvervet"
## [27] "rhebokgrey" "bushpig"
## [29] "foxcape" "unresolvable"
## [31] "duikercommongrey" "aardvarkantbear"
## [33] "steenbok" "caracal"
## [35] "reptilesamphibians" "warthog"
## [37] "reedbuckmountain" "foxbateared"
## [39] "mongooseyellow" "blank"
## [41] "bustardludwigs" "craneblue"
## [43] "bushbuck" "unidentifiable"
## [45] "harespring"#standardize col number and names from original report before cbind
rep_sp <- cols_snap_std(rep_sp1)
names(rep_sp)## [1] "capture_id" "season"
## [3] "site" "roll"
## [5] "capture" "capture_date_local"
## [7] "capture_time_local" "zooniverse_url_0"
## [9] "zooniverse_url_1" "zooniverse_url_2"
## [11] "subject_id" "question__species"
## [13] "question__count_max" "question__count_median"
## [15] "question__count_min" "question__standing"
## [17] "question__resting" "question__moving"
## [19] "question__eating" "question__interacting"
## [21] "question__young_present" "question__horns_count_max"
## [23] "question__horns_count_median" "question__horns_count_min"
## [25] "p_users_identified_this_species" "pielous_evenness_index"class(rep_sp$question__count_median)## [1] "character"class(rep_sp$question__count_median)## [1] "character"unique(rep_sp$site)## [1] "B04" "B05" "C04" "C06" "C07" "C08" "D03" "D04" "D05" "D06" "D07" "E03"
## [13] "E04" "E05" "E06" "F04" "F05" "G04" "B08"# check some columns, if there are or different strings to those in the snapshot source functions amend accordingly
colSums(is.na(rep_sp))## Warning: One or more parsing issues, see `problems()` for details
## capture_id season
## 0 0
## site roll
## 0 0
## capture capture_date_local
## 0 0
## capture_time_local zooniverse_url_0
## 0 0
## zooniverse_url_1 zooniverse_url_2
## 5465 5467
## subject_id question__species
## 0 0
## question__count_max question__count_median
## 7 7
## question__count_min question__standing
## 7 7
## question__resting question__moving
## 7 7
## question__eating question__interacting
## 7 7
## question__young_present question__horns_count_max
## 54 5409
## question__horns_count_median question__horns_count_min
## 5523 5560
## p_users_identified_this_species pielous_evenness_index
## 0 0unique(rep_sp$question__count_median)## [1] "1" "2" "3" "4" "8" "10" "5" "6"
## [9] "9" "7" "Nov-50" NAunique(rep_sp$question__count_max) # ## [1] "1" "3" "2" "Nov-50" "4" "6" "5" "7"
## [9] "10" "8" "9" "51+" NA#deal with NA
#THIS IS ASSUMING NA =1, please adapt according to your needs.
# important: Create DateTimeOriginal column for clean_table and other functions to work
rep_sp <-rep_sp %>%
mutate(question__count_median =replace_na(question__count_median, 1),
question__count_max = replace_na(question__count_max,1)) %>%
mutate(DateTimeOriginal = paste(capture_date_local, capture_time_local))
write.csv(rep_sp, "temp_rep_sp.csv")df <- standardise_names(rep_sp)
unique(df$question__species)## [1] "zebramountain" "ostrich"
## [3] "jackalblackbacked" "baboon"
## [5] "birdother" "springbok"
## [7] "eland" "hare"
## [9] "lion" "porcupine"
## [11] "hartebeestred" "gemsbokoryx"
## [13] "buffalo" "tortoise"
## [15] "kudu" "secretarybird"
## [17] "cheetah" "hyenabrown"
## [19] "genetcommonsmallspotted" "wildebeestblack"
## [21] "birdofprey" "blesbok"
## [23] "rhinocerosblack" "aardwolf"
## [25] "monkeyvervet" "rhebokgrey"
## [27] "bushpig" "foxcape"
## [29] "unresolvable" "duikercommon"
## [31] "aardvarkantbear" "steenbok"
## [33] "caracal" "reptilesamphibians"
## [35] "warthog" "reedbuckmountain"
## [37] "foxbateared" "mongooseyellow"
## [39] "blank" "bustardludwigs"
## [41] "craneblue" "bushbuck"
## [43] "harespring"There might be some things more to check in the values or strings of some columns. Although in function clean_table we deal with a lot of this SNAPSHOT_source_functions.You might want to double check generic terms such as “zebra” or “antelope” and change accordingly
Here we standardize species names, filter out no animals (e.g. fire), arrange some cols, force some strings to useful values, etc. There are two arguments here, you can include by_catch data by adding FALSE to rm_bycatch (birds etc, see formulation in the source functions to know what criteria was used), and you can also put only presence absence
df.clean <- clean_table(df, rm_bycatch = FALSE, pres_abs = FALSE)
unique(df.clean$question__species)## [1] zebramountain ostrich jackalblackbacked
## [4] baboon birdother springbok
## [7] eland hare lion
## [10] porcupine hartebeestred gemsbokoryx
## [13] buffalo tortoise kudu
## [16] secretarybird cheetah hyenabrown
## [19] genetcommonsmallspotted wildebeestblack birdofprey
## [22] blesbok rhinocerosblack aardwolf
## [25] monkeyvervet rhebokgrey bushpig
## [28] foxcape duikercommon aardvarkantbear
## [31] steenbok caracal reptilesamphibians
## [34] warthog reedbuckmountain foxbateared
## [37] mongooseyellow bustardludwigs craneblue
## [40] bushbuck harespring
## 41 Levels: aardvarkantbear aardwolf baboon birdofprey birdother ... zebramountain# before reorder chronologically in case, so:
df.clean <- df.clean %>% dplyr::arrange(site, DateTimeOriginal)
indep_records <- indep60(df.clean) #this takes time depending on the size of your file
#save
write.csv(indep_records, "data_out/MTZ_clean_indep_report_snap_form.csv")#check spp again
unique(indep_records$site)## [1] "B04" "B05" "B08" "C04" "C06" "C07" "C08" "D03" "D04" "D05" "D06" "D07"
## [13] "E03" "E04" "E05" "E06" "F04" "F05" "G04"unique(indep_records$question__species)## [1] zebramountain ostrich jackalblackbacked
## [4] baboon birdother springbok
## [7] hartebeestred eland hare
## [10] lion porcupine gemsbokoryx
## [13] buffalo hyenabrown tortoise
## [16] kudu secretarybird cheetah
## [19] genetcommonsmallspotted harespring wildebeestblack
## [22] birdofprey blesbok rhebokgrey
## [25] rhinocerosblack monkeyvervet aardwolf
## [28] bushbuck reedbuckmountain bushpig
## [31] foxcape duikercommon aardvarkantbear
## [34] steenbok caracal reptilesamphibians
## [37] warthog foxbateared mongooseyellow
## [40] bustardludwigs craneblue
## 41 Levels: aardvarkantbear aardwolf baboon birdofprey birdother ... zebramountainnames(indep_records)## [1] "capture_id" "season"
## [3] "site" "roll"
## [5] "capture" "capture_date_local"
## [7] "capture_time_local" "zooniverse_url_0"
## [9] "zooniverse_url_1" "zooniverse_url_2"
## [11] "subject_id" "question__species"
## [13] "question__count_max" "question__count_median"
## [15] "question__count_min" "question__standing"
## [17] "question__resting" "question__moving"
## [19] "question__eating" "question__interacting"
## [21] "question__young_present" "question__horns_count_max"
## [23] "question__horns_count_median" "question__horns_count_min"
## [25] "p_users_identified_this_species" "pielous_evenness_index"
## [27] "DateTimeOriginal" "row_id"df1 <- separate(indep_records,season, c("code_loc", "season1"), sep = "_")
df2 <-unite(df1, site_ID, code_loc, site, sep = "_",remove =FALSE) #
df2$site_ID <- as.factor(df2$site_ID) # need to convert to factor again
nlevels(df2$site_ID)## [1] 19names(df2)## [1] "capture_id" "site_ID"
## [3] "code_loc" "season1"
## [5] "site" "roll"
## [7] "capture" "capture_date_local"
## [9] "capture_time_local" "zooniverse_url_0"
## [11] "zooniverse_url_1" "zooniverse_url_2"
## [13] "subject_id" "question__species"
## [15] "question__count_max" "question__count_median"
## [17] "question__count_min" "question__standing"
## [19] "question__resting" "question__moving"
## [21] "question__eating" "question__interacting"
## [23] "question__young_present" "question__horns_count_max"
## [25] "question__horns_count_median" "question__horns_count_min"
## [27] "p_users_identified_this_species" "pielous_evenness_index"
## [29] "DateTimeOriginal" "row_id"unique(df2$site_ID)## [1] MTZ_B04 MTZ_B05 MTZ_B08 MTZ_C04 MTZ_C06 MTZ_C07 MTZ_C08 MTZ_D03 MTZ_D04
## [10] MTZ_D05 MTZ_D06 MTZ_D07 MTZ_E03 MTZ_E04 MTZ_E05 MTZ_E06 MTZ_F04 MTZ_F05
## [19] MTZ_G04
## 19 Levels: MTZ_B04 MTZ_B05 MTZ_B08 MTZ_C04 MTZ_C06 MTZ_C07 MTZ_C08 ... MTZ_G04#read file with traits that also contains the scientific names to be pulled
scient_name <- 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.# --- Merge tables
# Select a subset of columns from scient_name table
scient_name_sub <- scient_name %>% dplyr::select(Snapshot.Name, Scientific.Name)
#add a common column to join
scient_name_sub$question__species <- scient_name_sub$Snapshot.Name
# Merge the 2 tables, keeping all data in new filtered report (df.filtered) in the left hand side table (data table 'table")
df2$question__species <- as.character(df2$question__species)
mergenames <- dplyr::left_join(df2, scient_name_sub, by = "question__species")
mergenames$question__species <- as.factor(mergenames$question__species) # coerce back to factors
names(mergenames)## [1] "capture_id" "site_ID"
## [3] "code_loc" "season1"
## [5] "site" "roll"
## [7] "capture" "capture_date_local"
## [9] "capture_time_local" "zooniverse_url_0"
## [11] "zooniverse_url_1" "zooniverse_url_2"
## [13] "subject_id" "question__species"
## [15] "question__count_max" "question__count_median"
## [17] "question__count_min" "question__standing"
## [19] "question__resting" "question__moving"
## [21] "question__eating" "question__interacting"
## [23] "question__young_present" "question__horns_count_max"
## [25] "question__horns_count_median" "question__horns_count_min"
## [27] "p_users_identified_this_species" "pielous_evenness_index"
## [29] "DateTimeOriginal" "row_id"
## [31] "Snapshot.Name" "Scientific.Name"final.df <- cols_need(mergenames) #final clean df with independent records only, clear column names ready for use
names(final.df)## [1] "Reserve.Location" "Camera.Site" "Common.Name"
## [4] "Scientific.Name" "Photo.Date" "Photo.Time"
## [7] "Number.of.Individuals" "Photo.ID" "Season"
## [10] "Roll" "Zooniverse.Url.1" "Zooniverse.Url.2"
## [13] "Zooniverse.Url.3" "Consensus" "DateTimeOriginal"#quickly inspect sp
(spmergedrep <- unique(final.df$Common.Name))## [1] zebramountain ostrich jackalblackbacked
## [4] baboon birdother springbok
## [7] hartebeestred eland hare
## [10] lion porcupine gemsbokoryx
## [13] buffalo hyenabrown tortoise
## [16] kudu secretarybird cheetah
## [19] genetcommonsmallspotted harespring wildebeestblack
## [22] birdofprey blesbok rhebokgrey
## [25] rhinocerosblack monkeyvervet aardwolf
## [28] bushbuck reedbuckmountain bushpig
## [31] foxcape duikercommon aardvarkantbear
## [34] steenbok caracal reptilesamphibians
## [37] warthog foxbateared mongooseyellow
## [40] bustardludwigs craneblue
## 41 Levels: aardvarkantbear aardwolf baboon birdofprey birdother ... zebramountain# save doc
write.csv(final.df,"data_out/MTZ_final_sp_rep_ind60_S_merged.csv")FOR THE REPORTS WE NEED TO FILTER HERE WITHOUT THE BYCATCH…. and then run the other part of the script
basically the matrix of the design (cameras and days..) need: df with sites, coordinates, start and end day of survey (minimum): the metadata or covariates, need the species records cleaned
We will use the metadata table for this to put cam days and start, end of survey. All sites must be there but will better filter by reserve probably
cameras <- read_csv("data_in/1_Metadata_all_fixed_as_snapshot_TEMP_CSV.csv") #contains the covars/metadata too## New names:
## * Visib.1 -> Visib.1...24
## * Visib.1 -> Visib.1...25
## * Visib.1 -> Visib.1...26
## Rows: 711 Columns: 53
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (46): Reserve.Location, Code.Loc.Snap, Site.Original, Camera.Site.Conca...
## dbl (4): id, Lat_Y, Long_X, Elevation
## lgl (1): other.cols.in.metadata2
## date (1): Setup.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.str(cameras)## spec_tbl_df [711 x 53] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ id : num [1:711] 1 2 3 4 5 6 7 8 9 10 ...
## $ Reserve.Location : chr [1:711] "Augrabies" "Augrabies" "Augrabies" "Augrabies" ...
## $ Code.Loc.Snap : chr [1:711] "AUG" "AUG" "AUG" "AUG" ...
## $ Site.Original : chr [1:711] "B02" "B03" "B04" "B06" ...
## $ Camera.Site.Concatenate: chr [1:711] "AUG_B02" "AUG_B03" "AUG_B04" "AUG_B06" ...
## $ Camera.Site.Std : chr [1:711] "AUG_B02" "AUG_B03" "AUG_B04" "AUG_B06" ...
## $ Setup.Date : Date[1:711], format: "2018-11-25" "2018-11-25" ...
## $ Setup.Time : chr [1:711] "9:21" "8:50" "9:52" "10:34" ...
## $ Lat_Y : num [1:711] -28.5 -28.5 -28.5 -28.5 -28.5 ...
## $ Long_X : num [1:711] 20 20 20.1 20.1 20 ...
## $ Cam.Brand : chr [1:711] "Cuddeback" "Cuddeback" "Cuddeback" "Cuddeback" ...
## $ Flash : chr [1:711] "IR - Black" "IR - Black" "IR - Black" "IR - Black" ...
## $ Cam.Serial.No : chr [1:711] NA NA NA NA ...
## $ Height : chr [1:711] "50" "50" "50" "40" ...
## $ Fixture : chr [1:711] "Pole" "Pole" "Pole" "Pole" ...
## $ DRXN : chr [1:711] "NW" "SE" "E" "S" ...
## $ IMG# N : chr [1:711] "5622" "5617" "5627" "5582" ...
## $ IMG# E : chr [1:711] "5623" "5618" "5628" "5583" ...
## $ IMG# S : chr [1:711] "5624" "5619" "5629" "5584" ...
## $ IMG# W : chr [1:711] "5625" "5620" "5630" "5585" ...
## $ IMG# CT : chr [1:711] "5626" "5621" "5631" "5586" ...
## $ Habitat.Descriptor : chr [1:711] "Dry river bed" "Dry river bed" "Dry river bed" "Drainage line" ...
## $ Shade : chr [1:711] "0" "1" "0" "1" ...
## $ Visib.1...24 : chr [1:711] "10" "26" "12" "16" ...
## $ Visib.1...25 : num [1:711] 1 8 4 3 18 7 3 4 1 3 ...
## $ Visib.1...26 : chr [1:711] "3" "4" "12" "11" ...
## $ SITE_ID : chr [1:711] "B02" "B03" "B04" "B06" ...
## $ Dist.Tree.1 : chr [1:711] "1" "1" "1" "3" ...
## $ Dist.Tree.2 : chr [1:711] "1" "2" "2" "1" ...
## $ Dist.Tree.3 : chr [1:711] "3" "8" "8" "6" ...
## $ Dist.Tree.4 : chr [1:711] "5" "4" "9" "5" ...
## $ Dist.Tree.5 : chr [1:711] "4" "13" "3" "7" ...
## $ Dist.Tree.6 : chr [1:711] "5" "13" "3" "5" ...
## $ Dist.Tree.7 : chr [1:711] "8" "21" "5" "8" ...
## $ Dist.Tree.8 : chr [1:711] "6" "13" "7" "9" ...
## $ Dist.Tree.9 : chr [1:711] "13" "16" "10" "10" ...
## $ Dist.Tree.10 : chr [1:711] "14" "17" "15" "12" ...
## $ Dist.Road : chr [1:711] "253" "100" "128" "260" ...
## $ Dist.Water : chr [1:711] NA NA NA NA ...
## $ Dist.Outcrop : chr [1:711] "220" "140" "180" "200" ...
## $ Dist.Trail : chr [1:711] NA NA NA NA ...
## $ Gral.Comments : chr [1:711] NA NA NA NA ...
## $ other.cols.WATER (Km)P : chr [1:711] NA NA NA NA ...
## $ other.cols.in.metadata2: logi [1:711] NA NA NA NA NA NA ...
## $ Setup.Date.Original : chr [1:711] "25-Nov-2018" "25-Nov-2018" "25-Nov-2018" "24-Nov-2018" ...
## $ Management.Type : chr [1:711] "National Park" "National Park" "National Park" "National Park" ...
## $ Official.Name : chr [1:711] "Augrabies Falls National Park" "Augrabies Falls National Park" "Augrabies Falls National Park" "Augrabies Falls National Park" ...
## $ Country : chr [1:711] "South Africa" "South Africa" "South Africa" "South Africa" ...
## $ Province : chr [1:711] "Northern Cape" "Northern Cape" "Northern Cape" "Northern Cape" ...
## $ Municipality : chr [1:711] "Kai !Garib" "Kai !Garib" "Kai !Garib" "Kai !Garib" ...
## $ Vegetation : chr [1:711] "NAMAQUALAND BROKEN VELD" "NAMAQUALAND BROKEN VELD" "NAMAQUALAND BROKEN VELD" "NAMAQUALAND BROKEN VELD" ...
## $ Biome : chr [1:711] "Nama Karoo" "Nama Karoo" "Nama Karoo" "Nama Karoo" ...
## $ Elevation : num [1:711] 713 713 713 637 713 713 713 713 637 637 ...
## - attr(*, "spec")=
## .. cols(
## .. id = col_double(),
## .. Reserve.Location = col_character(),
## .. Code.Loc.Snap = col_character(),
## .. Site.Original = col_character(),
## .. Camera.Site.Concatenate = col_character(),
## .. Camera.Site.Std = col_character(),
## .. Setup.Date = col_date(format = ""),
## .. Setup.Time = col_character(),
## .. Lat_Y = col_double(),
## .. Long_X = col_double(),
## .. Cam.Brand = col_character(),
## .. Flash = col_character(),
## .. Cam.Serial.No = col_character(),
## .. Height = col_character(),
## .. Fixture = col_character(),
## .. DRXN = col_character(),
## .. `IMG# N` = col_character(),
## .. `IMG# E` = col_character(),
## .. `IMG# S` = col_character(),
## .. `IMG# W` = col_character(),
## .. `IMG# CT` = col_character(),
## .. Habitat.Descriptor = col_character(),
## .. Shade = col_character(),
## .. Visib.1...24 = col_character(),
## .. Visib.1...25 = col_number(),
## .. Visib.1...26 = col_character(),
## .. SITE_ID = col_character(),
## .. Dist.Tree.1 = col_character(),
## .. Dist.Tree.2 = col_character(),
## .. Dist.Tree.3 = col_character(),
## .. Dist.Tree.4 = col_character(),
## .. Dist.Tree.5 = col_character(),
## .. Dist.Tree.6 = col_character(),
## .. Dist.Tree.7 = col_character(),
## .. Dist.Tree.8 = col_character(),
## .. Dist.Tree.9 = col_character(),
## .. Dist.Tree.10 = col_character(),
## .. Dist.Road = col_character(),
## .. Dist.Water = col_character(),
## .. Dist.Outcrop = col_character(),
## .. Dist.Trail = col_character(),
## .. Gral.Comments = col_character(),
## .. `other.cols.WATER (Km)P` = col_character(),
## .. other.cols.in.metadata2 = col_logical(),
## .. Setup.Date.Original = col_character(),
## .. Management.Type = col_character(),
## .. Official.Name = col_character(),
## .. Country = col_character(),
## .. Province = col_character(),
## .. Municipality = col_character(),
## .. Vegetation = col_character(),
## .. Biome = col_character(),
## .. Elevation = col_double()
## .. )
## - attr(*, "problems")=<externalptr>#unique(cameras$Camera.Site.Concatenate)#read file (fixed report)
spp_rec <- final.df
#or load it from the file we created (some times is better to avoid issues)
#spp_rec <- read_csv("data_out/MTZ_final_sp_rep_ind60_S_merged.csv") #quick explo
str(spp_rec)## tibble [1,986 x 15] (S3: tbl_df/tbl/data.frame)
## $ Reserve.Location : chr [1:1986] "MTZ" "MTZ" "MTZ" "MTZ" ...
## $ Camera.Site : Factor w/ 19 levels "MTZ_B04","MTZ_B05",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Common.Name : Factor w/ 41 levels "aardvarkantbear",..: 41 29 29 41 24 24 29 3 24 41 ...
## $ Scientific.Name : chr [1:1986] "Equus zebra" NA NA "Equus zebra" ...
## $ Photo.Date : POSIXct[1:1986], format: "2017-08-23 19:00:00" "2017-08-26 19:00:00" ...
## $ Photo.Time : chr [1:1986] "12:18:04" "15:17:05" "16:24:15" "18:04:31" ...
## $ Number.of.Individuals: num [1:1986] 1 1 1 3 1 1 2 2 1 3 ...
## $ Photo.ID : num [1:1986] 31547018 30785302 30119622 30782166 32731999 ...
## $ Season : chr [1:1986] "S1" "S1" "S1" "S1" ...
## $ Roll : num [1:1986] 1 1 1 1 1 1 1 1 1 1 ...
## $ Zooniverse.Url.1 : chr [1:1986] "https://panoptes-uploads.zooniverse.org/production/subject_location/efd7b1d6-5dbd-4729-92a1-45d4cfaf2caf.jpeg" "https://panoptes-uploads.zooniverse.org/production/subject_location/ac9468de-6b08-40a2-ad5c-a41327f1e30a.jpeg" "https://panoptes-uploads.zooniverse.org/production/subject_location/a8cdf34a-74be-4084-b30a-743954f93ee7.jpeg" "https://panoptes-uploads.zooniverse.org/production/subject_location/a888f549-b8bf-41b2-b4be-3ad5ee097029.jpeg" ...
## $ Zooniverse.Url.2 : chr [1:1986] NA NA NA NA ...
## $ Zooniverse.Url.3 : chr [1:1986] NA NA NA NA ...
## $ Consensus : num [1:1986] 0.78 0.91 1 0.89 1 1 1 0.8 0.6 0.7 ...
## $ DateTimeOriginal : POSIXct[1:1986], format: "2017-08-24 12:18:04" "2017-08-27 15:17:05" ...class(spp_rec$Photo.Date)## [1] "POSIXct" "POSIXt"unique(spp_rec$Camera.Site)## [1] MTZ_B04 MTZ_B05 MTZ_B08 MTZ_C04 MTZ_C06 MTZ_C07 MTZ_C08 MTZ_D03 MTZ_D04
## [10] MTZ_D05 MTZ_D06 MTZ_D07 MTZ_E03 MTZ_E04 MTZ_E05 MTZ_E06 MTZ_F04 MTZ_F05
## [19] MTZ_G04
## 19 Levels: MTZ_B04 MTZ_B05 MTZ_B08 MTZ_C04 MTZ_C06 MTZ_C07 MTZ_C08 ... MTZ_G04unique(spp_rec$Common.Name)## [1] zebramountain ostrich jackalblackbacked
## [4] baboon birdother springbok
## [7] hartebeestred eland hare
## [10] lion porcupine gemsbokoryx
## [13] buffalo hyenabrown tortoise
## [16] kudu secretarybird cheetah
## [19] genetcommonsmallspotted harespring wildebeestblack
## [22] birdofprey blesbok rhebokgrey
## [25] rhinocerosblack monkeyvervet aardwolf
## [28] bushbuck reedbuckmountain bushpig
## [31] foxcape duikercommon aardvarkantbear
## [34] steenbok caracal reptilesamphibians
## [37] warthog foxbateared mongooseyellow
## [40] bustardludwigs craneblue
## 41 Levels: aardvarkantbear aardwolf baboon birdofprey birdother ... zebramountainso work only with mammals for reports
BYCATCH <- c("birdofprey", "birdother", "bustardkori", "bustardludwigs", "guineafowl",
"squirreltree", "reptilesamphibians", "human", "bustarddenhams",
"secretarybird","tortoise", "domesticanimal", "ostrich", "bat", "reptilesamphibians", "fowlguinea","spider", "reptile", "insect", "bustardludwig's","craneblue", "horse")
mammals <- filter(spp_rec,
!(Common.Name %in% BYCATCH))
unique(mammals$Common.Name)## [1] zebramountain jackalblackbacked baboon
## [4] springbok hartebeestred eland
## [7] hare lion porcupine
## [10] gemsbokoryx buffalo hyenabrown
## [13] kudu cheetah genetcommonsmallspotted
## [16] harespring wildebeestblack blesbok
## [19] rhebokgrey rhinocerosblack monkeyvervet
## [22] aardwolf bushbuck reedbuckmountain
## [25] bushpig foxcape duikercommon
## [28] aardvarkantbear steenbok caracal
## [31] warthog foxbateared mongooseyellow
## 41 Levels: aardvarkantbear aardwolf baboon birdofprey birdother ... zebramountainspp_rec <- mammalsspp_rec$Photo.Date <- as.Date(spp_rec$Photo.Date, format="%Y-%m-%d")
spp_rec$DateTimeOriginal <- as.POSIXct(spp_rec$DateTimeOriginal)
class(spp_rec$Photo.Date)## [1] "Date"max(spp_rec$Photo.Date)## [1] "2018-07-17"min(spp_rec$Photo.Date)## [1] "2017-08-23"max(spp_rec$Photo.Date)## [1] "2018-07-17"#using final spp report file cleaned with good var
unique(spp_rec$Reserve.Location)## [1] "MTZ"table(spp_rec$Camera.Site) ##
## MTZ_B04 MTZ_B05 MTZ_B08 MTZ_C04 MTZ_C06 MTZ_C07 MTZ_C08 MTZ_D03 MTZ_D04 MTZ_D05
## 209 179 4 15 126 53 100 30 145 17
## MTZ_D06 MTZ_D07 MTZ_E03 MTZ_E04 MTZ_E05 MTZ_E06 MTZ_F04 MTZ_F05 MTZ_G04
## 52 102 55 164 145 122 168 26 120# getting last picture date for all sites as end date of survey, if this is not the case, change
#tibble with new cols of survey period
surv_lenght <- survey_period(spp_rec) #IF UNSURE ABOUT STARTING DATES
surv_lenght## # A tibble: 19 x 4
## Camera.Site First.Photo.Date Last.Photo.Date Act.Days
## <fct> <date> <date> <drtn>
## 1 MTZ_B04 2017-08-24 2018-07-04 314 days
## 2 MTZ_B05 2017-09-01 2018-05-03 244 days
## 3 MTZ_B08 2018-05-03 2018-05-13 10 days
## 4 MTZ_C04 2017-08-26 2018-06-26 304 days
## 5 MTZ_C06 2017-08-23 2018-07-13 324 days
## 6 MTZ_C07 2017-09-03 2018-07-09 309 days
## 7 MTZ_C08 2018-03-28 2018-06-19 83 days
## 8 MTZ_D03 2018-03-28 2018-05-07 40 days
## 9 MTZ_D04 2017-08-23 2018-07-15 326 days
## 10 MTZ_D05 2017-08-24 2017-10-01 38 days
## 11 MTZ_D06 2017-08-27 2017-11-01 66 days
## 12 MTZ_D07 2017-08-25 2018-04-05 223 days
## 13 MTZ_E03 2018-03-28 2018-05-15 48 days
## 14 MTZ_E04 2017-08-24 2018-04-01 220 days
## 15 MTZ_E05 2017-08-23 2018-04-02 222 days
## 16 MTZ_E06 2017-08-24 2018-06-12 292 days
## 17 MTZ_F04 2017-08-24 2018-07-07 317 days
## 18 MTZ_F05 2017-11-02 2017-11-27 25 days
## 19 MTZ_G04 2017-08-24 2018-07-17 327 days#surv_lenght <- survey_period2(spp_rec) note: if you are sure about the starting date then don´t use “min(photo.date)as start date, use”Setup.Date” from cameras filter by site (e.g.MTZ_Cams); use function “survey_period2”, then manually add start date en mutate for subtraction to cam days, for example:
Working with the survey period demands deep exploration of data, be mindful that there might be gaps between rolls, within roles or between seasons and within seasons, so use exploration code for some visualizations and set the begin and end of survey correctly
now merge this info (first,last photo,cam days as a column into the cameras doc (metadata) explore this part carefully, there might be dates that don´t match and errors can occur
#1. rename col Camera.Site to join with camera df (Camera.Site.Concatenate which is the one in cameras)
surv_lengh2 <- surv_lenght %>% rename(Camera.Site.Concatenate = Camera.Site)
# 2. standardize dates in cameras
names(cameras)## [1] "id" "Reserve.Location"
## [3] "Code.Loc.Snap" "Site.Original"
## [5] "Camera.Site.Concatenate" "Camera.Site.Std"
## [7] "Setup.Date" "Setup.Time"
## [9] "Lat_Y" "Long_X"
## [11] "Cam.Brand" "Flash"
## [13] "Cam.Serial.No" "Height"
## [15] "Fixture" "DRXN"
## [17] "IMG# N" "IMG# E"
## [19] "IMG# S" "IMG# W"
## [21] "IMG# CT" "Habitat.Descriptor"
## [23] "Shade" "Visib.1...24"
## [25] "Visib.1...25" "Visib.1...26"
## [27] "SITE_ID" "Dist.Tree.1"
## [29] "Dist.Tree.2" "Dist.Tree.3"
## [31] "Dist.Tree.4" "Dist.Tree.5"
## [33] "Dist.Tree.6" "Dist.Tree.7"
## [35] "Dist.Tree.8" "Dist.Tree.9"
## [37] "Dist.Tree.10" "Dist.Road"
## [39] "Dist.Water" "Dist.Outcrop"
## [41] "Dist.Trail" "Gral.Comments"
## [43] "other.cols.WATER (Km)P" "other.cols.in.metadata2"
## [45] "Setup.Date.Original" "Management.Type"
## [47] "Official.Name" "Country"
## [49] "Province" "Municipality"
## [51] "Vegetation" "Biome"
## [53] "Elevation"class(cameras$Setup.Date)## [1] "Date"unique(cameras$Setup.Date)## [1] "2018-11-25" "2018-11-24" "2018-11-23" "2018-11-22" "2018-07-21"
## [6] "2018-07-22" "2018-07-20" "2018-07-24" "2018-07-25" "2019-10-18"
## [11] "2019-10-19" "2018-09-06" "2018-09-07" NA "2018-09-05"
## [16] "2018-09-04" "2018-09-08" "2018-12-03" "2018-12-04" "2019-03-04"
## [21] "2019-03-05" "2018-05-22" "2018-11-13" "2018-05-21" "2018-05-23"
## [26] "2018-11-27" "2019-01-12" "2018-11-28" "2019-01-15" "2019-01-16"
## [31] "2018-07-19" "2019-07-20" "2018-06-12" "2018-06-13" "2018-06-11"
## [36] "2018-06-14" "2018-06-21" "2018-06-20" "2019-01-20" "2018-06-24"
## [41] "2018-06-25" "2018-06-23" "2018-03-27" "2018-03-26" "2018-03-28"
## [46] "2019-02-27" "2019-02-28" "2019-02-05" "2019-06-17" "2019-06-18"
## [51] "2019-06-19" "2018-11-18" "2018-11-19" "2018-11-20" "2017-10-07"
## [56] "2018-05-31" "2018-06-02" "2019-07-21" "2019-08-14" "2019-08-17"
## [61] "2019-08-10" "2019-08-01" "2018-06-05" "2018-06-06" "2018-06-04"
## [66] "2018-06-01" "2018-06-07" "2018-06-09" "2017-04-16" "2017-04-11"
## [71] "2019-02-14" "2018-06-18" "2019-06-14" "2019-08-07" "2019-06-16"
## [76] "2019-08-09" "2019-09-04" "2019-08-27" "2019-09-06" "2019-08-16"
## [81] "2017-06-28" "2017-06-29" "2017-06-30" "2017-07-01" "2017-07-02"
## [86] "2017-07-03" "2017-07-04" "2017-07-05" "2017-07-06" "2017-07-07"
## [91] "2017-07-08" "2017-07-09" "2017-07-10" "2017-07-11" "2017-07-12"
## [96] "2017-07-13" "2018-07-13" "2018-10-02" "2018-10-01" "2018-08-31"
## [101] "2018-08-30" "2018-09-01" "2017-12-15" "2017-12-14" "2017-12-10"
## [106] "2019-10-05" "2019-10-06" "2019-10-07" "2019-10-08" "2020-03-07"#cameras$parse_Setup_date <- lubridate::parse_date_time(x = cameras$Setup.Date,
# order = c("dmY", "Ymd","dmy"))#now done from input file..no need here
# join
join <- dplyr::left_join(cameras, surv_lengh2, by = "Camera.Site.Concatenate") #there are some NA as soon as I have all sites, there should be no NA
#here we have to filter by the reserve to avoid NA and other stuff in future analysis
MTZ_cams <- join %>%
filter(Code.Loc.Snap == "MTZ") # object to work with
colSums(is.na(MTZ_cams)) #for Somkhanda, there are 11 sites with 0 nights!!## id Reserve.Location Code.Loc.Snap
## 0 0 0
## Site.Original Camera.Site.Concatenate Camera.Site.Std
## 0 0 0
## Setup.Date Setup.Time Lat_Y
## 0 0 0
## Long_X Cam.Brand Flash
## 0 0 1
## Cam.Serial.No Height Fixture
## 2 0 0
## DRXN IMG# N IMG# E
## 0 0 0
## IMG# S IMG# W IMG# CT
## 0 0 0
## Habitat.Descriptor Shade Visib.1...24
## 0 0 0
## Visib.1...25 Visib.1...26 SITE_ID
## 0 0 0
## Dist.Tree.1 Dist.Tree.2 Dist.Tree.3
## 0 0 0
## Dist.Tree.4 Dist.Tree.5 Dist.Tree.6
## 0 0 0
## Dist.Tree.7 Dist.Tree.8 Dist.Tree.9
## 0 0 0
## Dist.Tree.10 Dist.Road Dist.Water
## 0 0 19
## Dist.Outcrop Dist.Trail Gral.Comments
## 0 1 19
## other.cols.WATER (Km)P other.cols.in.metadata2 Setup.Date.Original
## 19 19 0
## Management.Type Official.Name Country
## 0 0 0
## Province Municipality Vegetation
## 0 0 0
## Biome Elevation First.Photo.Date
## 0 0 0
## Last.Photo.Date Act.Days
## 0 0MTZ_cams <-MTZ_cams %>%
filter(!is.na(MTZ_cams$Act.Days))
names(MTZ_cams)## [1] "id" "Reserve.Location"
## [3] "Code.Loc.Snap" "Site.Original"
## [5] "Camera.Site.Concatenate" "Camera.Site.Std"
## [7] "Setup.Date" "Setup.Time"
## [9] "Lat_Y" "Long_X"
## [11] "Cam.Brand" "Flash"
## [13] "Cam.Serial.No" "Height"
## [15] "Fixture" "DRXN"
## [17] "IMG# N" "IMG# E"
## [19] "IMG# S" "IMG# W"
## [21] "IMG# CT" "Habitat.Descriptor"
## [23] "Shade" "Visib.1...24"
## [25] "Visib.1...25" "Visib.1...26"
## [27] "SITE_ID" "Dist.Tree.1"
## [29] "Dist.Tree.2" "Dist.Tree.3"
## [31] "Dist.Tree.4" "Dist.Tree.5"
## [33] "Dist.Tree.6" "Dist.Tree.7"
## [35] "Dist.Tree.8" "Dist.Tree.9"
## [37] "Dist.Tree.10" "Dist.Road"
## [39] "Dist.Water" "Dist.Outcrop"
## [41] "Dist.Trail" "Gral.Comments"
## [43] "other.cols.WATER (Km)P" "other.cols.in.metadata2"
## [45] "Setup.Date.Original" "Management.Type"
## [47] "Official.Name" "Country"
## [49] "Province" "Municipality"
## [51] "Vegetation" "Biome"
## [53] "Elevation" "First.Photo.Date"
## [55] "Last.Photo.Date" "Act.Days"class(MTZ_cams$Last.Photo.Date)## [1] "Date"class(MTZ_cams$First.Photo.Date)## [1] "Date"class(MTZ_cams$Camera.Site.Concatenate)## [1] "character"#Rename back MTZ_CAMS FOR Camera.Site
MTZ_cams <- MTZ_cams %>% rename(Camera.Site = Camera.Site.Concatenate)
class(MTZ_cams$Camera.Site)## [1] "character"unique(spp_rec$Common.Name) ### [1] zebramountain jackalblackbacked baboon
## [4] springbok hartebeestred eland
## [7] hare lion porcupine
## [10] gemsbokoryx buffalo hyenabrown
## [13] kudu cheetah genetcommonsmallspotted
## [16] harespring wildebeestblack blesbok
## [19] rhebokgrey rhinocerosblack monkeyvervet
## [22] aardwolf bushbuck reedbuckmountain
## [25] bushpig foxcape duikercommon
## [28] aardvarkantbear steenbok caracal
## [31] warthog foxbateared mongooseyellow
## 41 Levels: aardvarkantbear aardwolf baboon birdofprey birdother ... zebramountain#temp file one reserve only with survey length and previous metadata
#write.csv(MTZ_cams, "data_out/MTZ/MTZ_cams.csv")
#MTZ_cams <- read_csv("data_out/MTZ/MTZ_cams.csv")
# name of stations
unique(MTZ_cams$Camera.Site) ## [1] "MTZ_B04" "MTZ_B05" "MTZ_B08" "MTZ_C04" "MTZ_C06" "MTZ_C07" "MTZ_C08"
## [8] "MTZ_D03" "MTZ_D04" "MTZ_D05" "MTZ_D06" "MTZ_D07" "MTZ_E03" "MTZ_E04"
## [15] "MTZ_E05" "MTZ_E06" "MTZ_F04" "MTZ_F05" "MTZ_G04"unique(spp_rec$Camera.Site)## [1] MTZ_B04 MTZ_B05 MTZ_B08 MTZ_C04 MTZ_C06 MTZ_C07 MTZ_C08 MTZ_D03 MTZ_D04
## [10] MTZ_D05 MTZ_D06 MTZ_D07 MTZ_E03 MTZ_E04 MTZ_E05 MTZ_E06 MTZ_F04 MTZ_F05
## [19] MTZ_G04
## 19 Levels: MTZ_B04 MTZ_B05 MTZ_B08 MTZ_C04 MTZ_C06 MTZ_C07 MTZ_C08 ... MTZ_G04Check “2_species_exploration” of records for better plots
cam_op <- cameraOperation(CTtable = MTZ_cams,
setupCol = "First.Photo.Date",
retrievalCol = "Last.Photo.Date",
stationCol = "Camera.Site",
writecsv = T,
hasProblems = FALSE,
outDir = "data_out")## CTtable was converted from tibble to data.framecamopPlot <- function(camOp){
which.tmp <- grep(as.Date(colnames(camOp)), pattern = "01$")
label.tmp <- format(as.Date(colnames(camOp))[which.tmp], "%Y-%m")
at.tmp <- which.tmp / ncol(camOp)
image(t(as.matrix(camOp)), xaxt = "n", yaxt = "n", col = c("blue", "grey70"))
axis(1, at = at.tmp, labels = label.tmp)
axis(2, at = seq(from = 0, to = 1, length.out = nrow(camOp)), labels = rownames(camOp), cex.axis=0.6,
las = 1, xlab = "wt", ylab = "mpg")
abline(v = at.tmp, col = rgb(0,0,0, 0.2))
box()
}
jpeg(file="MTZ_camplot.jpeg", width =571, height = 421, res = 96)
#check main folder for image and copy
camopPlot(cam_op)
#dev.off()survey_rep <- surveyReport(recordTable = spp_rec,
CTtable = MTZ_cams,
speciesCol = "Common.Name",
stationCol = "Camera.Site",
setupCol = "First.Photo.Date",
retrievalCol = "Last.Photo.Date",
recordDateTimeCol = "DateTimeOriginal",
CTHasProblems = F,
makezip = F, #
sinkpath = "data_out") # ## CTtable was converted from tibble to data.frame
## recordTable was converted from tibble to data.frame
## saved output to file
## data_out/survey_report_2021-10-08.txtBe careful, values in CTtable vs recordTable (stationcol) should match if there are problems you can try:
# show all unique entries of the sites ID in spp records that are not in the _cams (cameras base)
del_cams <-unique(spp_rec$Camera.Site)[!unique(spp_rec$Camera.Site) %in% MTZ_cams$Camera.Site]survey_rep[[1]] #camera trap operation (similar to input but with effort) survey_rep[[2]] #numMTZ of species by station!! IMPORMTZT survey_rep[[3]] #numMTZ of events and numMTZ of stations by species (rememMTZ not standardise by effort) survey_rep[[4]] #numMTZ of species and events by station survey_rep[[5]] #equal to 4 except for the fat that it contains unobserved species with n_eMTZts = 0
So save them to be able to retrieve them afterwards.
write.csv(survey_rep[[1]], "data_out/details_indiv_reports/1.MTZ_camtrap_operation.csv")
write.csv(survey_rep[[2]], "data_out/details_indiv_reports/2.MTZ_#spp_by_station.csv")
write.csv(survey_rep[[3]], "data_out/details_indiv_reports/3.MTZ_events+#station_by_spp.csv")
write.csv(survey_rep[[4]], "data_out/details_indiv_reports/4.MTZ_#spp+#events_by_station.csv")
write.csv(survey_rep[[5]], "data_out/details_indiv_reports/5.MTZ_#obs¬obsspp+#events_by_station.csv")r2 <- survey_rep[[2]]
ggplot(r2, aes(x = "", y= n_species)) +
geom_boxplot(alpha=0.3, width = 0.5, color = "blue",fill = "lightblue") +
theme(legend.position="none") +
ggtitle("Boxplot of observed species across sites") +
geom_jitter(color="black", size=1, alpha=0.9) +
labs(y="Number of species", x = "") +
theme(plot.margin = unit(c(0.5,0.5,0.5,0.5),"cm"))
ggsave((filename = "figures/MTZ_boxplot_sp_richness.jpg"))## Saving 7 x 5 in image(fig_r2 <- spp_station_r2(r2))
ggsave(filename = "figures/MTZ_barplot_sp_per_cam.jpg")## Saving 7 x 5 in imager3 <-survey_rep[[3]]
(fig_r3 <- station_spp_r3(r3))
ggsave(filename = "figures/MTZ_barplot_cams_per_species.jpg")## Saving 7 x 5 in imager3 <-survey_rep[[3]]
(fig_r3 <- events_spp_r3(r3))
ggsave(filename = "figures/MTZ_barplot_events_per_species.jpg")## Saving 7 x 5 in imagethe CTtable (cameras) must be just for each reserve otherwise will graph all sites untidy
1st we need to have each site with its corresponding coordinates. #so lets use report 2 (2.#spp_by_station)
spp_rich <- read.csv("data_out/details_indiv_reports/2.MTZ_#spp_by_station.csv", stringsAsFactors = FALSE) #add the location col from cameras/metadata (per reserve) #MTZ_cams <- read.csv(“data_out/MTZ/MTZ_cams.csv”)
lat.long <- select(MTZ_cams, "Camera.Site", "Lat_Y", "Long_X")
head(lat.long)## # A tibble: 6 x 3
## Camera.Site Lat_Y Long_X
## <chr> <dbl> <dbl>
## 1 MTZ_B04 -32.2 25.4
## 2 MTZ_B05 -32.2 25.4
## 3 MTZ_B08 -32.3 25.4
## 4 MTZ_C04 -32.2 25.4
## 5 MTZ_C06 -32.2 25.4
## 6 MTZ_C07 -32.3 25.4#merge by sites
mergesites <- dplyr::left_join(spp_rich, lat.long, by = "Camera.Site", row.names=NULL)
#mergesites$Camera.Site <- as.factor(mergesites$Camera.Site) # coerce back to factors
str(mergesites)## 'data.frame': 19 obs. of 5 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Camera.Site: chr "MTZ_B04" "MTZ_B05" "MTZ_B08" "MTZ_C04" ...
## $ n_species : int 15 9 1 8 15 6 13 6 18 8 ...
## $ Lat_Y : num -32.2 -32.2 -32.3 -32.2 -32.2 ...
## $ Long_X : num 25.4 25.4 25.4 25.4 25.4 ...#delete NA row
#mergesites <-mergesites %>% #kruger had NA in coordinates one site
#filter(!is.na(mergesites$Long_X))
#write.csv(mergesites, "data_out/MTZ/details_indiv_reports/2.MTZ_#spp_by_station+coord.csv", row.names = FALSE)
#mergesites <- read.csv("data_out/MTZ/2.#spp_by_station+coord.csv", row.names = 1)
min(mergesites$Long_X) # ## [1] 25.41295max(mergesites$Long_X) ### [1] 25.51562min(mergesites$Lat_Y)## [1] -32.28169max(mergesites$Lat_Y)## [1] -32.15953(basic scatter plot but tidy) To put a google maps in the background see 1_species_exploration
(spp_richnes_plot<- fig_sp_richness(mergesites))
ggsave(filename = "figures/MTZ_Sp_richnes_cams.jpg")## Saving 7 x 5 in imageCAPTURE FREQUENCIES OR RAI (RELATIVE ABUNDANCE INDEX) STANDARIZED BY EFFORT (OF STUDY AREA) we need the cam operation and rep5
effort<- read.csv("data_out/details_indiv_reports/1.MTZ_camtrap_operation.csv", header = TRUE)
rep5 <- read.csv("data_out/details_indiv_reports/5.MTZ_#obs¬obsspp+#events_by_station.csv", header = TRUE)
str(effort)## 'data.frame': 19 obs. of 9 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Camera.Site : chr "MTZ_B04" "MTZ_B05" "MTZ_B08" "MTZ_C04" ...
## $ setup_date : chr "2017-08-24" "2017-09-01" "2018-05-03" "2017-08-26" ...
## $ first_image_date: chr "2017-08-24" "2017-09-01" "2018-05-03" "2017-08-26" ...
## $ last_image_date : chr "2018-07-04" "2018-05-03" "2018-05-13" "2018-06-26" ...
## $ retrieval_date : chr "2018-07-04" "2018-05-03" "2018-05-13" "2018-06-26" ...
## $ n_nights_total : int 314 244 10 304 324 309 83 40 326 38 ...
## $ n_nights_active : int 314 244 10 304 324 309 83 40 326 38 ...
## $ n_cameras : int 1 1 1 1 1 1 1 1 1 1 ...effort <- effort[,-1]
effort <- effort%>%
filter(n_nights_active >0)
rep5 <- rep5[,-1]
#merge
new.mat <- merge(x = effort, y = rep5, by = "Camera.Site", all = TRUE)
write.csv(new.mat, "data_out/details_indiv_reports/MTZ_new.mat_for_RAI.csv")
#new.mat <- read.csv("data_out/MTZ/MTZ_new.mat_for_RAI")This table include information about Relative Abundance Index or capture frequencies standardized by effort at the grid level (RAI.Gral) and by each camera trap effort (RAImean), as well as number of sites occupied (Sites.Occ) and naive occupancy (Occ.Naive)
Here we have a df with important summary information, including richness, events, naive occupancy, etc
#new.mat <- read_csv("data_out/MTZ_new.mat_for_RAI")
# compute RAI´S, occ, and other in one go
#RAI() = events (not real abundance col)
RAI_calc_table <- RAI_events(new.mat)
library(kableExtra)##
## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
##
## group_rowsRAI_calc_table %>%
kbl() %>%
kable_paper("hover", full_width = F)| Common.Name | Total.Cam.Sites | Cam.Days | Total.Count | RAI.Gral | RAImean | RAI.sd | Sites.Occ | Occ.Naive |
|---|---|---|---|---|---|---|---|---|
| aardvarkantbear | 19 | 3732 | 7 | 0.19 | 0.22 | 0.51 | 5 | 0.26 |
| aardwolf | 19 | 3732 | 36 | 0.96 | 0.97 | 2.20 | 5 | 0.26 |
| baboon | 19 | 3732 | 106 | 2.84 | 3.04 | 3.44 | 14 | 0.74 |
| blesbok | 19 | 3732 | 5 | 0.13 | 0.11 | 0.47 | 1 | 0.05 |
| buffalo | 19 | 3732 | 35 | 0.94 | 0.84 | 1.13 | 10 | 0.53 |
| bushbuck | 19 | 3732 | 3 | 0.08 | 0.05 | 0.16 | 2 | 0.11 |
| bushpig | 19 | 3732 | 3 | 0.08 | 0.14 | 0.55 | 2 | 0.11 |
| caracal | 19 | 3732 | 2 | 0.05 | 0.03 | 0.10 | 2 | 0.11 |
| cheetah | 19 | 3732 | 3 | 0.08 | 0.05 | 0.22 | 1 | 0.05 |
| duikercommon | 19 | 3732 | 43 | 1.15 | 1.50 | 3.37 | 5 | 0.26 |
| eland | 19 | 3732 | 52 | 1.39 | 1.86 | 2.85 | 13 | 0.68 |
| foxbateared | 19 | 3732 | 13 | 0.35 | 0.23 | 0.81 | 2 | 0.11 |
| foxcape | 19 | 3732 | 1 | 0.03 | 0.06 | 0.28 | 1 | 0.05 |
| gemsbokoryx | 19 | 3732 | 34 | 0.91 | 1.02 | 1.63 | 8 | 0.42 |
| genetcommonsmallspotted | 19 | 3732 | 9 | 0.24 | 0.31 | 0.76 | 5 | 0.26 |
| hare | 19 | 3732 | 44 | 1.18 | 2.50 | 7.33 | 9 | 0.47 |
| harespring | 19 | 3732 | 2 | 0.05 | 0.05 | 0.14 | 2 | 0.11 |
| hartebeestred | 19 | 3732 | 79 | 2.12 | 2.26 | 3.24 | 13 | 0.68 |
| hyenabrown | 19 | 3732 | 12 | 0.32 | 0.25 | 0.42 | 6 | 0.32 |
| jackalblackbacked | 19 | 3732 | 197 | 5.28 | 7.44 | 14.11 | 15 | 0.79 |
| kudu | 19 | 3732 | 178 | 4.77 | 7.86 | 10.10 | 16 | 0.84 |
| lion | 19 | 3732 | 22 | 0.59 | 0.49 | 1.16 | 5 | 0.26 |
| mongooseyellow | 19 | 3732 | 2 | 0.05 | 0.05 | 0.21 | 1 | 0.05 |
| monkeyvervet | 19 | 3732 | 110 | 2.95 | 3.84 | 7.68 | 10 | 0.53 |
| porcupine | 19 | 3732 | 53 | 1.42 | 1.65 | 2.39 | 11 | 0.58 |
| reedbuckmountain | 19 | 3732 | 2 | 0.05 | 0.13 | 0.48 | 2 | 0.11 |
| rhebokgrey | 19 | 3732 | 20 | 0.54 | 2.71 | 9.25 | 4 | 0.21 |
| rhinocerosblack | 19 | 3732 | 29 | 0.78 | 1.14 | 2.34 | 10 | 0.53 |
| springbok | 19 | 3732 | 148 | 3.97 | 3.27 | 5.81 | 9 | 0.47 |
| steenbok | 19 | 3732 | 4 | 0.11 | 0.16 | 0.51 | 2 | 0.11 |
| warthog | 19 | 3732 | 41 | 1.10 | 0.98 | 3.14 | 2 | 0.11 |
| wildebeestblack | 19 | 3732 | 117 | 3.14 | 2.54 | 7.81 | 5 | 0.26 |
| zebramountain | 19 | 3732 | 420 | 11.25 | 12.26 | 11.22 | 17 | 0.89 |
write.csv(RAI_calc_table, "data_out/+MTZ_final_table_Calc.csv", row.names = FALSE)This plot shows the Relative abundance index (better called capture frequencies) of all species, using the total camera days so comparison should be taken cautiously
(fig_RAIgral <- RAI_gral_barplot(RAI_calc_table))
ggsave(filename = "figures/MTZ_barplot_RAI.Gral_per_species.jpg")## Saving 7 x 5 in imagenames(new.mat)## [1] "Camera.Site" "setup_date" "first_image_date" "last_image_date"
## [5] "retrieval_date" "n_nights_total" "n_nights_active" "n_cameras"
## [9] "Common.Name" "n_events"RAIalt <- with(new.mat, round((n_events/n_nights_active)*100, 2))
table.2 <- cbind(new.mat, RAIalt)
table.2## Camera.Site setup_date first_image_date last_image_date retrieval_date
## 1 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 2 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 3 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 4 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 5 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 6 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 7 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 8 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 9 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 10 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 11 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 12 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 13 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 14 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 15 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 16 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 17 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 18 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 19 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 20 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 21 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 22 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 23 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 24 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 25 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 26 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 27 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 28 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 29 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 30 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 31 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 32 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 33 MTZ_B04 2017-08-24 2017-08-24 2018-07-04 2018-07-04
## 34 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 35 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 36 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 37 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 38 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 39 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 40 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 41 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 42 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 43 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 44 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 45 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 46 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 47 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 48 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 49 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 50 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 51 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 52 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 53 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 54 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 55 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 56 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 57 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 58 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 59 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 60 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 61 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 62 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 63 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 64 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 65 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 66 MTZ_B05 2017-09-01 2017-09-01 2018-05-03 2018-05-03
## 67 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 68 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 69 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 70 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 71 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 72 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 73 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 74 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 75 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 76 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 77 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 78 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 79 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 80 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 81 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 82 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 83 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 84 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 85 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 86 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 87 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 88 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 89 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 90 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 91 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 92 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 93 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 94 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 95 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 96 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 97 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 98 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 99 MTZ_B08 2018-05-03 2018-05-03 2018-05-13 2018-05-13
## 100 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 101 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 102 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 103 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 104 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 105 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 106 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 107 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 108 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 109 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 110 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 111 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 112 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 113 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 114 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 115 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 116 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 117 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 118 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 119 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 120 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 121 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 122 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 123 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 124 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 125 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 126 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 127 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 128 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 129 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 130 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 131 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 132 MTZ_C04 2017-08-26 2017-08-26 2018-06-26 2018-06-26
## 133 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 134 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 135 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 136 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 137 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 138 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 139 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 140 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 141 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 142 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 143 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 144 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 145 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 146 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 147 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 148 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 149 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 150 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 151 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 152 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 153 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 154 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 155 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 156 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 157 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 158 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 159 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 160 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 161 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 162 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 163 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 164 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 165 MTZ_C06 2017-08-23 2017-08-23 2018-07-13 2018-07-13
## 166 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 167 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 168 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 169 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 170 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 171 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 172 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 173 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 174 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 175 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 176 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 177 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 178 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 179 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 180 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 181 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 182 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 183 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 184 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 185 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 186 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 187 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 188 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 189 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
## 190 MTZ_C07 2017-09-03 2017-09-03 2018-07-09 2018-07-09
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## 469 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 470 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 471 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 472 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 473 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 474 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 475 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 476 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 477 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 478 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 479 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 480 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 481 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 482 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 483 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 484 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 485 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 486 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 487 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 488 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 489 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 490 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 491 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 492 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 493 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 494 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 495 MTZ_E05 2017-08-23 2017-08-23 2018-04-02 2018-04-02
## 496 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 497 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 498 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 499 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 500 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 501 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 502 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 503 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 504 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 505 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 506 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 507 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 508 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 509 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 510 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 511 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 512 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 513 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 514 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 515 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 516 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 517 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 518 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 519 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 520 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 521 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 522 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 523 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 524 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 525 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 526 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 527 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 528 MTZ_E06 2017-08-24 2017-08-24 2018-06-12 2018-06-12
## 529 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 530 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 531 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 532 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 533 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 534 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 535 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 536 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 537 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 538 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 539 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 540 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 541 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 542 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 543 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 544 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 545 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 546 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 547 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 548 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 549 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 550 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 551 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 552 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 553 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 554 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 555 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 556 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 557 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 558 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 559 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 560 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 561 MTZ_F04 2017-08-24 2017-08-24 2018-07-07 2018-07-07
## 562 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 563 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 564 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 565 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 566 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 567 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 568 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 569 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 570 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 571 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 572 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 573 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 574 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 575 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 576 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 577 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 578 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 579 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 580 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 581 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 582 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 583 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 584 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 585 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 586 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 587 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 588 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 589 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 590 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 591 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 592 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 593 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 594 MTZ_F05 2017-11-02 2017-11-02 2017-11-27 2017-11-27
## 595 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 596 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 597 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 598 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 599 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 600 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 601 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 602 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 603 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 604 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 605 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 606 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 607 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 608 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 609 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 610 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 611 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 612 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 613 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 614 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 615 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 616 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 617 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 618 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 619 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 620 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 621 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 622 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 623 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 624 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 625 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 626 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## 627 MTZ_G04 2017-08-24 2017-08-24 2018-07-17 2018-07-17
## n_nights_total n_nights_active n_cameras Common.Name n_events
## 1 314 314 1 aardvarkantbear 0
## 2 314 314 1 aardwolf 0
## 3 314 314 1 baboon 14
## 4 314 314 1 blesbok 0
## 5 314 314 1 buffalo 1
## 6 314 314 1 bushbuck 0
## 7 314 314 1 bushpig 0
## 8 314 314 1 caracal 0
## 9 314 314 1 cheetah 3
## 10 314 314 1 duikercommon 0
## 11 314 314 1 eland 10
## 12 314 314 1 foxbateared 0
## 13 314 314 1 foxcape 0
## 14 314 314 1 gemsbokoryx 1
## 15 314 314 1 genetcommonsmallspotted 1
## 16 314 314 1 hare 3
## 17 314 314 1 harespring 0
## 18 314 314 1 hartebeestred 6
## 19 314 314 1 hyenabrown 3
## 20 314 314 1 jackalblackbacked 11
## 21 314 314 1 kudu 2
## 22 314 314 1 lion 1
## 23 314 314 1 mongooseyellow 0
## 24 314 314 1 monkeyvervet 0
## 25 314 314 1 porcupine 15
## 26 314 314 1 reedbuckmountain 0
## 27 314 314 1 rhebokgrey 0
## 28 314 314 1 rhinocerosblack 0
## 29 314 314 1 springbok 13
## 30 314 314 1 steenbok 0
## 31 314 314 1 warthog 0
## 32 314 314 1 wildebeestblack 0
## 33 314 314 1 zebramountain 125
## 34 244 244 1 aardvarkantbear 0
## 35 244 244 1 aardwolf 0
## 36 244 244 1 baboon 0
## 37 244 244 1 blesbok 5
## 38 244 244 1 buffalo 6
## 39 244 244 1 bushbuck 0
## 40 244 244 1 bushpig 0
## 41 244 244 1 caracal 0
## 42 244 244 1 cheetah 0
## 43 244 244 1 duikercommon 0
## 44 244 244 1 eland 0
## 45 244 244 1 foxbateared 0
## 46 244 244 1 foxcape 0
## 47 244 244 1 gemsbokoryx 0
## 48 244 244 1 genetcommonsmallspotted 0
## 49 244 244 1 hare 17
## 50 244 244 1 harespring 1
## 51 244 244 1 hartebeestred 2
## 52 244 244 1 hyenabrown 0
## 53 244 244 1 jackalblackbacked 4
## 54 244 244 1 kudu 0
## 55 244 244 1 lion 0
## 56 244 244 1 mongooseyellow 0
## 57 244 244 1 monkeyvervet 0
## 58 244 244 1 porcupine 0
## 59 244 244 1 reedbuckmountain 0
## 60 244 244 1 rhebokgrey 0
## 61 244 244 1 rhinocerosblack 0
## 62 244 244 1 springbok 48
## 63 244 244 1 steenbok 0
## 64 244 244 1 warthog 0
## 65 244 244 1 wildebeestblack 82
## 66 244 244 1 zebramountain 14
## 67 10 10 1 aardvarkantbear 0
## 68 10 10 1 aardwolf 0
## 69 10 10 1 baboon 0
## 70 10 10 1 blesbok 0
## 71 10 10 1 buffalo 0
## 72 10 10 1 bushbuck 0
## 73 10 10 1 bushpig 0
## 74 10 10 1 caracal 0
## 75 10 10 1 cheetah 0
## 76 10 10 1 duikercommon 0
## 77 10 10 1 eland 0
## 78 10 10 1 foxbateared 0
## 79 10 10 1 foxcape 0
## 80 10 10 1 gemsbokoryx 0
## 81 10 10 1 genetcommonsmallspotted 0
## 82 10 10 1 hare 0
## 83 10 10 1 harespring 0
## 84 10 10 1 hartebeestred 0
## 85 10 10 1 hyenabrown 0
## 86 10 10 1 jackalblackbacked 0
## 87 10 10 1 kudu 0
## 88 10 10 1 lion 0
## 89 10 10 1 mongooseyellow 0
## 90 10 10 1 monkeyvervet 0
## 91 10 10 1 porcupine 0
## 92 10 10 1 reedbuckmountain 0
## 93 10 10 1 rhebokgrey 4
## 94 10 10 1 rhinocerosblack 0
## 95 10 10 1 springbok 0
## 96 10 10 1 steenbok 0
## 97 10 10 1 warthog 0
## 98 10 10 1 wildebeestblack 0
## 99 10 10 1 zebramountain 0
## 100 304 304 1 aardvarkantbear 0
## 101 304 304 1 aardwolf 0
## 102 304 304 1 baboon 6
## 103 304 304 1 blesbok 0
## 104 304 304 1 buffalo 0
## 105 304 304 1 bushbuck 0
## 106 304 304 1 bushpig 0
## 107 304 304 1 caracal 0
## 108 304 304 1 cheetah 0
## 109 304 304 1 duikercommon 0
## 110 304 304 1 eland 1
## 111 304 304 1 foxbateared 0
## 112 304 304 1 foxcape 0
## 113 304 304 1 gemsbokoryx 0
## 114 304 304 1 genetcommonsmallspotted 0
## 115 304 304 1 hare 1
## 116 304 304 1 harespring 0
## 117 304 304 1 hartebeestred 0
## 118 304 304 1 hyenabrown 0
## 119 304 304 1 jackalblackbacked 1
## 120 304 304 1 kudu 1
## 121 304 304 1 lion 0
## 122 304 304 1 mongooseyellow 0
## 123 304 304 1 monkeyvervet 1
## 124 304 304 1 porcupine 0
## 125 304 304 1 reedbuckmountain 0
## 126 304 304 1 rhebokgrey 0
## 127 304 304 1 rhinocerosblack 1
## 128 304 304 1 springbok 0
## 129 304 304 1 steenbok 0
## 130 304 304 1 warthog 0
## 131 304 304 1 wildebeestblack 0
## 132 304 304 1 zebramountain 3
## 133 324 324 1 aardvarkantbear 0
## 134 324 324 1 aardwolf 26
## 135 324 324 1 baboon 8
## 136 324 324 1 blesbok 0
## 137 324 324 1 buffalo 0
## 138 324 324 1 bushbuck 2
## 139 324 324 1 bushpig 0
## 140 324 324 1 caracal 0
## 141 324 324 1 cheetah 0
## 142 324 324 1 duikercommon 0
## 143 324 324 1 eland 5
## 144 324 324 1 foxbateared 0
## 145 324 324 1 foxcape 0
## 146 324 324 1 gemsbokoryx 5
## 147 324 324 1 genetcommonsmallspotted 1
## 148 324 324 1 hare 4
## 149 324 324 1 harespring 0
## 150 324 324 1 hartebeestred 5
## 151 324 324 1 hyenabrown 3
## 152 324 324 1 jackalblackbacked 27
## 153 324 324 1 kudu 9
## 154 324 324 1 lion 0
## 155 324 324 1 mongooseyellow 0
## 156 324 324 1 monkeyvervet 3
## 157 324 324 1 porcupine 3
## 158 324 324 1 reedbuckmountain 0
## 159 324 324 1 rhebokgrey 0
## 160 324 324 1 rhinocerosblack 3
## 161 324 324 1 springbok 0
## 162 324 324 1 steenbok 0
## 163 324 324 1 warthog 0
## 164 324 324 1 wildebeestblack 0
## 165 324 324 1 zebramountain 22
## 166 309 309 1 aardvarkantbear 0
## 167 309 309 1 aardwolf 0
## 168 309 309 1 baboon 7
## 169 309 309 1 blesbok 0
## 170 309 309 1 buffalo 0
## 171 309 309 1 bushbuck 0
## 172 309 309 1 bushpig 0
## 173 309 309 1 caracal 0
## 174 309 309 1 cheetah 0
## 175 309 309 1 duikercommon 0
## 176 309 309 1 eland 0
## 177 309 309 1 foxbateared 0
## 178 309 309 1 foxcape 0
## 179 309 309 1 gemsbokoryx 0
## 180 309 309 1 genetcommonsmallspotted 0
## 181 309 309 1 hare 0
## 182 309 309 1 harespring 0
## 183 309 309 1 hartebeestred 0
## 184 309 309 1 hyenabrown 0
## 185 309 309 1 jackalblackbacked 0
## 186 309 309 1 kudu 3
## 187 309 309 1 lion 0
## 188 309 309 1 mongooseyellow 0
## 189 309 309 1 monkeyvervet 0
## 190 309 309 1 porcupine 1
## 191 309 309 1 reedbuckmountain 1
## 192 309 309 1 rhebokgrey 8
## 193 309 309 1 rhinocerosblack 0
## 194 309 309 1 springbok 0
## 195 309 309 1 steenbok 0
## 196 309 309 1 warthog 0
## 197 309 309 1 wildebeestblack 0
## 198 309 309 1 zebramountain 33
## 199 83 83 1 aardvarkantbear 0
## 200 83 83 1 aardwolf 4
## 201 83 83 1 baboon 2
## 202 83 83 1 blesbok 0
## 203 83 83 1 buffalo 1
## 204 83 83 1 bushbuck 0
## 205 83 83 1 bushpig 2
## 206 83 83 1 caracal 0
## 207 83 83 1 cheetah 0
## 208 83 83 1 duikercommon 0
## 209 83 83 1 eland 0
## 210 83 83 1 foxbateared 0
## 211 83 83 1 foxcape 1
## 212 83 83 1 gemsbokoryx 0
## 213 83 83 1 genetcommonsmallspotted 0
## 214 83 83 1 hare 0
## 215 83 83 1 harespring 0
## 216 83 83 1 hartebeestred 2
## 217 83 83 1 hyenabrown 1
## 218 83 83 1 jackalblackbacked 51
## 219 83 83 1 kudu 2
## 220 83 83 1 lion 1
## 221 83 83 1 mongooseyellow 0
## 222 83 83 1 monkeyvervet 0
## 223 83 83 1 porcupine 5
## 224 83 83 1 reedbuckmountain 0
## 225 83 83 1 rhebokgrey 7
## 226 83 83 1 rhinocerosblack 0
## 227 83 83 1 springbok 0
## 228 83 83 1 steenbok 0
## 229 83 83 1 warthog 0
## 230 83 83 1 wildebeestblack 0
## 231 83 83 1 zebramountain 21
## 232 40 40 1 aardvarkantbear 0
## 233 40 40 1 aardwolf 0
## 234 40 40 1 baboon 0
## 235 40 40 1 blesbok 0
## 236 40 40 1 buffalo 0
## 237 40 40 1 bushbuck 0
## 238 40 40 1 bushpig 0
## 239 40 40 1 caracal 0
## 240 40 40 1 cheetah 0
## 241 40 40 1 duikercommon 0
## 242 40 40 1 eland 1
## 243 40 40 1 foxbateared 0
## 244 40 40 1 foxcape 0
## 245 40 40 1 gemsbokoryx 2
## 246 40 40 1 genetcommonsmallspotted 0
## 247 40 40 1 hare 0
## 248 40 40 1 harespring 0
## 249 40 40 1 hartebeestred 5
## 250 40 40 1 hyenabrown 0
## 251 40 40 1 jackalblackbacked 0
## 252 40 40 1 kudu 11
## 253 40 40 1 lion 0
## 254 40 40 1 mongooseyellow 0
## 255 40 40 1 monkeyvervet 0
## 256 40 40 1 porcupine 0
## 257 40 40 1 reedbuckmountain 0
## 258 40 40 1 rhebokgrey 0
## 259 40 40 1 rhinocerosblack 4
## 260 40 40 1 springbok 0
## 261 40 40 1 steenbok 0
## 262 40 40 1 warthog 0
## 263 40 40 1 wildebeestblack 0
## 264 40 40 1 zebramountain 7
## 265 326 326 1 aardvarkantbear 3
## 266 326 326 1 aardwolf 2
## 267 326 326 1 baboon 3
## 268 326 326 1 blesbok 0
## 269 326 326 1 buffalo 3
## 270 326 326 1 bushbuck 0
## 271 326 326 1 bushpig 0
## 272 326 326 1 caracal 1
## 273 326 326 1 cheetah 0
## 274 326 326 1 duikercommon 1
## 275 326 326 1 eland 5
## 276 326 326 1 foxbateared 0
## 277 326 326 1 foxcape 0
## 278 326 326 1 gemsbokoryx 13
## 279 326 326 1 genetcommonsmallspotted 0
## 280 326 326 1 hare 3
## 281 326 326 1 harespring 0
## 282 326 326 1 hartebeestred 15
## 283 326 326 1 hyenabrown 3
## 284 326 326 1 jackalblackbacked 27
## 285 326 326 1 kudu 10
## 286 326 326 1 lion 0
## 287 326 326 1 mongooseyellow 0
## 288 326 326 1 monkeyvervet 8
## 289 326 326 1 porcupine 3
## 290 326 326 1 reedbuckmountain 0
## 291 326 326 1 rhebokgrey 0
## 292 326 326 1 rhinocerosblack 0
## 293 326 326 1 springbok 3
## 294 326 326 1 steenbok 3
## 295 326 326 1 warthog 0
## 296 326 326 1 wildebeestblack 0
## 297 326 326 1 zebramountain 39
## 298 38 38 1 aardvarkantbear 0
## 299 38 38 1 aardwolf 0
## 300 38 38 1 baboon 0
## 301 38 38 1 blesbok 0
## 302 38 38 1 buffalo 0
## 303 38 38 1 bushbuck 0
## 304 38 38 1 bushpig 0
## 305 38 38 1 caracal 0
## 306 38 38 1 cheetah 0
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## 482 222 222 1 jackalblackbacked 18
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## RAIalt
## 1 0.00
## 2 0.00
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## 5 0.32
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## 199 0.00
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## 201 2.41
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## 217 1.20
## 218 61.45
## 219 2.41
## 220 1.20
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## 223 6.02
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## 251 0.00
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## 409 0.00
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## 411 0.00
## 412 0.00
## 413 0.00
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## 415 0.00
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## 417 35.42
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## 419 0.00
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## 422 2.08
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## 515 10.62
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## 594 12.00
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## 611 0.00
## 612 7.95
## 613 0.31
## 614 1.22
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## 619 3.36
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## 623 1.83
## 624 0.00
## 625 0.00
## 626 1.83
## 627 2.75names(table.2)## [1] "Camera.Site" "setup_date" "first_image_date" "last_image_date"
## [5] "retrieval_date" "n_nights_total" "n_nights_active" "n_cameras"
## [9] "Common.Name" "n_events" "RAIalt"write.csv(table.2, "data_out/MTZ_RAIalt_station_events.csv")This plot is probably preferable as now we are accounting for the effort of each camera site, but this depends on tne question. Although comparison between species should still be taken cautiously, this is a good way to see patterns.
plot <- ggplot(table.2, aes(x=Common.Name, y= RAIalt, fill=Common.Name)) +
geom_boxplot(alpha=0.3) +
ylim(0, 50) + # can´t do with function as this y lim will vary easier one by one
theme(legend.position="none") +
theme(axis.text.x = element_text(angle = 90,hjust = 1, vjust = 0.5)) +
ggtitle("RAI alt (std) per species") +
labs(y="RAI alt", x = "Species")
ggsave(filename = "figures/MTZ_boxplot_RAI.alt_per_species.jpg")## Saving 7 x 5 in image
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).plot## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
#bring df with coordinates (from CTtable or metadata for the reserve in previous steps)
#MTZ_cams <- read_csv("data_out/MTZ/MTZ_cams.csv")
lat.long <- select(MTZ_cams, "Camera.Site", "Lat_Y", "Long_X")
mergesites2 <- dplyr::left_join(table.2, lat.long, by = "Camera.Site", row.names=NULL)
str(mergesites2)## 'data.frame': 627 obs. of 13 variables:
## $ Camera.Site : chr "MTZ_B04" "MTZ_B04" "MTZ_B04" "MTZ_B04" ...
## $ setup_date : chr "2017-08-24" "2017-08-24" "2017-08-24" "2017-08-24" ...
## $ first_image_date: chr "2017-08-24" "2017-08-24" "2017-08-24" "2017-08-24" ...
## $ last_image_date : chr "2018-07-04" "2018-07-04" "2018-07-04" "2018-07-04" ...
## $ retrieval_date : chr "2018-07-04" "2018-07-04" "2018-07-04" "2018-07-04" ...
## $ n_nights_total : int 314 314 314 314 314 314 314 314 314 314 ...
## $ n_nights_active : int 314 314 314 314 314 314 314 314 314 314 ...
## $ n_cameras : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Common.Name : chr "aardvarkantbear" "aardwolf" "baboon" "blesbok" ...
## $ n_events : int 0 0 14 0 1 0 0 0 3 0 ...
## $ RAIalt : num 0 0 4.46 0 0.32 0 0 0 0.96 0 ...
## $ Lat_Y : num -32.2 -32.2 -32.2 -32.2 -32.2 ...
## $ Long_X : num 25.4 25.4 25.4 25.4 25.4 ...write.csv(mergesites2, "data_out/details_indiv_reports/MTZ_RAIalt+coord.csv", row.names = FALSE)
# Bring the IUCN trait file to with RAIalt+coord so we can do selective plots per species or all IUCN cat
# at once
#traits file with scientific names, IUCN cat and others...
IUCN <- 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.merged.table <- mergesites2 %>% left_join(IUCN, by = c("Common.Name" = "Snapshot.Name"))
# Select only relevant IUCN categories
df.IUCN <- merged.table %>% dplyr::filter(IUCN.Cat %in% c("VU", "CR", "EN"))
unique(df.IUCN$Common.Name) ### [1] "cheetah" "lion" "reedbuckmountain" "rhinocerosblack"
## [5] "zebramountain"# which species are threatened?
sel_spp <- IUCN %>%
filter(IUCN.Cat %in% c("VU", "CR", "EN"))
sel_spp## # A tibble: 13 x 14
## id Scien.Code Comm.Code Snapshot.Name Common.Name Scientific.Name Order
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 12 FENI CATB catblackfooted Cat (Black-~ Felis nigripes Carni~
## 2 13 ACJU CHEE cheetah Cheetah Acinonyx jubat~ Carni~
## 3 19 LOAF ELEA elephant Elephant (A~ Loxodonta afri~ Probo~
## 4 30 GICA GIRA giraffe Giraffe Giraffa camelo~ Artio~
## 5 38 HIAM HIPO hippopotamus Hippopotamus Hippopotamus a~ Artio~
## 6 47 PAPA LEOP leopard Leopard Panthera pardus Carni~
## 7 48 PALE LION lion Lion Panthera leo Carni~
## 8 64 SMTE PANG pangolin Pangolin (G~ Smutsia temmin~ Pholi~
## 9 68 BUMO RARI rabbitriverine Rabbit (Riv~ Bunolagus mont~ Lagom~
## 10 70 REFU REMO reedbuckmount~ Reedbuck (M~ Redunca fulvor~ Artio~
## 11 72 DIBI RHBL rhinocerosbla~ Rhinoceros ~ Diceros bicorn~ Peris~
## 12 84 LYPI WIDO wilddog Wild dog Lycaon pictus Carni~
## 13 84 EQZE ZEBM zebramountain Zebra (Moun~ Equus zebra Peris~
## # ... with 7 more variables: Family <chr>, IUCN.Cat <chr>, Guild.Realm <chr>,
## # Specif.Guild <chr>, Body.Size.Cat <chr>, Mass.kg <dbl>, Simp.Guild <chr>#13 spp are threatened should not be more than that ever in the reservesfig_RAI_spp(df.IUCN, species = "zebramountain") #
# species is the snapshot name herefor this use the traits doc.
need two files: 1) scientific names and other info like IUCN cat: traits <- read_csv(“data_in/traits_spp_scientif_names_snapshot_names.csv”) and 2. file with RAIalt (created above) function to apply for each spp is fig_RAI_spp This will automatically create a file (jpg) for each threatened species without need of doing it one by one (saved in figures)
# ---- Loop across multiple species and store all graphs in a list
unique_names <- unique(df.IUCN$Common.Name)
grlist <- lapply(unique_names,
function(spp) fig_RAI_spp(df.IUCN, species = spp))
grlist## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
# Name list to access each species easier
# Change it to unique_names
names(grlist) <- unique_names
#unique(grlist)
# ---- Plot just one graph by hand
# grlist[[1]]
# grlist$rabbitriverine # Now works
# ---- Save each graph in a separate file in the current working directory
names <- names(grlist) # Plot all 7 graphs
for(s in names){
g <- grlist[[eval(s)]] # double brackets and store in object g
ggsave(filename = paste0("figures/MTZ_RAIalt_",s, ".jpg"), g) # draw object g #not reading the folder, capy from wd
}## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image#check main folder and copy```