deployment <- read.csv("deployments.csv",
header = TRUE,
sep = ",",
stringsAsFactors = FALSE)
camtraps <- read.csv("images.csv",
header = TRUE,
sep = ",",
stringsAsFactors = FALSE)1 Step 1 Load the data into R
2 Step 2 Ensure necessary packages are installed
# creates a list of packages needed for the following analysis
list.of.packages <- c(
"leaflet",
"plotly",
"kableExtra",
"tidyr",
"dplyr",
"viridis",
"corrplot",
"lubridate",
"taxize",
"sf",
"reshape",
"vegan",
"plotrix",
"chron",
"knitr")
# uses the previously created vector and checks the current R project if these packages have been installed in not it installs and loads them if it does contain these packages it just loads them from the library
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,repos = "http://cran.us.r-project.org")Installing package into 'C:/Users/Owen King/AppData/Local/R/win-library/4.4'
(as 'lib' is unspecified)Warning: package 'taxize' is not available for this version of R
A version of this package for your version of R might be available elsewhere,
see the ideas at
https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packageslapply(list.of.packages, require, character.only = TRUE)Loading required package: leafletWarning: package 'leaflet' was built under R version 4.4.2Loading required package: plotlyWarning: package 'plotly' was built under R version 4.4.2Loading required package: ggplot2
Attaching package: 'plotly'The following object is masked from 'package:ggplot2':
last_plotThe following object is masked from 'package:stats':
filterThe following object is masked from 'package:graphics':
layoutLoading required package: kableExtraWarning: package 'kableExtra' was built under R version 4.4.2Loading required package: tidyrLoading required package: dplyrWarning: package 'dplyr' was built under R version 4.4.2
Attaching package: 'dplyr'The following object is masked from 'package:kableExtra':
group_rowsThe following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionLoading required package: viridisWarning: package 'viridis' was built under R version 4.4.2Loading required package: viridisLiteLoading required package: corrplotWarning: package 'corrplot' was built under R version 4.4.2corrplot 0.95 loadedLoading required package: lubridate
Attaching package: 'lubridate'The following objects are masked from 'package:base':
date, intersect, setdiff, unionLoading required package: taxizeWarning in library(package, lib.loc = lib.loc, character.only = TRUE,
logical.return = TRUE, : there is no package called 'taxize'Loading required package: sfWarning: package 'sf' was built under R version 4.4.2Linking to GEOS 3.12.2, GDAL 3.9.3, PROJ 9.4.1; sf_use_s2() is TRUELoading required package: reshapeWarning: package 'reshape' was built under R version 4.4.2
Attaching package: 'reshape'The following object is masked from 'package:lubridate':
stampThe following object is masked from 'package:dplyr':
renameThe following objects are masked from 'package:tidyr':
expand, smithsThe following object is masked from 'package:plotly':
renameLoading required package: veganWarning: package 'vegan' was built under R version 4.4.2Loading required package: permuteWarning: package 'permute' was built under R version 4.4.2Loading required package: latticeThis is vegan 2.6-8Loading required package: plotrixLoading required package: chronWarning: package 'chron' was built under R version 4.4.2
Attaching package: 'chron'The following objects are masked from 'package:lubridate':
days, hours, minutes, seconds, yearsLoading required package: knitr[[1]]
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[1] TRUE3 Step 3 check the data is correct
#displays variables within the data set
names(deployment) [1] "project_id" "deployment_id"
[3] "longitude" "latitude"
[5] "start_date" "end_date"
[7] "bait_type" "bait_description"
[9] "feature_type" "feature_type_methodology"
[11] "camera_id" "camera_name"
[13] "quiet_period" "camera_functioning"
[15] "sensor_height" "height_other"
[17] "sensor_orientation" "orientation_other"
[19] "plot_treatment" "plot_treatment_description"
[21] "detection_distance" "subproject_name"
[23] "subproject_design" "event_name"
[25] "event_description" "event_type"
[27] "recorded_by" "fuzzed" names(camtraps) [1] "project_id" "deployment_id"
[3] "image_id" "sequence_id"
[5] "filename" "location"
[7] "is_blank" "identified_by"
[9] "wi_taxon_id" "class"
[11] "order" "family"
[13] "genus" "species"
[15] "common_name" "uncertainty"
[17] "timestamp" "age"
[19] "sex" "animal_recognizable"
[21] "individual_id" "number_of_objects"
[23] "individual_animal_notes" "behavior"
[25] "highlighted" "markings"
[27] "cv_confidence" "license"
[29] "fuzzed" #displays the structure of the data set
str(deployment)'data.frame': 300 obs. of 28 variables:
$ project_id : int 2001294 2001294 2001294 2001294 2001294 2001294 2001294 2001294 2001294 2001294 ...
$ deployment_id : chr "03B-19" "06B-19" "08B-19" "11A-19" ...
$ longitude : num -123 -123 -123 -123 -123 ...
$ latitude : num 42.1 42 42 42 42 ...
$ start_date : chr "12/09/2019 00:00" "13/09/2019 00:00" "13/09/2019 00:00" "13/09/2019 00:00" ...
$ end_date : chr "09/10/2019 00:00" "06/11/2019 00:00" "06/11/2019 00:00" "06/11/2019 00:00" ...
$ bait_type : chr "None" "None" "None" "None" ...
$ bait_description : logi NA NA NA NA NA NA ...
$ feature_type : chr "forest" "creek" "creek" "forest" ...
$ feature_type_methodology : logi NA NA NA NA NA NA ...
$ camera_id : int NA NA NA NA NA NA NA NA NA NA ...
$ camera_name : chr "" "" "" "" ...
$ quiet_period : int 0 0 0 0 0 0 0 0 0 0 ...
$ camera_functioning : chr "Camera Functioning" "Camera Functioning" "Camera Functioning" "Camera Functioning" ...
$ sensor_height : chr "Knee height" "Knee height" "Knee height" "Knee height" ...
$ height_other : logi NA NA NA NA NA NA ...
$ sensor_orientation : chr "Parallel" "Parallel" "Parallel" "Parallel" ...
$ orientation_other : logi NA NA NA NA NA NA ...
$ plot_treatment : logi NA NA NA NA NA NA ...
$ plot_treatment_description: logi NA NA NA NA NA NA ...
$ detection_distance : logi NA NA NA NA NA NA ...
$ subproject_name : chr "" "" "" "" ...
$ subproject_design : chr "" "" "" "" ...
$ event_name : logi NA NA NA NA NA NA ...
$ event_description : logi NA NA NA NA NA NA ...
$ event_type : logi NA NA NA NA NA NA ...
$ recorded_by : logi NA NA NA NA NA NA ...
$ fuzzed : logi FALSE FALSE FALSE FALSE FALSE FALSE ...str(camtraps)'data.frame': 547189 obs. of 29 variables:
$ project_id : int 2001294 2001294 2001294 2001294 2001294 2001294 2001294 2001294 2001294 2001294 ...
$ deployment_id : chr "34A-19" "01A-19" "01B-19" "15B-19" ...
$ image_id : chr "700d0357-4313-4724-9ae5-cd29a5cc0589" "ffc7f4b4-1551-43e6-b359-e149cd174975" "74bd6673-1586-4bc1-be5b-4023e7dcde5c" "aca78142-2d78-442d-8287-3b017c17c4c1" ...
$ sequence_id : logi NA NA NA NA NA NA ...
$ filename : chr "09260186.JPG" "10230418.JPG" "09300423.JPG" "11050556.JPG" ...
$ location : chr "https://app.wildlifeinsights.org/download/2007276/project/2001294/data-files/700d0357-4313-4724-9ae5-cd29a5cc0589" "https://app.wildlifeinsights.org/download/2007276/project/2001294/data-files/ffc7f4b4-1551-43e6-b359-e149cd174975" "https://app.wildlifeinsights.org/download/2007276/project/2001294/data-files/74bd6673-1586-4bc1-be5b-4023e7dcde5c" "https://app.wildlifeinsights.org/download/2007276/project/2001294/data-files/aca78142-2d78-442d-8287-3b017c17c4c1" ...
$ is_blank : logi NA NA NA NA NA NA ...
$ identified_by : chr "Ashley Newcomb" "Sierra Rose" "Sierra Rose" "Ashley Newcomb" ...
$ wi_taxon_id : chr "d01f67fd-836b-4b25-89fc-2239e59f56b0" "f1856211-cfb7-4a5b-9158-c0f72fd09ee6" "febff896-db40-4ac8-bcfe-5bb99a600950" "f67c4346-bbbd-43d5-9b19-b541d493019b" ...
$ class : chr "Mammalia" "" "Mammalia" "Mammalia" ...
$ order : chr "Rodentia" "" "Cetartiodactyla" "Rodentia" ...
$ family : chr "Sciuridae" "" "Cervidae" "Sciuridae" ...
$ genus : chr "Otospermophilus" "" "Odocoileus" "Tamiasciurus" ...
$ species : chr "beecheyi" "" "hemionus" "douglasii" ...
$ common_name : chr "California Ground Squirrel" "Blank" "Mule Deer" "Douglas's Squirrel" ...
$ uncertainty : logi NA NA NA NA NA NA ...
$ timestamp : chr "26/09/2019 13:22" "23/10/2019 10:25" "30/09/2019 00:18" "05/11/2019 10:09" ...
$ age : chr "" "" "" "" ...
$ sex : chr "" "" "" "" ...
$ animal_recognizable : logi NA NA NA NA NA NA ...
$ individual_id : logi NA NA NA NA NA NA ...
$ number_of_objects : int 1 1 2 1 2 1 1 1 1 1 ...
$ individual_animal_notes: logi NA NA NA NA NA NA ...
$ behavior : logi NA NA NA NA NA NA ...
$ highlighted : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ markings : logi NA NA NA NA NA NA ...
$ cv_confidence : logi NA NA NA NA NA NA ...
$ license : chr "CC-BY-NC" "CC-BY-NC" "CC-BY-NC" "CC-BY-NC" ...
$ fuzzed : logi FALSE FALSE FALSE FALSE FALSE FALSE ...4 Step 4 calculate the number of photos and the number of blanks
#Calculates the number of photos by working out how many rows are in the camtrap data set which gave the output ([1] "Number of photographs: 547189")
number <- nrow(camtraps)
print(paste("Number of photographs:", number))[1] "Number of photographs: 547189"#calculate the number of unique species within the data set. which had the output of [1] "Number of unique species: 71" this may be an overestimate due to humans being labbled as human, human research, ect.
camtraps$full_species <- paste(camtraps$genus, camtraps$species, sep = " ")
unique_species <- unique(camtraps$full_species)
print(paste("Number of unique species:",length(unique_species)))[1] "Number of unique species: 71"# we can also view these unique species with the code below we can now see the issue with the identification of Humans diluting the amount of species that were truely captured
print(unique_species) [1] "Otospermophilus beecheyi" " "
[3] "Odocoileus hemionus" "Tamiasciurus douglasii"
[5] "Homo sapiens" "Sciurus griseus"
[7] "Homo " "Ursus americanus"
[9] "Cervus canadensis" "Glaucomys sabrinus"
[11] "Bassariscus astutus" "Neotamias "
[13] "Sylvilagus bachmani" "Cyanocitta stelleri"
[15] "Procyon lotor" "Meleagris gallopavo"
[17] "Oreortyx pictus" "Bos taurus"
[19] "Pekania pennanti" "Urocyon cinereoargenteus"
[21] "Ixoreus naevius" "Colaptes cafer"
[23] "Catharus " "Turdus migratorius"
[25] "Callospermophilus lateralis" "Lynx rufus"
[27] "Puma concolor" "Canis familiaris"
[29] "Peromyscus " "Canis latrans"
[31] "Melanerpes formicivorus" "Hylatomus pileatus"
[33] "Neotoma " "Bonasa umbellus"
[35] "Junco hyemalis" "Colaptes auratus"
[37] "Mustela erminea" "Sialia mexicana"
[39] "Felis catus" "Leuconotopicus villosus"
[41] "Spilogale gracilis" "Tamias "
[43] "Dryobates " "Glaucomys "
[45] "Lepus californicus" "Catharus guttatus"
[47] "Catharus swainsoni" "Neotoma fuscipes"
[49] "Lepus americanus" "Dendragapus fuliginosus"
[51] "Mephitis mephitis" "Sceloporus "
[53] "Patagioenas fasciata" "Neotoma cinerea"
[55] "Aphelocoma californica" "Odocoileus "
[57] "Mustela frenata" "Eutamias "
[59] "Dryobates pubescens" "Aquila chrysaetos"
[61] "Mustela " "Accipiter gentilis"
[63] "Neovison vison" "Didelphis virginiana"
[65] "Euphagus cyanocephalus" "Turdus "
[67] "Corvus corax" "Sciurus "
[69] "Ammodramus savannarum" "Lepus "
[71] "Poecile gambeli" #it is useful to know the amount of blank images to understand how many misfires the camera traps had. the output was [1] 364922.
empty_genus <- sum(camtraps$genus == "")
print(empty_genus)[1] 364922#The output was [1] "Percentage of photographs without animal captures 66.6903026193875.
#however rounding this to 2 decimal places is better for interpretation therefore 66.69% which is a large amount of dataset however, the data is still useable due to how large the data set is.
proportion <- (empty_genus/nrow(camtraps))*100
print(paste("Percentage of photographs without animal captures:",proportion))[1] "Percentage of photographs without animal captures: 66.6903026193875"5 Step 5 check how many deployments have no caputures
#first count the number of deployments
unique_deployments <- unique(deployment$deployment_id)
num_all_deployments <- length(unique_deployments)
#then work out the number of deployments that where successful i.e. had captured images of animals
successful_deployments<- unique(camtraps$deployment_id)
num_successful_deployments <-length(successful_deployments)
#use the previous bit of code to inform how many deployments where not successful which will provide us with this output. [1] "Number of deployments with no captures: 0", which indicates every deployment was successful.
total<-num_all_deployments - num_successful_deployments
print(paste("Number of deployments with no captures:", total))[1] "Number of deployments with no captures: 0"6 Step 6 look at the start and end of the sampling
#this is to display the items in each column to inspect as seen here the format for the date is day:month:year_hours:minutes:seconds.
head(deployment$start_date)[1] "12/09/2019 00:00" "13/09/2019 00:00" "13/09/2019 00:00" "13/09/2019 00:00"
[5] "18/09/2019 00:00" "18/09/2019 00:00"head(deployment$end_date)[1] "09/10/2019 00:00" "06/11/2019 00:00" "06/11/2019 00:00" "06/11/2019 00:00"
[5] "07/11/2019 00:00" "07/11/2019 00:00"#after checking data in previous step it can be seen that the seconds are missing and have no values therefore we need to add them and can add them using the code below.
#the ifelse function checks the length of the date string to see if it has enough numbers in the string to contain seconds.
# Add ":00" to start_date if seconds are missing
deployment$start_date <- ifelse(nchar(deployment$start_date) == 16,
paste0(deployment$start_date, ":00"),
deployment$start_date)
# Add ":00" to end_date if seconds are missing
deployment$end_date <- ifelse(nchar(deployment$end_date) == 16,
paste0(deployment$end_date, ":00"),
deployment$end_date)library(lubridate)
# Convert start_date and end_date to POSIXct format directly in Pacific Time (America/Los_Angeles), ensuring that the date and time format is in line with how the data set is laid out, so ours is day/month/year_hours/minutes/seconds.
deployment$start_date <- dmy_hms(deployment$start_date, tz = "America/Los_Angeles")
deployment$end_date <- dmy_hms(deployment$end_date, tz = "America/Los_Angeles")
# Check for NAs after conversion to ensure it has been successful
print(sum(is.na(deployment$start_date)))[1] 0#[1] 0
print(sum(is.na(deployment$end_date)))[1] 0#[1] 0# Find the earliest start date
earliest_start_date <- min(deployment$start_date, na.rm = TRUE)
# Find the latest end date
latest_end_date <- max(deployment$end_date, na.rm = TRUE)
# View the results
print(paste("Earliest start date:", earliest_start_date))[1] "Earliest start date: 2019-08-10"print(paste("Latest end date:", latest_end_date))[1] "Latest end date: 2021-11-05"7 Step 7 check the intervals in which the samples where taken
# work out how long each deployment was deployed for.
deployment$interval <- interval(deployment$start_date, deployment$end_date)
# specify that you want to calculate the interval in days
deployment$duration_days <- as.numeric(deployment$interval) / (60 * 60 * 24)
# display the results in the following order start date, end date, and the interval between both of those which is called duration_days.
print(head(deployment[, c("start_date", "end_date", "duration_days")],5)) start_date end_date duration_days
1 2019-09-12 2019-10-09 27.00000
2 2019-09-13 2019-11-06 54.04167
3 2019-09-13 2019-11-06 54.04167
4 2019-09-13 2019-11-06 54.04167
5 2019-09-18 2019-11-07 50.04167#we can also sumarise the intervals using the summary comand to have the output. min = 6, 1st quart = 35, Median, = 44, Mean = 42.77, 3rd quart = 51.04, Max = 88.04.
#This is useful to see if there have been any mistakes in the conversion or during the data collection as sometimes mixing the start date and end date is possible.
summary(deployment$duration_days) Min. 1st Qu. Median Mean 3rd Qu. Max.
6.00 35.00 44.00 42.77 51.04 88.04 8 Step 8 Calculate the sampling effort
# Sum the duration_days to calculate total camera days (sampling effort)
total_camera_days <- sum(deployment$duration_days, na.rm = TRUE)
# round the effort to be a hole number as half days are useless in further analysis
total_camera_days_rounded <- round(total_camera_days, 0)
# View the result using the print function which gave the output [1] "Total camera days (sampling effort): 12831"
print(paste("Total camera days (sampling effort):", total_camera_days_rounded))[1] "Total camera days (sampling effort): 12831"9 Step 9 Plot the camera traps on a map
# the red dots indicating where the cameras where deployed on the map, the map should appear in the viewer.
library(leaflet)
m <- leaflet() %>%
addProviderTiles(providers$Esri.WorldImagery) %>% addCircleMarkers (lng = deployment$longitude, lat = deployment$latitude, color = "red", radius = 5, fill = TRUE, fillColor = "red", fillOpacity = 0.7) %>% addScaleBar(position = "topleft", options = scaleBarOptions(metric = TRUE, imperial = FALSE))
#this will display the map that was previously created
m10 Step 10 Conduct the Relative Index of Abundance
library(dplyr)
# Define a custom function called event.sp to calculate trapping events and Relative Index of Abundance (RIA)
event.sp <- function(camtraps,
deployment,
total_camera_days_rounded,
time_threshold = NULL) {
#we include 4 parameters in this function, camtraps and deployment which both represent a dataset which will be using in the RAI calculation.
#total camera days rounded which is the effort rounded to the nearest integer which aids in the RIA calculation.
#time threshold adds the ability to set a buffer after an observation as the RIA sees each row as a seperate event however a bear for example may have just sat in front of the camera for 20 minutes trigering the camera 4 times.
# the reason for using genus as some species could not be identified fully therefore identifying species via their genus makes the output fully representative of the whole dataset when producing the RIA.
# Checks for missing values in the column genus
missing_genus <- sum(is.na(camtraps$genus) | camtraps$genus == "")
# Print count of missing genus values
if (missing_genus > 0) {
cat("Warning: Found", missing_genus, "missing or empty values in 'genus'. Rows with missing genus will be excluded.\n")
}
# Filter out rows with missing or empty genus
camtraps <- camtraps %>%
filter(!is.na(genus) & genus != "")
# Combine 'genus' and 'species' into a new 'full_species' column in the camtraps dataset
camtraps <- camtraps %>%
mutate(
full_species = paste(genus, species, sep = " "),
full_species = trimws(full_species)
)
# Convert timestamp column in camtraps to POSIXct
camtraps$timestamp <- as.POSIXct(camtraps$timestamp, format = "%Y:%m:%d %H:%M:%S")
#calculate trapping events without a threshold of 30 mins
if (is.null(time_threshold)) {
trapping_events <- camtraps %>%
group_by(full_species, deployment_id) %>%
summarise(trapping_event_count = n(), .groups = 'drop')
} else {
camtraps <- camtraps %>%
arrange(deployment_id, full_species, timestamp)
trapping_events <- camtraps %>%
group_by(full_species, deployment_id) %>%
mutate(time_diff = c(NA, diff(as.numeric(timestamp)))) %>%
mutate(new_event = ifelse(is.na(time_diff) | time_diff > time_threshold, 1, 0)) %>%
summarise(trapping_event_count = sum(new_event), .groups = 'drop')
}
# Calculate RIA using the total camera days divided by the total trapping events.
trapping_events <- trapping_events %>%
mutate(RIA = trapping_event_count / total_camera_days_rounded) # Calculate RIA
# Return the trapping events summary with RIA
return(trapping_events)
}
#output should indicate the species, deployment, trapping event count, and the RIA. The RIA indicates the proportion of that species caught in comparison to the number of camera trap days overall. 11 Step 11 without time threshold
# Without time threshold
trapping_events_no_threshold <- event.sp(camtraps, deployment, total_camera_days_rounded)Warning: Found 364922 missing or empty values in 'genus'. Rows with missing genus will be excluded.#save the RIA as a csv so it can be used to inform the naive occupancy and the occupancy model
#write.csv(trapping_events_no_threshold, file = "trapping_events_no_threshold.csv", row.names = FALSE)12 Step 12 with time threshold
# With a 30-minute threshold (in seconds)
trapping_events_with_threshold <- event.sp(camtraps, deployment, total_camera_days_rounded, time_threshold = 30 * 60)Warning: Found 364922 missing or empty values in 'genus'. Rows with missing genus will be excluded.#save the RIA as a csv so it can be used to inform the naive occupancy and the occupancy model
#write.csv(trapping_events_with_threshold, file = "trapping_events_with_threshold.csv", row.names = FALSE)13 Step 13 Overall RIA without the threshold
library(knitr)
# this will create an overall RIA without the threshold assuming that every capture is a signular event.
# Summarize results across all deployments
species_summary <- trapping_events_no_threshold %>%
group_by(full_species) %>%
summarise(
total_trapping_event_count = sum(trapping_event_count, na.rm = TRUE), # Sum of trapping events
total_RAI = sum(RIA, na.rm = TRUE), # Sum of RAI
.groups = 'drop' # Drop grouping after summarization
) %>%
arrange(desc(total_trapping_event_count)) # Optional: sort by total trapping events
# Print a nicely formatted table
kable(species_summary,
format = "html", # C
col.names = c("Species", "Total Trapping Events", "Total RAI"),
caption = "Summary of Trapping Events and RAI by Species")| Species | Total Trapping Events | Total RAI |
|---|---|---|
| Tamiasciurus douglasii | 35673 | 2.7802198 |
| Homo sapiens | 34349 | 2.6770322 |
| Odocoileus hemionus | 31781 | 2.4768919 |
| Homo | 16411 | 1.2790118 |
| Sciurus griseus | 13545 | 1.0556465 |
| Ursus americanus | 13442 | 1.0476190 |
| Sceloporus | 7260 | 0.5658172 |
| Neotamias | 5689 | 0.4433793 |
| Otospermophilus beecheyi | 4589 | 0.3576494 |
| Bos taurus | 2863 | 0.2231315 |
| Urocyon cinereoargenteus | 2430 | 0.1893851 |
| Ixoreus naevius | 1658 | 0.1292183 |
| Glaucomys sabrinus | 1454 | 0.1133193 |
| Pekania pennanti | 1095 | 0.0853402 |
| Peromyscus | 817 | 0.0636739 |
| Oreortyx pictus | 764 | 0.0595433 |
| Cyanocitta stelleri | 679 | 0.0529187 |
| Tamias | 654 | 0.0509703 |
| Neotoma | 615 | 0.0479308 |
| Bassariscus astutus | 577 | 0.0449692 |
| Neotoma cinerea | 482 | 0.0375653 |
| Cervus canadensis | 479 | 0.0373315 |
| Neotoma fuscipes | 434 | 0.0338243 |
| Turdus migratorius | 417 | 0.0324994 |
| Lynx rufus | 414 | 0.0322656 |
| Meleagris gallopavo | 383 | 0.0298496 |
| Puma concolor | 383 | 0.0298496 |
| Lepus californicus | 353 | 0.0275115 |
| Sylvilagus bachmani | 351 | 0.0273556 |
| Eutamias | 298 | 0.0232250 |
| Canis latrans | 240 | 0.0187047 |
| Catharus | 182 | 0.0141844 |
| Procyon lotor | 154 | 0.0120022 |
| Colaptes cafer | 149 | 0.0116125 |
| Spilogale gracilis | 146 | 0.0113787 |
| Mephitis mephitis | 139 | 0.0108331 |
| Glaucomys | 112 | 0.0087289 |
| Catharus guttatus | 101 | 0.0078716 |
| Lepus | 98 | 0.0076378 |
| Bonasa umbellus | 77 | 0.0060011 |
| Felis catus | 76 | 0.0059232 |
| Ammodramus savannarum | 66 | 0.0051438 |
| Canis familiaris | 64 | 0.0049879 |
| Colaptes auratus | 63 | 0.0049100 |
| Callospermophilus lateralis | 55 | 0.0042865 |
| Patagioenas fasciata | 30 | 0.0023381 |
| Junco hyemalis | 29 | 0.0022602 |
| Hylatomus pileatus | 25 | 0.0019484 |
| Lepus americanus | 21 | 0.0016367 |
| Aphelocoma californica | 16 | 0.0012470 |
| Didelphis virginiana | 15 | 0.0011690 |
| Mustela erminea | 10 | 0.0007794 |
| Mustela | 7 | 0.0005456 |
| Dendragapus fuliginosus | 6 | 0.0004676 |
| Dryobates pubescens | 6 | 0.0004676 |
| Catharus swainsoni | 5 | 0.0003897 |
| Accipiter gentilis | 3 | 0.0002338 |
| Aquila chrysaetos | 3 | 0.0002338 |
| Corvus corax | 3 | 0.0002338 |
| Dryobates | 3 | 0.0002338 |
| Euphagus cyanocephalus | 3 | 0.0002338 |
| Leuconotopicus villosus | 3 | 0.0002338 |
| Melanerpes formicivorus | 3 | 0.0002338 |
| Mustela frenata | 3 | 0.0002338 |
| Neovison vison | 3 | 0.0002338 |
| Turdus | 3 | 0.0002338 |
| Poecile gambeli | 2 | 0.0001559 |
| Sialia mexicana | 2 | 0.0001559 |
| Odocoileus | 1 | 0.0000779 |
| Sciurus | 1 | 0.0000779 |
#save the RIA as a csv so it can be used to inform the naive occupancy and the occupancy model
#write.csv(species_summary, file = "species_summary.csv", row.names = FALSE)14 Step 14 Overall RIA with threshold
# using the exact code from above but changing the source data to the thresholded output from the previous RAI. ensuring that there will not be repeated captures.
# Summarize results across all deployments
species_summary_threshold <- trapping_events_with_threshold %>%
group_by(full_species) %>%
summarise(
total_trapping_event_count = sum(trapping_event_count, na.rm = TRUE), # Sum of trapping events
total_RAI = sum(RIA, na.rm = TRUE), # Sum of RAI
.groups = 'drop' # Drop grouping after summarization
) %>%
arrange(desc(total_trapping_event_count)) # Optional: sort by total trapping events to make the data more interpretable.
# print the output in a formatted table using the Kable function.
kable(species_summary_threshold,
format = "html", # C
col.names = c("Species", "Total Trapping Events", "Total RAI"),
caption = "Summary of Trapping Events and RAI by Species")| Species | Total Trapping Events | Total RAI |
|---|---|---|
| Tamiasciurus douglasii | 35673 | 2.7802198 |
| Homo sapiens | 34349 | 2.6770322 |
| Odocoileus hemionus | 31781 | 2.4768919 |
| Homo | 16411 | 1.2790118 |
| Sciurus griseus | 13545 | 1.0556465 |
| Ursus americanus | 13442 | 1.0476190 |
| Sceloporus | 7260 | 0.5658172 |
| Neotamias | 5689 | 0.4433793 |
| Otospermophilus beecheyi | 4589 | 0.3576494 |
| Bos taurus | 2863 | 0.2231315 |
| Urocyon cinereoargenteus | 2430 | 0.1893851 |
| Ixoreus naevius | 1658 | 0.1292183 |
| Glaucomys sabrinus | 1454 | 0.1133193 |
| Pekania pennanti | 1095 | 0.0853402 |
| Peromyscus | 817 | 0.0636739 |
| Oreortyx pictus | 764 | 0.0595433 |
| Cyanocitta stelleri | 679 | 0.0529187 |
| Tamias | 654 | 0.0509703 |
| Neotoma | 615 | 0.0479308 |
| Bassariscus astutus | 577 | 0.0449692 |
| Neotoma cinerea | 482 | 0.0375653 |
| Cervus canadensis | 479 | 0.0373315 |
| Neotoma fuscipes | 434 | 0.0338243 |
| Turdus migratorius | 417 | 0.0324994 |
| Lynx rufus | 414 | 0.0322656 |
| Meleagris gallopavo | 383 | 0.0298496 |
| Puma concolor | 383 | 0.0298496 |
| Lepus californicus | 353 | 0.0275115 |
| Sylvilagus bachmani | 351 | 0.0273556 |
| Eutamias | 298 | 0.0232250 |
| Canis latrans | 240 | 0.0187047 |
| Catharus | 182 | 0.0141844 |
| Procyon lotor | 154 | 0.0120022 |
| Colaptes cafer | 149 | 0.0116125 |
| Spilogale gracilis | 146 | 0.0113787 |
| Mephitis mephitis | 139 | 0.0108331 |
| Glaucomys | 112 | 0.0087289 |
| Catharus guttatus | 101 | 0.0078716 |
| Lepus | 98 | 0.0076378 |
| Bonasa umbellus | 77 | 0.0060011 |
| Felis catus | 76 | 0.0059232 |
| Ammodramus savannarum | 66 | 0.0051438 |
| Canis familiaris | 64 | 0.0049879 |
| Colaptes auratus | 63 | 0.0049100 |
| Callospermophilus lateralis | 55 | 0.0042865 |
| Patagioenas fasciata | 30 | 0.0023381 |
| Junco hyemalis | 29 | 0.0022602 |
| Hylatomus pileatus | 25 | 0.0019484 |
| Lepus americanus | 21 | 0.0016367 |
| Aphelocoma californica | 16 | 0.0012470 |
| Didelphis virginiana | 15 | 0.0011690 |
| Mustela erminea | 10 | 0.0007794 |
| Mustela | 7 | 0.0005456 |
| Dendragapus fuliginosus | 6 | 0.0004676 |
| Dryobates pubescens | 6 | 0.0004676 |
| Catharus swainsoni | 5 | 0.0003897 |
| Accipiter gentilis | 3 | 0.0002338 |
| Aquila chrysaetos | 3 | 0.0002338 |
| Corvus corax | 3 | 0.0002338 |
| Dryobates | 3 | 0.0002338 |
| Euphagus cyanocephalus | 3 | 0.0002338 |
| Leuconotopicus villosus | 3 | 0.0002338 |
| Melanerpes formicivorus | 3 | 0.0002338 |
| Mustela frenata | 3 | 0.0002338 |
| Neovison vison | 3 | 0.0002338 |
| Turdus | 3 | 0.0002338 |
| Poecile gambeli | 2 | 0.0001559 |
| Sialia mexicana | 2 | 0.0001559 |
| Odocoileus | 1 | 0.0000779 |
| Sciurus | 1 | 0.0000779 |
#save the RIA as a csv so it can be used to inform the naive occupancy and the occupancy model
#write.csv(species_summary_threshold, file = "species_summary_threshold.csv", row.names = FALSE)15 Step 15 Species accumulation curve
#Ensure the dates in deployments are classified as date types which ensures both the comparisons and calculations are accurate
deployment <- deployment %>%
mutate(start_date = as.Date(start_date),
end_date = as.Date(end_date))#Create a data sequence for each deployment, generating a sequence based on the deployment date, from the start date to end date.
deployment_dates <- deployment %>%
select(deployment_id, start_date, end_date) %>%
rowwise() %>%
do(data.frame(deployment_id = .$deployment_id,
date = seq(.$start_date, .$end_date, by = "day"))) %>%
ungroup() # Create a new variable called full species by combining the genus and species column. as well as converting the timestamp of each capture to a date format so it is compatible with the deployment data. due to the camtraps dataset timestamp being in a different format day/month/year where as the deployment was year/month/year so changing it is essential for both datasets to be compatible.
camtraps1 <- camtraps %>%
mutate(full_species = paste(genus, species, sep = " "),
timestamp = as.Date(timestamp, format = "%d/%m/%Y %H:%M"))#Create a count for number species captured for each day for each deployment.
species_per_day <- deployment_dates %>%
left_join(camtraps1, by = c("deployment_id", "date" = "timestamp")) %>%
group_by(deployment_id, date) %>%
summarise(species_detected = list(unique(full_species)), .groups = 'drop') %>%
ungroup()Warning in left_join(., camtraps1, by = c("deployment_id", date = "timestamp")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 1 of `x` matches multiple rows in `y`.
ℹ Row 54464 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.# Calculate the cumulative number of new species, per day. and create a loop working out how many new species where found in the next day.
cumulative_species <- numeric(nrow(species_per_day))
# go through each date and calculate cumulative new species that appear for every new day thereafter.
detected_species <- character(0)
for (i in seq_len(nrow(species_per_day))) {
# Get newly detected species for the day
new_species <- species_per_day$species_detected[[i]]
# add the new sum to the cumulative number of species
detected_species <- union(detected_species, new_species)
# store the cumulative number of species
cumulative_species[i] <- length(detected_species)
}
# Combine with dates to create the number of days
species_per_day <- species_per_day %>%
mutate(cumulative_species = cumulative_species)
# using the previous code count the number of camera days overall in response to the number of species.
camera_days <- species_per_day %>%
mutate(camera_day = row_number())
# Inspect the first few rows of species_per_day to ensure the accumulation of the number of species per day worked and the calculation ran correctly. which in this case it did and you can see this by an increasing number of species in the species_per_day column.
head(species_per_day)# A tibble: 6 × 4
deployment_id date species_detected cumulative_species
<chr> <date> <list> <dbl>
1 01A-19 2019-09-12 <chr [3]> 3
2 01A-19 2019-09-13 <chr [1]> 4
3 01A-19 2019-09-14 <chr [1]> 4
4 01A-19 2019-09-15 <chr [1]> 4
5 01A-19 2019-09-16 <chr [1]> 4
6 01A-19 2019-09-17 <chr [1]> 4# Check if species_detected contains useful data
species_per_day %>%
filter(lengths(species_detected) > 0) %>%
head()# A tibble: 6 × 4
deployment_id date species_detected cumulative_species
<chr> <date> <list> <dbl>
1 01A-19 2019-09-12 <chr [3]> 3
2 01A-19 2019-09-13 <chr [1]> 4
3 01A-19 2019-09-14 <chr [1]> 4
4 01A-19 2019-09-15 <chr [1]> 4
5 01A-19 2019-09-16 <chr [1]> 4
6 01A-19 2019-09-17 <chr [1]> 4library(ggplot2)
# using the package ggplot2, plot the data that was created from the previous calculations in step 15.
ggplot(camera_days, aes(x = camera_day, y = cumulative_species)) +
geom_line(color = "blue", linewidth = 1) +
geom_point(color = "red") +
labs(
x = "Number of Camera Days",
y = "Cumulative Number of New Species Detected",
title = "Species Accumulation Curve"
) +
theme_minimal()