Chapter 8 Analysis data exploration

Now things start to get really interesting - we are getting closer to analyzing our data. Before we get into building any models however, we must thoroughly explore our data. We want to ask questions like?

  • How many species did we detect?
  • Which are the most common?
  • Where did we detect them?
  • When did we detect them?
  • How do species detections relate to our covariates?

In the error checking section we focused our ‘data exploration’ on figures which would help us find issues with our data, now we want to shift gears and create plots which actually tell us about patterns in our data.

To reflect the change from error check to patterns, all of the datasets we use will now be coming out of the data/processed_data/ folder.

Create a new .R script

Call it 03_example_exploration.R.

Load the required packages

# Check you have them and load them
list.of.packages <- c("kableExtra", "tidyr", "leaflet", "dplyr", "viridis", "corrplot", "lubridate", "plotly")

new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
lapply(list.of.packages, require, character.only = TRUE)

8.1 Final locations plot

So lets read in the camera_locations.csv and plot the final survey locations in leaflet. We repeat this as we may have filtered out some stations in the error checking section - for example if they failed to collect any useful data:

locs <- read.csv("data/processed_data/AlgarRestorationProject_camera_locations_and_covariates.csv")

# If you want to color by a category do it here:
category <- "feature_type"
# First lets choose a category to color
locs[,category] <- factor(locs[,category])
col.cat <- turbo(length(levels(locs[,category])))
# Add it to the dataframe
locs$colours <- col.cat[locs[,category]]

m <- leaflet() %>%
  # Add a satellite image layer
  addProviderTiles(providers$Esri.WorldImagery, group="Satellite") %>%  
  addProviderTiles(providers$Esri.WorldTopoMap, group="Base") %>%     
  addCircleMarkers(lng=locs$longitude, lat=locs$latitude,
                   # Color the markers depending on the 'feature type'
                   color=locs$colours,
                   # Add a popup of the deployment code 
                   popup=paste(locs$placename, locs[,category])) %>%
  # Add a legend explaining what is going on
  addLegend("bottomleft", colors = col.cat,  labels = levels(locs[,category]),
    title = category,
    labFormat = labelFormat(prefix = "$"),
    opacity = 1
  ) %>%
  # add a layer control box to toggle between the layers
  addLayersControl(
    baseGroups = c("Satellite", "Base"),
    options = layersControlOptions(collapsed = FALSE)
  )
m

8.2 Independent detections summary

When you are writing papers or reports based on camera data, it is useful to have a capture summary table in the main text or as an appendix. We will use the species list we created to append summary information to:

# Also read in the species list
sp_summary <- read.csv("data/processed_data/AlgarRestorationProject_species_list.csv", header=T)

8.2.1 Total number of captures

To summaries the wildlife detections in this project we can make use of the ...total_observations.csv files:

total_obs <- read.csv("data/processed_data/AlgarRestorationProject_30min_independent_total_observations.csv", header=T)

The format of the output tables is what we would call ‘wide’ format - we have multiple species observation on the same row.

placename days Alces.alces Canis.latrans Canis.lupus Cervus.canadensis Lepus.americanus Lontra.canadensis Lynx.canadensis Martes.americana Odocoileus.virginianus Oryctolagus.cuniculus Rangifer.tarandus Tamiasciurus.hudsonicus Ursus.americanus Vulpes.vulpes
ALG027 358 4 4 13 1 0 0 10 0 3 0 0 0 32 0
ALG029 593 17 0 2 0 0 0 1 0 35 0 0 0 9 0
ALG031 593 11 0 8 0 1 1 0 6 1 1 21 0 2 1
ALG032 592 2 0 0 0 0 0 1 0 2 0 5 0 0 0
ALG035 594 11 0 2 0 7 0 1 0 1 0 6 3 2 0
ALG036 417 1 0 0 0 42 0 1 0 5 0 0 15 0 0
ALG037 592 2 0 0 0 0 0 0 0 0 0 3 0 2 0
ALG038 593 0 0 0 0 0 0 0 1 0 0 12 0 1 0
ALG039 591 2 0 6 0 0 0 0 0 0 0 5 0 3 0
ALG043 392 6 0 0 0 13 0 0 0 21 0 0 0 2 0
ALG044 392 15 0 3 0 2 0 0 1 10 0 0 0 7 2
ALG045 418 5 0 5 0 3 0 4 3 0 0 1 0 6 0
ALG046 592 9 0 0 0 14 0 1 0 18 0 0 0 0 1
ALG047 507 1 1 13 0 1 0 0 0 15 0 5 1 9 0
ALG048 593 0 0 2 0 0 0 1 1 1 0 7 0 4 0
ALG049 341 2 0 0 0 0 0 0 1 14 0 0 4 10 0
ALG052 590 13 0 3 0 9 0 1 1 65 0 1 0 17 0
ALG053 592 3 0 0 0 0 0 1 0 0 0 1 0 2 3
ALG054 595 1 0 1 0 25 0 3 0 28 0 0 0 2 0
ALG055 592 3 3 26 0 29 0 9 1 19 0 0 2 10 2
ALG056 595 8 0 0 0 0 0 1 0 6 0 0 0 2 0
ALG057 405 0 0 1 0 0 0 0 0 13 1 0 0 2 0
ALG058 595 2 0 0 0 58 0 3 0 51 0 1 0 11 0
ALG059 465 7 0 0 0 2 0 1 0 0 0 3 0 9 0
ALG060 592 1 0 0 0 1 0 1 2 0 0 0 0 6 0
ALG061 590 0 0 0 0 0 0 0 4 60 0 0 0 5 0
ALG062 592 1 0 4 0 4 0 1 0 48 0 0 0 16 0
ALG063 593 1 0 1 0 1 0 0 1 23 0 4 1 2 0
ALG064 592 2 0 0 0 0 0 0 0 0 0 10 0 0 0
ALG065 447 26 0 0 0 0 0 0 1 0 0 1 0 2 0
ALG066 591 2 0 0 0 0 0 1 0 1 0 6 0 0 0
ALG067 391 2 0 2 0 0 0 0 0 3 0 0 0 0 0
ALG068 592 3 0 0 0 0 0 0 0 0 0 0 0 2 0
ALG069 592 6 0 0 0 3 0 4 0 64 0 0 0 25 0
ALG070 408 2 0 0 0 0 0 0 1 4 0 3 0 1 0
ALG071 595 3 0 1 0 21 0 2 0 46 0 0 0 22 1
ALG072 595 1 0 0 0 5 0 4 0 5 0 1 0 3 0
ALG073 593 1 0 0 0 0 0 0 0 0 0 23 0 0 0

Sometimes, however, we might want a “longer” format where every row represents a unique species_site combination.

We can do this using the ‘pivot_longer’ function:

long_obs <- total_obs %>% 
  pivot_longer(cols=sp_summary$sp,  # The columns we want to create into rows - species
               names_to="sp",       # What we what the number column to be called
               values_to = "count") # Takes the values in the species columns and calls them `count`

We now have a dataframe where each row is a unique species at a given location (e.g. ALG027) - a.k.a. long format!

placename days sp count
ALG027 358 Alces.alces 4
ALG027 358 Cervus.canadensis 1
ALG027 358 Odocoileus.virginianus 3
ALG027 358 Rangifer.tarandus 0
ALG027 358 Canis.latrans 4
ALG027 358 Canis.lupus 13
ALG027 358 Vulpes.vulpes 0
ALG027 358 Lynx.canadensis 10
ALG027 358 Lontra.canadensis 0
ALG027 358 Martes.americana 0
ALG027 358 Ursus.americanus 32
ALG027 358 Lepus.americanus 0
ALG027 358 Oryctolagus.cuniculus 0
ALG027 358 Tamiasciurus.hudsonicus 0
ALG029 593 Alces.alces 17
ALG029 593 Cervus.canadensis 0
ALG029 593 Odocoileus.virginianus 35
ALG029 593 Rangifer.tarandus 0
ALG029 593 Canis.latrans 0
ALG029 593 Canis.lupus 2
ALG029 593 Vulpes.vulpes 0
ALG029 593 Lynx.canadensis 1
ALG029 593 Lontra.canadensis 0
ALG029 593 Martes.americana 0
ALG029 593 Ursus.americanus 9
ALG029 593 Lepus.americanus 0
ALG029 593 Oryctolagus.cuniculus 0
ALG029 593 Tamiasciurus.hudsonicus 0
ALG031 593 Alces.alces 11
ALG031 593 Cervus.canadensis 0
ALG031 593 Odocoileus.virginianus 1
ALG031 593 Rangifer.tarandus 21
ALG031 593 Canis.latrans 0
ALG031 593 Canis.lupus 8
ALG031 593 Vulpes.vulpes 1
ALG031 593 Lynx.canadensis 0
ALG031 593 Lontra.canadensis 1
ALG031 593 Martes.americana 6
ALG031 593 Ursus.americanus 2
ALG031 593 Lepus.americanus 1
ALG031 593 Oryctolagus.cuniculus 1
ALG031 593 Tamiasciurus.hudsonicus 0
ALG032 592 Alces.alces 2
ALG032 592 Cervus.canadensis 0
ALG032 592 Odocoileus.virginianus 2
ALG032 592 Rangifer.tarandus 5
ALG032 592 Canis.latrans 0
ALG032 592 Canis.lupus 0
ALG032 592 Vulpes.vulpes 0
ALG032 592 Lynx.canadensis 1
ALG032 592 Lontra.canadensis 0
ALG032 592 Martes.americana 0
ALG032 592 Ursus.americanus 0
ALG032 592 Lepus.americanus 0
ALG032 592 Oryctolagus.cuniculus 0
ALG032 592 Tamiasciurus.hudsonicus 0
ALG035 594 Alces.alces 11
ALG035 594 Cervus.canadensis 0
ALG035 594 Odocoileus.virginianus 1
ALG035 594 Rangifer.tarandus 6
ALG035 594 Canis.latrans 0
ALG035 594 Canis.lupus 2
ALG035 594 Vulpes.vulpes 0
ALG035 594 Lynx.canadensis 1
ALG035 594 Lontra.canadensis 0
ALG035 594 Martes.americana 0
ALG035 594 Ursus.americanus 2
ALG035 594 Lepus.americanus 7
ALG035 594 Oryctolagus.cuniculus 0
ALG035 594 Tamiasciurus.hudsonicus 3
ALG036 417 Alces.alces 1
ALG036 417 Cervus.canadensis 0
ALG036 417 Odocoileus.virginianus 5
ALG036 417 Rangifer.tarandus 0
ALG036 417 Canis.latrans 0
ALG036 417 Canis.lupus 0
ALG036 417 Vulpes.vulpes 0
ALG036 417 Lynx.canadensis 1
ALG036 417 Lontra.canadensis 0
ALG036 417 Martes.americana 0
ALG036 417 Ursus.americanus 0
ALG036 417 Lepus.americanus 42
ALG036 417 Oryctolagus.cuniculus 0
ALG036 417 Tamiasciurus.hudsonicus 15
ALG037 592 Alces.alces 2
ALG037 592 Cervus.canadensis 0
ALG037 592 Odocoileus.virginianus 0
ALG037 592 Rangifer.tarandus 3
ALG037 592 Canis.latrans 0
ALG037 592 Canis.lupus 0
ALG037 592 Vulpes.vulpes 0
ALG037 592 Lynx.canadensis 0
ALG037 592 Lontra.canadensis 0
ALG037 592 Martes.americana 0
ALG037 592 Ursus.americanus 2
ALG037 592 Lepus.americanus 0
ALG037 592 Oryctolagus.cuniculus 0
ALG037 592 Tamiasciurus.hudsonicus 0
ALG038 593 Alces.alces 0
ALG038 593 Cervus.canadensis 0
ALG038 593 Odocoileus.virginianus 0
ALG038 593 Rangifer.tarandus 12
ALG038 593 Canis.latrans 0
ALG038 593 Canis.lupus 0
ALG038 593 Vulpes.vulpes 0
ALG038 593 Lynx.canadensis 0
ALG038 593 Lontra.canadensis 0
ALG038 593 Martes.americana 1
ALG038 593 Ursus.americanus 1
ALG038 593 Lepus.americanus 0
ALG038 593 Oryctolagus.cuniculus 0
ALG038 593 Tamiasciurus.hudsonicus 0
ALG039 591 Alces.alces 2
ALG039 591 Cervus.canadensis 0
ALG039 591 Odocoileus.virginianus 0
ALG039 591 Rangifer.tarandus 5
ALG039 591 Canis.latrans 0
ALG039 591 Canis.lupus 6
ALG039 591 Vulpes.vulpes 0
ALG039 591 Lynx.canadensis 0
ALG039 591 Lontra.canadensis 0
ALG039 591 Martes.americana 0
ALG039 591 Ursus.americanus 3
ALG039 591 Lepus.americanus 0
ALG039 591 Oryctolagus.cuniculus 0
ALG039 591 Tamiasciurus.hudsonicus 0
ALG043 392 Alces.alces 6
ALG043 392 Cervus.canadensis 0
ALG043 392 Odocoileus.virginianus 21
ALG043 392 Rangifer.tarandus 0
ALG043 392 Canis.latrans 0
ALG043 392 Canis.lupus 0
ALG043 392 Vulpes.vulpes 0
ALG043 392 Lynx.canadensis 0
ALG043 392 Lontra.canadensis 0
ALG043 392 Martes.americana 0
ALG043 392 Ursus.americanus 2
ALG043 392 Lepus.americanus 13
ALG043 392 Oryctolagus.cuniculus 0
ALG043 392 Tamiasciurus.hudsonicus 0
ALG044 392 Alces.alces 15
ALG044 392 Cervus.canadensis 0
ALG044 392 Odocoileus.virginianus 10
ALG044 392 Rangifer.tarandus 0
ALG044 392 Canis.latrans 0
ALG044 392 Canis.lupus 3
ALG044 392 Vulpes.vulpes 2
ALG044 392 Lynx.canadensis 0
ALG044 392 Lontra.canadensis 0
ALG044 392 Martes.americana 1
ALG044 392 Ursus.americanus 7
ALG044 392 Lepus.americanus 2
ALG044 392 Oryctolagus.cuniculus 0
ALG044 392 Tamiasciurus.hudsonicus 0
ALG045 418 Alces.alces 5
ALG045 418 Cervus.canadensis 0
ALG045 418 Odocoileus.virginianus 0
ALG045 418 Rangifer.tarandus 1
ALG045 418 Canis.latrans 0
ALG045 418 Canis.lupus 5
ALG045 418 Vulpes.vulpes 0
ALG045 418 Lynx.canadensis 4
ALG045 418 Lontra.canadensis 0
ALG045 418 Martes.americana 3
ALG045 418 Ursus.americanus 6
ALG045 418 Lepus.americanus 3
ALG045 418 Oryctolagus.cuniculus 0
ALG045 418 Tamiasciurus.hudsonicus 0
ALG046 592 Alces.alces 9
ALG046 592 Cervus.canadensis 0
ALG046 592 Odocoileus.virginianus 18
ALG046 592 Rangifer.tarandus 0
ALG046 592 Canis.latrans 0
ALG046 592 Canis.lupus 0
ALG046 592 Vulpes.vulpes 1
ALG046 592 Lynx.canadensis 1
ALG046 592 Lontra.canadensis 0
ALG046 592 Martes.americana 0
ALG046 592 Ursus.americanus 0
ALG046 592 Lepus.americanus 14
ALG046 592 Oryctolagus.cuniculus 0
ALG046 592 Tamiasciurus.hudsonicus 0
ALG047 507 Alces.alces 1
ALG047 507 Cervus.canadensis 0
ALG047 507 Odocoileus.virginianus 15
ALG047 507 Rangifer.tarandus 5
ALG047 507 Canis.latrans 1
ALG047 507 Canis.lupus 13
ALG047 507 Vulpes.vulpes 0
ALG047 507 Lynx.canadensis 0
ALG047 507 Lontra.canadensis 0
ALG047 507 Martes.americana 0
ALG047 507 Ursus.americanus 9
ALG047 507 Lepus.americanus 1
ALG047 507 Oryctolagus.cuniculus 0
ALG047 507 Tamiasciurus.hudsonicus 1
ALG048 593 Alces.alces 0
ALG048 593 Cervus.canadensis 0
ALG048 593 Odocoileus.virginianus 1
ALG048 593 Rangifer.tarandus 7
ALG048 593 Canis.latrans 0
ALG048 593 Canis.lupus 2
ALG048 593 Vulpes.vulpes 0
ALG048 593 Lynx.canadensis 1
ALG048 593 Lontra.canadensis 0
ALG048 593 Martes.americana 1
ALG048 593 Ursus.americanus 4
ALG048 593 Lepus.americanus 0
ALG048 593 Oryctolagus.cuniculus 0
ALG048 593 Tamiasciurus.hudsonicus 0
ALG049 341 Alces.alces 2
ALG049 341 Cervus.canadensis 0
ALG049 341 Odocoileus.virginianus 14
ALG049 341 Rangifer.tarandus 0
ALG049 341 Canis.latrans 0
ALG049 341 Canis.lupus 0
ALG049 341 Vulpes.vulpes 0
ALG049 341 Lynx.canadensis 0
ALG049 341 Lontra.canadensis 0
ALG049 341 Martes.americana 1
ALG049 341 Ursus.americanus 10
ALG049 341 Lepus.americanus 0
ALG049 341 Oryctolagus.cuniculus 0
ALG049 341 Tamiasciurus.hudsonicus 4
ALG052 590 Alces.alces 13
ALG052 590 Cervus.canadensis 0
ALG052 590 Odocoileus.virginianus 65
ALG052 590 Rangifer.tarandus 1
ALG052 590 Canis.latrans 0
ALG052 590 Canis.lupus 3
ALG052 590 Vulpes.vulpes 0
ALG052 590 Lynx.canadensis 1
ALG052 590 Lontra.canadensis 0
ALG052 590 Martes.americana 1
ALG052 590 Ursus.americanus 17
ALG052 590 Lepus.americanus 9
ALG052 590 Oryctolagus.cuniculus 0
ALG052 590 Tamiasciurus.hudsonicus 0
ALG053 592 Alces.alces 3
ALG053 592 Cervus.canadensis 0
ALG053 592 Odocoileus.virginianus 0
ALG053 592 Rangifer.tarandus 1
ALG053 592 Canis.latrans 0
ALG053 592 Canis.lupus 0
ALG053 592 Vulpes.vulpes 3
ALG053 592 Lynx.canadensis 1
ALG053 592 Lontra.canadensis 0
ALG053 592 Martes.americana 0
ALG053 592 Ursus.americanus 2
ALG053 592 Lepus.americanus 0
ALG053 592 Oryctolagus.cuniculus 0
ALG053 592 Tamiasciurus.hudsonicus 0
ALG054 595 Alces.alces 1
ALG054 595 Cervus.canadensis 0
ALG054 595 Odocoileus.virginianus 28
ALG054 595 Rangifer.tarandus 0
ALG054 595 Canis.latrans 0
ALG054 595 Canis.lupus 1
ALG054 595 Vulpes.vulpes 0
ALG054 595 Lynx.canadensis 3
ALG054 595 Lontra.canadensis 0
ALG054 595 Martes.americana 0
ALG054 595 Ursus.americanus 2
ALG054 595 Lepus.americanus 25
ALG054 595 Oryctolagus.cuniculus 0
ALG054 595 Tamiasciurus.hudsonicus 0
ALG055 592 Alces.alces 3
ALG055 592 Cervus.canadensis 0
ALG055 592 Odocoileus.virginianus 19
ALG055 592 Rangifer.tarandus 0
ALG055 592 Canis.latrans 3
ALG055 592 Canis.lupus 26
ALG055 592 Vulpes.vulpes 2
ALG055 592 Lynx.canadensis 9
ALG055 592 Lontra.canadensis 0
ALG055 592 Martes.americana 1
ALG055 592 Ursus.americanus 10
ALG055 592 Lepus.americanus 29
ALG055 592 Oryctolagus.cuniculus 0
ALG055 592 Tamiasciurus.hudsonicus 2
ALG056 595 Alces.alces 8
ALG056 595 Cervus.canadensis 0
ALG056 595 Odocoileus.virginianus 6
ALG056 595 Rangifer.tarandus 0
ALG056 595 Canis.latrans 0
ALG056 595 Canis.lupus 0
ALG056 595 Vulpes.vulpes 0
ALG056 595 Lynx.canadensis 1
ALG056 595 Lontra.canadensis 0
ALG056 595 Martes.americana 0
ALG056 595 Ursus.americanus 2
ALG056 595 Lepus.americanus 0
ALG056 595 Oryctolagus.cuniculus 0
ALG056 595 Tamiasciurus.hudsonicus 0
ALG057 405 Alces.alces 0
ALG057 405 Cervus.canadensis 0
ALG057 405 Odocoileus.virginianus 13
ALG057 405 Rangifer.tarandus 0
ALG057 405 Canis.latrans 0
ALG057 405 Canis.lupus 1
ALG057 405 Vulpes.vulpes 0
ALG057 405 Lynx.canadensis 0
ALG057 405 Lontra.canadensis 0
ALG057 405 Martes.americana 0
ALG057 405 Ursus.americanus 2
ALG057 405 Lepus.americanus 0
ALG057 405 Oryctolagus.cuniculus 1
ALG057 405 Tamiasciurus.hudsonicus 0
ALG058 595 Alces.alces 2
ALG058 595 Cervus.canadensis 0
ALG058 595 Odocoileus.virginianus 51
ALG058 595 Rangifer.tarandus 1
ALG058 595 Canis.latrans 0
ALG058 595 Canis.lupus 0
ALG058 595 Vulpes.vulpes 0
ALG058 595 Lynx.canadensis 3
ALG058 595 Lontra.canadensis 0
ALG058 595 Martes.americana 0
ALG058 595 Ursus.americanus 11
ALG058 595 Lepus.americanus 58
ALG058 595 Oryctolagus.cuniculus 0
ALG058 595 Tamiasciurus.hudsonicus 0
ALG059 465 Alces.alces 7
ALG059 465 Cervus.canadensis 0
ALG059 465 Odocoileus.virginianus 0
ALG059 465 Rangifer.tarandus 3
ALG059 465 Canis.latrans 0
ALG059 465 Canis.lupus 0
ALG059 465 Vulpes.vulpes 0
ALG059 465 Lynx.canadensis 1
ALG059 465 Lontra.canadensis 0
ALG059 465 Martes.americana 0
ALG059 465 Ursus.americanus 9
ALG059 465 Lepus.americanus 2
ALG059 465 Oryctolagus.cuniculus 0
ALG059 465 Tamiasciurus.hudsonicus 0
ALG060 592 Alces.alces 1
ALG060 592 Cervus.canadensis 0
ALG060 592 Odocoileus.virginianus 0
ALG060 592 Rangifer.tarandus 0
ALG060 592 Canis.latrans 0
ALG060 592 Canis.lupus 0
ALG060 592 Vulpes.vulpes 0
ALG060 592 Lynx.canadensis 1
ALG060 592 Lontra.canadensis 0
ALG060 592 Martes.americana 2
ALG060 592 Ursus.americanus 6
ALG060 592 Lepus.americanus 1
ALG060 592 Oryctolagus.cuniculus 0
ALG060 592 Tamiasciurus.hudsonicus 0
ALG061 590 Alces.alces 0
ALG061 590 Cervus.canadensis 0
ALG061 590 Odocoileus.virginianus 60
ALG061 590 Rangifer.tarandus 0
ALG061 590 Canis.latrans 0
ALG061 590 Canis.lupus 0
ALG061 590 Vulpes.vulpes 0
ALG061 590 Lynx.canadensis 0
ALG061 590 Lontra.canadensis 0
ALG061 590 Martes.americana 4
ALG061 590 Ursus.americanus 5
ALG061 590 Lepus.americanus 0
ALG061 590 Oryctolagus.cuniculus 0
ALG061 590 Tamiasciurus.hudsonicus 0
ALG062 592 Alces.alces 1
ALG062 592 Cervus.canadensis 0
ALG062 592 Odocoileus.virginianus 48
ALG062 592 Rangifer.tarandus 0
ALG062 592 Canis.latrans 0
ALG062 592 Canis.lupus 4
ALG062 592 Vulpes.vulpes 0
ALG062 592 Lynx.canadensis 1
ALG062 592 Lontra.canadensis 0
ALG062 592 Martes.americana 0
ALG062 592 Ursus.americanus 16
ALG062 592 Lepus.americanus 4
ALG062 592 Oryctolagus.cuniculus 0
ALG062 592 Tamiasciurus.hudsonicus 0
ALG063 593 Alces.alces 1
ALG063 593 Cervus.canadensis 0
ALG063 593 Odocoileus.virginianus 23
ALG063 593 Rangifer.tarandus 4
ALG063 593 Canis.latrans 0
ALG063 593 Canis.lupus 1
ALG063 593 Vulpes.vulpes 0
ALG063 593 Lynx.canadensis 0
ALG063 593 Lontra.canadensis 0
ALG063 593 Martes.americana 1
ALG063 593 Ursus.americanus 2
ALG063 593 Lepus.americanus 1
ALG063 593 Oryctolagus.cuniculus 0
ALG063 593 Tamiasciurus.hudsonicus 1
ALG064 592 Alces.alces 2
ALG064 592 Cervus.canadensis 0
ALG064 592 Odocoileus.virginianus 0
ALG064 592 Rangifer.tarandus 10
ALG064 592 Canis.latrans 0
ALG064 592 Canis.lupus 0
ALG064 592 Vulpes.vulpes 0
ALG064 592 Lynx.canadensis 0
ALG064 592 Lontra.canadensis 0
ALG064 592 Martes.americana 0
ALG064 592 Ursus.americanus 0
ALG064 592 Lepus.americanus 0
ALG064 592 Oryctolagus.cuniculus 0
ALG064 592 Tamiasciurus.hudsonicus 0
ALG065 447 Alces.alces 26
ALG065 447 Cervus.canadensis 0
ALG065 447 Odocoileus.virginianus 0
ALG065 447 Rangifer.tarandus 1
ALG065 447 Canis.latrans 0
ALG065 447 Canis.lupus 0
ALG065 447 Vulpes.vulpes 0
ALG065 447 Lynx.canadensis 0
ALG065 447 Lontra.canadensis 0
ALG065 447 Martes.americana 1
ALG065 447 Ursus.americanus 2
ALG065 447 Lepus.americanus 0
ALG065 447 Oryctolagus.cuniculus 0
ALG065 447 Tamiasciurus.hudsonicus 0
ALG066 591 Alces.alces 2
ALG066 591 Cervus.canadensis 0
ALG066 591 Odocoileus.virginianus 1
ALG066 591 Rangifer.tarandus 6
ALG066 591 Canis.latrans 0
ALG066 591 Canis.lupus 0
ALG066 591 Vulpes.vulpes 0
ALG066 591 Lynx.canadensis 1
ALG066 591 Lontra.canadensis 0
ALG066 591 Martes.americana 0
ALG066 591 Ursus.americanus 0
ALG066 591 Lepus.americanus 0
ALG066 591 Oryctolagus.cuniculus 0
ALG066 591 Tamiasciurus.hudsonicus 0
ALG067 391 Alces.alces 2
ALG067 391 Cervus.canadensis 0
ALG067 391 Odocoileus.virginianus 3
ALG067 391 Rangifer.tarandus 0
ALG067 391 Canis.latrans 0
ALG067 391 Canis.lupus 2
ALG067 391 Vulpes.vulpes 0
ALG067 391 Lynx.canadensis 0
ALG067 391 Lontra.canadensis 0
ALG067 391 Martes.americana 0
ALG067 391 Ursus.americanus 0
ALG067 391 Lepus.americanus 0
ALG067 391 Oryctolagus.cuniculus 0
ALG067 391 Tamiasciurus.hudsonicus 0
ALG068 592 Alces.alces 3
ALG068 592 Cervus.canadensis 0
ALG068 592 Odocoileus.virginianus 0
ALG068 592 Rangifer.tarandus 0
ALG068 592 Canis.latrans 0
ALG068 592 Canis.lupus 0
ALG068 592 Vulpes.vulpes 0
ALG068 592 Lynx.canadensis 0
ALG068 592 Lontra.canadensis 0
ALG068 592 Martes.americana 0
ALG068 592 Ursus.americanus 2
ALG068 592 Lepus.americanus 0
ALG068 592 Oryctolagus.cuniculus 0
ALG068 592 Tamiasciurus.hudsonicus 0
ALG069 592 Alces.alces 6
ALG069 592 Cervus.canadensis 0
ALG069 592 Odocoileus.virginianus 64
ALG069 592 Rangifer.tarandus 0
ALG069 592 Canis.latrans 0
ALG069 592 Canis.lupus 0
ALG069 592 Vulpes.vulpes 0
ALG069 592 Lynx.canadensis 4
ALG069 592 Lontra.canadensis 0
ALG069 592 Martes.americana 0
ALG069 592 Ursus.americanus 25
ALG069 592 Lepus.americanus 3
ALG069 592 Oryctolagus.cuniculus 0
ALG069 592 Tamiasciurus.hudsonicus 0
ALG070 408 Alces.alces 2
ALG070 408 Cervus.canadensis 0
ALG070 408 Odocoileus.virginianus 4
ALG070 408 Rangifer.tarandus 3
ALG070 408 Canis.latrans 0
ALG070 408 Canis.lupus 0
ALG070 408 Vulpes.vulpes 0
ALG070 408 Lynx.canadensis 0
ALG070 408 Lontra.canadensis 0
ALG070 408 Martes.americana 1
ALG070 408 Ursus.americanus 1
ALG070 408 Lepus.americanus 0
ALG070 408 Oryctolagus.cuniculus 0
ALG070 408 Tamiasciurus.hudsonicus 0
ALG071 595 Alces.alces 3
ALG071 595 Cervus.canadensis 0
ALG071 595 Odocoileus.virginianus 46
ALG071 595 Rangifer.tarandus 0
ALG071 595 Canis.latrans 0
ALG071 595 Canis.lupus 1
ALG071 595 Vulpes.vulpes 1
ALG071 595 Lynx.canadensis 2
ALG071 595 Lontra.canadensis 0
ALG071 595 Martes.americana 0
ALG071 595 Ursus.americanus 22
ALG071 595 Lepus.americanus 21
ALG071 595 Oryctolagus.cuniculus 0
ALG071 595 Tamiasciurus.hudsonicus 0
ALG072 595 Alces.alces 1
ALG072 595 Cervus.canadensis 0
ALG072 595 Odocoileus.virginianus 5
ALG072 595 Rangifer.tarandus 1
ALG072 595 Canis.latrans 0
ALG072 595 Canis.lupus 0
ALG072 595 Vulpes.vulpes 0
ALG072 595 Lynx.canadensis 4
ALG072 595 Lontra.canadensis 0
ALG072 595 Martes.americana 0
ALG072 595 Ursus.americanus 3
ALG072 595 Lepus.americanus 5
ALG072 595 Oryctolagus.cuniculus 0
ALG072 595 Tamiasciurus.hudsonicus 0
ALG073 593 Alces.alces 1
ALG073 593 Cervus.canadensis 0
ALG073 593 Odocoileus.virginianus 0
ALG073 593 Rangifer.tarandus 23
ALG073 593 Canis.latrans 0
ALG073 593 Canis.lupus 0
ALG073 593 Vulpes.vulpes 0
ALG073 593 Lynx.canadensis 0
ALG073 593 Lontra.canadensis 0
ALG073 593 Martes.americana 0
ALG073 593 Ursus.americanus 0
ALG073 593 Lepus.americanus 0
ALG073 593 Oryctolagus.cuniculus 0
ALG073 593 Tamiasciurus.hudsonicus 0

It is often easier to use this long format to make summaries:

# We can them summaries those using dplyr
tmp <- long_obs %>%                   # Take the long observation data frame `long_obs` 
          group_by(sp) %>%            # Group by species
          summarise(count=sum(count)) # Sum all the independent observations

# Add it to the sp_summary dataframe
sp_summary <- left_join(sp_summary, tmp)

8.2.2 Raw occupancy

We can very quickly flip a count to a presence/absence using as.logical this converts all integers to 1 and keeps 0’s as 0!

# We use the mutate function to mutate the column
total_binary <-  total_obs %>%    # The total obs dataframe              
                    mutate(across(sp_summary$sp, ~+as.logical(.x)))  # across all of the species columns, make it binary

# Flip the dataframe to longer - as before
long_bin <- total_binary %>% 
  pivot_longer(cols=sp_summary$sp, names_to="sp", values_to = "count") # Takes the species names columns, and makes them unique rows with "sp" as the key 

Now when we do the same calculations, as before, we can calculate the number of sites occupied:

# We can now sum the presence/absences and divide by the number of survey locations
tmp <- long_bin %>% 
  group_by(sp) %>% 
  summarise(occupancy=sum(count)/nrow(locs)) # divided the sum by the number of sites

# add the results to the sp_summary
sp_summary <- left_join(sp_summary, tmp)

8.2.3 Comparison plot

Then we can use the dataframe created above to summaries the detections and the occupancy patterns.

Note - here we weave two plotly graphs together using the subplot() function!

# Lets put the dataframes in a sensible order
sp_summary <- sp_summary[order(sp_summary$count),]

yform <- list(categoryorder = "array",
              categoryarray = sp_summary$sp)

xform <- list(title="Captures")

# Capture rate
fig1 <- plot_ly(x = sp_summary$count, y = sp_summary$sp, type = 'bar', orientation = 'h') %>% 
 layout(yaxis = yform, xaxis=xform)

yform <- list(categoryorder = "array",
              categoryarray = sp_summary$sp,
              showticklabels=F)
xform <- list(title="Occupancy")


# Occupancy
fig2 <- plot_ly(x = sp_summary$occupancy, y = sp_summary$sp, type = 'bar', orientation = 'h') %>% 
 layout(yaxis = yform, xaxis=xform)

subplot(nrows=1,fig1, fig2, titleX = T) # We could stack them on top of one another using nrows=2

What does this output tell you about species-specific occurrences across the landscape?

8.3 Temporal patterns in capture rates

Next lets summaries the temporal patterns in the number of sites (placenames) surveyed, and the total number of animals captured. We will use the monthly dataframes in order to do this, but you could do it at the weekly or daily scale if required!

mon_obs <- read.csv("data/processed_data/AlgarRestorationProject_30min_independent_monthly_observations.csv", header=T)

We will first count the number of survey nights each location was active, then in the second step add the number of species detections.

# Count up the number of stations and the number of camera nights
mon_summary <- mon_obs %>%                  # Use the monthly observations dataframe
            group_by(date) %>%              # Group by the date
            summarise(locs_active=n(),      # Count the number of active cameras
                      cam_days=sum(days))   # And sum the active days 


# Add in the species specific counts - and join it with the mon_summary dataframe
mon_summary <- mon_obs %>% 
                group_by(date) %>%  
                summarise(across(sp_summary$sp, sum, na.rm=TRUE)) %>% # summarise across all of 
                                                                      # the species columns 
                left_join(x=mon_summary)   # Join with the mon_summary dataframe

Now lets use lubridate to convert the timestamp column to a date object and plot the output.

Each black dot represents the number of survey nights or average capture rate, respectively.

# We first need to convert the date column to a date object
mon_summary$date <- ym(mon_summary$date)

# Set up a two panel plot (side by side)
par(mfrow=c(1,2))

plot(mon_summary$date, mon_summary$locs_active,
     type="o", 
     pch=19,
     ylim=c(0, max(mon_summary$locs_active)),
     las=1, 
     ylab="Number of cameras active", xlab="Date")


# Sum all the captures rates for the species columns
mon_summary$all.sp <- rowSums(mon_summary[, sp_summary$sp])

# Plot them
plot(mon_summary$date, mon_summary$all.sp/(mon_summary$cam_days/100),
     type="o",
     pch=19,
     las=1, ylab="Detections per 100 cam days", xlab="Date")

As we saw in the error checking section, survey effort (number of cameras active) drops in early 2018 (left hand panel). The right hand panel shows the overall capture rate (for all species pooled), and you can see it is strongly seasonal - peaks in summers, and drops in the winter.

8.4 Species-specific capture rates

We should now split up this overall capture rate, and explore temporal patterns in species-specific detections. We can do this by looping the code with a for() loop.

par(mfrow=c(2,2))
i <- 1
for(i in 1:length(sp_summary$sp))
{
  plot(mon_summary$date, pull(mon_summary, sp_summary$sp[i])/(mon_summary$cam_days/100),  # The pull command allows you to grab a specific column in a dataframe and turn it into a vector!
     type="o",
     pch=19,
     las=1, ylab="Detections per 100 cam days", xlab="Date",
     main=sp_summary$sp[i])
}

Can you see any interesting patterns in here? What do black bears do in winter? What time of year do we get the most marten detections?

8.5 Spatial patterns in capture rates

We also often want to explore if there are any spatial patterns in capture rates, these can hint at any ecological relationships we might want to explore further. Here we do it for just a single species, the white-tailed deer (Odocoileus virginianus).

Here we make use of the ‘total_obs’ data frame we imported earlier. We also use the ‘locs’ dataframe.

total_obs <- left_join(total_obs, locs)

focal_species <- "Odocoileus.virginianus"

focal_cr <- pull(total_obs, focal_species)/(total_obs$days/100)

m <- leaflet() %>%
  addProviderTiles(providers$Esri.WorldTopoMap, group="Base") %>%     
  addCircleMarkers(lng=locs$longitude, lat=locs$latitude,
                   # Add a popup of the deployment code 
                   popup=paste(locs$placename),
                   radius=(focal_cr/max(focal_cr)*10)+1, stroke=F,
                   fillOpacity=0.6) 
m

Try it for some different species. Can you see any different patterns?

8.6 Species co-occurences

Camera trap data are being increasingly used to model multiple species communities. In the same way in which we used the corrplot package in the (analysis covariates section(#covariates), we can use it to explore the co-occurrence patterns of the species in the community.

The plot below uses the ‘total_obs’ dataframe, and performs pairwise correlations between the species on the left, and the species on the top row.

Blue colors = positive correlation -> at locations where you have high counts of one species, you also have high counts of the paired species.

Red colors = negative correlation -> at locations where you have high counts of one species, then you are likely to have low counts of the species pair (or vice-versa).

We implement a more nuanced form of this data analysis in the interactions chapter. To make this plot we use the total_obs dataframe.

# Reset the plot parameters
par(mfrow=c(1,1))

# Pull the data for each of the species from 
tmp <- total_obs[, sp_summary$sp]
M <- cor(tmp)

corrplot(M, method="color", 
         type="upper", 
         order="hclust",
         # addCoef.col = "black", # We suppress the coefs to make a cleaner plot
         tl.col="black", tl.srt=45, #Text label color and rotation
         diag=FALSE
         )

What would you conclude?

8.7 Covariate plots

So far we have explored temporal and spatial patterns in species counts - but what about the effects of the covariates we derived in the analysis covariates section? Before embarking on an in depth analysis, it is always sensible to plot your response terms against predictors.

Note we are often paranoid about “data dredging” or shopping around for “significant” predictors, as this isn’t good scientific practice. Here, we should only explore covariates for which we have a prior belief in there effects on the response term. We are not looking for significant relationships, rather trying to understand the structure of our data! You should know your data inside out before you start modelling.

Final note just because you do not see a strong effect in your raw data, doesn’t mean that it will not have an effect in your final models, particularly if you plan to account for multiple confounding variables or use random effects!

Lets have a quick look at what we have available to us in the camera_locations_and_covariates file:

str(locs)
## 'data.frame':    38 obs. of  13 variables:
##  $ project_id     : chr  "AlgarRestorationProject" "AlgarRestorationProject" "AlgarRestorationProject" "AlgarRestorationProject" ...
##  $ placename      : chr  "ALG027" "ALG029" "ALG031" "ALG032" ...
##  $ longitude      : num  -112 -113 -112 -112 -112 ...
##  $ latitude       : num  56.3 56.4 56.3 56.4 56.4 ...
##  $ feature_type   : Factor w/ 3 levels "HumanUse","NatRegen",..: 1 1 1 1 1 2 1 1 1 2 ...
##  $ line_of_sight_m: num  253 207 334 83 440 ...
##  $ water_depth_m  : num  0 0.385 0 0 0 ...
##  $ elevation      : int  528 510 526 510 505 522 529 528 529 509 ...
##  $ elev_units     : chr  "meters" "meters" "meters" "meters" ...
##  $ road_dist_m    : num  16077 15647 14618 20980 19146 ...
##  $ water_dist_m   : num  7429 6707 5873 9772 10020 ...
##  $ mean_ndvi      : num  7690 7662 7380 7130 7485 ...
##  $ colours        : chr  "#30123BFF" "#30123BFF" "#30123BFF" "#30123BFF" ...

We have - feature type, water_depth_m, line_of_sight_m, elevation, road_dist_m, water_dist_m, lcc_habitats and mean_ndvi as potential covariates.

Before we proceed, it is good practice to convert categorical variables (like feature_type and lcc_habitats) to factors. There is a very easy way to do that using the mutate_if() function of dplyr:

locs <- locs %>% 
            mutate_if(is.character,as.factor) # If a column is a character string, make it a factor

Run str(locs) again to see what has changed!

Before we explore patterns, we need to add the covariates to the response dataframes (e.g. total_obs). We will use left_join().

total_obs <- left_join(total_obs, locs)

Lets explore two different types of plot we can make for once particular species, then we will challenge you to explore some relationships of your own.

8.7.1 Continuous predictors

The best plot for two continuous predictors is a scatter plot.

In base R:

plot(data=total_obs, 
       Alces.alces ~   # Y variable
       line_of_sight_m, # X variable
       pch=19,
       las=1
     ) 

We can make the same plot in ggplot:

ggplot(data=total_obs, 
       aes(x=line_of_sight_m, y=Alces.alces)) + 
       geom_point() +                   # Specify a scatter plot
       theme_classic()                  # A nice theme

The great thing about ggplot is that it is easy to add trend lines:

ggplot(data=total_obs, 
       aes(x=line_of_sight_m, y=Alces.alces)) + 
       geom_point() +                   # Specify a scatter plot
       theme_classic() +  
       geom_smooth(method=lm, se=T, fullrange=TRUE)   # A nice theme

What do you think?

For more ggplot scatterplot examples (with code) see the R graph gallery - Scatterplots.

Let’s checkout another predictor:

ggplot(data=total_obs, 
       aes(x=mean_ndvi, y=Alces.alces)) + 
       geom_point() +                   # Specify a scatter plot
       theme_classic() +  
       geom_smooth(method=lm, se=T, fullrange=TRUE)   # A nice theme

8.7.2 Catagorical predictors

For categorical predictors boxplots are very useful!

Base R:

boxplot(data=total_obs,
          Alces.alces ~ feature_type)

Or if ggplot is your thing:

ggplot(total_obs, aes(x=feature_type, y=Alces.alces)) +
  geom_boxplot()+
  theme_classic()

For more ggplot boxplot examples (with code) see R Graph Galley - Boxplots. There is some cool stuff in there!

8.7.3 Do your own exploration

We will now list some potential relationships in the data, you should decide the best way to explore each one:

  • Wolves (Canis lupus) use locations with longer line_of_sight_m more frequently
  • Caribou (Rangifer tarandus) use locations where the water table is close to the surface (low water_depth_m)
  • White-tailed deer (Odocoileus virginianus) use locations with higher vegetation productivity (mean_ndvi)
  • Lynx (Lynx canadensis) select human use feature types over other feature types
  • Lynx (Lynx canadensis) select locations with higher snowshoe hare (Lepus americanus) activity

Can you find any evidence to support these? Are there any other things that interest you?

Remember we have the following species:

##  [1] "Cervus.canadensis"       "Lontra.canadensis"      
##  [3] "Oryctolagus.cuniculus"   "Canis.latrans"          
##  [5] "Vulpes.vulpes"           "Martes.americana"       
##  [7] "Tamiasciurus.hudsonicus" "Lynx.canadensis"        
##  [9] "Canis.lupus"             "Rangifer.tarandus"      
## [11] "Alces.alces"             "Ursus.americanus"       
## [13] "Lepus.americanus"        "Odocoileus.virginianus"

And the following covariates:

## [1] "feature_type"    "line_of_sight_m" "water_depth_m"   "elevation"      
## [5] "road_dist_m"     "water_dist_m"    "mean_ndvi"