Visualize deployment features

This vignette shows how to use function map_dep() to visualize important deployment features on a leaflet map:

number of identified species
number of observations
RAI (Relative Abundance Index)
effort (duration of a deployment)

Setup

Load packages:

library(camtraptor)
library(lubridate)
#> 
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#> 
#>     date, intersect, setdiff, union
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

This load automatically a camera trap data package, mica, containing camera trap data about musk rat and coypu. We will use this variable from now on.

Get taxonomic information

As some features must or could be used in combination with a species name, it’s sometimes useful to have an idea first about which species have been detected and the correspondent vernacular names if present:

get_species(mica)
#> # A tibble: 9 × 5
#>   taxonID taxonIDReference                scientificName     vernacula…¹ verna…²
#>   <chr>   <chr>                           <chr>              <chr>       <chr>  
#> 1 DGP6    https://www.catalogueoflife.org Anas platyrhynchos mallard     wilde …
#> 2 DGPL    https://www.catalogueoflife.org Anas strepera      gadwall     krakee…
#> 3 32FH    https://www.catalogueoflife.org Ardea              great hero… reigers
#> 4 GCHS    https://www.catalogueoflife.org Ardea cinerea      grey heron  blauwe…
#> 5 RQPW    https://www.catalogueoflife.org Castor fiber       Eurasian b… bever  
#> 6 6MB3T   https://www.catalogueoflife.org Homo sapiens       human       mens   
#> 7 3Y9VW   https://www.catalogueoflife.org Martes foina       beech mart… steenm…
#> 8 44QYC   https://www.catalogueoflife.org Mustela putorius   European p… bunzing
#> 9 5BSG3   https://www.catalogueoflife.org Vulpes vulpes      red fox     vos    
#> # … with abbreviated variable names ¹vernacularNames.en, ²vernacularNames.nl

Create maps

Basic usage

Number of species

You can visualize the number of species detected by each deployment by using the function map_dep() with feature argument set to n_species:

map_dep(mica,
        feature = "n_species")

Number of observations

To visualize the number of observations, set feature = n_obs:

map_dep(mica,
        feature = "n_obs")

You can also specify which species you want to calculate the number of observations:

map_dep(mica,
        feature = "n_obs",
        species = "Anas platyrhynchos")
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7

Notice how zero values are also visualized by a specific circle marker for ease detection. Color: red by default.

You can filter by sex:

map_dep(
  mica,
  "n_obs",
  species = "Anas platyrhynchos",
  sex = c("female", "unknown")
)
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7

and life stage:

map_dep(
  mica,
  "n_obs",
  life_stage = c("unknown", "subadult")
)
#> There are 2 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7

Number of individuals

To visualize the number of observed individuals, set feature = n_individuals:

map_dep(mica,
        feature = "n_individuals")

As for observations, you can specify a species:

map_dep(mica,
        feature = "n_individuals",
        species = "Anas platyrhynchos")
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7

and filter by sex and/or life stage:

map_dep(
  mica,
  "n_individuals",
  species = "Anas platyrhynchos",
  sex = c("female", "unknown")
)
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7

RAI

To visualize the Relative Abundance Index (RAI) for a species, set feature = rai and specify a species using its scientific name:

map_dep(mica,
        feature = "rai",
        species = "Anas platyrhynchos")
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7

Notice that in this package the RAI is normalized over a deployment activity period of 100 days.

As for number of observations and number of individuals, you can filter by sex and/or life stage:

map_dep(
  mica,
  "rai",
  species = "Anas platyrhynchos",
  sex = c("female", "unknown")
)
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7

Common names are allowed as values of species as well:

map_dep(mica,
        feature = "rai",
        species = "great herons")
#> Scientific name of great herons: Ardea
#> There are 3 deployments without observations: 29b7d356-4bb4-4ec4-b792-2af5cc32efa8, 577b543a-2cf1-4b23-b6d2-cda7e2eac372 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7

Values of species are also interpreted case insensitive:

map_dep(mica,
        feature = "rai",
        species = "CastoR FIBer")
#> There are 3 deployments without observations: 29b7d356-4bb4-4ec4-b792-2af5cc32efa8, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7

If species is not specified or is wrong, an informative error message listing all valid values is returned:

map_dep(mica,
          feature = "rai",
          species = "This is not a species name")
#> Error: Invalid value for species parameter: this is not a species name.
#> Valid inputs are: anas platyrhynchos, anas strepera, ardea, ardea cinerea, castor fiber, homo sapiens, martes foina, mustela putorius, vulpes vulpes, mallard, gadwall, great herons, grey heron, eurasian beaver, human, beech marten, european polecat, red fox, wilde eend, krakeend and others...

RAI (individuals)

You can also visualize the RAI based on number of detected individuals instead of the standard RAI which is based on the number of observations. Set feature = "rai_individuals":

map_dep(mica,
        feature = "rai_individuals",
        species = "Anas platyrhynchos")
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7

Everything described in previous section about visualizing RAI holds true for RAI based on individuals as well.

Effort

Visualize duration of the deployments, also called effort, as number of active hours:

map_dep(mica, 
        feature = "effort",
        effort_unit = "hour")

The same using days as time unit

map_dep(mica, 
        feature = "effort",
        effort_unit = "day")

or months

map_dep(mica, 
        feature = "effort",
        effort_unit = "month")

Clustering and hovering

You can specify which information you want to show while hovering with the mouse over the deployment. You can choose among all columns from deployments (see allowed fields in camera trap data package standard documentation) and n (number of species, number of observations or RAI).

Here below the lat/lon, the camera height and the tags are shown while hovering:

map_dep(mica,
        hover_columns = c("latitude",
                          "longitude",
                          "cameraHeight",
                          "tags"),
        feature = "n_obs"
)

Deactivating both cluster mode and hovering is also possible:

map_dep(mica,
        feature = "n_species", 
        cluster = FALSE,
        hover_columns = NULL)

Visualize deployments without detected animals

It can happen that some deployments didn’t observe any animal. While visualizing the number of species, they are shown in the map as red circles as deployments with zero values and a message is returned to the R console:

# create data package with less observations
mica_less_obs <- mica
mica_less_obs$data$observations <- 
  mica$data$observations %>% 
  filter(timestamp > lubridate::as_datetime("2020-06-30"))

# create new map
map_dep(mica_less_obs,
        feature = "n_species")
#> There are 2 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7

Notice that this allows you to visually distinguish - deployments without observations: grey circle as the number of species, the feature value, is NA - deployments with only observations of unknown species: red circle as the number of species, the feature value, is zero

Use a color palette

The default color palette is a viridis color palette called "inferno". You can specify another viridis color palette, e.g. "viridis" or "magma", or a RColorBrewer palette, e.g. "BuPu" or "Oranges". Below we use the viridis color palette:

map_dep(
  mica,
  "n_obs",
  palette = "viridis"
)

We can use a palette from RColorBrewer, e.g. the "BuPu" palette:

map_dep(
  mica,
  "n_obs",
  palette = "BuPu"
)

Another easy way to specify a palette is to create it by passing a vector of colors as names or hex colors, e.g. c("black", "blue", "#A3675F"):

map_dep(
  mica,
  "n_obs",
  palette = c("black", "blue", "#A3675F")
)

Use a specific icon and color for zero values

You can pass to zero_value_icon_url argument the URL to an icon and to zero_values_icon_size the size in pixels of such icon. There are several icon libraries you can choose from. A library with many free icons is icons8. Here, an example with the icon of Fry from Futurama animation series, color style, and size 50:

map_dep(
  mica,
  "n_obs",
  life_stage = "subadult",
  zero_values_icon_url = "https://img.icons8.com/color/48/000000/futurama-fry.png",
  zero_values_icon_size = 50
)
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7

Tipically the color is part of the URL. Here below two examples where we change the color of the default icon to green (2ECC71):

map_dep(
  mica,
  "n_obs",
  life_stage = "subadult",
  zero_values_icon_url = 
    "https://img.icons8.com/ios-glyphs/30/2ECC71/multiply.png"
)
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7

or the INBO fuchsia (#C04384):

map_dep(
  mica,
  "n_obs",
  life_stage = "subadult",
  zero_values_icon_url = 
    "https://img.icons8.com/ios-glyphs/30/C04384/multiply.png"
)
#> There are 3 deployments without observations: 577b543a-2cf1-4b23-b6d2-cda7e2eac372, 62c200a9-0e03-4495-bcd8-032944f6f5a1 and 7ca633fa-64f8-4cfc-a628-6b0c419056d7

Modifying the default value ("black") can be useful as the color of deployments with zero values can be sometimes too similar to one of the colors used in the palette.

Modify circle size

You can also modify the upper and lower limit of the circle sizes by specifying radius_range (default: c(10,50):

map_dep(mica,
        feature = "n_obs",
        radius_range = c(20, 150))

Use absolute scale

By default the upper limit of color palette and radius are defined based on the actual feature values. However, sometimes can be useful to set up an absolute upper limit. This can be done by setting argument relative_scale to FALSE and specifying the upper limit in max_scale.

Upper limit lower than number of observations:

map_dep(mica,
        feature = "n_obs",
        relative_scale = FALSE,
        max_scale = 2)

Upper limit higher than number of observations:

map_dep(mica,
        feature = "n_obs",
        relative_scale = FALSE,
        max_scale = 50)

Use filter predicates

You maybe would like to visualize deployment information for a subset of deployments. To do this, you can use filter predicates. E.g. visualize number of observations for the deployments with longitude equal or higher than 5.6:

map_dep(mica,
        pred_gt("longitude", 5.6),
        feature = "n_obs")
#> df %>% dplyr::filter((longitude > 5.6))

More about filter predicates in filter predicates article.

Damiano Oldoni

2023-03-14