Explore acoustic telemetry data

Introduction

In this vignette we show a typical workflow using the etn package to retrieve acoustic telemetry data from ETN. We will also use the packages dplyr for data exploration, lubridate to handle datetime data and leaflet for interactive visualizations.

To start, load the etn package:

library(etn)

And the other packages (with installation if necessary):

other_pkgs <- c("dplyr", "lubridate", "leaflet", "htmlwidgets", "htmltools")

# install missing packages
pkgs_to_install <- other_pkgs[!other_pkgs %in% rownames(installed.packages())]
install.packages(pkgs_to_install)

# load packages
library(dplyr)
library(lubridate)
library(leaflet)

User credentials

etn functions require credentials (username and password) to connect to the ETN database. See vignette("authentication") to configure credentials.

Select project(s) of interest

A researcher typically works in the context of one or more animal projects. As the project codes are not always easy to remember, let’s start by getting an overview of all projects:

all_projects <- get_animal_projects()

Show a preview:

all_projects |> head(10)

## # A tibble: 10 × 11
##    project_id project_code          project_type telemetry_type project_name   
##         <int> <chr>                 <chr>        <chr>          <chr>          
##  1        793 2004_Gudena           animal       Acoustic       Silver eel mig…
##  2         16 2010_phd_reubens      animal       Acoustic       2010_phd_reube…
##  3        841 2010_phd_reubens_sync animal       Acoustic       2010_phd_reube…
##  4        760 2011_Loire            animal       Acoustic       2011_Loire     
##  5        754 2011_Warnow           animal       Acoustic       Silver eel mig…
##  6         20 2011_rivierprik       animal       Acoustic       2011 Rivierprik
##  7         15 2012_leopoldkanaal    animal       Acoustic       2012 Leopoldka…
##  8        757 2013_Foyle            animal       Acoustic       2013_Foyle     
##  9         18 2013_albertkanaal     animal       Acoustic       2013 Albertkan…
## 10        801 2014_Frome            animal       <NA>           2014_Frome     
##    start_date end_date   latitude longitude moratorium imis_dataset_id
##    <date>     <date>        <dbl>     <dbl> <lgl>                <int>
##  1 2004-01-01 2005-12-31     56.4      9.91 FALSE                 6361
##  2 2010-08-01 2012-10-30     56.4      2.74 FALSE                 5846
##  3 2010-08-01 2012-10-30     NA       NA    FALSE                 6582
##  4 2011-11-11 2012-02-25     47.3     -1.98 FALSE                 6336
##  5 2011-06-01 2012-10-12     54.1     12.1  FALSE                 6332
##  6 2011-01-01 2012-09-03     50.9      3.66 FALSE                 5867
##  7 2012-01-01 2016-01-18     NA       NA    FALSE                 5873
##  8 2013-07-01 2014-03-01     54.9     -7.39 TRUE                  6333
##  9 2013-10-10 2018-12-31     51.1      5.11 FALSE                 5868
## 10 2014-10-01 2015-01-31     50.7     -2.11 FALSE                 6385

If you know the project code already and you are just interested to get more information about it, you can specify it in the get_animal_projects() function directly:

projects_code <- c("2014_demer", "2015_dijle")
projects_study <- get_animal_projects(animal_project_code = projects_code)
projects_study

## # A tibble: 2 × 11
##   project_id project_code project_type telemetry_type project_name start_date
##        <int> <chr>        <chr>        <chr>          <chr>        <date>    
## 1         21 2014_demer   animal       Acoustic       2014 Demer   2014-04-10
## 2         22 2015_dijle   animal       Acoustic       2015 Dijle   2015-04-16
##   end_date   latitude longitude moratorium imis_dataset_id
##   <date>        <dbl>     <dbl> <lgl>                <int>
## 1 2015-02-13     51.0      5.06 FALSE                 5871
## 2 2018-12-31     51.0      4.50 FALSE                 5872

This is exactly the same as retrieving all projects first and filtering them afterwards based on column project_code:

all_projects |>
  filter(project_code %in% c(projects_code))

## # A tibble: 2 × 11
##   project_id project_code project_type telemetry_type project_name start_date
##        <int> <chr>        <chr>        <chr>          <chr>        <date>    
## 1         21 2014_demer   animal       Acoustic       2014 Demer   2014-04-10
## 2         22 2015_dijle   animal       Acoustic       2015 Dijle   2015-04-16
##   end_date   latitude longitude moratorium imis_dataset_id
##   <date>        <dbl>     <dbl> <lgl>                <int>
## 1 2015-02-13     51.0      5.06 FALSE                 5871
## 2 2018-12-31     51.0      4.50 FALSE                 5872

To list all available animal project codes as a vector, you can use list_animal_project_codes(), one of the etn functions in the list_* family:

list_animal_project_codes() |> head(10)

##  [1] "2004_Gudena"           "2010_phd_reubens"      "2010_phd_reubens_sync"
##  [4] "2011_Loire"            "2011_rivierprik"       "2011_Warnow"          
##  [7] "2012_leopoldkanaal"    "2013_albertkanaal"     "2013_Foyle"           
## [10] "2014_demer"

Animals

By using get_animals() you can retrieve information about each animal (animal_id), such as scientific name, length, capture/release date and location, and the attached tag(s) (tag_serial_number):

animals <- get_animals(animal_project_code = projects_code)
animals |> head(10)

## # A tibble: 10 × 66
##    animal_id animal_project_code tag_serial_number tag_type tag_subtype
##        <int> <chr>               <chr>             <chr>    <chr>      
##  1       304 2014_demer          1187449           acoustic animal     
##  2       384 2014_demer          1157781           acoustic animal     
##  3       385 2014_demer          1157782           acoustic animal     
##  4       386 2014_demer          1157783           acoustic animal     
##  5       305 2014_demer          1187450           acoustic animal     
##  6       383 2014_demer          1157780           acoustic animal     
##  7       369 2014_demer          1171781           acoustic animal     
##  8       370 2014_demer          1171782           acoustic animal     
##  9       365 2014_demer          1171775           acoustic animal     
## 10       366 2014_demer          1171776           acoustic animal     
##    acoustic_tag_id acoustic_tag_id_alter…¹ scientific_name common_name aphia_id
##    <chr>           <chr>                   <chr>           <chr>          <int>
##  1 A69-1601-16129  ""                      Rutilus rutilus roach         154333
##  2 A69-1601-28296  ""                      Silurus glanis  wels catfi…   154677
##  3 A69-1601-28297  ""                      Silurus glanis  wels catfi…   154677
##  4 A69-1601-28298  ""                      Silurus glanis  wels catfi…   154677
##  5 A69-1601-16130  ""                      Rutilus rutilus roach         154333
##  6 A69-1601-28295  ""                      Squalius cepha… chub          282855
##  7 A69-1601-26535  ""                      Silurus glanis  wels catfi…   154677
##  8 A69-1601-26536  ""                      Silurus glanis  wels catfi…   154677
##  9 A69-1601-26529  ""                      Silurus glanis  wels catfi…   154677
## 10 A69-1601-26530  ""                      Silurus glanis  wels catfi…   154677
## # ℹ abbreviated name: ¹acoustic_tag_id_alternative
## # ℹ 56 more variables: animal_label <chr>, animal_nickname <chr>, tagger <chr>,
## #   capture_date_time <dttm>, capture_location <chr>, capture_latitude <dbl>,
## #   capture_longitude <dbl>, capture_method <chr>, capture_depth <chr>,
## #   capture_temperature_change <chr>, release_date_time <dttm>,
## #   release_location <chr>, release_latitude <dbl>, release_longitude <dbl>,
## #   recapture_date_time <dttm>, length1_type <chr>, length1 <dbl>, …

What species and how many individuals are tracked for the projects 2014_demer and 2015_dijle?

animals |>
  count(scientific_name)

## # A tibble: 7 × 2
##   scientific_name        n
##   <chr>              <int>
## 1 Anguilla anguilla      1
## 2 Cyprinus carpio        2
## 3 Petromyzon marinus     2
## 4 Platichthys flesus     8
## 5 Rutilus rutilus        6
## 6 Silurus glanis        20
## 7 Squalius cephalus      3

Detections

Let’s say we are interested in the tracking data of Wels catfish (Silurus glanis) in 2014. You can retrieve the detection history using get_acoustic_detections():

detections_silurus <- get_acoustic_detections(
  animal_project_code = projects_code,
  start_date = "2014-01-01",
  end_date = "2015-01-01", # The end date is exclusive
  scientific_name = "Silurus glanis"
)

Preview:

detections_silurus |> head(10)

## # A tibble: 10 × 20
##    detection_id date_time           tag_serial_number acoustic_tag_id
##           <int> <dttm>              <chr>             <chr>          
##  1     22603503 2014-08-24 00:48:26 1171775           A69-1601-26529 
##  2     22612214 2014-08-18 23:45:03 1171775           A69-1601-26529 
##  3     22613768 2014-06-26 22:54:46 1171775           A69-1601-26529 
##  4     22615492 2014-08-16 21:34:10 1171775           A69-1601-26529 
##  5     22617855 2014-08-16 21:05:51 1171775           A69-1601-26529 
##  6     22618631 2014-06-26 21:47:33 1171775           A69-1601-26529 
##  7     22621944 2014-09-06 03:08:05 1171775           A69-1601-26529 
##  8     22624153 2014-07-26 20:37:43 1171775           A69-1601-26529 
##  9     22624408 2014-09-16 00:48:53 1171775           A69-1601-26529 
## 10     22627314 2014-08-16 23:44:40 1171775           A69-1601-26529 
##    animal_project_code animal_id scientific_name acoustic_project_code
##    <chr>                   <int> <chr>           <chr>                
##  1 2014_demer                365 Silurus glanis  demer                
##  2 2014_demer                365 Silurus glanis  demer                
##  3 2014_demer                365 Silurus glanis  demer                
##  4 2014_demer                365 Silurus glanis  demer                
##  5 2014_demer                365 Silurus glanis  demer                
##  6 2014_demer                365 Silurus glanis  demer                
##  7 2014_demer                365 Silurus glanis  demer                
##  8 2014_demer                365 Silurus glanis  demer                
##  9 2014_demer                365 Silurus glanis  demer                
## 10 2014_demer                365 Silurus glanis  demer                
## # ℹ 12 more variables: receiver_id <chr>, station_name <chr>,
## #   deploy_latitude <dbl>, deploy_longitude <dbl>, sensor_value <dbl>,
## #   sensor_unit <chr>, sensor2_value <dbl>, sensor2_unit <chr>,
## #   signal_to_noise_ratio <int>, source_file <chr>, qc_flag <lgl>,
## #   deployment_id <int>

Which individuals have been detected (animal_id) and in which period?

detections_silurus_period <-
  detections_silurus |>
  mutate(date = date(date_time)) |>
  group_by(animal_id) |>
  summarize(
    start = min(date),
    end = max(date)
  )
detections_silurus_period

## # A tibble: 9 × 3
##   animal_id start      end       
##       <int> <date>     <date>    
## 1       365 2014-05-26 2014-11-16
## 2       366 2014-05-26 2014-09-22
## 3       367 2014-06-18 2014-06-19
## 4       368 2014-05-26 2014-09-01
## 5       369 2014-05-21 2014-07-01
## 6       370 2014-05-20 2014-11-16
## 7       384 2014-04-24 2014-04-26
## 8       385 2014-04-24 2014-12-19
## 9       386 2014-04-24 2014-12-28

Notice we group by animal_id, the unique identifier of the fish. However, if the fish has only been tagged once (as typically occurs), we could use acoustic_tag_id as well, i.e. the identifier picked up by acoustic receivers:

detections_silurus |>
  mutate(date = date(date_time)) |>
  group_by(acoustic_tag_id) |>
  summarize(
    start = min(date),
    end = max(date)
  )

## # A tibble: 9 × 3
##   acoustic_tag_id start      end       
##   <chr>           <date>     <date>    
## 1 A69-1601-26529  2014-05-26 2014-11-16
## 2 A69-1601-26530  2014-05-26 2014-09-22
## 3 A69-1601-26531  2014-06-18 2014-06-19
## 4 A69-1601-26534  2014-05-26 2014-09-01
## 5 A69-1601-26535  2014-05-21 2014-07-01
## 6 A69-1601-26536  2014-05-20 2014-11-16
## 7 A69-1601-28296  2014-04-24 2014-04-26
## 8 A69-1601-28297  2014-04-24 2014-12-19
## 9 A69-1601-28298  2014-04-24 2014-12-28

We can also get the tracking duration of each fish:

detections_silurus_duration <-
  detections_silurus |>
  group_by(animal_id) |>
  summarize(duration = max(date_time) - min(date_time))
detections_silurus_duration

## # A tibble: 9 × 2
##   animal_id duration       
##       <int> <drtn>         
## 1       365 173.143345 days
## 2       366 118.459780 days
## 3       367   1.589421 days
## 4       368  98.322998 days
## 5       369  41.792720 days
## 6       370 179.210486 days
## 7       384   1.435150 days
## 8       385 238.517766 days
## 9       386 248.323808 days

How many times has an individual has been detected?

detections_silurus |>
  group_by(animal_id) |>
  count()

## # A tibble: 9 × 2
## # Groups:   animal_id [9]
##   animal_id     n
##       <int> <int>
## 1       365  4857
## 2       366  2778
## 3       367   497
## 4       368  7690
## 5       369  1183
## 6       370  4462
## 7       384  2563
## 8       385 28004
## 9       386 18455

Stations

At how many detection stations have the individuals been detected?

detections_silurus |>
  group_by(animal_id) |>
  distinct(station_name) |>
  count()

## # A tibble: 9 × 2
## # Groups:   animal_id [9]
##   animal_id     n
##       <int> <int>
## 1       365    16
## 2       366     9
## 3       367     1
## 4       368    23
## 5       369     9
## 6       370    18
## 7       384     1
## 8       385    18
## 9       386    15

Which stations have been involved? You can retrieve them using list_values function applied to column station_name:

stations_silurus <-
  detections_silurus |>
  list_values(station_name)

## 23 unique station_name values

stations_silurus

##  [1] "de-7"   "s-2a"   "de-3"   "de-5"   "de-8"   "de-10"  "de-11"  "de-14a"
##  [9] "de-13"  "de-14"  "de-19"  "de-9"   "de-18"  "de-20"  "de-2"   "de-12" 
## [17] "de-1"   "de-4"   "de-6"   "de-21"  "de-16"  "de-23"  "de-22"

Notice how a detection station can be linked to multiple deployments:

detections_silurus |>
  distinct(station_name, deployment_id) |>
  group_by(station_name) |>
  add_tally() |>
  arrange(desc(n))

## # A tibble: 33 × 3
## # Groups:   station_name [23]
##    station_name deployment_id     n
##    <chr>                <int> <int>
##  1 de-12                 2938     3
##  2 de-12                 1656     3
##  3 de-12                 1470     3
##  4 de-7                  1378     2
##  5 de-3                  1381     2
##  6 de-8                  1427     2
##  7 de-14a                1431     2
##  8 de-14                 1433     2
##  9 de-9                  1437     2
## 10 de-3                  2869     2
## # ℹ 23 more rows

Sometimes it’s interesting to know the number of detections per station:

n_detect_station <-
  detections_silurus |>
  group_by(station_name) |>
  count()
n_detect_station

## # A tibble: 23 × 2
## # Groups:   station_name [23]
##    station_name     n
##    <chr>        <int>
##  1 de-1           368
##  2 de-10         1681
##  3 de-11          163
##  4 de-12         4203
##  5 de-13          166
##  6 de-14        12266
##  7 de-14a        6604
##  8 de-16          591
##  9 de-18          148
## 10 de-19          650
## # ℹ 13 more rows

It’s also interesting to know the number of unique individuals per station:

n_silurus_station <-
  detections_silurus |>
  distinct(station_name, animal_id) |>
  group_by(station_name) |>
  count()
n_silurus_station

## # A tibble: 23 × 2
## # Groups:   station_name [23]
##    station_name     n
##    <chr>        <int>
##  1 de-1             3
##  2 de-10            6
##  3 de-11            4
##  4 de-12            5
##  5 de-13            4
##  6 de-14            6
##  7 de-14a           6
##  8 de-16            4
##  9 de-18            3
## 10 de-19            5
## # ℹ 13 more rows

Acoustic tags

To get more information about the tags involved in detections_silurus, you can use the function get_tags, which returns tag related information such as serial number, manufacturer, model, and frequency:

tags_id <- list_values(detections_silurus, acoustic_tag_id)

## 9 unique acoustic_tag_id values

tags_silurus <- get_tags(acoustic_tag_id = tags_id)
tags_silurus

## # A tibble: 9 × 54
##   tag_serial_number tag_type tag_subtype sensor_type acoustic_tag_id
##   <chr>             <chr>    <chr>       <chr>       <chr>          
## 1 1157781           acoustic animal      <NA>        A69-1601-28296 
## 2 1157782           acoustic animal      <NA>        A69-1601-28297 
## 3 1157783           acoustic animal      <NA>        A69-1601-28298 
## 4 1171775           acoustic animal      <NA>        A69-1601-26529 
## 5 1171776           acoustic animal      <NA>        A69-1601-26530 
## 6 1171777           acoustic animal      <NA>        A69-1601-26531 
## 7 1171780           acoustic animal      <NA>        A69-1601-26534 
## 8 1171781           acoustic animal      <NA>        A69-1601-26535 
## 9 1171782           acoustic animal      <NA>        A69-1601-26536 
##   acoustic_tag_id_alternative manufacturer model  frequency status
##   <chr>                       <chr>        <chr>  <chr>     <chr> 
## 1 <NA>                        INNOVASEA    V13-1x 069k      ended 
## 2 <NA>                        INNOVASEA    V13-1x 069k      ended 
## 3 <NA>                        INNOVASEA    V13-1x 069k      ended 
## 4 <NA>                        INNOVASEA    V13-1x 069k      ended 
## 5 <NA>                        INNOVASEA    V13-1x 069k      ended 
## 6 <NA>                        INNOVASEA    V13-1x 069k      ended 
## 7 <NA>                        INNOVASEA    V13-1x 069k      ended 
## 8 <NA>                        INNOVASEA    V13-1x 069k      ended 
## 9 <NA>                        INNOVASEA    V13-1x 069k      ended 
## # ℹ 44 more variables: activation_date <dttm>, battery_estimated_life <chr>,
## #   battery_estimated_end_date <dttm>, length <dbl>, diameter <dbl>,
## #   weight <dbl>, floating <lgl>, archive_memory <chr>, sensor_slope <dbl>,
## #   sensor_intercept <dbl>, sensor_range <chr>, sensor_range_min <dbl>,
## #   sensor_range_max <dbl>, sensor_resolution <dbl>, sensor_unit <chr>,
## #   sensor_accuracy <dbl>, sensor_transmit_ratio <int>,
## #   accelerometer_algorithm <chr>, accelerometer_samples_per_second <dbl>, …

You can also retrieve such information by tag_serial_number:

tags_serial <- unique(detections_silurus$tag_serial_number)
tags_silurus <- get_tags(tag_serial_number = tags_serial)
tags_silurus

## # A tibble: 9 × 54
##   tag_serial_number tag_type tag_subtype sensor_type acoustic_tag_id
##   <chr>             <chr>    <chr>       <chr>       <chr>          
## 1 1157781           acoustic animal      <NA>        A69-1601-28296 
## 2 1157782           acoustic animal      <NA>        A69-1601-28297 
## 3 1157783           acoustic animal      <NA>        A69-1601-28298 
## 4 1171775           acoustic animal      <NA>        A69-1601-26529 
## 5 1171776           acoustic animal      <NA>        A69-1601-26530 
## 6 1171777           acoustic animal      <NA>        A69-1601-26531 
## 7 1171780           acoustic animal      <NA>        A69-1601-26534 
## 8 1171781           acoustic animal      <NA>        A69-1601-26535 
## 9 1171782           acoustic animal      <NA>        A69-1601-26536 
##   acoustic_tag_id_alternative manufacturer model  frequency status
##   <chr>                       <chr>        <chr>  <chr>     <chr> 
## 1 <NA>                        INNOVASEA    V13-1x 069k      ended 
## 2 <NA>                        INNOVASEA    V13-1x 069k      ended 
## 3 <NA>                        INNOVASEA    V13-1x 069k      ended 
## 4 <NA>                        INNOVASEA    V13-1x 069k      ended 
## 5 <NA>                        INNOVASEA    V13-1x 069k      ended 
## 6 <NA>                        INNOVASEA    V13-1x 069k      ended 
## 7 <NA>                        INNOVASEA    V13-1x 069k      ended 
## 8 <NA>                        INNOVASEA    V13-1x 069k      ended 
## 9 <NA>                        INNOVASEA    V13-1x 069k      ended 
## # ℹ 44 more variables: activation_date <dttm>, battery_estimated_life <chr>,
## #   battery_estimated_end_date <dttm>, length <dbl>, diameter <dbl>,
## #   weight <dbl>, floating <lgl>, archive_memory <chr>, sensor_slope <dbl>,
## #   sensor_intercept <dbl>, sensor_range <chr>, sensor_range_min <dbl>,
## #   sensor_range_max <dbl>, sensor_resolution <dbl>, sensor_unit <chr>,
## #   sensor_accuracy <dbl>, sensor_transmit_ratio <int>,
## #   accelerometer_algorithm <chr>, accelerometer_samples_per_second <dbl>, …

However, keep in mind that there is a fundamental difference between the arguments acoustic_tag_id and tag_serial_number: the tag_serial_number identifies the device, which could contain multiple tags or sensors and thus multiple acoustic_tag_id.

All possible acoustic_tag_id can be retrieved with the correspondent list_* function:

list_acoustic_tag_ids() |> head(10)

##  [1] "416kHz-485"  "416kHz-956"  "416kHz-1141" "416kHz-1164" "416kHz-1277"
##  [6] "416kHz-1935" "416kHz-2066" "416kHz-2219" "416kHz-2228" "416kHz-2248"

Acoustic network projects

The detection of Wels catfishes has been possible thanks to one or more acoustic network projects, mentioned in field acoustic_project_code. You can retrieve them via the list function list_values():

acoustic_project_codes <- detections_silurus |>
  list_values(acoustic_project_code)

## 2 unique acoustic_project_code values

acoustic_project_codes

## [1] "demer"      "zeeschelde"

To get more information about these acoustic networks, you can use function get_acoustic_projects()

acoustic_projects_silurus <- get_acoustic_projects(
  acoustic_project_code = acoustic_project_codes
)
acoustic_projects_silurus

## # A tibble: 2 × 11
##   project_id project_code project_type telemetry_type project_name start_date
##        <int> <chr>        <chr>        <chr>          <chr>        <date>    
## 1          7 demer        acoustic     Acoustic       Demer        2014-04-10
## 2          5 zeeschelde   acoustic     Acoustic       Zeeschelde   2014-04-10
##   end_date   latitude longitude moratorium imis_dataset_id
##   <date>        <dbl>     <dbl> <lgl>                <int>
## 1 2015-02-13     51.0      5.06 FALSE                 5879
## 2 NA             51.1      4.27 FALSE                 5860

You can retrieve the full list of acoustic network projects with the correspondent list_* function:

list_acoustic_project_codes() |> head(10)

##  [1] "2004_Gudena"              "2011_bovenschelde"       
##  [3] "2011_Loire"               "2011_Warnow"             
##  [5] "2013_Foyle"               "2013_Maas"               
##  [7] "2014_Frome"               "2014_Nene"               
##  [9] "2015_PhD_Gutmann_Roberts" "2016_Diaccia_Botrona"

Acoustic deployments

You can retrieve deployment information related to the acoustic networks in acoustic_project_codes by using get_acoustic_deployments() function:

deployments <- get_acoustic_deployments(
  acoustic_project_code = acoustic_project_codes
)
deployments

## # A tibble: 533 × 38
##    deployment_id receiver_id acoustic_project_code station_name
##            <int> <chr>       <chr>                 <chr>       
##  1          1424 VR2W-122350 demer                 de-1        
##  2          1658 VR2W-122345 demer                 de-2        
##  3          1478 VR2W-122325 demer                 de-2        
##  4          1661 VR2W-122344 demer                 de-3        
##  5          1381 VR2W-122339 demer                 de-3        
##  6          2869 VR2W-122339 demer                 de-3        
##  7          1662 VR2W-122323 demer                 de-4        
##  8          1425 VR2W-122337 demer                 de-5        
##  9          1663 VR2W-122341 demer                 de-6        
## 10          1378 VR2W-122320 demer                 de-7        
##    station_description   station_manager deploy_date_time    deploy_latitude
##    <chr>                 <chr>           <dttm>                        <dbl>
##  1 fietsersbrug aarschot 1               2014-05-13 00:00:00            51.0
##  2 Rommelaar             1               2014-05-14 00:00:00            51.0
##  3 rommelaar             1               2014-08-07 00:00:00            51.0
##  4 Messelbroek           1               2014-05-13 00:00:00            51.0
##  5 Messelbroek           <NA>            2014-08-07 00:00:00            51.0
##  6 Messelbroek           <NA>            2014-09-21 00:00:00            51.0
##  7 Testelt               1               2014-05-13 00:00:00            51.0
##  8 SO_ZICHEM_DEMER       1               2014-05-05 00:00:00            51.0
##  9 SA_RIF_LODEMER        1               2014-05-05 00:00:00            51.0
## 10 de-7                  <NA>            2014-05-05 00:00:00            51.0
## # ℹ 523 more rows
## # ℹ 30 more variables: deploy_longitude <dbl>, intended_latitude <dbl>,
## #   intended_longitude <dbl>, mooring_type <chr>, bottom_depth <chr>,
## #   riser_length <chr>, deploy_depth <chr>, battery_installation_date <dttm>,
## #   battery_estimated_end_date <dttm>, activation_date_time <dttm>,
## #   recover_date_time <dttm>, recover_latitude <dbl>, recover_longitude <dbl>,
## #   download_date_time <dttm>, download_file_name <chr>, …

These are the deployments of the acoustic receivers involved in detections_silurus:

deploys_silurus <-
  detections_silurus |>
  list_values(deployment_id)

## 33 unique deployment_id values

deploys_silurus

##  [1] 1378 1379 1381 1425 1427 1428 1429 1431 1432 1433 1436 1437 1439 1440 1478
## [16] 2869 2938 1424 1662 1663 1384 1434 1441 1656 1659 1660 2933 1470 1506 1573
## [31] 1588 1593 1592

More information about them can be retrieved via get_acoustic_deployments() function with argument deployment_id:

deployments_silurus <- get_acoustic_deployments(
  deployment_id = deploys_silurus
)
deployments_silurus

## # A tibble: 33 × 38
##    deployment_id receiver_id acoustic_project_code station_name
##            <int> <chr>       <chr>                 <chr>       
##  1          1424 VR2W-122350 demer                 de-1        
##  2          1478 VR2W-122325 demer                 de-2        
##  3          1381 VR2W-122339 demer                 de-3        
##  4          2869 VR2W-122339 demer                 de-3        
##  5          1662 VR2W-122323 demer                 de-4        
##  6          1425 VR2W-122337 demer                 de-5        
##  7          1663 VR2W-122341 demer                 de-6        
##  8          1378 VR2W-122320 demer                 de-7        
##  9          1593 VR2W-122320 demer                 de-7        
## 10          1427 VR2W-122330 demer                 de-8        
##    station_description   station_manager deploy_date_time    deploy_latitude
##    <chr>                 <chr>           <dttm>                        <dbl>
##  1 fietsersbrug aarschot 1               2014-05-13 00:00:00            51.0
##  2 rommelaar             1               2014-08-07 00:00:00            51.0
##  3 Messelbroek           <NA>            2014-08-07 00:00:00            51.0
##  4 Messelbroek           <NA>            2014-09-21 00:00:00            51.0
##  5 Testelt               1               2014-05-13 00:00:00            51.0
##  6 SO_ZICHEM_DEMER       1               2014-05-05 00:00:00            51.0
##  7 SA_RIF_LODEMER        1               2014-05-05 00:00:00            51.0
##  8 de-7                  <NA>            2014-05-05 00:00:00            51.0
##  9 de-7                  <NA>            2014-12-19 02:00:00            51.0
## 10 AAN_GB2_RODEMER       1               2014-05-05 00:00:00            51.0
## # ℹ 23 more rows
## # ℹ 30 more variables: deploy_longitude <dbl>, intended_latitude <dbl>,
## #   intended_longitude <dbl>, mooring_type <chr>, bottom_depth <chr>,
## #   riser_length <chr>, deploy_depth <chr>, battery_installation_date <dttm>,
## #   battery_estimated_end_date <dttm>, activation_date_time <dttm>,
## #   recover_date_time <dttm>, recover_latitude <dbl>, recover_longitude <dbl>,
## #   download_date_time <dttm>, download_file_name <chr>, …

Deployment duration:

deployments_silurus_duration <-
  deployments_silurus |>
  mutate(duration = as.duration(recover_date_time - deploy_date_time)) |>
  select(deployment_id, station_name, duration) |>
  arrange(deployment_id)
deployments_silurus_duration

## # A tibble: 33 × 3
##    deployment_id station_name duration                
##            <int> <chr>        <Duration>              
##  1          1378 de-7         19706400s (~32.58 weeks)
##  2          1379 s-2a         19440000s (~32.14 weeks)
##  3          1381 de-3         3888000s (~6.43 weeks)  
##  4          1384 de-21        5788800s (~9.57 weeks)  
##  5          1424 de-1         23760000s (~39.29 weeks)
##  6          1425 de-5         24451200s (~40.43 weeks)
##  7          1427 de-8         19706400s (~32.58 weeks)
##  8          1428 de-10        26611200s (~44 weeks)   
##  9          1429 de-11        26611200s (~44 weeks)   
## 10          1431 de-14a       16070400s (~26.57 weeks)
## # ℹ 23 more rows

Number of days a deployment detected the passage of one or more individuals:

n_active_days_deployments_silurus <-
  detections_silurus |>
  mutate(date = date(date_time)) |>
  distinct(deployment_id, station_name, date) |>
  group_by(deployment_id, station_name) |>
  summarize(n_days = n(), .groups = "drop") |>
  ungroup()
n_active_days_deployments_silurus

## # A tibble: 33 × 3
##    deployment_id station_name n_days
##            <int> <chr>         <int>
##  1          1378 de-7            125
##  2          1379 s-2a              7
##  3          1381 de-3              3
##  4          1384 de-21            25
##  5          1424 de-1              7
##  6          1425 de-5             48
##  7          1427 de-8            142
##  8          1428 de-10            35
##  9          1429 de-11            15
## 10          1431 de-14a           59
## # ℹ 23 more rows

Relative detection duration, i.e. number of days with at least one detection divided by deployment duration:

rel_det_duration_silurus <-
  n_active_days_deployments_silurus |>
  left_join(
    deployments_silurus_duration,
    by = c("deployment_id", "station_name")
  ) |>
  mutate(
    relative_detection_duration = n_days * (24 * 60 * 60) / as.numeric(duration)
  ) |>
  select(deployment_id, station_name, relative_detection_duration)
rel_det_duration_silurus

## # A tibble: 33 × 3
##    deployment_id station_name relative_detection_duration
##            <int> <chr>                              <dbl>
##  1          1378 de-7                              0.548 
##  2          1379 s-2a                              0.0311
##  3          1381 de-3                              0.0667
##  4          1384 de-21                             0.373 
##  5          1424 de-1                              0.0255
##  6          1425 de-5                              0.170 
##  7          1427 de-8                              0.623 
##  8          1428 de-10                             0.114 
##  9          1429 de-11                             0.0487
## 10          1431 de-14a                            0.317 
## # ℹ 23 more rows

Data visualization

Aside standard graphs, the geographical component of telemetry data makes interactive maps particularly useful. The package leaflet is quite popular to create such kind of visualizations.

We can for example create a map of the involved stations showing the station name and the acoustic project code it belongs to as pop-ups. First, we retrieve the coordinates of the stations:

geo_info_stations <-
  detections_silurus |>
  distinct(
    station_name,
    deploy_latitude,
    deploy_longitude,
    acoustic_project_code
  ) |>
  arrange(station_name)
geo_info_stations

## # A tibble: 23 × 4
##    station_name deploy_latitude deploy_longitude acoustic_project_code
##    <chr>                  <dbl>            <dbl> <chr>                
##  1 de-1                    51.0             4.84 demer                
##  2 de-10                   51.0             5.05 demer                
##  3 de-11                   51.0             5.05 demer                
##  4 de-12                   51.0             5.06 demer                
##  5 de-13                   51.0             5.06 demer                
##  6 de-14                   51.0             5.06 demer                
##  7 de-14a                  51.0             5.06 demer                
##  8 de-16                   51.0             5.06 demer                
##  9 de-18                   51.0             5.07 demer                
## 10 de-19                   51.0             5.08 demer                
## # ℹ 13 more rows

To be able to produce the desired map:

leaflet(geo_info_stations) |>
  addTiles() |>
  addMarkers(
    lng = ~deploy_longitude,
    lat = ~deploy_latitude,
    popup = ~paste0("Station: ", station_name, " (", acoustic_project_code, ")")
  ) |>
  htmlwidgets::saveWidget(file = "stations_map.html")

We can visualize the number of detections per station, n_detect_station, by joining it with geo_info_stations:

# Create a continuous colour palette function
pal <- colorNumeric(
  palette = "viridis",
  domain = n_detect_station$n
)

n_detect_station |>
  left_join(
    geo_info_stations,
    by = "station_name"
  ) |>
  leaflet() |>
  addTiles() |>
  addCircleMarkers(
    lng = ~deploy_longitude,
    lat = ~deploy_latitude,
    radius = ~log(n),
    color = ~pal(n),
    fillOpacity = 0.8,
    stroke = FALSE,
    popup = ~paste(
      sep = "<br/>",
      paste0("Station: ", station_name, " (", acoustic_project_code, ")"),
      paste0("# detections: ", n)
    )
  ) |>
  addLegend(
    title = "Detections",
    pal = pal,
    values = ~n
  ) |>
  htmlwidgets::saveWidget(file = "n_detections_per_station.html")

In a similar way we can visualize the number of detected individuals per station, n_silurus_station:

# Create a continuous colour palette function
pal <- colorNumeric(
  palette = "viridis",
  domain = n_silurus_station$n
)

n_silurus_station |>
  left_join(
    geo_info_stations,
    by = "station_name"
  ) |>
  leaflet() |>
  addTiles() |>
  addCircleMarkers(
    lng = ~deploy_longitude,
    lat = ~deploy_latitude,
    radius = ~n,
    color = ~pal(n),
    fillOpacity = 0.8,
    stroke = FALSE,
    popup = ~paste(
      sep = "<br/>",
      paste0("Station: ", station_name, " (", acoustic_project_code, ")"),
      paste0("# detected individuals: ", n)
    )
  ) |>
  addLegend(
    title = "Detected individuals",
    pal = pal,
    values = ~n
  ) |>
  htmlwidgets::saveWidget(file = "n_individuals_per_station.html")

We can also make a map of the relative detection duration of the deployments. First, we have to retrieve the deployment geographical coordinates:

geo_info_deploys <-
  detections_silurus |>
  distinct(
    deployment_id,
    deploy_latitude,
    deploy_longitude,
    station_name,
    acoustic_project_code
  ) |>
  arrange(station_name)
geo_info_deploys

## # A tibble: 33 × 5
##    deployment_id deploy_latitude deploy_longitude station_name
##            <int>           <dbl>            <dbl> <chr>       
##  1          1424            51.0             4.84 de-1        
##  2          1428            51.0             5.05 de-10       
##  3          1429            51.0             5.05 de-11       
##  4          2938            51.0             5.06 de-12       
##  5          1656            51.0             5.06 de-12       
##  6          1470            51.0             5.06 de-12       
##  7          1432            51.0             5.06 de-13       
##  8          1433            51.0             5.06 de-14       
##  9          1592            51.0             5.06 de-14       
## 10          1431            51.0             5.06 de-14a      
##    acoustic_project_code
##    <chr>                
##  1 demer                
##  2 demer                
##  3 demer                
##  4 demer                
##  5 demer                
##  6 demer                
##  7 demer                
##  8 demer                
##  9 demer                
## 10 demer                
## # ℹ 23 more rows

We are ready to create the desired map, where we show the deployment ID, the station name and the network project as popups:

# Create a continuous colour palette function
pal <- colorNumeric(
  palette = "viridis",
  domain = rel_det_duration_silurus$relative_detection_duration
)

rel_det_duration_silurus |>
  left_join(
    geo_info_deploys,
    by = c("deployment_id", "station_name")
  ) |>
  leaflet() |>
  addTiles() |>
  addCircleMarkers(
    lng = ~deploy_longitude,
    lat = ~deploy_latitude,
    radius = ~100 * relative_detection_duration,
    color = ~pal(relative_detection_duration),
    fillOpacity = 0.8,
    stroke = FALSE,
    clusterOptions = markerClusterOptions(),
    popup = ~paste(
      sep = "<br/>",
      paste0("DeploymentID: ", deployment_id),
      paste0("Station: ", station_name, " (", acoustic_project_code, ")"),
      paste0("# relative detection duration: ", round(relative_detection_duration, 2))
    )
  ) |>
  addLegend(
    title = "Relative detection duration",
    pal = pal,
    values = ~relative_detection_duration
  ) |>
  htmlwidgets::saveWidget(file = "relative_detection_duration.html")

Do you want to add the temporal component to visualize the fish movement? Take a look at the moveVis package.

Damiano Oldoni