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

Functions in etn need user credentials to connect to the database. You should have received those from VLIZ. You will be prompted (once per session) for these credentials. To avoid this you can save them as environment variables.

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
##         <int> <chr>        <chr>        <chr>         
##  1        793 2004_Gudena  animal       Acoustic      
##  2         16 2010_phd_re… animal       Acoustic      
##  3        841 2010_phd_re… animal       Acoustic      
##  4        760 2011_Loire   animal       Acoustic      
##  5        754 2011_Warnow  animal       Acoustic      
##  6         20 2011_rivier… animal       Acoustic      
##  7         15 2012_leopol… animal       Acoustic      
##  8        757 2013_Foyle   animal       Acoustic      
##  9         18 2013_albert… animal       Acoustic      
## 10        801 2014_Frome   animal       <NA>          
##    project_name         start_date end_date   latitude
##    <chr>                <date>     <date>        <dbl>
##  1 Silver eel migratio… 2004-01-01 2005-12-31     56.4
##  2 2010_phd_reubens     2010-08-01 2012-10-30     56.4
##  3 2010_phd_reubens_sy… 2010-08-01 2012-10-30     NA  
##  4 2011_Loire           2011-11-11 2012-02-25     47.3
##  5 Silver eel migratio… 2011-06-01 2012-10-12     54.1
##  6 2011 Rivierprik      2011-01-01 2012-09-03     50.9
##  7 2012 Leopoldkanaal   2012-01-01 2016-01-18     NA  
##  8 2013_Foyle           2013-07-01 2014-03-01     54.9
##  9 2013 Albertkanaal    2013-10-10 2018-12-31     51.1
## 10 2014_Frome           2014-10-01 2015-01-31     50.7
## # ℹ 3 more variables: longitude <dbl>, moratorium <chr>, imis_dataset_id <int>

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
##        <int> <chr>        <chr>        <chr>         
## 1         21 2014_demer   animal       Acoustic      
## 2         22 2015_dijle   animal       Acoustic      
##   project_name start_date end_date   latitude
##   <chr>        <date>     <date>        <dbl>
## 1 2014 Demer   2014-04-10 2015-02-13     51.0
## 2 2015 Dijle   2015-04-16 2018-12-31     51.0
## # ℹ 3 more variables: longitude <dbl>, moratorium <chr>, imis_dataset_id <int>

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
##        <int> <chr>        <chr>        <chr>         
## 1         21 2014_demer   animal       Acoustic      
## 2         22 2015_dijle   animal       Acoustic      
##   project_name start_date end_date   latitude
##   <chr>        <date>     <date>        <dbl>
## 1 2014 Demer   2014-04-10 2015-02-13     51.0
## 2 2015 Dijle   2015-04-16 2018-12-31     51.0
## # ℹ 3 more variables: longitude <dbl>, moratorium <chr>, imis_dataset_id <int>

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"     
##  [3] "2010_phd_reubens_sync" "2011_Loire"           
##  [5] "2011_rivierprik"       "2011_Warnow"          
##  [7] "2012_leopoldkanaal"    "2013_albertkanaal"    
##  [9] "2013_Foyle"            "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
##        <int> <chr>               <chr>            
##  1       304 2014_demer          1187449          
##  2       384 2014_demer          1157781          
##  3       385 2014_demer          1157782          
##  4       386 2014_demer          1157783          
##  5       305 2014_demer          1187450          
##  6       383 2014_demer          1157780          
##  7       369 2014_demer          1171781          
##  8       370 2014_demer          1171782          
##  9       365 2014_demer          1171775          
## 10       366 2014_demer          1171776          
##    tag_type tag_subtype acoustic_tag_id
##    <chr>    <chr>       <chr>          
##  1 acoustic animal      A69-1601-16129 
##  2 acoustic animal      A69-1601-28296 
##  3 acoustic animal      A69-1601-28297 
##  4 acoustic animal      A69-1601-28298 
##  5 acoustic animal      A69-1601-16130 
##  6 acoustic animal      A69-1601-28295 
##  7 acoustic animal      A69-1601-26535 
##  8 acoustic animal      A69-1601-26536 
##  9 acoustic animal      A69-1601-26529 
## 10 acoustic animal      A69-1601-26530 
## # ℹ 60 more variables: acoustic_tag_id_alternative <chr>,
## #   scientific_name <chr>, common_name <chr>, aphia_id <int>,
## #   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>, …

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
##           <int> <dttm>              <chr>            
##  1     22601488 2014-09-19 17:40:22 1171775          
##  2     22602183 2014-08-04 20:39:05 1171775          
##  3     22603017 2014-09-20 00:30:18 1171775          
##  4     22603503 2014-08-24 00:48:26 1171775          
##  5     22603679 2014-08-02 23:04:14 1171775          
##  6     22604431 2014-06-04 22:42:14 1171775          
##  7     22604560 2014-05-29 21:46:59 1171775          
##  8     22604598 2014-05-27 20:04:00 1171775          
##  9     22604698 2014-06-01 23:30:00 1171775          
## 10     22605122 2014-08-11 16:14:14 1171775          
##    acoustic_tag_id animal_project_code animal_id
##    <chr>           <chr>                   <int>
##  1 A69-1601-26529  2014_demer                365
##  2 A69-1601-26529  2014_demer                365
##  3 A69-1601-26529  2014_demer                365
##  4 A69-1601-26529  2014_demer                365
##  5 A69-1601-26529  2014_demer                365
##  6 A69-1601-26529  2014_demer                365
##  7 A69-1601-26529  2014_demer                365
##  8 A69-1601-26529  2014_demer                365
##  9 A69-1601-26529  2014_demer                365
## 10 A69-1601-26529  2014_demer                365
## # ℹ 14 more variables: scientific_name <chr>, acoustic_project_code <chr>,
## #   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 <chr>, 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-12"  "de-8"   "de-19"  "de-7"   "de-9"  
##  [6] "de-5"   "de-2"   "de-13"  "de-10"  "de-14" 
## [11] "s-2a"   "de-3"   "de-18"  "de-11"  "de-20" 
## [16] "de-14a" "de-4"   "de-6"   "de-1"   "de-16" 
## [21] "de-21"  "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-8                  1427     2
##  5 de-7                  1378     2
##  6 de-9                  1437     2
##  7 de-14                 1433     2
##  8 de-3                  2869     2
##  9 de-3                  1381     2
## 10 de-14a                1431     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
##   <chr>             <chr>    <chr>       <chr>      
## 1 1157781           acoustic animal      <NA>       
## 2 1157782           acoustic animal      <NA>       
## 3 1157783           acoustic animal      <NA>       
## 4 1171775           acoustic animal      <NA>       
## 5 1171776           acoustic animal      <NA>       
## 6 1171777           acoustic animal      <NA>       
## 7 1171780           acoustic animal      <NA>       
## 8 1171781           acoustic animal      <NA>       
## 9 1171782           acoustic animal      <NA>       
##   acoustic_tag_id acoustic_tag_id_alter…¹ manufacturer
##   <chr>           <chr>                   <chr>       
## 1 A69-1601-28296  <NA>                    INNOVASEA   
## 2 A69-1601-28297  <NA>                    INNOVASEA   
## 3 A69-1601-28298  <NA>                    INNOVASEA   
## 4 A69-1601-26529  <NA>                    INNOVASEA   
## 5 A69-1601-26530  <NA>                    INNOVASEA   
## 6 A69-1601-26531  <NA>                    INNOVASEA   
## 7 A69-1601-26534  <NA>                    INNOVASEA   
## 8 A69-1601-26535  <NA>                    INNOVASEA   
## 9 A69-1601-26536  <NA>                    INNOVASEA   
## # ℹ abbreviated name: ¹acoustic_tag_id_alternative
## # ℹ 47 more variables: model <chr>, frequency <chr>, status <chr>,
## #   activation_date <dttm>, battery_estimated_life <chr>,
## #   battery_estimated_end_date <dttm>, length <dbl>, diameter <dbl>,
## #   weight <dbl>, floating <chr>, 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>, …

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
##   <chr>             <chr>    <chr>       <chr>      
## 1 1157781           acoustic animal      <NA>       
## 2 1157782           acoustic animal      <NA>       
## 3 1157783           acoustic animal      <NA>       
## 4 1171775           acoustic animal      <NA>       
## 5 1171776           acoustic animal      <NA>       
## 6 1171777           acoustic animal      <NA>       
## 7 1171780           acoustic animal      <NA>       
## 8 1171781           acoustic animal      <NA>       
## 9 1171782           acoustic animal      <NA>       
##   acoustic_tag_id acoustic_tag_id_alter…¹ manufacturer
##   <chr>           <chr>                   <chr>       
## 1 A69-1601-28296  <NA>                    INNOVASEA   
## 2 A69-1601-28297  <NA>                    INNOVASEA   
## 3 A69-1601-28298  <NA>                    INNOVASEA   
## 4 A69-1601-26529  <NA>                    INNOVASEA   
## 5 A69-1601-26530  <NA>                    INNOVASEA   
## 6 A69-1601-26531  <NA>                    INNOVASEA   
## 7 A69-1601-26534  <NA>                    INNOVASEA   
## 8 A69-1601-26535  <NA>                    INNOVASEA   
## 9 A69-1601-26536  <NA>                    INNOVASEA   
## # ℹ abbreviated name: ¹acoustic_tag_id_alternative
## # ℹ 47 more variables: model <chr>, frequency <chr>, status <chr>,
## #   activation_date <dttm>, battery_estimated_life <chr>,
## #   battery_estimated_end_date <dttm>, length <dbl>, diameter <dbl>,
## #   weight <dbl>, floating <chr>, 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>, …

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"
##  [4] "416kHz-1164" "416kHz-1277" "416kHz-1935"
##  [7] "416kHz-2066" "416kHz-2219" "416kHz-2228"
## [10] "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
##        <int> <chr>        <chr>        <chr>         
## 1          7 demer        acoustic     Acoustic      
## 2          5 zeeschelde   acoustic     Acoustic      
##   project_name start_date end_date   latitude
##   <chr>        <date>     <date>        <dbl>
## 1 Demer        2014-04-10 2015-02-13     51.0
## 2 Zeeschelde   2014-04-10 NA             51.1
## # ℹ 3 more variables: longitude <dbl>, moratorium <chr>, imis_dataset_id <int>

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

list_acoustic_project_codes() |> head(10)

##  [1] "2004_Gudena"             
##  [2] "2011_bovenschelde"       
##  [3] "2011_Loire"              
##  [4] "2011_Warnow"             
##  [5] "2013_Foyle"              
##  [6] "2013_Maas"               
##  [7] "2014_Frome"              
##  [8] "2014_Nene"               
##  [9] "2015_PhD_Gutmann_Roberts"
## [10] "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: 511 × 38
##    deployment_id receiver_id acoustic_project_code
##            <int> <chr>       <chr>                
##  1          1424 VR2W-122350 demer                
##  2          1658 VR2W-122345 demer                
##  3          1478 VR2W-122325 demer                
##  4          1661 VR2W-122344 demer                
##  5          1381 VR2W-122339 demer                
##  6          2869 VR2W-122339 demer                
##  7          1662 VR2W-122323 demer                
##  8          1425 VR2W-122337 demer                
##  9          1663 VR2W-122341 demer                
## 10          1378 VR2W-122320 demer                
##    station_name station_description   station_manager
##    <chr>        <chr>                 <chr>          
##  1 de-1         fietsersbrug aarschot 1              
##  2 de-2         Rommelaar             1              
##  3 de-2         rommelaar             1              
##  4 de-3         Messelbroek           1              
##  5 de-3         Messelbroek           <NA>           
##  6 de-3         Messelbroek           <NA>           
##  7 de-4         Testelt               1              
##  8 de-5         SO_ZICHEM_DEMER       1              
##  9 de-6         SA_RIF_LODEMER        1              
## 10 de-7         de-7                  <NA>           
## # ℹ 501 more rows
## # ℹ 32 more variables: deploy_date_time <dttm>, deploy_latitude <dbl>,
## #   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>, …

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] 2938 1427 1436 1378 1437 1425 1478 1432 1428 1433
## [11] 1379 2869 1439 1429 1440 1381 1431 1662 1663 1424
## [21] 1434 1659 1441 2933 1384 1660 1656 1593 1573 1470
## [31] 1588 1506 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
##            <int> <chr>       <chr>                
##  1          1424 VR2W-122350 demer                
##  2          1478 VR2W-122325 demer                
##  3          1381 VR2W-122339 demer                
##  4          2869 VR2W-122339 demer                
##  5          1662 VR2W-122323 demer                
##  6          1425 VR2W-122337 demer                
##  7          1663 VR2W-122341 demer                
##  8          1378 VR2W-122320 demer                
##  9          1593 VR2W-122320 demer                
## 10          1427 VR2W-122330 demer                
##    station_name station_description   station_manager
##    <chr>        <chr>                 <chr>          
##  1 de-1         fietsersbrug aarschot 1              
##  2 de-2         rommelaar             1              
##  3 de-3         Messelbroek           <NA>           
##  4 de-3         Messelbroek           <NA>           
##  5 de-4         Testelt               1              
##  6 de-5         SO_ZICHEM_DEMER       1              
##  7 de-6         SA_RIF_LODEMER        1              
##  8 de-7         de-7                  <NA>           
##  9 de-7         de-7                  <NA>           
## 10 de-8         AAN_GB2_RODEMER       1              
## # ℹ 23 more rows
## # ℹ 32 more variables: deploy_date_time <dttm>, deploy_latitude <dbl>,
## #   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>, …

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
##            <int> <chr>       
##  1          1378 de-7        
##  2          1379 s-2a        
##  3          1381 de-3        
##  4          1384 de-21       
##  5          1424 de-1        
##  6          1425 de-5        
##  7          1427 de-8        
##  8          1428 de-10       
##  9          1429 de-11       
## 10          1431 de-14a      
##    relative_detection_duration
##                          <dbl>
##  1                      0.548 
##  2                      0.0311
##  3                      0.0667
##  4                      0.373 
##  5                      0.0255
##  6                      0.170 
##  7                      0.623 
##  8                      0.114 
##  9                      0.0487
## 10                      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
##    <chr>                  <dbl>            <dbl>
##  1 de-1                    51.0             4.84
##  2 de-10                   51.0             5.05
##  3 de-11                   51.0             5.05
##  4 de-12                   51.0             5.06
##  5 de-13                   51.0             5.06
##  6 de-14                   51.0             5.06
##  7 de-14a                  51.0             5.06
##  8 de-16                   51.0             5.06
##  9 de-18                   51.0             5.07
## 10 de-19                   51.0             5.08
##    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 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
##            <int>           <dbl>            <dbl>
##  1          1424            51.0             4.84
##  2          1428            51.0             5.05
##  3          1429            51.0             5.05
##  4          2938            51.0             5.06
##  5          1656            51.0             5.06
##  6          1470            51.0             5.06
##  7          1432            51.0             5.06
##  8          1433            51.0             5.06
##  9          1592            51.0             5.06
## 10          1431            51.0             5.06
##    station_name acoustic_project_code
##    <chr>        <chr>                
##  1 de-1         demer                
##  2 de-10        demer                
##  3 de-11        demer                
##  4 de-12        demer                
##  5 de-12        demer                
##  6 de-12        demer                
##  7 de-13        demer                
##  8 de-14        demer                
##  9 de-14        demer                
## 10 de-14a       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

2025-10-01