Explore acoustic telemetry data
Damiano Oldoni
2022-12-09
Source:vignettes/acoustic_telemetry.Rmd
acoustic_telemetry.RmdIntroduction
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:
And the other packages (with installation if necessary):
other_pkgs <- c("dplyr", "lubridate", "leaflet")
# 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)Connect with your user credentials (as received by VLIZ) to the database. To not expose such confidential information, you could opt to use environment variables:
con <- connect_to_etn(Sys.getenv("userid"), Sys.getenv("pwd"))
# This is the default, so you could also use connect_to_etn()Using con as variable to store the collection is not mandatory, but it makes your life much easier as con is the default value of the argument connection, present in every other function of this package.
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:
## # A tibble: 10 × 11
## proje…¹ proje…² proje…³ telem…⁴ proje…⁵ start_date end_date latit…⁶ longi…⁷
## <int> <chr> <chr> <chr> <chr> <date> <date> <dbl> <dbl>
## 1 793 2004_G… animal Acoust… 2004_G… 2004-01-01 2005-12-31 56.4 9.91
## 2 16 2010_p… animal Acoust… 2010_p… 2010-08-01 2012-10-30 NA NA
## 3 841 2010_p… animal Acoust… 2010_p… 2010-08-01 2012-10-30 NA NA
## 4 760 2011_L… animal Acoust… 2011_L… 2011-11-11 2012-02-25 47.3 -1.98
## 5 20 2011_r… animal Acoust… 2011 R… 2011-01-01 2012-09-03 NA NA
## 6 754 2011_W… animal Acoust… 2011_W… 2011-06-01 2012-10-12 54.1 12.1
## 7 15 2012_l… animal Acoust… 2012 L… 2012-01-01 2016-01-18 NA NA
## 8 18 2013_a… animal Acoust… 2013 A… 2013-10-10 2018-12-31 NA NA
## 9 757 2013_F… animal Acoust… 2013_F… 2013-07-01 2014-03-01 54.9 -7.39
## 10 21 2014_d… animal Acoust… 2014 D… 2014-04-10 2015-02-13 NA NA
## # … with 2 more variables: moratorium <chr>, imis_dataset_id <int>, and
## # abbreviated variable names ¹project_id, ²project_code, ³project_type,
## # ⁴telemetry_type, ⁵project_name, ⁶latitude, ⁷longitudeIf 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
## projec…¹ proje…² proje…³ telem…⁴ proje…⁵ start_date end_date latit…⁶ longi…⁷
## <int> <chr> <chr> <chr> <chr> <date> <date> <dbl> <dbl>
## 1 21 2014_d… animal Acoust… 2014 D… 2014-04-10 2015-02-13 NA NA
## 2 22 2015_d… animal Acoust… 2015 D… 2015-04-16 2018-12-31 NA NA
## # … with 2 more variables: moratorium <chr>, imis_dataset_id <int>, and
## # abbreviated variable names ¹project_id, ²project_code, ³project_type,
## # ⁴telemetry_type, ⁵project_name, ⁶latitude, ⁷longitudeThis is exactly the same as retrieving all projects first and filtering them afterwards based on column project_code:
## # A tibble: 2 × 11
## projec…¹ proje…² proje…³ telem…⁴ proje…⁵ start_date end_date latit…⁶ longi…⁷
## <int> <chr> <chr> <chr> <chr> <date> <date> <dbl> <dbl>
## 1 21 2014_d… animal Acoust… 2014 D… 2014-04-10 2015-02-13 NA NA
## 2 22 2015_d… animal Acoust… 2015 D… 2015-04-16 2018-12-31 NA NA
## # … with 2 more variables: moratorium <chr>, imis_dataset_id <int>, and
## # abbreviated variable names ¹project_id, ²project_code, ³project_type,
## # ⁴telemetry_type, ⁵project_name, ⁶latitude, ⁷longitudeTo 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_pr…¹ tag_s…² tag_t…³ tag_s…⁴ acous…⁵ acous…⁶ scien…⁷ commo…⁸
## <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 304 2014_demer 1187449 acoust… animal A69-16… "" Rutilu… roach
## 2 384 2014_demer 1157781 acoust… animal A69-16… "" Siluru… wels c…
## 3 385 2014_demer 1157782 acoust… animal A69-16… "" Siluru… wels c…
## 4 386 2014_demer 1157783 acoust… animal A69-16… "" Siluru… wels c…
## 5 305 2014_demer 1187450 acoust… animal A69-16… "" Rutilu… roach
## 6 383 2014_demer 1157780 acoust… animal A69-16… "" Squali… chub
## 7 369 2014_demer 1171781 acoust… animal A69-16… "" Siluru… wels c…
## 8 370 2014_demer 1171782 acoust… animal A69-16… "" Siluru… wels c…
## 9 365 2014_demer 1171775 acoust… animal A69-16… "" Siluru… wels c…
## 10 366 2014_demer 1171776 acoust… animal A69-16… "" Siluru… wels c…
## # … with 57 more variables: 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>,
## # 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?
## # 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 3Detections
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:
## # A tibble: 10 × 20
## detecti…¹ date_time tag_s…² acous…³ anima…⁴ anima…⁵ scien…⁶ acous…⁷
## <int> <dttm> <chr> <chr> <chr> <int> <chr> <chr>
## 1 22402411 2014-05-26 21:49:00 1171775 A69-16… 2014_d… 365 Siluru… demer
## 2 22207337 2014-05-26 21:49:20 1171775 A69-16… 2014_d… 365 Siluru… demer
## 3 20620757 2014-05-26 22:00:00 1171775 A69-16… 2014_d… 365 Siluru… demer
## 4 21701322 2014-05-26 22:00:08 1171775 A69-16… 2014_d… 365 Siluru… demer
## 5 20604027 2014-05-26 22:08:00 1171775 A69-16… 2014_d… 365 Siluru… demer
## 6 21283233 2014-05-26 22:08:12 1171775 A69-16… 2014_d… 365 Siluru… demer
## 7 22131424 2014-05-26 22:14:00 1171775 A69-16… 2014_d… 365 Siluru… demer
## 8 22454253 2014-05-26 22:14:24 1171775 A69-16… 2014_d… 365 Siluru… demer
## 9 22402759 2014-05-26 22:20:00 1171775 A69-16… 2014_d… 365 Siluru… demer
## 10 21751235 2014-05-26 22:20:19 1171775 A69-16… 2014_d… 365 Siluru… demer
## # … with 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 <chr>,
## # deployment_id <int>, and abbreviated variable names ¹detection_id,
## # ²tag_serial_number, ³acoustic_tag_id, ⁴animal_project_code, ⁵animal_id,
## # ⁶scientific_name, ⁷acoustic_project_codeWhich 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 2014-12-28Notice 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 2014-12-28We 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 daysHow many times has an individual has been detected?
## # A tibble: 9 × 2
## # Groups: animal_id [9]
## animal_id n
## <int> <int>
## 1 365 4872
## 2 366 2778
## 3 367 497
## 4 368 7690
## 5 369 1183
## 6 370 4462
## 7 384 2563
## 8 385 29068
## 9 386 18478Stations
At how many detection stations have the individuals been detected?
## # 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 15Which 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-8" "de-9" "de-7" "de-10" "de-12" "de-14" "de-14a" "de-5"
## [9] "de-11" "de-13" "s-2a" "de-18" "de-19" "de-20" "de-3" "de-2"
## [17] "de-6" "de-4" "de-1" "de-21" "de-22" "de-23" "de-16"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-9 1437 2
## 6 de-7 1378 2
## 7 de-14 1433 2
## 8 de-14a 1431 2
## 9 de-3 1381 2
## 10 de-3 2869 2
## # … with 23 more rowsSometimes it’s interesting to know the number of detections per 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 13353
## 7 de-14a 6604
## 8 de-16 591
## 9 de-18 148
## 10 de-19 650
## # … with 13 more rowsIt’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
## # … with 13 more rowsAcoustic 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_ser…¹ tag_t…² tag_s…³ senso…⁴ acous…⁵ acous…⁶ manuf…⁷ model frequ…⁸ status
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1157781 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## 2 1157782 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## 3 1157783 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## 4 1171775 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## 5 1171776 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## 6 1171777 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## 7 1171780 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## 8 1171781 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## 9 1171782 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## # … with 44 more variables: 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>, sensor_accuracy <dbl>,
## # sensor_transmit_ratio <int>, accelerometer_algorithm <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_ser…¹ tag_t…² tag_s…³ senso…⁴ acous…⁵ acous…⁶ manuf…⁷ model frequ…⁸ status
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1157781 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## 2 1157782 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## 3 1157783 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## 4 1171775 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## 5 1171776 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## 6 1171777 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## 7 1171780 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## 8 1171781 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## 9 1171782 acoust… animal NA A69-16… NA VEMCO V13-… 069k avail…
## # … with 44 more variables: 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>, sensor_accuracy <dbl>,
## # sensor_transmit_ratio <int>, accelerometer_algorithm <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] "\"A69-1602\"-34214" "\"A69-1602\"-34215" "\"A69-1602\"-34216"
## [4] "\"A69-1602\"-34217" "\"A69-1602\"-34218" "\"A69-1602\"-34219"
## [7] "\"A69-1602\"-34220" "\"A69-1602\"-34221" "\"A69-1602\"-34222"
## [10] "\"A69-1602\"-34223"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
## projec…¹ proje…² proje…³ telem…⁴ proje…⁵ start_date end_date latit…⁶ longi…⁷
## <int> <chr> <chr> <chr> <chr> <date> <date> <dbl> <dbl>
## 1 7 demer acoust… Acoust… Demer 2014-04-10 2015-02-13 NA NA
## 2 5 zeesch… acoust… Acoust… Zeesch… 2014-04-10 NA NA NA
## # … with 2 more variables: moratorium <chr>, imis_dataset_id <int>, and
## # abbreviated variable names ¹project_id, ²project_code, ³project_type,
## # ⁴telemetry_type, ⁵project_name, ⁶latitude, ⁷longitudeYou can retrieve the full list of acoustic network projects with the correspondent list_* function:
## [1] "2004_Gudena" "2011_Loire"
## [3] "2011_Warnow" "2013_Foyle"
## [5] "2013_Maas" "2014_Frome"
## [7] "2014_Nene" "2015_PhD_Gutmann_Roberts"
## [9] "2016_Diaccia_Botrona" "2017_Fremur"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: 415 × 38
## deploym…¹ recei…² acous…³ stati…⁴ stati…⁵ stati…⁶ deploy_date_time deplo…⁷
## <int> <chr> <chr> <chr> <chr> <chr> <dttm> <dbl>
## 1 1424 VR2W-1… demer de-1 fietse… 1 2014-05-13 00:00:00 51.0
## 2 1658 VR2W-1… demer de-2 Rommel… 1 2014-05-14 00:00:00 51.0
## 3 1478 VR2W-1… demer de-2 rommel… 1 2014-08-07 00:00:00 51.0
## 4 1661 VR2W-1… demer de-3 Messel… 1 2014-05-13 00:00:00 51.0
## 5 1381 VR2W-1… demer de-3 Messel… NA 2014-08-07 00:00:00 51.0
## 6 2869 VR2W-1… demer de-3 Messel… NA 2014-09-21 00:00:00 51.0
## 7 1662 VR2W-1… demer de-4 Testelt 1 2014-05-13 00:00:00 51.0
## 8 1425 VR2W-1… demer de-5 SO_ZIC… 1 2014-05-05 00:00:00 51.0
## 9 1663 VR2W-1… demer de-6 SA_RIF… 1 2014-05-05 00:00:00 51.0
## 10 1378 VR2W-1… demer de-7 de-7 NA 2014-05-05 00:00:00 51.0
## # … with 405 more rows, 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>, valid_data_until_date_time <dttm>, …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] 1427 1437 1378 1428 2938 1433 1431 1425 1429 1432 1379 1439 1436 1440 1381
## [16] 2869 1478 1663 1662 1424 1656 1659 1660 1441 2933 1434 1384 1506 1592 1470
## [31] 1588 1573 1593More 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
## deploym…¹ recei…² acous…³ stati…⁴ stati…⁵ stati…⁶ deploy_date_time deplo…⁷
## <int> <chr> <chr> <chr> <chr> <chr> <dttm> <dbl>
## 1 1424 VR2W-1… demer de-1 fietse… 1 2014-05-13 00:00:00 51.0
## 2 1478 VR2W-1… demer de-2 rommel… 1 2014-08-07 00:00:00 51.0
## 3 1381 VR2W-1… demer de-3 Messel… NA 2014-08-07 00:00:00 51.0
## 4 2869 VR2W-1… demer de-3 Messel… NA 2014-09-21 00:00:00 51.0
## 5 1662 VR2W-1… demer de-4 Testelt 1 2014-05-13 00:00:00 51.0
## 6 1425 VR2W-1… demer de-5 SO_ZIC… 1 2014-05-05 00:00:00 51.0
## 7 1663 VR2W-1… demer de-6 SA_RIF… 1 2014-05-05 00:00:00 51.0
## 8 1378 VR2W-1… demer de-7 de-7 NA 2014-05-05 00:00:00 51.0
## 9 1593 VR2W-1… demer de-7 de-7 NA 2014-12-19 02:00:00 51.0
## 10 1427 VR2W-1… demer de-8 AAN_GB… 1 2014-05-05 00:00:00 51.0
## # … with 23 more rows, 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>, valid_data_until_date_time <dttm>, …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)
## # … with 23 more rowsNumber 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 5
## 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
## # … with 23 more rowsRelative 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 0.111
## 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
## # … with 23 more rowsData 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
## acoustic_project_code station_name deploy_latitude deploy_longitude
## <chr> <chr> <dbl> <dbl>
## 1 demer de-1 51.0 4.84
## 2 demer de-10 51.0 5.05
## 3 demer de-11 51.0 5.05
## 4 demer de-12 51.0 5.06
## 5 demer de-13 51.0 5.06
## 6 demer de-14 51.0 5.06
## 7 demer de-14a 51.0 5.06
## 8 demer de-16 51.0 5.06
## 9 demer de-18 51.0 5.07
## 10 demer de-19 51.0 5.08
## # … with 13 more rowsTo 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, ")")
)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
)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$n
)
n_silurus_station %>%
left_join(
geo_info_stations,
by = "station_name"
) %>%
leaflet() %>%
addTiles() %>%
addCircleMarkers(
lng = ~deploy_longitude,
lat = ~deploy_latitude,
radius = ~n,
color = ~pal(n),
fillOpacity = 0.8,
stroke = FALSE,
popup = ~paste(
sep = "<br/>",
paste0("Station: ", station_name, " (", acoustic_project_code, ")"),
paste0("# detected individuals: ", n)
)
) %>%
addLegend(
title = "Detected individuals",
pal = pal,
values = ~n
)We can also make a map of the relative detection duration of the deployments. First, we have to retrieve the deployment geographical coordinates:
geo_info_deploys <-
detections_silurus %>%
distinct(
deployment_id,
deploy_latitude,
deploy_longitude,
station_name,
acoustic_project_code
) %>%
arrange(station_name)
geo_info_deploys## # A tibble: 33 × 5
## acoustic_project_code station_name deploy_latitude deploy_longitude deploym…¹
## <chr> <chr> <dbl> <dbl> <int>
## 1 demer de-1 51.0 4.84 1424
## 2 demer de-10 51.0 5.05 1428
## 3 demer de-11 51.0 5.05 1429
## 4 demer de-12 51.0 5.06 2938
## 5 demer de-12 51.0 5.06 1656
## 6 demer de-12 51.0 5.06 1470
## 7 demer de-13 51.0 5.06 1432
## 8 demer de-14 51.0 5.06 1433
## 9 demer de-14 51.0 5.06 1592
## 10 demer de-14a 51.0 5.06 1431
## # … with 23 more rows, and abbreviated variable name ¹deployment_idWe are ready to create the desired map, where we show the deployment ID, the station name and the network project as popups:
# Create a continuous colour palette function
pal <- colorNumeric(
palette = "viridis",
domain = rel_det_duration_silurus$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
)Do you want to add the temporal component to visualize the fish movement? Take a look at the moveVis package.