--- title: "Explore acoustic telemetry data" author: "Damiano Oldoni" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Explore acoustic telemetry data} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ## 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](https://dplyr.tidyverse.org/) for data exploration, [lubridate](https://lubridate.tidyverse.org/) to handle datetime data and [leaflet](https://rstudio.github.io/leaflet/) for interactive visualizations. To start, load the etn package: ``` r library(etn) ``` And the other packages (with installation if necessary): ``` r 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: ``` r all_projects <- get_animal_projects() ``` Show a preview: ``` r all_projects |> head(10) ``` ``` ## # A tibble: 10 × 11 ## project_id project_code project_type telemetry_type project_name ## ## 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 2014_Frome ## start_date end_date latitude longitude moratorium imis_dataset_id ## ## 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: ``` r 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 ## ## 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 ## ## 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`: ``` r 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 ## ## 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 ## ## 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](https://inbo.github.io/etn/reference/index.html#section-list-parameter-options): ``` r 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`): ``` r 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 ## ## 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 ## ## 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 , animal_nickname , tagger , ## # capture_date_time , capture_location , capture_latitude , ## # capture_longitude , capture_method , capture_depth , ## # capture_temperature_change , release_date_time , ## # release_location , release_latitude , release_longitude , ## # recapture_date_time , length1_type , length1 , … ``` What species and how many individuals are tracked for the projects `2014_demer` and `2015_dijle`? ``` r animals |> count(scientific_name) ``` ``` ## # A tibble: 7 × 2 ## scientific_name n ## ## 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()`: ``` r 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: ``` r detections_silurus |> head(10) ``` ``` ## # A tibble: 10 × 20 ## detection_id date_time tag_serial_number acoustic_tag_id ## ## 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 ## ## 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 , station_name , ## # deploy_latitude , deploy_longitude , sensor_value , ## # sensor_unit , sensor2_value , sensor2_unit , ## # signal_to_noise_ratio , source_file , qc_flag , ## # deployment_id ``` Which individuals have been detected (`animal_id`) and in which period? ``` r 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 ## ## 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: ``` r 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 ## ## 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: ``` r 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 ## ## 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? ``` r detections_silurus |> group_by(animal_id) |> count() ``` ``` ## # A tibble: 9 × 2 ## # Groups: animal_id [9] ## animal_id n ## ## 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? ``` r detections_silurus |> group_by(animal_id) |> distinct(station_name) |> count() ``` ``` ## # A tibble: 9 × 2 ## # Groups: animal_id [9] ## animal_id n ## ## 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`: ``` r stations_silurus <- detections_silurus |> list_values(station_name) ``` ``` ## 23 unique station_name values ``` ``` r 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: ``` r 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 ## ## 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: ``` r n_detect_station <- detections_silurus |> group_by(station_name) |> count() n_detect_station ``` ``` ## # A tibble: 23 × 2 ## # Groups: station_name [23] ## station_name n ## ## 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: ``` r 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 ## ## 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: ``` r tags_id <- list_values(detections_silurus, acoustic_tag_id) ``` ``` ## 9 unique acoustic_tag_id values ``` ``` r 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 ## ## 1 1157781 acoustic animal A69-1601-28296 ## 2 1157782 acoustic animal A69-1601-28297 ## 3 1157783 acoustic animal A69-1601-28298 ## 4 1171775 acoustic animal A69-1601-26529 ## 5 1171776 acoustic animal A69-1601-26530 ## 6 1171777 acoustic animal A69-1601-26531 ## 7 1171780 acoustic animal A69-1601-26534 ## 8 1171781 acoustic animal A69-1601-26535 ## 9 1171782 acoustic animal A69-1601-26536 ## acoustic_tag_id_alternative manufacturer model frequency status ## ## 1 INNOVASEA V13-1x 069k ended ## 2 INNOVASEA V13-1x 069k ended ## 3 INNOVASEA V13-1x 069k ended ## 4 INNOVASEA V13-1x 069k ended ## 5 INNOVASEA V13-1x 069k ended ## 6 INNOVASEA V13-1x 069k ended ## 7 INNOVASEA V13-1x 069k ended ## 8 INNOVASEA V13-1x 069k ended ## 9 INNOVASEA V13-1x 069k ended ## # ℹ 44 more variables: activation_date , battery_estimated_life , ## # battery_estimated_end_date , length , diameter , ## # weight , floating , archive_memory , sensor_slope , ## # sensor_intercept , sensor_range , sensor_range_min , ## # sensor_range_max , sensor_resolution , sensor_unit , ## # sensor_accuracy , sensor_transmit_ratio , ## # accelerometer_algorithm , accelerometer_samples_per_second , … ``` You can also retrieve such information by `tag_serial_number`: ``` r 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 ## ## 1 1157781 acoustic animal A69-1601-28296 ## 2 1157782 acoustic animal A69-1601-28297 ## 3 1157783 acoustic animal A69-1601-28298 ## 4 1171775 acoustic animal A69-1601-26529 ## 5 1171776 acoustic animal A69-1601-26530 ## 6 1171777 acoustic animal A69-1601-26531 ## 7 1171780 acoustic animal A69-1601-26534 ## 8 1171781 acoustic animal A69-1601-26535 ## 9 1171782 acoustic animal A69-1601-26536 ## acoustic_tag_id_alternative manufacturer model frequency status ## ## 1 INNOVASEA V13-1x 069k ended ## 2 INNOVASEA V13-1x 069k ended ## 3 INNOVASEA V13-1x 069k ended ## 4 INNOVASEA V13-1x 069k ended ## 5 INNOVASEA V13-1x 069k ended ## 6 INNOVASEA V13-1x 069k ended ## 7 INNOVASEA V13-1x 069k ended ## 8 INNOVASEA V13-1x 069k ended ## 9 INNOVASEA V13-1x 069k ended ## # ℹ 44 more variables: activation_date , battery_estimated_life , ## # battery_estimated_end_date , length , diameter , ## # weight , floating , archive_memory , sensor_slope , ## # sensor_intercept , sensor_range , sensor_range_min , ## # sensor_range_max , sensor_resolution , sensor_unit , ## # sensor_accuracy , sensor_transmit_ratio , ## # accelerometer_algorithm , accelerometer_samples_per_second , … ``` 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: ``` r 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()`: ``` r acoustic_project_codes <- detections_silurus |> list_values(acoustic_project_code) ``` ``` ## 2 unique acoustic_project_code values ``` ``` r acoustic_project_codes ``` ``` ## [1] "demer" "zeeschelde" ``` To get more information about these acoustic networks, you can use function `get_acoustic_projects()` ``` r 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 ## ## 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 ## ## 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: ``` r 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: ``` r deployments <- get_acoustic_deployments( acoustic_project_code = acoustic_project_codes ) deployments ``` ``` ## # A tibble: 533 × 38 ## deployment_id receiver_id acoustic_project_code station_name ## ## 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 ## ## 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 2014-08-07 00:00:00 51.0 ## 6 Messelbroek 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 2014-05-05 00:00:00 51.0 ## # ℹ 523 more rows ## # ℹ 30 more variables: deploy_longitude , intended_latitude , ## # intended_longitude , mooring_type , bottom_depth , ## # riser_length , deploy_depth , battery_installation_date , ## # battery_estimated_end_date , activation_date_time , ## # recover_date_time , recover_latitude , recover_longitude , ## # download_date_time , download_file_name , … ``` These are the deployments of the acoustic receivers involved in `detections_silurus`: ``` r deploys_silurus <- detections_silurus |> list_values(deployment_id) ``` ``` ## 33 unique deployment_id values ``` ``` r 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`: ``` r deployments_silurus <- get_acoustic_deployments( deployment_id = deploys_silurus ) deployments_silurus ``` ``` ## # A tibble: 33 × 38 ## deployment_id receiver_id acoustic_project_code station_name ## ## 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 ## ## 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 2014-08-07 00:00:00 51.0 ## 4 Messelbroek 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 2014-05-05 00:00:00 51.0 ## 9 de-7 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 , intended_latitude , ## # intended_longitude , mooring_type , bottom_depth , ## # riser_length , deploy_depth , battery_installation_date , ## # battery_estimated_end_date , activation_date_time , ## # recover_date_time , recover_latitude , recover_longitude , ## # download_date_time , download_file_name , … ``` Deployment duration: ``` r 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 ## ## 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: ``` r 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 ## ## 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: ``` r 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 ## ## 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: ``` r 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 ## ## 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: ``` r 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`: ``` r # 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 = "
", 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`: ``` r # Create a continuous colour palette function pal <- colorNumeric( palette = "viridis", domain = n_silurus_station$n ) n_silurus_station |> left_join( geo_info_stations, by = "station_name" ) |> leaflet() |> addTiles() |> addCircleMarkers( lng = ~deploy_longitude, lat = ~deploy_latitude, radius = ~n, color = ~pal(n), fillOpacity = 0.8, stroke = FALSE, popup = ~paste( sep = "
", paste0("Station: ", station_name, " (", acoustic_project_code, ")"), paste0("# detected individuals: ", n) ) ) |> addLegend( title = "Detected individuals", pal = pal, values = ~n ) |> htmlwidgets::saveWidget(file = "n_individuals_per_station.html") ``` We can also make a map of the relative detection duration of the deployments. First, we have to retrieve the deployment geographical coordinates: ``` r geo_info_deploys <- detections_silurus |> distinct( deployment_id, deploy_latitude, deploy_longitude, station_name, acoustic_project_code ) |> arrange(station_name) geo_info_deploys ``` ``` ## # A tibble: 33 × 5 ## deployment_id deploy_latitude deploy_longitude station_name ## ## 1 1424 51.0 4.84 de-1 ## 2 1428 51.0 5.05 de-10 ## 3 1429 51.0 5.05 de-11 ## 4 2938 51.0 5.06 de-12 ## 5 1656 51.0 5.06 de-12 ## 6 1470 51.0 5.06 de-12 ## 7 1432 51.0 5.06 de-13 ## 8 1433 51.0 5.06 de-14 ## 9 1592 51.0 5.06 de-14 ## 10 1431 51.0 5.06 de-14a ## acoustic_project_code ## ## 1 demer ## 2 demer ## 3 demer ## 4 demer ## 5 demer ## 6 demer ## 7 demer ## 8 demer ## 9 demer ## 10 demer ## # ℹ 23 more rows ``` We are ready to create the desired map, where we show the deployment ID, the station name and the network project as popups: ``` r # Create a continuous colour palette function pal <- colorNumeric( palette = "viridis", domain = rel_det_duration_silurus$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 = "
", 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](https://movevis.org/index.html) package.