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#glatos2020 RI, REI, and VTrack workshop

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Receiver Efficiency Index

Overview

Teaching: 20 min
Exercises: 0 min
Questions

  • How do I calculate receiver efficiency with GLATOS?

Objectives

  • Demonstrate how to calculate receiver efficiency

The receiver efficiency index is number between 0 and 1 indicating the amount of relative activity at each receiver compared to the entire set of receivers, regardless of positioning. The function takes a set detections and a deployment history of the receivers to create a context for the detections. Both the amount of unique tags and number of species are taken into consideration in the calculation.

The receiver efficiency index implement is implemented based on the paper Acoustic telemetry array evolution: From species- and project-specific designs to large-scale, multispecies, cooperative networks. Each receiver’s index is calculated on the formula of:

\(REI = \frac{T_r}{T_a} \times \frac{S_r}{S_a} \times \frac{DD_r}{DD_a} \times \frac{D_a}{D_r}\)

\(REI\) = Receiver Efficiency Index

\(T_r\) = The number of tags detected on the receievr

\(T_a\) = The number of tags detected across all receivers

\(S_r\) = The number of species detected on the receiver

\(S_a\) = The number of species detected across all receivers

\(DD_a\) = The number of unique days with detections across all receivers

\(DD_r\) = The number of unique days with detections on the receiver

\(D_a\) = The number of days the array was active

\(D_r\) = The number of days the receiver was active

Importing Libraries

We will import dplyr,glatos and plotly to run then visualize the REI.

library(dplyr)
library(glatos)
library(plotly)

Importing Data

We will import the sample data below using glatos::read_glatos_detections() and glatos::read_glatos_receivers()

detection_file <- system.file("extdata", "walleye_detections.csv", package = "glatos")
receiver_file <- system.file("extdata", "sample_receivers.csv", package = "glatos")

receivers <- read_glatos_receivers(receiver_file)
detections <- read_glatos_detections(detection_file)

Cleaning Data

Below we use dplyr::mutate() to ensure that any recovery times that are set as NA are set to the current date and time. You can replace Sys.time() with the last known download time if you know it.

receivers <- receivers %>% 
  mutate( recover_date_time = replace(recover_date_time,
                                      is.na(recover_date_time), 
                                      Sys.time()))

Running REI

REI() takes two arguments. The first is a dataframe of detections the detection timstamp, the station identifier, the species, and the tag identifier. The next is a dataframe of deployments for each station. The station name should match the stations in the detections. The deployments need to include a deployment date and recovery date or last download date. Details on the columns metnioned see the preparing data section.

rei <- glatos::REI(detections,receivers)

Plotting with Plotly

Below is the code for plotting the RI using plotly. plotly allows us to interact with the map rather than haveing a static image. More about plotly can be found here.

geo <- list(
  scope = 'north america',
  showland = TRUE,
  landcolor = toRGB("white"),
  showocean = TRUE,
  oceancolor = toRGB("gray"),
  showcountreies = TRUE,
  showlakes = TRUE,
  lakecolor = plotly::toRGB("gray"),
  
  resolution = 50,
  center = list(lat = median(rei$latitude),
                lon = median(rei$longitude)),
  lonaxis = list(range=c(min(rei$longitude)-1, max(rei$longitude)+1)),
  lataxis = list(range=c(min(rei$latitude)-1, max(rei$latitude)+1))
)


map <- rei %>% 
  plot_geo(lat = ~latitude, lon = ~longitude, color = ~rei,width=900 )%>%
  add_markers(
    text = ~paste(station, ': ', rei),
    hoverinfo = "text",
    size = ~c(rei * 5 +5)
  )%>%
  layout(title = "REI", geo = geo)

To show the map you can just type out the variable name.

map

Plotting with Mapbox

Mapbox is a Live Location Platform that can serve up map tiles for use. You can create a free account and get an access token here. Below we set the access token as an environment variable that plotly can call.

Please note that the provided token will be deleted after the end of the workshop, so if you want use mapbox later, you will need your own token.

Sys.setenv('MAPBOX_TOKEN' = 'pk.eyJ1Ijoic29mdHdhcmVtb25rIiwiYSI6ImNrNzBubnVoazAwZjIzZW42c29ybGEzcXAifQ.pWqh1qxrRibjGMmHMThoiA')

From there, we can just call the plot_mapbox() function and pass whatever arguments we need for the map.

rei_mapbox <- rei %>% 
  plot_mapbox(lat = ~latitude, lon = ~longitude, color = ~rei , width=900) %>%
  add_markers(
    text = ~paste(station, ':', rei), 
    hoverinfo = "text",
    size = ~c(rei*6 +10)
  )%>%
  layout(title = "REI Mapbox", 
    mapbox = list(zoom = 5,
                        center = list(lat = ~median(latitude),
                                      lon = ~median(longitude))
  ))

To show the map you can just type out the variable name.

rei_mapbox

Key Points

  • GLATOS provides a method to calculate receiver efficiency using the GLATOS data objects.

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