Station usage - hours

Published

2026-03-06

This page (finally!) describes and summarises where (Motus station) and when (tidal condition) the birds were.

The total amount of hours and the usage rate are assessed for each individual being detected within the Motus array and across the different tidal conditions such as:

  • Diurnal High Tide
  • Diurnal Low Tide
  • Nocturnal High Tide
  • Nocturnal Low Tide

Plus, globally visualise the conditions of detection across the tagged population.

It is surprising to see how much we thought we knew and yet we ignore…

Load your data in your R environment - see Load & Format > Reproducibility

library(motus)
library(dplyr)
library(here)
library(forcats) 
library(ggplot2)
library(lubridate)
library(tidyr)
library(purrr)
library(readr)
library(bioRad)
library(hms)
library(dplyr)
library(ggplot2)
library(scales)
library(gt)

And load your activity table too!

recv.act <- tbl(sql.motus, "activity")  %>% 
  collect() %>% 
  as.data.frame() %>%
  rename(deviceID = "motusDeviceID") %>%
  # keep our deployed antennas only
  filter(deviceID %in% unique(recv$deviceID)) %>% 
  # Set the time properly - IMPORTANT
  mutate(date = as_datetime(as.POSIXct(hourBin* 3600,
                                       origin = "1970-01-06", 
                                       tz = "UTC")),
         dateAus = as_datetime(as.POSIXct(hourBin* 3600, 
                                          origin = "1970-01-06", 
                                          tz = "UTC"), 
                               tz = "Australia/Sydney"))

Pre-requisite

This analysis compares the time available for each tidal category (Diurnal High Tide, Nocturnal High Tide, Diurnal Low Tide and Nocturnal Low tide) with the time each shorebird species spent at Motus stations.

First, we need to determine the available time for each species group and for each Motus station. Second, we need to calculate the time used for each bird in every species and for each MOTUS stations too.

The below procedure breaks down how we assessed:

  • Tide variable
  • The time available
  • The time used

Schema 1: Definition of available and used periods across bird individuals and Motus stations

Schema: Definition of available and used periods across bird individuals and Motus stations

TIDE VARIABLE

Based on New South Wales Tide Tables we were able to extract and classify the total time for each of the four categories of tide that occurred within the Hunter estuary. Note that one data-set is provided at the estuary scale, therefore, we assume the tide is occurring at the same scale, time, frequency and amplitude across our MOTUS stations.

Tide table informs for Low and High tide peaks. Half of the period between peak n and peak n-1 is taken, added to the period between peak n and peak n+1, then expanded in hours for further analysis. The tide associated to peak n defines its variable period (i.e. red lines below is defined as high tide since peak n is a high tide peak).

Schema: Definition of tide variables (high or low) for one period (red line)

tide_data <- tide_data %>%
  
  arrange(tideDateTimeAus) %>%
  mutate(prev_time = lag(tideDateTimeAus),
         next_time = lead(tideDateTimeAus) ) %>%
  filter(!is.na(prev_time) & !is.na(next_time)) %>%
  
  # Get the duration of the tide centered around Peak 2 (P2), with start halfway between P1 and P2, and with end btw P2-P3
  mutate(duration_h = as.numeric(difftime(tideDateTimeAus, prev_time, units = "hours")/2 + 
                                    difftime(next_time, tideDateTimeAus, units = "hours")/2)) %>%
  
  # Rename for consistency
  rename(tideHighLow = high_low,
         timeAus = tideDateTimeAus,
         tideDiel = day_night) %>%
  select(timeAus, tideCategory, tideHighLow, tideDiel, duration_h, sunriseNewc, sunsetNewc) %>%
  
  # Factorise
  mutate(tideCategory = as_factor(tideCategory))

TIME AVAILABLE

Now tide data ready to be processed, we need to define for each individual (birds) and across each Motus stations, what was the amount of time available for the four tide categories.

Available time for each Motus stations is accessed with activity table: each row is a dated record of noise or a tag detected - see Survey effort > Activity table.

We need to extract the temporal coverage of each station, starting from deployment date to the last recorded activity, and expand this period in hours.


# Sort the terminated serno (if terminated, ie. one box removed from one antenna site, a date comes along)
# but still needed for accessing survey effort as the station is currently running with another serno 
recv.act.term <- recv.act %>%
  left_join(recv %>% 
              filter(!is.na(timeEndAus)) %>%
              select(deviceID, serno, recvDeployName),
            "deviceID") %>%
  filter(!is.na(recvDeployName)) %>%
  mutate(SernoStation = paste0(recvDeployName, "_", serno))

# Sort the currently running serno
recv.act.runn <- recv.act %>%
  left_join(recv %>% 
              filter(is.na(timeEndAus)) %>%
              select(deviceID, serno, recvDeployName),
            "deviceID") %>%
  filter(!is.na(recvDeployName)) %>%
  mutate(SernoStation = paste0(recvDeployName, "_", serno))

# Merging in one data-set to use Station's name further + pick-up the rounded hours
recv.act <- bind_rows(recv.act.runn, recv.act.term) %>%
  mutate(hour_dt = round_date(dateAus, "hour"))

# Providing helpful variables
recv <- recv %>%
  mutate(SernoStation = paste0(recvDeployName, "_", serno),
         lisStart = timeStartAus,
         lisEnd = if_else(
           is.na(timeEndAus), # means the station is still running since the last data downloading
           with_tz(Sys.time(), "Australia/Sydney"),
           with_tz(as_datetime(timeEndAus, tz = "UTC"), "Australia/Sydney")) )

# Generating hourly sequences per SernoStation from start to end dates of the deviceID at particular sites
recv_hours <- recv %>%
  select(recvDeployName, deviceID, SernoStation, lisStart, lisEnd) %>%
  group_by(SernoStation) %>%
  rowwise() %>%
  mutate(hour_dt = list(seq(from = round_date(lisStart, unit = "hour"),
                            to = round_date(lisEnd, unit = "hour"),
                            by = "hour")) ) %>%
  unnest(cols = c(hour_dt)) %>%
  ungroup()

# Simplify station variables (recvDeployName)
recv <- recv %>% 
  select(!recvDeployName) 
recv$recvDeployName <- sub("_SG-.*", "", recv$SernoStation)

recv_hours <- recv_hours %>% 
  select(!recvDeployName) 
recv_hours$recvDeployName <- sub("_SG-.*", "", recv_hours$SernoStation)


We now how the complete temporal coverage, expanded in hours, for each Motus station.

However, due to some temporary failures or maintenance (see Survey effort > Operational periods), the actual period a station was operational may be different than the complete temporal coverage. So, we must determine when a station was not operational and substract these off-hours to the complete temporal coverage.


# Distinguish station from mixed recv + Giving operational variable (= TRUE when existing values from act table)
recv_hours <- recv_hours %>%
  left_join(recv.act %>%
              distinct(recvDeployName, hour_dt) %>%
              mutate(operational = TRUE),
            by = c("recvDeployName", "hour_dt")) %>%
  mutate(operational = if_else(is.na(operational), FALSE, TRUE))

# /!\ Due to unknown error? Have to set this manually
recv_hours <- recv_hours %>%
  mutate(operational = case_when(
    recvDeployName == "Fullerton Entrance" & 
      hour_dt > as.POSIXct("2023-04-02") & 
      hour_dt < as.POSIXct("2023-04-05") ~ FALSE,
    TRUE ~ operational))

# Summary table                                                                     
off_runs <- recv_hours %>% 
  arrange(recvDeployName, hour_dt) %>%
  group_by(recvDeployName) %>%
  mutate(off_run_id = consecutive_id(operational == FALSE)) %>%
  ungroup() %>%
  
  filter(operational == FALSE) %>%
  
  group_by(recvDeployName, off_run_id) %>%
  summarise(
    start_off = min(hour_dt),
    end_off = max(hour_dt),
    tot_off_hours = n(),
    .groups = "drop") %>%
  
  filter(tot_off_hours > 24)

# Unique recvDeployNames from off_runs
recv_names <- unique(recv_hours$recvDeployName)

# Split tide_data into a named list with one element per recvDeployName
tide_data_list <- setNames(vector("list", length(recv_names)), recv_names)

for(name in recv_names) {
  # Get off intervals for this recvDeployName
  intervals <- off_runs %>%
    filter(recvDeployName == name) %>%
    select(start_off, end_off)
  
  # Get deployment start and end dates for this recvDeployName
  deploy <- recv_hours %>%
    filter(recvDeployName == name) %>%
    summarise(
      lisStart = min(lisStart, na.rm = TRUE),
      lisEnd = max(lisEnd, na.rm = TRUE)
    )
  
  # Filter tide_data by deployment period
  td <- tide_data %>%
    filter(timeAus >= deploy$lisStart & timeAus <= deploy$lisEnd) %>%
    mutate(recvDeployName = name) 
  
  if(nrow(intervals) > 0) {
    # Vectorized exclusion of off intervals
    is_in_off <- sapply(td$timeAus, function(t) {
      any(t >= intervals$start_off & t <= intervals$end_off)
    })
    td <- td[!is_in_off, ]
  }
  tide_data_list[[name]] <- td
}

tide_data_df <- bind_rows(tide_data_list) %>%
  mutate(hour_dt = round_date(timeAus, unit = "hour"))            # AVAILABLE TIME (tide categories covering same time as recv survey effort) 

# Finalise data set for receiver ON with tidal data, per hour
total_recv_tide_data <- tide_data_df %>%
  group_by(recvDeployName) %>%
  
  # Expand per hour bin
  summarise(hour_seq = list(seq(min(hour_dt), max(hour_dt), by = "hour")), .groups = "drop") %>% 
  unnest(hour_seq) %>%
  rename(hour_dt = hour_seq) %>%
  
  # Match tide data to hourly grid
  # Hours WITHOUT tide data get NA values across all columns but we'll deal with this later
  left_join(tide_data_df, by = c("recvDeployName", "hour_dt"))


Temporal coverage for each station is now reduced to its operational periods, and ready to be processed.

We now assign each bird its own available time period, spanning from its tagging date to its last Motus detection, and expanded to hourly resolution too.


# Get the monitored period of each bird
period_sp <- data_all  %>%
  group_by(Band.ID) %>%
  reframe(DateAUS.Trap = first(DateAUS.Trap), 
          last_dateAus = max(dateAus),
          speciesEN = speciesEN) %>%
  unique()

# Expand one row per hours to each individual across its whole period (this is the available time)
bird_hours <- period_sp %>%
  group_by(Band.ID) %>%
  rowwise() %>%
  mutate(hour_dt = list(seq(from = as.POSIXct(ymd(DateAUS.Trap), tz = "UTC"),
                            to = as.POSIXct(last_dateAus, tz = "UTC"),
                            by = "hour"))) %>%
  unnest(cols = c(hour_dt)) %>%
  ungroup() 

# Duplicate in as many list as many stations
recv_names <- unique(recv_hours$recvDeployName)
bird_data_list <- setNames(vector("list", length(recv_names)), recv_names)

for(name in recv_names) {
  
  valid_hours_recv <- total_recv_tide_data %>%
    filter(recvDeployName == name) %>%
    pull(hour_dt)
  
  valid_hours_bird_recv <- bird_hours %>%
    filter(hour_dt %in% valid_hours_recv)
  
  valid_hours_bird_recv$name <- name
  
    
  bird_data_list[[name]] <- valid_hours_bird_recv
} 

# ADD TIDE TO AVAILABLE TIME
# The NA cols left over before from recv table, now also filtered out based on available time coming from bird periods table
get.tideIndex <- function(time){return(which.min(abs(tide_data$timeAus - time)))}

available_bird_recv_time <- bind_rows(bird_data_list) %>%
  mutate(hour_dt = force_tz(hour_dt, "Australia/Sydney")) %>%
  # Remove rows with ANY NA
  filter(if_all(everything(), ~ !is.na(.))) %>%
  mutate(tideIndex = map_dbl(hour_dt, get.tideIndex))

# Extract tide categories thanks to tide index (row order in available_bird_recv_time)
tide_values <- tide_data[available_bird_recv_time$tideIndex, c("tideCategory")]

# Merge tide categories to available_bird_recv_time and factorise the variables
available_bird_recv_time <- available_bird_recv_time %>%
  mutate(tideCategory = as_factor(tide_values$tideCategory))

# FINALISE AVAILABLE TIME 
available_bird_recv_time <- available_bird_recv_time %>%
  rename(recvDeployName = name) %>%
  group_by(Band.ID, speciesEN, recvDeployName, tideCategory)  %>%
  summarise(duration_h = n()) %>%
  mutate(tideDiel = if_else(grepl("Diurnal", tideCategory), "Diurnal", "Nocturnal"),
         tideHighLow = if_else(grepl("High", tideCategory), "High", "Low")) %>%
  select(Band.ID, speciesEN, recvDeployName, tideCategory, tideDiel, tideHighLow, duration_h)

The available time is now defined for each bird across each Motus station, taking into account the periods where stations where ON only. The available periods are expanded in hours and the tide condition is associated.

TIME USED

Let’s now determine the time used for each bird for each Motus station.

The time used is defined by multiplying the number of detections (meaning the number of rows) by the duration of a pulse (pulse interval). We assume that if an individual is detected by a receiver once, the individual was at least in the station coverage for the duration of the signal pulse. Duration differ depending tag models - see table below.

Table X: Pulse Interval depending Lotek nanotag models

Pulse Interval depending Lotek nanotag models

# USED TIME (amount of time each bird spent during each category of tide and at each station)
# Provide the burst interval value depending Lotek-nano tag model (scd)
data_bird <- data_all %>%
  mutate(burst_inter = ifelse(tagModel == "NTQB2-6-2", dseconds(7.1), dseconds(13.1))) %>%
  select(timeAus, sunriseNewc, sunsetNewc, tideCategory, tideHighLow, tideDiel, 
         speciesEN, tagModel, recvDeployName, recv,speciesSci, Band.ID, burst_inter)

used_bird_recv_time <- data_bird %>%
  group_by(Band.ID, speciesEN, recvDeployName, tideCategory)  %>%
  summarise(duration_sec = sum(burst_inter)) %>%
  mutate(duration_h = round(duration_sec / 3600, 0),
         tideDiel = if_else(grepl("Diurnal", tideCategory), "Diurnal", "Nocturnal"),
         tideHighLow = if_else(grepl("High", tideCategory), "High", "Low")) %>%
  select(Band.ID, speciesEN, recvDeployName, tideCategory, tideDiel, tideHighLow, duration_h)

Usage rate

Now that the used and available time have been determined for each individual and for each Motus station, we can relate these two elements together with the Usage rate (or rate of use).

\[ \text{Usage rate} = \frac{\text{Time used}}{\text{Time available}} \times 100 \quad \textit{across stations and species} \]

Some cases may happen where the time used was over the available. It may occur because multiplying each bird detection by its pulse interval duration overestimates the actual time used. For example, if a bird is detected twice with a 13-second pulse interval—once at second 1 and again at second 14—this results in 2 detections over 14 seconds. However, simply multiplying the detections by the pulse interval (2 × 13 seconds) incorrectly estimates the total time used as 26 seconds.

Band.ID speciesEN recvDeployName tideCategory tideDiel tideHighLow available_t used_t rate_use
6318617 Pacific Golden-Plover Curlew Point Diurnal_Low Diurnal Low 346 351 101.4451
6318617 Pacific Golden-Plover Curlew Point Diurnal_High Diurnal High 437 461 105.4920
7176824 Bar-tailed Godwit Curlew Point Nocturnal_High Nocturnal High 6 8 133.3333
7176824 Bar-tailed Godwit Tomago Nocturnal_High Nocturnal High 6 9 150.0000
7394027 Bar-tailed Godwit Tomago Nocturnal_High Nocturnal High 49 52 106.1224
7394040 Bar-tailed Godwit Curlew Point Diurnal_High Diurnal High 6 8 133.3333
7394040 Bar-tailed Godwit Curlew Point Nocturnal_Low Nocturnal Low 6 13 216.6667

Note: We decided to force n = 5 out of 1512 cases where the rate used was between 100 and 140 % to equal 100 %.
And we decided to remove n = 2 cases where the rate used was over 140 %, considering the tag might simply have fell of the bird within the vicinity of the station.
Reminder: one case is made of the combination from an individual recorded at a Motus station for a specific tide condition. - See table above.

figure_plot <- figure_plot %>%
  
  mutate(rate_use = ifelse(rate_use > 100 & rate_use < 140, 100, rate_use)) %>% # force 100-140 rate values to equal 100 %
  mutate(rate_use = ifelse(rate_use >= 140, NA, rate_use)) %>%                  # remove rates above 140 %
  
  mutate(speciesType = factor(shorebird_class[speciesEN], 
                               levels = c("migratory", "resident"))) %>%
  
  filter(!speciesEN %in% c("Masked Lapwing"))

Note: We filtered out Masked Lapwing from the analysis, since they have too few data.

Now, everything is ready to format the data for plot and to visualise the results for Usage rate and Hourly usage across the Motus stations of our local array.

# Track size sample
counts <- used_bird_recv_time %>%
      group_by(speciesEN) %>%
      summarise(n = n_distinct(Band.ID)) %>%
      mutate(label = paste0(speciesEN, " (n = ", n, ")"))
label_vec <- setNames(counts$label, counts$speciesEN)
# Tide and species groupings
tide_levels <- c("Low", "High")
species_types <- unique(shorebird_class)  

# Simplified function - no min_n_nonzero check
make_plot <- function(tide_level, species_type) {
  data_sub <- figure_plot %>%
    filter(tideHighLow == tide_level) %>%
    mutate(species_class = shorebird_class[speciesEN]) %>%
    filter(species_class == species_type)
  
  p <- ggplot(data_sub,
              aes(x    = factor(recvDeployName, levels = sort(unique(recvDeployName))),
                  y    = rate_use,
                  fill = tideDiel))
  
  # Always add boxplot for all available data
  p <- p + 
    geom_boxplot(outlier.shape = NA,
                 varwidth = FALSE,
                 position = position_dodge(width = 0.8, preserve = "single")) +
    
    # Always show individual points
    geom_point(aes(shape = tideDiel), 
               position = position_dodge(width = 0.8),
               alpha = 1, size = 1.5,
               show.legend = FALSE) +
    scale_shape_manual(values = c("Diurnal" = 21, "Nocturnal" = 16)) +
    
    facet_wrap(~ speciesEN,
               labeller = labeller(speciesEN = label_vec)) +
    labs(x     = "Receiver Deployment",
         y     = "Rate of Use (%)",
         title = paste(ifelse(species_type == "migratory",
                              "Migratory species", "Resident species"),
                       "during", tide_level, "tide")) +
    coord_cartesian(ylim = c(0, 100)) +
    theme_minimal() +
    scale_fill_manual(values = c("Diurnal" = "white", "Nocturnal" = "darkgrey")) +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))
  
  return(p)
}

# Generate all plots
plots_used_rate <- cross2(tide_levels, species_types) %>%
  purrr::map(~ make_plot(.x[[1]], .x[[2]]))
# General function to provide one table with stats values
make_summary_table <- function(tide_level, species_type) {
  data_sub <- figure_plot %>%
    filter(tideHighLow == tide_level) %>%
    mutate(species_class = shorebird_class[speciesEN]) %>%
    filter(species_class == species_type)
  
  summary_table <- data_sub %>%
    group_by(speciesEN, recvDeployName, tideDiel) %>%
    summarise(
      n_individual = n(),
      min = round(min(rate_use, na.rm = TRUE), 1),
      q1 = round(quantile(rate_use, 0.25, na.rm = TRUE),1),
      median = round(median(rate_use, na.rm = TRUE),1),
      q3 = round(quantile(rate_use, 0.75, na.rm = TRUE),1),
      max = round(max(rate_use, na.rm = TRUE),1),
      mean = round(mean(rate_use, na.rm = TRUE),1),
      sd = round(sd(rate_use, na.rm = TRUE),1),
      .groups = 'drop'
    ) %>%
    arrange(speciesEN, recvDeployName, tideDiel) %>%
    rename(Species = "speciesEN", Station = "recvDeployName", condition = "tideDiel")
  
  return(summary_table)
}

# One table per conditions
high_migratory <- make_summary_table("High", "migratory")
high_resident <- make_summary_table("High", "resident")
low_migratory <- make_summary_table("Low", "migratory")
low_resident <- make_summary_table("Low", "resident")

RESIDENT BIRDS

Statistics - High Tide Resident Species
Species Station condition n_individual min q1 median q3 max mean sd
Pied Stilt Corrie Island Diurnal 3 Inf NA NA NA -Inf NaN NA
Pied Stilt Corrie Island Nocturnal 3 Inf NA NA NA -Inf NaN NA
Pied Stilt Curlew Point Diurnal 4 6.1 6.3 6.5 6.7 6.9 6.5 0.6
Pied Stilt Curlew Point Nocturnal 4 0.5 0.5 0.5 0.5 0.5 0.5 NA
Pied Stilt Davies Point Diurnal 3 Inf NA NA NA -Inf NaN NA
Pied Stilt Davies Point Nocturnal 3 Inf NA NA NA -Inf NaN NA
Pied Stilt Fullerton Entrance Diurnal 6 1.5 3.8 35.7 41.7 52.1 27.0 23.0
Pied Stilt Fullerton Entrance Nocturnal 6 0.3 4.0 4.1 15.4 24.6 9.7 10.1
Pied Stilt Hexham Swamp Diurnal 3 Inf NA NA NA -Inf NaN NA
Pied Stilt Hexham Swamp Nocturnal 3 Inf NA NA NA -Inf NaN NA
Pied Stilt Milhams Pond Diurnal 6 2.6 2.6 2.6 2.6 2.6 2.6 NA
Pied Stilt Milhams Pond Nocturnal 6 0.4 0.4 0.4 0.4 0.4 0.4 NA
Pied Stilt Ramsar Road Diurnal 6 1.5 1.9 2.3 2.7 3.1 2.3 1.1
Pied Stilt Ramsar Road Nocturnal 6 0.0 1.0 2.0 3.0 4.0 2.0 2.9
Pied Stilt Swan Pond Diurnal 6 0.4 1.0 1.7 2.3 3.0 1.7 1.8
Pied Stilt Swan Pond Nocturnal 6 0.3 0.6 1.0 1.3 1.7 1.0 1.0
Pied Stilt Tomago Diurnal 3 Inf NA NA NA -Inf NaN NA
Pied Stilt Tomago Nocturnal 3 Inf NA NA NA -Inf NaN NA
Pied Stilt Windeyers Diurnal 1 Inf NA NA NA -Inf NaN NA
Pied Stilt Windeyers Nocturnal 1 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Corrie Island Diurnal 3 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Corrie Island Nocturnal 3 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Curlew Point Diurnal 3 4.5 6.7 8.9 9.1 9.3 7.6 2.6
Red-necked Avocet Curlew Point Nocturnal 3 1.6 1.6 1.6 1.6 1.6 1.6 NA
Red-necked Avocet Davies Point Diurnal 3 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Davies Point Nocturnal 3 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Fullerton Entrance Diurnal 3 16.4 21.3 26.2 26.7 27.3 23.3 6.0
Red-necked Avocet Fullerton Entrance Nocturnal 3 16.7 20.0 23.4 25.0 26.6 22.2 5.1
Red-necked Avocet Hexham Swamp Diurnal 3 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Hexham Swamp Nocturnal 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Red-necked Avocet Milhams Pond Diurnal 3 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Milhams Pond Nocturnal 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Red-necked Avocet Ramsar Road Diurnal 3 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Ramsar Road Nocturnal 3 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Swan Pond Diurnal 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Red-necked Avocet Swan Pond Nocturnal 3 0.0 0.0 0.0 0.0 0.0 0.0 NA
Red-necked Avocet Tomago Diurnal 3 0.0 0.5 0.9 2.3 3.7 1.6 1.9
Red-necked Avocet Tomago Nocturnal 3 20.2 21.0 21.8 22.6 23.3 21.8 1.6

Statistics - Low Tide Resident Species
Species Station condition n_individual min q1 median q3 max mean sd
Pied Stilt Corrie Island Diurnal 3 Inf NA NA NA -Inf NaN NA
Pied Stilt Corrie Island Nocturnal 3 Inf NA NA NA -Inf NaN NA
Pied Stilt Curlew Point Diurnal 4 0.0 0.1 0.2 0.3 0.4 0.2 0.3
Pied Stilt Curlew Point Nocturnal 4 0.7 0.8 1.0 1.1 1.3 1.0 0.4
Pied Stilt Davies Point Diurnal 3 Inf NA NA NA -Inf NaN NA
Pied Stilt Davies Point Nocturnal 3 Inf NA NA NA -Inf NaN NA
Pied Stilt Fullerton Entrance Diurnal 6 3.4 3.8 12.2 25.0 31.7 15.2 12.7
Pied Stilt Fullerton Entrance Nocturnal 6 1.8 3.3 6.2 16.7 21.3 9.8 8.7
Pied Stilt Hexham Swamp Diurnal 3 Inf NA NA NA -Inf NaN NA
Pied Stilt Hexham Swamp Nocturnal 3 Inf NA NA NA -Inf NaN NA
Pied Stilt Milhams Pond Diurnal 6 5.4 5.4 5.4 5.4 5.4 5.4 NA
Pied Stilt Milhams Pond Nocturnal 6 0.0 0.0 0.0 0.0 0.0 0.0 NA
Pied Stilt Ramsar Road Diurnal 6 0.9 1.7 2.4 3.2 3.9 2.4 2.2
Pied Stilt Ramsar Road Nocturnal 6 0.0 0.8 1.7 2.5 3.3 1.7 2.3
Pied Stilt Swan Pond Diurnal 6 0.5 0.9 1.4 1.8 2.3 1.4 1.3
Pied Stilt Swan Pond Nocturnal 6 0.2 0.5 0.8 1.1 1.4 0.8 0.9
Pied Stilt Tomago Diurnal 3 Inf NA NA NA -Inf NaN NA
Pied Stilt Tomago Nocturnal 3 Inf NA NA NA -Inf NaN NA
Pied Stilt Windeyers Diurnal 1 Inf NA NA NA -Inf NaN NA
Pied Stilt Windeyers Nocturnal 1 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Corrie Island Diurnal 3 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Corrie Island Nocturnal 3 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Curlew Point Diurnal 3 0.0 0.3 0.6 0.8 1.1 0.6 0.8
Red-necked Avocet Curlew Point Nocturnal 3 0.0 0.2 0.3 0.5 0.6 0.3 0.5
Red-necked Avocet Davies Point Diurnal 3 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Davies Point Nocturnal 3 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Fullerton Entrance Diurnal 3 2.8 8.0 13.3 14.9 16.6 10.9 7.2
Red-necked Avocet Fullerton Entrance Nocturnal 3 0.0 2.9 5.8 6.2 6.5 4.1 3.6
Red-necked Avocet Hexham Swamp Diurnal 3 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Hexham Swamp Nocturnal 3 0.0 0.0 0.0 0.0 0.0 0.0 NA
Red-necked Avocet Milhams Pond Diurnal 3 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Milhams Pond Nocturnal 3 0.0 0.0 0.0 0.0 0.0 0.0 NA
Red-necked Avocet Ramsar Road Diurnal 3 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Ramsar Road Nocturnal 3 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Swan Pond Diurnal 3 Inf NA NA NA -Inf NaN NA
Red-necked Avocet Swan Pond Nocturnal 3 0.0 0.2 0.3 0.5 0.6 0.3 0.5
Red-necked Avocet Tomago Diurnal 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Red-necked Avocet Tomago Nocturnal 3 0.0 2.3 4.5 5.8 7.1 3.9 3.6

MIGRATORY BIRDS

Statistics - High Tide Migratory Species
Species Station condition n_individual min q1 median q3 max mean sd
Bar-tailed Godwit Corrie Island Diurnal 7 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Corrie Island Nocturnal 7 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Curlew Point Diurnal 8 1.2 36.8 59.8 81.5 100.0 56.4 36.4
Bar-tailed Godwit Curlew Point Nocturnal 8 0.4 9.8 11.3 67.3 100.0 36.4 44.2
Bar-tailed Godwit Davies Point Diurnal 7 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Davies Point Nocturnal 7 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Fullerton Entrance Diurnal 9 0.0 0.0 0.0 0.0 4.2 0.8 1.9
Bar-tailed Godwit Fullerton Entrance Nocturnal 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Bar-tailed Godwit Hexham Swamp Diurnal 7 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Hexham Swamp Nocturnal 7 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Milhams Pond Diurnal 10 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Milhams Pond Nocturnal 10 0.0 0.0 0.0 0.0 0.0 0.0 NA
Bar-tailed Godwit Ramsar Road Diurnal 9 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Ramsar Road Nocturnal 9 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Swan Pond Diurnal 10 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Swan Pond Nocturnal 10 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Tomago Diurnal 7 5.1 5.2 5.4 5.5 5.6 5.4 0.4
Bar-tailed Godwit Tomago Nocturnal 7 0.0 0.7 49.6 98.7 100.0 49.8 57.0
Bar-tailed Godwit Windeyers Diurnal 4 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Windeyers Nocturnal 4 Inf NA NA NA -Inf NaN NA
Curlew Sandpiper Corrie Island Diurnal 5 Inf NA NA NA -Inf NaN NA
Curlew Sandpiper Corrie Island Nocturnal 5 Inf NA NA NA -Inf NaN NA
Curlew Sandpiper Curlew Point Diurnal 5 12.2 22.7 39.8 42.8 43.0 32.1 13.9
Curlew Sandpiper Curlew Point Nocturnal 5 2.0 9.7 10.1 11.3 15.7 9.8 5.0
Curlew Sandpiper Davies Point Diurnal 5 Inf NA NA NA -Inf NaN NA
Curlew Sandpiper Davies Point Nocturnal 5 Inf NA NA NA -Inf NaN NA
Curlew Sandpiper Fullerton Entrance Diurnal 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Curlew Sandpiper Fullerton Entrance Nocturnal 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Curlew Sandpiper Hexham Swamp Diurnal 5 Inf NA NA NA -Inf NaN NA
Curlew Sandpiper Hexham Swamp Nocturnal 5 Inf NA NA NA -Inf NaN NA
Curlew Sandpiper Milhams Pond Diurnal 5 0.0 0.0 0.0 0.0 0.0 0.0 NA
Curlew Sandpiper Milhams Pond Nocturnal 5 0.0 0.0 0.0 0.0 0.0 0.0 NA
Curlew Sandpiper Ramsar Road Diurnal 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Curlew Sandpiper Ramsar Road Nocturnal 5 0.0 0.0 0.1 0.2 0.2 0.1 0.1
Curlew Sandpiper Swan Pond Diurnal 5 0.0 0.0 0.1 0.1 0.1 0.1 0.1
Curlew Sandpiper Swan Pond Nocturnal 5 0.0 0.0 0.2 0.2 0.2 0.1 0.1
Curlew Sandpiper Tomago Diurnal 5 0.0 0.0 0.0 0.0 0.1 0.0 0.1
Curlew Sandpiper Tomago Nocturnal 5 0.0 0.0 0.0 0.2 0.2 0.1 0.1
Curlew Sandpiper Windeyers Diurnal 5 Inf NA NA NA -Inf NaN NA
Curlew Sandpiper Windeyers Nocturnal 5 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Corrie Island Diurnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Corrie Island Nocturnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Curlew Point Diurnal 2 0.0 0.5 0.9 1.4 1.9 0.9 1.3
Eurasian Whimbrel Curlew Point Nocturnal 2 0.0 1.1 2.2 3.3 4.4 2.2 3.1
Eurasian Whimbrel Davies Point Diurnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Davies Point Nocturnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Fullerton Entrance Diurnal 2 2.9 3.3 3.6 4.0 4.4 3.6 1.0
Eurasian Whimbrel Fullerton Entrance Nocturnal 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Eurasian Whimbrel Hexham Swamp Diurnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Hexham Swamp Nocturnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Milhams Pond Diurnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Milhams Pond Nocturnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Ramsar Road Diurnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Ramsar Road Nocturnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Swan Pond Diurnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Swan Pond Nocturnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Tomago Diurnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Tomago Nocturnal 2 0.8 0.8 0.8 0.8 0.8 0.8 NA
Eurasian Whimbrel Windeyers Diurnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Windeyers Nocturnal 2 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Corrie Island Diurnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Corrie Island Nocturnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Curlew Point Diurnal 1 1.2 1.2 1.2 1.2 1.2 1.2 NA
Far Eastern Curlew Curlew Point Nocturnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Davies Point Diurnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Davies Point Nocturnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Fullerton Entrance Diurnal 2 0.0 0.0 0.0 0.0 0.0 0.0 NA
Far Eastern Curlew Fullerton Entrance Nocturnal 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Far Eastern Curlew Hexham Swamp Diurnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Hexham Swamp Nocturnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Milhams Pond Diurnal 2 18.0 18.0 18.0 18.0 18.0 18.0 NA
Far Eastern Curlew Milhams Pond Nocturnal 2 8.5 8.5 8.5 8.5 8.5 8.5 NA
Far Eastern Curlew Ramsar Road Diurnal 2 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Ramsar Road Nocturnal 2 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Swan Pond Diurnal 2 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Swan Pond Nocturnal 2 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Tomago Diurnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Tomago Nocturnal 1 1.4 1.4 1.4 1.4 1.4 1.4 NA
Far Eastern Curlew Windeyers Diurnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Windeyers Nocturnal 1 Inf NA NA NA -Inf NaN NA
Pacific Golden-Plover Corrie Island Diurnal 14 Inf NA NA NA -Inf NaN NA
Pacific Golden-Plover Corrie Island Nocturnal 14 Inf NA NA NA -Inf NaN NA
Pacific Golden-Plover Curlew Point Diurnal 14 0.0 5.0 13.3 60.2 100.0 31.5 34.8
Pacific Golden-Plover Curlew Point Nocturnal 14 0.0 0.0 2.6 11.6 22.3 6.2 7.4
Pacific Golden-Plover Davies Point Diurnal 14 Inf NA NA NA -Inf NaN NA
Pacific Golden-Plover Davies Point Nocturnal 14 Inf NA NA NA -Inf NaN NA
Pacific Golden-Plover Fullerton Entrance Diurnal 14 0.0 0.0 0.0 0.0 0.4 0.1 0.1
Pacific Golden-Plover Fullerton Entrance Nocturnal 14 0.0 0.0 0.0 0.3 0.9 0.2 0.4
Pacific Golden-Plover Hexham Swamp Diurnal 14 0.0 0.0 1.9 5.8 11.0 3.5 4.2
Pacific Golden-Plover Hexham Swamp Nocturnal 14 0.0 0.0 0.9 1.4 3.1 1.0 1.1
Pacific Golden-Plover Milhams Pond Diurnal 14 0.0 0.0 0.0 42.8 85.6 28.5 49.4
Pacific Golden-Plover Milhams Pond Nocturnal 14 0.0 0.0 0.0 11.7 46.7 11.7 23.4
Pacific Golden-Plover Ramsar Road Diurnal 14 0.0 0.0 0.0 0.0 0.6 0.1 0.3
Pacific Golden-Plover Ramsar Road Nocturnal 14 0.0 0.0 0.0 0.0 1.3 0.2 0.5
Pacific Golden-Plover Swan Pond Diurnal 14 0.0 0.0 0.0 0.0 0.2 0.0 0.1
Pacific Golden-Plover Swan Pond Nocturnal 14 0.0 0.0 0.0 0.0 0.6 0.1 0.2
Pacific Golden-Plover Tomago Diurnal 14 Inf NA NA NA -Inf NaN NA
Pacific Golden-Plover Tomago Nocturnal 14 Inf NA NA NA -Inf NaN NA
Pacific Golden-Plover Windeyers Diurnal 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Pacific Golden-Plover Windeyers Nocturnal 12 0.0 0.0 0.0 0.3 1.1 0.3 0.6

Statistics - Low Tide Migratory Species
Species Station condition n_individual min q1 median q3 max mean sd
Bar-tailed Godwit Corrie Island Diurnal 7 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Corrie Island Nocturnal 7 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Curlew Point Diurnal 8 0.0 0.8 3.8 6.1 83.3 16.3 33.0
Bar-tailed Godwit Curlew Point Nocturnal 8 0.0 0.6 1.3 3.4 25.0 6.0 10.7
Bar-tailed Godwit Davies Point Diurnal 7 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Davies Point Nocturnal 7 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Fullerton Entrance Diurnal 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Bar-tailed Godwit Fullerton Entrance Nocturnal 9 0.0 0.0 0.4 0.9 1.3 0.5 0.6
Bar-tailed Godwit Hexham Swamp Diurnal 7 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Hexham Swamp Nocturnal 7 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Milhams Pond Diurnal 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Bar-tailed Godwit Milhams Pond Nocturnal 10 0.0 0.0 0.0 0.0 0.0 0.0 NA
Bar-tailed Godwit Ramsar Road Diurnal 9 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Ramsar Road Nocturnal 9 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Swan Pond Diurnal 10 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Swan Pond Nocturnal 10 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Tomago Diurnal 7 0.0 0.0 0.0 0.0 0.0 0.0 NA
Bar-tailed Godwit Tomago Nocturnal 7 0.1 6.3 12.5 19.3 27.1 13.0 11.6
Bar-tailed Godwit Windeyers Diurnal 4 Inf NA NA NA -Inf NaN NA
Bar-tailed Godwit Windeyers Nocturnal 4 Inf NA NA NA -Inf NaN NA
Curlew Sandpiper Corrie Island Diurnal 5 Inf NA NA NA -Inf NaN NA
Curlew Sandpiper Corrie Island Nocturnal 5 Inf NA NA NA -Inf NaN NA
Curlew Sandpiper Curlew Point Diurnal 5 10.4 24.3 30.6 30.8 34.0 26.0 9.4
Curlew Sandpiper Curlew Point Nocturnal 5 3.2 9.2 11.0 11.6 14.3 9.8 4.2
Curlew Sandpiper Davies Point Diurnal 5 Inf NA NA NA -Inf NaN NA
Curlew Sandpiper Davies Point Nocturnal 5 Inf NA NA NA -Inf NaN NA
Curlew Sandpiper Fullerton Entrance Diurnal 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Curlew Sandpiper Fullerton Entrance Nocturnal 5 0.0 0.2 0.2 0.2 0.3 0.2 0.1
Curlew Sandpiper Hexham Swamp Diurnal 5 Inf NA NA NA -Inf NaN NA
Curlew Sandpiper Hexham Swamp Nocturnal 5 Inf NA NA NA -Inf NaN NA
Curlew Sandpiper Milhams Pond Diurnal 5 0.0 0.0 0.0 0.0 0.0 0.0 NA
Curlew Sandpiper Milhams Pond Nocturnal 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Curlew Sandpiper Ramsar Road Diurnal 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Curlew Sandpiper Ramsar Road Nocturnal 5 0.0 0.1 0.2 0.2 0.2 0.1 0.1
Curlew Sandpiper Swan Pond Diurnal 5 0.0 0.0 0.0 0.0 0.1 0.0 0.1
Curlew Sandpiper Swan Pond Nocturnal 5 0.0 0.2 0.2 0.3 0.3 0.2 0.1
Curlew Sandpiper Tomago Diurnal 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Curlew Sandpiper Tomago Nocturnal 5 0.0 0.0 0.2 0.2 0.3 0.1 0.1
Curlew Sandpiper Windeyers Diurnal 5 Inf NA NA NA -Inf NaN NA
Curlew Sandpiper Windeyers Nocturnal 5 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Corrie Island Diurnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Corrie Island Nocturnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Curlew Point Diurnal 2 1.4 1.4 1.4 1.4 1.4 1.4 NA
Eurasian Whimbrel Curlew Point Nocturnal 2 1.1 2.7 4.2 5.7 7.2 4.2 4.3
Eurasian Whimbrel Davies Point Diurnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Davies Point Nocturnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Fullerton Entrance Diurnal 2 0.0 0.1 0.2 0.3 0.4 0.2 0.3
Eurasian Whimbrel Fullerton Entrance Nocturnal 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Eurasian Whimbrel Hexham Swamp Diurnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Hexham Swamp Nocturnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Milhams Pond Diurnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Milhams Pond Nocturnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Ramsar Road Diurnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Ramsar Road Nocturnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Swan Pond Diurnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Swan Pond Nocturnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Tomago Diurnal 2 0.0 0.0 0.0 0.0 0.0 0.0 NA
Eurasian Whimbrel Tomago Nocturnal 2 0.6 0.6 0.6 0.6 0.6 0.6 NA
Eurasian Whimbrel Windeyers Diurnal 2 Inf NA NA NA -Inf NaN NA
Eurasian Whimbrel Windeyers Nocturnal 2 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Corrie Island Diurnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Corrie Island Nocturnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Curlew Point Diurnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Curlew Point Nocturnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Davies Point Diurnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Davies Point Nocturnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Fullerton Entrance Diurnal 2 0.0 0.0 0.0 0.0 0.0 0.0 NA
Far Eastern Curlew Fullerton Entrance Nocturnal 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Far Eastern Curlew Hexham Swamp Diurnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Hexham Swamp Nocturnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Milhams Pond Diurnal 2 19.6 19.6 19.6 19.6 19.6 19.6 NA
Far Eastern Curlew Milhams Pond Nocturnal 2 44.7 44.7 44.7 44.7 44.7 44.7 NA
Far Eastern Curlew Ramsar Road Diurnal 2 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Ramsar Road Nocturnal 2 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Swan Pond Diurnal 2 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Swan Pond Nocturnal 2 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Tomago Diurnal 1 0.0 0.0 0.0 0.0 0.0 0.0 NA
Far Eastern Curlew Tomago Nocturnal 1 1.5 1.5 1.5 1.5 1.5 1.5 NA
Far Eastern Curlew Windeyers Diurnal 1 Inf NA NA NA -Inf NaN NA
Far Eastern Curlew Windeyers Nocturnal 1 Inf NA NA NA -Inf NaN NA
Pacific Golden-Plover Corrie Island Diurnal 14 Inf NA NA NA -Inf NaN NA
Pacific Golden-Plover Corrie Island Nocturnal 14 Inf NA NA NA -Inf NaN NA
Pacific Golden-Plover Curlew Point Diurnal 14 3.6 9.9 13.2 53.9 100.0 31.9 33.4
Pacific Golden-Plover Curlew Point Nocturnal 14 0.0 2.0 4.2 19.2 38.8 10.0 12.2
Pacific Golden-Plover Davies Point Diurnal 14 Inf NA NA NA -Inf NaN NA
Pacific Golden-Plover Davies Point Nocturnal 14 Inf NA NA NA -Inf NaN NA
Pacific Golden-Plover Fullerton Entrance Diurnal 14 0.0 0.0 0.0 0.1 0.6 0.1 0.2
Pacific Golden-Plover Fullerton Entrance Nocturnal 14 0.0 0.0 0.0 0.2 1.0 0.2 0.4
Pacific Golden-Plover Hexham Swamp Diurnal 14 0.0 2.7 4.3 7.7 11.6 5.2 4.3
Pacific Golden-Plover Hexham Swamp Nocturnal 14 0.0 0.1 0.9 1.1 1.9 0.7 0.7
Pacific Golden-Plover Milhams Pond Diurnal 14 0.0 0.0 0.0 0.0 81.4 16.3 36.4
Pacific Golden-Plover Milhams Pond Nocturnal 14 0.0 0.0 1.2 15.7 55.9 14.5 27.6
Pacific Golden-Plover Ramsar Road Diurnal 14 0.0 0.0 0.0 0.0 1.3 0.2 0.5
Pacific Golden-Plover Ramsar Road Nocturnal 14 0.0 0.0 0.0 0.0 2.7 0.3 0.9
Pacific Golden-Plover Swan Pond Diurnal 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Pacific Golden-Plover Swan Pond Nocturnal 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Pacific Golden-Plover Tomago Diurnal 14 0.0 0.0 0.0 0.0 0.0 0.0 NA
Pacific Golden-Plover Tomago Nocturnal 14 Inf NA NA NA -Inf NaN NA
Pacific Golden-Plover Windeyers Diurnal 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Pacific Golden-Plover Windeyers Nocturnal 12 0.0 0.0 0.0 0.1 0.5 0.1 0.3

Hourly usage

USED vs AVAILABLE

Legend: Shorebird movement and spatial ecology vary across species and within the local MOTUS array in NSW. Each tab displays one tide category from the pairs “high/low” and “nocturnal/diurnal,” showing used time in hours (colored boxes) for each species at different Motus stations. The grey boxes represent the total available time for the corresponding tide category and Motus station, across individuals. Note that available time might be relatively similar across stations as it is based on the general Newcastle tide table and is not station-specific.

USED

Since the size sample at the current date is too low, it might be hard to read in details how much each bird or species spent time (hours) at each Motus station. So here below is displayed the used only for each stations.

# Plot function used ONLY
plot_used <- function(tide_cat) {
  data_subset <- used_bird_recv_time %>% filter(tideCategory == tide_cat)
  
  ggplot(data_subset, 
         aes(x = recvDeployName, y = duration_h, fill = speciesEN)) +
    
    geom_boxplot(position = position_dodge2(width = 0.9, preserve = "single")) +
    scale_fill_manual(values = species_colors, name = "Species") +
    
    facet_wrap(~ speciesEN, scales = "free_y", 
               labeller = labeller(speciesEN = label_vec)) +
    
    theme_minimal(base_size = 12) +
    theme(strip.text = element_text(face = "bold", size = 10),
          axis.text.x = element_text(angle = 45, hjust = 1),
          legend.position = "none",
          plot.margin = margin(0, 0, 0, 0, "in"))  +
    
    coord_cartesian(ylim = c(0, NA), expand = FALSE) + 
    labs(x = "Motus stations",
         y = "Detection duration (hours)",
         title = paste("Tide Category:", tide_cat),
         fill = "Species")
  }

# Get unique tideCategory levels
tide_categories <- combined_data %>%
  filter(!is.na(tideCategory)) %>%
  pull(tideCategory) %>%
  unique()

# Generate a list of plots for all tide categories
plots_list_used <- purrr::map(tide_categories, plot_used)

Legend: Shorebird movement and spatial ecology vary across species and within the local Motus array in NSW. Each tab displays one tide category from the pairs “high/low” and “nocturnal/diurnal,” showing how many hours each species have been recorded at different Motus stations.

Detection conditions

Visualise here the difference between the total number of hours recorded across the four tidal conditions for each species and for the whole population of tagged shorebirds, “Total used”. And see the difference with the “Usage rate”, which measures bird detections by the Motus array as a proportion of the total available detection time.

POPULATION

TOTAL SURVEY EFFORT (%) - TOTAL AVAILABLE -

Legend: Find the values of the Usage rate (or rate of use) from the whole tagged shorebirds population detected within the Motus array and for each tide category.

TOTAL DETECTED (hours) - TOTAL USED -

Legend: Find the total of hours (or total uses) the whole tagged shorebirds population has been recorded within the Motus array and for each tide category.

SPECIES

TOTAL SURVEY EFFORT (%) - TOTAL AVAILABLE -

Legend: Find the values of the Usage rate (or rate of use) across each shorebirds species detected within the Motus array and for each tide category.

TOTAL DETECTED (hours) - TOTAL USED -

Legend: Find the total of hours (or total uses) each species has been recorded within the Motus array and for each tide category.

SITES

TOTAL DETECTED (hours) - TOTAL USED -

Species Site Use Summary
Detected / Survey effort (Hours) per Tide Categories
N Birds Total Hours
Detected / Survey effort
Hours per Tide Category
Detection per Tide Category (%)
Noct. Low Noct. High Diur. Low Diur. High Noct. Low Noct. High Diur. Low Diur. High
BAR-TAILED GODWIT
Total 10 508 / 72875 (0.7 %) 69 / 18152 (0.4 %) 198 / 18171 (1.1 %) 22 / 18252 (0.1 %) 219 / 18300 (1.2 %) 13.6 39 4.3 43.1
Curlew Point 6 316 / 14445 (2.2 %) 35 / 3525 (1 %) 52 / 3409 (1.5 %) 22 / 3684 (0.6 %) 207 / 3827 (5.4 %) 11.1 16.5 7 65.5
Fullerton Entrance 9 6 / 22641 (0 %) 2 / 5723 (0 %) 1 / 5819 (0 %) < 1 / 5589 (0 %) 3 / 5510 (0.1 %) 33.3 16.7 0 50
Milhams Pond 2 0 / 22943 (0 %) < 1 / 5801 (0 %) < 1 / 5899 (0 %) < 1 / 5658 (0 %) < 1 / 5585 (0 %) NaN NaN NaN NaN
Tomago 5 186 / 12846 (1.4 %) 32 / 3103 (1 %) 145 / 3044 (4.8 %) < 1 / 3321 (0 %) 9 / 3378 (0.3 %) 17.2 78 0 4.8
CURLEW SANDPIPER
Total 5 2429 / 63678 (3.8 %) 255 / 14154 (1.8 %) 215 / 12306 (1.7 %) 815 / 17550 (4.6 %) 1144 / 19668 (5.8 %) 10.5 8.9 33.6 47.1
Curlew Point 5 2402 / 10613 (22.6 %) 240 / 2359 (10.2 %) 208 / 2051 (10.1 %) 814 / 2925 (27.8 %) 1140 / 3278 (34.8 %) 10 8.7 33.9 47.5
Fullerton Entrance 5 4 / 10613 (0 %) 4 / 2359 (0.2 %) < 1 / 2051 (0 %) < 1 / 2925 (0 %) < 1 / 3278 (0 %) 100 0 0 0
Milhams Pond 3 0 / 10613 (0 %) < 1 / 2359 (0 %) < 1 / 2051 (0 %) < 1 / 2925 (0 %) < 1 / 3278 (0 %) NaN NaN NaN NaN
Ramsar Road 5 5 / 10613 (0 %) 3 / 2359 (0.1 %) 2 / 2051 (0.1 %) < 1 / 2925 (0 %) < 1 / 3278 (0 %) 60 40 0 0
Swan Pond 5 12 / 10613 (0.1 %) 5 / 2359 (0.2 %) 3 / 2051 (0.1 %) 1 / 2925 (0 %) 3 / 3278 (0.1 %) 41.7 25 8.3 25
Tomago 5 6 / 10613 (0.1 %) 3 / 2359 (0.1 %) 2 / 2051 (0.1 %) < 1 / 2925 (0 %) 1 / 3278 (0 %) 50 33.3 0 16.7
EURASIAN WHIMBREL
Total 2 96 / 7710 (1.2 %) 33 / 1779 (1.9 %) 16 / 1428 (1.1 %) 9 / 2052 (0.4 %) 38 / 2451 (1.6 %) 34.4 16.7 9.4 39.6
Curlew Point 2 65 / 2570 (2.5 %) 32 / 593 (5.4 %) 15 / 476 (3.2 %) 7 / 684 (1 %) 11 / 817 (1.3 %) 49.2 23.1 10.8 16.9
Fullerton Entrance 2 29 / 2570 (1.1 %) < 1 / 593 (0 %) < 1 / 476 (0 %) 2 / 684 (0.3 %) 27 / 817 (3.3 %) 0 0 6.9 93.1
Tomago 1 2 / 2570 (0.1 %) 1 / 593 (0.2 %) 1 / 476 (0.2 %) < 1 / 684 (0 %) < 1 / 817 (0 %) 50 50 0 0
FAR EASTERN CURLEW
Total 2 144 / 2439 (5.9 %) 69 / 552 (12.5 %) 17 / 646 (2.6 %) 33 / 666 (5 %) 25 / 575 (4.3 %) 47.9 11.8 22.9 17.4
Curlew Point 1 1 / 313 (0.3 %) < 1 / 68 (0 %) < 1 / 74 (0 %) < 1 / 88 (0 %) 1 / 83 (1.2 %) 0 0 0 100
Fullerton Entrance 2 0 / 859 (0 %) < 1 / 196 (0 %) < 1 / 236 (0 %) < 1 / 234 (0 %) < 1 / 193 (0 %) NaN NaN NaN NaN
Milhams Pond 1 141 / 954 (14.8 %) 68 / 220 (30.9 %) 16 / 262 (6.1 %) 33 / 256 (12.9 %) 24 / 216 (11.1 %) 48.2 11.3 23.4 17
Tomago 1 2 / 313 (0.6 %) 1 / 68 (1.5 %) 1 / 74 (1.4 %) < 1 / 88 (0 %) < 1 / 83 (0 %) 50 50 0 0
MASKED LAPWING
Total 1 0 / 721 (0 %) < 1 / 168 (0 %) < 1 / 133 (0 %) < 1 / 192 (0 %) < 1 / 228 (0 %) NaN NaN NaN NaN
Curlew Point 1 0 / 721 (0 %) < 1 / 168 (0 %) < 1 / 133 (0 %) < 1 / 192 (0 %) < 1 / 228 (0 %) NaN NaN NaN NaN
PACIFIC GOLDEN-PLOVER
Total 14 4819 / 150283 (3.2 %) 633 / 33472 (1.9 %) 498 / 32839 (1.5 %) 1797 / 41108 (4.4 %) 1891 / 42864 (4.4 %) 13.1 10.3 37.3 39.2
Curlew Point 13 3094 / 19237 (16.1 %) 342 / 4295 (8 %) 198 / 4189 (4.7 %) 1199 / 5253 (22.8 %) 1355 / 5500 (24.6 %) 11.1 6.4 38.8 43.8
Fullerton Entrance 12 13 / 19237 (0.1 %) 4 / 4295 (0.1 %) 4 / 4189 (0.1 %) 3 / 5253 (0.1 %) 2 / 5500 (0 %) 30.8 30.8 23.1 15.4
Hexham Swamp 11 330 / 19237 (1.7 %) 19 / 4295 (0.4 %) 36 / 4189 (0.9 %) 149 / 5253 (2.8 %) 126 / 5500 (2.3 %) 5.8 10.9 45.2 38.2
Milhams Pond 8 1346 / 19237 (7 %) 255 / 4295 (5.9 %) 248 / 4189 (5.9 %) 439 / 5253 (8.4 %) 404 / 5500 (7.3 %) 18.9 18.4 32.6 30
Ramsar Road 10 29 / 19237 (0.2 %) 12 / 4295 (0.3 %) 7 / 4189 (0.2 %) 7 / 5253 (0.1 %) 3 / 5500 (0.1 %) 41.4 24.1 24.1 10.3
Swan Pond 12 4 / 19237 (0 %) < 1 / 4295 (0 %) 3 / 4189 (0.1 %) < 1 / 5253 (0 %) 1 / 5500 (0 %) 0 75 0 25
Tomago 1 0 / 19237 (0 %) < 1 / 4295 (0 %) < 1 / 4189 (0 %) < 1 / 5253 (0 %) < 1 / 5500 (0 %) NaN NaN NaN NaN
Windeyers 7 3 / 15624 (0 %) 1 / 3407 (0 %) 2 / 3516 (0.1 %) < 1 / 4337 (0 %) < 1 / 4364 (0 %) 33.3 66.7 0 0
PIED STILT
Total 6 1746 / 114107 (1.5 %) 333 / 29164 (1.1 %) 314 / 28625 (1.1 %) 441 / 27789 (1.6 %) 658 / 28529 (2.3 %) 19.1 18 25.3 37.7
Curlew Point 4 58 / 15271 (0.4 %) 5 / 3792 (0.1 %) 3 / 3561 (0.1 %) 2 / 3823 (0.1 %) 48 / 4095 (1.2 %) 8.6 5.2 3.4 82.8
Fullerton Entrance 5 1031 / 24611 (4.2 %) 187 / 6320 (3 %) 137 / 6241 (2.2 %) 250 / 5968 (4.2 %) 457 / 6082 (7.5 %) 18.1 13.3 24.2 44.3
Milhams Pond 1 20 / 24801 (0.1 %) < 1 / 6368 (0 %) 1 / 6293 (0 %) 12 / 6012 (0.2 %) 7 / 6128 (0.1 %) 0 5 60 35
Ramsar Road 2 566 / 24525 (2.3 %) 130 / 6300 (2.1 %) 159 / 6211 (2.6 %) 154 / 5942 (2.6 %) 123 / 6072 (2 %) 23 28.1 27.2 21.7
Swan Pond 2 71 / 24899 (0.3 %) 11 / 6384 (0.2 %) 14 / 6319 (0.2 %) 23 / 6044 (0.4 %) 23 / 6152 (0.4 %) 15.5 19.7 32.4 32.4
RED-NECKED AVOCET
Total 3 378 / 8946 (4.2 %) 39 / 2058 (1.9 %) 128 / 1668 (7.7 %) 57 / 2388 (2.4 %) 154 / 2832 (5.4 %) 10.3 33.9 15.1 40.7
Curlew Point 3 46 / 1491 (3.1 %) 1 / 343 (0.3 %) 2 / 278 (0.7 %) 2 / 398 (0.5 %) 41 / 472 (8.7 %) 2.2 4.3 4.3 89.1
Fullerton Entrance 3 244 / 1491 (16.4 %) 19 / 343 (5.5 %) 67 / 278 (24.1 %) 55 / 398 (13.8 %) 103 / 472 (21.8 %) 7.8 27.5 22.5 42.2
Hexham Swamp 2 0 / 1491 (0 %) < 1 / 343 (0 %) < 1 / 278 (0 %) < 1 / 398 (0 %) < 1 / 472 (0 %) NaN NaN NaN NaN
Milhams Pond 2 0 / 1491 (0 %) < 1 / 343 (0 %) < 1 / 278 (0 %) < 1 / 398 (0 %) < 1 / 472 (0 %) NaN NaN NaN NaN
Swan Pond 2 1 / 1491 (0.1 %) 1 / 343 (0.3 %) < 1 / 278 (0 %) < 1 / 398 (0 %) < 1 / 472 (0 %) 100 0 0 0
Tomago 3 87 / 1491 (5.8 %) 18 / 343 (5.2 %) 59 / 278 (21.2 %) < 1 / 398 (0 %) 10 / 472 (2.1 %) 20.7 67.8 0 11.5

Legend: Summary for each species and per Motus station of the number of hours a species has been detected and the number of hours the station has been listening while the species had at least one individual with an activated tag on (survey effort). NA are when the group holds individuals that are detected less than one hour total.

The only remaining limitation is the high number of hours sharing the same date and time across several Motus stations, for the survey effort. Which is not realistic since the birds can be at only one place at once.

This page has been last updated on