---
title: "Estimate activity centers from acoustic telemetry data"
author: "Megan Winton"
date: "2018-06-06, updated 2025-08-26"
output: rmarkdown::github_document
vignette: >
  %\VignetteIndexEntry{Estimate activity centers from acoustic telemetry data}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## Introduction

This vignette will walk you through the analyses presented in Winton et al. 2018, who describe the use of spatial point process models to estimate individual centers of activity from passive acoustic telemetry data. The vignette progresses from applying the simplest case, which assumes that detection probabilities/receiver detection ranges remain constant over time, to application of a test-tag integrated model, which incorporates detection data from one or more stationary test transmitters to estimate time-varying detection ranges. The models are fitted in a Bayesian framework using the Stan software (Carpenter et al. 2017); code was modified from that provided in Royle et al. 2013 for fitting spatial point process models to data from camera traps. We prefer the Bayesian approach for COA estimation due to its treatment of uncertainty, but realize the longer computational time required may be prohibitive for some applications. In the near future, we will update to include an option to fit in a maximum likelihood framework, which will reduce run times. We'd also like to note that the models described can support varying degrees of complexity - not all applications will require (or have the data to support) the most complex version of the model. The simpler the model, the shorter the run-time.

We realize that many users will have unique situations and may need to modify the base code to suit their purposes. Users can copy the `.stan` files contained in the `src/stan_files` folder to their local machine to do so. We will be happy to accommodate user requests (as our schedules allow). Our hope is to make this a collaborative package that will evolve based on the needs of the acoustic telemetry community and continue to improve over time. We have tried to make the instructions outlined in this vignette user-friendly since we are a group of applied biologists with varying degrees of statistical experience. If some of the statistical notation outlined here or in the paper remains unclear, feel free to contact us with questions/for clarification. Most of it is actually very intuitive, and we would like to make sure that comes through in the documentation. This is a new package, so if you find bugs, places where code efficiency could be improved, or instances where the documentation could be made more user-friendly, please let us know! 
 

## Data preparation

The package includes two data sets: 

1. 153 hours of detection data from a black sea bass (note that we have assigned time as the number of cumulative hours since the tag was deployed rather than providing the raw date/time stamp), and
2. hourly detections from a stationary test transmitter over the same time period. Two files containing the receiver coordinates and the location of the test tag are also included. We have provided the data in this format to illustrate data preparation specific to fitting these types of models; however, you will need to aggregate your date/time stamps to the time period of interest. Receiver and test tag coordinates provided are in the Universal Transverse Mercator (zone 18) projection, which have previously been mean-centered and scaled into kilometers. *This scaling step is highly recommended to reduce run-times. In other words, do it to your dataset prior to starting the code chunks here.*

To access the provided data, run:


``` r
library(TelemetrySpace)
library(dplyr)
library(tidyr)
library(ggplot2)
library(hexbin)
library(ggpubr)

rlocs # Receiver locations
```

```
## # A tibble: 30 × 3
##    Station   east  north
##    <chr>    <dbl>  <dbl>
##  1 SB1     -1.33  1.61  
##  2 SB2     -0.579 1.91  
##  3 SB3      0.219 2.00  
##  4 SB4      1.00  1.81  
##  5 SB5     -1.69  0.896 
##  6 SB6     -0.879 0.950 
##  7 SB7     -0.231 1.20  
##  8 SB8      0.464 1.21  
##  9 SB9      1.28  1.04  
## 10 SB10    -1.77  0.0952
## # ℹ 20 more rows
```

``` r
testloc # Test tag location
```

```
## # A tibble: 1 × 2
##     east north
##    <dbl> <dbl>
## 1 -0.329 0.713
```

``` r
head(testdat) # Hourly detection data from the test tag
```

```
## # A tibble: 6 × 5
##   Station Transmitter   east north  hour
##   <chr>         <dbl>  <dbl> <dbl> <dbl>
## 1 SB12           3447 -0.326 0.513     1
## 2 SB13           3447  0.125 0.575     1
## 3 SB7            3447 -0.231 1.20      1
## 4 SB6            3447 -0.879 0.950     1
## 5 SB13           3447  0.125 0.575     1
## 6 SB6            3447 -0.879 0.950     1
```

``` r
head(fishdat) # Hourly detection data from a black sea bass
```

```
## # A tibble: 6 × 5
##   Station Transmitter   east north  hour
##   <chr>         <dbl>  <dbl> <dbl> <dbl>
## 1 SB15           3425  0.190 0.209     1
## 2 SB14           3425 -0.261 0.159     1
## 3 SB15           3425  0.190 0.209     1
## 4 SB14           3425 -0.261 0.159     1
## 5 SB12           3425 -0.326 0.513     1
## 6 SB14           3425 -0.261 0.159     1
```

Prior to doing anything else, we need to specify our 'state space' - the spatial extent of interest. This will consist of your receiver array and a 'buffer' area around the array. The buffer needs to be large enough to allow for individuals that may have activity centers outside of the receiver array. There is no general rule of thumb for this, but it should scale with the geographic extent covered by your array. I would suggest selecting a buffer that is large enough to allow you to discriminate between individuals that are detected along the periphery of the array and individuals that are not detected at all - a COA will be estimated for all time steps unless you specify the individual has left the area; time steps with 0 detections will have an estimated COA somewhere outside the detection range of all receivers (note that this could be within the array bounds if your array has non-continuous coverage). If there are many time steps with zeros, you may want to modify your data file to omit time periods with 0 detections or modify the relevant `.stan` code chunk included in the `src/stan_files` folder on your local machine to skip time periods with no detections, which will speed up computing times. As always, it is up to the user to make sure that the results make sense in the context of the species/array of interest.


``` r
# Define state-space of point process - 'buffer' adds a fixed buffer to the outer extent of the recs
buffer <- 1
xlim <- c(min(rlocs$east - buffer), max(rlocs$east + buffer))
ylim <- c(min(rlocs$north - buffer), max(rlocs$north + buffer))
```

Next, we need to prepare our test tag data. First, calculate the distance between the test tag's location and each receiver. This can be done using the included 'distf' function.


``` r
# Set up a blank vector for storage
D <- NULL
# Loop over each hour
for(i in 1:nrow(testdat)){
  D[i] <- distf(testloc[, c('east', 'north')], testdat[i, c('east', 'north')])
}

testdat$D <- unlist(D) # Assign to testdat data frame

# Plot to examine variation in detection rate over time
testdat |> 
  count(Station, hour, D, east, north) |> 
  mutate(
    dist = round(D * 1000), #Round distance for plotting
    label = paste(dist, "m", "(", Station, ")") #Create label by merging station name and distance
  ) |>

  #Plot using ggplot
  ggplot(aes(x = hour, y = n)) +
    geom_bar(stat = "identity", fill = '#008080') +
    facet_wrap(~ as.factor(label)) +
    theme(text = element_text(size = 16)) +
    scale_y_continuous(breaks = seq(0,30,10)) +
    labs(x = "Hours since deployment",y = "Number of detections")
```

![plot of chunk unnamed-chunk-3](figure/unnamed-chunk-3-1.png)

Tally how many time intervals each receiver was operational for - this allows for individual receivers deployed for different periods and/or receivers that were lost.


``` r
# The full analysis takes several hours to run, so we'll subset out 10 hours to illustrate
fishdat <- fishdat |> 
  filter(hour < 11)
testdat <- testdat |> 
  filter(hour < 11)

# Create a copy of the receiver locations for tallying
rs <- rlocs

# Add column for each time interval to indicate whether receiver was operational or not
rs[, c(4:(max(fishdat$hour) + 3))] <- 1

# Create vector of the number of sampling occasions for each receiver 
tsteps <- rowSums(rs[, 4:ncol(rs)])
tsteps # Will all be the same here because all operational over the entire time span
```

```
##  [1] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [26] 10 10 10 10 10
```

Since detection records only include non-zero events (here assuming we'd like to keep time steps with zeros), we'll need to convert the number of detections to include zeros for all receivers that did not detect the tag during that time period.


``` r
## Starting with the test tag data
test_agg <- testdat |> 
  mutate(
    rec = as.numeric(substr(Station, 3, 4)),
    # Add a column that indicates each record corresponds to 1 detection
    count = 1
  ) |> 

  # Aggregate the number of detections for each individual at each receiver
  # in each time step
  count(Transmitter, rec, east, north, hour) |> 

  # Create a numeric identifier for each transmitter
  mutate(tag = as.numeric(as.factor(Transmitter))) |> 

  # Rename hour to time for consistency when plotting below
  rename(time = "hour")

# Specify quantities for indexing
#   number of individual tags (here just the one test tag)
ntest <- length(unique(test_agg$Transmitter))

#   number of receivers
nrec <- nrow(rlocs)

#   number of time steps
tsteps <- max(test_agg$time)

# This chunk saves the total number of encounters of each individual (rows) in 
#each trap (cols) at each sampling occasion (array elements)
testY <- array(NA, dim=c(ntest, nrec, tsteps))

for(t in 1:tsteps){
  for (i in 1:ntest){
    h1 <- test_agg[test_agg$tag == i,]

    for(j in 1:nrec){
      testY[i, j, t] <- ifelse(

        # If there are no detections at that receiver in that time period, set to 0
        nrow(h1[h1$time == t & h1$rec == j,]) == 0,
        0,

        # otherwise set to the number of detections
        h1[h1$time == t & h1$rec == j,]$n
      )
    }
  }  
}
```

Now we'll do the same thing for the black sea bass detection data. 


``` r
## Now do the same for each tagged fish
fish_agg <- fishdat |> 
  mutate(
    rec = as.numeric(substr(Station, 3, 4)),
    # Add a column that indicates each record corresponds to 1 detection
    count = 1
  ) |> 

  # Aggregate the number of detections for each individual at each receiver
  # in each time step
  count(Transmitter, rec, east, north, hour) |> 

  # Create a numeric identifier for each transmitter
  mutate(tag = as.numeric(as.factor(Transmitter))) |> 

  # Rename hour to time for consistency when plotting below
  rename(time = "hour")

# Specify quantities for indexing
#   number of individual tags (here just the one test tag)
nind <- length(unique(fish_agg$Transmitter))

# This chunk saves the total number of encounters of each individual (rows) in each trap (cols) at each sampling occasion (array elements)
Y <- array(NA, dim=c(nind, nrec, tsteps))

for(t in 1:max(tsteps)){
  for (i in 1:nrow(Y)){

    h1 <- fish_agg[fish_agg$tag == i,]

    for(j in 1:nrow(rlocs)){
      # If there are no detections at that receiver in that time period, set to 0; otherwise set to the number of detections
      Y[i, j, t] <- ifelse(

        # If there are no detections at that receiver in that time period, set to 0
        nrow(h1[h1$time == t & h1$rec == j,]) == 0,
        0,

        # otherwise set to the number of detections
        h1[h1$time == t & h1$rec == j,]$n
      )
    }
  }
}
```

## Fit the standard COA model  

After all of that data processing, we're finally ready to fit our model! We'll start by fitting the model assuming that detection probabilities are the same among all receivers and remain constant over time (As we all know, this will pretty much never be the case in 'real-life' but best to start simple). Besides, this model is handy when you don't have test-tag (or other) data available to inform detection probabilities but would like to be able to estimate COAs along the periphery of the array. The only other information you'll need to provide is the expected number of transmissions per tag per time interval (`ntrans` below).


``` r
### Format data for model fitting
fit <- COA_Standard(
  nind = nind,    # number of individuals
  nrec = nrec,    # number of receivers
  ntime = tsteps, # number of time steps
  ntrans = 30,    # number of expected transmissions per tag per time interval
  y = Y,          # array of detections
  recX = as.vector(rlocs$east),  # E-W receiver coordinates
  recY = as.vector(rlocs$north), # N-S receiver coordinates
  xlim = xlim, # E-W boundary of spatial extent (receiver array + buffer)
  ylim = ylim  # N-S boundary of spatial extent (receiver array + buffer)
)
```

```
##         warmup sample
## chain:1  2.298  1.985
## chain:2  1.830  2.315
## chain:3  1.749  1.337
## chain:4  1.759  2.078
```

The function will automatically spit out the run-time associated with each of the 4 chains used for fitting and returns a list with four objects:


``` r
summary(fit)
```

```
##               Length Class      Mode   
## model           1    stanfit    S4     
## summary        20    -none-     numeric
## time            1    -none-     numeric
## coas            7    data.frame list   
## all_estimates 325    data.frame list
```

The first contains the Stan model object (accessible via `fit$Model`, which will allow you to use `rstan` plotting tools and diagnostic plots - see rstan documentation for details).

The second element is a table of parameter estimates and associated quantiles from the posterior distribution. The table also includes the effective sample size and the Rhat statistic (which should be ~1).


``` r
fit$summary
```

```
##            mean      se_mean         sd      2.5%       25%       50%       75%
## p0    0.2822800 0.0004478783 0.02520083 0.2361652 0.2644027 0.2813702 0.2994305
## sigma 0.2845763 0.0002817552 0.01493595 0.2554774 0.2742519 0.2845919 0.2947178
##           97.5%    n_eff     Rhat
## p0    0.3329404 3165.990 1.000794
## sigma 0.3141591 2810.101 1.001010
```

The third returns the time required to run the model (in minutes). Note that Stan will automatically detect and use multiple cores. If the computer used to run this has multiple cores, the time returned will be longer than the actual run time (because it will sum the time for each core). To return the realized run time, divide `fit$Time` by the number of cores.


``` r
fit$time
```

```
## [1] 0.25585
```

The fourth returns an array of COA estimates, where each matrix corresponds to one individual, the rows correspond to each time step, and the columns include the median posterior estimate of the east-west (x) and north-south (y) coordinates. The 95% credible interval (Bayesian version of a confidence interval) for each coordinate is also provided.


``` r
fit$coas
```

```
##    time           x         y    x_lower     x_upper    y_lower   y_upper
## 1     1 -0.04419208 0.2936790 -0.1462210  0.05951826 0.18845035 0.4023171
## 2     2 -0.02330482 0.3578317 -0.1420680  0.09773196 0.21932718 0.5005321
## 3     3 -0.09875625 0.3839595 -0.2569494  0.04730306 0.19878372 0.5686053
## 4     4 -0.12329253 0.2816735 -0.2786449  0.02481079 0.10911757 0.4669253
## 5     5 -0.16831392 0.3242430 -0.3259290 -0.02445424 0.15206068 0.5067735
## 6     6 -0.13019674 0.7062093 -0.4725975  0.17890991 0.17035957 0.8814318
## 7     7 -0.05163113 0.3667484 -0.1700821  0.07165783 0.23090152 0.4937734
## 8     8 -0.15522780 0.4049252 -0.2793242 -0.03514842 0.27458678 0.5346351
## 9     9 -0.05002765 0.3329523 -0.2220192  0.11698386 0.12850061 0.5501859
## 10   10 -0.09807742 0.3133353 -0.2885934  0.07443582 0.08394051 0.5651978
```

The last element (`fit$All_estimates`) contains the estimates from each non-warm-up iteration for all 4 chains. (If you're not familiar with Bayesian lingo and this just seems like statistical jargon, all you need to know is that this is what you'll use to plot the uncertainty cloud around COA estimates and/or the distribution of parameter estimates as presented in the paper.) This includes estimates of all latent parameters for each individual in each time step (aka this element contains tons of parameter estimates), so we will need to subset out parameters for plotting.


``` r
# Extract east-west and north-south coordinates of COAs from each iteration of each chain
# Note that 'sx' and 'sy' are the variable names used to store the coordinates for each individual (rows) in each time step (columns).
# We'll store them as a list, where each element corresponds to an individual
EN_fit <- list(NA)

for (i in 1:nind){
  EN_fit[[i]] <- bind_cols(
    # East-West
    fit$all_estimates |> 
      select(starts_with(paste("sx[", i, ",", sep=''))) |> 
      pivot_longer(everything(), values_to = "X"),
    # North-South
    fit$all_estimates |> 
      select(starts_with(paste("sy[", i, ",", sep=''))) |> 
      pivot_longer(everything(), values_to = "Y") |> 
      select(Y)
  ) |> 
  # The ID field corresponds to the time step.
  mutate(time = as.numeric(gsub(".*,|\\]", "", name)))
}

## Plot posterior estimates
# Select individual for plotting
post_fit <- EN_fit[[1]]
coa_fit <- as.data.frame(fit$coas[, , 1])

# Plot in ggplot
plotCOAs <- ggplot(aes(x = X, y = Y), data = post_fit) +
  geom_hex(alpha = 1, bins = 100)  +
  geom_point(aes(x = x, y = y), data = coa_fit, pch = 25, cex = 1.5, alpha = .8, fill = NA) +
  facet_wrap(~ time, ncol = 2) +
  scale_fill_gradientn(colours = c("white", "blue"), name = "Frequency", na.value = NA) +
  geom_point(aes(x = east, y = north), data = rlocs, cex = 1) +
  geom_point(aes(x = east, y = north), data = fish_agg, pch = 21, cex = 1.5, fill = '#00BFC4') +
  
  # Unhash line below to include test tag location
  #geom_point(aes(x = east, y = north), data = testloc, cex = 1.5, pch = 21, fill = '#F8766D') +
  
  coord_fixed(xlim = c(-1, 1), ylim = c(-0.5, 1.5)) +
  scale_x_continuous(breaks = c(-1, 0, 1)) +
  #scale_y_continuous(breaks = c(-1, 0, 1)) +
  theme(legend.position = "none") +
  labs(x = 'East-West (km)', y = 'North-South (km)')
```

## Fit a COA model that allows for receiver- and time-specific detection probabilities

Now let's fit a model that allows for variation in detection probabilities. This model is a simple extension of the previous model - it just allows the detection probability to vary between time steps and receivers. The only other information you'll need to provide is the expected number of transmissions per tag per time interval (`ntrans`).


``` r
### Format data for model fitting
fit_vary <- COA_TimeVarying(
  nind = nind,    # number of individuals
  nrec = nrec,    # number of receivers
  ntime = tsteps, # number of time steps
  ntrans = 30,    # number of expected transmissions per tag per time interval
  y = Y,          # array of detections from tagged fish
  recX = as.vector(rlocs$east),  # E-W receiver coordinates
  recY = as.vector(rlocs$north), # N-S receiver coordinates
  xlim = xlim, # E-W boundary of spatial extent (receiver array + buffer)
  ylim = ylim  # N-S boundary of spatial extent (receiver array + buffer)
)
```

```
##         warmup sample
## chain:1 24.266 14.747
## chain:2 22.624 17.264
## chain:3 23.072 14.642
## chain:4 22.762 14.569
```

``` r
summary(fit_vary)
```

```
##                 Length Class      Mode   
## model              1   stanfit    S4     
## summary         3010   -none-     numeric
## time               1   -none-     numeric
## coas               7   data.frame list   
## detection_probs  300   -none-     numeric
## all_estimates    923   data.frame list
```

``` r
fit_vary$time
```

```
## [1] 2.565767
```

``` r
# As before, extract east-west and north-south coordinates of COAs from each iteration of each chain
# Note that 'sx' and 'sy' are the variable names used to store the coordinates for each individual (rows) in each time step (columns).
# We'll store them as a list, where each element corresponds to an individual
EN_vary <- list(NA)

for (i in 1:nind){
  EN_vary[[i]] <- bind_cols(
    # East-West
    fit_vary$all_estimates |> 
      select(starts_with(paste("sx[", i, ",", sep=''))) |> 
      pivot_longer(everything(), values_to = "X"),
    # North-South
    fit_vary$all_estimates |> 
      select(starts_with(paste("sy[", i, ",", sep=''))) |> 
      pivot_longer(everything(), values_to = "Y") |> 
      select(Y)
  ) |> 
  # The ID field corresponds to the time step.
  mutate(time = as.numeric(gsub(".*,|\\]", "", name)))
}

## Plot posterior estimates
# Select individual for plotting
post_vary <- EN_vary[[1]]
coa_vary <- as.data.frame(fit_vary$coas[, , 1])

# Plot in ggplot
plotTVary <- ggplot(aes(x = X, y = Y), data = post_vary) +
    geom_hex(alpha = 1, bins = 100)  +
    geom_point(aes(x = x, y = y), data = coa_vary,
      pch = 25, cex = 1.5, alpha = .8, fill = NA
    ) +
    facet_wrap(~time, ncol = 2) +
    scale_fill_gradientn(colours=  c("white", "blue"), name = "Frequency", na.value=NA) +
    geom_point(aes(x = east, y = north), data = rlocs, cex = 1) +
    geom_point(aes(x = east,y = north), data = fish_agg, pch = 21, cex = 1.5, fill = '#00BFC4') +
    
    # Unhash line below to include test tag location  
    #geom_point(aes(x=east,y=north),data=testloc,cex=1.5,pch=21,fill='#F8766D') +
    
    coord_fixed(xlim = c(-1, 1), ylim = c(-0.5, 1.5)) +
    scale_x_continuous(breaks = c(-1, 0, 1)) +
    #scale_y_continuous(breaks = c(-1, 0, 1)) +
    theme(legend.position = "none") +
    labs(x = 'East-West (km)', y = 'North-South (km)')
```

## Fit a model that uses detections from a moored test tag to inform detection probabilities

Now we'll fit the most exciting model - the model that integrates test tag data to inform detection probabilities. This model is a simple extension of the previous models - it just adds a component that specifies the distance of each test tag from each receiver, and shifts the probability of detection by comparing the number of detections logged at each receiver versus those emitted from the test tag in each time step. In other words, it treats test tags as tagged individuals but with known COAs. The only other information you'll need to provide is the expected number of transmissions per tag per time interval (`ntrans`).


``` r
### Format data for model fitting
## Specify the number of sentinel tags (this step is necessary because of issues that arise with Stan indexing if you have only 1 test tag)
nsentinel <- 1

fit_tag <- COA_TagInt(
  nind = nind,       # number of individuals
  nrec = nrec,       # number of receivers
  ntime = tsteps,    # number of time steps
  ntest = nsentinel, # number of test tags
  ntrans = 30,       # number of expected transmissions per tag per time interval
  y = Y,             # array of detections from tagged fish
  test = testY,      # array of detections from test tags
  recX = as.vector(rlocs$east),  # E-W receiver coordinates
  recY = as.vector(rlocs$north), # N-S receiver coordinates 
  xlim = xlim, # E-W boundary of spatial extent (receiver array + buffer)
  ylim = ylim, # N-S boundary of spatial extent (receiver array + buffer)
  testX = array(testloc$east, dim = c(nsentinel)),
  testY = array(testloc$north, dim = c(nsentinel))
)
```

```
##         warmup sample
## chain:1 19.695 10.553
## chain:2 18.609 10.535
## chain:3 19.143 10.438
## chain:4 20.521  9.958
```

``` r
summary(fit_tag)
```

```
##                 Length Class      Mode   
## model              1   stanfit    S4     
## summary         3010   -none-     numeric
## time               1   -none-     numeric
## coas               7   data.frame list   
## detection_probs  300   -none-     numeric
## all_estimates    953   data.frame list
```

``` r
fit_tag$time # See the comment about run time when your computer has multiple cores above.
```

```
## [1] 1.990867
```

``` r
# As before, extract east-west and north-south coordinates of COAs from each iteration of each chain
# Note that 'sx' and 'sy' are the variable names used to store the coordinates for each individual (rows) in each time step (columns).
# We'll store them as a list, where each element corresponds to an individual
EN_tag <- list(NA)

for (i in 1:nind){
  EN_tag[[i]] <- bind_cols(
    # East-West
    fit_tag$all_estimates |> 
      select(starts_with(paste("sx[", i, ",", sep=''))) |> 
      pivot_longer(everything(), values_to = "X"),
    # North-South
    fit_tag$all_estimates |> 
      select(starts_with(paste("sy[", i, ",", sep=''))) |> 
      pivot_longer(everything(), values_to = "Y") |> 
      select(Y)
  ) |> 
  # The ID field corresponds to the time step.
  mutate(time = as.numeric(gsub(".*,|\\]", "", name)))
}

## Plot posterior estimates
# Select individual for plotting
post_tag <- EN_tag[[1]]
coa_tag <- as.data.frame(fit_tag$coas[, , 1])

# Plot in ggplot
plotTagInt <- ggplot(aes(x = X, y = Y), data = post_tag) +
  geom_hex(alpha = 1, bins = 100)  +
  geom_point(aes(x = x, y = y),
    data = coa_tag, pch = 25, cex = 1.5, alpha = 0.8, fill = NA
  ) +
  facet_wrap(~time, ncol = 2) +
  scale_fill_gradientn(colours = c("white", "blue"), name = "Frequency", na.value = NA) +
  geom_point(aes(x = east, y = north), data = rlocs, cex = 1) +
  geom_point(aes(x = east, y = north), data = fish_agg, pch = 21, cex = 1.5, fill = '#00BFC4') +

  # Unhash line below to plot test tag location
  #geom_point(aes(x=east,y=north),data=testloc,cex=1.5,pch=21,fill='#F8766D') +

  coord_fixed(xlim = c(-1, 1), ylim = c(-0.5, 1.5)) +
  scale_x_continuous(breaks = c(-1, 0, 1)) +
  theme(legend.position = "none") +
  labs(x = 'East-West (km)', y = 'North-South (km)')
```

## Comparison

Now plot them all up together to see how they compare!


``` r
ggarrange(plotCOAs, plotTVary, plotTagInt,
          labels = c("Standard", "Time-varying", "Tag-integrated"),
          ncol = 3, nrow = 1,
          align = "hv",
          #widths = c(8,8,8), heights = c(2,2,2),
          common.legend = F)
```

![plot of chunk unnamed-chunk-15](figure/unnamed-chunk-15-1.png)


## References

Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., ... & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of statistical software, 76, 1-32.

Royle, J. A., Chandler, R. B., Sollmann, R., & Gardner, B. (2013). Spatial capture-recapture. Academic press.

Winton, M. V., Kneebone, J., Zemeckis, D. R., & Fay, G. (2018). A spatial point process model to estimate individual centres of activity from passive acoustic telemetry data. Methods in Ecology and Evolution, 9(11), 2262-2272.
