Quickstart example

This is a quickstart guide to model and visualize animal movements from radio-telemetry data using the movetrack package.

library(movetrack)
library(ggplot2)
theme_set(theme_bw(base_size = 15))

# Load example data
data(motusData)

Estimate locations

The first step involves estimating animal locations based on antenna bearings and signal strength at two-minute intervals using the locate function.

loc <- locate(motusData, dTime = 2)

## Removed 10 detections containing missing values.

Model flight paths

Next, we model the animal’s flight paths using Markov Chain Monte Carlo (MCMC) with four parallel chains utilising the track function and return a summary of the results.

fit <- track(loc, parallel_chains = 4, refresh = 1e3)

## Running MCMC with 4 parallel chains...
## 
## Chain 1 Iteration:    1 / 2000 [  0%]  (Warmup)

## Chain 2 Iteration:    1 / 2000 [  0%]  (Warmup)

## Chain 3 Iteration:    1 / 2000 [  0%]  (Warmup)

## Chain 4 Iteration:    1 / 2000 [  0%]  (Warmup)

## Chain 1 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
## Chain 1 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
## Chain 4 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
## Chain 4 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
## Chain 2 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
## Chain 2 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
## Chain 1 Iteration: 2000 / 2000 [100%]  (Sampling) 
## Chain 1 finished in 120.4 seconds.
## Chain 2 Iteration: 2000 / 2000 [100%]  (Sampling) 
## Chain 2 finished in 122.1 seconds.
## Chain 3 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
## Chain 3 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
## Chain 4 Iteration: 2000 / 2000 [100%]  (Sampling) 
## Chain 4 finished in 129.4 seconds.
## Chain 3 Iteration: 2000 / 2000 [100%]  (Sampling) 
## Chain 3 finished in 156.9 seconds.
## 
## All 4 chains finished successfully.
## Mean chain execution time: 132.2 seconds.
## Total execution time: 157.1 seconds.

fit

Click to expand

##        ID                time  lon  lat distance  speed
## 1   49237 2023-08-20 20:16:00 8.80 54.8       NA     NA
## 2   49237 2023-08-20 20:18:00 8.80 54.8     1134   9.45
## 3   49237 2023-08-20 20:20:00 8.79 54.8     1380  11.50
## 4   49237 2023-08-20 20:22:00 8.79 54.8     1506  12.55
## 5   49237 2023-08-20 20:24:00 8.78 54.8     1549  12.91
## 6   49237 2023-08-20 20:26:00 8.77 54.8     1678  13.99
## 7   49237 2023-08-20 20:28:00 8.76 54.8     1881  15.67
## 8   49237 2023-08-20 20:30:00 8.75 54.8     2035  16.96
## 9   49237 2023-08-20 20:32:00 8.74 54.7     1965  16.38
## 10  49237 2023-08-20 20:34:00 8.74 54.7     1799  14.99
## 11  49237 2023-08-20 20:36:00 8.74 54.7     1749  14.57
## 12  49237 2023-08-20 20:38:00 8.75 54.7     1706  14.21
## 13  49237 2023-08-20 20:40:00 8.76 54.7     1576  13.14
## 14  49237 2023-08-20 20:42:00 8.77 54.7     1431  11.92
## 15  49237 2023-08-20 20:44:00 8.78 54.7     1371  11.42
## 16  49237 2023-08-20 20:46:00 8.78 54.7     1421  11.84
## 17  49237 2023-08-20 20:48:00 8.79 54.7     1553  12.94
## 18  49237 2023-08-20 20:50:00 8.80 54.6     1890  15.75
## 19  49237 2023-08-20 20:52:00 8.80 54.6     2235  18.63
## 20  49237 2023-08-20 20:54:00 8.81 54.6     2342  19.52
## 21  49237 2023-08-20 20:56:00 8.82 54.6     2301  19.18
## 22  49237 2023-08-20 20:58:00 8.82 54.6     2445  20.37
## 23  49237 2023-08-20 21:00:00 8.83 54.5     2774  23.11
## 24  49237 2023-08-20 21:08:00 8.83 54.5     2966   6.18
## 25  49237 2023-08-20 21:10:00 8.82 54.5     2716  22.64
## 26  49237 2023-08-20 21:12:00 8.82 54.5     2239  18.66
## 27  49237 2023-08-20 21:14:00 8.81 54.5     1908  15.90
## 28  49237 2023-08-20 21:16:00 8.79 54.4     1808  15.07
## 29  49237 2023-08-20 21:18:00 8.78 54.4     1717  14.31
## 30  49237 2023-08-20 21:20:00 8.77 54.4     1697  14.14
## 31  49237 2023-08-20 21:22:00 8.76 54.4     1854  15.45
## 32  49237 2023-08-20 21:24:00 8.75 54.4     2012  16.77
## 33  49237 2023-08-20 21:26:00 8.75 54.4     1893  15.78
## 34  49237 2023-08-20 21:28:00 8.75 54.4     1729  14.41
## 35  49237 2023-08-20 21:30:00 8.76 54.4     1620  13.50
## 36  49237 2023-08-20 21:32:00 8.76 54.3     1692  14.10
## 37  49237 2023-08-20 21:34:00 8.76 54.3     1770  14.75
## 38  49237 2023-08-20 21:36:00 8.77 54.3     1597  13.31
## 39  49237 2023-08-20 21:38:00 8.77 54.3     1466  12.21
## 40  49237 2023-08-20 21:40:00 8.77 54.3     1410  11.75
## 41  49237 2023-08-20 21:42:00 8.77 54.3     1520  12.67
## 42  49237 2023-08-20 21:44:00 8.77 54.3     1812  15.10
## 43  49237 2023-08-20 21:46:00 8.77 54.3     2133  17.78
## 44  49237 2023-08-20 21:48:00 8.77 54.2     2264  18.87
## 45  49237 2023-08-20 21:50:00 8.77 54.2     2511  20.93
## 46  49237 2023-08-20 21:52:00 8.76 54.2     2836  23.63
## 47  49237 2023-08-20 22:10:00 8.76 54.2     2789   2.58
## 48  49237 2023-08-20 22:12:00 8.75 54.1     2454  20.45
## 49  49237 2023-08-20 22:14:00 8.74 54.1     2060  17.17
## 50  49237 2023-08-20 22:16:00 8.73 54.1     2031  16.93
## 51  49237 2023-08-20 22:24:00 8.72 54.1     2299   4.79
## 52  49237 2023-08-20 22:26:00 8.71 54.1     2708  22.57
## 53  49237 2023-08-20 22:28:00 8.70 54.0     3354  27.95
## 54  49237 2023-08-20 22:30:00 8.69 54.0     4280  35.66
## 55  49237 2023-08-20 22:34:00 8.68 54.0     5308  22.12
## 56  49237 2023-08-20 23:00:00 8.65 53.9     5997   3.84
## 57  49237 2023-08-20 23:02:00 8.63 53.9     6725  56.04
## 58  49237 2023-08-20 23:04:00 8.59 53.8     7587  63.23
## 59  49237 2023-08-21 00:16:00 8.55 53.7     8207   1.90
## 60  49237 2023-08-21 00:18:00 8.50 53.7     8335  69.45
## 61  49237 2023-08-21 00:20:00 8.45 53.6     8062  67.19
## 62  49237 2023-08-21 00:22:00 8.40 53.5     7784  64.87
## 63  49237 2023-08-21 00:24:00 8.34 53.5     7720  64.33
## 64  49237 2023-08-21 00:26:00 8.28 53.4     7966  66.38
## 65  49237 2023-08-21 01:44:00 8.23 53.3     7959   1.70
## 66  49237 2023-08-21 01:46:00 8.18 53.3     7468  62.23
## 67  49237 2023-08-21 01:48:00 8.14 53.2     6486  54.05
## 68  49237 2023-08-21 01:50:00 8.11 53.2     5430  45.25
## 69  49237 2023-08-21 01:52:00 8.08 53.1     4545  37.88
## 70  49237 2023-08-21 01:54:00 8.06 53.1     4031  33.59
## 71  49237 2023-08-21 01:56:00 8.04 53.1     3797  31.64
## 72  50893 2023-10-26 18:38:00 8.78 54.3       NA     NA
## 73  50893 2023-10-26 18:40:00 8.78 54.3     4520  37.67
## 74  50893 2023-10-26 18:42:00 8.78 54.2     6174  51.45
## 75  50893 2023-10-26 18:44:00 8.79 54.2     7731  64.42
## 76  50893 2023-10-26 18:46:00 8.80 54.2     4138  34.49
## 77  50893 2023-10-26 18:48:00 8.81 54.2     3595  29.96
## 78  50893 2023-10-26 18:50:00 8.82 54.2     3561  29.68
## 79  50893 2023-10-26 18:52:00 8.84 54.2     3562  29.69
## 80  50893 2023-10-26 18:54:00 8.86 54.2     3747  31.23
## 81  50893 2023-10-26 18:56:00 8.87 54.1     4037  33.64
## 82  50893 2023-10-26 18:58:00 8.88 54.1     4687  39.06
## 83  50893 2023-10-26 19:00:00 8.88 54.1     4800  40.00
## 84  50893 2023-10-26 19:02:00 8.88 54.0     4606  38.38
## 85  50893 2023-10-26 19:04:00 8.86 54.0     4406  36.72
## 86  50893 2023-10-26 19:06:00 8.83 54.0     3958  32.98
## 87  50893 2023-10-26 19:08:00 8.77 54.0     4899  40.83
## 88  50893 2023-10-26 19:10:00 8.67 54.0     7420  61.83
## 89  50893 2023-10-26 19:12:00 8.53 53.9    12498 104.15
## 90  50893 2023-10-26 19:50:00 8.37 53.8    15450   6.78
## 91  50893 2023-10-26 19:52:00 8.24 53.7    13860 115.50
## 92  50893 2023-10-26 19:54:00 8.19 53.7     7947  66.22
## 93  50893 2023-10-26 19:56:00 8.15 53.6     5106  42.55
## 94  50893 2023-10-26 19:58:00 8.10 53.6     4401  36.68
## 95  50893 2023-10-26 20:00:00 8.07 53.6     3619  30.16
## 96  50893 2023-10-26 20:02:00 8.05 53.6     3410  28.41
## 97  50893 2023-10-26 20:04:00 8.05 53.6     3233  26.94
## 98  50893 2023-10-26 20:06:00 8.06 53.6     3287  27.39
## 99  50893 2023-10-26 20:08:00 8.08 53.6     3501  29.18
## 100 50893 2023-10-26 20:10:00 8.07 53.6     3339  27.83
## 101 50893 2023-10-26 20:12:00 8.00 53.7     6200  51.67
## 102 50893 2023-10-26 20:14:00 7.81 53.7    13765 114.71
## 103 50893 2023-10-26 20:16:00 7.75 53.7     5652  47.10
## 104 50893 2023-10-26 20:18:00 7.73 53.7     3612  30.10
## 105 50893 2023-10-26 20:20:00 7.64 53.7     6893  57.44
## 106 50893 2023-10-26 20:34:00 7.44 53.7    14093  16.78
## 107 50893 2023-10-26 20:36:00 7.26 53.7    12380 103.16
## 108 50893 2023-10-26 20:38:00 7.22 53.7     4132  34.43
## 109 50893 2023-10-26 20:40:00 7.22 53.7     3057  25.47
## 110 50893 2023-10-26 20:42:00 7.23 53.7     3056  25.47
## 111 50893 2023-10-26 20:44:00 7.23 53.7     3036  25.30
## 112 50893 2023-10-26 20:46:00 7.22 53.7     3051  25.43
## 113 50893 2023-10-26 20:48:00 7.20 53.6     3287  27.39
## 114 50893 2023-10-26 20:50:00 7.19 53.6     3333  27.78
## 115 50893 2023-10-26 20:52:00 7.16 53.6     3848  32.07
## 116 50893 2023-10-26 20:54:00 7.14 53.6     4022  33.52
## 117 50893 2023-10-26 20:56:00 7.13 53.6     4487  37.39
## 118 50893 2023-10-26 21:04:00 7.09 53.6     5545  11.55
## 119 50893 2023-10-26 21:06:00 7.08 53.5     4469  37.25
## 120 50893 2023-10-26 21:08:00 7.08 53.5     3512  29.27
## 121 50893 2023-10-26 21:10:00 7.08 53.5     3219  26.83
## 122 50893 2023-10-26 21:12:00 7.08 53.5     3075  25.62
## 123 50893 2023-10-26 21:14:00 7.08 53.5     3095  25.79
## 124 50893 2023-10-26 21:20:00 7.08 53.5     3734  10.37
## 125 50893 2023-10-26 21:22:00 7.09 53.5     3443  28.69
## 126 50893 2023-10-26 21:24:00 7.09 53.5     3504  29.20
## 127 50893 2023-10-26 21:26:00 7.08 53.5     5118  42.65
## 128 50893 2023-10-26 21:28:00 6.93 53.4    13146 109.55
## 129 50893 2023-10-26 21:30:00 6.51 53.3    30851 257.09
## 130 50893 2023-10-26 23:26:00 5.92 53.2    41745   6.00
## 131 50893 2023-10-26 23:28:00 5.35 53.1    40354 336.28
## 132 50893 2023-10-26 23:30:00 5.00 53.0    26137 217.81
## 133 50893 2023-10-26 23:38:00 4.84 52.9    12099  25.21
## 134 50893 2023-10-26 23:40:00 4.82 52.9     4526  37.72
## 135 50893 2023-10-26 23:42:00 4.82 52.9     3691  30.76
## 136 50893 2023-10-26 23:44:00 4.82 52.9     3422  28.51
## 137 50893 2023-10-26 23:46:00 4.81 52.9     3541  29.51
## 138 50893 2023-10-26 23:48:00 4.78 52.9     3821  31.84
## 139 50893 2023-10-26 23:50:00 4.76 52.9     3715  30.96
## 140 50893 2023-10-26 23:52:00 4.75 52.9     3600  30.00
## 141 50893 2023-10-26 23:54:00 4.74 52.9     3674  30.62
## 142 50893 2023-10-26 23:56:00 4.71 52.9     3758  31.31
## 143 50893 2023-10-26 23:58:00 4.69 52.8     3779  31.49
## 144 50893 2023-10-27 00:00:00 4.67 52.8     3556  29.63
## 145 50893 2023-10-27 00:02:00 4.67 52.8     3501  29.18
## 146 50893 2023-10-27 00:04:00 4.67 52.8     3563  29.69
## 147 50893 2023-10-27 00:06:00 4.65 52.8     4033  33.61
## 148 50893 2023-10-27 00:08:00 4.62 52.8     4119  34.32
## 149 50893 2023-10-27 00:10:00 4.60 52.8     3757  31.31
## 150 50893 2023-10-27 00:12:00 4.59 52.8     3612  30.10
## 151 50893 2023-10-27 00:14:00 4.59 52.8     3664  30.53
## 152 50893 2023-10-27 00:16:00 4.57 52.7     4988  41.57
## 153 50893 2023-10-27 00:18:00 4.55 52.7     8526  71.05
## 154 50893 2023-10-27 00:46:00 4.53 52.6     7484   4.45
## 155 50893 2023-10-27 00:48:00 4.52 52.6     5596  46.64
## 156 50893 2023-10-27 00:50:00 4.52 52.6     3921  32.67
## 157 50893 2023-10-27 00:52:00 4.52 52.6     3567  29.72
## 158 50893 2023-10-27 00:54:00 4.52 52.6     3528  29.40
## 159 50893 2023-10-27 00:56:00 4.53 52.6     3555  29.62
## 160 50893 2023-10-27 00:58:00 4.53 52.6     3620  30.17
## 161 50893 2023-10-27 01:00:00 4.53 52.6     4399  36.66

Plot flight speed

We can plot the results per individual and output variable using the plot function:

plot(fit, id = 49237)

Plot flight paths

We can visualise the results on a map using the mapTrack function:

mapTrack(fit) +
    geom_point(aes(recvDeployLon, recvDeployLat), data = motusData)

Modelled movement trajectories per individual. Posterior means are shown together with 50 posterior draws, circles on the map indicate receiver locations with detections of the animals.

We could also create a Leaflet map using the following code:

library(sfheaders)
library(leaflet)

# Extract draws
draws <- getDraws(fit) |>
    sf_linestring("lon", "lat", linestring_id = "tID")

# Leaflet map
fit |>
    as.data.frame() |>
    sf_linestring("lon", "lat", linestring_id = "ID") |>
    leaflet() |>
    addTiles() |>
    addPolylines(data = draws, color = "grey", weight = 1, opacity = 0.2) |>
    addPolylines(color = ~ c("orange", "blue"))