Quickstart example
Georg Rüppel
2024-06-05
Source:vignettes/movetrack_example.Rmd
movetrack_example.RmdThis 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...
##
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## Chain 1 Iteration: 2000 / 2000 [100%] (Sampling)
## Chain 1 finished in 143.0 seconds.
## Chain 3 Iteration: 2000 / 2000 [100%] (Sampling)
## Chain 3 finished in 143.2 seconds.
## Chain 4 Iteration: 2000 / 2000 [100%] (Sampling)
## Chain 4 finished in 154.8 seconds.
## Chain 2 Iteration: 1000 / 2000 [ 50%] (Warmup)
## Chain 2 Iteration: 1001 / 2000 [ 50%] (Sampling)
## Chain 2 Iteration: 2000 / 2000 [100%] (Sampling)
## Chain 2 finished in 348.1 seconds.
##
## All 4 chains finished successfully.
## Mean chain execution time: 197.3 seconds.
## Total execution time: 348.2 seconds.
fitClick 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 1180 9.83
## 3 49237 2023-08-20 20:20:00 8.79 54.8 1430 11.92
## 4 49237 2023-08-20 20:22:00 8.79 54.8 1519 12.66
## 5 49237 2023-08-20 20:24:00 8.78 54.8 1584 13.20
## 6 49237 2023-08-20 20:26:00 8.77 54.8 1696 14.13
## 7 49237 2023-08-20 20:28:00 8.76 54.8 1902 15.85
## 8 49237 2023-08-20 20:30:00 8.75 54.8 2068 17.23
## 9 49237 2023-08-20 20:32:00 8.74 54.7 1977 16.48
## 10 49237 2023-08-20 20:34:00 8.74 54.7 1869 15.57
## 11 49237 2023-08-20 20:36:00 8.74 54.7 1785 14.88
## 12 49237 2023-08-20 20:38:00 8.75 54.7 1724 14.36
## 13 49237 2023-08-20 20:40:00 8.76 54.7 1614 13.45
## 14 49237 2023-08-20 20:42:00 8.77 54.7 1474 12.29
## 15 49237 2023-08-20 20:44:00 8.78 54.7 1392 11.60
## 16 49237 2023-08-20 20:46:00 8.79 54.7 1403 11.69
## 17 49237 2023-08-20 20:48:00 8.79 54.7 1577 13.15
## 18 49237 2023-08-20 20:50:00 8.80 54.6 1900 15.83
## 19 49237 2023-08-20 20:52:00 8.80 54.6 2253 18.77
## 20 49237 2023-08-20 20:54:00 8.81 54.6 2354 19.62
## 21 49237 2023-08-20 20:56:00 8.81 54.6 2256 18.80
## 22 49237 2023-08-20 20:58:00 8.82 54.6 2424 20.20
## 23 49237 2023-08-20 21:00:00 8.82 54.5 2806 23.39
## 24 49237 2023-08-20 21:08:00 8.83 54.5 3054 6.36
## 25 49237 2023-08-20 21:10:00 8.82 54.5 2746 22.89
## 26 49237 2023-08-20 21:12:00 8.82 54.5 2227 18.56
## 27 49237 2023-08-20 21:14:00 8.81 54.5 1898 15.82
## 28 49237 2023-08-20 21:16:00 8.79 54.4 1806 15.05
## 29 49237 2023-08-20 21:18:00 8.78 54.4 1722 14.35
## 30 49237 2023-08-20 21:20:00 8.77 54.4 1717 14.30
## 31 49237 2023-08-20 21:22:00 8.76 54.4 1864 15.54
## 32 49237 2023-08-20 21:24:00 8.75 54.4 2043 17.03
## 33 49237 2023-08-20 21:26:00 8.75 54.4 1899 15.82
## 34 49237 2023-08-20 21:28:00 8.75 54.4 1703 14.19
## 35 49237 2023-08-20 21:30:00 8.76 54.4 1638 13.65
## 36 49237 2023-08-20 21:32:00 8.76 54.3 1698 14.15
## 37 49237 2023-08-20 21:34:00 8.76 54.3 1787 14.89
## 38 49237 2023-08-20 21:36:00 8.77 54.3 1622 13.52
## 39 49237 2023-08-20 21:38:00 8.77 54.3 1453 12.11
## 40 49237 2023-08-20 21:40:00 8.77 54.3 1422 11.85
## 41 49237 2023-08-20 21:42:00 8.77 54.3 1537 12.81
## 42 49237 2023-08-20 21:44:00 8.77 54.3 1827 15.22
## 43 49237 2023-08-20 21:46:00 8.77 54.3 2172 18.10
## 44 49237 2023-08-20 21:48:00 8.77 54.2 2282 19.01
## 45 49237 2023-08-20 21:50:00 8.77 54.2 2555 21.29
## 46 49237 2023-08-20 21:52:00 8.76 54.2 2908 24.24
## 47 49237 2023-08-20 22:10:00 8.76 54.2 2816 2.61
## 48 49237 2023-08-20 22:12:00 8.75 54.1 2472 20.60
## 49 49237 2023-08-20 22:14:00 8.74 54.1 2049 17.08
## 50 49237 2023-08-20 22:16:00 8.73 54.1 2029 16.91
## 51 49237 2023-08-20 22:24:00 8.72 54.1 2296 4.78
## 52 49237 2023-08-20 22:26:00 8.71 54.1 2667 22.22
## 53 49237 2023-08-20 22:28:00 8.70 54.0 3337 27.81
## 54 49237 2023-08-20 22:30:00 8.69 54.0 4288 35.73
## 55 49237 2023-08-20 22:34:00 8.68 54.0 5338 22.24
## 56 49237 2023-08-20 23:00:00 8.66 53.9 5967 3.82
## 57 49237 2023-08-20 23:02:00 8.63 53.9 6737 56.14
## 58 49237 2023-08-20 23:04:00 8.59 53.8 7659 63.82
## 59 49237 2023-08-21 00:16:00 8.55 53.7 8269 1.91
## 60 49237 2023-08-21 00:18:00 8.50 53.7 8403 70.02
## 61 49237 2023-08-21 00:20:00 8.46 53.6 8064 67.20
## 62 49237 2023-08-21 00:22:00 8.40 53.5 7770 64.75
## 63 49237 2023-08-21 00:24:00 8.34 53.5 7712 64.26
## 64 49237 2023-08-21 00:26:00 8.28 53.4 7974 66.45
## 65 49237 2023-08-21 01:44:00 8.23 53.3 8041 1.72
## 66 49237 2023-08-21 01:46:00 8.18 53.3 7504 62.53
## 67 49237 2023-08-21 01:48:00 8.14 53.2 6458 53.82
## 68 49237 2023-08-21 01:50:00 8.11 53.2 5377 44.81
## 69 49237 2023-08-21 01:52:00 8.08 53.1 4476 37.30
## 70 49237 2023-08-21 01:54:00 8.06 53.1 3983 33.19
## 71 49237 2023-08-21 01:56:00 8.04 53.1 3737 31.14
## 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 4405 36.71
## 74 50893 2023-10-26 18:42:00 8.78 54.2 6218 51.82
## 75 50893 2023-10-26 18:44:00 8.79 54.2 7751 64.59
## 76 50893 2023-10-26 18:46:00 8.80 54.2 4112 34.27
## 77 50893 2023-10-26 18:48:00 8.81 54.2 3572 29.77
## 78 50893 2023-10-26 18:50:00 8.82 54.2 3510 29.25
## 79 50893 2023-10-26 18:52:00 8.84 54.2 3575 29.79
## 80 50893 2023-10-26 18:54:00 8.86 54.2 3816 31.80
## 81 50893 2023-10-26 18:56:00 8.87 54.1 4121 34.34
## 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 4834 40.28
## 84 50893 2023-10-26 19:02:00 8.88 54.0 4685 39.04
## 85 50893 2023-10-26 19:04:00 8.86 54.0 4415 36.79
## 86 50893 2023-10-26 19:06:00 8.83 54.0 4024 33.53
## 87 50893 2023-10-26 19:08:00 8.77 54.0 4949 41.24
## 88 50893 2023-10-26 19:10:00 8.67 54.0 7375 61.46
## 89 50893 2023-10-26 19:12:00 8.52 53.9 12610 105.08
## 90 50893 2023-10-26 19:50:00 8.36 53.8 15503 6.80
## 91 50893 2023-10-26 19:52:00 8.24 53.7 13822 115.18
## 92 50893 2023-10-26 19:54:00 8.19 53.7 7916 65.97
## 93 50893 2023-10-26 19:56:00 8.15 53.6 5121 42.67
## 94 50893 2023-10-26 19:58:00 8.10 53.6 4492 37.43
## 95 50893 2023-10-26 20:00:00 8.07 53.6 3612 30.10
## 96 50893 2023-10-26 20:02:00 8.05 53.6 3440 28.67
## 97 50893 2023-10-26 20:04:00 8.05 53.6 3252 27.10
## 98 50893 2023-10-26 20:06:00 8.06 53.6 3297 27.47
## 99 50893 2023-10-26 20:08:00 8.08 53.6 3559 29.66
## 100 50893 2023-10-26 20:10:00 8.07 53.6 3380 28.16
## 101 50893 2023-10-26 20:12:00 8.00 53.7 6166 51.38
## 102 50893 2023-10-26 20:14:00 7.81 53.7 13844 115.37
## 103 50893 2023-10-26 20:16:00 7.75 53.7 5725 47.71
## 104 50893 2023-10-26 20:18:00 7.73 53.7 3616 30.13
## 105 50893 2023-10-26 20:20:00 7.65 53.7 6877 57.31
## 106 50893 2023-10-26 20:34:00 7.44 53.7 14080 16.76
## 107 50893 2023-10-26 20:36:00 7.26 53.7 12527 104.39
## 108 50893 2023-10-26 20:38:00 7.22 53.7 4200 35.00
## 109 50893 2023-10-26 20:40:00 7.22 53.7 3174 26.45
## 110 50893 2023-10-26 20:42:00 7.23 53.7 3059 25.49
## 111 50893 2023-10-26 20:44:00 7.23 53.7 3102 25.85
## 112 50893 2023-10-26 20:46:00 7.22 53.7 3115 25.96
## 113 50893 2023-10-26 20:48:00 7.20 53.6 3322 27.69
## 114 50893 2023-10-26 20:50:00 7.19 53.6 3340 27.84
## 115 50893 2023-10-26 20:52:00 7.16 53.6 3871 32.26
## 116 50893 2023-10-26 20:54:00 7.14 53.6 4039 33.66
## 117 50893 2023-10-26 20:56:00 7.13 53.6 4471 37.26
## 118 50893 2023-10-26 21:04:00 7.09 53.6 5633 11.74
## 119 50893 2023-10-26 21:06:00 7.08 53.5 4529 37.74
## 120 50893 2023-10-26 21:08:00 7.08 53.5 3494 29.12
## 121 50893 2023-10-26 21:10:00 7.08 53.5 3169 26.41
## 122 50893 2023-10-26 21:12:00 7.08 53.5 3075 25.63
## 123 50893 2023-10-26 21:14:00 7.08 53.5 3070 25.59
## 124 50893 2023-10-26 21:20:00 7.08 53.5 3692 10.26
## 125 50893 2023-10-26 21:22:00 7.09 53.5 3463 28.86
## 126 50893 2023-10-26 21:24:00 7.09 53.5 3508 29.23
## 127 50893 2023-10-26 21:26:00 7.08 53.5 5173 43.10
## 128 50893 2023-10-26 21:28:00 6.93 53.4 13121 109.34
## 129 50893 2023-10-26 21:30:00 6.51 53.3 30706 255.88
## 130 50893 2023-10-26 23:26:00 5.92 53.2 41820 6.01
## 131 50893 2023-10-26 23:28:00 5.36 53.1 40420 336.83
## 132 50893 2023-10-26 23:30:00 4.99 53.0 26287 219.06
## 133 50893 2023-10-26 23:38:00 4.84 52.9 12133 25.28
## 134 50893 2023-10-26 23:40:00 4.82 52.9 4531 37.76
## 135 50893 2023-10-26 23:42:00 4.83 52.9 3699 30.83
## 136 50893 2023-10-26 23:44:00 4.82 52.9 3462 28.85
## 137 50893 2023-10-26 23:46:00 4.81 52.9 3565 29.71
## 138 50893 2023-10-26 23:48:00 4.78 52.9 3857 32.14
## 139 50893 2023-10-26 23:50:00 4.76 52.9 3732 31.10
## 140 50893 2023-10-26 23:52:00 4.75 52.9 3691 30.75
## 141 50893 2023-10-26 23:54:00 4.74 52.9 3686 30.72
## 142 50893 2023-10-26 23:56:00 4.71 52.9 3823 31.86
## 143 50893 2023-10-26 23:58:00 4.68 52.8 3829 31.90
## 144 50893 2023-10-27 00:00:00 4.67 52.8 3604 30.03
## 145 50893 2023-10-27 00:02:00 4.67 52.8 3532 29.43
## 146 50893 2023-10-27 00:04:00 4.67 52.8 3587 29.90
## 147 50893 2023-10-27 00:06:00 4.64 52.8 4139 34.50
## 148 50893 2023-10-27 00:08:00 4.62 52.8 4162 34.69
## 149 50893 2023-10-27 00:10:00 4.60 52.8 3717 30.98
## 150 50893 2023-10-27 00:12:00 4.59 52.8 3564 29.70
## 151 50893 2023-10-27 00:14:00 4.59 52.8 3662 30.52
## 152 50893 2023-10-27 00:16:00 4.57 52.7 4985 41.54
## 153 50893 2023-10-27 00:18:00 4.55 52.7 8597 71.64
## 154 50893 2023-10-27 00:46:00 4.53 52.6 7481 4.45
## 155 50893 2023-10-27 00:48:00 4.53 52.6 5550 46.25
## 156 50893 2023-10-27 00:50:00 4.52 52.6 3961 33.01
## 157 50893 2023-10-27 00:52:00 4.52 52.6 3606 30.05
## 158 50893 2023-10-27 00:54:00 4.52 52.6 3598 29.98
## 159 50893 2023-10-27 00:56:00 4.53 52.6 3616 30.13
## 160 50893 2023-10-27 00:58:00 4.53 52.6 3717 30.97
## 161 50893 2023-10-27 01:00:00 4.53 52.6 4512 37.60Plot 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"))