Overview • aniMotum

Disclaimer

This vignette is an extended set of examples to highlight aniMotum’s functionality. Please, do NOT interpret these examples as instructions for conducting analysis of animal movement data. Numerous essential steps in a proper analysis have been left out of these vignettes. It is your job to understand your data, ensure you are asking the right questions of your data, and that the analyses you undertake appropriately reflect those questions. We can not do this for you!

Models

This is an overview of how to use aniMotum to filter animal track locations obtained via the Argos satellite system or (possibly) another tracking system. aniMotum provides three state-space models (SSM’s), two for filtering (ie. estimating “true” locations and associated movement model parameters, while accounting for error-prone observations), and another for filtering and estimating move persistence - an index of movement behaviour:

a simple Random Walk model, rw
a Correlated Random Walk model, crw
a Move Persistence model, mp

the models are continuous-time models, that is, they account for the time intervals between successive observations, thereby naturally accounting for the often irregularly-timed nature of animal tracking data. We won’t dwell on the details of the models here (see Jonsen et al. 2020 for details on the crw model), except to say there may be advantages to choosing one over the other in certain circumstances. The Random Walk model tends not to deal well with small to moderate gaps (relative to a specified time step) in observed locations and can over-fit to particularly noisy data. The Correlated Random Walk model can often deal better with these small to moderate data gaps and appropriately smooth through noisy data but tends to estimate nonsensical (i.e., ‘looping’ artefacts) movement through longer data gaps, especially when animals are mostly stationary during those gaps. The Move Persistence model can be more robust to data gaps up to a point, and estimates time-varying move persistence $\gamma_t$ along the track, which provides an index of how an animal’s movement behaviour varies in space and time based on the autocorrelation of successive movements. If you have Argos location data and your goal is to infer changes in movement behaviour along tracks then estimating locations and move persistence simultaneously within a state-space model is the preferred approach, as location uncertainty is accounted for in the move persistence estimates.

Additionally, aniMotum provides separate models (mpm, jmpm) for estimating move persistence after fitting either the rw or crw SSM (see Jonsen et al. 2019 for details). Using the fit_mpm function, the mpm is fit to individual tracks, whereas the jmpm is fit to multiple tracks simultaneously with a variance parameter that is estimated jointly across the tracks. This latter model may better resolve subtle changes in movement behaviour along tracks that lack much contrast in movements. Both models can be fit to time-regularized locations or to time-irregular locations. See Auger-Méthé et al. (2017) for an example of the latter. These models can be fit to animal tracks, regardless of the geolocation technology used, but they assume that locations are known without error.

Data

aniMotum can accept input data in several possible formats. Here, we outline the default formats but users are not restricted to using these. Input data with variable names and/or column ordering that differs from the defaults can be used, provided the essential minimum data elements are present to allow the SSM’s to be fitted: a unique animal/track identifier, date-time of each observation, location quality class, and longitude and latitude (or projected x and y) coordinates. We expand on these and additional variables in the following sections.

A pre-processing function format_data() can be used to correctly structure the input data for use in aniMotum. This function checks for the presence of the essential variables, converts date-time to POSIX format, maps custom variable names onto the default names, and puts the variables into the default column order expected by aniMotum. Users may choose to call format_data() themselves, prior to fitting an SSM with fit_ssm(), or they can allow fit_ssm() to format the data automatically. We illustrate these approaches in the “Fitting a model” section, below.

Argos Least-Squares (LS) location data

We’ll start out with the original Argos data, CLS Argos’ Least-Squares-based (LS) locations. These data contain the minimum information required by aniMotum’s SSM’s. A minimal input data.frame or tibble, containing only the information required to fit the SSM, looks like this:

#>             id                date lc   lon    lat
#> 1 ct109-085-14 2015-02-03 00:11:02  B 70.45 -49.93
#> 2 ct109-085-14 2015-02-03 13:26:37  B 71.00 -50.21
#> 3 ct109-085-14 2015-02-03 21:53:15  B 71.31 -50.37
#> 4 ct109-085-14 2015-02-04 04:05:35  A 71.64 -50.43
#> 5 ct109-085-14 2015-02-04 17:12:42  B 72.04 -50.46
#> 6 ct109-085-14 2015-02-05 02:05:44  B 72.44 -50.47

The (default) column names represent the following:
- id a unique identifier for each animal (or track) in the data.frame
- date a date-time variable in the form YYYY-mm-dd HH:MM:SS (or YYYY/mm/dd HH:MM:SS). These can be text strings, in which case aniMotum converts them to POSIX format and assumes the timezone is UTC. If the date-times are from a local timezone then you must specify this via the tz argument to format_data(). A list of valid timezone names can be viewed via OlsonNames().
- lc the location class variable common to Argos data, with classes in the set: 3, 2, 1, 0, A, B, Z.
- lon the longitude variable
- lat the latitude variable
The lc values determine the measurement error variances (based on independent data, see Jonsen et al. 2020) used in the SSM’s for each observation.

Argos Kalman Filter (KF) location data

Since 2011, the default Argos location data uses CLS Argos’ Kalman Filter (KF) algorithm. These data include error ellipse information for each observed location in the form of 3 variables: ellipse semi-major axis length, ellipse semi-minor axis length, and ellipse orientation. A minimal input data.frame or tibble for Argos KF data looks like this:

#>      id                date lc      lon       lat  smaj smin eor
#> 1 54591 2012-03-05 05:09:33  1 110.5707 -66.42752  2442  416  42
#> 2 54591 2012-03-06 04:55:14  0 110.3402 -66.39579 49660  391  90
#> 3 54591 2012-03-07 04:23:10  A 110.4778 -66.45266  5032  472  93
#> 4 54591 2012-03-07 21:23:06  A 110.3749 -66.39622  4007  286 116
#> 5 54591 2012-03-09 04:27:49  B 110.4732 -66.48743 13063  956  82
#> 6 54591 2012-03-10 00:10:41  A 110.5014 -66.43516  5099  478  79

The column names follow those for Argos LS data, with the following additions:
- smaj the Argos error ellipse semi-major axis length (m)
- smin the Argos error ellipse semi-minor axis length (m)
- eor the Argos error ellipse ellipse orientation (degrees from N)

Here, the error ellipse parameters for each observation define the measurement error variances used in the SSM’s (Jonsen et al. 2020). Missing error ellipse values are allowed, in which case, those observations are treated as Argos LS data.

GPS location data

The aniMotum SSM’s can be fit to GPS location data, for example to deal with (rare) extreme locations and/or to time-regularise locations. The input data format is the same as for Argos LS data, but the lc class is set to G for all GPS locations:

#>         id                date lc  lon   lat
#> 1 F02-B-17 2024-11-07 13:40:23  G 70.1 -49.2
#> 2 F02-B-17 2024-11-07 14:40:23  G 70.6 -48.2
#> 3 F02-B-17 2024-11-07 15:40:23  G 71.1 -47.2
#> 4 F02-B-17 2024-11-07 16:40:23  G 71.6 -46.2
#> 5 F02-B-17 2024-11-07 17:40:23  G 72.1 -45.2

Light-level geolocation data

The aniMotum SSM’s can be fit to processed light-level geolocation data, when they include longitude and latitude error SD’s (lonerr, laterr; units in degrees). In this case, the lc class is set to GL for all geolocation observations:

#>      id                date lc   lon lat    lonerr     laterr
#> 1 54632 2024-11-07 13:40:23 GL 100.0 -55 1.1857219 1.27832424
#> 2 54632 2024-11-08 01:40:23 GL 100.5 -54 0.3343330 0.69864596
#> 3 54632 2024-11-08 13:40:23 GL 101.0 -53 0.1007609 0.81756453
#> 4 54632 2024-11-09 01:40:23 GL 101.5 -52 0.8219809 0.03560142
#> 5 54632 2024-11-09 13:40:23 GL 102.0 -51 2.5876548 1.46203516

We caution users, that light-level geolocation errors can be (highly) non-Gaussian, depending on the species, tag technology, and algorithm used to estimate location from the light-level data. For example, we have observed strongly non-Gaussian location uncertainty in tracks of highly migratory tunas that traverse very different water masses in both the vertical and horizontal directions. Because aniMotum’s SSM’s assume bi-variate Normal location errors, fitting to geolocations with non-Gaussian errors would not be appropriate and likely result in highly biased location estimates. Geolocation data collected from birds may be less prone to this issue, but we urge users to explore both the uncertainty in their tracking data and the algorithm used to estimate locations from light-level data.

Projected location data

Users can provide projected location data as an sf-tibble or sf data.frame. By default, aniMotum’s SSM’s fit to locations that are transformed to the Mercator projection (EPSG 3395) and estimated locations are back-transformed to spherical coordinates (lon, lat). When an sf-projected data set is supplied, the aniMotum SSM’s are fit to locations on the supplied projection (aniMotum ensures that distance units are set to km). Here is an example sf-tibble Argos data set in a polar stereographic projection:

#> Simple feature collection with 6 features and 6 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1110.769 ymin: -948.5686 xmax: 1118.995 ymax: -936.6922
#> Projected CRS: +proj=stere +lat_0=-60 +lon_0=85 +ellps=WGS84 +units=km +no_defs
#> # A tibble: 6 × 7
#>   id    date                lc     smaj  smin   eor             geometry
#>   <chr> <dttm>              <chr> <dbl> <dbl> <dbl>         <POINT [km]>
#> 1 54591 2012-03-05 05:09:33 1      2442   416    42 (1118.995 -944.0405)
#> 2 54591 2012-03-06 04:55:14 0     49660   391    90 (1110.769 -936.6922)
#> 3 54591 2012-03-07 04:23:10 A      5032   472    93 (1114.021 -945.0264)
#> 4 54591 2012-03-07 21:23:06 A      4007   286   116  (1112.198 -937.343)
#> 5 54591 2012-03-09 04:27:49 B     13063   956    82 (1112.307 -948.5686)
#> 6 54591 2012-03-10 00:10:41 A      5099   478    79 (1115.773 -943.6181)

A mixture of location data types

Users can fit SSM’s to data where a combination of Argos, GPS or light-level geolocation observations are inter-mixed. Below is an example of mixed Argos KF and GPS observations, as one might obtain from a double-tagged individual:

#>         id                date lc  lon   lat  smaj smin eor
#> 1 F02-B-17 2017-09-17 05:20:00  G 70.1 -49.2    NA   NA  NA
#> 2 F02-B-17 2017-10-04 14:35:01  2 70.2 -49.1  1890   45  77
#> 3 F02-B-17 2017-10-05 04:03:25  G 70.1 -49.3    NA   NA  NA
#> 4 F02-B-17 2017-10-05 06:28:20  A 71.1 -48.7 28532 1723 101
#> 5 F02-B-17 2017-10-05 10:21:18  B 70.8 -48.5 45546 3303  97

These mixtures are possible because the aniMotum SSM’s apply the most appropriate measurement error model for each observation, based on its location class and error information (i.e., numeric values vs NA’s).

Fitting a model

Model fitting for quality control of locations is comprised of 3 steps: a data formatting step, a pre-filtering step, and the actual model fitting step. A number of checks are made on the input data during the formatting and pre-filtering steps, including applying the trip::sda filter to identify extreme observations (see ?fit_ssm for details). The pre-filter step is fully automated, although various arguments can be used to modify it’s actions, and called via the fit_ssm function:

## format, prefilter and fit Random Walk SSM using a 24 h time step
fit <-
  fit_ssm(
    x = ellie,
    model = "rw",
    time.step = 24
  )

These are the minimum arguments required: the input data, the model (rw, crw, or mp) and the time.step (in h) to which locations are predicted. Additional control can be exerted over the data formatting and pre-filtering steps, via the id, date, lc, coord, epar and sderr variable name arguments, and via the vmax, ang, distlim, min.dt, and spdf pre-filtering arguments (see ?fit_ssm for details). The defaults for these arguments are quite conservative (for non-flying species), usually leading to relative few observations being flagged to be ignored by the SSM. Additional control over the optimization can also be exerted via the control = ssm_control() argument, see see vignette('SSM_fitting') and ?ssm_control for more details.

Custom data formats

Users do not need to adhere to the default aniMotum data formatting presented above, so long as their input data contain the essential variables. The variables can have arbitrary names and be in any order. Here is an example using a custom formatted data.frame of Argos LS data as input:

data(sese2_n)
sese2_n
#> # A tibble: 295 × 5
#>    longitude latitude time                lc    id       
#>        <dbl>    <dbl> <chr>               <fct> <chr>    
#>  1      72.5    -50.2 2009-02-01 17:50:46 A     ct36-E-09
#>  2      73.0    -50.4 2009-02-02 03:30:26 A     ct36-E-09
#>  3      73.8    -50.8 2009-02-02 17:50:48 B     ct36-E-09
#>  4      74.6    -51.2 2009-02-03 07:39:08 A     ct36-E-09
#>  5      74.9    -51.4 2009-02-03 15:29:59 A     ct36-E-09
#>  6      75.5    -51.6 2009-02-04 01:21:08 A     ct36-E-09
#>  7      76.0    -52.2 2009-02-04 16:34:15 A     ct36-E-09
#>  8      76.7    -52.2 2009-02-04 21:05:50 A     ct36-E-09
#>  9      77.0    -52.7 2009-02-05 10:57:22 A     ct36-E-09
#> 10      77.8    -52.8 2009-02-05 16:03:58 A     ct36-E-09
#> # ℹ 285 more rows

Note the columns are in a different order than the default expected by aniMotum, and the first three variables have different names: longitude, latitude and time. There are two approaches to format these data. The first, is to call format_data as a data pre-processing step prior to fitting an SSM. The data are linked to the expected names via the following arguments to format_data: id, date, lc, coord, epar and sderr. In this example, we only need to use the date and coord arguments:

d <- format_data(sese2_n, date = "time", coord = c("longitude", "latitude"))
d
#> # A tibble: 295 × 10
#>    id        date                lc      lon   lat  smaj  smin   eor  x.sd  y.sd
#>    <chr>     <dttm>              <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 ct36-E-09 2009-02-01 17:50:46 A      72.5 -50.2    NA    NA    NA    NA    NA
#>  2 ct36-E-09 2009-02-02 03:30:26 A      73.0 -50.4    NA    NA    NA    NA    NA
#>  3 ct36-E-09 2009-02-02 17:50:48 B      73.8 -50.8    NA    NA    NA    NA    NA
#>  4 ct36-E-09 2009-02-03 07:39:08 A      74.6 -51.2    NA    NA    NA    NA    NA
#>  5 ct36-E-09 2009-02-03 15:29:59 A      74.9 -51.4    NA    NA    NA    NA    NA
#>  6 ct36-E-09 2009-02-04 01:21:08 A      75.5 -51.6    NA    NA    NA    NA    NA
#>  7 ct36-E-09 2009-02-04 16:34:15 A      76.0 -52.2    NA    NA    NA    NA    NA
#>  8 ct36-E-09 2009-02-04 21:05:50 A      76.7 -52.2    NA    NA    NA    NA    NA
#>  9 ct36-E-09 2009-02-05 10:57:22 A      77.0 -52.7    NA    NA    NA    NA    NA
#> 10 ct36-E-09 2009-02-05 16:03:58 A      77.8 -52.8    NA    NA    NA    NA    NA
#> # ℹ 285 more rows

We then fit the SSM to the formatted data d via fit_ssm:

fit <- fit_ssm(d,
               model = "crw",
               time.step = 24)

Alternatively, we can use a shortcut and have fit_ssm format the sese2_n data by adding the variable name arguments to the call:

fit <- fit_ssm(sese2_n,
               date = "time",
               coord = c("longitude","latitude"),
               model = "crw",
               time.step = 24)

Original variable names are not preserved in the output object fit but rather transformed to the default expected names. The grab function can be used to access the data and the SSM estimates (see Access Results, below).

fit_ssm can be applied to single or multiple tracks, without modification. The specified SSM is fit to each individual separately and the resulting output is a compound tibble with rows corresponding to each individual ssm_df fit object. The converged column indicates whether each model fit converged successfully.

## fit to data with two individuals
fit <- fit_ssm(sese2,
               model = "crw",
               time.step=24,
               control = ssm_control(verbose = 0))

## list fit outcomes for both seals
fit

#> # A tibble: 2 × 5
#>   id        ssm          converged pdHess pmodel
#>   <chr>     <named list> <lgl>     <lgl>  <chr> 
#> 1 ct36-E-09 <ssm [15]>   TRUE      TRUE   crw   
#> 2 ct36-F-09 <ssm [15]>   TRUE      TRUE   crw

Individual id is displayed in the 1st column, all fit output resides in a list (ssm) in the 2nd column, convergence status (whether the optimizer found a global minimum) of each model fit is displayed in the 3rd column, whether the Hessian matrix was positive-definite and could be solved to obtain parameter standard errors (pdHess) is displayed in the 4th column, and the specified process model (rw, crw, or mp) in the 5th column. In some cases, the optimizer will converge but the Hessian matrix is not positive-definite, which typically indicates the optimizer converged on a local minimum. In this case, some standard errors may be calculated but not all. One possible solution is to try specifying a longer time.step or set time.step = NA to turn off predictions and return only fitted values (location estimates at the pre-filtered observation times). If pdHess = FALSE persists then careful inspection of the supplied data is warranted to determine if suspect observations not identified by prefilter are present. The excellent glmmTMB troubleshooting vignette may also provide hints at solutions. Convergence failures should be examined for potential data issues, however, in some cases changes to the optimization parameters via ssm_control() (see ?fit_ssm and ?ssm_control on usage) may overcome mild issues (see ?nlminb or ?optim for details on optimization control parameters).

Access results

Summary information about the fit can be obtained via the summary function:

summary(fit)
#>  Animal id Model Time n.obs n.filt n.fit n.pred n.rr converged   AICc
#>  ct36-E-09   crw   24   170     26   144     58    .      TRUE 2948.3
#>  ct36-F-09   crw   24   125     24   101    110    .      TRUE 2366.4
#> 
#> --------------
#> ct36-E-09 
#> --------------
#>  Parameter Estimate Std.Err
#>        D_x   0.1921  0.0508
#>        D_y   0.2354  0.0525
#>      rho_p  -0.5046  0.1357
#>      rho_o   0.2435  0.1163
#>      tau_x     0.96  0.0764
#>      tau_y   0.6683  0.0571
#> 
#> --------------
#> ct36-F-09 
#> --------------
#>  Parameter Estimate Std.Err
#>        D_x   0.0234  0.0069
#>        D_y   0.1559  0.0368
#>      rho_p  -0.6488  0.1131
#>      rho_o   0.4365  0.1208
#>      tau_x   1.5913  0.1456
#>      tau_y   1.9959  0.2029

The summary table lists information about the fit, including the number of observations in the input data (n.obs), the number of observation flagged to be ignored by the SSM (n.filt), the number of fitted location estimates (n.fit), the number of predicted location estimates (n.pred), the number of rerouted location estimates (if present, n.rr), model convergence status, and AICc. When fitting to multiple individuals, these statistics are repeated on separate lines for each individual. Separate tables of SSM parameter estimates and their SE’s are also printed for each individual. The parameter estimates displayed vary depending on the SSM process model selected by the user (rw, crw, or mp) and the automatically chosen measurement model(s). Here, sigma_x and sigma_y are the process error standard deviations in the x and y directions, rho_p is the correlation parameter in the covariance term. The Std. Error column lists the standard errors, calculated via the Delta method (see TMB documentation for details), for each estimated parameter.

fit_ssm usually returns two sets of estimated locations in the model fit object: fitted values and predicted values. The fitted values occur at the times of the observations to which the SSM was fit (i.e., the observations that passed the pre-filter step). The predicted values occur at the regular time intervals specified by the time.step argument. If time.step = NA, then no predicted values are estimated or returned in the model fit object.

Users can obtain the fitted or predicted locations as a data.frame by using grab():

## grab fitted locations
floc <- grab(fit, what = "fitted")
floc[1:5,]
#> # A tibble: 5 × 14
#>   id     date                  lon   lat     x      y   x.se   y.se     u      v
#>   <chr>  <dttm>              <dbl> <dbl> <dbl>  <dbl>  <dbl>  <dbl> <dbl>  <dbl>
#> 1 ct36-… 2009-02-01 17:50:46  72.5 -50.2 8069. -6450.  0.467  0.513 0.340 -0.253
#> 2 ct36-… 2009-02-02 03:30:26  72.8 -50.4 8108. -6479. 10.6    9.35  4.05  -3.03 
#> 3 ct36-… 2009-02-02 17:50:48  73.7 -50.8 8201. -6553. 17.5   13.8   6.51  -5.11 
#> 4 ct36-… 2009-02-03 07:39:08  74.5 -51.2 8292. -6622. 14.2   10.2   6.56  -5.01 
#> 5 ct36-… 2009-02-03 15:29:59  74.9 -51.4 8338. -6657. 13.3    9.50  5.92  -4.48 
#> # ℹ 4 more variables: u.se <dbl>, v.se <dbl>, s <dbl>, s.se <lgl>

## grab predicted locations in projected form
ploc <- grab(fit, what = "predicted", as_sf = TRUE)
ploc[1:5,]
#> Simple feature collection with 5 features and 10 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 8068.437 ymin: -6911.071 xmax: 8676.385 ymax: -6449.872
#> Projected CRS: +proj=merc +lon_0=0 +datum=WGS84 +units=km +no_defs
#> # A tibble: 5 × 11
#>   id      date                       u         v    u.se    v.se    x.se    y.se
#>   <chr>   <dttm>                 <dbl>     <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#> 1 ct36-E… 2009-02-01 17:00:00 9.26e-11 -2.12e-11 1.00e-5 1.00e-5 1.00e-5 1.00e-5
#> 2 ct36-E… 2009-02-02 17:00:00 6.50e+ 0 -5.11e+ 0 1.04e+0 9.52e-1 1.69e+1 1.33e+1
#> 3 ct36-E… 2009-02-03 17:00:00 5.86e+ 0 -4.48e+ 0 1.22e+0 1.18e+0 1.37e+1 1.00e+1
#> 4 ct36-E… 2009-02-04 17:00:00 5.99e+ 0 -5.26e+ 0 9.58e-1 8.35e-1 1.26e+1 9.08e+0
#> 5 ct36-E… 2009-02-05 17:00:00 9.24e+ 0 -5.77e+ 0 1.14e+0 1.13e+0 1.21e+1 8.96e+0
#> # ℹ 3 more variables: s <dbl>, s.se <lgl>, geometry <POINT [km]>

Here, the output from the crw SSM is returned as a fitted location data.frame (floc) that includes individual id, date, longitude, latitude, x and y (typically from the default Mercator projection) and their standard errors (x.se, y.se in km), u, v (and their standard errors, u.se, v.se in km/h) are estimates of signed velocity in the x and y directions. The u, v velocities should generally be ignored as their estimation uses time intervals between consecutive locations, whether they are observation times or prediction times. The columns s and s.se provide a more reliable 2-D velocity estimate, although standard error estimation is turned off by default as this generally increases computation time for the crw SSM. Standard error estimation for s can be turned on via the control argument to fit_ssm (i.e. control = ssm_control(se = TRUE), see ?ssm_control for futher details).

The predicted location data.frame (ploc) is an sf-tibble with geometry and Coordinate Reference System information. This sf output format can be useful for custom mapping or calculating derived variables from the estimated locations.

The formatted and prefiltered version of the input data can also be extracted from the output:

fp.data <- grab(fit, what = "data")
fp.data[1:5,]
#> # A tibble: 5 × 14
#>   id      date                lc      lon   lat  smaj  smin   eor obs.type keep 
#>   <chr>   <dttm>              <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>    <lgl>
#> 1 ct36-E… 2009-02-01 17:50:46 A      72.5 -50.2    NA    NA    NA LS       TRUE 
#> 2 ct36-E… 2009-02-02 03:30:26 A      73.0 -50.4    NA    NA    NA LS       TRUE 
#> 3 ct36-E… 2009-02-02 17:50:48 B      73.8 -50.8    NA    NA    NA LS       TRUE 
#> 4 ct36-E… 2009-02-03 07:39:08 A      74.6 -51.2    NA    NA    NA LS       TRUE 
#> 5 ct36-E… 2009-02-03 15:29:59 A      74.9 -51.4    NA    NA    NA LS       TRUE 
#> # ℹ 4 more variables: x <dbl>, y <dbl>, emf.x <dbl>, emf.y <dbl>

Here, fp.data is in the form that is passed to the crw SSM. The first 5 columns (id, date, lc, lon, lat) are preserved from the formatted input data, and the error ellipse parameter columns (smaj, smin, eor) are appended and filled with NA’s, if missing from the input data. The observation type, obs.type, is determined for each observation during the prefiltering stage based on the combination of lc value and the presence of error ellipse parameters with non-NA values. The keep column indicates whether each record passed the pre-filtering stage (see ?prefilter for details), observations with keep = FALSE are ignored by the SSM. The x,y columns are the Mercator-projected coordinates (in km) that fitted to by the SSM. The emf.x and emf.y columns are the error multiplication factors used to scale the measurement error variances, used by the SSM, for each Argos Least-Squares location class - these are relevant only for obs.type = 'LS' and for GPS observations.

Visualising a model fit

A generic plot (see ?plot.ssm_df) method allows a quick visual of the SSM fit to the data:

# plot time-series of the fitted values
plot(fit, what = "fitted", type = 1, pages = 1)

Here, the fitted values (state estimates corresponding to the time of each observation; orange points) are plotted on top of the observations that passed the prefilter stage (blue points and blue rug at bottom) and as separate time-series for the x and y coordinates by default. Uncertainty in the estimates is displayed as 2 x SE intervals (orange-filled ribbon). Observations that failed the prefilter stage are also displayed (black x’s and black rug at bottom).

A 2-D time series plot of the track is invoked by the argument type = 2:

# plot fitted values as a 2-d track
plot(fit, what = "predicted", type = 2, pages = 1)

The predicted values (orange points) are the state estimates predicted at regular time intervals, specified by time.step (here a 24 h interval). 95 % confidence ellipses (orange-filled ellipses) around the predicted values are also displayed, but can be faded away by choosing a low alpha value (e.g., plot(fit, what = "predicted", type = 2, alpha = 0.05)). Observations that failed the prefilter stage are displayed (black x’s) by default but can be turned off with the argument outlier = FALSE).

References

Auger-Méthé M, Albertsen CM, Jonsen ID, Derocher AE, Lidgard DC, Studholme KR, Bowen WD, Crossin GT, Flemming JM (2017) Spatiotemporal modelling of marine movement data using Template Model Builder (TMB). Marine Ecology Progress Series 565:237-249.

Jonsen ID, McMahon CR, Patterson TA, Auger-Méthé M. Harcourt R, Hindel MA, Bestley S (2019) Movement responses to environment: fast inference of variation among southern elephant seals with a mixed effect model. Ecoloogy 100:e02566.

Jonsen ID, Patterson TA, Costa DP, Doherty PD, Godley BJ, Grecian WJ, Guinet C, Hoenner X, Kienle SS, Robinson PW, Votier SC, Whiting S, Witt MJ, Hindel MA, Harcourt RG, McMahon CR (2020) A continuous-time state-space model for rapid quality control of Argos locations from animal-borne tags. Movement Ecology 8:31.