An example with simulated data

library(sspm)
#> Loading required package: sf
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE
#> Loading required package: mgcv
#> Loading required package: nlme
#> This is mgcv 1.9-1. For overview type 'help("mgcv-package")'.
library(mgcv)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following object is masked from 'package:nlme':
#> 
#>     collapse
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

The following example shows the typical sspm workflow. We will use simulated data.

sfa_boundaries
#> Simple feature collection with 4 features and 2 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -64.18658 ymin: 46.00004 xmax: -46.6269 ymax: 60.84333
#> Geodetic CRS:  WGS 84
#>   sfa                       geometry               area
#> 1   4 MULTIPOLYGON (((-59.36453 5...  47575648300 [m^2]
#> 2   5 MULTIPOLYGON (((-55 53.75, ...  62857719164 [m^2]
#> 3   6 MULTIPOLYGON (((-49.9269 49... 178716060645 [m^2]
#> 4   7 MULTIPOLYGON (((-54.48779 4... 147799572836 [m^2]
borealis_simulated
#> # A tibble: 1,800 × 8
#>    year_f sfa   weight_per_km2 temp_at_bottom lon_dec lat_dec   row uniqueID  
#>    <fct>  <chr>          <dbl>          <dbl>   <dbl>   <dbl> <int> <chr>     
#>  1 1995   7               14.7          1.31    -62.8    59.2     1 y1995s7r1 
#>  2 1995   7              366.           0       -56.7    55.2     2 y1995s7r2 
#>  3 1995   7              108.           0.982   -60.6    56.9     3 y1995s7r3 
#>  4 1995   7               62.3          0       -58.0    56.4     4 y1995s7r4 
#>  5 1995   7                0            0.698   -59.0    57.1     5 y1995s7r5 
#>  6 1995   7                0            1.45    -58.8    56.3     6 y1995s7r6 
#>  7 1995   7              236.           1.05    -51.1    50.7     7 y1995s7r7 
#>  8 1995   7               68.2          0       -54.1    54.5     8 y1995s7r8 
#>  9 1995   7               93.6          1.86    -54.9    53.7     9 y1995s7r9 
#> 10 1995   7               25.4          1.74    -54.7    50.5    10 y1995s7r10
#> # ℹ 1,790 more rows
predator_simulated
#> # A tibble: 10,200 × 7
#>    year_f sfa   weight_per_km2 lon_dec lat_dec   row uniqueID  
#>    <fct>  <chr>          <dbl>   <dbl>   <dbl> <int> <chr>     
#>  1 1995   7               9.90   -61.0    60.0     1 y1995s7r1 
#>  2 1995   7             128.     -60.0    57.6     2 y1995s7r2 
#>  3 1995   7             358.     -58.0    55.6     3 y1995s7r3 
#>  4 1995   7               0      -58.0    55.9     4 y1995s7r4 
#>  5 1995   7             103.     -60.5    57.2     5 y1995s7r5 
#>  6 1995   7              57.3    -53.7    51.8     6 y1995s7r6 
#>  7 1995   7              58.6    -53.6    50.7     7 y1995s7r7 
#>  8 1995   7               0      -50.2    50.1     8 y1995s7r8 
#>  9 1995   7               0      -54.6    54.0     9 y1995s7r9 
#> 10 1995   7             131.     -52.8    51.0    10 y1995s7r10
#> # ℹ 10,190 more rows
catch_simulated
#> # A tibble: 2,020 × 7
#>    year_f sfa   catch lon_dec lat_dec   row uniqueID  
#>    <fct>  <chr> <dbl>   <dbl>   <dbl> <int> <chr>     
#>  1 1991   4     2527.   -62.2    59.5     1 y1991s4r1 
#>  2 1991   4     4194.   -61.0    58.0     2 y1991s4r2 
#>  3 1991   4     7438.   -62.4    60.3     3 y1991s4r3 
#>  4 1991   4        0    -58.2    55.2     4 y1991s4r4 
#>  5 1991   4     3837.   -57.8    55.5     5 y1991s4r5 
#>  6 1991   4     3196.   -58.6    56.3     6 y1991s4r6 
#>  7 1991   4     3214.   -57.9    55.7     7 y1991s4r7 
#>  8 1991   4        0    -56.4    54.4     8 y1991s4r8 
#>  9 1991   4      539.   -58.9    55.6     9 y1991s4r9 
#> 10 1991   4     4234.   -55.2    53.2    10 y1991s4r10
#> # ℹ 2,010 more rows
  1. The first step of the sspm workflow is to create a sspm_boundary from an sf object, providing the boundary that delineates the boundary regions. The object can then be plotted with spm_plot (as can most sspm objects).
bounds <- spm_as_boundary(boundaries = sfa_boundaries, 
                          boundary = "sfa")

plot(bounds)

  1. The second step consists in wrapping a data.frame, tibble or sf object into a sspm_data object, with a few other pieces of relevant information, such as the name, dataset type (biomass, predictor or catch, depending on the type of information contained), time column and coordinates column (i not sf) and unique row identifier. Here we wrap the borealis dataset that contains the biomass information.
biomass_dataset <- 
  spm_as_dataset(borealis_simulated, name = "borealis",
                 density = "weight_per_km2",
                 time = "year_f",
                 coords = c('lon_dec','lat_dec'), 
                 uniqueID = "uniqueID")
#> ℹ  Casting data matrix into simple feature collection using columns: lon_dec, lat_dec
#> !  Warning: sspm is assuming WGS 84 CRS is to be used for casting

biomass_dataset
#> 
#> ‒‒ Dataset borealis ‒‒
#> →  [1800 rows, 9 columns]
#> →  Density : weight_per_km2
#> →  Time : year_f
  1. We do the same with the predator data, which are of the predictor type.
predator_dataset <- 
  spm_as_dataset(predator_simulated, name = "all_predators", 
                 density = "weight_per_km2",
                 time = "year_f",
                 coords = c("lon_dec", "lat_dec"),
                 uniqueID = "uniqueID")
#> ℹ  Casting data matrix into simple feature collection using columns: lon_dec, lat_dec
#> !  Warning: sspm is assuming WGS 84 CRS is to be used for casting

predator_dataset
#> 
#> ‒‒ Dataset all_predators ‒‒
#> →  [10200 rows, 8 columns]
#> →  Density : weight_per_km2
#> →  Time : year_f
  1. The sspm workflow relies on the discretization of the boundary objects, the default method being voronoi tesselation.
bounds_voronoi <- bounds %>% 
  spm_discretize(method = "tesselate_voronoi",
                 with = biomass_dataset, 
                 nb_samples = 30)
#> ℹ  Discretizing using method tesselate_voronoi

bounds_voronoi
#> 
#> ‒‒ Boundaries (Discrete) ‒‒
#> →  [4 rows, 3 columns]
#>   ★ Points — [120 features, 10 columns]
#>   ★ Patches — [85 features, 4 columns]
#> →  Column : sfa
#> →  Area : area_sfa

The other available method is triangulate_delaunay for delaunay triangulation. Here the a argument is used to set the size of the mesh (see RTriangle::triangulate for more details).

## Not run
bounds_delaunay <- bounds %>%
  spm_discretize(method = "triangulate_delaunay", a = 1, q = 30)
bounds_delaunay
  1. Plotting the object shows the polygons that have been created.
plot(bounds_voronoi)

## Not run
plot(bounds_delaunay)
  1. The results of the discretization can also be explored with spm_patches() and spm_points().
spm_patches(bounds_voronoi)
#> Simple feature collection with 85 features and 3 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -64.18658 ymin: 46.00004 xmax: -46.6269 ymax: 60.84489
#> Geodetic CRS:  WGS 84
#> # A tibble: 85 × 4
#>    sfa   patch_id patch_area                                            geometry
#>  * <fct> <fct>        [km^2]                                       <POLYGON [°]>
#>  1 4     P1            3328. ((-63.11402 59.66424, -62.83338 60.43615, -63 60.8…
#>  2 4     P2            4099. ((-62.00856 58.53059, -61.78999 58.61768, -61.4823…
#>  3 4     P3            3308. ((-61.80265 59.43396, -61.97171 59.81804, -62.0892…
#>  4 4     P4            6814. ((-61.48231 59.06057, -61.78999 58.61768, -61.1115…
#>  5 4     P5            1677. ((-59.37831 57.67423, -59.43377 57.71988, -59.4892…
#>  6 4     P6            2282. ((-59.70893 58.16354, -60.11445 58.34307, -60.2929…
#>  7 4     P7            9080. ((-60.07796 59.21579, -60.11318 59.24152, -60.1409…
#>  8 4     P8            3003. ((-62.7581 59.09816, -62.49637 59.37714, -62.69224…
#>  9 4     P9           10478. ((-62.08921 59.41056, -61.97171 59.81804, -61.8026…
#> 10 4     P10           1601. ((-61.38138 58.03343, -61.11151 58.34397, -61.7899…
#> # ℹ 75 more rows
spm_points(bounds_voronoi)
#> Simple feature collection with 120 features and 9 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -63.35709 ymin: 46.12884 xmax: -47.23827 ymax: 60.77991
#> Geodetic CRS:  WGS 84
#> # A tibble: 120 × 10
#> # Groups:   sfa [4]
#>    year_f weight_per_km2 temp_at_bottom lon_dec lat_dec   row uniqueID   
#>  * <fct>       [kg/km^2]          <dbl>   <dbl>   <dbl> <int> <chr>      
#>  1 1995               0           0.676   -49.0    47.6    16 y1995s7r16 
#>  2 1995               0           0.808   -60.2    57.3    46 y1995s5r46 
#>  3 1995               0           0       -60.0    57.4    47 y1995s5r47 
#>  4 1996               0           1.36    -60.5    57.7    81 y1996s6r81 
#>  5 1996           14731.          2.46    -55.5    54.8    86 y1996s6r86 
#>  6 1996            9112.          3.47    -56.1    55.2   104 y1996s5r104
#>  7 1996            5442.          1.74    -55.8    50.0   109 y1996s5r109
#>  8 1996           19154.          0.747   -54.8    53.1   112 y1996s5r112
#>  9 1996           11333.          3.43    -47.2    46.5   120 y1996s5r120
#> 10 1996            2746.          1.51    -55.9    54.5   123 y1996s4r123
#> # ℹ 110 more rows
#> # ℹ 3 more variables: geometry <POINT [°]>, sfa <fct>, area_sfa [km^2]
  1. The next step in this workflow is to smooth the variables to be used in the final sspm model, by using spatial-temporal smoothers, by passing each dataset through spm_smooth. Here we first smooth weight_per_km2 as well as temp_at_bottom. Note that the boundary column sfa can be used in the formula as the data will be first joined to the provided boundaries.
biomass_smooth <- biomass_dataset %>%  
  spm_smooth(weight_per_km2 ~ sfa + smooth_time(by = sfa) + 
               smooth_space() + 
               smooth_space_time(),
             boundaries = bounds_voronoi, 
             family=tw)%>% 
  spm_smooth(temp_at_bottom ~ smooth_time(by=sfa, xt = NULL) +
               smooth_space() +
               smooth_space_time(xt = NULL),
             family=gaussian)
#> ℹ  Fitting formula: weight_per_km2 ~ sfa + smooth_time(by = sfa) + smooth_space() + smooth_space_time() for dataset 'borealis'
#> ℹ  Note:  response variable temp_at_bottom is NOT a biomass density variable
#> ℹ  Fitting formula: temp_at_bottom ~ smooth_time(by = sfa, xt = NULL) + smooth_space() + smooth_space_time(xt = NULL) for dataset 'borealis'

biomass_smooth
#> 
#> ‒‒ Dataset borealis (Mapped) ‒‒
#> →  [1801 rows, 12 columns]
#> →  Density : weight_per_km2
#> →  Time : year_f
#> →  Smoothed data : [2040 rows, 8 columns]
#>    ★ Smoothed vars: temp_at_bottom — weight_per_km2
  1. The smoothed results for any smoothed variables (listed in “smoothed vars” above) can be easily plotted. Here the 4 panels represent 4 different patches in the same SFA. The panels show a very similar pattern due to the nature of the simulated data we are using. The data points are actually different, but the differences are not noticeable in this example case.
plot(biomass_smooth, var = "weight_per_km2", log = FALSE, interval = T)

You can also make a spatial plot

plot(biomass_smooth, var = "weight_per_km2", use_sf = TRUE)

  1. We also smooth the weight_per_km2 variable in the predator data.
predator_smooth <- predator_dataset %>%  
  spm_smooth(weight_per_km2 ~ smooth_time() + smooth_space(),
             boundaries = bounds_voronoi,
             drop.unused.levels = F, family=tw, method= "fREML")
#> ℹ  Fitting formula: weight_per_km2 ~ smooth_time() + smooth_space() for dataset 'all_predators'

predator_smooth
#> 
#> ‒‒ Dataset all_predators (Mapped) ‒‒
#> →  [10201 rows, 11 columns]
#> →  Density : weight_per_km2
#> →  Time : year_f
#> →  Smoothed data : [3400 rows, 7 columns]
#>    ★ Smoothed vars: weight_per_km2
  1. Before we assemble the full model with our newly smoothed data, we need to deal with the catch data. We first load the dataset.
catch_dataset <- 
  spm_as_dataset(catch_simulated, name = "catch_data", 
                 biomass = "catch",
                 time = "year_f", 
                 uniqueID = "uniqueID", 
                 coords = c("lon_dec", "lat_dec"))
#> ℹ  Casting data matrix into simple feature collection using columns: lon_dec, lat_dec
#> !  Warning: sspm is assuming WGS 84 CRS is to be used for casting

catch_dataset
#> 
#> ‒‒ Dataset catch_data ‒‒
#> →  [2020 rows, 8 columns]
#> →  Biomass : catch
#> →  Time : year_f
  1. We then need to aggregate this data. This illustrate using the spm_aggregate functions. Here we use spm_aggregate_catch:
biomass_smooth_w_catch <- 
  spm_aggregate_catch(biomass = biomass_smooth, 
                      catch = catch_dataset, 
                      biomass_variable = "weight_per_km2",
                      catch_variable = "catch",
                      fill = mean)
#> ℹ  Offsetting biomass with catch data using columns: weight_per_km2, catch

biomass_smooth_w_catch
#> 
#> ‒‒ Dataset borealis (Mapped) ‒‒
#> →  [1801 rows, 12 columns]
#> →  Density : weight_per_km2
#> →  Time : year_f
#> →  Smoothed data : [2040 rows, 13 columns]
#>    ★ Smoothed vars: temp_at_bottom — weight_per_km2
#>    ★ Vars with catch: weight_per_km2_borealis_with_catch
  1. Once data has been smoothed, we can assemble a sspm model object, using one dataset of type biomass, one dataset of type predictor and (optionnaly) a dataset of type catch.
sspm_model <- sspm(biomass = biomass_smooth_w_catch, 
                   predictors = predator_smooth)
#> ℹ  Joining smoothed data from all datasets

sspm_model
#> 
#> ‒‒ Model (2 datasets) ‒‒
#> →  Smoothed data : [2040 rows, 14 columns]
#>    ★ Smoothed vars: temp_at_bottom — weight_per_km2_all_predators — weight_per_km2_borealis
#>    ★ Vars with catch: weight_per_km2_borealis_with_catch
  1. Before fitting the model, we must split data into test/train with spm_split.
sspm_model <- sspm_model %>% 
  spm_split(year_f %in% c(1990:2017))

sspm_model
#> 
#> ‒‒ Model (2 datasets) ‒‒
#> →  Smoothed data : [2040 rows, 15 columns] / [1955 train, 85 test]
#>    ★ Smoothed vars: temp_at_bottom — weight_per_km2_all_predators — weight_per_km2_borealis
#>    ★ Vars with catch: weight_per_km2_borealis_with_catch
  1. To fit the model, we might be interested in including lagged values. This is done with spm_lag.
sspm_model <- sspm_model %>% 
  spm_lag(vars = c("weight_per_km2_borealis", 
                   "weight_per_km2_all_predators"), 
          n = 1)

sspm_model
#> 
#> ‒‒ Model (2 datasets) ‒‒
#> →  Smoothed data : [2040 rows, 17 columns] / [1955 train, 85 test]
#>    ★ Smoothed vars: temp_at_bottom — weight_per_km2_all_predators — weight_per_km2_borealis
#>    ★ Vars with catch: weight_per_km2_borealis_with_catch
#>    ★ lagged vars: weight_per_km2_all_predators_lag_1 — weight_per_km2_borealis_lag_1
  1. We can now fit the final spm model with spm.
sspm_model_fit <- sspm_model %>% 
  spm(log_productivity ~ sfa +
        weight_per_km2_all_predators_lag_1 +
        smooth_space(by = weight_per_km2_borealis_lag_1) +
        smooth_space(), 
      family = mgcv::scat)
#> ℹ  Fitting SPM formula: log_productivity ~ sfa + weight_per_km2_all_predators_lag_1 + smooth_space(by = weight_per_km2_borealis_lag_1) + smooth_space()

sspm_model_fit
#> 
#> ‒‒ Model fit ‒‒
#> →  Smoothed data : [2040 rows, 17 columns] / [1955 train, 85 test]

It is possible to access the GAM fit object in order to look at it in more details and, for example, evaluate the goodness of fit.

gam_fit <- spm_get_fit(sspm_model_fit)
summary(gam_fit)
#> 
#> Family: Scaled t(7.001,0.195) 
#> Link function: identity 
#> 
#> Formula:
#> log_productivity ~ sfa + weight_per_km2_all_predators_lag_1 + 
#>     s(patch_id, k = 30, bs = "mrf", xt = list(penalty = pen_mat_space), 
#>         by = weight_per_km2_borealis_lag_1) + s(patch_id, k = 30, 
#>     bs = "mrf", xt = list(penalty = pen_mat_space))
#> 
#> Parametric coefficients:
#>                                      Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)                         6.871e-01  9.457e-02   7.265 5.49e-13 ***
#> sfa5                                1.033e-01  9.140e-02   1.130   0.2585    
#> sfa6                                3.456e-02  1.117e-01   0.310   0.7570    
#> sfa7                                9.776e-02  1.201e-01   0.814   0.4158    
#> weight_per_km2_all_predators_lag_1 -2.346e-05  1.054e-05  -2.225   0.0262 *  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Approximate significance of smooth terms:
#>                                             edf Ref.df      F p-value    
#> s(patch_id):weight_per_km2_borealis_lag_1 20.58     30 42.486  <2e-16 ***
#> s(patch_id)                               15.36     29  6.623  <2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> R-sq.(adj) =  0.611   Deviance explained = 42.1%
#> -REML = 2748.4  Scale est. = 1         n = 1870

You can also use the summary method.

summary(sspm_model_fit, biomass = "weight_per_km2_borealis")
#> ── Productivity ────────────────────────────────────────────────────────────────
#>   sfa      mean       min       max        sd
#> 1   4 0.9832311 0.6525740  1.162395 0.1264615
#> 2   5 0.9997828 0.5368252  1.411977 0.1888340
#> 3   6 1.0212750 0.6745112  1.315847 0.1341058
#> 4   7 1.2698200 0.6146555 11.900818 1.3920693
#> ── Biomass density ─────────────────────────────────────────────────────────────
#>   sfa               mean                 min                max        sd
#> 1   4 2841.074 [kg/km^2] 2660.9860 [kg/km^2] 2975.946 [kg/km^2]  63.03606
#> 2   5 3041.163 [kg/km^2] 2551.7907 [kg/km^2] 3590.994 [kg/km^2] 143.79971
#> 3   6 3226.922 [kg/km^2] 2115.0442 [kg/km^2] 4173.462 [kg/km^2] 528.03240
#> 4   7 3209.607 [kg/km^2]  667.0919 [kg/km^2] 4397.809 [kg/km^2] 242.45339
#> ── Biomass (with catch) ────────────────────────────────────────────────────────
#>   sfa          mean              min           max       sd
#> 1   4 12303685 [kg] 4273382.667 [kg] 31182213 [kg]  8392040
#> 2   5 11289489 [kg] 5414528.649 [kg] 23610412 [kg]  4525524
#> 3   6 19247927 [kg] 3459958.807 [kg] 65388041 [kg] 10251234
#> 4   7 17633584 [kg]    4549.233 [kg] 60226353 [kg] 11329851
  1. Plotting the object produces a actual vs predicted plot (with TEST/TRAIN data highlighted.
plot(sspm_model_fit, train_test = TRUE, scales = "free")
#> Warning: Removed 85 rows containing missing values or values outside the scale range
#> (`geom_point()`).

  1. We can also extract the predictions.
preds <- predict(sspm_model_fit)
head(preds)
#> Simple feature collection with 6 features and 6 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -64.18658 ymin: 59.66424 xmax: -62.83338 ymax: 60.80996
#> Geodetic CRS:  WGS 84
#>      pred_log      pred patch_id year_f sfa      patch_area
#> 1 -0.08863805 0.9151768       P1   1995   4 3328.183 [km^2]
#> 2 -0.22422079 0.7991387       P1   1996   4 3328.183 [km^2]
#> 3  0.01638812 1.0165231       P1   1997   4 3328.183 [km^2]
#> 4  0.06999493 1.0725027       P1   1998   4 3328.183 [km^2]
#> 5  0.07119763 1.0737934       P1   1999   4 3328.183 [km^2]
#> 6  0.10258953 1.1080365       P1   2000   4 3328.183 [km^2]
#>                         geometry
#> 1 POLYGON ((-63.11402 59.6642...
#> 2 POLYGON ((-63.11402 59.6642...
#> 3 POLYGON ((-63.11402 59.6642...
#> 4 POLYGON ((-63.11402 59.6642...
#> 5 POLYGON ((-63.11402 59.6642...
#> 6 POLYGON ((-63.11402 59.6642...

We can also get the predictions for biomass by passing the biomass variable name.

biomass_preds <- predict(sspm_model_fit, biomass = "weight_per_km2_borealis")
head(biomass_preds)
#> Simple feature collection with 6 features and 8 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -64.18658 ymin: 59.66424 xmax: -62.83338 ymax: 60.80996
#> Geodetic CRS:  WGS 84
#> # A tibble: 6 × 9
#>   year_f patch_id sfa   patch_area biomass_with_catch  biomass
#>    <dbl> <fct>    <fct>     [km^2]               [kg]     [kg]
#> 1   1995 P1       4          3328.                NA       NA 
#> 2   1996 P1       4          3328.           9908910. 9904493.
#> 3   1997 P1       4          3328.           9627427. 9623010.
#> 4   1998 P1       4          3328.           9401565. 9393490.
#> 5   1999 P1       4          3328.           9414290. 9409129.
#> 6   2000 P1       4          3328.           9273921. 9269504.
#> # ℹ 3 more variables: biomass_density_with_catch [kg/km^2],
#> #   biomass_density [kg/km^2], geometry <POLYGON [°]>

We can also predict the biomass one step ahead.

biomass_one_step <- predict(sspm_model_fit, biomass = "weight_per_km2_borealis", 
                            next_ts = TRUE)
head(biomass_one_step)
#> Simple feature collection with 6 features and 5 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -64.18658 ymin: 57.66667 xmax: -59.36453 ymax: 60.80996
#> Geodetic CRS:  WGS 84
#> # A tibble: 6 × 6
#>   patch_id year_f sfa     biomass patch_area                            geometry
#>   <fct>     <dbl> <fct>      [kg]     [km^2]                       <POLYGON [°]>
#> 1 P1         2019 4      8986220.      3328. ((-63.11402 59.66424, -62.83338 60…
#> 2 P2         2019 4     11093752.      4099. ((-62.00856 58.53059, -61.78999 58…
#> 3 P3         2019 4      8939170.      3308. ((-61.80265 59.43396, -61.97171 59…
#> 4 P4         2019 4     18479004.      6814. ((-61.48231 59.06057, -61.78999 58…
#> 5 P5         2019 4      4534086.      1677. ((-59.37831 57.67423, -59.43377 57…
#> 6 P6         2019 4      6205773.      2282. ((-59.70893 58.16354, -60.11445 58…
  1. We can produce an array of plots, as timeseries or as spatial plots
plot(sspm_model_fit, log = T, scales = 'free')
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 85 rows containing missing values or values outside the scale range
#> (`geom_point()`).

plot(sspm_model_fit, log = T, use_sf = TRUE)

plot(sspm_model_fit, biomass = "weight_per_km2_borealis",  scales = "free")
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 85 rows containing missing values or values outside the scale range
#> (`geom_point()`).

plot(sspm_model_fit, biomass = "weight_per_km2_borealis", use_sf = TRUE)

plot(sspm_model_fit, biomass = "weight_per_km2_borealis",
     next_ts = TRUE, aggregate = TRUE, scales = "free", 
     smoothed_biomass = TRUE, interval = T)
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 4 rows containing missing values or values outside the scale range
#> (`geom_point()`).
#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
#> -Inf
#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
#> -Inf
#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
#> -Inf
#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
#> -Inf
#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
#> -Inf
#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
#> -Inf
#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
#> -Inf
#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
#> -Inf