Extract a relative biomass/abundance index, center of gravity, or effective area occupied

Usage

get_index(obj, bias_correct = TRUE, level = 0.95, area = 1, silent = TRUE, ...)

get_index_split(
  obj,
  newdata,
  bias_correct = FALSE,
  nsplit = 1,
  level = 0.95,
  area = 1,
  silent = FALSE,
  predict_args = list(),
  ...
)

get_cog(
  obj,
  bias_correct = FALSE,
  level = 0.95,
  format = c("long", "wide"),
  area = 1,
  silent = TRUE,
  ...
)

get_eao(obj, bias_correct = FALSE, level = 0.95, area = 1, silent = TRUE, ...)

Arguments

obj: Output from predict.sdmTMB() with return_tmb_object = TRUE. Alternatively, if sdmTMB() was called with do_index = TRUE or if using the helper function get_index_split(), an object from sdmTMB().
bias_correct: Should bias correction be implemented TMB::sdreport()? This is recommended to be TRUE for any final analyses, but one may wish to set this to FALSE for slightly faster calculations while experimenting with models.
level: The confidence level.
area: Grid cell area. A vector of length newdata from predict.sdmTMB() or a value of length 1 which will be repeated internally to match or a character value representing the column used for area weighting.
silent: Silent?
...: Passed to TMB::sdreport().
newdata: New data (e.g., a prediction grid by year) to pass to predict.sdmTMB() in the case of get_index_split().
nsplit: The number of splits to do the calculation in. For memory intensive operations (large grids and/or models), it can be helpful to do the prediction, area integration, and bias correction on subsets of time slices (e.g., years) instead of all at once. If nsplit > 1, this will usually be slower but with reduced memory use.
predict_args: A list of arguments to pass to predict.sdmTMB() in the case of get_index_split().
format: Long or wide.

Value

For get_index(): A data frame with a columns for time, estimate, lower and upper confidence intervals, log estimate, and standard error of the log estimate.

For get_cog(): A data frame with a columns for time, estimate (center of gravity in x and y coordinates), lower and upper confidence intervals, and standard error of center of gravity coordinates.

For get_eao(): A data frame with a columns for time, estimate (effective area occupied; EAO), lower and upper confidence intervals, log EAO, and standard error of the log EAO estimates.

References

Geostatistical model-based indices of abundance (along with many newer papers):

Shelton, A.O., Thorson, J.T., Ward, E.J., and Feist, B.E. 2014. Spatial semiparametric models improve estimates of species abundance and distribution. Canadian Journal of Fisheries and Aquatic Sciences 71(11): 1655–1666. doi:10.1139/cjfas-2013-0508

Thorson, J.T., Shelton, A.O., Ward, E.J., and Skaug, H.J. 2015. Geostatistical delta-generalized linear mixed models improve precision for estimated abundance indices for West Coast groundfishes. ICES J. Mar. Sci. 72(5): 1297–1310. doi:10.1093/icesjms/fsu243

Geostatistical model-based centre of gravity:

Thorson, J.T., Pinsky, M.L., and Ward, E.J. 2016. Model-based inference for estimating shifts in species distribution, area occupied and centre of gravity. Methods Ecol Evol 7(8): 990–1002. doi:10.1111/2041-210X.12567

Geostatistical model-based effective area occupied:

Thorson, J.T., Rindorf, A., Gao, J., Hanselman, D.H., and Winker, H. 2016. Density-dependent changes in effective area occupied for sea-bottom-associated marine fishes. Proceedings of the Royal Society B: Biological Sciences 283(1840): 20161853. doi:10.1098/rspb.2016.1853

Bias correction:

Thorson, J.T., and Kristensen, K. 2016. Implementing a generic method for bias correction in statistical models using random effects, with spatial and population dynamics examples. Fisheries Research 175: 66–74. doi:10.1016/j.fishres.2015.11.016

Examples

# \donttest{
library(ggplot2)

# use a small number of knots for this example to make it fast:
mesh <- make_mesh(pcod, c("X", "Y"), n_knots = 60)

# fit a spatiotemporal model:
m <- sdmTMB(
 data = pcod,
 formula = density ~ 0 + as.factor(year),
 time = "year", mesh = mesh, family = tweedie(link = "log")
)

# prepare a prediction grid:
nd <- replicate_df(qcs_grid, "year", unique(pcod$year))

# Note `return_tmb_object = TRUE` and the prediction grid:
predictions <- predict(m, newdata = nd, return_tmb_object = TRUE)

# biomass index:
ind <- get_index(predictions, bias_correct = TRUE)
ind
#>   year      est       lwr      upr  log_est        se  type
#> 1 2003 277556.6 198537.77 388025.4 12.53378 0.1709448 index
#> 2 2004 399682.6 311373.58 513036.9 12.89843 0.1273887 index
#> 3 2005 430225.5 327502.38 565168.4 12.97206 0.1391935 index
#> 4 2007 119509.7  86514.07 165089.6 11.69115 0.1648452 index
#> 5 2009 210258.6 151807.71 291215.1 12.25609 0.1661887 index
#> 6 2011 339420.7 262199.67 439384.3 12.73500 0.1317035 index
#> 7 2013 351455.2 258755.05 477365.7 12.76984 0.1562276 index
#> 8 2015 383241.4 285819.07 513870.4 12.85642 0.1496487 index
#> 9 2017 192367.9 136764.93 270576.6 12.16716 0.1740572 index
ggplot(ind, aes(year, est)) + geom_line() +
  geom_ribbon(aes(ymin = lwr, ymax = upr), alpha = 0.4) +
  ylim(0, NA)


# do that in 2 chunks
# only necessary for very large grids to save memory
# will be slower but save memory
# note the first argument is the model fit object:
ind <- get_index_split(m, newdata = nd, nsplit = 2, bias_correct = TRUE)
#> Calculating index in 2 chunks ■■■■■■■■■■■■■■■■                  50% | ETA:  0s
#> Calculating index in 2 chunks ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:  0s

# center of gravity:
cog <- get_cog(predictions, format = "wide")
#> Bias correction is turned off.
#> It is recommended to turn this on for final inference.
cog
#>   year    est_x    lwr_x    upr_x     se_x    est_y    lwr_y    upr_y      se_y
#> 1 2003 463.5260 446.4142 480.6378 8.730670 5757.861 5739.855 5775.867  9.187109
#> 2 2004 476.7402 466.4506 487.0297 5.249871 5732.504 5720.879 5744.129  5.931164
#> 3 2005 470.6887 457.7494 483.6280 6.601796 5763.031 5750.153 5775.910  6.570990
#> 4 2007 480.8948 464.5560 497.2336 8.336280 5738.231 5716.843 5759.620 10.912733
#> 5 2009 477.2029 457.9185 496.4872 9.839144 5734.029 5713.361 5754.697 10.545101
#> 6 2011 470.5112 457.6004 483.4221 6.587284 5747.104 5733.628 5760.579  6.875389
#> 7 2013 471.9877 455.6078 488.3676 8.357252 5747.645 5728.969 5766.320  9.528488
#> 8 2015 463.0289 449.6443 476.4136 6.829028 5753.970 5736.844 5771.096  8.737855
#> 9 2017 470.5219 455.4189 485.6249 7.705734 5755.973 5739.644 5772.301  8.331045
#>   type
#> 1  cog
#> 2  cog
#> 3  cog
#> 4  cog
#> 5  cog
#> 6  cog
#> 7  cog
#> 8  cog
#> 9  cog
ggplot(cog, aes(est_x, est_y, colour = year)) +
  geom_point() +
  geom_linerange(aes(xmin = lwr_x, xmax = upr_x)) +
  geom_linerange(aes(ymin = lwr_y, ymax = upr_y)) +
  scale_colour_viridis_c()


# effective area occupied:
eao <- get_eao(predictions)
eao
#>   year      est       lwr      upr  log_est        se type
#> 1 2003 2408.074 1407.0230 4121.340 7.786583 0.2741638  eoa
#> 2 2004 1807.151  849.4148 3844.759 7.499507 0.3851904  eoa
#> 3 2005 1660.807  943.6930 2922.859 7.415059 0.2884024  eoa
#> 4 2007 1854.122  793.7681 4330.947 7.525166 0.4328524  eoa
#> 5 2009 3227.459 2437.0094 4274.293 8.079450 0.1433310  eoa
#> 6 2011 3056.811 2192.3985 4262.042 8.025128 0.1695827  eoa
#> 7 2013 2887.839 1895.2602 4400.246 7.968264 0.2148775  eoa
#> 8 2015 1739.803  801.6399 3775.904 7.461527 0.3953480  eoa
#> 9 2017 1975.478  703.8844 5544.253 7.588566 0.5265156  eoa
ggplot(eao, aes(year, est)) + geom_line() +
  geom_ribbon(aes(ymin = lwr, ymax = upr), alpha = 0.4) +
  ylim(0, NA)

# }