get_index: Extract a relative biomass/abundance index, center of...

In pbs-assess/sdmTMB: Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB'

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

Description

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

Usage

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

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.
bias_correct	Should bias correction be implemented TMB::sdreport()?
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.
silent	Silent?
...	Passed to TMB::sdreport().
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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.fishres.2015.11.016")}

See Also

get_index_sims()

Examples

library(ggplot2)

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

# fit a spatiotemporal model:
m <- sdmTMB(
 data = pcod,
 formula = density ~ 0 + as.factor(year),
 time = "year", mesh = pcod_spde, 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)
ind
ggplot(ind, aes(year, est)) + geom_line() +
  geom_ribbon(aes(ymin = lwr, ymax = upr), alpha = 0.4) +
  ylim(0, NA)

# center of gravity:
cog <- get_cog(predictions, format = "wide")
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
ggplot(eao, aes(year, est)) + geom_line() +
  geom_ribbon(aes(ymin = lwr, ymax = upr), alpha = 0.4) +
  ylim(0, NA)

pbs-assess/sdmTMB documentation built on Dec. 20, 2024, 1:51 p.m.