Estimate productivity curve

`est.productivity()` estimates a productivity relationship for the IAMSE operating model, typically linking biomass (or depletion) to surplus production or long-term yield. The operating model is simulated over a range of fishing mortalities up to `fmax` for `ny` years, and summary quantities are extracted to characterise the stock's productivity.

Usage

est.productivity(
  dat,
  set = NULL,
  ny = 100,
  fmax = 10,
  nf = 1000,
  tsSplit = 8,
  plot = TRUE
)

Arguments

dat

Data object as returned by [check.dat()], containing life-history and stock information for one or more stocks.

set

Optional settings list as returned by [check.set()]. If `NULL` (default), internal defaults are used where possible. When supplied, `set` controls aspects such as the number of replicates, noise structure, and other operating-model options.

ny

Integer giving the number of years to simulate when estimating the productivity relationship. Larger values allow the system to approach equilibrium or typical dynamic behaviour under each fishing level. Default is `100`.

fmax

Numeric value giving the maximum fishing mortality (or F-multiplier) considered when constructing the productivity curve. Default is `10`.

nf

Integer giving the number of fishing-mortality levels (or points along the F gradient) to consider between 0 and `fmax`. Default is `1e3` (1000 points), providing a relatively fine grid.

tsSplit

Integer controlling how the simulated time series are split into segments when summarising productivity (for example, discarding an initial burn-in and using the remaining periods for analysis). The precise interpretation depends on the internal implementation. Default is `8`.

plot

Logical; if `TRUE` (default), produce diagnostic plots of the productivity relationship (e.g. yield or surplus production vs. biomass or F). If `FALSE`, no plots are produced.

Value

An object (typically a list or data frame) containing productivity summaries as a function of fishing mortality and/or biomass for each stock. The exact structure depends on the internal implementation. The object is usually returned invisibly if the function is called primarily for its side effects (e.g. plotting).

Details

The function can be used to construct productivity curves or clouds that relate state variables (e.g. biomass, depletion) to production, which can then be analysed or plotted for diagnostics and comparison across stocks.