## Implementation of MPs to Control Effort

This section includes parameters describing the management agency’s ability to implement an MP that institutes an effort limit (TAE). For example, an MP may specify a certain TAE, but the managing agency may be unable to stop fishing at the exact moment that the effort limit is reached, resulting in underages or overages.

TAEFrac and TAESD allow the user to specify the actual effort employed as a fraction of the desired TAE in the form of a distribution that allows variability between simulations and years.

### TAEFrac

Mean fraction of recommended TAE that is actually taken. For each historical simulation a single value is drawn from a uniform distribution specified by the upper and lower bounds provided. This value is the mean TAE fraction obtained across all years of that simulation, and a yearly TAE frac is drawn from a log-normal distribution with the simulation mean and a coefficient of variation specified by the value of TAESD drawn for that simulation. If the value drawn is greater than 1 the amount of effort employed is greater than that recommended by the TAE, and if it is less than 1 the amount of effort employed is less than that recommended by the TAE. Positive real numbers.

### TAESD

Log-normal coefficient of variation in the fraction of recommended TAE that is actually taken. For each historical simulation a single value is drawn from a uniform distribution specified by the upper and lower bounds provided. This value is used, along with the TAEFrac drawn for that simulation, to create a log-normal distribution that yearly values speciying the actual amount of efort employed are drawn from. Positive real numbers.

### Custom Parameters

See Custom Imp Parameters for information on specifying simulation- and year-specific implementation error values.

### Interactive App

Choose upper and lower bounds for TAEFrac and TAESD. Based on these ranges, the parameter values for 5 simulations will show in the table. Click on any line of the table to view how those parameter values affect the implemented vs. desired TAE in a hypothetical situation where the MP modeled recommends a decline in TAE over time.

• Figure 1 shows the effects of implementation error in the recommended TAE over 50 projection years in a single simulation. The red line shows the TAE recommended by the MP, while the blue line shows the actual effort limit applied by the model. The location of the blue line relative to the red line is influenced by whether TAEFrac is greater or less than 1, and the variability of the blue line over time is influenced by the value of TAESD, with larger values resulting in greater variation between years.