3.1 Overview of Red Snapper Management

The Red Snapper fishery has separate management for the recreational and commercial sectors.

The regulations for the recreational fishery are set each year. If NOAA Fisheries determines that a season is allowed, the recreational season for the Red Snapper fishery opens the second Friday in July. The recreational sector season length varies based on previous years landings. The recreational season in 2022 and 2023 was 2 days. The season was then closed until the following July. The recreational fishery is managed with a bag limit of one fish per day per angler.

The commercial fishery has a 75 lb gutted weight trip limit, and a commercial annual catch limit (ACL; 124,815 lbs whole weight in 2023). The commercial fishery opens the second Monday in July. The commercial fishery remains open until the ACL is met. In recent years (2022 and 2023), the commercial fishery has closed in August is closed until the following year.

The Red Snapper fishery currently does not have a size limit for the recreational or commercial sectors.

The annual catch limit for the entire stock is split between the recreational (71.93%) and the commercial (28.07%) fleets.

More information on the current management regulations for the Red Snapper fishery is available here.

3.2 Overview of Gag Grouper Management

The Gag Grouper fishery has separate management for the recreational and commercial sectors.

The recreational fishery is closed to retention from the beginning of of January 1 through April 30 each year. Recreational fishers have a bag limit of 1 fish per person per day.

The commercial fishery is closed to retention from the beginning of January 1 through April 30 each year. Commercial fishers have a trip limit of 1000 lbs (gutted weight).

A minimum legal length of 24 inch (total length) in in place for both commercial and recreational fishers.

The annual catch limit for the entire stock is split between the recreational (49%) and the commercial (51%) fleets.

More information on the current management regulations for the Gag Grouper fishery is available here.

4.1 Process to Create Seasonal Fleet Structure

The recent assessments of Red Snapper and Gag Grouper were used to generate the operating models for these fisheries. The assessments were conducted with the Beaufort Assessment Model (BAM). The assessments modelled the landings and discards for each fleet as two separate fleets: Landings and Discards. The Discards fleet includes both discards that occured during the fishing season, and discards that occur when the fishery was closed to retention (closed season).

To be able to evaluate management actions such as different season lengths, or minimum size limits, the operating model requires a different fleet structure to that used in the assessment.

The fleet structure for the operating model requires the removals to be split into On-Season (landings and discards that occur during the mini-seasons when the fishery is open) and Off-Season (discards of all catch when the fishery is closed).

The general process for re-structuring the fleets is described in the steps below. The next section describes the implementation of this process for the Base Case models for the Red Snapper and Gag Grouper.

4.2 Red Snapper

The Red Snapper OM was based on the most recent stock assessment (SEDAR 73). This assessment included six fleets:

1. Commercial Handline (cHL)
2. Commercial Handline - Discards (cHL.D)
3. Recreational Headboat (rHB)
4. Recreational Headboat - Discards (rHB.D)
5. General Recreational (rGN).
6. General Recreational - Discards (rGN.D).

Following the process described above, the operating model was developed with a different fleet structure.:

1. Commercial Handline - On-Season
2. Commercial Handline - Off-Season
3. Recreational Headboat - On-Season
4. Recreational Headboat - Off-Season
5. General Recreational - On-Season
6. General Recreational - Off-Season

The On- and Off-Season fleet structure was generated by calculating the proportion of total removals for each fleet that were taken during the open and closed seasons.

4.2.3 General Recreational

The relative pattern in fishing mortality for the On- and Off-Season components of the General Recreational fleet was assumed to be the same as the Recreational Headboat fleet (Figure 4.1).

Further analyses of data from the Marine Recreational Information Program (MRIP) could be used to improve the estimates of total removals that occur during the open and closed seasons for the General Recreational fleet.

Figure 4.1: The proportion of overall fishing mortality for the On-Season and Off-Season fleets for the Red Snapper fishery.

This results in an operating model with 6 fleets, with estimated landings and discards that occur during both the open and closed seasons for each fleet (Figure 4.2). The selectivity pattern of the On-Season and Off-Season fleets was assumed to be the same.

Figure 4.2: The landings and discards for the On-Season and Off-Season fleets for the Red Snapper fishery.

4.3 Gag Grouper

The gag OM was based on the most recent stock assessment (SEDAR 71). This assessment included seven fleets:

1. Commercial Handline (cHL)
2. Commercial Handline - Discards (cHL.D)
3. Recreational Headboat (rHB)
4. Recreational Headboat - Discards (rHB.D)
5. General Recreational (rGN).
6. General Recreational - Discards (rGN.D).
7. Commercial Dive (cDV)

Similar to the Red Snapper model described above, the fleet structure of the operating model was modified to include the seasonal aspect of the fishery:

1. Commercial Handline - On-Season
2. Commercial Handline - Off-Season
3. Recreational Headboat - On-Season
4. Recreational Headboat - Off-Season
5. General Recreational - On-Season
6. General Recreational - Off-Season
7. Commercial Dive - On-Season

The Commercial Dive fleet does not have discards. As it is a targeted fishery, it does not operate during the closed seasons and therefore is only modeled as an On-Season fleet

The SEDAR 71 model was converted to the OM with the On- and Off-Season structure following the same approach used for the Red Snapper described above.

The landings- and discard-at-age from the SEDAR 73 model for the three fleets (cHL, rHB, and rGN) were combined into total removals. This results in a model that describe the total removals-at-age by year for the three fleets. The On- and Off-Season fleet structure was then generated by calculating the proportion of total removals that were taken during the open and closed seasons.

4.3.3 General Recreational

Following the approach used for Red Snapper, the relative pattern in fishing mortality for the On- and Off-Season components of the General Recreational fleet was assumed to be the same as the Recreational Headboat fleet (Figure 4.3).

Figure 4.3: The proportion of overall fishing mortality for the On-Season and Off-Season fleets for the Gag Grouper fishery.

The operating model was constructed by allocating, for each fleet, the total fishing mortality (removals) in each year between On- and Off-Season components of the fleet according to the pattern in relative F described above.

As it is a targeted fishery, the Commercial Dive fleet only operates in the On-Season. This results in an operating model with 7 fleets, with estimated landings and discards that occur during both the open and closed seasons for each fleet (Figure 4.4).

Figure 4.4: The landings and discards for the On-Season and Off-Season fleets for the Gag Grouper fishery.

4.4 Generating Multi-Species Operating Model

A multi-species operating model was generated by combining the Red Snapper and Gag Grouper OMs together. The fishery dynamics for each stock within the multi-species operating model must have the same structure: i.e., same number of historical years, and the same fleet structure.

The initial year of the Gag Grouper assessment is 1962, 12 years later than the initial year for Red Snapper (1950). To match the initial year of the Red Snapper OM, the Gag model was extended back for 12 years, with no fishing mortality for any of the fleets.

To maintain the same fleet structure, the Commercial Dive fleet was added to the Red Snapper model, but fishing mortality set to 0 for all years.

5.1 Selectivity and Retention Schedules

Figures 5.1 and 5.2 show the selectivity- and retention-at-age curves in the last historical year (2019) for the On- and Off-Season fleets for the Red Snapper and Gag Grouper respectively. The selectivity pattern of the On-Season and Off-Season fleets is assumed to be the same.

The selectivity and retention curves remain the same in the projection period unless changed by a management procedure. Retention curves with a maximum value less than one indicate a general level of discarding.

Figure 5.1: The selectivity- (solid line) and retention-at-age (dashed line) curves for the Red Snapper in the multi-species operating model. The plot shows the curves from the last historical year (2019).

Figure 5.2: The selectivity- (solid line) and retention-at-age (dashed line) curves for the Gag Grouper in the multi-species operating model. The plot shows the curves from the last historical year (2019).

5.2 Removals, Landings, and Discards

Figures 5.3, 5.4, and 5.5 show historical overall removals, and landings and discards by fleet respectively for the Red Snapper and Gag Grouper multi-species Base Case operating model.

Figure 5.3: The overall removals (1,000 t) from the historical period of the Red Snapper and Gag Grouper multi-species operating model.

Figure 5.4: The landings (1,000 t) by fleet from the historical period of the Red Snapper and Gag Grouper multi-species operating model. Note the different scale on the y-axes.

Figure 5.5: The discards (1,000 t) by fleet from the historical period of the Red Snapper and Gag Grouper multi-species operating model. Note the different scale on the y-axes.

5.3 Calculation of Reference Points

The reference points are calculated following the same method described in the SEDAR reports. This section describes the calculation of reference points for the Red Snapper and Gag Grouper. The calculation for other species added to the MSE analysis will follow a similar process.

5.3.1 Red Snapper

The maximum fishing mortality threshold (MFMT) for Red Snapper is defined by the SAMFC as \(F_{30\%}\), and the minimum stock size threshold (MSST) as \(75\%\text{SSB}_{F30\%}\). Overfishing is defined as F > MFMT and overfished as SSB < MSST, with \(\text{SSB}_{F30\%}\) defined as the rebuilding target.

The relationship between fishing mortality and the spawning potential ratio (SPR) was calculated using the selectivity curve representing total removals in the terminal historical year (2019; Figure 5.6). This resulted in a \(F_{30\%}\) = 0.21, and MSST of 510417 (eggs 1E8; Figure 5.6).

It should be noted that the calculation of \(F_{30\%}\) and associated metrics is a function of the overall selectivity curve for the fishery, and the values will be different if the overall selectivity pattern in the future (for example, introduction of size limits, or changes in relative exploitation levels of the different fleets).

Figure 5.6: The relationship between a) fishing mortality (F) and equilibrium spawning biomass (SB) and b) spawning potential ratio (SPR) for Red Snapper. The maximum fishing mortality threshold (MFMT) is indicated with the vertical dashed line. The equilbrium SB and SPR corresponding with MFMT are indicated with horizontal dashed lines. The horizontal dotted line indicates the minimum stock size threshold (MSST), defined as 75% of SB_MFMT.

5.3.2 Gag Grouper

The maximum fishing mortality threshold (MFMT) for Gag Grouper is defined by the SAMFC as \(F_{\text{MSY}}\), and the minimum stock size threshold (MSST) as \(75\%\text{SSB}_{\text{MSY}}\). Overfishing is defined as F > MFMT and overfished as SSB < MSST.

\(F_{\text{MSY}}\) was calculated using the selectivity curve representing total removals in the terminal historical year (2019; Figure 5.7). This resulted in a \(F_{\text{MSY}}\) = 0.42, and MSST of 1330 (Figure 5.6).

It should be noted that the calculation of \(F_{\text{MSY}}\) and associated metrics is a function of the overall selectivity curve for the fishery, and the values will be different if the overall selectivity pattern in the future (for example, introduction of size limits, or changes in relative exploitation levels of the different fleets).

Figure 5.7: The relationship between a) fishing mortality (F) and equilibrium removals (metric tons) and b) spawning stock biomass (mt) for Gag Grouper. The maximum fishing mortality threshold (MFMT) is indicated with the vertical dashed line. The equilbrium spawning biomass corresponding with MFMT is indicated with the horizontal dashed line. The horizontal dotted line indicates the minimum stock size threshold (MSST), defined as 75% of SB_MFMT.

5.4 Biomass and Reference Points

Figures 5.8 and 5.9 show the predicted spawning biomass and the corresponding reference points for the Red Snapper and Gag Grouper stocks in the Base Case multi-stock operating model. Both stocks are below the MSST reference point in the terminal year (2019). Red Snapper appears to be rebuilding towards MSST, while the trend for Gag Grouper is a continuing decline.

Figure 5.8: The spawning stock (eggs) for Red Snapper from the Base Case OM. The horizontal lines indicate the reference points (see Reference Point section above).

Figure 5.9: The spawning stock (1000 t) for Gag Grouper from the Base Case OM. The horizontal lines indicate the reference points (see Reference Point section above).

6.1 Definition of Spatial Structure

The spatial structure of the MSE has been defined as 6 areas, including 3 geographic regions (Figure 6.1) and a Nearshore (<100ft) and Offshore (>100ft) component for each region (black line in Figure 6.1).

The relative size of each area was determined by first calculating the surface area of the entire area included in the MSE (all colored regions in Figure 6.1), and then calculating the proportion of the total area that was within the Nearshore and Offshore region of each geographic area (Table 6.1).

Note: this calcutation assumes that suitable potential habitat is equally distributed across the entire region.

Figure 6.1: The six spatial areas defined in the MSE, with three geographic regions (colors) and a division of each in Nearshore (<100ft) and Offshore (>100ft) (black line).

6.2 Latitudinal Distribution of Unfished Abundance

Information on the unfished distribution of Red Snapper and Gag is not available. Instead, the assumed unfished distribution was derived from various sources of information, including the South-East Reef Fish Survey (SERFS) (Bubley et al., 2023) and studies reported in the literature.

It may also be possible to use other data, such as CPUE information from MRIP and headboat surveys or a deepwater longline survey, to calculate improved estimate of the relative abundance of Red Snapper and Gag Grouper across the three regions.

Red Snapper

Data from SERFS (Bubley et al., 2023) was used to calculate the average relative abundance from 1990 – 2022 of Red Snapper and Gag in each of the three geographic areas included in the MSE.

This analysis reports that the highest abundance of Red Snapper is in the Georgia - Cape Canaveral region, and the abundance in North and South Carolina is about 1/4 of that in the Georgia - Cape Canaveral region. The data from the survey suggests that the relative abundance in the Cape Canaveral - Florida region is about 7% of that in the Georgia - Cape Canaveral region.

However, the SERFS survey did not include the entire Florida region included in the SAFMC management area, but stopped at the St. Lucie Inlet, FL. Consequently, the SERFS data may significantly under-estimate the relative average abundance of Red Snapper in the Florida region included in the MSE.

The most recent assessment of Red Snapper from the Gulf of Mexico (SEDAR 52) estimates an unfished biomass in the east of the GoM about 6 times higher than the unfished biomass estimated for Red Snapper in the SAMFC region (SEDAR 73). This suggests that the abundance of Red Snapper is considerably higher in the Gulf of Mexico, and indicates a pattern of decreasing abundance of Red Snapper with increasing latitude, a result supported by the SERFS survey (Bubley et al., 2023).

Based on this information, the Base Case OM currently assumes that the unfished abundance of Red Snapper in the Cape Canaveral - Florida region is twice as high as that in the Georgia - Cape Canaveral region (Table 6.2).

Alternative operating models can be developed to evaluate the sensitivity of the results to this assumption.

Gag

The analysis of the SERFS (Bubley et al., 2023) data reveals that the abundance of Gag in the North and South Carolina region is about 2.5 times higher than that in the Georgia - Cape Canaveral region, and the lowest abundance south of Cape Canaveral. This suggests a pattern of increasing abundance of Gag with increasing latitude. This is supported by the study of Grüss et al. (2017), who report that Gag are most common in the north-east region of the Gulf of Mexico compared to regions further south.

Given this information, and the fact that the SERFS survey likely under-estimates the abundance of Gag in the Cape Canaveral - Florida region, the Base Case OM assumes that the unfished abundance in the Cape Canaveral - Florida region is half of that in the Georgia - Cape Canaveral region (Table 6.2).

Alternative operating models can be developed to evaluate the sensitivity of the results to this assumption.

6.3 Age-Specific Distribution by Depth Zone

The relative geographic distribution of each species is combined with information about the relative distribution by depth (Nearshore and Offshore) of each age-class to calculate the overall distribution across the 6 areas (Figure 6.1).

Red Snapper

Mitchell et al. (2014) used data from two fishery-independent surveys in the southeastern US Atlantic Ocean to calculate the depth distribution of Red Snapper. They found that most recruitment occurred in the shallow nearshore waters, but after about 3 years (> 50 cm FL) there was no detectable difference in the depth-distribution of Red Snapper by age or length.

Following the results reported in Mitchell et al. (2014), the Base Case OM assumes that almost all age-0 fish (recruits) are located in the Nearshore region, with a decline in the fraction in the Nearshore region from 95% at age-0 to 50% at age-4. All fish older than age-4 are assumed to be equally distributed between Nearshore and Offshore regions (Figure 6.2).

Gag

The age-specific depth distribution of Gag was based on the analysis of Carruthers et al. (2015), who used a spatial populations dynamics model to estimate the fraction of unfished individuals by age-class in the nearshore and offshore regions of the Gulf of Mexico. They found that juvenile Gag are most likely to be in the nearshore region, but move offshore as they increase in age (Fig 8 in Carruthers et al. (2015)).

Based on this analysis, the Base Case OM assumed the age-specific unfished distribution of Gag in the SAMFC management area is similar to that in the Gulf of Mexico (Figure 6.2).

Figure 6.2: The assumed unfished distribution by age, depth, and geographic region for the Red Snapper and Gag Base Case operating model.

6.4 Probability of Movement

The final piece of information required for adding spatial structure to the MSE is a prior on the the probability of fish remaining in an area in any given year, and for those that move, the relative probability of moving to the each of the other areas.

Given the relatively high spatial structure of the Red Snapper and Gag, particularly the latitudinal gradient in abundance, and the reported site fidelity of Red Snapper (Topping & Szedlmayer, 2011), the Base Case OM assumed a prior probability of 0.95 that fish remain within an area in a given year.

Of those that move, the OM assumed that the probability of moving to a geographically distant area (e.g., Area 1 to Area 5, or vice versa) was 100 times less than the probability of moving to a closer area (e.g., Area 1 to Area 2).

6.5 Calculating Movement Matrix

A movement matrix is calculated within the MSE framework using the specified information on the assumed age-specific unfished distribution by area and depth, and the probability of moving to any given area. This model uses the specified relative probability of moving to each area, and optimizes the probability of remaining in a given area to solve for the specified relative distribution by area. See ?makemov2 in the MSEtool R package for more information on this method.

7.1 Recruitment Process Error

The variance-covariance matrix was estimated from the historical log recruitment deviations of each species in the analysis. A truncated multivariate normal distribution, truncated at 2 standard deviations, was then used to generate correlated random log recruitment deviations for the projection period, with the standard deviation and lag-1 auto-correlation factor reported by the stock assessments (Figure 7.1).

Figure 7.1: Distribution of the log recruitment deviations for a) Red Snapper and b) Gag Grouper, with a fitted normal distribution shown in blue. Panels c) and d) show the deviations in normal space, and include the log-normal distribution used to generate recruitment process error for the projection period.

For the Base Case OM, the standard deviation of the log recruitment deviations for Red Snapper was 0.81 with an auto-correlation factor of 0.51. Figure 7.2 shows an example of the recruitment deviations for 9 simulations for Red Snapper.

Figure 7.2: An example of the recruitment deviations from 9 simulations from the Red Snapper stock in the Base Case OM. The black lines indicates the recruitment deviations during the historical period (identical across all simulations). The blue line indicates the recruitment deviations in the projection period, which were generated from a distribution with the statistical properties (standard deviation and auto-correlation) reported by SEDAR 73.

For the Base Case OM, the standard deviation of the log recruitment deviations for Gag Grouper was 0.48 with an auto-correlation factor of 0.62. Figure 7.3 shows an example of the recruitment deviations for 9 simulations for Gag Grouper.

Figure 7.3: An example of the recruitment deviations from 9 simulations from the Gag Grouper stock in the Base Case OM. The black lines indicates the recruitment deviations during the historical period (identical across all simulations). The blue line indicates the recruitment deviations in the projection period, which were generated from a distribution with the statistical properties (standard deviation and auto-correlation) reported by SEDAR 71

7.2 Life-History

In the Base Case OM, the life-history parameters in the projections are assumed to be stationary; i.e., remain at the same values as the terminal historical year.

7.3 Selectivity

The selectivity pattern of each fleet in the projection period is assumed to remain unchanged from the terminal historical year, unless the selectivity or retention curve is modified by a management procedure.

7.4 Implementation Error

The Base Case OM does not assume any implementation error.

Any error in the implementation of management regulations set by the Management Procedures is set within the MPs.

7.5 Observation Error

Currently the Base Case OM assumes no observation error on the simulated data. This assumption will be revised when management procedures that use specific data to set management advice are developed.

8.1 OM_02: Lower M

The natural mortality rate (M) is reduced from the Base Case value to the lower value considered in the assessment sensitivity tests.

Red Snapper

M for mature adults is reduced from 0.107 (Base Case) to 0.07.

Gag

M for mature adults is reduced from 0.172 (Base Case) to 0.10.

8.2 OM_03: Higher M

The natural mortality rate (M) is increased from the Base Case value to the higher value considered in the assessment sensitivity tests.

Red Snapper

M for mature adults is increased from 0.107 (Base Case) to 0.15

Gag

M for mature adults is reduced from 0.172 (Base Case) to 0.25.

8.3 OM_04: Reduced Rec Landings

The Landings and Discards from the General Recreational Fleet are reduced by 40% from the reported values used in the Base Case OM. The reduction is applied to all historical years.

8.4 OM_05: Increased PE

Productivity changes in the projection years through an increase in recruitment variability.

The historical fishery reconstruction is the same as the Base Case OM, but the variability in recruitment is increased in the projection years by a factor of 1.5.

A comparison of the recruitment deviations for two simulations (black and dashed lines) for the Base Case (OM_01) and OM_05 where there is increased variability in recruitment in the projection years

8.5 OM_06: Increased Rec Effort

The historical fishery reconstruction is the same as the Base Case OM, but in the projection years the fishing effort from the General Recreational fleet is assumed to increase by 2% each year.

8.6 OM_07: Lower FL Biomass

Spatial Alternative OM not developed yet. Will be finalized once spatial management strategies are determined.

8.7 OM_08: Higher FL Biomass

Spatial Alternative OM not developed yet. Will be finalized once spatial management strategies are determined.

9.1 Fleet-Specific Fishing Mortality or Removals

A generic function Fleet_MMP is used to set the fishing mortality or catch (total removals) for each fleet. Fleet_MMP is used inside wrapper functions that define the specific management recommendations for each simulation and year (see below for examples).

The Fleet_MMP function has two standard arguments for MMP (multi-stock/fleet management procedures) x: simulation number, and DataList: a list of Data objects, and a third argument Fleet_Management.

Fleet_Management is a data frame that specifies the management in terms of catch, fishing mortality (F), and/or a minimum legal length (MLL), for each stock and fleet:

##                   Fleets                             Fleet Category Season
## 1    Commercial Handline    Commercial Handline: On-Season     Comm     On
## 2  Recreational Headboat  Recreational Headboat: On-Season      Rec     On
## 3   General Recreational   General Recreational: On-Season      Rec     On
## 4    Commercial Handline   Commercial Handline: Off-Season     Comm    Off
## 5  Recreational Headboat Recreational Headboat: Off-Season      Rec    Off
## 6   General Recreational  General Recreational: Off-Season      Rec    Off
## 7        Commercial Dive                   Commercial Dive     Comm     On
## 8    Commercial Handline    Commercial Handline: On-Season     Comm     On
## 9  Recreational Headboat  Recreational Headboat: On-Season      Rec     On
## 10  General Recreational   General Recreational: On-Season      Rec     On
## 11   Commercial Handline   Commercial Handline: Off-Season     Comm    Off
## 12 Recreational Headboat Recreational Headboat: Off-Season      Rec    Off
## 13  General Recreational  General Recreational: Off-Season      Rec    Off
## 14       Commercial Dive                   Commercial Dive     Comm     On
##          Stock Catch  F MLL
## 1  Red Snapper    NA NA  NA
## 2  Red Snapper    NA NA  NA
## 3  Red Snapper    NA NA  NA
## 4  Red Snapper    NA NA  NA
## 5  Red Snapper    NA NA  NA
## 6  Red Snapper    NA NA  NA
## 7  Red Snapper    NA NA  NA
## 8  Gag Grouper    NA NA  NA
## 9  Gag Grouper    NA NA  NA
## 10 Gag Grouper    NA NA  NA
## 11 Gag Grouper    NA NA  NA
## 12 Gag Grouper    NA NA  NA
## 13 Gag Grouper    NA NA  NA
## 14 Gag Grouper    NA NA  NA

Either F or Catch must be set for each stock-fleet (but not both for same stock-fleet). The minimum legal length (mm) is assumed to be implemented as a knife-edge retention curve, and only applies to the On-Season fleets. If MLL is NA, the retention curve is unchanged.

Note that the specified Catch refers to the total removals (landings + dead discards).

The following sub-sections include some simple examples of management procedures that set management in terms of F, catch, and/or a minimum legal length for each stock and fleet.

StatusQuo <- function(x, DataList, ...) {

  stocks <- unique(Fleet_Management$Stock)
  fleets <- unique(Fleet_Management$Fleet)
  nstocks <- length(stocks)
  nfleets <- length(fleets)

  # copy the internal `Fleet_Management` object
  this_Fleet_Management <- Fleet_Management

  # loop over stocks and fleets
  for (s in 1:nstocks) {
    for (f in 1:nfleets) {
      # calculate mean F from 3 last historical years
      meanF <- mean(DataList[[s]][[f]]@Misc$FleetPars$Fishing_Mortality[x,68:70])
      # populate the `F` value in `this_Fleet_Management` object
      this_Fleet_Management <- this_Fleet_Management %>%
        dplyr::mutate(F=replace(F, Stock==stocks[s] &Fleet==fleets[f], meanF))
    }
  }
  # call internal `Fleet_MMP` function with `this_Fleet_Management` object
  Fleet_MMP(x, DataList, Fleet_Management=this_Fleet_Management)
}
# define as class `MMP`
class(StatusQuo) <- 'MMP'

StatusQuo_MLL <- function(x, DataList, ...) {

  stocks <- unique(Fleet_Management$Stock)
  fleets <- unique(Fleet_Management$Fleet)
  nstocks <- length(stocks)
  nfleets <- length(fleets)

  # copy the internal `Fleet_Management` object
  this_Fleet_Management <- Fleet_Management

  # loop over stocks and fleets
  for (s in 1:nstocks) {
    for (f in 1:nfleets) {
      # calculate mean F from 3 last historical years
      meanF <- mean(DataList[[s]][[f]]@Misc$FleetPars$Fishing_Mortality[x,68:70])
      # populate the `F` value in `this_Fleet_Management` object
      this_Fleet_Management <- this_Fleet_Management %>%
        dplyr::mutate(F=replace(F, Stock==stocks[s] &Fleet==fleets[f], meanF))
    }
  }

  # Set MLL
  MLL_RS <- inch2mm(20) # 20 inch MLL
  this_Fleet_Management <- this_Fleet_Management %>%
    dplyr::mutate(MLL=replace(MLL, Stock=='Red Snapper', MLL_RS))

  MLL_GG <- inch2mm(25) # 25 inch MLL
  this_Fleet_Management <- this_Fleet_Management %>%
    dplyr::mutate(MLL=replace(MLL, Stock=='Gag Grouper', MLL_GG))

  # call internal `Fleet_MMP` function with `this_Fleet_Management` object
  Fleet_MMP(x, DataList, this_Fleet_Management)
}
# define as class `MMP`
class(StatusQuo_MLL) <- 'MMP'

Ftarget <- function(x, DataList, ...) {

  MFMT <- data.frame(Stock=c('Red Snapper', 'Gag Grouper'),
                     MFMT=c(0.21, 0.42))

  stocks <- unique(Fleet_Management$Stock)
  fleets <- unique(Fleet_Management$Fleet)
  nstocks <- length(stocks)
  nfleets <- length(fleets)

  # copy the internal `Fleet_Management` object
  this_Fleet_Management <- Fleet_Management

  # loop over stocks and fleets
  for (s in 1:nstocks) {
    for (f in 1:nfleets) {
      # calculate mean F from 3 last historical years
      meanF <- mean(DataList[[s]][[f]]@Misc$FleetPars$Fishing_Mortality[x,68:70])

      # populate the `F` value in `this_Fleet_Management` object
      this_Fleet_Management <- this_Fleet_Management %>%
        dplyr::mutate(F=replace(F, Stock==stocks[s] &Fleet==fleets[f], meanF))
    }
  }

  # Calculate relative F for each fleet (by Stock)
  this_Fleet_Management <- this_Fleet_Management %>% group_by(Stock) %>% mutate(Frat=F/sum(F))


  # Set overall F to MFMT for each stock
  this_Fleet_Management <- left_join(this_Fleet_Management, MFMT, by='Stock')
  this_Fleet_Management <- this_Fleet_Management %>% mutate(F=MFMT*Frat)


  # call internal `Fleet_MMP` function with `this_Fleet_Management` object
  Fleet_MMP(x, DataList, this_Fleet_Management)
}
# define as class `MMP`
class(Ftarget) <- 'MMP'

fixedC_mean3 <- function(x, DataList, ...) {
  
  stocks <- unique(Fleet_Management$Stock)
  fleets <- unique(Fleet_Management$Fleet)
  nstocks <- length(stocks)
  nfleets <- length(fleets)
  
  this_Fleet_Management <- Fleet_Management
  nyears <- ncol(DataList[[1]][[1]]@Misc$FleetPars$Find)
  
  for (s in 1:nstocks) {
    for (f in 1:nfleets) {
      # mean last 3 years historical removals
      meanC <- mean(apply(DataList[[s]][[f]]@Misc$FleetPars$CB[x,,(nyears-2):nyears,], 2, sum))
      
      # populate the `Catch` value in `this_Fleet_Management` object
      this_Fleet_Management <- this_Fleet_Management %>%
        dplyr::mutate(Catch=replace(Catch, Stock==stocks[s] &Fleet==fleets[f], meanC))
    }
  }
  # call internal `Fleet_MMP` function with `this_Fleet_Management` object
  Fleet_MMP(x, DataList, this_Fleet_Management)
}
# define as class `MMP`
class(fixedC_mean3) <- 'MMP'

9.2 Setting an Annual Catch Limit (ACL)

Setting management recommendations in terms of an annual catch limit (ACL) is a little more complicated.

It is straightforward enough to set the ACL for the landings (On-Season fleets), and to calculate the corresponding fishing mortality for the On-Season fleets.

The challenge is to calculate the fishing mortality for the Off-Season fleets. This will depend on the length of the fishing season, and the targeting behavior of the fleets during the closed-seasons. A simple assumption is to assume that the Off-Season fishing mortality will be some multiple of the On-Season fishing mortality. More sophisticated approaches could use an effort dynamics model to predict the fishing effort for each species during the Off-Season.

Additional functions for setting management recommendations in terms of an ACL will be added to the SAMSE package.