TropFishR (version 1.6.1)

predict_mod: Prediction models

Description

This function applies Beverton & Holt's yield per recruit model as well as the Thompson & Bell model. These models predict catch, yield, biomass and economic values for different fishing mortality scenarions (in combination with gear changes).

Usage

predict_mod(param, type, FM_change = NA, E_change = NA,
  FM_relative = FALSE, Lc_change = NULL, tc_change = NULL, s_list = NA,
  stock_size_1 = NA, age_unit = "year", curr.E = NA, curr.Lc = NA,
  plus_group = NA, Lmin = NA, Lincr = NA, plot = FALSE, mark = TRUE,
  hide.progressbar = FALSE)

Arguments

param

a list consisting of following parameters (not all are required):

• Linf or Winf: infinite length or weight, respectively, for investigated species in cm [cm],

• K: growth coefficent for investigated species per year [1/year],

• t0: theoretical time zero, at which individuals of this species hatch,

• M: natural mortality or

• Z: total mortality,

• FM: fishing mortality,

• a: length-weight relationship coefficent (W = a * L^b),

• b: length-weight relationship coefficent (W = a * L^b),

• Lr or tr: length or age of recruitment;

additional list objects for the Thompson and Bell model:

• midLengths or age: midpoints of the length classes (length-frequency data) or ages (age composition data),

• meanWeight: vector with mean weight per length group or age class,

• meanValue: vector with mean value per length group or age class,

type

indicating which model should be applied: "ypr" for Beverton and Holt's yield per recruit model and "ThompBell" for the Thompson and Bell model

FM_change

vector with ascending fishing mortalities (if FM_relative is set to TRUE, values can also be relative (only for Thompson and Bell model). Default are absolute values from 0 to 10). Or

E_change

vector with ascending absolute exploitation rates;

FM_relative

logical; indicating whether FM_change is relative or absolute. Default is FALSE (absolute fishing mortalities in FM_change).

Lc_change

vector with ascending lengths at first capture (Lc), or

tc_change

vector with ascending ages at first capture (tc)

s_list

list with selectivity parameters

stock_size_1

stock size of smallest size class, if NA values are calculated relative to a stock size of 1000 individuals

age_unit

in which time unit the data is provided? "month" or "year"

curr.E

current exploitation rate if available

curr.Lc

current Lc (length at first capture) if available

plus_group

if a value is provided, a plus group is created comprising this size class and all above

Lmin

smallest length group where to start with selection ogive. Not required for "knife_edge" selection type

Lincr

arbitrary length increment between length groups for estimation of selection ogive. The smaller the higher the resolution but the slower the model run. Not required for "knife_edge" selection type

plot

logical; if TRUE results are displayed graphically

mark

logical; if value of choosen points should be displayed in graph (default: TRUE)

hide.progressbar

logical; should progressbar be displayed or hidden? (Default: FALSE)

Value

A list with the input parameters and dependent on the model type following list objects:

• type = 'ypr'

  • FM: fishing mortalities,

  • Lc or tc: lengths or ages at first capture,

  • list_Lc_runs: a list with dataframes for each Lc value:

    • FM_change: fishing mortalities

    • E: expoitation rates

    • Ty: mean age in annual yield

    • LY: mean length in annual yield

    • Wy: mean weight in annual yield

    • Y_R.rel: relative yield per recruit (change in catch in weigth per recruit relative to initial Y/R value)

    • B_R.rel: relative biomass per recruit

    • Y_R: yield per recurit (catch in weight per recruit)

    • B_R: biomass per recruit

    • B_R.percent: percentage biomass per recurit in relation to virgin biomass per recruit

  • df_Es: a dataframe with references points (columns) for different Lc values (rows)

  • df_current: a dataframe with the exploitation status, yield and biomass values of current exploitation or selectivity (if E_curr or Lc_tc_curr provided).

• type = 'ThomBell'

  • dt: delta t,

  • N: population number,

  • dead: deaths due to natural reasons,

  • C: catch,

  • Y: yield,

  • B: biomass,

  • V: value,

  • totals: summed up values (total catch, total yield, total value, average biomass),

  • totC: total catches for different x factors,

  • totY: total yield values for different x factors,

  • totV: total values for different x factors,

  • meanB: average biomasses for different x factors,

  • F_change: fishing mortality changes;

• type = 'ThomBell' and Lc_change provided

  • FM_change: fishing mortality changes,

  • Lc_change: changes in length at first capture,

  • Lt: lengths at age,

  • sel: probability of capture,

  • mat_FM_Lc_com.C: catch matrix for all fishing mortality and Lc/tc combinations,

  • mat_FM_Lc_com.Y: yield matrix for all fishing mortality and Lc/tc combinations,

  • mat_FM_Lc_com.V: value matrix for all fishing mortality and Lc/tc combinations,

  • mat_FM_Lc_com.B: biomass matrix for all fishing mortality and Lc/tc combinations;

Details

The Thompson and Bell model incorporates an iteration step simulating the stock by means of the stock_sim function. In case changes in gear characteristics - here measured in terms of Lc or tc, the length or age at first capture, respectively - should be explored, a list with selectivity information about the gear has to be provided and the prediction models make use of the selectivity select_ogive function. Sparre and Venema (1998) recommend to treat the last length class always as plus group. This model is very sensitive to zero observations in the ultimate length classes. If unrealistic results are returned, it is recommended to cut length classes with zero observations, group them in a plus group or to change the interval between length classes. Equations which are used in this function assume isometric growth, an assumption often not met. Further, the assumption that there is no relationship between the parental stock size and progeny over a wide range of fishing mortalities or exploitation values, respectively, is also said to be untrue. By default, the functions assume knife-edge recruitment and selection of gears (Sparre and Venema, 1998). If E_change instead of FM_change is used the range is cut at E=0.9, because higher values of E correspond to unrealistic high values of fishing mortality. If no selectivity information is given (by use of s_list), knife edge selectivity with L50 equal to the first argument of Lc_change is assumed.

References

Berkeley, S.A., and Houde, E.D., 1980. Swordfish, Xiphias gladius, dynamics in the Straits of Florida. ICES C.M., 11.

Beverton, R.J.H., and Holt, S.J., 1964. Table of yield functions for fishery management. FAO Fish. Tech. Pap. 38, 49 p.

Beverton, R.J.H., and Holt, S.J., 1966. Manual of methods for fish stock assessment. Pt. 2: Tables of yield functions. FAO Fisheries Technical Paper, (38)Rev.1:67 p.

Boerema, L.K., and J.A. Gulland, 1973. Stock assessment of the Peruvian anchovy (Engraulis ringens) and management of the fishery. Journal of the Fisheries Board of Canada, 30(12):2226-2235

Garcia, S. and N.P. van Zalinge, 1982. Shrimp fishing in Kuwait: methodology for a joint analysis of the artisanal and industrial fisheries. pp. 119-142 In: Report on the Workshop on assessment of the shrimp stocks of the west coast of the Gulf between Iran and the Arabian Peninsula. Fisheries development in the Gulf. Rome, FAO, FI:DP/RAB/80/015/1, 163 p.

Gulland, J.A., 1983. Fish stock assessment: a manual of basic methods. FAO/Wiley, New York.

Gulland, J.A. and Boerema, L., 1973. Scientific advice on catch levels. Fish. Bull. (US) 71:325-335.

Jones, R.E. 1957. A much simplified version of the fish yield equation. Doc. No. P. 21. Paper presented at the Lisbon joint meeting of International Commission Northwest Atlantic-Fisheries, International Council for the Exploration of the Sea, and Food and Agriculture Organization of the United Nations. 8 p. [Mimeo].

Millar, R.B., and Holst, R., 1997. Estimation of gillnet and hook selectivity using log-linear models. ICES Journal of Marine Science: Journal du Conseil, 54(3):471-477

Pauly, D., 1980. A selection of simple methods for the assessment of tropical fish stocks. FAO Fisheries Circulars (FAO). no. 729.

Pauly, D., 1984. Fish population dynamics in tropical waters: a manual for use with programmable calculators. ICLARM Stud. Rev. 8, 325 p.

Pauly, D. and M. Soriano. 1986. Some practical extensions to Beverton and Holt's relative yield-per-recruit model, p. 491-495. In J.L. Maclean, L.B. Dizon and L.V. Hosillos (eds.) The First Asian Fisheries Forum. Asian Fisheries Society, Manila.

Schaefer, M.B., 1954. Some aspects of the dynamics of populations important to the management of the commercial marine fisheries. Inter-Am. Trop. Tuna Comm., Bull. 1(2):27-56.

Schaefer, M.B., 1957. A study of the dynamics of the fishery for yellowfin tuna in the eastern tropical Pacific Ocean. Inter-Am. Trop. Tuna Comm., Bull. 2:247-268.

Sparre, P., and Venema, S.C., 1998. Introduction to tropical fish stock assessment. Part 1. Manual. FAO Fisheries Technical Paper, (306.1, Rev. 2). 407 p.

Examples

# NOT RUN {
#______________________________________
# Yiel Per Recruit (YPR) / Beverton and Holt's model
#______________________________________
# age structured data
# Nemipterus marginatus
threadfin <- list(Winf = 286, K = 0.37, t0 = -0.2, M = 1.1, tr = 0.4)

predict_mod(threadfin, FM_change = seq(0,6,0.1),
   tc_change = seq(0.2,1,0.2), type = 'ypr')  #where it is maximal  = MSY

# Leiognathus spendens (Pauly, 1980)
ponyfish <- list(Winf = 64, K = 1, t0 = -0.2, M = 1.8, tr = 0.2)

predict_mod(ponyfish, tc_change = c(0.2,0.3,1.0), type = 'ypr', plot=TRUE)

#______________________________________
# length structured data
# Xiphias gladius (Berkeley and Houde, 1980)
swordfish <- list(Linf = 309, K = 0.0949, M = 0.18,
                  a = 0.0003, b = 3, Lr = 90)

select.list <- list(selecType = 'trawl_ogive', L50 = 120, L75 = 132)
#swordfish$midLengths <- seq(60,300,5)

output <- predict_mod(param = swordfish, Lc_change = c(100,118,150,180),
            s_list = select.list, type = 'ypr', Lmin = 90, Lincr = 8)
plot(output)

data(hake)
hake$Lr <- 35
select.list <- list(selecType = 'trawl_ogive', L50 = 50, L75 = 54)
output <- predict_mod(param = hake, FM_change = seq(0,3,0.05),
                      Lc_change = seq(30,70,1), s_list = select.list,
                      type = 'ypr', plot = FALSE, curr.Lc = 50, curr.E = 0.73)
plot(output, type = "Isopleth", xaxis1 = "FM", yaxis1 = "Y_R.rel", mark = TRUE)

output <- predict_mod(param = hake, E_change = seq(0,1,0.1),
                      Lc_change = seq(2,120,2), #s_list = select.list,
                      type = 'ypr', plot = FALSE)
plot(output, type = "Isopleth", xaxis1 = "E", yaxis1 = "B_R")

#______________________________________
#      Thompson and Bell model
#______________________________________
# with age structured data
data(shrimps)

output <- predict_mod(param = shrimps, FM_change = seq(0.1,20,0.1),
     type = "ThompBell", age_unit = "month", plot = TRUE)

#______________________________________
# with length structured data
data(hake)
par(mar = c(5, 4, 4, 7))
predict_mod(param = hake,FM_change = seq(0.1,3,0.05),
     type = 'ThompBell', plot = TRUE)

# create list with selectivity information
select.list <- list(selecType = 'trawl_ogive', L50 = 50, L75 = 55)

output <- predict_mod(param = hake, FM_change = seq(0,2,0.1),
     Lc_change = seq(20,70,1),
     curr.E = 0.4, curr.Lc = 50,
     type = 'ThompBell', s_list = select.list)
plot(output, xaxis1 = "FM", yaxis_iso = "Lc", yaxis1 = "B_R", mark = TRUE)


# }