ALADYM (Age-Length Based Dynamic Model): a stochastic simulation tool to predict population dynamics and management scenarios using fishery-independent information WORKSHOP ON TRAWL SURVEY BASED MONITORING FISHERY SYSTEM IN THE MEDITERRANEAN Rome, Italy, March 2007 Lembo G. 1, S. Martino 1, A. J. Abella 2, F. Fiorentino 3, M.T. Spedicato 1 SCIENTIFIC ADVISORY COMMITTEE SUB-COMMITTEE STOCK ASSESSMENT SUB-COMMITTEE STOCK ASSESSMENT 1 COISPA Tecnologia & Ricerca, Via dei trulli 18-20, Bari, Italy 2 ARPAT Toscana, Italy. 3 IAMC CNR, Mazara del Vallo, Italy

ALADYM (Age-Length Based Dynamic Model) is designed to predict, through simulations, the consequences of management scenarios, in terms of different metrics and indicators. Removals are simulated on the basis of the total mortality rate modulated using selectivity pattern and a fishing activity coefficient. Aladym can work in absence of fishery-dependent data, although its predictive capability of real catch levels can be verified using information referred to the commercial catches or fishing activity per month. ALADYM GENERAL FRAMEWORK (EU FISBOAT-Fisheries Independent Survey Based Operational Assessment Tools)

The Aladym model is composed of two complementary tools: A. the quasi-deterministic dynamic tool defined as Aladym-r; B. the stochastic dynamic tool defined as Aladym-q. The tool B) Aladym-q adds to the same mathematical model of Aladym-r the capability to deal with the stochastic representation of some input parameters in order to evaluate the corresponding distribution functions of the output variables. This feature aims to build up a procedure suited to associate to indicators and/or reference points a probability. ALADYM TOOLS

The simulation approach is used as a tool to convert survey biological information and relative assessment into quantitative HCRs. The options implemented in the simulation model are based on total mortality, gear selectivity (size at first capture L50% and selection range) and fishing activity (alone or in combination). The effects of HCRs (based on total mortality and selectivity) are then analysed in terms of sustainability for the population in the long-term (e.g. the ratio SSB/USSB given in the outputs). Yield by time is also simulated using the catch equation Harvest control rules

The model is designed to simulate population dynamics of a given species accounting for differences by sex in growth, maturity and mortality. All the quantities are calculated as vectors with an associated time step Δt (time slice=1 month). A. THE QUASI-DETERMINISTIC DYNAMIC TOOL ALADYM R 06 The population dynamics is formulated following the simultaneous evolution of several cohorts at month scale through the exponential population decline model, both in absence and in presence of fishing mortality

ALADYM Q 06 FRAMEWORK B. THE STOCHASTIC DYNAMIC TOOL DYNAMIC TOOL

von Bertalanffy growth parameters by sex with associated variability;von Bertalanffy growth parameters by sex with associated variability; length-weight relationship parameters by sex;length-weight relationship parameters by sex; maturity ogive parameters by sex (Lm50% and Lm25%-Lm75% range);maturity ogive parameters by sex (Lm50% and Lm25%-Lm75% range); natural mortality by sex (a constant value or a vector);natural mortality by sex (a constant value or a vector); seed values (minimum, maximum, log-mean and log-standard deviation) of recruitment by sex;seed values (minimum, maximum, log-mean and log-standard deviation) of recruitment by sex; proportion of offsprings entering in the stock by month;proportion of offsprings entering in the stock by month; stock-recruitment relationship parameters or a vector of recruit number by month with associated variability;stock-recruitment relationship parameters or a vector of recruit number by month with associated variability; time elapsing from spawning to birth;time elapsing from spawning to birth; sex-ratio (female/total) at initial time;sex-ratio (female/total) at initial time; total mortality Z by sex;total mortality Z by sex; selection ogive parameters of the gear used by the fleet;selection ogive parameters of the gear used by the fleet; fishing activity coefficient by month (0, in case of absence of fishing activity);fishing activity coefficient by month (0, in case of absence of fishing activity); In Aladym-q the following inputs are also provided: the number of realizations;the number of realizations; the parameters of the defined pdfs.the parameters of the defined pdfs. INPUTS

SOURCE OF DATA AND PARAMETERS MEDITS and GRU.N.D. trawl survey time series in the south Tyrrhenian sea Selectivity experiments in the area

a vector of natural mortality by a vector of natural mortality by age and sex provided by the user. age and sex provided by the user. ALADYM INPUTS (M) ALADYM INPUTS (Z)

INPUTS OF STOCHASTIC ALADYM Q 06

SOME OUTPUTS OF STOCHASTIC ALADYM Q 06 (1) related to the exploited population (biomass)

SOME OUTPUTS OF STOCHASTIC ALADYM Q 06 (2) related to the exploited population (mean length)

SOME OUTPUTS OF STOCHASTIC ALADYM Q 06 (3) related to simulated yield

Ratio between exploited and unexploited spawning stock biomass Z=current SOME OUTPUTS OF STOCHASTIC ALADYM Q 06 (4) (sustainability indicator)

EXERCISE WITH STOCHASTIC ALADYM Q 06 All the other inputs unchanged

CUMULATIVE PROBABILITY DISTRIBUTIONS DECREASING Z Z -25% Z -5%

CUMULATIVE PROBABILITY DISTRIBUTIONS INCREASING Z Z +5% Z +25%

Probability of the BRP ESSB/USSB of exceeding reported values

Further reference points (Z MBP and Z MSY ) derived using Aladym outputs at different setting of total mortality (from -25% to +25% of the current value).

Comparison between monthly yields simulated by Aladym along three different years and monthly observed landings (data source: IREPA)