example of results (gross return)

Example of results (gross return)Revenue computation (per month or per year) from catches on studied species + effort proportional ‘a priori’ revenue from other species

Benthic Intermediate’ trawler metiers

BenthicLarge (TTBLN) & HakeNetLarge (GNS) & SpainMainHakeON30-39 (VHVO)

Example of results (selectivity sce.)

‘Gear Selectivity’ scenario:

Better evolution for stockswith increased mesh sizes

Abundance trajectories

Abundance trajectories

Example of results (catch.wt)hake:Catches in weight for trawlersare lesser for 110mm than for 70 mm…

…and greater for netters.

NephropsLarge

BenthicLarge

SpainMainHake

nephrops:Catches in weight are greater for 110mm

hake, catch.wt

hake, catch.wtnephrops, catch.wt

Example of results (hake/nephrops)Quantifying technical interactions…

Hake :Decreasing catches for trawlers (up to -50%)

slight increase for netters (~2%).

…because high decrease of discards for both stocks

But also…decrease of landings,landings for nephrops

remains identical !

% b

alan

ce

trawler mesh size

- -

++

Effect on first year

hk, catch.wt hk, discards.wt hk, landings.wt

neph, landings.wtneph, discards.wtneph, catch.wt

Example of results (hake landings.wt)And quantifying the impact on landings in weight (ref: 70 mm versus 110 mm)…

-

Why? Because hake abundance trajectory is better…abundance of older fishes (i.e. bigger) increasing after 5 years. See also the effect on grossreturn and the price evolution?

First year=

+ after 5 years

surplus

deficit

% balance of hake landings

% b

alan

ce

Example of results (nephrops landings.wt)And quantifying the impact on landings in weight (ref: 70 mm versus 110 mm)…

surplus

deficit

% balance of nephrops landings

% b

alan

ce

Example of results (gross return)Selectivity’ scenario:Impact in terms of revenues

Trawlers: inversion after 5 years

Higher for netters

Fish price: Inversion for 110mm sce. due to higher total landing(french market)…But positivegross return balance

price

% balance

Example of results (MPA sce.)

Applying this MPAseems to be beneficial for hake stock…

Abundance trajectories

Example of results (hake abund. - MPA sce.)

Which are the protected ages?- age 2 in recru squares-2-9 ages in repro -& recru squares

23E5 or 20E7 or 19E7 23E4 or 22E4 or 22E5

23E5 or 20E7 or 19E7 23E5 or 20E7 or 19E7

No MPA

MPA

Example of results (hake abundance, all ages) MPANo MPA

12 months, Year5, All Ages, log.scale, topo.colors

+

better recruitment

+

Example of results (hake abundance, ages 4-9)

MPANo MPA

12 months, Year5, Ages 4:9, log.scale, topo.colors

+

Example of results (exploitation)catches.wt discards.wt landings.wt

Catches on hake for all fleets decrease near the MPA season but increase other times…

…due to a dominant decrease of discards but also a decrease of landings for spanish fleets

% b

alan

ce

-~+ +

wei

ght

SpainMainHakeON3039, Effort met 42,Year5, log.scale

Effort re-allocation in space and timeNo MPA MPA

Example of results (SpainMainHakeON3039)

Example of results (SpainMainHakeON3039)

No MPA

MPA: the reallocation of effortis large but unefficient for this fleet because no available fish in the remaining zone!

Landing.wt Landing.wt

effort

OK

-

Example of results (BenthicLarge)

No MPAMPA: minor reallocation (only one square).Increasing landings rather an indirect effect from the decrease for spanish netters…

Landing.wt Landing.wt

effort

+

Example of results (IV)How MPA impact occurs? Due to the preservation of total biomass or, more subtle, to the preservation of critical stages in the life cycle? Effort is reallocated on the inshore zone…but effort on recruitment zone remain constant because of inshore.zone Λ recru.zone

MPAnoMPA MPA

Example of results (IV)How to choose a priori MPA settings?Protect juvenile => Discards.wt mapProtect spawner biomass => hake abundance 4-9 ages map(A posteriori = protect fishermen’ landings?)

An other way to connect ISIS-FISH with FLR…

Isis-Fish & FLR : creating a connection

Isis-Fish : freeware in Java for spatial and seasonal simulation of multi-stocks and multi-fleets interactions with complex management rules (ex. MPA, etc…).

FLR : data structuration in R and set of methods for evaluation of uncertainties in stock assessmentsand simulation of (multi)stock(s) and (multi)fleet(s) interactionswith simple management rules (i.e. HCR).

>

>

http://www.ifremer.fr/isis-fish/

c.voidEval("my.xsa <- FLXSA(my.stock, indices, FLXSA.control())");

> using Rserve package

Isis-Fish & FLR : creating a connection

(http://stats.math.uni-augsburg.de/Rserve/)

R commands can be encapsulated in java methods

var SSB = c.eval("apply(my.stock@stock.n[,myyear,1,myseason,], 1, sum)").asDouble();

c.voidEval("my.stock@stock.n <- my.xsa@stock.n");

// each time step, running an xsa on the stock and updating the stock object

// subsetting SSB

// impacting the isis’ management rules if (SSB < referencePoint) { do…}

etc.

// in a simulation loop:

Northern Hake Base Case using Isis & FLR

Age 0 Age 1 Age 2

1 area1 fleet with F=qE with E =1and q=Fwg

Stock parameters from TECTAC base

FLXSA

abundances

catchesThe match of these results with stock assessment is planned…

Northern Hake Base Case using Isis & FLR

Age 0 Age 1 Age 2

FLXSA output as input for Isis’ management rules:Hake box if SSB <Bpa

FLXSA

abundances

catches

start end

Why a link…

Alternative case couldBe easily implemented