Stock Assessment Form

Demersal species Reference year:2014

Reporting year:2015

Pérez-Gil J.L., González M., García T., García C., Serna J.M., Meléndez M.J., Acosta J. and Ciércoles C.

IEO- Centro Oceanográfico e Málaga, Puerto pesquero S/N, Fuengirola,Málaga. (Spain.)

European hake is a target demersal species of the Mediterranean fishing fleets. It is largely exploited in GSA01, mainly by trawlers on the shelf and slope (91% landings), but also by small-scale fisheries using long lines (3%) and gillnets and trammel nets (6%) (average percents estimated between 2012 and 2014). The trawler fleet (124 boats in 2014) is the largest in landings (245 tons in 2014). The state of exploitation of this stock was assessed by means of VPA Extended Survivor Analysis (XSA) (Shepherd, 1999). The software used was the Lowestoft suite (Darby and Flatman 1994) and FLR (Fisheries Libraries in R).

The assessment was carried out using official landings and data on the size composition of trawl fleet for the years 2003-2014. XSA results show that Spawning biomass (SSB) and yield (Y) show a decreasing trend from 2011 to 2014. Recruitment (R) showed fluctuations over the all series,after a sharp decrease from 2010, seems to have stabilized during the last three years (around 11000 thousands).

Fishing mortality (Fbar0-3) showed a drastic increase from 2008 to 2011. After this, a decreasing trend is observed in the last 4

years. Y/R analysis shows that the Fref = Fcurrent (1.4) exceeds the Y/R F0.1 reference point (0.2).

1

Stock Assessment Form version 1.0 (January 2014)

Uploader: Pérez-Gil J.L., González M., García T., García C., Serna J.M.,

Meléndez M.J., Acosta J. and Ciércoles C.

* IEO- Centro Oceanográfico de Málaga, Puerto pesquero S/N, Fuengirola,Málaga. (Spain.)

Stock assessment form

1 Basic Identification Data .............................................................................................................. 2

2 Stock identification and biological information ........................................................................... 3

2.1 Stock unit ............................................................................................................................... 3 2.2 Growth and maturity ............................................................................................................. 3

3 Fisheries information ................................................................................................................... 5

3.1 Description of the fleet .......................................................................................................... 5 3.2 Historical trends .................................................................................................................... 7

4 Fisheries independent information ........................................................................................... 10

4.1 MEDITS_ES ....................................................................................................................... 10

4.1.1 Brief description of the direct method used .................................................... 10

4.1.2 Spatial distribution of the resources ............................................................... 12 4.1.3 Historical trends ............................................................................................. 13

5 Ecological information ............................................................................................................... 14

5.1 Protected species potentially affected by the fisheries ........................................................ 14

6 Stock Assessment ....................................................................................................................... 15

6.1 Extended Survivor Analysis (XSA) .................................................................................... 15

6.1.1 Model assumptñ ............................................................................................. 16 6.1.2 Scripts ............................................................................................................ 16 6.1.3 Input data and Parameters ............................................................................. 16 6.1.4 Tuning data .................................................................................................... 16

6.1.5 Results ........................................................................................................... 17 6.1.6 Robustness analysis ...................................................................................... 18

6.1.7 Retrospective analysis, comparison between model runs, sensitivity analysis, etc. ....................................................................................................................... 19 6.1.8 Assessment quality ........................................................................................ 20

Exploratory data analysis ............................................................................................ 20 Tunning Data analysis. ............................................................................................... 21

Yield per recruit analysis................................................................................................................ 21

7 Stock predictions ........................................................................................................................ 23

7.1 Short term predictions ......................................................................................................... 23

7.2 Medium term predictions .................................................................................................... 25 7.3 Long term predictions ......................................................................................................... 25

25

Draft scientific advice ......................................................................................................................... 26

Scientific advice for management ...................................................................................................... 26

2

1 Basic Identification Data

Scientific name: Common name: ISCAAP Group:

Merluccius merluccius - HKE European hake 32 HKE

1st Geographical sub-area: 2nd Geographical sub-area: 3rd Geographical sub-area:

Northern Alboran Sea GSA01

4th Geographical sub-area: 5th Geographical sub-area: 6th Geographical sub-area:

1st Country 2nd Country 3rd Country

SPAIN

4th Country 5th Country 6th Country

Stock assessment method: (direct, indirect, combined, none)

XSA (tuning with MEDITS indices) and Y/R

Authors:

(Pérez-Gil J.L., González M., García T., García C., Serna J.M., Meléndez M.J., Acosta J. and Ciércoles C.)

Affiliation:

* IEO- Centro Oceanográfico de Málaga, Puerto pesquero S/N, Fuengirola,Málaga. (Spain.)

3

2 Stock identification and biological information

2.1 Stock unit

The assessment cover the complete stock unit in the GSA01 (Northern Alboran Sea).

2.2 Growth and maturity

Table 2.2-1: Maximum size, size at first maturity and size at recruitment.

Somatic magnitude measured

(LT, LC, etc)

Total length

(LT) Units cm

Sex Fem Mal Combined

Reproduction

season

All year: Feb and June

Maximum

size

observed

90 61 90

Recruitment

season

All year (higher picks

in winter and spring)

Size at first

maturity 26

Spawning area Shelf and upper

Slope

Recruitment

size to the

fishery

14.5

Nursery area Continental Shelf

4

Table 2-2.2: M vector and proportion of matures by size or age (Combined Males-Females))

Age Natural mortality ** Proportion of matures

0 1.24 0

1 0.58 0.15

2 0.45 0.82

3 0.4 0.98

4 0.37 1

5+ 0.35 1

**Natural mortality vector, PROBIOM. Abella A, Caddy J.F, Serena F. 1999.

Table 2-3: Growth and length weight model parameters

Sex

Units female male Combined Years

Growth model

L∞ 110*

K 0.178*

t0

Data source *Mellon-Duval et al. (2010) (tagging surveys).

**DCF-EU (Spain. 2012)

Length weight

relationship

a 0.00677**

b 3.035097**

M

(scalar)

0.4 (Vector

average)

sex ratio

(% females/total)

0.36

5

3 Fisheries information

3.1 Description of the fleet

European hake is a target demersal species of the Mediterranean fishing fleets. It is largely exploited

in GSA01, mainly by trawlers on the shelf and slope (91% landings), but also by small-scale fisheries

using long lines (3%) and gillnets and trammel nets (6%) (average percents estimated between 2012

and 2014). The trawler fleet is the largest in number of boats, around 130 trawlers are involved in

this fishery (average 2012-2014), with total annual landings oscillating around an average value of

417 tons for the period 2003-2014 (275 tons in 2014).

The total annual landings used in the assessment come from the official data of the GSA01 area.

Discard data are included in the assessment and come from sampling program on board from

commercial fleet.

Figure 1. Hake landings by fishing gear.

In the GSA 01, for sampling purpose only the major métier need be considered In order to identify

the metiers to be sampled, the DCF (EU) proposed the use of a Ranking System, based on four

criteria of selection (Landings, Economical value, effort and métier with special importance).

With this methodology the métiers GNS_DEF (Gillnets) and LLS_DEF (Long lines) haven´t been

selected in the GSA1, and consequently we don´t have sampled them. It is therefore desirable to

assess the resource considering the rate of capture by fleet and distribution of sizes available

(analytical assessment), in this case the trawl fleet.

6

Figure 2. Percentage

landings by fleet in GSA01 for

hake (Average 2012-

2014)

Table 3-1: Description of operational units exploiting the stock

Country GSA Fleet Segment

Fishing Gear

Class

Group of Target

Species Species

Operational

Unit 1* ESP 01 E-Trawl 12-24 m 03-Trawls

33 – Demersal shelf

species HKE

3.1-2: Catch, bycatch, discards and effort by operational unit in the reference year

Operational Units

Fleet

(n° of

boats.

Averag

e 2012-

2014)*

Landings/Catc

h (T average

2012-2014

species

assessed)

Other

species

caught

(names and

weight )

Discards

(species

assessed)

Discard

s

(other

species

caught)

Effort

average

2012-2014

(days)

03-Trawls 130 339/359 20 21890

Total

7

3.2 Historical trends

Figure 3. Hake total landings 2003-2014 – GSA1

Landings have shown important oscillations along the period of the data series. However, in the last four years from 2011 onwards, a decreasing trend in landings is observed with the minimum values in the time series data (244t).

*Including discard fraction.

Table 3-2: Catches as used in the assessment (tones)

Year 2004 2005 2006 2007 2008 2009

Fleet; OTB 181 187 188 182 184 188

Year 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Catch (t) 427.8 519.8 313.2 291.9 298.9 294.8 584.4 544.8 653.5 458 347.2 275.11

Landings(t) 415 515 295 282 274 282 563 529 648 437 336 244

Minimum size* 5 8 6 5 7 8 6 5 6 5 5 5

Average size 15.2 20.1 17.6 16.3 19.8 21.3 21.9 20.5 25.1 24.4 26.2 23.2

Maximum size 67 63 75 71 66 70 68 74 70 67

0

100

200

300

400

500

600

700

0

5000

10000

15000

20000

25000

30000

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Ton

ne

s

Fish

ing

day

s

Merluccius merluccius - OTB_DES (Trawl) - GSA01

fishing days Landings

8

Year 2010 2011 2012 2013 2014

Fleet; OTB 154 149 139 129 124

Table 3-4: Selectivity

L25* 12.19 cm

L50* 13.55 cm

L75* 14.99 cm

Selection factor 2.8

*It corresponds to 40 mm square mesh in the codend in force from 2012.

Data source: Baro J, Meléndez M.J. y del Árbol J. Informe del seguimiento científico sobre una acción piloto de

selectividad con artes de arrastre en aguas del litoral de Málaga. (RAI-AP-25/2003).IEO. 2004

Discards

Discards were included in the assessment. The percentage of discards (mainly caught by trawlers)

was around 4% (average 2009-2014).

Figure 4. Catch an landings for Hake in GSA01 (Trawl fleet)

0

100

200

300

400

500

600

700

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

ton

ne

s

Merluccius merluccius - OTB - GSA01

Landings

Catch

9

Figure 5. Catch matrix (Total landings by age

and year) and Length frequency distribution

(Total length) of Trawl catches in the

geographical sub-area 01 (Northern

Alboran Sea) for the period (2003-2014).

Composition Landings were largely composed of age 0 immature individuals from 2003 to 2008. However, this pattern changed from age 0 to age 1 from 2009 onwards.

Management regulations

- Fishing license: fully observed

- Engine power limited to 316 KW or 500 HP: not fully observed

- Mesh size in the codend (40 mm square or 50 mm rhomboidal): fully observed

- Fishing forbidden within upper 50 m depth: not fully observed

- Time at sea (12 hours per day and 5 days per week): fully observed

- Minimum landing size (20 cm TL), (EC regulation 1967/2006): mostly fully observed

10

4 Fisheries independent information

4.1 MEDITS_ES

4.1.1 Brief description of the direct method used

The Spanish Institute of Oceanography carries out two scientific surveys under the Data

Collection Regulation: MEDITS and MEDIAS. Both are international coordinated surveys.

MEDITS is an international bottom trawl survey, the IEO is involved in it from 1994. The

survey takes place in all European Mediterranean countries and the main target species

are the demersal species.

The Spanish Medits survey carries out about 170 – 180 hauls in spring. It samples 4 GSAs,

including Balearic Islands, and the sampling procedure is based on the common

methodology included in the MEDITS instruction manual. The GSAs sampled are: GSA1,

GSA2, GSA5 and GSA6.

Direct methods: trawl based abundance indices

Table 4.1-1: Trawl survey basic information

Survey MEDITS_ES Trawler/RV Trawler

Sampling season MAY-JUN

Sampling design Random stratified with number of haul by stratum

proportional to stratum surface

Sampler (gear used) GOC-73

Cod –end mesh size as opening

in mm

20

Investigated depth range (m) 40-750

11

Table 4.1-2: Trawl survey sampling area and number of hauls

Stratum Total surface

(km2)

Trawlable surface

(km2)

Swept area

(km2)

Number of

hauls

A (-50m) 510 4

B (50-100m) 1951 16

C (100-200m) 1086 7

D (200-500 m) 3461 14

E (500-800m) 4912 14

Total (km2) 2384 55

Figure 6. Medits hauls in the GSA01 (2014 survey).

12

4.1.2 Spatial distribution of the resources

Figure 7. MEDITS_ES trawl survey 2014. Hake Spatial distribution of estimated abundances.

13

4.1.3 Historical trends

Figure 8. MEDITS_ES (1995-2013). Trends in abundance indices (n/km2) and biomass indices (kg/day).

14

5 Ecological information

5.1 Protected species potentially affected by the fisheries

Table 5.1. Estimated percentage species catches from commercial trips reported by Scientific observers on board in GSA01 (Trawl fleet). 2012 period.

15

6 Stock Assessment

An Extended Survivor Analysis (XSA) tuned with MEDITS survey data was carried out over the period

2003-2014, for the trawl fleet data, considering age classes from 0 to 5+.

6.1 Extended Survivor Analysis (XSA)

Ad hoc methods for tuning single species VPA's to fleet catch per unit effort (CPUE) data are sensitive to

observation errors in the final year because they make the assumption that the data for that year are exact.

In addition, the methods fail to utilize all of the year class strength information contained within the catches

taken from a cohort by the tuning fleets.

Extended Survivors Analysis (XSA), (Shepherd, 1992, 1999), an extension of Survivors Analysis (Doubleday,

1981), is an alternative approach which overcomes these deficiencies. In general, the algorithms used within

the ad hoc tuning procedures, exploit the relationship between fishing effort and fishing mortality.

XSA focuses on the relationship between catch per unit effort and population abundance, allowing the use

of a more complicated model for the relationship between CPUE and year class strength at the youngest

ages. (Darby and Flatman, 1994).

The XSA assessments were performed using the Lowestoft VPA Suite stock assessment software package

(Darby and Flatman, 1994) and the open-source framework FLR (Fisheries Library for R) (Kett et al, 2007).

Their results were analyzed and compared. FLR packages were also used to perform Exploratory Data

Analysis, Sensitivity Analysis, Retrospective Analysis, Reference Points Estimation and Short Term

Projections.

Shepherd J. G., 1999. Extended survivors analysis: An improved method for the analysis of catch-at-age data and

abundance indices. ICES J. Mar. Sci 56: 584–591.

Darby, C. D., and S. Flatman. "1994. Virtual population analysis: version 3.1 (Windows/DOS) user guide." Info. Tech.

Ser. MAFF Direct. Fish. Res., Lowestoft 1: 85.

Kell L.T., Mosqueira I., Grosjean P., Fromentin J-M., Garcia D., Hillary R., Jardim E., Pastoors M., Poos J.J., Scott F. &

Scott R.D. 2007. FLR: an open-source framework for the evaluation and development of management strategies. ICES

J. of Mar. Sci. 20: 289-290.

16

6.1.1 Model assumptñ

Imput Parameters Catch time series 2002-2014 (official landings and Discard data) from trawl fleet. Length distributions 2002-2014 (monthly onboard and port sampling). Catch-at-Length data converted to Catch-at-Age data using cohort slicing. Growth Parameters from Mellon et al, 2010 and DCF-Spain (2012). Biological sampling 2003-2014 for Maturity and Length-Weight relationships. M vector by age using PROBION spreadsheet (Abella et al, 1997). Tuning data 1996-2014 from MEDITS survey and commercial fleet.

Main Settings Ages 0 to 5+ (Age 5 is a Plus Group) Fbar 0-3. Catchability independent of size and age for ages older than 1 and 2 respectively. Survivor estimates shrunk towards the mean F of the final 5yrs or the 4 oldest ages. S.E. of the mean to which the estimates are shrunk = 0.5 Minimum standard error for population estimates derived from each fleet = 0.3.

6.1.2 Scripts

FLR (Fisheries Libraries in R)

FLR Project - http://flr-project.org/

6.1.3 Input data and Parameters

6.1.4 Tuning data

17

6.1.5 Results

Figure 9. XSA results.

XSA results show that Spawning biomass (SSB) and yield (Y) show a decreasing trend from 2011 to 2014. Recruitment (R) showed fluctuations over the all series but seems to have stabilized during the last three years (around 11000 thousands).

Fishing mortality (Fbar0-3) showed a drastic increase from 2008 to 2011. After this, a decreasing trend is observed in the last 4 years.

18

Figure10. 33rd and 66th percentiles of the XSA outputs and abundance indices time series 2003-2014 and 1995-2014 respectively.

(N): stock numbers, (B): biomass, (SSB): spawning stock biomass and the abundance indices MEDITS survey in n/km2, reach values above the 66th percentile in the last years. Fishing mortality (Fbar0-3), reach higher values (opposite percentile scale) in 2013-2014 period.

6.1.6 Robustness analysis

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

F

N (Stock)

> 66th percentile B

33rd-66th percentile R

< 33rd percentile SSB

CPUE

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

MEDITS (N/km2)

19

6.1.7 Retrospective analysis, comparison between model runs, sensitivity analysis, etc.

6.1.7.1 Retrospective analysis.

Figure 11. Retrospective analysis was applied in the XSA model for the hake in GSA01 and the period 2003-2014 up to 4 years backward. Results show no particular retrospective bias.

6.1.7.2 Sensitivity analysis

Sensitivity analysis on catchability independent of age “qage”

20

Sensitivity analysis on shrinkage weight “fse”

Sensitivity analysis on shrinkage ages “shk.ages”

Figure 12. Sensitivity analysis on different qage, fse and shk.ages values.

6.1.8 Assessment quality

Exploratory data analysis

Figure 13. Catch weight

at age, age proportion and maturity ogive plots by year.

21

Tunning Data analysis.

Figure 14. Catchability residuals plots by fleet (MEDITS Surveys and Commercial Fleet).

Yield per recruit analysis.

Yield per recruit analyses was conducted based on the exploitation pattern resulting from the XSA model and population parameters. Minimum and maximum ages for the analysis were considered to be age group 0 and 5. Stock weight at age, catch weight at age and maturity ogive was estimated as mean values between 2003 and 2014. Natural mortality vector values were applied per age group using ProBiom (Abella et al., 1998). Fishing mortalities were the mean exploitation pattern F between 2012 and 2014. Reference F was considered to be mean F for ages 0 to 3 during the last 3 years (2012-2014).

22

Figure 15. Equilibrium Yield (t) vs F.Curves with plot including the corresponding yield reference point

(F0.1 = 0..2, Fmax=0.3) and the observed historical data (dots connected with lines).

2014

2003

Fc=1.4F0.1=0.2

23

Figure 15. Yield per recruit and SSB per recruit curve.

Current F (Fref = FBAR 0-3) was estimated to be 1.4. Fref exceeds F0.1 reference point (0.2) indicating the

stock is subjected to over-fishing (or overexploitation) from a precautionary point of view.

7 Stock predictions

7.1 Short term predictions

Deterministic projections for three years (2015-2017) were produced. These projections are based on the arithmetic mean of recruitment, catches and weights at age of the last three years (2012-2014). F Status Quo is the geometric mean of FBAR0-3 during the last three years (2012-2014).

*F0.1

**Fcurrent

Change_SSB Change_Catch

2016 2017 2016 2017 2016-2017(%) 2014-2016(%)

0 0 0 0 191.3 743.7 288.7 -100

0.1 0.1 60 138.3 191.3 636.1 232.5 -78.2

0.1 0.2 75.9 168.2 191.3 608 217.8 -72.4

0.2 0.3 111.5 225.9 191.3 545.4 185.1 -59.5

0.3 0.4 155.6 279.6 191.3 468.9 145.1 -43.4

0.4 0.6 193.6 310.9 191.3 404.3 111.3 -29.6

0.5 0.7 226.4 327.6 191.3 349.8 82.8 -17.7

0.6 0.9 254.8 335 191.3 303.7 58.7 -7.4

0.7 1 279.5 336.5 191.3 264.7 38.3 1.6

0.8 1.1 300.9 334.5 191.3 231.6 21.1 9.4

0.9 1.3 319.6 330.6 191.3 203.6 6.4 16.2

1 1.4 336 325.7 191.3 179.9 -6 22.1

1.1 1.6 350.4 320.4 191.3 159.7 -16.5 27.4

1.2 1.7 363.1 315.2 191.3 142.6 -25.5 32

1.3 1.9 374.4 310.3 191.3 128 -33.1 36.1

1.4 2 384.4 305.7 191.3 115.6 -39.6 39.7

1.5 2.2 393.3 301.6 191.3 105 -45.1 43

1.6 2.3 401.3 298 191.3 96 -49.8 45.9

1.7 2.4 408.4 294.8 191.3 88.3 -53.9 48.5

1.8 2.6 414.9 292 191.3 81.7 -57.3 50.8

1.9 2.7 420.8 289.5 191.3 76 -60.3 53

2 2.9 426.1 287.4 191.3 71.1 -62.8 54.9

Ffactor FbarSSBCatch

24

Figure 16. Short term predictions results.

Fishing at Fcurrent from 2014 to 2016 would produce an increase in catches of 22% with a decrease in SSB for the 2016-2017 period -6%).

Fishing at F0.1 from 2014 to 2016 would generate a decrease in catches of 72% of the catches and an increase of 218% in SSB for the 2016-2017 period.

-150

-100

-50

0

50

100

150

200

250

300

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

% c

han

ge i

n l

and

ings

or

SSB

F factor

Short Term Prediction

Change_SSB_2016-2017(%) Change_Catch_2014-2016(%)

F0.1=0.2 FCurrent=1.4

25

7.2 Medium term predictions

7.3 Long term predictions

Medium and long term forecast depends on having a reasonable Stock-Recruitment relationship (SSR). European hake Merluccius merluccius does not show a reasonable SRR (Fig 12) and therefore no medium or long term predictions were performed for this species.

Figure 17.Stock-Recruitment Relationship

26

Draft scientific advice

Based on Indicator Analytic al

reference

point (name

and value)

Current

value from

the analysis

(name and

value)

Empirical reference

value (name and

value)

Trend

(time

period)

Stock

Status

Fishing

mortality

Fishing

mortality

(F0.1 = 0.2)

(Fmax= 0.28)

(Fc=1.4)

Fc/F0.1=7 N

5

IO _Oh

Catch (t)

275 33% percentile, 308

66% percentile, 474

N

6

OI

Stock

abundance

Biomass(t)

763 33% percentile; 795

66% percentile; 1033

N

6

OL

SSB (t)

181 33% percentile; 265

66% percentile; 334

N

11

OL

Recruitment R1 (6

years)

R2 (2013) thousands

14075

11121

Final Diagnosis - In overexplotation (Fcurren =1.4t>F0.1=0.2)

- Relative low biomass; Bcurrent= 763 (t); Biomass at 33rd percentile =

795(t)

Scientific advice for management

- Reduce Fcurrent towards F0.1

- Progressive reduction of the fishing effort

27

Explanation of codes

Trend categories

1) N - No trend 2) I - Increasing 3) D – Decreasing 4) C - Cyclic

Stock Status

Based on Fishing mortality related indicators

1) N - Not known or uncertain – Not much information is available to make a judgment; 2) U - undeveloped or new fishery - Believed to have a significant potential for expansion in

total production; 3) S - Sustainable exploitation- fishing mortality or effort below an agreed fishing mortality or

effort based Reference Point; 4) IO –In Overfishing status– fishing mortality or effort above the value of the agreed fishing

mortality or effort based Reference Point. An agreed range of overfishing levels is provided;

Range of Overfishing levels based on fishery reference points

In order to assess the level of overfishing status when F0.1 from a Y/R model is used

as LRP, the following operational approach is proposed:

If Fc*/F0.1 is below or equal to 1.33 the stock is in (OL): Low overfishing

If the Fc/F0.1 is between 1.33 and 1.66 the stock is in (OI): Intermediate overfishing

If the Fc/F0.1 is equal or above to 1.66 the stock is in (OH): High overfishing

*Fc is current level of F

5) C- Collapsed- no or very few catches;

Based on Stock related indicators

1) N - Not known or uncertain: Not much information is available to make a judgment 2) S - Sustainably exploited: Standing stock above an agreed biomass based Reference Point; 3) O - Overexploited: Standing stock below the value of the agreed biomass based Reference

Point. An agreed range of overexploited status is provided;

Empirical Reference framework for the relative level of stock biomass index

Relative low biomass: Values lower than or equal to 33rd percentile of biomass index in the time series (OL)

Relative intermediate biomass: Values falling within this limit and 66th percentile (OI)

Relative high biomass: Values higher than the 66th percentile (OH)

28

4) D – Depleted: Standing stock is at lowest historical levels, irrespective of the amount of fishing effort exerted;

5) R –Recovering: Biomass are increasing after having been depleted from a previous period;

Agreed definitions as per SAC Glossary

Overfished (or overexploited) - A stock is considered to be overfished when its abundance is below

an agreed biomass based reference target point, like B0.1 or BMSY. To apply this denomination, it

should be assumed that the current state of the stock (in biomass) arises from the application of

excessive fishing pressure in previous years. This classification is independent of the current level of

fishing mortality.

Stock subjected to overfishing (or overexploitation) - A stock is subjected to overfishing if the fishing

mortality applied to it exceeds the one it can sustainably stand, for a longer period. In other words,

the current fishing mortality exceeds the fishing mortality that, if applied during a long period, under

stable conditions, would lead the stock abundance to the reference point of the target abundance

(either in terms of biomass or numbers)