Stock Assessment Form Small Pelagics Reporting year: 2016 Reference year: 2015 Sprat represent a unit stock shared among the Black Sea countries. Its key role is determined by the importance from both commercial and ecological point of view. The sprat fishery takes place in the Black Sea (GFCM Fishing Sub-area 37.4 (Division 37.4.2) and Geographical Sub-area (GSA) 29). The sprat landings highly varied as for 2000-2013 the average catch accounted to 62097.69 tons. In 2014 the total catch accounted 58380 tons, in 2015 almost doubled to 109009 tons, as the major landings belong to Turkey (76996t in 2015). Discards of sprat are evidently very low. The stock was exploited unsustainably during 2010, 2011 and 2012 (but not during 2013). Still in 2014 1nd 2015 the sprat stock been exploited sustainably.
Transcript
Stock Assessment Form
Small Pelagics Reporting year: 2016
Reference year: 2015 Sprat represent a unit stock shared among the Black Sea countries. Its key role is determined by the
importance from both commercial and ecological point of view. The sprat fishery takes place in the Black
Sea (GFCM Fishing Sub-area 37.4 (Division 37.4.2) and Geographical Sub-area (GSA) 29). The sprat landings
highly varied as for 2000-2013 the average catch accounted to 62097.69 tons. In 2014 the total catch
accounted 58380 tons, in 2015 almost doubled to 109009 tons, as the major landings belong to Turkey
(76996t in 2015). Discards of sprat are evidently very low. The stock was exploited unsustainably during
2010, 2011 and 2012 (but not during 2013). Still in 2014 1nd 2015 the sprat stock been exploited
sustainably.
Stock Assessment Form version 0.9
Uploader: Violin Raykov
Stock assessment form
1 Basic Identification Data .............................................................................................................. 2
2 Stock identification and biological information ........................................................................... 4
2.1 Stock unit ............................................................................................................................... 5
2.2 Growth and maturity ............................................................................................................. 5
3 Fisheries information ................................................................................................................... 6
3.1 Description of the fleet ......................................................................................................... 7
Combined method: you can choose both a direct and an indirect method and the name of the combined
method (if it does exist)
2 Stock identification and biological information
The Black Sea sprat (Sprattus sprattus L.) is a key species in the Black Sea ecosystem. Sprat is a marine
pelagic schooling species, sometimes entering in the estuaries (especially as juveniles) and the Azov Sea
and tolerating salinities as low as 4‰. Sprat is one of the most important fish species, being fished and
consumed traditionally in the Black Sea countries. It is most abundant small pelagic fish species in the
region, together with anchovy and horse mackerel and accounts for most of the landings in the north-
western part of the Black Sea. Whiting is also taken as a by-catch in the sprat fishery, although there is no
targeted fishery beyond this (Raykov, 2006) except for Turkish waters. Sprat fishing takes place on the
continental shelf on 15-110 m of depth (Shlyakhov, Shlyakhova, 2011). The harvesting of the Black Sea sprat
is conducted during the day time when its aggregations become denser and are successfully fished with
trawls. The main fishing gears are mid-water otter trawl, pelagic pair trawls and uncovered pound nets. The
species is fast growing; age comprises 4-5 age groups. Sprat has lengths comprised between 50 and 120
mm, the highest frequency pertaining to the individuals of 70-100 mm lengths. The age corresponding to
these lengths was 0+ - 4-4+, the ages 2-2+ - 3-3+ having a significant participation. By 1982, the age classes
4-4+ years had a share of 34% from the catch of this species, then the percentage continually decreased up
to 1995 when this age was not signalled, meaning the increase of the pressure through fishing exerted on
the populations. While the share of this age decreased, the prevalence of 0+ especially 1-1+ ages became
increased. During last years the age structure show the presence of the specimens of 1-1+ and 3; 3+ years,
the catch base being the individuals of 1-1+ and 2-2+ years. The sprat fishery is taking place in the Black Sea
(GFCM Fishing Sub-area 37.4 (Division 37.4.2) and Geographical Sub-area (GSA) 29). The opportunities of
marine fishing are limited by the specific characteristics of the Black Sea. The exploitation of the fish
recourses is limited in the shelf area. The water below 100-150 m is anoxic and contains hydrogen sulphide.
In Bulgarian, Romanian, Russian and Ukrainian waters the most intensive fisheries of Black Sea sprat is
conducted in April till October with mid-water trawls on vessels 15- 40 m long and a small number
vessels >40m. Beyond the 12-mile zone a special permission is needed for fishing. Harvesting of Black Sea
sprat is conducted during the day, when the sprat aggregations become denser and are successfully fished
with mid-water trawls.
The significance of the sprat fishery in Turkey in the last three years has increased and the landings reached
77 thous.t. t in 2015. The main gears used for sprat fishery in Turkey (fishing area is constrained in front of
the city of Samsun) are pelagic pair trawls working in spring at 20-40m depth and in autumn - in deeper
water: 40-80m depths.
2.1 Stock unit
It is assumed that sprat represent a unit stock shared among the Black Sea countries
2.2 Growth and maturity
No maturity studies carried out in 2015
Table 2.2-1:Maximum size, size at first maturity and size at recruitment.
Somatic magnitude measured (LH, LC, etc)* Units*
Sex Fem Mal Both Unsexed
Maximum size observed 13
Reproduction
season
Nov-March
Size at first maturity 6
Reproduction
areas
North western
Black Sea
Recruitment size
Nursery areas North western
Black Sea coastal
zone and marginal
habitats
Table 2.2-2: Growth and length weight model parameters
L∞ k t0 a b
Bulgaria 12.08 0.66 -1.33 0.008 2.78426
Romania 12.1 0.3497 -1.67 0.00642 2.974
Ukraine 12.42 0.286 -1.504 0.008475 2.9691
Turkey 13.039 0.445 -1.096 0.004 1.878849
Sex
Units female male both unsexed
Growth model
L∞
K
t0
Data source SGSABS GFCM
Length weight
relationship
A
B
M
(vector by length or age) 0.95
sex ratio
(% females/total) F51:M49
3 Fisheries information
The sprat fishery is taking place in the Black Sea (GFCM Fishing Sub-area 37.4 (Division 37.4.2) and
Geographical Sub-area (GSA) 29). The opportunities of marine fishing are limited by the specific
characteristics of the Black Sea. The exploitation of the fish recourses is limited in the shelf area. The water
below 100-150 m is anoxic and contains hydrogen sulphide. In Bulgarian, Romanian, Russian and Ukrainian
waters the most intensive fisheries of Black Sea sprat is conducted in April till October with mid-water
trawls on vessels 15- 40 m long and a small number vessels > 40m. Beyond the 12-mile zone a special
permission is needed for fishing. Harvesting of Black Sea sprat is conducted during the day, when the sprat
aggregations become denser and are successfully fished with mid-water trawls.
The significance of the sprat fishery in Turkey in the last three years has increased and the landings reached
77 000 t in 2015. The main gears used for sprat fishery in Turkey (fishing area is constrained in front of the
city of Samsun) are pelagic pair trawls working in spring at 20-40m depth and in autumn - in deeper water:
40-80m depths.
3.1 Description of the fleet
Table 3.1-1: Description of operational units in the stock
Country GSA Fleet
Segment
Fishing
Gear
Class
Group of Target
Species Species
Operational
Unit 1 Bulgaria 29
24<40
12<18
18<24
6<12
OTM Sprat, horse
mackerel,bluefish,anchovy
Alosa immaculata,Atherina
pontica,Raja clavata, Dasyatis
pastinavca,M.merlangius,
Squalus acanthias etc
Operational
Unit 2 Bulgaria 29 -
FPN
GNS
Sprat, anchovy, horse
mackerel
Alosa immaculata,Atherina
pontica,Raja clavata, Dasyatis
pastinavca,M.merlangius,Squalus
acanthias etc
Operational
Unit 3 Romania 29 24<40 OTM
Sprat, anchovy, horse
mackerel
Alosa immaculate,Atherina
pontica,Raja clavata, Dasyatis
pastinavca,M.merlangius,
Squalus acanthias etc
Operational
Unit 4 Romania 29 - FPN,GNS
Sprat, anchovy,horse
mackerel
Alosa immaculata,Atherina
pontica,Raja clavata, Dasyatis
pastinavca,M.merlangius,
Squalus acanthias etc
Operational
Unit 5 Ukraine 29
24<40
12<18
18<24
6<12
Sprat, anchovy,horse
mackerel
Alosa immaculata,Atherina
pontica,Raja clavata, Dasyatis
pastinavca,M.merlangius,
Squalus acanthias etc
Operationa
l Unit 6 Turkey 29
24<40
12<18
18<24
6<12
OTM, Pair
trawls,
Purse
seiners
Sprat, horse
mackerel,bluefish,anchovy,b
onito
Alosa immaculata,Atherina
pontica,Raja clavata, Dasyatis
pastinavca,M.merlangius,
Squalus acanthias etc
Operational
Unit 7
Russian
Federation 29
24<40
12<18
18<24
6<12
OTM Sprat, horse
mackerel,bluefish,anchovy
Alosa immaculata,Atherina
pontica,Raja clavata, Dasyatis
pastinavca,M.merlangius,
Squalus acanthias etc
Table 3.1-2: Catch, bycatch, discards and effort by operational unit
Operational
Units*
Fleet
(n° of
boats)*
Kilos or
Tons
Cat
ch
(sp
ecie
s
ass
ess
ed)
Other species caught
Discards
(species
assessed
)
Discards
(other
species
caught)
Effort units
1 32 2880 spra
t
M.merlangius,D.pastin
aca,Raja
clavata,Sq.acanthias,Al
osa immaculata, etc no -
Kw*days/GT*
days
2
51
stationary
nets and
2000 GNS 1200 spra
t
M.merlangius,D.pastin
aca,Raja
clavata,Sq.acanthias,Al
osa
immaculata,Atherina
pontica etc no -
Days
deployed
3 2 51
spra
t
M.merlangius,D.pastin
aca,Raja
clavata,Sq.acanthias,Al
osa
immaculata,Atherina
pontica etc no -
Kw*days,
GT*days
4 22 39
spr
at
M.merlangius,D.pastin
aca,Raja
clavata,Sq.acanthias,Al
osa
immaculata,Atherina
pontica etc - -
Days, hours
deployed
5 23 8250 spr
at
M.merlangius,D.pastin
aca,Raja
clavata,Sq.acanthias,Al
osa
immaculata,Atherina
pontica etc - -
Kw*days,
GT*days
6 80 68740
spra
t
M.merlangius,D.pastin
aca,Raja
clavata,Sq.acanthias,Al
osa
immaculata,Atherina
pontica etc - -
Kw*days,GT*
days
7 33 2706 spra
t
M.merlangius,D.pastin
aca,Raja
clavata,Sq.acanthias,Al
osa
immaculata,Atherina
pontica etc - -
Kw*days,GT*
days
Total
109009
Table: Catches as used in the assessment
Classification Catch (tn)
BG 3287
GE 2
RO 110
UKR 2237
TU 76996
RU 26377
Total 109009
3.2 Historical trends
The following Tables list the fishing effort data received from Member States through the official DCF data
call in units of kW*days at sea and number of vessels. According to the first table 76% of the total sprat
landings in Bulgarian marine area were realized by fleet segment 24<40 m LOA. In Romania only one
fishing vessel using OTM targeting sprat has been operating in Black Sea. Major fishing gears used for sprat
fishery were stationary uncovered pound nets.
Table DCF nominal fishing effort (GT and kw*days at sea) associated to the LOA segments and % from the
total catch as submitted to JRC through the DCF 2015 Med and Black Sea data call by major gear type 2015
in Bulgaria.
Table 3.2.1. Fishing effort of LOAs vessels using OTM in Bulgarian part for 2015 (NAFA,2015)
Varibale Value measure
unit year segment LOA Fishing
gear
totGTFishDays 10593.38 GTDAYS 2015 TM VL0612 OTM
totkWFishDays 104251.1 KWDAYS 2015 TM VL0612 OTM
totGTFishDays 905571.1 GTDAYS 2015 TM VL1218 OTM
totkWFishDays 8394966 KWDAYS 2015 TM VL1218 OTM
totGTFishDays 267637.8 GTDAYS 2015 TM VL1824 OTM
totkWFishDays 1002344 KWDAYS 2015 TM VL1824 OTM
totGTFishDays 2483836 GTDAYS 2015 TM VL2440 OTM
totkWFishDays 6654107 KWDAYS 2015 TM VL2440 OTM
Commercial CPUE
Commercial CPUE kg.h-1 increased in 2015 in comparison with 2014 in Bulgarian part. The same trend is
detected for the 2008-2015 in Turkey sprat fishery. In Romanian waters a significant drop of CPUE has been
observed due to drastic reduction of the fishing fleet (Figure 3.2.2.).
Table 3.2.2. Effort of vessels using OTM and FPO targeting sprat in 2015, Bulgaria (NAFA,2015)
The main fishing gears targeting sprat in Bulgaria are OTM, FPO and BS. The distribution of CPUE to the
corresponding fishing fleet segments are presented on Table.
initial year Description: OTM Description:FPO
… N vessels kW*days GT*days Hrs fished N vessels kW*days GT*days Hrs fished
2008 29 8272 2396
2009 51 10354 2910 36 1150 123
2010 58 11011 5422 49 1418 151
2011 39 7302.89 2066.43 69 2063 179
2012 8 18.53 223.52
2013 48 2193.11 8240.39
2014 36 7002 1823.3
2015 58 16155.67 3667.638 46 1344.54 138.14
Table 3.2.1. Data regarding sprat fishery fleet, total landings and CPUE (TUIK Fishery Statistics)
Table 3.2.3. CPUE kg/h *1000 of Ukrainian fishing vessels. 1996-2012 (Shlyakhov et al.. 2012);for 2014-2016
Odessa Center of YugNIRO.
TURKEYTotal landing (tons) No of vessels CPUE (tons/years/vessel)
1993 940 2 470
1994 933 2 466.5
1995 1639 2 819.5
1996 1608 2 804
1997 500 4 125
1998 1500 4 375
1999 965 4 241.25
2000 6225 8 778.125
2001 1000 6 166.6667
2002 2050 8 256.25
2003 6025 12 502.0833
2004 5411 16 338.1875
2005 5500 30 183.3333
2006 7311 34 215.0294
2007 11921 40 298.025
2008 39303 54 727.8333
2009 53385 60 889.75
2010 57023 70 814.6143
2011 87141 82 1062.695
2012 12091.7 64 188.9328
2013 9764 60 162.7333
2014 41647.9 82 507.9012
2015 76995.6 118 652.5051
3.3 Management regulations
A quota is allocated in EU waters of the Black Sea (Bulgaria and Romania). No fishery management agreement exists among other Black Sea countries. In the EU Black Sea waters a global (both Romania and Bulgaria) TAC 12 750 tons has been allocated in 2009 and 2010. In 2011 and in 2012 allocated quota in Bulgarian waters was at the rate of 8 032.5 t sprat (Council Regulation 5/2012) and 3 442.49 t for Romanian waters .The decreasing trend in indices since 2008 was observed despite of quotas regime in force in community waters. From the catches of fish only the turbot species (Scophthalmus maximus) and sprat (Sprattus sprattus) are subject to quotas and are included in the National data collection program (NDCP). The applied quotas are precautionary because it is not possible their biomass to be calculated for the whole water basin of the Black Sea.
Jan-Mar Apr-Jun Jul-Sep Oct-Dec average
1996 0.41 0.96 1.27 0.64 820
1997 0.36 0.84 1.11 0.56 720
1998 0.46 1.08 1.42 0.72 920
1999 0.5 1.2 1.58 0.8 1020
2000 0.85 2.22 2.8 1.41 1820
2001 0.65 1.55 2 1.03 1310
2002 0.85 2.12 2.75 1.39 1780
2003 0.45 1.1 1.45 0.65 910
2004 0.4 1.2 1.5 0.75 960
2005 0.48 1.1 1.55 0.75 970
2006 0.5 1.25 1.67 0.85 1070
2007 0.45 1.2 1.55 0.8 1000
2008 0.83 2 2.6 1.3 1680
2009 0.85 2.1 2.75 1.4 1780
2010 0.8 2.15 2.8 1.4 1790
2011 0.55 1.77 2.17 1.15 1440
2012 240 1580 1710 550 1020
2013 na na na na na
2014* 900
2015* 1000
2016* 900
*North-Western part of Black Sea
Ukrainian commercial fleet CPUE kg*h-1 by years and quarters
Table 3.3.1. EC quota and recommended Total allowable catch of sprat in EU waters for 2008-2010,
and2014-2015.
Year
National data
2008 2009
2010
2014
2015
Spicies Sprat
(SPR)
Sprat
(SPR)
Sprat
(SPR)
Sprat
(SPR)
Sprat (SPR)
Quota. t
15 0002 12 7502 12 7502
11 4752
8032.51
11 4752
8032.51
Total catch. t 4 300.0363(BG)
234 (RO)
4 541.348
(BG)
92(RO)
4 039. 966
(BG)
39(RO)
3 957.895
(BG)
131.3
(RO)
3 156.832
(BG)
87.458(RO)
Biomass. t 32 718.33
60 0005
41 761.3983
60 0005
75 080.204
59 6005
48 201.74
-
44 282
60 0005
Recommended
TAC
average
13 746.573
11 469.93
12 5004
- -
NB: 1 - quota according to Regulation (EU) № 1579/2007. Regulation (EU) № 1139/2008. Regulation (EU)
3 - Source of data: Institute of Oceanology – BAS. Bulgaria
4 - Source of data: Institute of Oceanology – BAS. Bulgaria and NIMRD,Romania
5National Institute for Marine Research and Development. Romania
Sprat fishery in Turkey was firstly promoted by the Commercial Fishery Advice of General
Directorate of Fishery with date of 02.08.2002 and number of 24 834 regarding the years 2002-2004
(Section 2. Article 5) (Anonymous 2002). New management criteria were brought into force for sprat
fishery. These criteria were summed up in four topics as:
(1) Regulations about fishing area: Sprat fishery by pelagic trawls should be conducted only along Samsun shelf area. The coordinates of this area were specified. But except sprat. the fishery was allowed for anchovy. horse mackerel and bluefish along other trawling areas in Black Sea.
(2) Regulations about fishing gear: In Turkey pelagic trawls operate as paired vessels. Vessels engaged in sprat fishery need to receive licence eligible only for one fishing period from Samsun City Directorate of Food. Agriculture and Livestock. The single vessel operation in pelagic fishery seems to be inconvenient for Turkey at least for now as the fisherman can quickly change the gear to bottom trawling during operation.
(3) Regulations about time periods: Though pelagic fishing period starts in 15 September as same as bottom trawling. it lasts to 15 May. Bottom trawling ends with 15 April. There is no limitation in distance from land for pelagic trawling.
(4) Regulations about depth: The pelagic fishery is banned in waters shallower than 18 m in fishing area between 15 September and 15 April. But between 15 April-15 May it is allowed in waters deeper than 36 m limited with offshore of Çayağzı Cape (Samsun-Yakakent) in west and Akçay estuary (Samsun - Ordu city border) in east (Anonymous. 2006). Sprat catch reaches a maximum in this one month-period and provide a great economic input for fishermen. Conversely with bottom trawling depth limitations are in force in pelagic fishery instead distance from land. But as mentioned above the depth limitation is increased to 36 m by 15 April in order to protect spawning adults and juveniles on coastal zone.
Table Minimum landing size of sprat in the Black sea region
BG GE RO RU TR UA
Sprattus
sparttus TL=7cm SL=6cm TL=7cm SL= 6cm NO SL=6cm
Legend: TL-total length; SL-standard length;
3.4 Reference points
Table 3.4-1: List of reference points
Criterion Current
value Units
Reference
Point Trend Comments
B
SSB 278 745 t
increasi
ng
F 0.36 =F≤0.64
decrea
sing
Y
78 490.
in the range
of 39 907 to
45 504t
increasi
ng
CPUE
4 Fisheries independent information
4.1 Pelagic survey
The spring survey, in depths between 13.8 m and 80 m, is covered almost entirely continental shelf
Romanian coast, between St. Gheorghe and Vama Veche. During the research from survey, sweeping area
procedures were conducted on an surface of 2,725 Mm2. The analysis of structure by lengths and mass cards of sprat during survey, has highlighted the presence of
mature specimens and a high homogeneity of cards. The length of turbot individuals are within the limits of
classes of length 50,0-115,0 mm / 1.155 – 8.473 g. The dominant classes are those of 70.0 - 95.0 mm / 2.639
– 5.627 g (Fig. xxx). The dominant females 59.32 %, males (40.68 %). The average body length was 82.468
mm and the average mass of 3.674 g.
Fig. 4.1.1. Structure by lengths and mass cards of sprat during survey
Age composition of sprat catches indicates the presence of individuals from 1 to 3 years. Most of the
individuals caught are 1 years old (56.0 % of all specimens analyzed), followed closely by those of 2 years
(35.3 %) and 3 years (8.7 %)(Fig. 4.1.12).
Fig.4.1.2. Structure by age composition of sprat during survey
Pursuant to the Bilateral Agreement, Romania performed the pelagic surveys of 2015 in the Romanian Black
Sea area, using the ”Steaua de Mare” R/V, during quarters 2 (June) and 4 (November). The following
parameters were considered during the surveys:
0.0
5.0
10.0
15.0
20.0
25.0
%
Size class (mm)
TOTAL 2015sprat
no. 10.061
Female Male
1 …
2 years35.3 %
3 years8.7 %
TOTAL 2015Sprat
no. 4.763
Mid water trawl (57/63 - 62 m):
➢ trawling speed - 2.2 - 2,3 Kts;
➢ horizontal opening - 22 m;
➢ trawling time - 30 min.
The results obtained were presented as maps and tables comprising data on:
➢ surface of the swept grid (Nm2, m2);
➢ mean weight per area unit (g/m2, t/Mm2);
➢ weight variation ranges per area unit;
➢ total biomass values (t).
In depths between 13.8 m and 62 m, is covered almost entirely continental shelf Romanian coast, between St.
Gheorghe and Vama Veche. During the research from july survey 2015, sweeping area procedures were
conducted on an surface of 2,725 Mm2. The analysis of data obtained by sweeping area procedures
conducted with pelagic trawl reveals a low average of sprat catches 9.78 t / Nm2 (table 4.1.1.). Estimated
biomass of sprat agglomerations for spring survey in the investigated area was of about 26,650.5 tones, and the estimated one for the Romanian platform was considered only up to distance of 50 Mm, was about
48,903 tones. In 2015, sprat agglomerations biomass increased compared to the previous year by about
25.40%, but the biomass was smaller by 18.5 % compared to 2013.
Table 4.1.1. - Assessment of sprat agglomerations in June 2015, pelagic trawl survey, Romanian area
Depth range (m) 0 - 30m 30 – 50m 50-70 m Total
Investigated area (Nm2) 625 1125 975 2725
Variation of the catches (t/ Nm2) 24.2 0.25-28.37 0.074-24.46 0.074-
28.37 Average catch (t/ Nm2) 24.2 12.33 4.75 9.78
Biomass of the fishing agglomerations (t) 15125 13871.25 4631.25 26650.5
Biomass extrapolated the Romanian shelf (t) 48,903
Fig. 4.1.3. Distribution of the sprat agglomerations in the 2nd Quarter 2015 in Romanian marine waters
PARAMETERS - Sprat
b = 2.834801
q = - 2.05059
a = 0.0084
L∞ = 12.105
k = 0.264
to = - 3.28
M = 0.569
4.2.Pelagic survey - Bulgaria
Figure 4.2.1. Stations for PS in Bulgarian waters –dec.2015
Total 36 hauls were performed in Bulgarian waters on board of F/V Egeo3.Total number of the species was
17,13 fish,1 decapoda, 1 molusc, 1 macrozzoplankton 1 ascidia.Most often were sprat (100%), Aurelia
aurita (95.28%) as the rest of species were presented with low percents
ma).Themost dense agglomerations were detected in stratum 50-75m 7318.46 kg.km2 and mean value for
all stratums 4890 kg.km2.
Figure 4.2.2. Biomass index, t in Bulgarian waters, 2015
Figure 4.2.3. CPUA kg.km2 in different depth strata.
Figure 4.2.4. Spatial distribution and CPUA kg.km2 in Bulgarian waters, December 2015
5 Stock Assessment
In this section there will be one subsection for each different model used, and also different model assumptions runs should be documented when all are presented as alternative assessment options.
5.1 Integrated catch-at-age analysis (ICA)
5.1.1 Model assumptions
We used Integrated Catch-at-age Analysis (ICA; Patterson and Melvin, 1996). ICA is a statistical catch-at-age
method based on the Fournier and Deriso models (Deriso et al., 1985). It applies a statistical optimization
procedure to calculate population numbers and fishing mortality coefficients-at-age from data of catch
numbers-at-age and natural mortality. The dynamics of a cohort (generation) in the stock are expressed by
two non-linear equations referred to as a survival equation (exponential decay) and a catch equation:
where C, N, M, and F are catch, abundance, natural mortality, and fishing mortality, respectively, and a and
y are subscript indices for age and year.
The algorithm initially estimates population numbers and fishing mortality fitting a separable model, when
F is assumed to conform to a constant selection pattern (fishing mortality-at-age), but fishing mortality by
year is allowed to vary. The F matrix is then modelled as a multiplication of the year-specific F and the
specified selection pattern. This procedure substantially diminishes the number of parameters in the
model.
In its second stage, the ICA algorithm minimizes the weighted Sum of Square Residuals (SSR) of observed
and modelled catch and relative abundance indices (CPUE), assuming Gaussian distribution of the log
residuals:
min [a,y pca,y (log Ca,y – log Ĉa,y)2 + a,y,f pia,f (log Ia,y,f – log Î a,y,f)2,
where C, Ĉ, I, and Î are observed and estimated catch and age-structured index, respectively, and a, y, and f
are subscript indices for age, year, and fleet, respectively. Weights associated with catches and different
indices (pc, pi) are ideally set equal to the inverse variances of catch and index data, and can be calculated
based on the residuals between modelled and observed values. However, weights are usually set by the
user on the basis of some information about the reliability of different indices and current experience with
modelling the stock. Indices are defined as related to population numbers by the equations:
Î a,y = Na,y*exp(–Fa,y – M)
Î a,y = qa*Na,y*exp(–Fa,y – M)
Î a,y = qa*(Na,y*exp(–Fa,y – M))ka .
The two unknown parameters (qa, an age-specific catchability, and k, a constant) are estimated according
to the assumed relationship between the population and the abundance index, which has to be specified as
being one of the above – identity, linear, or power, respectively.
5.1.2 Scripts
If a script is available which incorporates the stock assessment run (e.g. if using FLR in R) it should be provided here in order to create a library of scripts.
5.1.3 Input data and Parameters Output Generated by ICA Version 1.4 ------------------------------------ SPRAT 2015 ----------
ICA was run assuming a constant selection pattern in 2009-2015 (Fig. 6.1.3.2) with reference F at age 2 and
Selection at the last ‘real’ age (S4) equal 1.
The results of the ICA show a reasonable agreement with tuning data (Fig. 6.1.3.3, 6.1.3.4, 6.1.3.5, 6.1.3.6)
The overall fit and partial SSR converged to unique minima (Fig. 6.1.3.1).
Analyses of the main population parameters (abundance, catch, fishing mortality, Fig. 6.1.3.7) indicate that
the sprat stock has recovered from the depression in the 1990s due to good recruitment in 1999-2001 and
the biomass and catches have gradually increased over the 1990s and during the 2000s reached levels
comparable to the previous periods of high abundance (Fig. 6.1.3.8). The stock estimates reveal the cyclic
nature the sprat population dynamics. The years with strong recruitment were followed by years of low to
medium recruitment which leads to corresponding changes in the the Spawning Stock Biomass (SSB). High
fishing mortalities (F1-3) were observed during the stock collapse in the early 1990s. in 2004-2005. and
2009-2011. In 2011 the highest ever total catch of 120 708t was recorded due mainly to the intensive
development of the Turkish sprat fishery. Over 2007-2011 years the levels of biomass and catches were
comparable with the highest figures reported, but in 2009-2011 - a decreasing trend in recruitment
becomes evident (Fig. 6.1.3.7.). In 2012 and 2013 catches have dropped probably related to lower
recruitment and subsequently lower stock densities combined with unfavorable environmental conditions,
but in 2014-2015 catches increased up comparable to the highest recorded levels (Fig. 6.1.3.8.). Over the
last few years recruitment is relatively high, whilst the SSB and fishing mortality are kept at average level
preconditioning the good state of the stock.
Fig. 6.1.3.1 Trajectories of the total Sum of Squared Residuals (SSR) and the partial SSRs of the two tuning
fleets as functions of the reference F.
Fig. 6.1.3.2 Selection pattern estimated by the separable model
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F
SS
R
Total SSR
Bulgaria CPUE
Ukraine CPUE
Rom PTS
Turkey CPUE
0
0.5
1
1.5
2
2.5
0 1 2 3 4 5
Age (y)
Sele
cti
on
Fig. 6.1.3.3 Adjustment of ICA: time-series of estimated abundance-at-age and age-structured Bulgarian
CPUE (best fit is given by linear relationships and r2 are displayed): (a) Age 1. (b) Age 2. (c) Age 3. (d) Age 4.
Figure 6.1.3.4. Adjustment of ICA: time-series of estimated abundance-at-age and age-structured Ukrainian
CPUE (best fit is given by linear relationships and r2 are displayed): (a) Age 1. (b) Age 2. (c) Age 3. (d) Age 4.
0
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lga
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n C
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Ab
un
da
nc
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(10
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e a
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R2 = 0.44b
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lga
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n C
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4 (
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Ab
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da
nc
e a
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(10
-9)
R2 = 0.51
d
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-3)
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e a
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R2 = 0.36c
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rain
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(1
0-3
)
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(10
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R2 = 0.39a
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rain
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(1
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(10
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(1
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un
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nc
e a
ge
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(10
-9)
R2 = 0.30
d
Figure 6.1.3.5. Adjustment of ICA: time-series of estimated abundance-at-age and age-structured Ukrainian CPUE (best fit is given by linear relationships and r2 are displayed): (a) Age 1. (b) Age 2. (c) Age 3. (d) Age 4.
Figure 6.1.3.6. Adjustment of ICA: time-series of estimated abundance-at-age and age-structured Romanian
PTS (best fit is given by linear relationships and r2 are displayed): (a) Age 1. (b) Age 2. (c) Age 3.
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(10
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R2 = 0.19c
Fig. 6.1.3.7. Time-series of sprat population estimates: A. recruitment (line) and SSB (grey); B. landings
(grey) and average fishing mortality (ages 2–4. line).
1999
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2006
2007
2008
2009
2010
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Landings F 1-3B
Fig. 6.1.3.8. Time-series of sprat population estimates – present results combined with historical estimates:
A. recruitment (line) and SSB (grey); B. landings (grey) and average fishing mortality (ages 2–4. line).
5.1.5 Robustness analysis
Comparison was done between ICA runs with no shrinkage and shrinkage (5 years, var=0.2, Fig. Fig. 6.2.1). Current results are very similar to previous year estimates.
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Fis
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Ca
tch
10
-3
Landings F 1-3B
Fig. 6.2.1. Comparing sprat skok estimates from ICA in 2015 and 2014 A. Average (years 1-3) fishing
mortality; B. Recruitment, C. SSB.
Diagnostics from the ICA model:
No of years for separable analysis : 7 Age range in the analysis : 0 . . . 5 Year range in the analysis : 1996 . . . 2015 Number of indices of SSB : 0 Number of age-structured indices : 5 Parameters to estimate : 40 Number of observations : 266 Conventional single selection vector model to be fitted.
0
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1
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1.61995
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2015 2015 shrinkage 2014A. F1_3
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C. SSB
PARAMETER ESTIMATES іParm.і і Maximum і і і і і і Mean of і і No. і і Likelh. і CV і Lower і Upper і -s.e. і +s.e. і Param. і і і і Estimateі (%)і 95% CL і 95% CL і і і Distrib.і Separable model : F by year 1 2009 0.5077 20 0.3418 0.7542 0.4149 0.6213 0.5182 2 2010 0.5731 18 0.3960 0.8292 0.4746 0.6919 0.5833 3 2011 1.0427 16 0.7612 1.4284 0.8881 1.2243 1.0563 4 2012 0.5251 19 0.3594 0.7671 0.4328 0.6371 0.5350 5 2013 0.2217 21 0.1444 0.3403 0.1781 0.2759 0.2270 6 2014 0.4009 20 0.2663 0.6035 0.3253 0.4939 0.4097 7 2015 0.4993 26 0.2989 0.8341 0.3843 0.6487 0.5167 Separable Model: Selection (S) by age 8 0 0.0522 26 0.0310 0.0878 0.0400 0.0681 0.0541 9 1 0.2794 19 0.1899 0.4109 0.2294 0.3402 0.2848 2 1.0000 Fixed : Reference Age 10 3 1.9777 15 1.4696 2.6614 1.6996 2.3011 2.0005 4 1.0000 Fixed : Last true age Separable model: Populations in year 2015 11 0 126927992 64 35647409 451946311 66399505 242633059 156573694 12 1 61062875 27 35460070 105151363 46275367 80575799 63456214 13 2 27684711 19 18761805 40851252 22700383 33763450 28235531 14 3 7722813 18 5416156 11011838 6444082 9255288 7850381 15 4 1027071 21 679261 1552976 831738 1268279 1050180 Separable model: Populations at age 16 2009 1229324 30 678689 2226699 907896 1664549 1287106 17 2010 1087981 23 692602 1709067 864074 1369909 1117250 18 2011 924012 22 590685 1445436 735417 1160970 948405 19 2012 419805 24 258330 682212 327687 537817 432886 20 2013 382889 22 246098 595714 305585 479749 392751 21 2014 964446 19 664085 1400657 797255 1166698 982083 Age-structured index catchabilities Bul Linear model fitted. Slopes at age : 22 1 Q .1871E-02 17 .1585E-02 .3120E-02 .1871E-02 .2643E-02 .2257E-02 23 2 Q .5110E-02 17 .4330E-02 .8516E-02 .5110E-02 .7216E-02 .6164E-02 24 3 Q .7129E-02 17 .6034E-02 .1192E-01 .7129E-02 .1009E-01 .8610E-02 25 4 Q .4508E-02 17 .3802E-02 .7625E-02 .4508E-02 .6430E-02 .5470E-02 Ukr Linear model fitted. Slopes at age : 26 1 Q .3761E-02 18 .3159E-02 .6436E-02 .3761E-02 .5407E-02 .4584E-02 27 2 Q .8531E-02 17 .7200E-02 .1439E-01 .8531E-02 .1215E-01 .1034E-01 28 3 Q .1494E-01 17 .1260E-01 .2527E-01 .1494E-01 .2131E-01 .1812E-01 29 4 Q .5001E-02 17 .4217E-02 .8458E-02 .5001E-02 .7133E-02 .6067E-02 Rom survey Linear model fitted. Slopes at age : 30 1 Q .1213E-02 26 .9447E-03 .2619E-02 .1213E-02 .2040E-02 .1627E-02 31 2 Q .3132E-02 25 .2445E-02 .6723E-02 .3132E-02 .5248E-02 .4192E-02 32 3 Q .5121E-02 26 .3981E-02 .1113E-01 .5121E-02 .8650E-02 .6888E-02 Turkey CPUE Linear model fitted. Slopes at age : 33 1 Q .9681E-03 22 .7802E-03 .1883E-02 .9681E-03 .1518E-02 .1243E-02 34 2 Q .3827E-02 23 .3059E-02 .7631E-02 .3827E-02 .6100E-02 .4965E-02
35 3 Q .5930E-02 23 .4734E-02 .1188E-01 .5930E-02 .9482E-02 .7708E-02 36 4 Q .3128E-02 23 .2504E-02 .6212E-02 .3128E-02 .4973E-02 .4051E-02 RESIDUALS ABOUT THE MODEL FIT ------------------------------
------+------------------------------------------------------------------------ Turkey ------ ------+-------------------------------------------------------------------------------------------------------- Age | 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 ------+-------------------------------------------------------------------------------------------------------- 1 | 0.774 -0.015 -0.273 -0.124 -0.066 0.680 -0.197 0.468 ******* -0.400 -0.766 -0.273 0.194 2 | -0.418 0.874 -0.671 -0.329 -0.416 0.130 -0.079 0.449 ******* 0.149 -0.615 ******* 0.931 3 | -0.073 -1.465 -0.386 -0.992 -0.533 0.181 0.573 1.582 ******* 0.884 -0.597 ******* 0.833 4 | 0.200 -1.790 -1.126 -1.468 0.046 0.639 0.869 1.207 ******* 0.476 -0.290 0.848 0.398 ------+-------------------------------------------------------------------------------------------------------- PARAMETERS OF THE DISTRIBUTION OF ln(CATCHES AT AGE) ----------------------------------------------------- Separable model fitted from 2009 to 2015 Variance 0.2133 Skewness test stat. -0.5056 Kurtosis test statistic -0.5755 Partial chi-square 0.2048 Significance in fit 0.0000 Degrees of freedom 14 PARAMETERS OF THE DISTRIBUTION OF THE AGE-STRUCTURED INDICES ------------------------------------------------------------ DISTRIBUTION STATISTICS FOR Bul Linear catchability relationship assumed Age 1 2 3 4 Variance 0.0289 0.0285 0.0974 0.2100 Skewness test stat. 0.4833 0.9616 0.4582 -0.8920 Kurtosis test statisti -0.2751 -0.6495 -0.5769 -0.5994 Partial chi-square 0.0495 0.0509 0.1982 0.5857 Significance in fit 0.0000 0.0000 0.0000 0.0000 Number of observations 20 20 20 20 Degrees of freedom 19 19 19 19 Weight in the analysis 0.2500 0.2500 0.2500 0.2500 DISTRIBUTION STATISTICS FOR Ukr Linear catchability relationship assumed Age 1 2 3 4 Variance 0.0387 0.0787 0.1333 0.5263 Skewness test stat. -0.1626 -0.2628 -1.7989 -1.5875 Kurtosis test statisti -0.3112 -0.0620 1.7592 -0.5221 Partial chi-square 0.0560 0.1266 0.2337 1.3590 Significance in fit 0.0000 0.0000 0.0000 0.0000 Number of observations 18 19 19 20 Degrees of freedom 17 18 18 19 Weight in the analysis 0.2500 0.2500 0.2500 0.2500 DISTRIBUTION STATISTICS FOR Rom survey Linear catchability relationship assumed Age 1 2 3 Variance 0.1201 0.0366 0.1536 Skewness test stat. -0.0865 0.0995 -0.5507
Kurtosis test statisti -0.5711 -0.4281 -0.4457 Partial chi-square 0.0668 0.0211 0.1009 Significance in fit 0.0000 0.0000 0.0000 Number of observations 7 7 7 Degrees of freedom 6 6 6 Weight in the analysis 0.3333 0.3333 0.3333
DISTRIBUTION STATISTICS FOR Turkey Linear catchability relationship assumed Age 1 2 3 4 Variance 0.0516 0.0791 0.2066 0.2382 Skewness test stat. 0.4654 0.6833 0.1657 -0.9945 Kurtosis test statisti -0.4709 -0.6943 -0.5842 -0.5770 Partial chi-square 0.0540 0.0753 0.2183 0.3706 Significance in fit 0.0000 0.0000 0.0000 0.0000 Number of observations 12 11 11 12 Degrees of freedom 11 10 10 11 Weight in the analysis 0.2500 0.2500 0.2500 0.2500 ANALYSIS OF VARIANCE -------------------------- Unweighted Statistics Variance SSQ Data Parameters d.f. Variance Total for model 120.2160 266 40 226 0.5319 Catches at age 3.3454 35 21 14 0.2390 Aged Indices Bul 27.7218 80 4 76 0.3648 Ukr 57.8951 76 4 72 0.8041 Rom survey 5.5850 21 3 18 0.3103 Turkey 24.1804 46 4 42 0.5757 Weighted Statistics Variance SSQ Data Parameters d.f. Variance Total for model 10.5624 266 40 226 0.0467 Catches at age 2.9865 35 21 14 0.2133 Aged Indices Bul 1.7326 80 4 76 0.0228 Ukr 3.6184 76 4 72 0.0503 Rom survey 0.6206 21 3 18 0.0345 Turkey 1.5113 46 4 42 0.0360
5.1.6 Retrospective analysis, comparison between model runs, sensitivity analysis, etc.
5.1.7 Assessment quality
ICA combines the power and accuracy of a statistical model with the flexibility of setting different options
of the parameters (e.g. a separable model accounting for age effects) and for this raison is suitable for a
short living species (age 5 at maximum) such as the Black Sea sprat. ICA has previously been applied to
Black Sea sprat by Daskalov (1998), Pilling et al. 2009, and Daskalov et al. 2010.
6 Stock predictions
Table 7.1. Sprat in the Black Sea. Input to short term prediction.
2016
age stock size (000) M maturity weight in stock (kg) exploitation pattern weight in catch (kg)
0 142668864 0.6400 0.0000 0.001 0.0261 0.0018
1 73291590 0.9500 1.0000 0.0024 0.1395 0.0031
2 20540028 0.9500 1.0000 0.0033 0.4993 0.0056
3 6497378 0.9500 1.0000 0.0041 0.9874 0.0073
4 1112275 0.9500 1.0000 0.0054 0.4993 0.0094
5 241774 0.9500 1.0000 0.01 0.4993 0.0082
2017
age stock size (000) M maturity weight in stock (kg) exploitation pattern weight in catch (kg)
0 142668864 0.6400 0.0000 0.0010 0.0261 0.0018
1 0.9500 1.0000 0.0024 0.1395 0.0031
2 0.9500 1.0000 0.0033 0.4993 0.0056
3 0.9500 1.0000 0.0041 0.9874 0.0073
4 0.9500 1.0000 0.0054 0.4993 0.0094
5 0.9500 1.0000 0.0100 0.4993 0.0082
2018
age stock size (000) M maturity weight in stock (kg) exploitation pattern weight in catch (kg)
0 142668864 0.6400 0.0000 0.0010 0.0261 0.0018
1 0.9500 1.0000 0.0024 0.1395 0.0031
2 0.9500 1.0000 0.0033 0.4993 0.0056
3 0.9500 1.0000 0.0041 0.9874 0.0073
4 0.9500 1.0000 0.0054 0.4993 0.0094
5 0.9500 1.0000 0.0100 0.4993 0.0082
The status quo fishing in 2016 would result in landings 78 490. and SSB of 278 745 t. Thus the forecasted
2016 SSB is expected to increase by about 13 % compared to 2015 (SSB=275 120 t) and total catch to
decrease by about 28% from the catch recorded in 2015 – 108 400t. In 2017 and 2018 the status quo model
predicts a slight increase in biomass and catches relative to 2016 (Table 6.7.2).
Recruitment estimates are rather imprecise due to the lack of survey data. Recruitment have increased up
to 2008, afterward the trend reversed. In short-term forecast we used a geometric mean over 2011-2013
equal of 142 668 864 000 specimens.
Catches have been very high during 2009-2011 due to quickly expending Turkish fishery. In 2012 and 2013
total catches decreased. The largest drop in the catches was due to the low catches by the Turkish fishery.
Due to increasing recruitment, catches raised again in 2014 and 2015. The status quo prediction is for
relatively high catches (around 80 000 t) for the 2017-2018.
Given that the state of the stock depends greatly on a variable recruitment, the dynamic nature of
developing Turkish sprat fishery and the lack of quota constraints on the sprat fisheries, the status quo
assumption must be taken with a caution when considered in management advice.
More management options through multiplications of the fishing mortality are given in Table 6.7.2. The
Fmsy level of fishing mortality of 0.64 (corresponding to exploitation rate of 0.4. Patterson 1992) would
yield catches about 90 000 t.
Table 6.7.2 Sprat in the Black Sea. Management option table providing short term prediction.
SSB Catch
F-factor reference F stock biomass sp. stock biomass catch in weight F-factor reference F stock biomass sp. stock biomasscatch in weight stock biomass sp. stock biomasscatch 2018-2016 2017-2016
Final Diagnosis Example: In intermediate level of overfishing and overexploited with
low level of biomass
State the rationale behind that diagnoses, explaining if it is based on analytical or on empirical
references
7.1 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)
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)
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