Evaluating the use of the Daily Egg Production Method for stock assessment of blue mackerel, Scomber australasicus
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
CSIRO PUBLISHING
Marine and Freshwater Research, 2009, 60, 112–128 www.publish.csiro.au/journals/mfr
Evaluating the use of the Daily Egg Production Method for
stock assessment of blue mackerel, Scomber australasicus
T. M. WardA,B , P. J. RogersA , L. J. McLeayA and R. McGarveyA
A South Australian Research and Development Institute, Aquatic Sciences,
PO Box 120, Henley Beach, SA 5022, Australia.
B Corresponding author. Email: ward.tim@saugov.sa.gov.au
Abstract. The present study evaluates the suitability of the Daily Egg Production Method (DEPM) for stock assessment of
blue mackerel, Scomber australasicus and assesses methodological options for future applications. In southern Australia,
estimates of mean daily egg production were higher for Californian Vertical Egg Tow (CalVET) nets than bongo nets, and in
eastern Australia, were higher in October 2003 than July 2004. Estimates of spawning area for southern Australia were three
times higher for bongo nets than CalVET nets. Similar estimates of spawning area were obtained using standard (manual)
gridding and natural neighbour methods. Large samples and reliable estimates of all adult parameters were obtained
for southern Australia. Relatively few spawning adults were collected off eastern Australia. Preliminary best estimates
of spawning biomass for southern and eastern Australia were 56 228 t and 29 578 t, respectively, with most estimates
within the ranges of 45 000–68 000 t and 20 000–40 000 t respectively. The DEPM is suitable for stock assessment of
S. australasicus. Several technical refinements are required to enhance future applications, including: genetic techniques
for identifying early stage eggs; a temperature–egg development key; improved methods for sampling adults off eastern
Australia; and measurements of the degeneration rates of post-ovulatory follicles at several temperatures.
Additional keywords: batch fecundity, scombrids, sex ratio, spawning area, spawning biomass, spawning fraction.
Introduction The central tenet of the DEPM is that spawning biomass can
The Daily Egg Production Method (DEPM) is acknowledged be calculated by dividing the mean number of pelagic eggs pro-
to be a suitable technique for stock assessment of small duced per day throughout the spawning area (i.e. total daily egg
pelagic fishes, especially anchovy (Engraulis spp.) and sardine production) by the mean number of eggs produced per unit mass
(Sardinops sagax) (Stratoudakis et al. 2006). For example, in of adult fish (i.e. mean daily fecundity, Parker 1980, 1985). Total
Australia, the method has been applied successfully to S. sagax daily egg production is the product of mean daily egg production
off Western Australia (Fletcher et al. 1996; Gaughan et al. (P0 ) and total spawning area (A). Mean daily fecundity is calcu-
2004), South Australia (Ward and McLeay 1998; Ward et al. lated by dividing the product of mean sex ratio (by weight R),
2001) and Queensland (Staunton Smith and Ward 2000) and mean batch fecundity (number of oocytes in a batch, F) and mean
was recently assessed as a tool for estimating the spawning spawning fraction (proportion of mature females spawning each
biomass of redbait (Emmelichthys nitidus) off Tasmania (Neira night S) by mean female weight (W). Hence, spawning biomass
et al. 2008). The DEPM has been evaluated and used for stock (SB) is calculated according to the equation:
assessment of chub mackerel (Scomber japonicus) in Japan, SB = P0 × A/(R × F × S/W)
which is morphologically and genetically similar to blue mack-
erel (Scomber australasicus) (Watanabe et al. 1999). A related The DEPM can be applied to fishes that spawn multiple
study of the reproductive biology of S. australasicus suggested batches of pelagic eggs during an extended spawning season
that the DEPM may be a suitable method for stock assessment (e.g. Lasker 1985). Data used to estimate the DEPM parame-
of this species (Rogers et al. 2009). ters are obtained during fishery-independent surveys during the
S. australasicus Cuvier, 1832 is a key species for recreational spawning season. The key assumptions are that: (1) the survey
anglers throughout Australia, being targeted both as live-bait is conducted during the main (preferably the peak) spawning
for larger pelagic species and as a table-fish. This species is season; (2) the entire spawning area is sampled; (3) eggs are
also taken in small quantities in multi-species fisheries in all sampled without loss and identified without error; (4) levels of
Australian states (Ward and Rogers 2007). S. australasicus is a egg production and mortality are consistent across the spawning
prescribed quota species for the Commonwealth Small Pelagic area; and (5) representative samples of spawning adults are col-
Fishery (SPF). The newly developed draft Harvest Strategy for lected during the survey period (Lasker 1985; Stratoudakis et al.
the SPF identifies the DEPM as the preferred technique for stock 2006). The degree to which these assumptions are met affects the
assessment of quota species. accuracy and precision of estimates of spawning biomass. Some
© CSIRO 2009 10.1071/MF08134 1323-1650/09/020112Evaluating the DEPM for Scomber australasicus Marine and Freshwater Research 113
assumptions, such as that the levels of daily egg production and Sampling methods
mortality are consistent across the spawning area, are rarely, if Ichthyoplankton samples were collected using CalVET
ever, fully upheld. (Californian Vertical Egg Tow) nets (Smith et al. 1985) and
Estimates of each DEPM parameter, especially mean bongo nets. CalVET nets were used in southernAustralian waters
daily egg production, typically have high levels of variance only and had an internal mouth diameter of 300 mm and mesh
(Stratoudakis et al. 2006). This factor combined with the multi- size of 330 µm. Nets were deployed to within 10 m of the seabed
plicative nature of the model means that estimates of spawning at depths 80 m. Bongo
biomass are usually accurate but imprecise (Smith 1993; Hunter nets used in southern and eastern Australian waters had inter-
and Lo 1997; Stratoudakis et al. 2006). nal mouth diameters of 580 and 600 mm respectively. The mesh
Successful application of the DEPM is more reliant on sizes of bongo nets were 330 and 500 µm in southern Australia
detailed knowledge of the biological and ecological charac- and 300 and 500 µm in eastern Australia. In southern Australia,
teristics of the species and population than most other stock bongo nets were deployed to within 10 m of the seabed at depths
assessment methods (Stratoudakis et al. 2006). Initial studies 110 m. In eastern
necessarily focus on identifying the timing and location of the Australian waters, bongo nets were deployed to within 2–5 m of
spawning area and tend to place comparatively less emphasis the seabed in waters up to 200 m deep.
on sampling spawning adults. Collecting representative sam- Conductivity–depth–temperature recorders (CTDs, usually
ples of spawning adults has proven difficult for some species Sea-bird Electronics, Bellevue, WA) were usually deployed with
in some locations. As a result, many studies in which the DEPM the plankton nets. General Oceanics (Miami, FL) flowmeters
has been used for stock assessment of small pelagic fishes have were used to estimate the distance travelled by each net. Factory
failed to obtain temporally coherent estimates of all parameters calibrations were used to estimate volumes of water filtered by
(e.g. Lo et al. 1996, 2005). Despite the absence of all the infor- the two nets. During the surveys in southern Australia, wire
mation required to estimate mean daily fecundity precisely for length during each net deployment was measured to the near-
the spawning period and/or location being studied, many studies est cm using a digital meter (General Oceanics). In the eastern
have still succeeded in providing information for fisheries man- Australian surveys, depth was estimated using a CTD and depth
agement purposes (e.g. Hill et al. 2005). This is because three sensor (Scanmar, Åsgårdstrand, Norway).
of the population parameters used to estimate mean daily fecun- In southern Australia, plankton samples from the two cod-
dity, i.e. female weight, sex ratio and batch fecundity, tend to ends were combined and stored in 5% buffered formaldehyde
be stable between years. Hence, estimates of spawning biomass and seawater solution. Eastern Australian samples were also
tend to be less sensitive to uncertainty in estimates of mean daily combined and fixed in 98% ethanol solution.
fecundity than estimates of total mean daily egg production. A
good example of the use of spawning biomass estimates obtained Egg identification and staging
without the data required to estimate daily fecundity directly Eggs of S. australasicus were identified using the morpho-
is the sardine stock assessments off the west coast of north- logical criteria in Ward and Rogers (2007). Identifications were
ern and central America, where estimates of spawning biomass confirmed using genetic techniques (Ward and Rogers 2007).
were used as indices of abundance for stock assessment mod- Owing to the uncertainties associated with the identification
elling between 1993–94 and 2004–05, even though adult samples of early-stage eggs (Ward and Rogers 2007), we only included
required to calculate estimates of reproductive parameters were early-stage eggs that could be identified as S. australasicus with
not collected between 1995–96 and 2000–01 (Lo et al. 1996, a high degree of confidence in our analyses. Hence, there may be
2005; Hill et al. 2005). a negative bias in the number of S. australasicus eggs reported.
The present paper tests the hypothesis that the DEPM is The potential for bias in the estimates of egg abundance resulting
a suitable technique for estimating the spawning biomass of from potential misidentification of young eggs was assessed by
S. australasicus in southern and eastern Australia. To do this, staging sub-samples of eggs from each location as being Day 1
we obtained estimates of P0 , A, W, R, F and S off southern and or Day 2 to determine the proportion of early-stage eggs in sam-
eastern Australia and assessed the sensitivity of estimates of ples. Eggs were determined to be either Day 1 or Day 2 based
spawning biomass to variations in estimates of each parameter. on the criteria of S. japonicus described by Watanabe (1970).
Methods Egg density
Total mean daily egg production Egg density under one square metre (m2 ) of water (P) was
estimated at each station:
Timing and location of ichthyoplankton surveys
C×D
We conducted extensive surveys (with reduced sampling P=
intensity) to determine the extent of the spawning area, rather V
than conducting intensive surveys to estimate spawning area where C is the number of eggs in each sample, V is the volume
and mean daily egg production with high levels of precision. of water filtered (m3 ) estimated using the flowmeters and D is
The need to identify the timing and location of spawning meant the maximum depth (m) to which the net was deployed.
that sampling designs were refined between surveys, which lim-
ited the potential for direct comparison of annual results. The Estimating egg production
sites where ichthyoplankton samples were collected are shown Mean daily egg production is typically estimated by fit-
in Fig. 1. ting mortality models to estimates of egg abundance by114 Marine and Freshwater Research T. M. Ward et al.
(d ) Fraser Is. (e)
(a)
Pacific
NT Ocean
WA QLD
SA
NSW
Queensland
35°S
VIC
Southern Ocean
TAS
130°E
(b) South Australia
f
ul
rG
Eyre Peninsula
ce
Great
en
Australian
Sp
Gulf St
Bight Vincent
New South Wales
35°S 200 m
Investigator St.
Forster
Kangaroo Is.
Newcastle
100 km 135°E Encounter
Bay
(c)
200 m
35°S
100 km
151°E
Fig. 1. (a) Locations where ichthyoplankton samples were collected using (b) a CalVET net and (c) a bongo net from waters off southern Australia during
February–March 2005 and using a bongo net in waters off eastern Australia in (d) October 2003 and (e) July 2004.
age (Picquelle and Stauffer 1985). This approach requires range of values determined for similar pelagic fishes, to calculate
temperature–development keys such as those that have been P0 (Bunn et al. 2000).
developed for S. sagax (White and Fletcher 1996) and north-
ern anchovy (Engraulis mordax) (Lo 1985). Temperature– Spawning area
development keys of this type have not yet been developed for Spawning area has typically been estimated by manually
S. australasicus. Data from Watanabe (1970) were not suitable dividing the sampling area into a series of contiguous grids
for assigning ages to egg stages. (Lasker 1985). The effect of grid size (which is related to
McGarvey and Kinloch (2001) described a method for esti- sampling intensity) on estimates of spawning area is poorly
mating mean daily egg production from mean egg density and understood. New methods have been developed for optimising
assumed rates of egg mortality. This method assumes that: the distribution of grid boundaries based on the distance between
P0 = if Z > 0 stations, such as the Voronoi natural neighbour (VNN) method.
The VNN is a geometric estimation technique that generates
where P is the mean density and z is egg mortality. We used regions around each point in the dataset. The method uses an
assumed egg mortality rates of 0.1–0.5 day−1 , which reflect the area-weighting technique to determine a new value for every gridEvaluating the DEPM for Scomber australasicus Marine and Freshwater Research 115
(a) (c)
Great 200 Great 200
Australian m Australian m
Bight Bight
35°S
100 km
135°E
(b) (d )
Great 200 20
Australian m Great 0
m
Bight Australian
Bight
(e)
Great 20
0
m
Australian
Bight
Fig. 2. Grid areas used to estimate spawning area using contiguous and Voronoi natural neighbour (VNN) grids at sites sampled using a Californian Vertical
Egg Tow (CalVET) net (a and b, respectively), and using small uniform, contiguous and VNN grids at sites using a bongo net (c, d and e respectively) in
waters off southern Australia during February–March 2005.
node. At each node, a region is generated that overlies portions of and VNN methods were also compared (MAPINFO version 8
the surrounding regions defining each point. Natural neighbour- Vertical Mapper, Figs 2, 3).
hoods are built around data points using Delaunay triangulation.
Adult reproductive parameters
A network of polygons is generated from the point locations
(MAPINFO version 8; Pitney Bowes MapInfo Corporation, Adult sampling
North Greenbush, NY, USA; www.mapinfo.com). We examined Most fish were collected using lines and lures, small baits or
the effect of grid size on estimates of spawning area by compar- baitfish jigs. Some samples were also collected using a monofil-
ing estimates calculated from data obtained using the CalVET ament gill-net (mesh size 65 mm). In southern Australia, samples
and bongo nets using small uniformly-sized grids and larger non- were collected from research cruises in Gulf St Vincent (GSV),
uniform contiguous grids that covered the entire spawning area. Backstairs Passage (BP), Investigator Strait (IS), Spencer Gulf
Estimates of spawning area calculated using the Lasker (1985) (SG) and the eastern Great Australian Bight (GAB) (EGAB,116 Marine and Freshwater Research T. M. Ward et al.
(a) (b) (c) (d ) (e)
35°S
100 km
151°E
Fig. 3. Grid areas used to estimate spawning area for sites sampled in waters off eastern Australian in October 2003 using (a) small uniform and (b) large
uniform grids and in July 2004 using (c) uniform grids, (d) contiguous grids and (e) Voronoi natural neighbour (VNN) generated grids.
Fig. 4). Off eastern Australia, samples were collected from Batch fecundity
research surveys, gamefishing tournaments and recreational Mean-weighted batch fecundity was estimated from stage
catches (Fig. 4). Gonads were removed from mature females and IV (hydrated) ovaries collected in South Australia using the
fixed in 5% buffered formaldehyde solution. Immature females gravimetric method (Hunter et al. 1985). Both ovaries were
and males were frozen. weighed and the number of hydrated oocytes in three ovarian
Female weight sub-sections were counted and weighed. The total batch fecun-
Mature females from each sample were removed from dity for each female was calculated by multiplying the mean
formaldehyde and weighed (±0.01 g). The mean weight of number of oocytes per gram of ovary segment by the total weight
mature females in the population was calculated from the average of the ovaries.
of sample means weighted by proportional sample size: The relationship between ovary-free fish weight and batch
ni
fecundity was determined by linear and allometric regression
W = Wi × analysis and used to estimate the batch fecundity of mature
N females. Shapiro–Wilk tests (Origin version 7.5; OriginLab
where W i is the mean female weight of each sample i, n is the Corporation, Northampton, MA, USA; www.OriginLab.com)
number of fish in each sample and N is the total number of fish showed the normality of the error terms and estimates.
collected in all samples.
Spawning fraction
Sex ratio
Ovaries of mature females were sectioned and stained with
The mean sex ratio of mature fish in the population was calcu- haematoxylin and eosin. Several sections from each ovary were
lated from the average of sample means weighted by proportional examined to determine the presence or absence of post-ovulatory
sample size: follicles (POFs). POFs were assigned approximate ages accord-
ni
R = Ri × ing to the criteria developed by Hunter and Macewicz (1985)
N for northern anchovy (Engraulis australis) and Dickerson et al.
where n is the number of fish in each sample, N is the total (1992) for chub mackerel S. japonicus.
number of fish collected in all samples and Ri is the mean sex The spawning fraction of each sample was estimated:
ratio of each sample
[(d0 + d1+ POFs)/2]
Ri =
F Si =
(F + M) ni
where F and M are the respective total weights (g) of mature where d0 POFs includes the number of females with hydrated
females and males in each sample i. ovaries and d0 POFs, d1+ POFs is the number of females withEvaluating the DEPM for Scomber australasicus Marine and Freshwater Research 117
South Australia
North
New South Wales
Spencer Gulf
Middle
Great Gulf St Vincent
Australian
35°S Bight
Investigator Strait Pacific Ocean
Encounter
Bay
South
Victoria
Southern Ocean 200 m 200 m
Bass
Strait
Tasman Sea
Australia
Tasmania
km
0 100 200
150°E
Fig. 4. Locations in southern and eastern Australia where adult samples were collected for the estimation of adult reproductive parameters.
POFs > 24-h old and ni is the total number of females in a daily fecundity was calculated using the overall estimates of
sample. mean female weight, mean sex ratio, mean batch fecundity and
The mean spawning fraction of the population was estimated mean spawning fraction obtained for southern Australia.
from the average of sample means weighted by proportional The minimum and maximum spawning biomass of S. aus-
sample size. tralasicus for southernAustralia in 2005 was calculated using the
ni estimate of each parameter that produced the minimum (i.e. most
S = Si ×
N conservative) and maximum (i.e. least conservative) estimate of
spawning biomass respectively. Minimum estimates were cal-
where n is the number of fish in each sample, N is the total number culated using data from the bongo net, an egg mortality rate of
of fish collected in all samples and S i is the mean spawning 0.1 day−1 , uniform/similar-sized grid squares and the estimate
fraction estimate of each sample. of each adult parameter for a single season that produced the
minimum estimate of spawning biomass. Maximum estimates
Spawning biomass were calculated using data from the bongo net, an egg mortality
We calculated minimum, best and maximum estimates of the rate of 0.5 day−1 , the VNN method and the least conservative
spawning biomass of S. australasicus for southern Australia estimates of mean female weight, mean sex ratio, mean batch
in 2005. The best estimate of mean daily egg production was fecundity and mean spawning fraction for a single season off
calculated using data from the bongo net and an egg mortal- southern Australia.
ity rate of 0.3 day−1 . The best estimate of spawning area was For eastern Australia, the minimum, best and maximum esti-
calculated using data from the bongo net and the contiguous mates of spawning biomass were calculated using egg data from
‘original’ (Lasker 1985) technique. The best estimate of mean the July 2004 survey, daily egg mortality rates of 0.1, 0.3 and118 Marine and Freshwater Research T. M. Ward et al.
Table 1. Summary of ichthyoplankton surveys conducted in southern and eastern Australian waters to evaluate the application of the Daily Egg
Production Method (DEPM)
CalVET = Californian Vertical Egg Tow
Location Survey date Net type No. No. % No. eggs Mean egg
stations positive positive (% Day 1) density (eggs m−2 )
sampled stations stations (± s.e.)
Southern Australia 5 Feb–19 Mar 2005 CalVET 334 35 10.5 127 (59) 23.5 (±6.1)
Bongo 152 54 35.5 512 (55) 18.0 (±5.7)
Eastern Australia 1–7 October 2003 Bongo 74 29 39.2 1 639 (10) 41.7 (±25.4)
20–27 July 2004 Bongo 85 45 52.9 873 (42) 12.4 (±5.1)
2005 CalVET 2005 bongo
34°S
Egg density m2
0
0–10
10–50
50–100 km
100–1000 135°E 0 25 50 75 100 km
0 25 50 75 100
Fig. 5. Distribution and abundance of Scomber australasicus eggs at sites sampled using a Californian Vertical Egg Tow (CalVET) net and a bongo net in
waters off southern Australia during February–March 2005.
0.5 day−1 and uniform grids, contiguous grids and the VNN biomass. The sensitivity analyses were done by calculating esti-
method respectively. Estimates of mean female weight and mean mates of spawning biomass for southern and eastern Australia
sex ratio were calculated using data from eastern Australia. using the best estimates of five parameters (e.g. P, A, W, R, S) and
Mean batch fecundity was calculated by applying the relation- by varying the estimate of the parameter being tested (e.g. F) over
ship between ovary-free female weight and batch fecundity from an appropriate range of values. The range of values examined
southern Australia to the estimate of mean female size from east- for each parameter were those used to calculate the minimum,
ern Australia. Estimates of mean spawning fraction for southern best and maximum estimates of the spawning biomass in each
Australia were used in the analysis owing to the lack of data from location.
eastern Australia.
Standard errors for the estimates of spawning biomass for
southern and eastern Australia were calculated using the delta Results
method of Parker (1985), modified to include sample variance in Egg abundance, density and daily egg production
the sex ratio and ignoring the covariance terms. A delta method Off southern Australia, more S. australasicus eggs were col-
approximation was also used to estimate a s.e. for P0 , when lected using the bongo net than the CalVET (Table 1). The
the s.e. of mean egg density is known and P0 is inferred from patterns of distribution and abundance of eggs determined using
an assumed egg mortality value (i.e. 0.3 day−1 ) using the equa- the two types of net were generally similar, with eggs present at
tion of McGarvey and Kinloch (2001), which assumes an egg sites located in the southern parts of both gulfs, in Investigator
duration of 2 days. Strait and in shelf waters south of the Eyre Peninsula (Figs 1, 5).
Approximately 35.5% of samples obtained using the bongo net
Z
s.e.(P0 ) = × s.e. × P contained eggs, whereas only 10.5% of CalVET net samples
1 − exp[−2 · Z] contained eggs.
Mean egg density estimated using the CalVET data was
Sensitivity analysis higher than the estimate obtained from bongo net data (Table 1,
Sensitivity analyses were undertaken to determine the effects Mann–Whitney Test, Z = −3.578, P < 0.001). Estimates of
of variations in each parameter on the estimates of spawning mean daily egg production obtained from the CalVET net wereEvaluating the DEPM for Scomber australasicus Marine and Freshwater Research 119
Table 2. Estimates of egg production calculated for different spawning higher than for the bongo net (Table 2). Day-1 eggs comprised
area grid weightings for surveys in southern and eastern Australia less than 60% of eggs obtained using both bongo and CalVET
CalVET = Californian Vertical Egg Tow nets, which suggest that the potential for a significant positive
bias in egg production resulting from the misidentification of
Location Year/net type Egg mortality Egg production young eggs was low (Table 1).
rate (Z day−1 ) In October 2003 and July 2004, 1639 and 837 eggs, respec-
05 CalVET 05 bongo tively, were collected from easternAustralia (Table 1). In October
Southern 2005 0.1 12.99 9.94 2003, S. australasicus eggs were abundant in shelf waters in
Australia CalVET/bongo 0.2 14.28 10.93 the northern portion of the sampling area only (mainly north
0.3 15.65 11.98 of Forster), whereas in July 2004 eggs were abundant in shelf
0.4 17.10 13.10 waters throughout the entire area surveyed between Indian
0.5 18.62 14.25 Head, Fraser Island, Queensland and Newcastle, NSW (Table 1,
03 bongo 04 bongo Figs 1, 6).
Eastern 2003–04 bongo 0.1 23.01 6.82 Mean egg density and P0 were higher in 2003 than in 2004
Australia 0.2 25.31 7.50 (Tables 1, 2). Day-1 eggs comprised less than 10 and 42% of
0.3 27.74 8.22 eggs collected in 2003 and 2004, respectively (Table 1), which
0.4 30.30 8.98 suggests that the potential for a positive bias in egg production
0.5 33.00 9.78
resulting from the misidentification of young eggs was low.
October 2003 July 2004
35°S
Egg density m2
0
0–10
10–50
50–100
100–1000
151°E 0 25 50 75 100
Fig. 6. Distribution and abundance of Scomber australasicus eggs at sites in eastern Australian in October 2003 and July 2004.120 Marine and Freshwater Research T. M. Ward et al.
Spawning area combined was 0.46. There were no geographical or temporal
The total survey area in southern Australia in 2005 was similar trends in sex ratio.
for the CalVET net and bongo net (Figs 2, 3, Table 3). As the The mean weighted sex ratio for eastern Australia for all years
percentage of samples containing eggs was much higher for the was 0.50 (Table 5).
bongo net than for the CalVET net (Table 1), the estimates of
spawning area obtained using the bongo net were higher than Batch fecundity
those for the CalVET net (Table 3). For both the bongo net and Ovaries contained between 14 349 and 105 193 hydrated oocytes
the CalVET net, estimates obtained using the contiguous grids for females weighing 234.9 g and 607.4 g (gonad free weight)
and VNN were similar (Table 3; Figs 2, 3). respectively. The allometric model fitted the data better than a
The estimate of spawning area for eastern Australia obtained linear one (Fig. 7). Estimates of weighted mean batch fecundity
using the small uniform grid squares, which covered ∼45% of for individual samples calculated using the relationship shown in
the total survey area was much lower than the estimate obtained Fig. 7 ranged from 37 284 g in Spencer Gulf in 2001–02 to 91 113
using the larger grid squares that covered the entire sampling in Encounter Bay in 2003–04 (Table 4). Whole-of-season means
area (Table 3). Limitations in the sampling design prevented ranged from 46 468 eggs in 2002–03 to 55 053 in 2003–04.
estimation of the spawning area using theVNN for October 2003. The overall mean batch fecundity in southern Australia was
Estimates of spawning area obtained off eastern Australia in 52 182 eggs (Table 4).
July 2004 using uniformly sized grids, which covered ∼83% of Based on the relationship between ovary-free fish weight and
the sampling area, were larger than those obtained using larger batch fecundity for southern Australia, the overall mean batch
continuous grids that covered the entire sampling area and the fecundity in eastern Australia was 22 085 eggs (Table 5).
VNN method (Fig. 3, Table 3).
Spawning fraction
Female weight Of the 702 mature females collected from southern Australian
Estimates of weighted mean female weights for individual waters, 153 had Day-0 POFs, 49 had Day 1+ POFs and 61 had
regions off southern Australia within a season ranged between hydrated oocytes (Table 6). Estimates of weighted mean spawn-
357.8 g in Spencer Gulf in 2001–02 and 668.4 g in Encounter Bay ing fraction for individual regions within a season ranged from
in 2003–04. All estimates of mean female weight for Encounter 0.0 at Encounter Bay (EB) in 2002–03 and 2003–04 and 0.23 in
Bay, Investigator Strait, and the Great Australian Bight were SG in 2003–04 (Table 6). Whole-of-season means ranged from
>500 g, whereas all estimates of mean female weight for Gulf 0.05 in 2002–03 and 0.18 in 2001–02. The overall estimate of
St Vincent and Spencer Gulf were less than 470 g. Whole- weighted mean spawning fraction was 0.14.
of-season means ranged from 408.2 g in 2002–03 to 473.6 in No data were collected on spawning fraction in eastern
2003–04, which reflects the predominance of samples from the Australia.
two gulfs. The weighted mean value for all samples combined
was 452 g. Spawning biomass
Weighted mean weight of mature females off eastern The best estimate of spawning biomass for southern Australia
Australia was calculated from 50 samples containing a total of in 2005 was ∼56 288 t (±19 157 s.e.). The minimum and
186 (stage III–V) females (Table 5). Mean female weight was maximum estimates were 11 342 t and 293 456 t respectively
267.3 g. (Table 7).
The best estimate of spawning biomass for eastern Australia
Sex ratio was ∼29 578 t (±12 853 s.e.). The minimum and maximum
Estimates of weighted mean sex ratio for individual regions estimates were 11 096 t and 157 166 t respectively (Table 7).
off southern Australia within a season ranged between 0.31 in
Spencer Gulf in 2004–05 and 0.76 in Spencer Gulf in 2002–03 Sensitivity analysis
(Table 4). Whole-of-season means ranged from 0.36 in 2004–05 The sensitivity analysis showed that estimates of spawning
to 0.65 in 2002–03. The mean weighted sex ratio for all years biomass for southern Australia were most affected by variations
Table 3. Estimates of spawning area calculated using uniform grids, contiguous grids and the Voronoi natural neighbour (VNN) method using
a Californian Vertical Egg Tow (CalVET) net and a bongo net in waters off southern Australia during February–March 2005 and a bongo net in
waters off eastern Australia in October 2003 and July 2004
Location Year/net type No. stations Survey Spawning area
sampled area Uniform/similar Contiguous VNN method
sized grids grids grids
Southern Australia 2005 CalVET 334 108 961 11 840 11 840 11 898
2005 bongo 152 119 603 17 451 34 895 36 370
Eastern Australia 2003 bongo 74 30 422 5931 10 078
2004 bongo 85 38 974 17 503 20 811 21 019Table 4. Adult reproductive parameters, mean female weight, sex ratio and batch fecundity of blue mackerel obtained from samples collected from South Australian waters between
2001 and 2006
IS = Investigator Strait, SG = Spencer Gulf, EB = Encounter Bay, GSV = Gulf St Vincent, EGAB = eastern Great Australian Bight
Season Sampling period Region No. No. Males Females Mean Mean Total Total Sex Mean wt Mean batch
samples fish wt (g) wt (g) W i wt (g) wt (g) ratio Ri (gonad free) (g) fecundity
Evaluating the DEPM for Scomber australasicus
2001–02 2 Feb 2002 IS 1 46 19 27 480.6 502.7 9132 13 572 0.60 485.8 61 158
11 Apr 2002 SG 1 48 26 22 325.2 357.8 8456 7871 0.48 353.8 37 284
94A 45A 49A 437.6B 0.54B 50 439B
2002–03 17–24 Mar 2003 EB 2 30 17 13 615.7 645.4 10 467 8390 0.44 614.8 88 345
28 Jan–25 Mar 2003 GSV 4 44 22 22 292.1 394.7 6426 8682 0.57 376.5 41 083
12 Mar–7 Apr 2003 SG 3 97 23 74 367.7 370.5 8458 27 420 0.76 360.7 38 427
171A 62A 109A 408.2B 0.65B 46 468B
2003–04 5 Feb 2004 EB 1 25 11 14 581.8 668.4 6400 9358 0.59 627.0 91 113
25 Jan–18 Mar 2004 EGAB 3 190 109 81 549.8 531.2 59 925 43 029 0.42 515.5 67 114
5 Nov 2003–27 Apr 2004 GSV 15 493 294 199 427.0 442.0 125 536 87 951 0.41 419.3 48 601
14 Dec 2003–14 Apr 2004 SG 7 390 197 193 415.9 468.0 81 939 90 316 0.52 442.4 52 853
1098A 611A 487A 473.6B 0.46B 55 053B
2004–05 8 Nov 2004–20 Apr 2005 GSV 9 185 107 78 373.9 404.5 40 008 31 548 0.44 385.5 42 627
17 Dec 2004–21 Apr 2005 SG 5 277 195 82 400.9 437.6 78 168 35 886 0.31 446.4 53 606
462A 302A 160A 421.5B 0.36B 48 961B
2005–06 19 Jan 2006 SG 1 12 5 7 358.3 428.2 1792 2997 0.63 415.0 47 832
12A 5A 7A 428.2B 0.63B 47 832B
Grand totals/means – all seasons 1837A 1025A 812A 452.00B 0.46B 52 182B
A Total number of fish collected. B Mean value weighted by individual sample size.
Marine and Freshwater Research
121122 Marine and Freshwater Research T. M. Ward et al.
Mean batch
in estimates of spawning area and spawning fraction. Esti-
fecundity
29 127B
25 105B
21 095B
24 421B
22 085B
17 764
31 946
29 605
25 836
23 737
25 430
20 920
20 716
22 015
18 884
24 757
mates of spawning biomass obtained by varying other param-
eters within the bounds of information obtained in the study
suggest that the spawning biomass in the survey area in south-
Table 5. Adult reproductive parameters, mean female weight and sex ratio of blue mackerel obtained from samples collected from NSW waters between 2002 and 2005
ern Australia during 2005 ranged between ∼45 000 and 68 000 t.
(gonad free) (g)
(Fig. 8). Only the estimate of spawning area obtained using small
Mean wt
256.1B
uniform grid squares produced a lower estimate of spawning
220.1
320.5
305.3
279.8
265.0
276.9
244.4
242.9
252.5
228.9
272.2
biomass (∼28 000 t). Only values of spawning fraction less than
0.10 produced estimates of spawning biomass that were greater
than 80 000 t.
The sensitivity analysis showed that estimates of spawning
ratio Ri
0.39B
0.53B
0.52B biomass for eastern Australia were most affected by variations
Sex
0.5B
0.5B
0.28
0.48
0.38
0.45
0.26
0.37
0.52
0.46
0.55
0.6
in estimates of spawning fraction (Fig. 9). Estimates of spawn-
1
ing biomass obtained by varying the other parameters within the
bounds of information obtained in our study suggest that the
3145A
7648A
34 096A
4835A
49 724A
Total
wt (g)
230
673
2243
858
3332
3458
502
1778
25 836
5980
4835
spawning biomass in the survey area off eastern Australia dur-
ing 2005 ranged between ∼20 000 and 40 000 t. Only assumed
values of spawning fraction less than 0.10 produced estimates
of spawning biomass that were greater than 40 000 t.
5013A
6358A
35 609A
3929A
50 909A
Total
wt (g)
582
727
3703
0
2189
4169
1441
3004
24 243
6922
3929
Discussion
wt (g) W i
Mean
314.5B
283.2B
258.3B
284.4B
267.3B
229.6
336.4
320.4
277.7
288.2
251.2
263.6
239.2
284.4
Mean daily egg production
286
254
The large differences in the number of eggs collected using the
CalVET and bongo nets in southern Australia in 2005 reflects
the larger quantity of water sampled by bongo nets compared
Mean
wt (g)
291.3
242.3
336.7
243.2
288.1
300.4
266.2
261.9
0
278
240
with CalVET nets. The finding that estimates of mean egg den-
sity are greater for the CalVET than the bongo net suggests that
the type or size of plankton net affects the estimates of egg pro-
10A
27A
132A
17A
186A
duction. This result has implications for other DEPM studies that
n
1
2
7
3
12
12
2
7
98
25
17
typically use one type of net (usually a CalVET net) to estimate
egg production. At this stage, it is unclear which of the estimates
16A
24A
142A
15A
197A
of egg production (i.e. those obtained using CalVET or bongo
n
2
3
11
0
9
15
5
10
101
26
15
nets) are more suitable for estimating egg production, or why
number of fish collected. B Mean value weighted by individual sample size.
the estimates differ. On this basis, we used data from the bongo
N fish
26A
51A
274A
32A
383A
nets (which are more conservative, and also available for eastern
3
5
18
3
21
27
7
17
199
51
32
Australia) to determine the ‘best’ estimates of egg production
for the present study.
N samples
There are several other reasons why the estimates of mean
50A
daily egg production for both southern and eastern Australia may
1
2
3
1
6
5
1
6
13
8
4
be conservative. Most importantly, estimates of egg production
obtained using the method of McGarvey and Kinloch (2001) are
consistently lower than those obtained using the internationally
Unknown
Unknown
accepted method of Picquelle and Stauffer (1985). For exam-
Middle
Middle
Middle
Region
North
North
North
South
South
South
ple, the estimate of mean daily egg production of sardine off
eastern Australia in July 2004 (i.e. 35.63 eggs m−2 ) obtained
using the method of McGarvey and Kinloch (2001) was almost
13 July–11 Aug 2005
13 Aug–28 Oct 2004
9 Aug–22 Sept 2003
50% lower than the value obtained using the internationally
16 July–4 Aug 2004
30 Aug–4 Oct 2002
14 Aug–3 Oct 2004
6 July–17 Oct 2003
20–23 Sept 2002
accepted linear version of the exponential mortality model (i.e.
Sampling period
69.96 eggs day−1 m−2 , Ward and Rogers 2007) of Picquelle and
23 Oct 2002
10 Oct 2003
8 Jul 2004
Stauffer (1985). As shown in Figs 8 and 9, variations in estimates
of egg production have a directly proportional effect on estimates
of spawning biomass, i.e. a two-fold increase in egg production
results in a doubling of the spawning biomass. The estimates of
daily egg mortality (i.e. Z = 0.1, 0.3, 0.5 day−1 ) used to calculate
Grand total
the minimum, best and maximum estimates of egg production in
A Total
the present study are also conservative values for small pelagic
2002
2003
2004
2005
Year
fishes (see Bunn et al. 2000).Evaluating the DEPM for Scomber australasicus Marine and Freshwater Research 123
200 000 Batch fecundity 3.9ovary-free wt1.56 Batch fecundity 3e-08FL4.88
Batch fecundity (n hydrated oocytes)
r2 0.68 r2 0.71
160 000 N 58
120 000
80 000
40 000
0
0 200 400 600 800 200 250 300 350 400 450
Ovary-free weight (g) Fork length (mm)
Fig. 7. Relationship between (left) ovary-free bodyweight and (right) fork length and batch fecundity for Scomber australasicus sampled in South Australian
between 2002 and 2005.
Table 6. Spawning fraction estimates of blue mackerel obtained from samples collected from South Australian waters between 2001–02 and
2004–05
POF = post-ovulatory follicles, SG = Spencer Gulf, EB = Encounter Bay, GSV = Gulf St Vincent, EGAB = eastern Great Australian Bight
Season Sampling period Region Sample No. Day-0 No. Day-1+ No. Si (Day 0 +
size POFs POFs hydrated Day 1+)
2001–02 11 April 2002 SG 20 0 7 0 0.18
20A 0A 7A 0 0.18B
2002–03 17–24 Mar 2003 EB 13 0 0 0 0.00
11–25 Mar 2003 GSV 18 0 1 0 0.03
12 Mar–7 April 2003 SG 52 1 6 1 0.07
83A 1A 7A 1A 0.05B
2003–04 05 Feb 2004 EB 14 0 0 0 0.00
5 Nov 2003–27 April 2004 GSV 190 32 12 10 0.12
14 Dec 2003–14 April 2004 SG 181 68 14 38 0.23
25 Jan 2004–18 Mar 2004 EGAB 81 18 5 0 0.14
466A 118A 31A 48A 0.16B
2004–05 8 Nov 2004–20 April 2005 GSV 61 10 2 3 0.10
17 Dec 2004–21 April 2005 SG 72 24 2 9 0.18
133A 34A 4A 12A 0.11B
Grand totals/means – all seasons 702A 153A 49A 61A 0.14B
A Total number of fish collected. B Mean value weighted by individual sample size.
The results of the surveys conducted off southern Australia Eggs of S. australasicus were widespread and abundant in
also show that the type and size of net has a large effect on the waters of southern Australia during the surveys conducted in
estimate of spawning area. A much higher proportion of stations February and March 2005. This finding supports adult reproduc-
were identified as positive (i.e. containing eggs) using the bongo tive data presented by Rogers et al. (2009), which suggests the
net than the CalVET. This finding probably reflects the larger peak spawning season of S. australasicus in southern Australia
quantity of water sampled by bongo nets compared with CalVET extends from December to March. Hence, surveys conducted off
nets and suggests that estimates of spawning area obtained using southern Australia in February–March 2005 appear to have been
CalVET nets may be negatively biased, especially when eggs are suitably timed for a DEPM study.
in low abundance. Bongo nets may be more suitable than Cal- The estimates of spawning area used to calculate the spawn-
VET nets for estimating the spawning area of S. australasicus in ing biomass of S. australasicus in southern Australia during
southern Australia. All estimates of spawning biomass presented 2005 may be conservative because significant levels of spawning
in the present study were calculated using data from bongo nets. occurred outside the area sampled in this study. Ward and Rogers
Only the methods used to estimate spawning area (i.e. uniform (2007) collected large numbers of S. australasicus eggs from
grids, contiguous grids, VNN method) were varied to establish the western GAB during 2006. It is not clear whether significant
minimum, best and maximum estimates of spawning area. spawning occurs outside the area surveyed off the east coast, but124 Marine and Freshwater Research T. M. Ward et al.
Table 7. Range of estimates of each parameter and spawning biomass of blue mackerel calculated for southern Australia and eastern Australia
in 2005
Best estimates are listed with (±s.e.). P0 = mean daily egg production, A = total spawning area, R = mean sex ratio by weight, W = mean female weight,
F = number of oocytes in a batch, S = mean proportion of mature females spawning each night
Location Minimum Best (±s.e.) Maximum
Southern Australia P0 (eggs day−1 m−2 ) 9.94 11.98 (3.777) 14.25
A (km2 ) 17 451 34 895 36 370
W (g) 408.20 452.00 (±5.258) 473.60
R 0.63 0.46 (±0.025) 0.36
F (eggs) 55 053 52 182 (±853) 46 468
S 0.18 0.14 (±0.016) 0.05
Spawning biomass 11 342 56 288 (19 157) 293 456
Eastern Australia P0 6.82 8.22 (3.335) 9.78
A 17 503 20 811 21 019
W 258.3 267.3 (±8.160) 314.5
R 0.53 0.50 (±0.042) 0.39
F 29 127 22 085 (±1246) 21 095
S 0.18 0.14 (±0.016) 0.05
Spawning biomass 11 096 29 578 (12 853) 157 166
the occurrence of eggs on the southernmost transects of the July entire spawning area, which does not appear to have been the
2004 survey suggests that spawning could have occurred further case for the surveys of southern Australia (see Ward and Rogers
south. 2007). Results obtained in the present study suggest that if the
The results from the surveys conducted off eastern Australia survey design is appropriate, reliable estimates of spawning area
suggest that S. australasicus eggs are widespread and abun- can be calculated using both Lasker’s ‘original’ method and the
dant in shelf waters between northern Fraser Island (southern VNN approach.
Qld) and Newcastle (central NSW) during winter and spring.
The higher egg abundances recorded during October 2003 com- Total mean daily fecundity
pared with July 2004 suggest that the peak spawning season Estimates of all parameters were obtained from the large num-
may occur after July. Adult reproductive data presented in Ward ber of adult samples collected from southern Australian waters
and Rogers (2007) suggest that future surveys would ideally be between 2001 and 2006. In contrast, samples obtained from east-
conducted during August–September. Much higher estimates of ern Australia did not include large fish, which are known to occur
egg production (23.01–33.00 eggs m−2 day−1 ) were obtained in in the region, and are unlikely to provide unbiased estimates of
October 2003 than in July 2004. However, spawning biomass adult parameters. The higher estimate of mean female weight
could not be estimated for this survey owing to limitations in the in southern Australia (452 g) compared with eastern Australia
sampling design (e.g. non-parallel transects). Data from eastern (267 g) may reflect differences in the locations from which sam-
Australia show the importance of implementing the correct sam- ples were collected in the two regions. Most of the samples
pling design when estimating spawning area. To obtain reliable from easternAustralia were collected from inshore sites, whereas
estimates of spawning area, it is important that surveys are some of the samples from southernAustralia were collected from
designed along parallel transects with the minimum logistically offshore waters. Mean female weights in samples from inshore
feasible distances between transects and stations. waters of southern Australia (i.e. the two gulfs) were signifi-
The July 2004 survey was suitable for estimating spawning cantly lower (500 g).
season of S. australasicus off eastern Australia. Estimates of As few adult samples were collected from offshore waters of
spawning area (and spawning biomass) obtained from this eastern Australia, but large numbers of eggs were obtained from
survey may be negatively biased. If egg production estimates sites located over the mid-shelf, we suggest that estimates of
for October 2003 were used to calculate spawning biomass for adult reproductive parameters obtained from these inshore fish
July 2004, the best estimate of spawning biomass for eastern may not be representative of the spawning population. Obtain-
Australia would have been 77 648 t rather than 29 578 t. ing representative adult samples from eastern Australia during
The distances between transects and sites are critical elements the spawning season is a high priority for future research on
of the sampling design because grid size increases as these dis- S. australasicus in Australia.
tances increase and estimates of spawning area are positively Estimates of adult parameters for southern Australia are
correlated with grid size. However, our results from southern remarkably similar to those obtained for the morphologically
Australia show that the effect of grid size on spawning area and and genetically similar S. japonicus in waters off California and
estimates of spawning biomass can be smaller than the effect of Japan. For example, Dickerson et al. (1992) presented similar
net type (e.g. bongo or CalVET) in determining whether sites estimates of mean female weight for samples of S. japonicus
are positive or negative, i.e. with or without eggs respectively. It obtained from the Southern Californian Bight (i.e. 355.05–
is also important that the sampling design (and grids) covers the 528.55 g). Estimates of mean female weight in samples ofEvaluating the DEPM for Scomber australasicus Marine and Freshwater Research 125
250 000 250 000
200 000 200 000
Biomass (t)
150 000 150 000
100 000 100 000
Max
Best
Max
50 000 50 000
Min Min Best
0 0
0 20 40 60 80 100 0 10 000 20 000 30 000 40 000 50 000
Egg production (eggs m2 day1) Spawning area (km2)
250 000 250 000
200 000 200 000
Max
Biomass (t)
150 000 150 000
100 000 100 000
Best
50 000 Max 50 000 Min
Min Best
0 0
0 200 400 600 800 1000 0 0.05 0.10 0.15 0.20
Female weight (g) Spawning fraction
250 000 250 000
200 000 200 000
Biomass (t)
150 000 150 000
100 000 100 000
Max x t
Best Ma Bes in
M
50 000 Min 50 000
0 0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 10 000 20 000 30 000 40 000 50 000 60 000
Sex ratio Batch fecundity
Fig. 8. Sensitivity analysis showing minimum, best and maximum values of biomass calculated from upper, best and lower parameter estimates applied in
the Daily Egg Production Method (DEPM) model for southern Australia.
S. japonicus obtained by Yamada et al. (1998) from waters off markedly within the Australian population of S. australasicus.
Japan (i.e. 402.3–797.2 g) were higher than those obtained in Hence, the parameters obtained from southern Australia may be
the present study or by Dickerson et al. (1992). The similarity suitable for calculating preliminary estimates of the spawning
of these parameters for these two separate species of Scomber biomass of S. australasicus off eastern Australia. Calculations
in several locations suggests that these parameters may not vary made using the best estimates of adult parameters from southern126 Marine and Freshwater Research T. M. Ward et al.
250 000 250 000
200 000 200 000
150 000 150 000
Biomass (t)
100 000 100 000
Max
50 000 50 000 Min Best/Max
Best
Min
0 0
0 20 40 60 80 100 0 10 000 20 000 30 000 40 000 50 000
Egg production (eggs m2 day1) Spawning area (km 2)
250 000 250 000
200 000 200 000
Biomass (t)
150 000 150 000
100 000 100 000 Max
Min Max
50 000 50 000 Best
Min
Best
0 0
0 200 400 600 800 1000 0 0.05 0.10 0.15 0.20
Female weight (g) Spawning fraction
250 000 250 000
200 000 200 000
Biomass (t)
150 000 150 000
100 000 100 000
Max
Max
50 000 Best 50 000 Best
Min
Min
0 0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 10 000 20 000 30 000 40 000 50 000 60 000
Sex ratio Batch fecundity
Fig. 9. Sensitivity analysis showing minimum, best and maximum values of biomass calculated from upper, best and lower parameter estimates applied in
the Daily Egg Production Method (DEPM) model for eastern Australia.
Australia suggest that the spawning biomass S. australasicus of oocytes. In contrast, Dickerson et al. (1992) presented an esti-
eastern Australia in 2004 was ∼23 009 t (cf. 29 578 t based on mate of mean batch fecundity (68 356 eggs) based on 13 females
data available for eastern Australia). with oocytes in the late migratory nucleus stage. Similarly, the
The estimate of mean batch fecundity provided in this report estimate of Yamada et al. (1998, i.e. 89 200 eggs) was based on
(i.e. 52 182 eggs) was based on 58 females with hydrated 12 females with hydrated oocytes. These findings suggest thatEvaluating the DEPM for Scomber australasicus Marine and Freshwater Research 127
our estimates of mean batch fecundity are, by international stan- samples of S. australasicus for eastern Australia that are required
dards, based on relatively large numbers of female fish and are to estimate adult reproductive parameters is a high priority for
likely to be reliable. However, further investigations are war- future management-orientated research on this species.
ranted regarding the spatial variations in the size and fecundity
of females, such as those between gulf and shelf waters of South Future DEPM surveys
Australia. Additional data on batch fecundity is required for Our results, combined with those of Ward and Rogers (2007)
eastern Australia. and Rogers et al. (2009), show that the DEPM is a suitable
The estimates of mean spawning fraction obtained in the tool for stock assessment of S. australasicus. It appears that
present study (0.14) is considerably higher than the overall esti- bongo nets should be used to sample eggs in future applications.
mate for S. japonicus in waters off California (0.087), but lower Future survey designs should involve parallel transects with the
than the estimate for the peak spawning month in that location minimum logistically feasible distances between transects and
(i.e. 0.206, Dickerson et al. 1992). The mean spawning fraction stations. Hook-and-line methods appear to be suitable for sam-
for S. japonicus in waters off Japan reported by Yamada et al. pling adults, but alternative methods should also be investigated.
(1998) is similar to our estimate (0.17). Hence, our estimates Off southern Australia, future surveys should be conducted
of spawning fraction are similar to those obtained in previous during February–March in waters between the western Great
studies of similar species and are likely to be reliable because Australian Bight and the eastern end of Kangaroo Island. Off
they are based on a large number of mature females (i.e. 702), eastern Australia, they should be conducted during August–
compared with previous studies in California (271, Dickerson September from southern Queensland to central or southern
et al. 1992) and Japan (192, Yamada et al. 1998). New South Wales. Ward and Rogers (2007) show that the cost-
The large discrepancy between the number of mature females effectiveness of DEPM surveys can be maximised by collecting
with Day-0 POFs (153) and Day-1+ POFs (49) that was observed data for several species concurrently. Future DEPM surveys for
in the present study warrants further investigation. We minimised stock assessment of S. australasicus should be coordinated with
the potential biases in our estimates of spawning fraction that those for other species, especially Sardinops sagax.
may have been associated with this discrepancy by using females Several important technical refinements are required to max-
with hydrated oocytes, Day-0 POFs and Day-1+ POFs in our imise the reliability of the estimates of spawning biomass that
calculations. Future studies of the reproductive biology of S. aus- are obtained using the DEPM. The highest immediate priori-
tralasicus should investigate the rates of degeneration of POFs. ties for additional research are: (1) developing cost-effective
and reliable genetic techniques for identifying early stage
Spawning biomass eggs; (2) establishing a temperature–egg development key;
As discussed above, there are several reasons why the best esti- (3) identifying locations and methods for collecting samples
mates of spawning biomass for southern and eastern Australia to estimate adult reproductive parameters off eastern Australia;
may be conservative (i.e. negatively biased). In both cases, and (4) measuring the degeneration rates of POFs to ensure that
egg production was calculated using a method (McGarvey and estimates of spawning fraction are reliable.
Kinloch 2001) that produces conservative estimates. In addition,
the estimate of egg mortality used to calculate egg production Acknowledgements
was conservative. For southern Australia, there is also clear evi-
This project was funded by the Fisheries Research and Development Corpo-
dence of significant spawning activity outside the area surveyed ration and Australian Fisheries Management Authority. We are grateful for
(i.e. in the western GAB), which suggests that the estimates of the efforts of the crews of the RV Ngerin and RV Bluefin for their efforts dur-
spawning area and spawning biomass for southern Australia are ing surveys. Dr Francisco Neira and Mr John Keane (TAFI) led the surveys
negatively biased. Spawning may also have occurred outside the off eastern Australia, sorted the samples and collated ichthyoplankton
area surveyed off eastern Australia. The estimate of egg produc- data. Mr Wetjens Dimmlich, Mr Nathan Strong, Mr David Schmarr, Mr Paul
tion for eastern Australia in 2004 may also be negatively biased Van Ruth, MrAlex Ivey, Mr David Fleer and Ms Michelle Roberts helped col-
as it was taken outside the peak spawning season. lect and sort plankton samples and catch adult blue mackerel during research
Estimates of adult parameters for southern Australia appear surveys off southern Australia. Ms Sandra Leigh, Mr Chris Leigh and Dr Bill
Breed (University ofAdelaideAnatomy Department) conducted the histolog-
to be reliable, as they were based on relatively large samples
ical analyses. We thank Cameron Dixon (SARDI), Dale McNeil (SARDI),
of adult fish collected over several years and are comparable
three anonymous reviewers and MFR editorial staff for valuable comments
to those obtained for a similar species in different locations. on drafts of this manuscript.
A major uncertainty, and potential source of positive bias for the
estimates of spawning biomass off eastern Australia, lies with
References
the use of reproductive data from samples that may not have
been representative of the adult population. If the size distribu- Bunn, N. A., Fox, C. J., and Webb, T. (2000). A literature review of studies
on fish egg mortality: implications for the estimation of spawning stock
tion of S. australasicus off eastern Australia is similar to that
biomass by the annual egg production method. Science Series Technical
off southern Australia, then the estimates of spawning biomass
Report 111. Centre for Environment, Fisheries and Aquaculture Science,
calculated using data from easternAustralia may be biased. How- Lowestoft.
ever, the effect of this bias may be relatively small as the estimate Dickerson, T. L., Macewicz, B. J., and Hunter, J. R. (1992). Spawning fre-
of spawning biomass obtained using data from eastern Australia quency and batch fecundity of chub mackerel, Scomber japonicus, during
(i.e. 29 578 t) is similar to the estimate calculated using adult data 1985. California Cooperative Oceanic Fisheries Investigations Reports
from southern Australia (23 009 t). Obtaining the representative 33, 130–140.128 Marine and Freshwater Research T. M. Ward et al.
Fletcher, W. J., Lo, N. C. H., Hayes, E. A., Tregonning, R. J., and Blight, Services. National Oceanic and Atmospheric Administration. (South
S. J. (1996). Use of the daily egg production method to estimate the West Fisheries Science Center: La Jolla, CA.)
stock size of western Australian sardines (Sardinops sagax). Marine and Picquelle, S., and Stauffer, G. (1985). Parameter estimation for an egg pro-
Freshwater Research 47, 819–825. doi:10.1071/MF9960819 duction method of anchovy biomass assessment. In ‘An Egg Production
Gaughan, D. J., Leary, T. I., Mitchell, R. W., and Wright, I. W. (2004). A Method for Estimating Spawning Biomass of Pelagic Fish: Application
sudden collapse in distribution of Pacific sardine (Sardinops sagax) off to the Northern Anchovy, Engraulis mordax’. (Ed. R. Lasker.) pp. 7–17.
southwestern Australia enables an objective re-assessment of biomass Technical Report 36. National Marine Fisheries Services. National
estimates. Fishery Bulletin 102, 617–633. Oceanic and Atmospheric Administration. (South West Fisheries Science
Hill, K. T., Lo, N. C. H., Macewicz, B. J., and Felix-Uraga, R. (2005). Center: La Jolla, CA.)
Assessment of the Pacific sardine (Sardinops sagax). National Oceanic Rogers, P. J., Ward, T. M., McLeay, L. J., Lowry, M., Williams, D., and
and Atmospheric Administration Fisheries. South West Fisheries Science Saunders, R. (2009). Reproductive biology of blue mackerel (Scomber
Center, La Jolla, CA. australasicus) off southern and eastern Australia: suitability of the Daily
Hunter, J. R., and Lo, N. C. H. (1997). The daily egg production method Egg Production Method for stock assessment. Marine and Freshwater
of biomass estimation: some problems and potential improvements. Research 60, in press.
Ozeanografika 2, 41–69. Smith, P. E. (1993). Balancing sampling precision and fisheries management
Hunter, J. R., and Macewicz, B. J. (1985). Measurement of spawning fre- objectives: minimal methods. Bulletin of Marine Science 53, 282–292.
quency in multiple spawning fishes. In ‘An Egg Production Method Smith, P. E., Flex, W., and Hewitt, R. P. (1985). The CalCOFI vertical egg tow
for Estimating Spawning Biomass of Pelagic Fish: Application to the (CalVET) net. In ‘An Egg Production Method for Estimating Spawn-
Northern Anchovy, Engraulis mordax’. (Ed. R. Lasker.) pp. 79–94. Tech- ing Biomass of Pelagic Fish: Application to the Northern Anchovy,
nical Report 36. National Marine Fisheries Services. National Oceanic Engraulis mordax’. (Ed. R. Lasker.) pp. 27–32. Technical Report 36.
and Atmospheric Administration. (South West Fisheries Science Center: National Marine Fisheries Services. National Oceanic and Atmospheric
La Jolla, CA.) Administration. (South West Fisheries Science Center: La Jolla, CA.)
Hunter, J. R., Lo, N. C. H., and Roderick, J. H. L. (1985). Batch fecundity in Staunton Smith, J., and Ward, T. M. (2000). Stock assessment of pelagic
multiple spawning fishes. In ‘An Egg Production Method for Estimating bait fishes in southern Queensland with special reference to pilchards
Spawning Biomass of Pelagic Fish:Application to the NorthernAnchovy, (Sardinops sagax). Final Report to the Fisheries Research and Devel-
Engraulis mordax’. (Ed. R. Lasker.) pp. 67–78. Technical Report 36. opment Corporation. Queensland Department of Primary Industries,
National Marine Fisheries Services. National Oceanic and Atmospheric Brisbane.
Administration. (South West Fisheries Science Center: La Jolla, CA.) Stratoudakis, Y., Bernal, M., Ganias, K., and Uriarte, A. (2006). The
Lasker, R. (1985). ‘An Egg Production Method for Estimating Spawning daily egg production method: recent advances, current applications and
Biomass of Pelagic Fish:Application to the NorthernAnchovy, Engraulis future challenges. Fish and Fisheries 7, 35–57. doi:10.1111/J.1467-
mordax.’(Ed. R. Lasker.) Technical Report 36. National Marine Fisheries 2979.2006.00206.X
Services. National Oceanic and Atmospheric Administration. (South Ward, T. M., and McLeay, L. J. (1998). Use of the Daily Egg Production
West Fisheries Science Center: La Jolla, CA.) Method to estimate the spawning biomass of pilchards (Sardinops sagax)
Lo, N. C. H. (1985). A model for temperature dependent northern anchovy in shelf waters of central and western South Australia in 1998. Report to
egg development and an automated procedure for the assignment of age PIRSA Fisheries. SARDI Aquatic Sciences, Adelaide.
to staged eggs. In ‘An Egg Production Method for Estimating Spawn- Ward, T. M., and Rogers, P. J. (Eds) (2007). Development and evaluation of
ing Biomass of Pelagic Fish: Application to the Northern Anchovy, egg-based stock assessment methods for blue mackerel Scomber aus-
Engraulis mordax’. (Ed. R. Lasker.) pp. 43–51. Technical Report 36. tralasicus in southern Australia. Final report to the Fisheries Research
National Marine Fisheries Services. National Oceanic and Atmospheric and Development Corporation on Project 2002/061. SARDI Aquatic
Administration. (South West Fisheries Science Center: La Jolla, CA.) Sciences, Adelaide.
Lo, N. C. H., Green Ruiz, Y. A., Cervantes, M. J., Moser, H. G., and Ward, T. M., Hoedt, F., McLeay, L., Dimmlich, W. F., Kinloch, M., et al.
Lynn, R. J. (1996). Egg production and spawning biomass of Pacific (2001). Effects of the 1995 and 1998 mass mortality events on the spawn-
sardine (Sardinops sagax) in 1994, determined by the daily egg produc- ing biomass of Sardinops sagax in SouthAustralian waters. ICES Journal
tion method. California Cooperative Oceanic Fisheries Investigations of Marine Science 58, 865–875. doi:10.1006/JMSC.2001.1077
Reports 37, 160–174. Watanabe, C. (1970). Morphology and ecology of early stages of life in com-
Lo, N. C. H., Macewicz, B. J., and Griffith, D. A. (2005). Spawning biomass mon mackerel, Scomber japonicus Houttuyn, with special reference to
of Pacific sardine (Sardinops sagax), from 1994–2004 off California. fluctuation of population. Report 62. Bulletin ofTokai Regional Fisheries
California Cooperative Oceanic Fisheries Investigations Reports 46, Research Laboratory, Tokai Regional Fisheries Research Laboratory,
93–112. Tokyo.
McGarvey, R., and Kinloch, M. A. (2001). An analysis of the sensitivity of Watanabe, C., Hanai, T., Meguro, K., Ogino, R., and Kimura, R. (1999).
stock biomass estimates derived from the daily egg production method Spawning biomass estimates of chub mackerel Scomber japonicus of
(DEPM) to uncertainty in egg mortality rates. Fisheries Research 49, a Pacific subpopulation off central Japan by a Daily Egg Production
303–307. doi:10.1016/S0165-7836(00)00208-3 Method. Nippon Suisan Gakkai Shi 65, 695–702.
Neira, F. J., Lyle, J. M., Ewing, G. P., Keane, J. P., and Tracey, S. R. White, K. V., and Fletcher, W. J. (1996). Identifying the developmental stages
(2008). Evaluation of egg production as a method of estimating spawning for eggs of the Australian pilchard, Sardinops sagax. Fisheries Research
biomass of redbait of the east coast of Tasmania. Final Report to the Fish- Report 103. Fisheries Western Australia, Perth.
eries Research and Development Corporation on Project No. 2004/039. Yamada, T., Aoki, I., and Mitani, I. (1998). Spawning time, spawning fre-
Tasmanian Fisheries and aquaculture Institute, Hobart. quency and fecundity of Japanese chub mackerel, Scomber japonicus in
Parker, K. (1980). A direct method for estimating northern anchovy, the waters around the Izu Islands, Japan. Fisheries Research 38, 83–89.
Engraulis mordax, spawning biomass. Fishery Bulletin 84, 541–544. doi:10.1016/S0165-7836(98)00113-1
Parker, K. (1985). Biomass model for the egg production method. In
‘An Egg Production Method for Estimating Spawning Biomass of
Pelagic Fish: Application to the Northern Anchovy, Engraulis mordax’.
(Ed. R. Lasker.) pp. 5–6. Technical Report 36. National Marine Fisheries Manuscript received 28 April 2008, accepted 20 October 2008
http://www.publish.csiro.au/journals/mfrYou can also read
