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Estimating the Impact of a Seasonal
Fishing Moratorium on the East China
Sea Ecosystem From 1997 to 2018
Lingyan Xu
1,2†
, Puqing Song
2†
, Yuyu Wang
3
, Bin Xie
2
, Lingfeng Huang
4
, Yuan Li
2
,
Xinqing Zheng
2,5
*and Longshan Lin
2
*
1
College of Marine Sciences, Shanghai Ocean University, Shanghai, China,
2
Third Institute of Oceanography, Ministry of
Natural Resources, Xiamen, China,
3
School of Ecology and Nature Conservation, Beijing Forestry University, Beijing, China,
4
College of the Environment and Ecology, Xiamen University, Xiamen, China,
5
Observation and Research Station of Island
and Coastal Ecosystems in the Western Taiwan Strait, Ministry of Natural Resources, Xiamen, China
Fisheries management policies (FMPs) have been implemented in coastal countries to
ensure a sustainable supply of seafood and the recovery of species diversity. Because of
the depletion of fishery stocks, China has introduced a series of FMPs since 1995,
including a seasonal fishing moratorium (SFM), a zero-growth strategy, and a minimum
mesh size for fishing nets. Here, we built two mass balance models for 1997–2000
(M1997) and 2018–2019 (M2018) using Ecopath with Ecosim 6.6 to illustrate the
interannual changes over the past two decades in the East China Sea (ECS). We then
simulated two dynamic scenarios from 1997 to 2018, SFM (M2018
SFM
) and no SFM
(M2018
no-SFM
), to test the role of the SFM under fishing pressure in the ECS. Ecopath
showed that the ECS ecosystem is becoming more mature, although it is still unstable,
featuring lower total primary production/total respiration, longer cycles, faster organic
material circulation speed, and a higher omnivorous degree. This suggests a slow
recovery for the ECS ecosystem in the past two decades. The biomass of fish in the
ECS—especially the planktivores, dominated by small-sized Benthosema pterotum—
significantly increased in M2018 versus M1997, but there were fewer medium- and large-
sized fish. The keystone species switched from the planktivores/piscivores dominated by
Decapterus maruadsi in M1997 to planktivores in M2018. Ecosim illustrated that the SFM
has positive effects on fishery resources recovery, especially for commercial fishes (i.e.,
large yellow croakers and hairtails), as reflected by the significantly higher predicted
biomass of fish in M2018
SFM
compared to M2018
no-SFM
and M1997, although the
bioaccumulation was consumed by the intense fishing pressure after the SFM.
However, the M2018
SFM
prediction for nektons was still lower than the actual value,
especially for planktivores, which display a sharp increase in biomass. This should be
partly attributable to the policy of the minimum mesh size (<5 cm was banned), which
benefits B. pterotum due to its 3.5 cm maximum body size. Therefore, a series of FMPs,
Frontiers in Marine Science | www.frontiersin.org June 2022 | Volume 9 | Article 8656451
Edited by:
Jun Xu,
Institute of Hydrobiology (CAS), China
Reviewed by:
Zhongxin Wu,
Dalian Ocean University, China
Chongliang Zhang,
Ocean University of China, China
*Correspondence:
Xinqing Zheng
zhengxinqing@tio.org.cn
Longshan Lin
lslin@tio.org.cn
†
These authors have contributed
equally to this work and share
first authorship
Specialty section:
This article was submitted to
Marine Ecosystem Ecology,
a section of the journal
Frontiers in Marine Science
Received: 30 January 2022
Accepted: 20 April 2022
Published: 09 June 2022
Citation:
Xu L, Song P, Wang Y, Xie B, Huang L,
Li Y, Zheng X and Lin L (2022)
Estimating the Impact of a Seasonal
Fishing Moratorium on the East China
Sea Ecosystem From 1997 to 2018.
Front. Mar. Sci. 9:865645.
doi: 10.3389/fmars.2022.865645
ORIGINAL RESEARCH
published: 09 June 2022
doi: 10.3389/fmars.2022.865645
rather than only the SFM, functioned together in the ECS ecosystem. However, the mixed
trophic impact indicated a negative impact if the fisheries were further developed. Fishery
management in the ECS needs to be strengthened by extending the SFM and reducing
fishing pressure after the SFM.
Keywords: Ecopath with Ecosim, East China Sea, seasonal fishing moratorium, commercial fish, fisheries
management policies
1 INTRODUCTION
Aquatic products are a primary protein source for humans (Lira
et al., 2021). As such, global marine capture production had
reached 92.51 million tons by 2017, with an average increase of
10-fold relative to 1950, resulting in the catch per unit effort
declining by 50% to 80% (Zhou et al., 2015;Link and Watson,
2019). Therefore, to ensure a sustainable supply of seafood and
the recovery of species diversity, fisheries management policies
(FMPs) such as total allowable catches, individual transferable
quotas, seasonal and area closures, and stock assessments have
been widely implemented by coastal countries (Bromley, 2005;
Fulton et al., 2014). Similarly, China has introduced a series of
FMPs since 1995 that include zero and minus growth targets, a
seasonal fishing moratorium (SFM), a minimum mesh size for
fishing nets, and a minimum catch size for fishing targets (Cao
et al., 2017). There has also been a ban on destructive fishing
methods, the construction of artificial fish reefs, and the release
of some commercial fish (i.e., Larimichthys crocea) to restore the
disturbed marine ecosystem (Han, 2018;Xin et al., 2020).
Fisheries management policies (FMPs) have received
considerable critical attention for their protective effect on
resource recoveries. Many studies have reported the positive
effects of FMPs. For example, Yue et al. (2015) found that after
the SFM, the catch increased in the East China Sea (ECS) and the
South China Sea. Wang et al. (2020) found that the reduction of
fishermen, fishing vessels, and catches all had a positive effect on
the recovery of fishery stocks in the Pearl River Delta. Lee and
Midani (2014) also showed that the catch per unit effort of
sandfish nearly doubled from 2005 to 2011 in the East Sea of
Korea after the implementation of a fishing stock-rebuild plan.
However, some studies have found that the effectiveness of these
fishery strategies had a spatiotemporal limit. Yan et al. (2019a)
concluded that in the ECS, the accumulation of biomass during
the SFM was rapidly removed by the subsequent intense fishing
pressure. In addition, the Fujian fishery statistical yearbook
shows that the landing of large yellow croaker (L. crocea) has
been maintained at a low level since 2000, although millions of L.
crocea larvae have been released (Wu et al., 2021). Di Franco
et al. (2009) also reported no differences in the fish assemblages
between partially protected areas and a location outside the
marine protected area in northeast Sardinia (Italy).
The ECS is one of the most important fishing areas, providing
about 40% of the total catch in China (Zhang et al., 2018). In the
1990s, the number of fishing vessels in this region exceeded 100,000
and accounted for nearly half of the total fishing vessels in the
China Sea (Shi, 1995). Under such intense fishing pressure, the ECS
ecosystem had already been overloaded. Many traditional
commercial fish stocks such as the large yellow croaker were
exhausted, and the length of the fishing season has declined in
some fishing grounds (Liu et al., 2012;Mei, 2019;Xu et al., 2021).
Similarly, much uncertainty remains about whether the FMPs in
the ECS work in the long term. For the ECS ecosystem, FMPs such
as the minimum mesh size (Tokai et al., 2019), zero and minus
growth targets (Ye and Rosenberg, 1991), SFM (Cheng et al., 2004),
and release enhancement (Lü et al., 2008) have been recognized to
be the major drivers rebuilding the fishery resources (Shih et al.,
2009;Shen and Heino, 2014;Zhou et al., 2019). However, the
previous evaluations of FMPs in the ECS have been focused at the
short-term (Yan et al., 2019b), single-policy (Liu and Cheng, 2015;
Yue et al., 2015), or species level (Xu and Liu, 2007) rather than at
the long-term, multiple-policy, or ecosystem level. Therefore,
whether or not FMPs, especially the SFM and reduced fishing
pressure, drove the variances in the structure and function of the
ECS ecosystem over the last 20 years needs to be further verified.
Ecopath with Ecosim (EwE), a widely used tool to support
ecosystem-based fisheries management, prioritizes the ecosystem
rather than a single species population (Pikitch et al., 2004;
Halpern et al., 2008; Surma et al., 2019;Reum et al., 2021), and it
can explore the long-term performances of multiple FMPs under
different scenarios (Li, 2009;Russo et al., 2017;Papapanagiotou
et al., 2020;Wang et al., 2020;Paradell et al., 2021). Therefore, to
verify the hypothesis on FMPs’effects, this work attempted to
estimate the interannual variation of the ECS ecosystem from
1997 to 2018 with EwE and explored the long-term effects of the
SFM under the actual fishing pressures present during the two
decades. We constructed two mass balance models for 1997–
2000 (referred to as M1997) and 2018–2019 (referred to as
M2018) in the ECS, to depict the variation in the ecosystem’s
structure and function over the past two decades. We further
conducted scenario simulations based on the M1997 model to
evaluate the contribution of the SFM under fishing pressure to
the variations of the ECS ecosystem during the two decades.
These results reveal the combined effects of the SFM and fishing
pressures on the rehabilitation of the structure and function of
the ECS ecosystem and offer advice on the management of
fishery resources in the ECS for governmental policymakers.
2 MATERIALS AND METHODS
2.1 Study Area
The ECS is located in the western Pacific Ocean and is connected
with the Sea of Japan through the Tsushima Strait and with the
Xu et al. Impact of Seasonal Fishing Moratoriums
Frontiers in Marine Science | www.frontiersin.org June 2022 | Volume 9 | Article 8656452
South China Sea through the Taiwan Strait (Figure 1;Li and
Zhang, 2012). It is influenced by the Kuroshio Current and dilute
water from the Yangtze River. The ECS is one of the most
productive regions globally, leading to many important fishing
grounds such as the Zhoushan and Minzhong (Liu, 2013). The
ECS has experienced three periods associated with dramatically
increased fishing equipment and changes in fishing methods:
slow growth (1951 to the 1990s), rapid growth (1991 to the
2000s), and high yield (after the 2000s; Chen et al., 1997;Mei,
2019). High-intensity fishing pressure has brought immense
economic benefits, but it has also driven a great change in the
catch composition from the ECS (Chen et al., 2004).
The SFM, which can offer a suitable time for adult spawning
and larvae growth, is considered the utmost protection to rebuild
fish stocks (Su et al., 2019). Initially, it was implemented in the
northern ECS (27°N to 35°N) from 1 June to 31 August in 1995.
During this period, only the trawl and sailing nets were banned.
In 2017, all fishing gear with the exception of hooks and lines was
banned in the ECS from noon on 1 May to noon on 16
September (Yan et al., 2019a). A minimum mesh size for nets
was implemented in 2004 to protect recruitment. In 2017, the
Ministry of Agriculture and Rural Affairs announced a minimum
allowable size for 15 marine economic fish species. A series of
FMPs were also introduced to reduce the fishing pressure,
including zero and minus growth targets, a licensing system, a
vessel buyback program, dual control, and a fishermen relocation
program. The Ministry of Agriculture designed the zero and
minus growth system in 1999 and the fishing quota management
system based on the maximum sustainable yield in 2000. In 2017,
species quota fishing was implemented in the Zhejiang and
Shandong provinces (Mei, 2019). The licensing system, vessel
buyback program, dual control, and fishermen relocation
program were also implemented after 2002 (Cao et al., 2017).
2.2 Ecopath Model Construction
and Parameterization
Ecopath offers a static snapshot that reflects the structure and
function of the ecosystem at a specific time. The Ecopath model
is based on a set of linear equations for each function group in
the system. It is formed by the food consumption equation and
energy theory (Polovina, 1984;Ulanowicz, 1986). Ecopath needs
the following input parameters: biomass (B), the production/
biomass ratio (P/B), the consumption/biomass ratio (Q/B), diet
composition (DC), and ecotrophic efficiency (EE). Usually, only
three of the four groups of parameters need to be entered, and
the model can then automatically obtain the fourth parameter.
The EE is difficult to obtain, and thus, the other three parameters
are usually entered (Christensen et al., 2005).
The basic equation is
Bi·P=BðÞ
i=on
j=1Bj·Q=BðÞ
j·DCji +P=BðÞ
i·Bi·1−EEi
ðÞ+Yi
+Ei+BAi,
where (P/B)
i
and B
i
are the production/biomass ratio and
biomass of group i, respectively; (Q/B)
j
is the consumption/
biomass ratio of group j,DC
ji
is the proportion of the diet that
FIGURE 1 | The study area in the East China Sea.
Xu et al. Impact of Seasonal Fishing Moratoriums
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predator group iobtains from prey group j,EE
i
is the ecotrophic
efficiency, B
i
(1 - EE
i
) is other mortality, Y
i
is the catch of group i,
E
i
is the net migration of group i, and BA
i
is the biomass
accumulation rate for i.
To simplify the trophic structure of the ecosystem, species
with a similar ecological niche were aggregated in one function
group (Christensen et al., 2005). The function group can also be
composed of a single species or the different age structures of a
species (Zheng et al., 2020;Lin et al., 2021). There were 24
function groups in the ECS ecosystem, including phytoplankton,
zooplankton, polychaetes, mollusks, benthic crustaceans,
echinoderms, other invertebrates, crabs, shrimps, cephalopods,
planktivores, benthivores, piscivores, planktivores/piscivores,
planktivores/benthivores, benthivores/piscivores, omnivores,
sharks, marine mammals, and detritus (Table S1). In addition,
large yellow croakers, small yellow croakers, hairtails, and
Bombay duck were treated as separate function groups due to
their high economic values and resources.
The biomass data were collected from field surveys and
published literature. Data for the first model was from a 1997–
2000 marine living resources supplementary survey and resource
evaluation. The second model data were from a joint survey in
the ECS in the autumn of 2018 and the spring of 2019. The P/B
and Q/B values were obtained using empirical equations and
from published literature (Palomares and Pauly, 1989;Pauly
et al., 1990;Christensen et al., 2005;Cheng et al., 2009;OuYang
and Guo, 2010;Li and Zhang, 2012). The diet data were
estimated based on stomach content analyses in the published
literature (Tables S2,S3). The food matrix of the function groups
was calculated based on the biomass weight of each species in the
function group. We used similar diet matrices in the M1997 and
M2018 models but made slight changes in several groups, e.g.,
hairtails, piscivores, and planktivores. Information on catch was
obtained from the China Fishery Statistical Yearbook (CFSY),
but there was no discard data in the two models due to a lack
of data.
2.2.1 Ecological Indicators
Ecological indicators, which can be used as measures to assess
ecosystem status, are included and presented along with other
Chinese models and models at similar latitudes. The Finn’s
cycling index indicates the speed of organic material circulation
in the ecosystem, and the mean path length represents the total
number of trophic links divided by the number of pathways
(Finn, 1976;Christensen et al., 2005). The connectivity index
reveals the interaction between species in terms of predation, and
the system omnivory index (SOI) is defined as the average
omnivorous degree of the consumers (Nee, 1990;Pauly and
Christensen, 1993). These indicators are linked with the
maturity of the ecosystem (Ulanowicz, 2012). Total system
throughput (TST) sums all flows in this model according to
Ulanowicz (2012). Mixed trophic impact (MTI) can reveal a
direct or indirect influence of one function group on another
function group, which can explain the relationship between
groups in the ecosystem (Ulanowicz and Puccia, 1990). The
keystone index can identify the key species of the ecosystem by
selecting indexes greater than zero. The keystones play a primary
role in maintaining stability and complexity and have a
disproportionate impact on biomass (Libralato et al., 2006).
2.2.2 Model Balance and Sensitivity Analysis
After all data were input, the EE should be below 1, and most of
the gross efficiency values should be between 0.1 and 0.3, with the
exception of some fast-growing organisms (Christensen et al.,
2005). Pedigree and a sensitivity analysis were used to verify the
reliability of the model. The pedigree can mark the source and
calculate the credibility of the input data (Majkowski, 1982;
Funtowicz and Ravetz, 1990). It is also the reference used to
adjust the parameters of the model. The parameter with the
lowest confidence would thus be adjusted when the model is
unbalanced (Funtowicz and Ravetz, 1990). The sensitivity analysis
can evaluate the uncertainty of the output data of the model when
the input data fluctuate. The sensitive analysis routine was set to
±20% uncertainty for all input parameters (Han et al., 2017).
2.3 Dynamic Simulation
The Ecosim model conducts a temporal dynamic analysis with
key original parameters from the Ecopath model, which is used
as a reference to estimate changes in the biomass of function
groups driven by time series data (Christensen et al., 2005). In
the modeling framework, a series of differential equations that
consider predator–prey interactions and foraging behaviors
inherent to Ecopath can be expressed as follows (Christensen
et al., 2005):
dBi=dt =P=QðÞ
io
j
Qji −o
j
Qij +Ii−Mi+Fi+ei
ðÞBi,
where dB
i
/dt is the change in the biomass of group iover time,
(P/Q)
i
is the net growth efficiency, M
i
is the non-predation
mortality rate, F
i
is the fishing mortality rate, e
i
is the
emigration rate, I
i
is the immigration rate, B
i
is the biomass of
group i,S
j
Q
ji
is the total consumption rate by group i, and S
j
Q
ij
is the predation by all predators on the same group i.
The Ecosim incorporated historical data, including biomass,
catches, and fishing mortalities for different function groups to
facilitate accurate model predictions. The partial biomass time
series calculated for hairtails, small yellow croakers, and
piscivores were derived from the SAU database (http://www.
seaaroundus.org/). For most nektonic groups, the time series on
absolute biomass, catch, and fishing mortalities were obtained
from the CFSY and China’s offshore marine comprehensive
survey and evaluation project. The time series Chl-awas from
the dataset of Aqua MODIS and SeaWiFS and was used to
calibrate primary production anomalies. Once the time series
data (Table S4) were included in the model, the fit model with
the lowest Akaike information criterion value was selected
(Burnham and Anderson, 2004). The vulnerabilities (v), which
represent the impacts of predator biomass for a given prey, are an
important parameter in the process of model fitting to time series
data (Christensen et al., 2005). To reduce human error and
obtain actual vin the calibrating process, the automated
“stepwise fitting”procedure was used (Scott et al., 2016).
As mentioned, a series of FMPs have been implemented in the
ECS since the 1990s (Cao et al., 2017). However, in this study, we
Xu et al. Impact of Seasonal Fishing Moratoriums
Frontiers in Marine Science | www.frontiersin.org June 2022 | Volume 9 | Article 8656454
only evaluated the impacts of the SFM under actual fishing
pressure (Figure S1)ontherecoveryoffishery resources. Then,
two Ecosim models that did or did not integrate the SFM under the
actual fishing pressures were built, which are expressed by
M2018
SFM
and M2018
no-SFM
. Because the annual landing in
Zhoushan (Zhejiang province) can account for 20~40% of the
total landing in the ECS (http://zstj.zhoushan.gov.cn/)—there was
significant correlation for landing between the ECS and Zhoushan
(r = 0.628, p < 0.05)—the relative fishing effort of every month in
the ECS was assumed to be consistent with that in the Zhoushan in
M2018
SFM
. In addition, we also employed the fleet data in Global
Fishing Watch (https://globalfishingwatch.org/data-download/
datasets/public-fishing-effort) to fit the actual fishing effort in the
ECS (Table S5), whereas the fishing effort of every month in a year
was assumed to be the same in the M2018
no-SFM
model. Although a
minimum mesh size could be beneficial to the juveniles of nektonic
groups by selecting suitable lengths for the species (Heikinheimo
et al., 2006;Nguyen et al., 2021), the model cannot load this policy
due to the lack of body length distribution data for the kinds of
function groups. Climate change was considered to affect the
physiology, distribution, and biomass of the marine species and
alter the community composition of the marine ecosystems
(Cheung et al., 2013;Kroeker et al., 2013;Zeng et al., 2019).
However, it was negligible from 2000 to 2018 in this study, as
shown by Zeng et al. (2019) in the Pearl River estuary, where the
non-producer biomass decreased by only 5% from 2000 to 2060.
Therefore, the variants for climate change (seawater surface
temperature, pH, and dissolved oxygen) were not included in the
two Ecosim models.
3 RESULTS
3.1 Quality of the Ecopath Model and the
Sensitivity Analysis
The basic data and output results for M1997 and M2018 are
shown in Table 1. Except for the plankton and high-trophic
groups, the EE of most function groups was close to 1, suggesting
that there was a high utilization rate of most groups in the ECS.
The gross efficiency was almost in the 0.1–0.3 range. Finally, the
pedigrees of the two Ecopath models were both 0.497, which is a
reasonable interval; 0.16–0.68 is the range of pedigrees for most
EwE models (Morissette, 2007), indicating that the quality of our
Ecopath models was acceptable.
A sensitivity analysis was used to evaluate the influence of the
variation in input data on the output results. A similar pattern was
observed in the two models (Figure 2).Thebiomassofthefunction
groups appeared to be the most influential parameter, with a range
of ±30%. The diet appeared to be the least influential parameter on
the two balanced models, which is consistent with observations by
Han et al. (2017). This further indicated that adjusting the food
matrix had the lowest impact on the overall model output.
3.2 The 20-Year Change in the East China
Sea Ecosystem
The flow diagram for the ECS showed similar trophic levels (TLs)
during the two periods, from 1.00–4.05 and 1.00–4.24 in M1997
and M2018, respectively (Table 1 and Figure S2). The mean TLs
of caught fishes showed a slight increase, from 3.11 in M1997 to
3.33 in M2018. Transfer efficiency was 10.56% in the M2018
TABLE 1 | Input and output (bold) parameters for the East China Sea ecosystem mass balance models M1997 (1997–2000) and M2018 (2018–2019).
Groups TL B (t/km
2
/year) P/B (/year) Q/B (/year) EE
M1997 M2018 M1997 M2018 M1997 M2018 M1997 M2018 M1997 M2018
1. Phytoplankton 1.000 1.000 16.52 43.20 170.7 82.75 ––0.205 0.411
2. Zooplankton 2.000 2.000 4.703 12.82 40.00 40.00 160.0 160.0 0.308 0.274
3. Polychaetes 2.000 2.000 3.130 1.841 6.700 6.700 24.20 24.20 0.541 0.462
4. Mollusks 2.169 2.169 9.510 0.343 3.000 3.000 7.000 7.000 0.300 0.957
5. Benthic Crustaceans 2.161 2.151 1.600 1.530 6.560 6.560 26.90 26.90 0.748 0.955
6. Echinoderms 2.220 2.212 3.460 6.427 1.200 1.200 3.700 3.700 0.182 0.232
7. Other invertebrates 2.000 2.000 3.160 1.359 1.000 2.000 9.000 9.000 0.616 0.795
8. Crabs 2.322 2.334 0.143 0.0534 4.500 4.500 12.00 12.00 0.916 0.996
9. Shrimps 2.314 2.314 0.161 0.288 5.100 5.100 19.20 19.20 0.999 0.998
10. Cephalopods 2.818 2.955 0.549 0.894 3.000 3.000 10.00 10.00 0.984 0.946
11. Planktivores 2.953 2.919 0.710 4.701 3.588 4.762 14.74 22.30 0.998 0.979
12. Benthivores 2.848 2.876 0.0397 0.278 2.116 2.156 7.176 8.497 0.995 0.965
13. Piscivores 3.571 3.940 0.348 0.275 2.130 1.643 6.868 6.160 0.804 0.324
14. Hairtails 3.169 3.369 1.336 3.369 1.104 2.900 6.467 5.600 0.998 0.338
15. Bombay duck 2.905 3.420 0.110 1.497 2.120 2.120 8.964 6.190 0.956 0.953
16. Planktivores/Benthivores 2.958 2.964 0.609 2.103 1.176 1.048 10.68 11.40 0.916 0.667
17. Planktivores/piscivores 3.116 3.522 2.588 1.294 0.885 1.287 11.72 12.23 0.353 0.695
18. Benthivores/piscivores 3.139 3.459 0.534 0.349 3.910 1.451 9.327 9.921 0.985 0.952
19. Omnivores 3.136 3.433 0.136 0.439 3.279 3.279 7.992 7.992 0.999 0.826
20. Large yellow croakers 3.379 3.498 0.00107 0.0339 2.130 1.441 4.913 3.767 0.997 0.307
21. Small yellow croakers 3.085 3.206 0.356 0.0832 4.300 2.410 8.997 6.074 0.226 0.990
22. Sharks 4.035 4.236 0.00889 0.00935 0.500 0.500 3.200 3.200 0.000 0.000
23. Marine mammals 3.809 3.939 0.00404 0.00404 0.050 0.050 30.00 30.00 0.000 0.000
24. Detritus 1.000 1.000 100 100 ————0.147 0.241
TL, trophic level; B, biomass; P/B, production/biomass; Q/B, consumption/biomass; EE, ecotrophic efficiency.
Xu et al. Impact of Seasonal Fishing Moratoriums
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model, which was less than the M1997 model (12.01%) but close
to the theoretical ranges (10%; Christensen and Pauly, 1992). The
TST developed further, from 6503.85 t/km
2
/year to 8925.33 t/km
2
/
year in the two decades, with detritus flow accounting for 33.33%
in M2018 and 40.36% in M1997 (Table 2). The consumption and
respiration in the TST significantly increased by 14.33% and 5.77%
in M2018, respectively, compared to M1997 (Table 2).
Total biomass increased considerably, from a value of
49.72 t/km
2
/year in M1997 to 83.19 t/km
2
/year in M2018
(Figure 3). The biomass of the plankton and fish in M2018 was
nearly doubled relative to M1997, especially the planktivores,
whose biomass in M2018 increased nearly seven times that in
M1997. However, obvious and significant downtrends were noted
in the benthic organisms, especially mollusks.
There was a major shift in the keystone indexes of the
function groups in the two models (Figure 4). Several groups
increased, including the benthic crustaceans, Bombay duck, large
yellow croakers, sharks, and planktivores. The keystone species
in the ECS changed from planktivores/piscivores to planktivores.
Zooplankton and planktivores/piscivores (e.g., Decapterus
maruadsi and Trachurus japonicus) were the keystone species
revealed by the keystone indexes close to zero in M1997 (−0.0355
and −0.0711, respectively), and they had the largest influence on
the ecosystem structure. However, piscivores, hairtails, and small
yellow croakers decreased in importance during the study period.
The MTI showed the increasingly negative impact of fisheries
in the ECS between M1997 and M2008 (Figure 5). In M1997,
36% of the function groups such as large yellow croakers,
piscivores, and benthivores/piscivores were negatively affected
by the fisheries. The positive impacts on the planktivores and
benthivores (around 24%) were ascribed to the indirect effect of
removing predators. In M2018, the negative impact of the
fisheries further extended to small yellow croakers and
omnivores. In addition, the MTI of the fisheries on important
fish species such as hairtails and large yellow croakers slightly
increased in M2018 relative to M1997, from −0.295 to −0.034 for
hairtails and from −0.729 to −0.516 for large yellow croakers.
Small yellow croakers had opposite values, 0.0635 in 1997 and
−0.395 in 2018. However, the impact of the fisheries on Bombay
duck appeared neutral over the two periods due to the limited
and poor fishery data.
Ecosystem indicators were used to evaluate the status and
variation of the structure and function of the ecosystem
(Table 2). The ECS ecosystem was more mature and stable in
M2018, as reflected by the higher total primary production/total
respiration (TPP/TR) and SOI as well as by the lower Finn’s
cycling index and mean path length (Table 2). The TPP/TR
declined from 4.89 to 2.74 between M1997 and M2018. The Flow
to Detritus/TST was 33.33% in M2018 and 40.36% in M1997,
indicating that more energy flowed into production rather than
A
B
DC
FIGURE 2 | The sensitivity analysis results for the 20% uncertainty of the Ecopath input parameters. TPP, total primary production; TR, total respiration; EE, ecotrophic
efficiency; B, biomass; P/B, production/biomass; Q/B, consumption/biomass; DC, diet composition. The sensitivity analysis results for the 20% uncertainty of the Ecopath
input parameters. TPP, total primary production; TR, total respiration; EE, ecotrophic efficiency; B, biomass; P/B, production/biomass; Q/B, consumption/biomass; DC,
diet composition. (A) represents the impact of the input parameters (B, P/B, Q/B, DC) with 20% uncertainty on the EE of the phytoplankton (M1997); (B) represents the
impact of the input parameters (B, P/B, Q/B, DC) with 20% uncertainty on the TPP/TR (M1997); (C) represents the impact of the input parameters (B, P/B, Q/B, DC) with
20% uncertainty on the EE of the phytoplankton (M2018); (D) represents the impact of the input parameters (B, P/B, Q/B, DC) with 20% uncertainty on the TPP/TR
(M2018).
Xu et al. Impact of Seasonal Fishing Moratoriums
Frontiers in Marine Science | www.frontiersin.org June 2022 | Volume 9 | Article 8656456
detritus. Ascendency decreased from 40.54% to 35.65% from
1997 to 2018, and overhead increased from 59.46% to 64.26%,
suggesting that the ECS ecosystem was more robust to resist
external disturbance. In addition, the Finn’s cycling index values
approximately doubled from 1997 to 2018, and there was a slight
increase in mean path length (from 2.31 in M1997 to 2.50 in
M2018). This further implied an increase in the proportion of
material recycling. The same performance was also observed in
FIGURE 3 | Biomass of the function groups in the East China Sea ecosystem in the M1997 (1997–2000) and M2018 (2018–2019) mass balance models and the
M2018
no-SFM
(no seasonal fishing moratorium) and M2018
SFM
(with a seasonal fishing moratorium) dynamic simulations (1997–2018).
TABLE 2 | Comparison of ecosystem indicators for the East China Sea Ecopath mass balance models M1997 (1997–2000) and M2018 (2018–2019) with other
available Ecopath models in adjacent waters (Bohai, Northern South China, and Southwestern Yellow Seas) and in other seas at similar latitudes (Northern Oman Sea
and Northern Gulf of Mexico).
Parameters The Northern
Oman Sea
The Northern Gulf of
Mexico
The Bohai
Sea
The Northern South
China Sea
The Southwestern
Yellow Sea
This Study
M1997 M2018
Sum of all consumption (t/km
2
/year) 6,591.37 1,908 1,213.72 7,951.491 1,031.03 1,058.40 2,374.89
Sum of all exports (t/km
2
/year) 8,736.78 7,530 3,922.88 3,613.73 991.67 2,243.97 2,268.27
Sum of respiration flows (t/km
2
/
year)
3,398.37 1,046 894.61 4,137.55 643.73 576.02 1,306.51
Sum of all flows into detritus (t/km
2
/
year)
8,855.18 8,078 4,467.43 3,588.56 1,330.83 2,624.96 2,975.58
Total system throughput (t/km
2
/
year) (TST)
2,7581.7 18,563 10,499.00 15,698.05 2,825.00 6,503.35 8,929.
63
Flows to Detritus/TST (FD/TST) 32.11% 43.52% 42.55% 22.86% 47.11% 40.36% 33.37%↑
Total primary production/total
respiration (TPP/TR)
3.57 8 5.38 1.005 8.68 4.89 2.74↑
Transfer efficiency (%) (TE) 10.60 16.93 11.35 21.94 13.22 12.03 11.52
Connectance index (CI) 0.44 0.396 0.33 0.31 0.280 0.35 0.35
System omnivory index (SOI) 0.42 0.410 0.14 0.33 0.217 0.12 0.17↑
Finn’s cycling index (FCI) 5.70 ––13.68 3.983 2.832 5.175↑
Mean path length (MPL) 2.27 ––3.78 2.444 2.306 2.497↑
Ascendency (%) (A) 45.50 –– – –40.54 35.65↑
Overhead (%) (O) 54.60 –– – –59.46 64.35↑
Bold arrows represent the greater maturity and complexity in the M2018 model.
Xu et al. Impact of Seasonal Fishing Moratoriums
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the SOI. Compared with M1997, the SOI increased from 0.13 to
0.18, whereas the connectivity index remained constant between
M1997 and M2018. In summary, a series of ecosystem indicators
showed that the maturity and stability of the ECS ecosystem in
2018 had further developed. However, the ECS is still a
developing ecosystem with a mass of unused energy.
3.3 Effect of the Seasonal
Fishing Moratorium
The optimal model for the stepwise fitting process based on time
series data was selected by the lowest Akaike information
criterion for important economic species, in which the vvalues
were caught (Table S6 and Figure S3). There were significant
differences in the biomasses of the kinds of function groups for
the four models. There was a higher biomass of low-TL function
groups in M2018
no-SFM
than in M2018
SFM
and M2018, i.e.,
polychaetes, mollusks, and shrimps; conversely, the higher
biomass in the high-TL function groups was in M2018
SFM
, i.e.,
piscivores and benthivores/piscivores. By linearly fitting the
actual biomass in M2018 with the one in M1997 and the
predicted biomasses in M2018
no-SFM
and M2018
SFM
(Figure 6), we found that the slope for M2018
SFM
was the
highest, followed by M2018
no-SFM
and M1997. The M2018
SFM
and M2018
no-SFM
models could explain 69.87% and 45.39% of
the biomass change, respectively. Note that the linear fit excluded
planktivores, planktivores/piscivores, and planktivores/
benthivores due to the poor prediction quality for plankton
organisms in the two Ecosim models.
Increased biomasses were observed for most function groups
during the SFM, but they sharply decreased after the SFM due to
the intense fishing intensity (Figure 7). The predicted biomass
for most function groups was higher compared to M1997,
suggesting reduced fishing pressure has a positive influence on
resource recovery. However, discrepancies in the biomasses
between function groups were found in the two simulated
scenarios. Similar patterns emerged in hairtails, benthivores/
piscivores, omnivores, and large yellow croakers (Figures 7G,
H, J, L, respectively). In M2018
no-SFM
, the biomass of these
A
B
FIGURE 4 | Keystoneness index and overall effects of each function group from the East China Sea ecosystem in the (A) M1997 (1997–2000) and (B) M2018 (2018–
2019) mass balance models. 1, Phytoplankton; 2, Zooplankton; 3, Polychaetes; 4, Mollusks; 5, Benthic crustaceans; 6, Echinoderms; 7, Other invertebrates; 8, Crabs; 9,
Shrimps; 10, Cephalopods; 11, Planktivores; 12, Benthivores; 13, Piscivores; 14, Hairtail; 15, Bombay duck; 16, Planktivores/Benthivores; 17, Planktivores/Piscivores; 18,
Benthivores/Piscivores; 19, Omnivores; 20, Large yellow croakers; 21, Small yellow croakers; 22, Sharks; 23, Marine mammals; 24, Detritus.
Xu et al. Impact of Seasonal Fishing Moratoriums
Frontiers in Marine Science | www.frontiersin.org June 2022 | Volume 9 | Article 8656458
groups declined substantially from that in M1997 but increased
dramatically in M2018
SFM
. This suggests that the SFM facilitates
the recovery of these function groups. There were no differences
in crabs; shrimps; benthivores, piscivores, and Bombay duck;
planktivores/benthivores; and small yellow croakers in the two
Ecosim models (Figures 7A, B, D–F, I, K, respectively). The
landing of benthivores and Bombay duck was not recorded in the
CFSY, so the change in fishing effort provided a limited effect on
these groups. The excessive exploitation of piscivores after the
SFM led to a similar trend in the two Ecosim models. As to crabs
and shrimps, the combined effect of fishing and trophic
interaction rapidly removed the bioaccumulation from the
SFM and brought similar results in the two scenarios.
Although the SFM facilitated the recovery of large yellow
croakers and benthivores/piscivores, the small yellow croakers
and planktivores/benthivores did not increase in the M2018
SFM
model. The biomass of the cephalopods was lower in M2018
SFM
than in M2018
no-SFM
(Figure 7C), which was mainly due to the
stronger feeding pressure from higher TL organisms.
4 DISCUSSION
4.1 Analysis of the Variations in the East
China Sea Ecosystem Structure
The widely used EwE software provides for the easy
implementation of different network indices that can describe
the developing status of an ecosystem structure. Our results
indicated a 20-year increase in the ecosystem’s maturity and
stability after a series of FMPs featuring a higher TST, a lower
A
B
FIGURE 5 | Mixed trophic impacts of the function groups in the East China Sea ecosystem in the (A) M1997 (1997–2000) and (B) M2018 (2018–2019) mass
balance models.
Xu et al. Impact of Seasonal Fishing Moratoriums
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AB D
EF G
I
H
JK L
C
FIGURE 7 | Biomass of important function groups in the M2018
no-SFM
and M2018
SFM
dynamic simulations (1997–2018), which are the predicted model without
and with a seasonal fishing moratorium, respectively; the peak value in M2018
SFM
represents the seasonal fishing moratorium. (A) Crabs, (B) Shrimps, (C)
Cephalopods, (D) Benthivores, (E) Piscivores, (F) Bombay duck, (G) Hairtails, (H) Benthivores/Piscivores, (I) Planktivores/Benthivores, (J) Omnivores, (K) Small
yellow croakers, (L) Large yellow croakers.
FIGURE 6 | The linear fit of the actual biomass in M2018 (2018–2019) with the ones predicted by the M2018
no-SFM
and M2018
SFM
dynamic simulations (1997–
2018), which represent the predicted model without and with a seasonal fishing moratorium (SFM), respectively. Dotted lines represent the actual biomasses in
M1997 (yellow) and M2018 (black).
Xu et al. Impact of Seasonal Fishing Moratoriums
Frontiers in Marine Science | www.frontiersin.org June 2022 | Volume 9 | Article 86564510
TPP/TR, longer cycles, faster organic material circulation speed,
a higher degree of omnivory in the consumers, and less energy
flowing into detritus. Although TST is not necessarily linked with
ecosystem status, i.e., degradation or recovery, increased TST
means an increase in the “size”of the entire system after a 20-
year recovery. The expectation is that there are changes in TST
consistent with changes in productivity (Coll et al., 2008b).
However, the TPP/TR value indicative of the maturity of the
ecosystem in M2018 still reached 2.76. Despite being far less than
those of two adjacent seas—the southwest Yellow Sea (Wang
et al., 2019) and the Bohai Sea (Lin et al., 2018)—as well as two
seas at a comparable latitude, the northern Oman Sea
(Tajzadehnamin et al., 2020) and northern Gulf of Mexico
(Sagarese et al., 2017), this value was far higher than the 1 that
represents a mature status (Christensen et al., 2005)(Table 2).
This value was also lower than that for the northern South China
Sea (Ma et al., 2020). These data suggest that the ECS ecosystem
is still immature and unstable, with much energy that still cannot
be utilized directly.
The Ecopath results showed a substantial increase in fish
biomass, especially for the planktivores in M2018. These
findings are consistent with other studies, where an increase in
low-TL fish resulted from implementing FMPs after intensive
exploitation (Pitcher, 2001;Vijverberg et al., 2012;Gebremedhin
et al., 2021). In particular, the biomass of the planktivores
increased from 11.06% of the total fish biomass in M1997 to
34.5% in M2018. This has led to an obvious variation in the species
composition and a keystone species change from planktivores/
piscivores to planktivores (Figure 4). This variation in keystone
species is consistent with findings in the Bohai Sea and the
northern South China Sea, where the dominant species were
also low-TL organisms such as cephalopods and mollusks
(Chen, 2017;Li, 2020). This might be ascribed to the policy on
the minimum mesh size. Since 2004, the minimum mesh size of
fishing nets in the China Sea has been 5 cm, but B. pterotum,the
dominant planktivore species, has only a 3.5-cm maximum body
length (Fishbase); thus, this species can benefit from this policy.
These low-TL fishes have a short growth period and high
reproductive rate that allows them to adapt to intense fishing
pressure (Reum et al., 2021). Jiang et al. (2009) also showed that
after a fishing moratorium, the community tends to be dominated
by fast-growing small groups, which facilitates the expansion of
fish in the community. Small fishes also play a considerable role in
bridging the low and high TLs (Lira et al., 2021).
The MTI showed the increasingly negative impacts of fishery
on the function groups in M2018 versus M1997. The negative
effect of fishing activity on large/medium-sized species such as
benthivores/piscivores (Pennahia argentatus and Nemipterus
virgatus)andpiscivores(Scomberomorus niphonius)was
obvious in M1997. This negative impact had further developed
in M2018. The negative impact of harvesting on piscivores
increased due to increased fishing efforts (MTI values declined
from −5.30 to −5.81). Moreover, the negative impacts expanded
to other groups that had a positive or neutral impact in M1997,
including planktivores and small yellow croakers. At this point,
the direct harvest effect from fishing exceeds the predator
removal effect.
The MTI and keystone index of important commercial fish
showed different performances during these two decades. The
MTI results showed that the fishery still had a negative impact on
hairtails and large yellow croakers over the two periods, but there
was a slight increase in the MTI of the fishery. This is also
reflected in the keystone index, which had a more important role
in the ecosystem. The increase in biomass is responsible for this
pattern. Compared with M1997, the biomass of hairtails was
tripled and that of large yellow croakers was doubled in M2018.
Conversely, the fishing effort for these two commercial fish
decreased. Many fish larvae, especially large yellow croakers,
have been released into the ECS ecosystem to rebuild the fish
stocks (Zhang et al., 2010). However, a significant decrease in the
MTI index of small yellow croakers was observed. Fishery
landings (according to the SAU database) show that the
harvest of small yellow croakers has exceeded the fishing
maximum sustainable yield since 2007. Therefore, its keystone
index decreased versus that in M1997. In addition, Bombay duck
had significant increases in its MTI and keystone index versus
other groups, indicating that its ecological roles broadened.
Consistent with Liu et al. (2021) and Zhang et al. (2021),
Bombay duck has been the dominant species in the ECS, but
no catch data were recorded in the CFSY.
4.2 Impact of Fisheries Management
Policies on Fishery Stocks in
the East China Sea
The SFM simulations illustrated that the SFM in the ECS could
increase the short-term fish biomass during the period and play a
positive role in the long-term bioaccumulation of fish biomass.
Compared with M2018
no-SFM
, the biomass for most function
groups increased in M2018
SFM
. Consistent with the observations
of Yan et al. (2019a), a significant increase in fish biomass was
found in 2017, when there was a longer moratorium and fewer
fishery landings (Figure 7). However, these efforts were
weakened by the intense fishing pressure after the SFM, with
the species with high exploitation rates influenced more than
others (Chagaris et al., 2020). This result has also been reported
in the Western Mediterranean Sea by Samy-Kamal et al. (2015)
and in the Visayan Sea (Philippines) by Napata et al. (2020). The
fishing effort after the SFM accounted for half of the annual
fishing effort, so the effect revealed by the SFM implementation
was not significant in the annual biomass survey (Chen et al.,
1997;Lu and Zhao, 2015;Yan et al., 2019a).
The SFM led to about a 70% change in biomass, suggesting
that there were other factors that promoted the biomass increase
in the ECS in M2018. Increased primary production facilitated
the biomass of fish communities, which is revealed by a strong
linkage between the higher primary production and secondary
production of higher-TL organisms or fishery resources (Chassot
et al., 2007;Friedland et al., 2012). Compared with M1997, the
biomass of the phytoplankton increased approximately three-
fold in M2018. Phytoplankton is a basic compartment in an
ecosystem to provide food sources for low-TL species and fish
larvae (Sun and Liang, 2016). We suspect that the increase in
primary production might be due to the high concentrations of
dissolved inorganic nitrogen and dissolved inorganic
Xu et al. Impact of Seasonal Fishing Moratoriums
Frontiers in Marine Science | www.frontiersin.org June 2022 | Volume 9 | Article 86564511
phosphorus and the high rate of N/P from the nearshore
(Ehrnsten et al., 2019;Yang et al., 2020). However, nutrient
load was not included in the time series data to verify this
pattern, and the predicted plankton biomass in the two
simulations was lower than the actual in M2018. In addition,
increased biomasses for planktivores and large yellow croakers
were also observed by Zhai et al. (2020). However, the change in
the fishing net mesh size and the release enhancement were not
considered in the Ecosim models, perhaps resulting in the low
planktivoreandlargeyellowcroaker biomasses in the two
dynamic simulations. The seven-fold increase in planktivores
(Figure 3) also confirmed the role of the policy on minimum
mesh size. Release enhancement is also considered a means of
restoring recruitment (Moore et al., 2007). For example, from
2001 to 2006, 2~6 million juvenile large yellow croakers were
released annually in Zhejiang Province, and tagged adults were
recaptured in a fishery survey (Lü et al., 2008). Unfortunately,
there was no integrity database to offer reliable and detailed
release data, including the release numbers and efforts (Yang
et al., 2013). Therefore, it was very difficult to evaluate the effect
of the release enhancement in the Ecosim model.
The FMPs in the ECS increased the fish biomass, but the effect
was to some extent limited to the recovery of commercial fishes
(Figure 7). In the 1990s, the total number of landings in the ECS
have become quite intense and destructive, e.g., bottom trawling is
still widely used (Han et al., 2017), which can destroy benthic
communities (Olsgard et al., 2008;Van Denderen et al., 2015;
Hiddink et al., 2019). The biomass of the zoobenthos, especially
mollusks and polychaetes, sharply declined in M2018 (Table S1).
This, in turn, influences the transfer of material and energy from
primary producers or detritus to higher TLs. Therefore, the
government should strengthen the implementation of the FMPs
by limiting the depth of trawling and the numberof catches as well
as by prolonging the duration of the rest period. This would
reduce the pressure from overfishing (Dimarchopoulou et al.,
2019;Russo et al., 2019) and protect vulnerable benthic habitats
(Clark et al., 2019). These measures were simulated in the Gulf of
Gabes (Tunisia) by Halouani et al. (2016), who found that limiting
the trawling depth and lengthening the rest period duration can
both increase the TL of the catch. In addition, lessons can be
drawn from successful programs implemented around the world
and applied to the FMP system in China. Enhancing the selectivity
of species, selecting an optimal body length for species, and
evaluating the total allowable catch via historical data would also
be beneficial to the recovery of fisheries (Coll et al., 2008a;Colloca
et al., 2013).
5 CONCLUSION
The ECS ecosystem is becoming more mature, although it is still
unstable. The high biomass of plankton stimulated an increase in
other groups, especially planktivores. FMPs such as the SFM and
minimum-mesh size also play positive roles in fish recovery.
Despite this, fishing management still requires further
development due to the decrease in high-TL groups and the
change in keystone species. The commercial fish are still in an
unrecovered state. A SFM could promote the fishery recovery,
but extending the SFM and reducing fishing pressure after it
would play a greater role in rehabilitating the depleted fisheries
resources in the ECS.
It must be said that the dynamic simulation in Ecosim in this
study only included the SFM and fishing pressure. We cannot
evaluate the impacts of the minimum-mesh size and release
enhancement due to the difficulty in obtaining precise estimates
for fishing effort. The aquatic product and fleet can only represent
the tendency of the fishing effort. Therefore, in the future long-
term monitoring of keystone species and commercial fish, data on
bycatch as well as climate and oceanographic variables are critical
to evaluating and predicting future changes in the ECS ecosystem.
Reliable and detailed fishery data are also missing, especially the
discard data for non-commercial species, which contributes at
least 8% to the entire fish yield (Cao et al., 2017). Therefore, it is
critical to developing an integrity database for better stock
assessment and fishery management.
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in
the article/Supplementary Material. Further inquiries can be
directed to the corresponding authors.
ETHICS STATEMENT
The animal study was reviewed and approved by the Third
Institute of Oceanography, Ministry of Natural Resources,
Xiamen 361005, China.
AUTHOR CONTRIBUTIONS
XZ and LL contributed to the conception and design of the study.
LX wrote the first draft of the manuscript. XZ, YW, LX, and PS
analyzed the data and reviewed the manuscript. All authors
contributed to the article and approved the submitted version.
FUNDING
This work was funded by the National Key Research and
Development Program of China (Grant Numbers:
2018YFC1406301 and 2018YFC1406302) and the Scientific
Research Foundation of Third Institute of Oceanography,
MNR (Grant Number: 2019017).
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fmars.2022.
865645/full#supplementary-material
Xu et al. Impact of Seasonal Fishing Moratoriums
Frontiers in Marine Science | www.frontiersin.org June 2022 | Volume 9 | Article 86564512
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