Home Journals Articles Updates About
CN
image
收藏切换
Response of Japanese anchovy (Engraulis japonicus) to the Pacific Decadal Oscillation in the Yellow Sea over the past 400 a
收藏切换
PDF
Haoyu Li1, 2, Qisheng Tang2, Yao Sun2, *
Acta Oceanologica Sinica | 2022, 41(8) : 31 - 40
Less
收藏切换
Acta Oceanologica Sinica | 2022, 41(8): 31-40
• Marine Biology •
Response of Japanese anchovy (Engraulis japonicus) to the Pacific Decadal Oscillation in the Yellow Sea over the past 400 a
Full
Haoyu Li1, 2, Qisheng Tang2, Yao Sun2, *
Affiliations
  • 1 College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
  • 2 Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China
Published: 2022-08-25 doi: 10.1007/s13131-021-1914-z
Outline
收藏切换

Quantitative identification of long-term changes in the abundance of Japanese anchovy (Engraulis japonicus) in the Yellow Sea is particularly important for understanding evolutionary processes of the Yellow Sea ecosystem. Unfortunately, the driving mechanisms of climate variability on the anchovy are still unclear due to the lack of long-term observational data. In this study, we used the fish scale deposition rate in the central Yellow Sea to reconstruct the time series of the anchovy stock over the past 400 a. On this basis, we further explored the impacts of the Pacific Decadal Oscillation (PDO) on the anchovy. Our results show that the anchovy stock is positively correlated with the PDO on a decadal time scale. In addition, anchovy abundance was relatively high during 1620–1860 AD (the Little Ice Age, LIA), though in a state of constant fluctuation; anchovy abundance maintained at a relatively low level after ~1860 AD. In particular, followed by overfishing since the 1980s, the anchovy stock has declined sharply. Based on these findings, we infer that fluctuations of the anchovy stock may be regulated by basin-scale “atmosphere–ocean” interactions. Nevertheless, the role of overfishing should not be ignored.

Japanese anchovy  /  scale deposition rate  /  population fluctuation  /  Pacific Decadal Oscillation  /  Yellow Sea
Haoyu Li, Qisheng Tang, Yao Sun. Response of Japanese anchovy (Engraulis japonicus) to the Pacific Decadal Oscillation in the Yellow Sea over the past 400 a[J]. Acta Oceanologica Sinica, 2022 , 41 (8) : 31 -40 . DOI: 10.1007/s13131-021-1914-z
Full Text
Less
1 Introduction
Less
Knowledge of fish population dynamics is particularly important for our understanding of marine ecosystems and exploited populations. In particular, small pelagic fishes such as anchovies, sardines, and herrings have an r-type life history strategy that causes their populations to be highly sensitive to environmental stress (Stenseth et al., 2002; Alheit et al., 2012). In addition, such fishes are ideal target species for indicating the changes in marine ecosystems due to their great plasticity in growth, survival, and other life history characteristics (Alheit et al., 2012). Meanwhile, they are playing an increasingly important role in many marine ecosystems, both ecologically and economically. In the past few decades, small pelagic fish populations have shown frequent large fluctuations in abundance. Well-known examples include sardine (Sardinops sagax) in the California Current (Kawasaki, 2002), the Peruvian anchovy (Engraulis ringens) in the Humboldt Current (Alheit and Niquen, 2004), the sardine (Sardinops sagax) in the Benguela Current (Cury and Shannon, 2004), and the Japanese sardine (Sardinops melanostictus) in the Kuroshio and Oyashio Currents (Yasuda et al., 1999). Overall, small pelagic fishes directly and indirectly contribute approximately 16.9 billion dollar to the global fishery economy each year, accounting for 20% of the global marine fishery economy (Pikitch et al., 2014). Therefore, identifying driving mechanisms behind anchovy stock fluctuations will help us to better understand dynamic characteristics of the marine food web.
Data employed in previous studies of fish population dynamics have generally come from fishery records. Due to the short time scale, results of such studies may be biased to varying degrees. Fortunately, marine sediment records as precious archives can be used to help us understand the long-term relationship between fish populations and their environment, meaning that a bridge could be built between fish dynamics and the evolution of marine ecosystems. In the late 1960s, Soutar and Isaacs (1969) pioneered the idea of reconstructing fish abundance in historical periods, based on the fish scale deposition rate (SDR) for the Pacific sardine (Sardinops caerulea), northern anchovy (Engraulis mordax), and the Pacific hake (Merluccius productus) in anaerobic sediments off the California coast. Subsequently, to reconstruct long-term changes in small pelagic fish abundance, the SDR was carried out in several regions worldwide, for example the west coast of Canada (Patterson et al., 2005), northern Mexico (Holmgren-Urba and Baumgartner, 1993), central Peru (Gutiérrez et al., 2009), northern Chile (Valdés et al., 2008), and the southwest coast of Japan (Kuwae et al., 2017). All such studies have shown that fish population fluctuations are closely related to climate variability, which provide scientific basis for future fisheries management in a warming world.
China has also conducted similar work in the Yellow Sea (YS) and obtained some preliminary results. Specifically, Jia et al. (2008) extracted and identified the well-preserved scales for major fish species in the YS and East China Sea (ECS) in the sediments, such as anchovy (Engraulis japonicus), small yellow croaker (Pseudosciaena polyactis), Pacific herring (Clupea pallasii), half-fin anchovy (Setipinna taty), white croaker (Argyrosomus argentatus), and firefly fish (Acropoma japonicum). Further, Huang et al. (2014) discussed the necessary conditions of preservation and spatial distribution of fish scales in the YS and ECS, and confirmed the reliability of using SDR to reconstruct the abundance of fish populations in historical periods. Moreover, Huang et al. (2016) analyzed the fluctuation characteristics of anchovy in the past 150 a and compared it with anchovy SDR in other regions around the Pacific, suggesting that the Pacific Decadal Oscillation (PDO) may be the principal driver for the simultaneous fluctuations of anchovy populations on a basin scale. The above-mentioned studies showed that before industrial fishing, fish populations were in constant fluctuation between high and low densities. In particular, the PDO was considered one of the most significant climate signals in the North Pacific (Newman et al., 2016), and it is well documented and largely associated with changes in fish populations (Ma et al., 2019). In reality, there are numerous papers on the relationships between the PDO and fish populations, and though some of the links between the two have been identified (Mantua et al., 1997; Mantua and Hare, 2002; Chavez et al., 2003; Salvatteci et al., 2018; Litzow et al., 2020; Li et al., 2020), no solid conclusion has been reached. Besides, Zhou et al. (2015) showed that the PDO may indirectly affect the anchovy fluctuations in the YS through changes in sea surface temperature (SST). In other words, the positive phase of the PDO (cold SST) corresponds to high anchovy abundance, while the negative phase of the PDO (warm SST) corresponds to low anchovy abundance. Hence, the PDO could be used to reveal the reasons behind anchovy fluctuations to some degree.
A large amount of evidence suggests that both climate variability and human activities are two major driving forces that affect fish population fluctuations (Tang et al., 2016; Lindegren et al., 2013; Ma et al., 2019). There are many reports on the dynamics of fish populations, especially concerning small pelagic fishes and climate variability (Checkley et al., 2017; Lindegren et al., 2018; Salvatteci et al., 2018). However, due to limited data availability, relevant research progress has been relatively slow. We do not yet have a complete understanding of the reasons behind fish population fluctuations, though some important findings have been made during this period. In this study, we further explore the relationship between the PDO and the anchovy in the YS on a larger spatial and longer temporal scale than previous research. We added an additional sampling site to the original sites used by Zhou et al. (2015), and we also greatly extended the length of the time series (i.e., from 1860–2005 AD to 1620–2005 AD). The results will contribute to further understand the history of anchovy and facilitate future predictions, thereby providing guidance for sustainable development and utilization of the anchovy in the YS.
2 Materials and methods
Less
2.1 Study area and sample collection
Located between the Korean Peninsula and mainland China, the YS is a semi-closed shelf shallow sea in the western North Pacific. The average water depth is about 44 m with a maximum of 140 m, and the total area is about 400 000 km2. The east side is affected by the Kuroshio Current (KC) and the Yellow Sea Warm Current (YSWC), a branch of the Tsushima Warm Current (TSWC); the west side is mainly affected by the Yellow Sea Coastal Current (YSCC) (Fig. 1). Generally, the SST in winter is lower than in other seasons, together with the stronger East Asian Winter Monsoon (EAWM), and hence both the YSWC and the YSCC are strengthened. Therefore, the YSWC and the YSCC have the greatest impacts on the YS in winter. For the past few decades, the YS has become one of the most active fishing grounds in the world, with distinctive topography, hydrology, and productivity (Jin et al., 2010; Tang et al., 2016). The anchovy in the YS was a bycatch fishery prior to the 1980s (Zhao, 2006). The development of an industrial fishery soon after led to peak landings in the mid-to-late 1990s, and the subsequent increase in fishing effort resulted in the anchovy resources almost collapsing at the beginning of the 21st century (Zhao, 2006).
Jia et al. (2008) found that the central YS, as the main wintering ground for the anchovy, corresponds to the largest SDR. Additionally, Huang et al. (2014) showed that the wintering anchovy in the central YS can well represent the entire anchovy size in the YS. The sampling sites are shown in Fig. 1: these are designated as Sites 10594 (34°59.9′N, 122°29.9′E, water depth: 64 m), 10694 (35°0.0′N, 123°0.0′E, water depth: 70 m), and 10794 (35°0.1′N, 123°18.2′E, water depth: 76 m). The sedimentary samples consist of two periods: (1) 1860–2005 AD: obtained by a non-disturbance box sampler (30 cm×30 cm×100 cm) during the summer cruise of R/V Beidou in August 2005, with core lengths for samples at Sites 10594, 10694, and 10794 being 50 cm, 30 cm, and 31 cm, respectively (Huang, 2015); (2) 1620–1860 AD: obtained by a gravity corer (11 cm in diameter) during the summer cruise of R/V Beidou in August 2018, where the core lengths of samples at Sites 10594, 10694, and 10794 were 136 cm, 132 cm, and 128 cm, respectively. The sampling process was detailed in Huang et al. (2014). Overall, the three cores were gray-black, while the color intensity significantly changes with the increase in depth (above ~20 cm for light gray-black; below ~20 cm in dark gray-black) and were mainly composed of silty clay. In addition, the water content of the sediment decreases with the increase in the depth of the cores.
2.2 Extraction and identification of the fish scales
Cores were sub-sampled at 1-cm intervals for the top 30 cm, and at 2-cm intervals below 30 cm. Pre-treatment method for sub-samples was conducted as described by Holmgren (2001). In order to obtain enough scales to reconstruct a reliable time series, the amount of sediment used is an important criterion that must be considered. Unlike Salvatteci et al. (2019), who used less sediment, we used relatively large amount (the larger diameter of 11 cm of the gravity sampler used in this study) to avoid the low density of fish scales affecting the counting. In short, approximately 100–150 g of wet sub-sample was incubated with H2O2 solution (5%) for 3–5 h, then passed through a 250-μm mesh sieve gently. Fish scales were identified using a light microscope by comparing fossil scales with modern scales from fresh fishes captured in the same area.
2.3 Chronology
In this study, the three cores ages were determined by 210Pb dating during 1860–2005 AD (Yang et al., 2012; Huang et al., 2014). The sedimentation rates (SRs) of Sites 10594, 10694, and 10794 were estimated as 0.35 cm/a, 0.14 cm/a, and 0.10 cm/a, respectively. This was basically consistent with the results of Qiao et al. (2017), with the SR in the central YS estimated as 0.1–0.5 cm/a. Additionally, Kim et al. (1998) showed that the sea level of the YS has been relatively high for the past 7 000 a, and hence experienced a low SR with a relatively stable sedimentary environment. Therefore, we assumed that the SR in the central YS was relatively constant during the study period. The chronological time series required for this study was obtained by extrapolation (Liu et al., 2013).
2.4 Reconstruction of the anchovy stock
In order to avoid the interference of excessive counting of anchovy scale fragments, only one anchovy scale can be counted when an intact focus is found (Patterson et al., 2005). We adopted the method proposed by McClatchie et al. (2017) to calculate the SDR. That is, SDR was calculated using the following equation:
$ {\rm{SDR}} = 1\;000\times N\times {\rm{SR}}, $
where SDR is the scale deposition rate (1000 cm−2·a−1, according to the number of scales); N is the fish scale count in per unit volume (cm-3, according to the number of scales); and SR is the linear sedimentation rate in yearly (cm/a). Because the SR of Site 10794 was 0.10 cm/a, the SDR in all cores was interpolated to 10-a intervals. Then, we followed the method of Zhou et al. (2015) to reconstruct the anchovy stock. Specially, we first averaged the size of wintering anchovy by acoustic estimation (Jin et al., 2001; Zhao, 2006) and our SDR from 1985 to 2005 at 5-a intervals, and then to fit a regression model, which could be used to reconstruct longer time series of anchovy. Here, the SDR was based on the composition of two time periods (i.e., 1620–1860 AD and 1860–2005 AD) to greatly extend the length of the time series for the anchovy.
2.5 Analysis with the Ensemble Empirical Mode Decomposition (EEMD)
The anchovy stock fluctuation cycles of 50–70 a on a decadal time scale (Zhou et al., 2015) will inevitably induce some misunderstandings if the original PDO is used directly. To find multiscale consistency between anchovy stock, climatic indices, and the oceanic variable, we used an EEMD approach (Wu and Huang, 2009). EEMD analysis is applicable to extracting meaningful signals from noisy, nonlinear, and nonstationary data. The data are decomposed by a filtering process into several limited Intrinsic Mode Functions (IMFs) with different timescales. The results of the EEMD are detailed in the supplementary materials (Figs S2–S7). We then conducted Pearson’s correlation to explore the relationship between the anchovy and the PDO (with corresponding IMFs). Likewise, Pearson’s correlations were conducted between the anchovy and other climate variables (with corresponding IMFs) and biogeochemical proxies.
3 Results
Less
3.1 Fish debris composition in the cores
Similar to the results of Jia et al. (2008), the fish debris in the sediments were mainly composed of fish scales, and most of them were relatively complete in morphology, which helps confirm their species; only a very small amount of other fish debris has been found (i.e., bones, tooth, and vertebrae). We also identified these scales such as small yellow croaker, Pacific herring, half-fin anchovy, white croaker, and firefly fish, except for the most abundant anchovy scales. Here, considering the topic of this study, we do not discuss further fish scales for other species.
3.2 Fluctuation characteristics of the anchovy SDR
In general, the SDR has seven peak intervals for the past ~400 a: 1620–1650 AD, 1680–1700 AD, 1740–1760 AD, 1800–1820 AD, 1850–1870 AD, 1910–1930 AD, and 1970–1990 AD (Fig. 2).
Specifically, the SDR between cores was relatively high during the period 1620–1860 AD (the Little Ice Age, LIA); after the end of the LIA, the SDR between cores was maintained at a low level. Additionally, in the LIA, the mean SDR of Sites 10594, 10694, and 10794 showed decreasing trends in order eastward; after the LIA, the mean SDR of the three cores basically stayed the same (Fig. 3). Compared with LIA, the mean SDR after LIA is smaller.
In addition, due to the SDR between cores having zero values with a non-Gaussian distribution, this excludes the use of most parametric methods. Therefore, we conducted a non-parametric Spearman correlation analysis to confirm the relationship between the SDR from the three cores. The results showed that the SDR between cores had a significant positive correlation (Table 1).
3.3 Time series of the anchovy stock
According to results of the fitted linear regression equation, we found that there was a significant positive correlation between the SDR and the wintering anchovy size by acoustic estimation for the period from 1985 to 2005 (Fig. 4). Hence, this relationship enables us to effectively estimate the anchovy size in historical periods. We then used the regression equation to reconstruct the time series of the anchovy stock in the YS for the past 400 a (1620–2005 AD) (Table S1).
As shown in Fig. 5, the anchovy stock size fluctuated between (0.13±0.57)×106 t and (6.10±1.28)×106 t and continually alternated between high and low abundances. In general, the anchovy abundance was relatively high during the period 1620–1860 AD (LIA), and maintained at a lower level after ~1860 AD. Moreover, our sedimentary records show that anchovy stock has continued to decline since the late 1980s and collapsed by the early 2000s. This result is more consistent with previous reports based on anchovy landings.
4 Discussion
Less
4.1 Preservation of anchovy scales in the sediments
Whether fish scales can be well-preserved in the sediments is the primary concern that needs to be considered when using the SDR to trace the dynamics of fish populations in historical periods. Previously, fish scales have been demonstrated to be well-preserved in the sediments for the past ~150 a (Jia et al., 2008; Huang et al., 2014). We also examined this point based on the scale integrity index (SII) proposed by Salvatteci et al. (2012) (i.e., the ratio of whole relative to fragmentary scales for each sample). In fact, most of the fish debris in the sediments were composed of intact anchovy scales, as shown in the Section 3.1. Therefore, we did not strictly rate for the anchovy scales as described by Salvatteci et al. (2012). Here, we improved on the SII based on Salvatteci et al. (2012). That is, we used the average values in the SII of all samples in each core to evaluate the effect of degradation. These ratios were 84% for Core 10594, 81% for Core 10694, and 78% for Core 10794, indicating that the scales were well preserved with less degradation. Moreover, the degradation rate of fish scales would likely increase with depth. However, the accumulation rate of the SDR on average for three cores was significantly positively correlated with age (Fig. S1), indicating that the degradation of scales may be weak.
4.2 Relationship between the anchovy stock and the PDO
During the period 1620–2005 AD, there was a weak positive correlation between the reconstructed anchovy and the PDO (Fig. 6). Although the specific causes for this difference are unclear, we speculate that the following two factors may be involved. First, the results of Shen et al. (2006) suggested that the PDO mainly oscillated in a quasi-centennial period (75–115 a) before 1850 AD, and in a pentadecadal (50–70 a) period after 1850. In this study, the average period of the PDO (the fifth component, IMF 5) selected by EEMD was about 76 a, in line with the quasi-centennial pattern. Second, the impact of the LIA on the anchovy may significantly exceed the PDO, masking the signal of the PDO.
In order to better determine the role of the LIA, we made a comparison to explore the impacts of the LIA and after the LIA on anchovy. In the LIA, anchovy had no correlation with the PDO, while a strong positive correlation existed with PDO after the LIA (Fig. 7), which is consistent with the research of Zhou et al. (2015). This suggests that the LIA may have a significant impact on the PDO, with large regime shifts resulting in the anchovy fluctuations. However, more evidence is needed to support this argument. As a consequence, we suggest that the positive correlation between the anchovy and the PDO is reliable, though there are noises to varying degrees.
It is generally accepted that the PDO is negatively correlated with SST in the central-western North Pacific, i.e., the SST is below average during the positive phase of the PDO, and vice versa in the eastern North Pacific (Mantua and Hare, 2002; Newman et al., 2016). As a wintering ground for the anchovy, the central YS is located at the intersection of the YSWC and the YSCC. During the positive phase of the PDO followed by colder periods, both the YSWC and the YSCC are strengthened (Yuan and Hsueh, 2010), and nutrients are enriched in the central YS, providing favorable conditions for wintering anchovy.
Based on the landings, Izquierdo-Peña et al. (2019) and Oozeki et al. (2019) found that the anchovy populations in the Humboldt and Kuroshio Currents have fluctuated in-phase over the past few decades. They suggest that this mode may be controlled by large-scale climate variability. In particular, the Peruvian anchovy was greatly reduced during the LIA in the Humboldt Current (Gutiérrez et al., 2009), which is opposite to the anchovy in the YS (Fig. 5). In the meantime, the Peruvian anchovy showed signs of migrating southward during the LIA (Gutiérrez et al., 2009), which may also explain our results to some degree. In other words, the intensified EAWM led to the strong YSWC (Hu et al., 2012; Zhang et al., 2019), with a relatively high SST during the LIA (Li et al., 2020), and the anchovy stock around the seas of Japan (lower SST) may have migrated southward to the central YS for overwintering, as described by Zhou et al. (2015). Therefore, anchovy abundance in the YS was relatively high during the LIA. Moreover, temperature change has important effects on the reproduction, growth, and migration for the anchovy. Taken together, our suggestion is that the responses of the anchovy to the PDO have both indirect effects on food availability and direct effects on physiological behavior.
4.3 Fishing effects on the anchovy stock
In general, anchovy was maintained at relatively high density before the 1980s (with moderate fishing pressure), though some fluctuations in abundance were inevitable. The traditional viewpoint was that overfishing was the fundamental reason for the rapid decline of the anchovy in the YS (Zhao, 2006). However, the long-term changes in reconstructed anchovy stock in our results show that there were continuous fluctuations before the 1980s, when fishing pressure was approximately zero (Fig. 5). Furthermore, research in other Pacific locations also indicated natural fluctuations for the anchovy populations based on the SDR with little or no fishing pressure (Baumgartner et al., 1992; Valdés et al., 2008; Guiñez et al., 2014; McClatchie et al., 2017; Kuwae et al., 2017). Therefore, the reason behind the anchovy depletion was possibly different from the traditional view, and that it may be the result of natural fluctuations. Nevertheless, this does not suggest that the decline in anchovy abundance has nothing to do with overfishing.
In brief, fishing may lead to an age truncation effect (Hsieh et al., 2006; Deyle et al., 2013) and changes in life history traits (Kuparinen et al., 2016; Britten et al., 2016; Pinsky and Byler, 2015). Both of these can magnify the sensitivity of fish populations to climate variability (Planque et al., 2010), thus leading to stock fluctuations that are greater than just climate impact alone. This may increase variability of the exploited populations, reducing the capacity of populations to buffer climate events, and hence increased risks of collapse. Particularly, with the increase in the number of fishing vessels with greater horsepower, combined with the updating of fishing gears and methods (Tang et al., 2016), the anchovy resources in the YS drastically declined from the mid-to-late 1990s to the early 2000s (Zhao, 2006). This combination of overfishing and unfavorable climate conditions likely led to the decline that the YS anchovy stock is currently experiencing.
4.4 Potential mechanism under anchovy fluctuations
Understanding the mechanism driving fish population dynamics is essential to accurately predicting changes under climate variability and overexploitation. To date, several hypotheses (e.g., bottom-up and top-down control) have been proposed to explain the fluctuations in fish populations. There is evidence that in the absence of fishing, many large and highly productive marine ecosystems such as those in the North Pacific and North Atlantic are subject to bottom-up processes that are also widely recognized as a response to climate variability (Chavez et al., 2003; Ware and Thomson, 2005; Greene and Pershing, 2007; Pitois et al., 2012; Salvatteci et al., 2018; Capuzzo et al., 2018). In contrast, other studies have shown that bottom-up processes would switch to top-down control and potentially lead to trophic cascading effects on food webs in marine ecosystems (Worm and Myers, 2003; Frank et al., 2005; Möllmann et al., 2008). In fact, these hypotheses are not mutually exclusive, making it more difficult to elucidate fish population fluctuations. However, with the accumulation of evidence, some oceanographers are gradually inclined to accept the view of multi-factor control (Tang et al., 2016; Mcowen et al., 2015), though most do not deny the possibility of a single dominant factor. Undoubtedly, this provides a new way for us to understand fish population fluctuations from a novel perspective.
Extensive studies have shown that global warming results in a deeper thermocline and enhanced water column stratification with a shallow mixed layer, thereby reducing the supply of nutrients to the euphotic zone and thus lowering fish productivity (Roemmich and McGowan, 1995; Behrenfeld et al., 2006; Doney et al., 2012; Rykaczewski and Dunne, 2010). On the one hand, though the YS lacks coastal upwelling, the EAWM has a significant impact on this area that may provide a function similar to coastal upwelling. In the western North Pacific, the winter monsoon may affect the biological productivity by controlling the mixed layer depth. That is, the strong winter monsoon brings greater nutrient input into the euphotic zone and ultimately increases ecosystem productivity and hence fish productivity. On the other hand, the overall shoaling in the mixing layer due to intensified upper-ocean stratification caused by surface warming affects phytoplankton dynamics through controlling the availability of nutrients and hence biological productivity (Jang et al., 2011; Jacox et al., 2015) and ultimately fish abundance. Moreover, in order to identify the reasons behind the anchovy fluctuations in the YS, we explored the relationship between the anchovy and environmental proxies from 1860 AD to 2005 AD. During the period 1860–1950 AD, the anchovy and proxies for primary and secondary productivity were in good agreement (Table 2).
Since the People’s Republic of China was founded in 1949, Chinese marine fisheries entered a new era (Shen and Heino, 2014). From 1950, especially since the end of the 1980s, the above-mentioned consistency has been almost completely absent due to the intensive fishing (Fig. 8). Consequently, we suggested that anchovy fluctuations are regulated by bottom-up effects via climate variability under moderate fishing pressure. In other words, climate variability seems to indirectly influence the anchovy stock through bottom-up control via a cascading effect from the changes in the PDO. The impacts of these changes on the hydrodynamic features of the YS in turn influence the productivity of plankton prey for the anchovy.
Here, taking the anchovy in the YS as a case, we attempted to make a reasonable explanation of anchovy fluctuations based on our findings and perspectives (Fig. 8; Table 2). Overall, a physical-biological mechanism of fluctuations in anchovy abundance could be constructed based on a bottom-up control hypothesis under lessened (or absent) fishing pressure (Fig. 9). That is, when the PDO is in a positive phase, the Aleutian Low Pressure is intensified, followed by strong EAWM results in low SST, which drives weaker stratification of the upper water column. This enhances mixing in some parts of the ocean with more nutrient availability, increasing biological productivity with more food availability and thus high anchovy abundance.
However, it is important to emphasize that the PDO itself does not directly affect anchovy–sardine fluctuations. For example, Lindegren et al. (2013) suggested that fluctuations of anchovy–sardine in the California Current System are explained by interacting density-dependent processes (i.e., through species-specific life-history traits) and climate forcing. In the Humboldt Current system, changes in the 3D habitat driven by large-scale climate forcing and regional processes were the main drivers of anchovy–sardine fluctuations (Salvatteci et al., 2018). These studies suggest that the mechanism behind small pelagic fish fluctuations are more complicated than previously stated in other studies (Chavez et al., 2003). Hence, this present study is still a preliminary result and needs more supporting evidence in the near future.
5 Conclusions
Less
We reconstructed the time series of the anchovy stock in the YS over the past 400 a. Like many small pelagic fishes, the anchovy has frequently experienced historic “boom and bust” cycles between very high and very low abundance. We first proposed a conceptual framework of potential driving forces. We suggest that the PDO may be the principal driving force for the long-term changes in the anchovy stock under moderate fishing pressure. However, we should also realize that under the combined pressures of climate variability and overfishing, the decline of the anchovy abundance is not simply attributed to either of them. Therefore, a comprehensive consideration of the relative effects of climate and fishing on the dynamics of the anchovy could ultimately achieve the goal of scientific management and sustainable utilization. Particularly, climate variability should be integrated into the development of adaptive management strategies of anchovy. In the future, we plan to collect data for other biogeochemical proxies as well as observational data, and to conduct integrated studies to further verify the reliability of the mechanism proposed by the present study.
Acknowledgements
Less
We thank Hongxia Qiu for her help in subsampling the cores. We would like to express our sincere appreciation to the two anonymous reviewers for their insightful comments that have greatly improved the quality of the manuscript. The captain and crew aboard the R/V Beidou are thanked for their excellent cooperation during sample collection.
Funding
Less
  • The National Natural Science Foundation of China under contract No. 31600397.
References
Less
Alheit J, Niquen M. 2004. Regime shifts in the Humboldt Current ecosystem. Progress in Oceanography, 60(2–4): 201–222, doi: 10.1016/j.pocean.2004.02.006
Alheit J, Pohlmann T, Casini M, et al. 2012. Climate variability drives anchovies and sardines into the North and Baltic Seas. Progress in Oceanography, 96(1): 128–139, doi: 10.1016/j.pocean.2011.11.015
Baumgartner T R, Soutar A, Ferreira-Bartrina V. 1992. Reconstruction of the history of Pacific sardine and northern anchovy populations over the past two millennia from sediments of the Santa Barbara Basin, California. CalCOFI Reports, 33: 24–40
Behrenfeld M J, O’Malley R T, Siegel D A, et al. 2006. Climate-driven trends in contemporary ocean productivity. Nature, 444(7120): 752–755, doi: 10.1038/nature05317
Britten G L, Dowd M, Worm B. 2016. Changing recruitment capacity in global fish stocks. Proceedings of the National Academy of Sciences of the United States of America, 113(1): 134–139, doi: 10.1073/pnas.1504709112
Capuzzo E, Lynam C P, Barry J, et al. 2018. A decline in primary production in the North Sea over 25 years, associated with reductions in zooplankton abundance and fish stock recruitment. Global Change Biology, 24(1): e352–e364, doi: 10.1111/gcb.13916
Chavez F P, Ryan J, Lluch-Cota S E, et al. 2003. From anchovies to sardines and back: multidecadal change in the Pacific Ocean. Science, 299(5604): 217–221, doi: 10.1126/science.1075880
Checkley D M, Asch R G, Rykaczewski R R. 2017. Climate, anchovy, and sardine. Annual Review of Marine Science, 9: 469–493, doi: 10.1146/annurev-marine-122414-033819
Cury P, Shannon L. 2004. Regime shifts in upwelling ecosystems: observed changes and possible mechanisms in the northern and southern Benguela. Progress in Oceanography, 60(2–4): 223–243, doi: 10.1016/j.pocean.2004.02.007
Deyle E R, Fogarty M, Hsieh C H, et al. 2013. Predicting climate effects on Pacific sardine. Proceedings of the National Academy of Sciences of the United States of America, 110(16): 6430–6435, doi: 10.1073/pnas.1215506110
Doney S C, Ruckelshaus M, Duffy J E, et al. 2012. Climate change impacts on marine ecosystems. Annual Review of Marine Science, 4: 11–37, doi: 10.1146/annurev-marine-041911-111611
Frank K T, Petrie B, Choi J S, et al. 2005. Trophic cascades in a formerly cod-dominated ecosystem. Science, 308(5728): 1621–1623, doi: 10.1126/science.1113075
Greene C H, Pershing A J. 2007. Climate drives sea change. Science, 315(5815): 1084–1085, doi: 10.1126/science.1136495
Guiñez M, Valdés J, Sifeddine A, et al. 2014. Anchovy population and ocean-climatic fluctuations in the Humboldt Current System during the last 700 years and their implications. Palaeogeography, Palaeoclimatology, Palaeoecology, 415: 210–224
Gutiérrez D, Sifeddine A, Field D B, et al. 2009. Rapid reorganization in ocean biogeochemistry off Peru towards the end of the Little Ice Age. Biogeosciences, 6(5): 835–848, doi: 10.5194/bg-6-835-2009
Holmgren D. 2001. Decadal-centennial variability in marine ecosystems of the Northeast Pacific Ocean: the use of fish scales depositions in sediments [dissertation]. Washington: University of Washington
Holmgren-Urba D, Baumgartner T R. 1993. A 250-year history of pelagic fish abundances from the anaerobic sediments of the central Gulf of California. CalCOFI Reports, 34: 60–68
Hsieh C H, Reiss C S, Hunter J R, et al. 2006. Fishing elevates variability in the abundance of exploited species. Nature, 443(7113): 859–862, doi: 10.1038/nature05232
Hu Bangqi, Yang Zuosheng, Zhao Meixun, et al. 2012. Grain size records reveal variability of the East Asian Winter Monsoon since the Middle Holocene in the Central Yellow Sea mud area, China. Science China: Earth Sciences, 55(10): 1656–1668, doi: 10.1007/s11430-012-4447-7
Huang Jiansheng, 2015. Sedimentary record of anchovy population dynamics in the Yellow Sea and its response to climate changes (in Chinese) [dissertation]. Xiamen: Xiamen University
Huang Jiansheng, Sun Yao, Jia Haibo, et al. 2014. Spatial distribution and reconstruction potential of Japanese anchovy (Engraulis japonicus) based on scale deposition records in recent anaerobic sediment of the Yellow Sea and East China Sea. Acta Oceanologica Sinica, 33(12): 138–144, doi: 10.1007/s13131-014-0573-8
Huang Jiansheng, Sun Yao, Jia Haibo, et al. 2016. Last 150-year variability in Japanese anchovy (Engraulis japonicus) abundance based on the anaerobic sediments of the Yellow Sea Basin in the western North Pacific. Journal of Ocean University of China, 15(1): 131–136, doi: 10.1007/s11802-016-2605-9
Izquierdo-Peña V, Lluch-Cota S E, Hernandez-Rivas M E, et al. 2019. Revisiting the regime problem hypothesis: 25 years later. Deep-Sea Research Part II: Topical Studies in Oceanography, 159: 4–10, doi: 10.1016/j.dsr2.2018.11.003
Jacox M G, Bograd S J, Hazen E L, et al. 2015. Sensitivity of the California Current nutrient supply to wind, heat, and remote ocean forcing. Geophysical Research Letters, 42(14): 5950–5957, doi: 10.1002/2015GL065147
Jang C J, Park J, Park T, et al. 2011. Response of the ocean mixed layer depth to global warming and its impact on primary production: a case for the North Pacific Ocean. ICES Journal of Marine Science, 68(6): 996–1007, doi: 10.1093/icesjms/fsr064
Jia Haibo, Sun Yao, Zhao Meixun, et al. 2008. Fish-scale-deposition information and spatial distribution in typical fishery area of the Yellow Sea and East China Sea. Journal of Fisheries of China, 32(4): 584–591
Jin Xianshi, Hamre J, Zhao Xianyong, et al. 2001. Study on the quota management of anchovy (Engraulis japonicus) in the Yellow Sea. Journal of Fishery Sciences of China, 8(3): 27–30
Jin Xianshi, Zhang Bo, Xue Ying. 2010. The response of the diets of four carnivorous fishes to variations in the Yellow Sea ecosystem. Deep-Sea Research Part II: Topical Studies in Oceanography, 57(11–12): 996–1000, doi: 10.1016/j.dsr2.2010.02.001
Kawasaki T. 2002. Climate change, regime shift and stock management. Fisheries Science, 68(S1): 148–153
Kim D, Park B K, Shin I C. 1998. Paleoenvironmental changes of the Yellow Sea during the Late Quaternary. Geo-Marine Letters, 18(3): 189–194, doi: 10.1007/s003670050067
Kuparinen A, Boit A, Valdovinos F S, et al. 2016. Fishing-induced life-history changes degrade and destabilize harvested ecosystems. Scientific Reports, 6(1): 22245, doi: 10.1038/srep22245
Kuwae M, Yamamoto M, Sagawa T, et al. 2017. Multidecadal, centennial, and millennial variability in sardine and anchovy abundances in the western North Pacific and climate–fish linkages during the late Holocene. Progress in Oceanography, 159: 86–98, doi: 10.1016/j.pocean.2017.09.011
Li Haoyu, Yang Shu, Tang Qisheng, et al. 2020. Long-term variation in the abundance of Pacific herring (Clupea pallasii) from the Yellow Sea in the western North Pacific and its relation to climate over the past 590 years. Fisheries Oceanography, 29(1): 56–65, doi: 10.1111/fog.12449
Lindegren M, Checkley Jr D M, Rouyer T, et al. 2013. Climate, fishing, and fluctuations of sardine and anchovy in the California Current. Proceedings of the National Academy of Sciences of the United States of America, 110(33): 13672–13677, doi: 10.1073/pnas.1305733110
Lindegren M, Van Deurs M, MacKenzie B R, et al. 2018. Productivity and recovery of forage fish under climate change and fishing: North Sea sandeel as a case study. Fisheries Oceanography, 27(3): 212–221, doi: 10.1111/fog.12246
Litzow M A, Malick M J, Bond N A, et al. 2020. Quantifying a novel climate through changes in PDO-climate and PDO-salmon relationships. Geophysical Research Letters, 47(16): e2020GL087972
Liu Yi, Zhou Xin, Huang Wen, et al. 2013. Responses of primary productivity to current and climate changes in the mud area to the Southwest of Cheju Island during the past 800 years. Journal of Ocean University of China, 12(4): 605–610, doi: 10.1007/s11802-013-2205-x
Ma Shuyang, Liu Yang, Li Jianchao, et al. 2019. Climate-induced long-term variations in ecosystem structure and atmosphere-ocean-ecosystem processes in the Yellow Sea and East China Sea. Progress in Oceanography, 175: 183–197, doi: 10.1016/j.pocean.2019.04.008
Mantua N J, Hare S R. 2002. The Pacific Decadal Oscillation. Journal of Oceanography, 58(1): 35–44, doi: 10.1023/A:1015820616384
Mantua N J, Hare R S, Zhang Yuan, et al. 1997. A Pacific interdecadal climate oscillation with impacts on salmon production. Bulletin of the American Meteorological Society, 78(6): 1069–1080, doi: 10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2
McClatchie S, Hendy I L, Thompson A R, et al. 2017. Collapse and recovery of forage fish populations prior to commercial exploitation. Geophysical Research Letters, 44(4): 1877–1885, doi: 10.1002/2016GL071751
Mcowen C J, Cheung W W L, Rykaczewski R R, et al. 2015. Is fisheries production within large marine ecosystems determined by bottom-up or top-down forcing?. Fish and Fisheries, 16(4): 623–632, doi: 10.1111/faf.12082
Möllmann C, Müller-Karulis B, Kornilovs G, et al. 2008. Effects of climate and overfishing on zooplankton dynamics and ecosystem structure: regime shifts, trophic cascade, and feedback loops in a simple ecosystem. ICES Journal of Marine Science, 65(3): 302–310, doi: 10.1093/icesjms/fsm197
Newman M, Alexander M A, Ault T R, et al. 2016. The Pacific Decadal Oscillation, revisited. Journal of Climate, 29(12): 4399–4427, doi: 10.1175/JCLI-D-15-0508.1
Oozeki Y, Ñiquen Carranza M, Takasuka A, et al. 2019. Synchronous multi-species alternations between the northern Humboldt and Kuroshio Current systems. Deep-Sea Research Part II: Topical Studies in Oceanography, 159: 11–21, doi: 10.1016/j.dsr2.2018.11.018
Patterson R T, Prokoph A, Kumar A, et al. 2005. Late Holocene variability in pelagic fish scales and dinoflagellate cysts along the west coast of Vancouver Island, NE Pacific Ocean. Marine Micropaleontology, 55(3-4): 183–204, doi: 10.1016/j.marmicro.2005.02.006
Pikitch E K, Rountos K J, Essington T E, et al. 2014. The global contribution of forage fish to marine fisheries and ecosystems. Fish and Fisheries, 15(1): 43–64, doi: 10.1111/faf.12004
Pinsky M L, Byler D. 2015. Fishing, fast growth and climate variability increase the risk of collapse. Proceedings of the Royal Society B:Biological Sciences, 282(1813): 20151053, doi: 10.1098/rspb.2015.1053
Pitois S G, Lynam C P, Jansen T, et al. 2012. Bottom-up effects of climate on fish populations: data from the Continuous Plankton Recorder. Marine Ecology Progress Series, 456: 169–186, doi: 10.3354/meps09710
Planque B, Fromentin J M, Cury P, et al. 2010. How does fishing alter marine populations and ecosystems sensitivity to climate?. Journal of Marine Systems, 79(3-4): 403–417, doi: 10.1016/j.jmarsys.2008.12.018
Qiao Shuqing, Shi Xuefa, Wang Guoqing, et al. 2017. Sediment accumulation and budget in the Bohai Sea, Yellow Sea and East China Sea. Marine Geology, 390: 270–281, doi: 10.1016/j.margeo.2017.06.004
Rayner N A, Brohan P, Parker D E, et al. 2006. Improved analyses of changes and uncertainties in sea surface temperature measured in situ since the mid-nineteenth century: The HadSST2 dataset. Journal of Climate, 19(3): 446–469, doi: 10.1175/JCLI3637.1
Roemmich D, McGowan J. 1995. Climatic warming and the decline of zooplankton in the California Current. Science, 267(5202): 1324–1326, doi: 10.1126/science.267.5202.1324
Rykaczewski R R, Dunne J P. 2010. Enhanced nutrient supply to the California Current Ecosystem with global warming and increased stratification in an earth system model. Geophysical Research Letters, 37(21): L21606
Salvatteci R, Field D, Baumgartner T, et al. 2012. Evaluating fish scale preservation in sediment records from the oxygen minimum zone off Peru. Paleobiology, 38(1): 52–78, doi: 10.1666/10045.1
Salvatteci R, Field D, Gutiérrez D, et al. 2018. Multifarious anchovy and sardine regimes in the Humboldt Current System during the last 150 years. Global Change Biology, 24(3): 1055–1068, doi: 10.1111/gcb.13991
Salvatteci R, Gutierrez D, Field D, et al. 2019. Fish debris in sediments from the last 25 kyr in the Humboldt Current reveal the role of productivity and oxygen on small pelagic fishes. Progress in Oceanography, 176: 102114, doi: 10.1016/j.pocean.2019.05.006
Shen Gongming, Heino M. 2014. An overview of marine fisheries management in China. Marine Policy, 44: 265–272, doi: 10.1016/j.marpol.2013.09.012
Shen Caiming, Wang W C, Gong Wei, et al. 2006. A Pacific Decadal Oscillation record since 1470 AD reconstructed from proxy data of summer rainfall over eastern China. Geophysical Research Letters, 33(3): L03702
Soutar A, Isaacs J D. 1969. History of fish populations inferred from fish scales in anaerobic sediments off California. California Committee on the Fishing Industry Reports, 13: 63–70
Stenseth N C, Mysterud A, Ottersen G, et al. 2002. Ecological effects of climate fluctuations. Science, 297(5585): 1292–1296, doi: 10.1126/science.1071281
Surry A M, King J R. 2015. A New Method for Calculating ALPI: The Aleutian Low Pressure Index. Nanaimo: Fisheries and Oceans Canada, 1–31
Tang Qisheng, Ying Yiping, Wu Qiang. 2016. The biomass yields and management challenges for the Yellow Sea large marine ecosystem. Environmental Development, 17(S1): 175–181
Valdés J, Ortlieb L, Gutierrez D, et al. 2008. 250 years of sardine and anchovy scale deposition record in Mejillones Bay, northern Chile. Progress in Oceanography, 79(2–4): 198–207, doi: 10.1016/j.pocean.2008.10.002
Ware D M, Thomson R E. 2005. Bottom-up ecosystem trophic dynamics determine fish production in the Northeast Pacific. Science, 308(5726): 1280–1284, doi: 10.1126/science.1109049
Worm B, Myers R A. 2003. Meta-analysis of cod-shrimp interactions reveals top-down control in oceanic food webs. Ecology, 84(1): 162–173, doi: 10.1890/0012-9658(2003)084[0162:MAOCSI]2.0.CO;2
Wu Zhaohua, Huang N E. 2009. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(1): 1–41, doi: 10.1142/S1793536909000047
Xing Lei, Zhao Meixun, Zhang Hailong, et al. 2009. Biomarker records of phytoplankton community structure changes in the Yellow Sea over the last 200 years. Periodical of Ocean University of China, 39(2): 317–322
Yang Qian, Song Xianli, Sun Yao, et al. 2012. Application of biologic silicon in modern sedimentary section to reconstruction of phytoplankton changes in the East China Sea and the Huanghai Sea during last 200 years. Acta Oceanologica Sinica, 31(2): 70–77, doi: 10.1007/s13131-012-0193-0
Yasuda I, Sugisaki H, Watanabe Y, et al. 1999. Interdecadal variations in Japanese sardine and ocean/climate. Fisheries Oceanography, 8(1): 18–24, doi: 10.1046/j.1365-2419.1999.00089.x
Yuan Dongliang, Hsueh Y. 2010. Dynamics of the cross-shelf circulation in the Yellow and East China Seas in winter. Deep-Sea Research Part II: Topical Studies in Oceanography, 57(19–20): 1745–1761, doi: 10.1016/j.dsr2.2010.04.002
Zhang Ziyin, Guo Wenli, Gong Daoyi, et al. 2013. Evaluation of the twentieth century reanalysis dataset in describing East Asian winter monsoon variability. Advances in Atmospheric Sciences, 30(6): 1645–1652, doi: 10.1007/s00376-012-2226-1
Zhang Zhaohui, Qu Fangyuan, Wang Shouqiang. 2019. Sustainable development of the Yellow Sea Large Marine Ecosystem. Deep-Sea Research Part II: Topical Studies in Oceanography, 163: 102–107, doi: 10.1016/j.dsr2.2018.08.009
Zhao Xianyong. 2006. Population dynamic characteristics and sustainable utilization of the anchovy stock in the Yellow Sea (in Chinese) [dissertation]. Qingdao: Ocean University of China
Zhou Xin, Sun Yao, Huang Wen, et al. 2015. The Pacific Decadal Oscillation and changes in anchovy populations in the Northwest Pacific. Journal of Asian Earth Sciences, 114: 504–511, doi: 10.1016/j.jseaes.2015.06.027
Appendix
Less
Year 2022 volume 41 Issue 8
PDF
73
40
Cite this Article
BibTeX
Article Info
doi: 10.1007/s13131-021-1914-z
  • Receive Date：2021-06-08
  • Online Date：2025-11-21
  • Published：2022-08-25
Article Data
Affiliations
History
  • Received：2021-06-08
  • Accepted：2021-07-14
Funding
The National Natural Science Foundation of China under contract No. 31600397.
Affiliations
    1 College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
    2 Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China

Corresponding:

References
Share
https://castjournals.cast.org.cn/joweb/aos/EN/10.1007/s13131-021-1914-z
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House