Wei Yu, Xinjun Chen

a College of Marine Sciences, Shanghai Ocean University, Shanghai, 201306, China

b Laboratory for Marine Fisheries Science and Food Production Processes, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, 266237,China

c National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai, 201306, China

d Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai, 201306, China

e Key Laboratory of Oceanic Fisheries Exploration, Ministry of Agriculture and Rural Affairs, Shanghai, 201306, China

f Scientific Observing and Experimental Station of Oceanic Fishery Resources, Ministry of Agriculture and Rural Affairs, Shanghai, 201306, China

Keywords:

ABSTRACT

1. Introduction

It is well-established that sea level change has become a serious challenge faced by wildlife worldwide (Kern & Shriver, 2014). Due to anthropogenic warming-driven glacial or ice melt and oceanic thermal expansion, researchers expect global average sea level to rise considerably and accelerate during the coming decades and even centuries(Merrifield et al., 2009, 2012). Sea level rose nearly 15 cm over the 20th century and the consequences of these rising sea levels yielded significantly negative impacts on low-lying coastal areas, wetlands, and small islands (Sagoe-Addy & Addo, 2013). Sea level changes increase the risk of extinction for many populations and species and raise very important conservation concerns for marine ecosystems (Golam, Haroon Yousuf, &Dayanthi, 2017). Therefore, understanding the potential impacts of sea level changes on species’ habitat should be a high priority for researchers and managers. To date, efforts have been directed toward exploring how habitats in coastal zones respond to sea level changes.However, the potential consequences of sea level changes for the habitat suitability of pelagic fish species in the open ocean are poorly understood, particularly for short-lived and climate-sensitive squid species.

Squid in the family Ommastrephidae are distributed throughout the neritic and oceanic oceans (Anderson & Rodhouse, 2001).Ommastrephid squid fisheries have been developed due to an expanding demand of protein for human consumption. Commercial squid catches account for a large proportion of world fisheries (Wang, Chen, Tanaka,Cao, & Chen, 2017). Ommastrephid squid are characterized by a 1-year lifespan, rapid growth, and high fecundity (Vijai, Sakai, Kamei, &Sakurai, 2014). As opportunistic species, Ommastrephid squid are extremely susceptible to climatic and environmental variability on a range of spatial and temporal scales (Coelho, 1984). Furthermore,Ommastrephid squid are an important ecosystem component and fluctuations in their stock sizes can affect both their predators and prey.Ommastrephid squid are also indicators of environmental changes and ecosystem conditions (Anderson & Rodhouse, 2001), given the importance of environmental conditions as determinants of habitat quality for Ommastrephid squid (Rodhouse, 2001).

The neon flying squid Ommastrephes bartramii, one of the most important targeted Ommastrephid squids, is widely distributed in the subtropical and temperate waters in the northern Pacific Ocean (Yatsu,Midorikawa, Shimada, & Uozumi, 1997). The O. bartramii population is comprised of two reproductive cohorts: the autumn cohort and the winter-spring cohort (Bower & Ichii, 2005). The winter-spring cohort of O. bartramii consists of two geographical stocks across the northern Pacific Ocean (Fig.1) and is the traditional fishing target of the Chinese squid-jigging fishery (Chen, Chen, Tian, Liu and Qian, 2008). Typically,O. bartramii undergoes a seasonal migration from feeding grounds in the north to spawning grounds in the south (Fig.1) (Ichii, Mahapatra, Sakai,& Okada, 2009). O. bartramii is exploited by multinational fishing vessels from Japan, Korea, Russia, China, Chinese Taipei, and other countries and regions due to its high value (Chen, Chen, et al., 2008; Chen,Chen, et al., 2008). In 2015, the O. bartramii stock was formally regulated by the North Pacific Fisheries Commission (NPFC), an inter-governmental organization, to ensure the long-term conservation and sustainable use of this fishery. Thus, it is urgent to make efforts to identify the biological response of O. bartramii to climate changes,including how to evaluate, monitor, and mitigate impacts of sea level changes.

O. bartramii is highly sensitive to the effects of large-scale climate change and regional oceanographic variability (Cao, Chen, & Chen,2009). If the biotic and abiotic environmental conditions in the area where squids inhabit are not suitable for feeding and breeding, squids tend to move to more favorable areas (Yu, Chen, Yi, Gao, & Chen, 2016).Therefore, the habitat quality of O. bartramii stock largely depends on the regional environmental conditions (Alabia et al., 2015a). Sea level height (SLH) is coupled with the dynamics and thermodynamics of the upper layers of the ocean and is a key environmental factor for O. bartramii, having profound effects on its stock (Chen, Tian, Liu, &Chen, 2011). SLH is also an effective proxy for O. bartramii habitats, due to O. bartrmii typically occurring in a limited/specific range of SLH,regarded as its representative habitat (Chen, Tian, Chen, & Liu, 2010).During 1993-2012, rates of SLH rise in the western Pacific Ocean were about three times faster than the global mean rate (Cazenave &Cozannet, 2014). The SLH rise in the northwest Pacific Ocean (NWPO) is likely to affect the habitats of short-lived O. bartramii stocks. Thus,assessing the potential impacts of SLH changes on climate-sensitive O. bartramii stock is essential and has important implications for Ommastrephid squid management and conservation.

In this study, the impacts of SLH changes on the western winterspring cohort of O. bartramii, an ecologically and commercially important species in the NWPO, were first quantified under three different SLH change scenarios (5 cm, 20 cm, and 35 cm). A habitat suitability index(HSI) modeling approach was applied, based on available fishery and environmental data. With these analyses, this study aims to: (1) quantify the relationship between the habitat of O. bartramii and SLH and predict the habitat of O. bartramii; (2) evaluate the impacts of SLH changes on habitat suitability and distribution of O. bartramii in an important region of the current fishing ground; and (3) highlight the conservation and management concerns for O. bartramii stocks as well as short-lived Ommastrephid squid species.

2. Materials and methods

2.1. Study area

The study area covers the northwestern Pacific Ocean, extending from 35N to 50N, and from 150E to 180E (Fig.1), located between the sub-arctic and sub-tropical domains. Two major ocean currents, the warm Kuroshio Current and the cold Oyashio Current, meet and interact in this region to form one of the most biologically productive feeding grounds in the world. Many economically important fish species such as tunas, saury, sardines, and squids inhabit this zone.

Fig.1. Migration pattern of the winter-spring cohort of neon flying squid Ommastrephes bartramii in the North Pacific Ocean. Location of fishing grounds for the Chinese squid-jigging fishery was overlapped in space with feeding habitat of the western stock of O. bartramii in the northwest Pacific Ocean.

2.2. Fishery data

Logbook data for the Chinese squid-jigging O. bartramii fishery were collected from the fishing grounds in the NWPO and included information on daily catch (tonnes), fishing effort (days fished), and fishing location (as longitude and latitude) during July to November from 2006 to 2015. These data were compiled into monthly data and grouped using 0.5× 0.5grid cells. Possible outliers in this dataset, such as data outside biological reality, were excluded from the study. Fishery data were obtained from the Chinese Squid-jigging Science and Technology Group of Shanghai Ocean University. Fishing locations occurred throughout the entire study area. The western stock of winter-spring O. bartramii was targeted with no bycatch. The monthly O. bartramii CPUEs (catch-per-unit-effort) were calculated within each 0.5× 0.5fishing grid cell.

2.3. Sea level height

The daily delayed time and both updated and merged products of mapped absolute dynamic topography (MADT) for SLH were obtained from the Archiving, Validation, and Interpretation of Satellite Oceanographic Data (AVISO) at a pixel resolution of 0.25for both latitude and longitude during July-November from 2006 to 2015 (https://www.aviso.altimetry.fr/en/my-aviso.html). The environmental data were then re-gridded to the same resolution as the fishery data by matlab software.

2.4. Habitat suitability index model construction and validation

The habitat suitability index (HSI) modeling approach is a type of species distribution model and has been commonly applied to describe species-habitat relationships (Bernal, DeAngelis, Schofield, & Sealey,2015; Xue et al., 2017). In recent years, the HSI model has been applied more widely and become a common ecological tool to identify species’responses to climate variability for management, conservation, and restoration (Tanaka & Chen, 2016). The empirical climate-driven HSI models are broadly based on suitability indices (SI) that reflect habitat quality by quantifying associations between one or more key environmental factors and relevant species abundance or occurrence for a given species (Vayghan, Poorbagher, Shahraiyni, Fazli, & Saravi, 2013). The habitat suitability is represented by predicted HSI values ranging from 0 to 1, the probability that a habitat is suitable for a species. The value ‘0’indicates a fully unsuitable habitat, whereas the value ‘1’ indicates a fully suitable habitat (Guan, Chen, & Wilson, 2017).

The HSI empirical model was adopted in this study to understand and analyze the impacts of SLH changes on the habitat suitability of O. bartramii in the NWPO. For squid-jigging O. bartramii fisheries, the areas with high fishing effort implied suitability for squid growth and survival. Fishing effort could be also used to indicate squid occurrence and was a better proxy for estimating habitat suitability than the CPUE data (Tian, Chen, Chen, Xu, & Dai, 2009). Therefore, fishing effort was used for the HSI model development in this study. As the response variable, fishing effort was correlated with the SLH using a histogram analysis. The total fishing effort in each SLH class interval (5 cm) was determined for each month. Based on this analysis, the SI value was then calculated. In this study, the SI value at the i class interval of SLH in each month was defined as the frequency of total fishing effort at the i class interval divided by the maximum frequency of the total fishing effort.

The calculated SI values for SLH were then utilized as observed values to fit the SI model with each class interval value. The relationship between observed SI and SLH was fitted as the following equation (Yu,Chen, Yi, Chen, & Zhang, 2015):

where a, b, and c were the estimated model parameters and SLH was the class interval value. The SI model for each month was created using Matlab 2012a software. The range of SLH corresponding to SI values greater than 0.6 was taken as the suitable range for O. bartramii (Chang et al., 2013). Due to only one habitat variable being included in the model, the output monthly SI model (i.e. the final HSI model) was then directly employed to predict the habitat suitability of O. bartramii within the fishing grounds of the northwest Pacific Ocean. The areas with HSI≥0.6, 0.2 ＜HSI＜0.6, and HSI≤0.2 were defined as the suitable, fair,and poor habitat for O. bartramii, respectively.

The HSI model performances were evaluated with a cross-validation test. The fishery and environmental data from 2006 to 2014 were selected as the training data for developing the model. The remaining data in 2015 were used as testing data to evaluate the HSI performance.In order to examine the consistency between the HSI and actual fishing activity, the predicted spatial distribution HSI maps were overlaid on the CPUE data in 2015. Moreover, the distributions of catch and fishing effort on each of the HSI groups ([0.0 0.2]; [0.2 0.6]; [0.6 1.0]) were also presented.

2.5. SLH change scenarios and impacts on habitat of Ommastrephes bartramii

Given the adequate predictions of critical habitat variables, the HSI models were used to predict the impacts of climate change on abundance and distribution of squid species. To predict the habitat suitability and distribution of O. bartramii with various levels of SLH rise, the HSI models were forced by scenarios of projected changes in SLH from the Global Climate Models (GCMs) developed in the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC)under several emission scenarios (Sagoe-Addy & Addo, 2013).

In order to model O. bartramii habitat suitability variation, three well-supported predictions of SLH change for the years 2020, 2060, and 2100 from IPCC B2 emission scenarios were selected, with associated SLH increases of 5 cm, 20 cm, and 35 cm, respectively (Sagoe-Addy &Addo, 2013). The IPCC B2 scenarios were considered appropriate for this study because of the urgent need to solve socio-economic, environmental, and issues associated with increased resource use. The fishery and SLH data were combined from 2006 to 2015 and input into the base model. Then, SLH increases of 5 cm, 20 cm, and 35 cm in the NWPO were added and applied to the climate-driven HSI model. The three SLH change scenarios, plus the original SLH, were applied to the habitat response models for O. bartramii in the NWPO.

The relationships between SLH and both the habitat suitability and latitudinal gravity center of HSI values (LATG) were examined from 2006 to 2015. For each SLH scenario, the new habitat conditions were also evaluated by estimating habitat suitability and the areas of both suitable and poor habitats in the fishing grounds. The LATGwere also determined. The equation used to calculate the LATGwas (Li, Cao,Zou, Chen, & Runnebaum, 2016):

where Latitudewas the latitude of the ith fishing unit in month m and HSIwas the HSI value within the ith fishing unit in month m.

3. Results

3.1. Variations in sea level height and catch-per-unit-effort

Fig.2a and e shows the spatial maps for the 10-year climatological SLH mean in the NWPO. The monthly SLH spatial pattern exhibited seasonal variability. SLH was generally high in the south and low in the north. Zonal expansion of the warm Kuroshio Current Extension could be observed across 30-35N from July to November. Monthly-averaged SLH (Fig.2f) significantly changed between months, increasing from 64.5 cm in July to 71.6 cm in October and then decreasing in November.

Fig.2. Spatial distribution of sea level height (SLH) in the fishing grounds of Ommastrephes bartramii in the northwest Pacific Ocean in (a) July; (b) August; (c)September; (d) October; (e) November. (f) Monthly-averaged SLH in the fishing grounds of O. bartramii from July to November.

Time series of SLH from July to November during 2006-2015 revealed interannual fluctuations with a increasing linear trend (R=0.4755, P ＜0.001) (Fig.3a). By contrast, a decreasing linear trend was found in the O. bartramii CPUEs (Fig.3b). Correlations between annual SLH and CPUE were estimated by month with a clear negative relationship between them, though the relationship was not significant in October and November.

Fig.3. (a) Time series of sea level height (SLH) in the fishing grounds of Ommastrephes bartramii from July to November over 2006-2015. (b) Time series of catchper-unit-effort (CPUE, t/d)of Ommastrephes bartramii from July to November over 2006-2015.

3.2. Fitted SI model

SI curves between fishing effort and SLH were established for each month (Fig.4). The performance of each fitted SI model was statistically assessed (Table 1). Results suggested that all the fitted SI models were statistically significant (P ＜0.001) with low Root Mean Squared Error(RMSE).

3.3. HSI model validation

The HSI models for O. bartramii developed using 2006-2014 data were tested using the fishery data from 2015 (Fig.5). The HSI values from July to November in 2015 were predicted for the fishing grounds in the NWPO using the model and overlaid on the CPUE data. The spatial distributions of the fishing locations and suitable habitat were very consistent for each month. Most catches were from the suitable habitat area (HSI≥0.6). Additionally, catch and fishing effort were verified for each HSI class interval (Table 2). A positive relationship was found between HSI and both catch and fishing effort for O. bartramii. In the suitable areas with HSI between 0.6 and 1.0, percentages of catches and fishing effort accounted for 64.3% and 60.2% of the total amounts,respectively. However, areas with HSI below 0.2 (i.e., poor habitat)

Table 1 Estimates of statistical parameters for the habitat suitability index (HSI) model for neon flying squid Ommastrephes bartramii from July to November in the northwest Pacific Ocean. SSE: Sum of Squares for Error; RMSE: Root Mean Squared Error.

Fig.4. Monthly variability of suitability index curve of sea level height (SLH) for Ommastrephes bartramii from July to November in the northwest Pacific Ocean. The histogram plot is presented as an example showing the fishing effort distribution in relation to SLH during August.

Fig.5. Monthly catch-per-unit-effort (CPUE, t/d) distribution for Ommastrephes bartramii caught by Chinese squid-jigging fishing vessels in the northwest Pacific Ocean during 2015 overlaid on the habitat suitability index map (suitable habitat with HSI≥0.6) predicted using sea level height data from 2006 to 2014.

HSI Frequency of catch (%)Fishing grids cell with catch Poor habitat with HSI between 0.0 and 0.2 Frequency of fishing effort (%)11.2 12.5 578 Fair habitat with HSI between 0.2 and 0.6 24.5 27.3 1010 Suitable habitat with HSI between 0.6 and 1.0 64.3 60.2 1847

Table 2 The parameters used for each HSI class interval correspond to: frequency of catch, frequency of fishing effort, and fishing grid cells with catch.produced just 11% of catches and 12.5% of fishing effort and the number of fishing grid cells with catch was also the lowest, further implying that these areas were not suitable for O. bartramii. In the fair habitat, with HSI values between 0.2 and 0.6, percentages of catches and fishing efforts accounted for about 25% of the respective totals.

3.4. Habitat suitability and distribution of O. bartramii in relation to SLH changes

Habitat suitability and the centers of gravity in each month from 2006 to 2015 tended to be closely related to the SLH in the fishing grounds of the NWPO (Figs. 6 and 7). First, regression analyses suggested that the LATGof O. bartramii was strongly and positively related to SLH during all fishing months, from July to November,meaning that suitable habitats were likely to move northward with increased SLH. Second, a higher average HSI value of O. bartramii was associated with lower SLH in the fishing grounds during September and October. In July, the correlation between them was significantly positive. No significant relationships were found between SLH and the average HSI values in August and November.

The monthly suitable and fair habitats of O. bartramii under the scenarios of 5 cm, 20 cm, and 35 cm increases in SLH are shown in Fig.8.With the SLH rise, the habitat of O. bartramii experienced dramatic changes. Compared to the habitat distribution during 2006-2015, the suitable habitat from July to November tended to expand under SLH+5 cm scenario, while the fair habitat contracted from August to October.The greatest change in the habitat of O. bartramii occurred under the SLH+35 cm scenario. Squid suitable habitat tended to completely disappear in the feeding grounds of the NWPO during August to November, while expanding in July. The location of suitable and fair habitat of O. bartramii also changed with different SLH scenarios. The suitable habitat tended to move northward and then disappear with the SLH increase. Fair habitat was divided into two geographical distributions (north and south) on either side of the suitable habitat. The northern fair habitat tended to disappear under the SLH+20 cm scenario. However, the southern fair habitat was likely to enlarge and move northward with the rising SLH.

By estimating the average HSI values and areas of suitable and poor habitat, the temporal and spatial variability of habitat of O. bartramii were further quantified for each SLH change scenario (Figs. 9 and 10).SLH change scenarios revealed that the predicted average HSI and the area of suitable habitat exhibited a general decreasing trend from August to October for increasing SLH. For example, the average HSI was 0.17 in August, 0.13 in September, and 0.20 in October with no SLH change. With a SLH rise of 20 cm, the HSI values significantly decreased to 0.13, 0.01, 0.16 in August, September, and October, respectively.With a SLH rise of 35 cm, both the HSI values and suitable habitat area were very much reduced. In July and November, the HSI values and suitable habitat area initially increased at projections of +5 cm and +20 cm, but largely declined under the +35 cm scenario. The area of poor habitat for O. bartramii largely increased with the SLH rise. The percentage of poor habitat was 72.2% in July, 72.6% in August, 79.9% in September, 69.7% in October, and 81.7% in November under the SLH scenarios. With a SLH increase of 35 cm, it increased to 77.3%, 95.2%,100%, 85.9% and 87.9%, in July, August, September, October, and November, respectively. Moreover, a clear poleward shift of LATGwas observed under all scenarios. The initial LATGfrom July to November was generally located south of 45N, but with SLH rise, it moved to the north of 47N for each month. The northernmost location of LATGwas 48N in September under the SLH+35 cm scenario.

4. Discussion

4.1. HSI modeling

Fishing effort and CPUE data are considered to be reliable indicators of fish species’ presence and abundance, respectively, and have been frequently used as the response variable for detecting habitat hotspots for fish species within the HSI modeling construction (Andrade, 2003;Chen et al., 2010). However, the CPUE-based HSI model was not applicable to the short-lived O. bartramii stock. A special analysis was conducted to compare the CPUE-based and fishing effort-based HSI model to explore the suitable habitat for O. bartramii and it concluded that the CPUE-based HSI model tended to overestimate the ranges of suitable habitats and underestimate monthly variations in the spatial distribution of suitable habitats (Tian et al., 2009). The fishing effort-based HSI model performs better than CPUE-based models for predicting the potential habitats for O. bartramii stock (Alabia et al.,2015b; Yu, Chen, Qi et al., 2015) and also other squid species, such as Japanese common squid Todarodes Pacificus (Alabia, Dehara, Saitoh, &Hirawake, 2016) and jumbo flying squid Dosidicus gigas (Yu, Yi, Chen, &Chen, 2016). Moreover, fishing effort for squid-jigging fisheries is a reflection of the density of fishing vessels, with large amounts of fishing effort implying good production and high squid abundance (Yu, Chen,Qian, & Tian, 2015). Fishing effort is a better index of resource distribution relative to CPUE for a system where potential competition for space occurs (Swain & Wade, 2003). Therefore, fishing effort was chosen to develop the HSI model in this study.

Fig.6. Relationship between the gravity centers of habitat suitability index and sea level height in the fishing grounds of Ommastrephes bartramii from July to November during 2006-2015.

Although only one environmental variable SLH was incorporated into the model, the HSI model yielded robust estimates of habitat suitability for O. bartramii. The fitted SI curves with low RMSE and high Rvalues (Fig.4) indicated a close relationship between SLH and squid habitat. The monthly suitable SLH range tended to change for O. bartramii (Fig.4). Due to their north-to-south migratory features(O. bartramii migrates from July to November), squid individuals inhabit different regions of the feeding ground on a monthly scale, leading to different SLH preferences from month to month (Bower & Ichii, 2005).The consistency between spatial distribution of suitable habitat and catch and fishing effort (Table 2 and Fig.5) proved that our model exhibited high performance and made good predictions of suitable habitat distributions. These findings suggested that the SLH had a good capacity for predicting the habitat suitability of O. bartramii stock in the northwestern Pacific Ocean. However, there are several reasons to expect our estimates are conservative. To truly assess the climate-driven habitat changes, future analyses should include more biotic and abiotic predictors within the model construction and also make efforts to evaluate the interactions among the environmental variables.

4.2. Climate change and the O. bartramii stock

Rapid climate change has impacted O. bartramii stocks (Cao et al.,2009). In the past five years, a growing number of studies have related the abundance and distribution of O. bartramii to climate events, such as the El Ni˜no and La Ni˜na phenomena. Such anomalous events are reflected by fluctuating O. bartramii catches (Chen, Zhao and Chen, 2007).El Ni˜no has had a variety of impacts on O. bartramii (Alabia et al., 2016b;Yu, Chen, Qi et al., 2015, Yu, Chen, Yi et al., 2016). As the Pacific climate varies on an interannual time scale, El Ni˜no and La Ni˜na events influence O. bartramii stocks by perturbing environmental conditions within spawning and feeding areas (Igarashi et al., 2017).

Additionally, the rising sea level is a potential climatic factor that yields strong impacts on coastal habitats, coastal aquaculture, and fisheries in the world’s oceans. It increases coastal erosion, extreme marine flooding, and saltwater intrusion in coastal aquifers (Cazenave &Cozannet, 2014), and other potential consequences for coastal ecosystems based on biophysical interactive processes (Nicholls & Cazenave,2010). This has motivated increasing efforts to explore the dynamic response of coastal systems to SLH change (Tsch´a et al., 2017; Wetzel,Beissmann, Penn, & Jetz, 2013). However, how pelagic fish species respond to climate change remains uncertain. Researchers have mainly focused on revealing the causes of annual fluctuations in fish catches in relation to short term climatic events that have significant impacts(Dupont, Bagøien, & Melle, 2017). It is easy to ignore the inconspicuous impacts of SLH changes on fisheries in the high seas. In fact, with the rising sea level, the pelagic fish habitat is changing on different spatial-temporal scales.

Fig.7. Relationship between habitat suitability index values and sea level height in the fishing grounds of Ommastrephes bartramii from July to November during 2006-2015.

4.3. Possible consequences of SLH changes on O. bartramii stock

O. bartramii habitats in the NWPO are at risk from SLH changes.Evidence from this study verified the close association between SLH and squid abundance and occurrence. The SLH incorporated in the HSI model could be considered a critical environmental factor for identifying and exploring the habitat of O. bartramii. Other squids or pelagic fish species are closely linked to SLH, which influence their abundance and spatial distribution pattern. For example, the shortfin squid (Illex argentines) in the southeast Atlantic Ocean prefer to inhabit waters with zero or negative SLH (Chen, Liu and Chen, 2008). Likewise, chub mackerel (Scomber japonicas) have a high probability of occurrence in areas with low SLH in the East China Sea (Chen, Li, Feng, & Tian, 2009).SLH was also used as a reliable environmental proxy to detect habitat hotspots for albacore (Thunnus alalunga) in the NWPO (Zainuddin,Kiyofuji, Saitoh, & Saitoh, 2006).

High SLH was also likely to result in decreased CPUE, low habitat quality, and a northward shift of suitable habitat for O. bartramii during 2006-2015 (Fig.11). Moreover, the relationships between HSI and SLH by month were quite different (Fig.7), which might be due to the seasonal changes of SLH within the fishing grounds. In this study, catches of western O. bartramii from Chinese squid-jigging fishery accounted for more than 80% of the total catch for this stock (Chen, Liu, et al., 2008).The large amount of data over an extensive distribution reflected the habitat condition across the study years. The squid fishery during 2006-2015 reflected that high SLH was not favorable for squid aggregation and the formation of fishing grounds with abundant O. bartramii.Similarly, changes in abundance for the autumn cohort of O. bartramii were also significantly related to the SLH. Ichii et al. (2011) examined the very low stock level of autumn O. bartramii from 1999 to 2002 based on the Japanese squid fishery and found it could be explained by an abrupt increase of the sea level anomaly in the Subtropical Frontal Zone and the Transition Zone.

The projection results from the HSI model in this study suggested that the habitat of western O. bartramii stock in the NWPO was vulnerable to various SLH change scenarios. In general, O. bartramii abundance is typically high from August to October. As a consequence, these three months are the primary fishing months responsible for the majority of the total catch (Chen, Liu, et al., 2008). It indicated that SLH rise was likely to result in significant loss of suitable habitat and enlarged poor habitat in the NWPO, particularly under a 35 cm SLH rise scenario during these three months, which projected the complete disappearance of suitable habitat, and the poor habitat extent increased to between 80 and 90% of the study area. This implies that the feeding grounds in the NWPO will not be suitable for the growth and survival of adult O. bartramii. Furthermore, the suitable habitat will shift northward with the increased SLH. Spatial and temporal variability in the NWPO habitat of O. bartramii would induce serious social and economic problems for countries with this fishery. For example, the projected habitat loss,associated decline in squid catch, and consequent decrease the food supply for humans would cause marine fisheries-related food security issues, especially in Asia-Pacific countries (China, Japan, and Korea,etc.). Fuel costs for fishing O. bartramii would also increase due to fishing locations moving further north.

Fig.8. Spatial distribution of monthly potential suitable habitat and fair habitat for Ommastrephes bartramii in the northwest Pacific Ocean under scenarios of sea level height (SLH) in recent years during 2006-2015 and with SLH increases of 5 cm, 20 cm, and 35 cm, respectively.

The process by which rising sea levels in the NWPO drive variations in habitat suitability of the western O. bartramii stock was inferred by this research. The SLH increased in the squid feeding grounds, indicating that the heat content and depth of the upper thermocline and nutricline would also increase (Kahru, Fiedler, Gille, Manzano, & Mitchell, 2007).This directly resulted in reduced primary productivity across the fishing grounds. Feeding conditions for squid would become worse and directly produce large areas of low-quality habitat, leading to a contraction of suitable habitat and enlarged poor habitat. However, with SLH rise, the northern regions of the feeding ground would become more favorable for O. bartramii, thus, the suitable habitat would move further north.SLH rise was usually associated with an increase in sea surface temperature (SST) and both the favorable SST and SLH range for O. bartramii shifted northward (Xu, Chen, Chen, Ding, & Tian, 2016). The O. bartramii stock would follow the favorable environmental conditions and migrate northward.

4.4. Implications for Ommastrephid squid conservation and management

Despite previous studies that have evaluated the influences of climate variability on Ommastrephid squid species, significant gaps still exist in understanding the links between climatic factors and the direction and magnitude of responses by squid species. This study provides valuable information about how one important Ommastrephid squid species responds to increased sea level height using quantitative analysis. Such a modeling approach should be repeated and applied to other ecologically and economically important Ommastrephid squids, such as the jumbo flying squid Dosidicus gigas in the Eastern Pacific Ocean(Medellín-Ortiz, Cadena-C´ardenas, & Santana-Morales, 2016), the Japanese flying squid Todarodes pacificus in the East China Sea and the Sea of Japan (Zhang, Saitoh, & Hirawake, 2016), and the purpleback flying squid Sthenoteuthis oualaniensis in the Northwest Indian Sea (Chen, Liu,Tian, Qian, and Zhao, 2007).

There is little doubt that sea level will continue to rise for the foreseeable future, but the extent of future SLH change remains highly uncertain (Nicholls & Cazenave, 2010). Therefore, the conservation and management of pelagic Ommastrephid squid species is challenging.With further rises in sea level and relevant environmental changes, some squid species will experience stress, decrease in abundance, and possibly become extinct, while other species may take advantage of these changes. The important thing is the need to identify who are the “winners and losers” among the Ommastrephid squid in the next 100 years due to the sea level changes. This issue should be addressed in a broad sense, considering not only the commercial species, but also other species with critical roles in food webs.

Responses to SLH changes are likely species-specific (Baker, Littnan,& Johnston, 2006; Lafever, Lopez, Feagin, & Silvy, 2007). This analysis represented a first step towards predicting the impacts of SLH changes on the O. bartramii stock in the NWPO. The suitable habitats of O. bartramii will decrease or disappear due to the adverse impacts of SLH changes and mitigating these negative impacts is a challenge. One important measure is to reduce deep emissions to prevent global sea-level rise, which should be regarded as a global policy and effectively implemented in the future (Vermeer & Rahmstorf, 2009). Careful conservation and management of squid fisheries that considers climate change is also needed to prevent habitat loss and facilitate persistence(Hannah, Midgley, & Millar, 2002). To maintain the contribution of squid to food security, adaptation measures must minimize the gap between the increasing squid demand and sustainable fisheries (Bell et al.,2013). Key adaptation measures include: flexible fishing effort schemes under different anomalous environments, such as decreasing the fishing efforts under unfavorable climatic conditions; conservative fishing practices to maintain the recruitment potential of squid stocks, such as prohibiting the fishing of spawning squid; protection of the coastal spawning grounds for squids with an inshore-to-offshore spawning migration (e.g. D. gigas); and exploring new fishing grounds created by climate-induced squid redistribution.

Fig.9. Monthly-averaged habitat suitability index and the percentage of suitable (HSI≥0.6) and poor (HSI≤0.2) habitat occupying the fishing grounds of Ommastrephes bartramii in the northwest Pacific Ocean under different sea-level rise scenarios.

Fig.10. Monthly gravity centers of habitat suitability index on the fishing ground of Ommastrephes bartramii in the northwest Pacific Ocean under different sea-level rise scenarios.

Fig.11. Schematic of the habitat suitability response by the western stock of Ommastrephes bartramii to sea-level rise in the northwest Pacific Ocean.

In conclusion, this study provided the first insight and best current available assessment of the SLH change impacts on the western O. bartramii stock in the NWPO through an HSI modeling method. These findings suggested that O. bartramii habitats in the NWPO were at risk to SLH rise, which would cause serious social and economic problems.Advanced scientific research and climate change-integrated conservation management strategies are recommended to reduce the negative impacts of SLH rise. Conclusions from this study also provided important implications for pelagic short-lived Ommastrephid squid in global oceans for better conservation and fishery management.

Data availability statement

Data available upon request from the authors.

CRediT authorship contribution statement

Wei Yu: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing -original draft. Xinjun Chen: Conceptualization, Project administration,Resources, Writing - review & editing.

Declaration of competing interest

The authors have no conflict of interest to declare.

Acknowledgments

This study was financially supported by the National Key R&D Program of China (2019YFD0901405), National Natural Science Foundation of China (41906073), the Natural Science Foundation of Shanghai(19ZR1423000) and the Shanghai Universities First-Class Disciplines Project (Fisheries A).