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Ocean warming-induced range-shifting of potential habitat for jumbo flying squid Dosidicus gigas in the Southeast Pacific Ocean off Peru
Fisheries Research 204 (2018) 137–146
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Ocean warming-induced range-shifting of potential habitat for jumbo flying squid Dosidicus gigas in the Southeast Pacific Ocean off Peru Wei Yua,b,c,d, Xinjun Chena,b,c,d,
T
⁎
a
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China c Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China d Collaborative Innovation Center for Distant-Water Fisheries, Shanghai 201306, China b
A R T I C L E I N F O
A B S T R A C T
Handled by B. Arara
Climate-induced ocean warming may have significant influences on abundance and geographic distribution of fish species and cause range shifts and/or expansions in their habitats. The jumbo flying squid Dosidicus gigas in the Eastern Pacific Ocean is an ecologically and commercially important species. With 1-year short lifespan, D. gigas is significantly affected by climatic and environmental variability. This study used the logbook data of the 2011–2015 Chinese squid-jigging fishery off Peruvian waters, coupled with sea surface temperature (SST) data, to explore the variations in seasonal habitat suitability and habitat distribution pattern for D. gigas in the Southeast Pacific Ocean under five scenarios: SST in recent years (2011–2015) and with 0.5 °C, 1.0 °C, 2.0 °C and 4.0 °C increases in relation to the climate variability. A fishing effort-based habitat suitability index (HSI) model was developed to estimate habitat quality of D. gigas and spatial distribution of suitable and optimal habitats in relation to ocean warming. Results indicated that obvious seasonal variations were observed in the SST on the fishing ground of D. gigas with spatial variability. HSI modeling approach in this study essentially captured habitat characteristics of D. gigas from spring to winter during 2011–2015. SST increase scenarios revealed that seasonal habitat suitability of D. gigas significantly reduced, and the percentages of suitable (HSI ≥ 0.6) and optimal (HSI ≥ 0.8) habitats occupying the fishing ground dramatically decreased due to the rising SST. Moreover, an obvious southeastward movement was observed in gravity centers of D. gigas habitat under the above-mentioned five scenarios. Our findings suggested that ocean warming was likely to result in the shrinkage and southeastward range-shift in the potential high-quality habitats of D. gigas in the Southeast Pacific Ocean off Peruvian waters.
Keywords: Dosidicus gigas Habitat suitability index Rising sea surface temperature Potential habitat Distribution shift
1. Introduction Climate-induced recent ocean warming causes significant changes in the marine species, with extremely strong impacts on potential habitat of marine species in space and time (Malcolm et al., 2002). Evidence has shown that birds, coral reefs and large marine predator species react to these ocean water temperature changes with rapid and extensive shifts in their geographical distributions and stock abundance (Veit et al., 1996; Pandolfi and Cohen, 2011; Bograd and Foley, 2013). For example, based on the 80 years of Japanese in situ sea surface temperature (SST) data, four major reef corals, the habitat-forming species in the tropical areas, have shown poleward expansions since the 1930s (Yamano et al., 2011). For fish species, sea surface water temperature increases may strongly drive in changes in their distribution and abundance due to the temperature-related variability in the habitat
⁎
quality and the occurrence of range shifts in their habitats (Donelson et al., 2015; Montero-Serra et al., 2015). However, these responses are likely to vary with species due to their different life histories. ommastrephid squid, a group with fast generation times, is particularly sensitive to climatic and environmental variability at different time scales (Anderson and Rodhouse, 2001). Adverse climatic and environmental conditions can cause mass loss in areas of suitable habitats for squids in a short time and consequently induce stock decline or fishery collapse. Squids are also temperature-dependent animals whose life cycle is closely related to ocean water surface temperature (Yu et al., 2015). The jumbo flying squid Dosidicus gigas is an ecologically important species in pelagic marine ecosystem due to its critical role in food-webs and trophic dynamics (Markaida, 2006). Dosidicus gigas exhibits northto-south and inshore-to-offshore round-trip migration patterns (Nigmatullin et al., 2001). Life history of D. gigas is complex but plastic,
Corresponding author at: College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China. E-mail address: [email protected] (X. Chen).
https://doi.org/10.1016/j.fishres.2018.02.016 Received 7 July 2017; Received in revised form 7 February 2018; Accepted 17 February 2018 Available online 20 March 2018 0165-7836/ © 2018 Elsevier B.V. All rights reserved.
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fisheries in this study were primarily distributed in the Southeast Pacific Ocean off Peruvian waters. The longitudinal and latitudinal gravity centers of fishing effort were calculated for D. gigas fishery (Li et al., 2014). Furthermore, the CPUE (catch per unit effort) within a 0.5° × 0.5° fishing grid for this squid species in each season was calculated by the following equation (Cao et al., 2009):
such as fast growth, high fecundity and strong movement capacity (Argüelles et al., 2001; Markaida et al., 2004). With huge commercial values, D. gigas supports large-scale fisheries in the Eastern Pacific Ocean (Nevárez-Martínez et al., 2006). The Peruvian offshore water is an area with high-level biological productivity. Due to its location in the Humboldt Current System combined with complicated currents and coastal wind-driven upwelling (Carr et al., 2002). Therefore, this area is a productive habitat for D. gigas and is one of the most important fishing grounds globally. The biomass of D. gigas and commercial catches account for a large proportion of all fisheries in the Humboldt Current region (Waluda et al., 2006; Yu et al., 2016). However, annual catches of D. gigas tended to be fluctuant from year to year (Chen et al., 2008). With only 1-year short lifespan, D. gigas is significantly affected by climatic and environmental variability (Medellín-Ortiz et al., 2016). Therefore, understanding the linkage between D. gigas stock and climate variability is important for better fisheries management and accurate fishing forecasts. Increasingly many studies have focused on evaluating impacts of the El Niño-Southern Oscillation (ENSO) event on spatial distribution and abundance of D. gigas (Waluda et al., 2006; Ichii et al., 2002). For instance, Waluda and Rodhouse (2006) reported that abundance of D. gigas was positively correlated with SST between 17 °C and 22 °C in July during fishing season, and with SST between 24 °C and 28 °C in September prior to fishing season. Squid abundance was also linked to mesoscale variability in oceanographic conditions (i.e., ENSO), with weaken upwelling off Peru during El Niño of 1997–1998 leading to low catches of D. gigas (Waluda and Rodhouse, 2006). Yu et al. (2016) suggested that habitat quality of D. gigas off Peru would be poor in El Niño years and favorable in La Niña years. Meanwhile, the El Niño conditions might yield enlarged unsuitable habitat and cause reduced catches. Recently, some researchers have paid increasing attentions to ocean warming-induced redistribution of potential habitat for fish species in the North Pacific Ocean (Christian and Holmes, 2016). However, there are hardly any studies investigating the influence of ocean warming on habitat quality of squids especially in the Southeast Pacific Ocean. Water temperature is usually considered as the dominant abiotic environmental factor in regulating distribution and abundance of squid species (Yu et al., 2015). Given that surface water temperature may define the limits of the habitat of squids, ocean warming events are likely to lead to individual movements and further range-shift of potential habitat of squid species like D. gigas. Climate change scenarios have predicted an average surface water temperature increase of 1.08–4.08 °C by 2100 (IPCC, 2007). We explored the possible changes of habitat of D. gigas in the Southeast Pacific Ocean off Peru in relation to this ocean warming. In this study, we used the logbook data of the 2011–2015 Chinese squid-jigging fishery off Peruvian waters, coupled with the SST data, to examine variations in seasonal habitat suitability and suitable and optimal habitat distributions for D. gigas in the Southeast Pacific Ocean under five ocean warming scenarios: SST in recent years (2011–2015) and with 0.5 °C, 1.0 °C, 2.0 °C and 4.0 °C increases. A fishing effort-based habitat suitability index (HSI) model was developed to estimate the habitat quality and spatial distribution pattern of suitable and optimal habitats in relation to above-mentioned five ocean warming scenarios.
CPUE =
∑ Catch ∑ Fishing effort
(1)
where ΣCatch was the sum of catches for all the fishing vessels within a 0.5° × 0.5° fishing grid per month; and ΣFishing effort was the sum of fishing efforts for all the fishing vessels within a 0.5° × 0.5° fishing grid per month. 2.2. Sea surface temperature data Sea surface temperature (SST) was considered as a critical environmental driver in regulating the distribution and abundance of D. gigas stock (Paulino et al., 2016; Yu et al., 2016). Thus, it was considered as a good proxy to characterize the habitat of D. gigas. In this study, the monthly SST data was sourced from the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation (OI) SST Version 2 with spatial resolution of 0.25° × 0.25° (https://www. ncdc.noaa.gov/oisst/data-access). The OI analysis was constructed by combining observations from different platforms (satellites, ships, buoys) on a regular global grid. Before the analysis was computed, the satellite data was adjusted for biases using the method from Reynolds (1988) and Reynolds and Marsico (1993). The SST data were grouped on a 0.5° × 0.5° latitude/longitude grid to match with the spatial resolution of the fishery data. 2.3. Habitat suitability modeling Habitat suitability index (HSI) model is one of the most important and effective tools to identify and explore fish habitats (Thomasma et al., 1991; Xue et al., 2017). Generally, by initially determining the suitability index (SI) as a function of one or more critical environmental factors, an empirical HSI model is then constructed by combining all input factors and further describing the suitability of a give habitat of target species (Li et al., 2016). The HSI values range from 0 to 1, indicating a poor habitat and the most environmental-favorable habitat, respectively. In this study, the HSI empirical model would present seasonal distribution of potential habitat of D. gigas in the Southeast Pacific Ocean off Peru. The season in the Southern Hemisphere was defined as follows: spring (September–November), summer (December–February), autumn (March–May), and winter (June–August). Regarding the HSI model, the first step was to quantify the statistical or parameterized functional relationships between environmental variable and the probability of fish occurrence. For short-lived squid species, CPUE-based HSI model may overestimate the ranges of optimal habitats and underestimate variations in the spatial distribution of optimal habitats (Tian et al., 2009). Therefore, fishing effort was a better proxy than CPUE to estimate the probability of squid occurrence. The SST was used as the environmental factor in the model inputs. As the response factor, fishing effort was correlated with the SST by histogram analysis. All the 3669 fishing points were divided by season. Then, the total number of fishing efforts in each SST class interval was determined for each season. Based on the above analysis, we further determined the SI values and output the environmental preference for D. gigas. The SI value at the i class interval of SST from spring to winter was calculated using the frequency of total fishing efforts at the i class interval divided by the maximum frequency of the total fishing efforts. The formula was established by the equation as follows:
2. Materials and methods 2.1. Fisheries data collection Commercial fisheries data for D. gigas grouped by 0.5° × 0.5° grid cell and by month were obtained from the Chinese Squid-jigging Science and Technology Group of Shanghai Ocean University. Data including 3669 observations from January to December during 2011–2015 were used in the analysis. The data contained fishing effort (days fished), the location of fishing ground (latitude and longitude in degrees) and catch (unit: tonnes). Fishing locations for Chinese D. gigas
SIi = 138
Fishing efforti Fishing effort max
(2)
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where i was the index representing the ith class interval of the SST; Fishing efforti was the total fishing efforts at the ith class interval; and Fishing effortmax was the maximum fishing efforts at a certain class interval. The second step was to develop the SI model, which was continuous and ranged from 0 to 1, based on the observed SI values calculated from the Eq. (2). The SI model of SST in each season was fitted in the form of:
SI = a× exp ⎡−⎛ ⎢ ⎣ ⎝
SST − b ⎞ 2⎤ c ⎠ ⎥ ⎦
Table 1 Location of Chinese squid-jigging vessels for Dosidicus gigas fishery from spring to winter in the Southeast Pacific Ocean off Peru from 2011 to 2015. Latitudinal gravity (°S)
Spring Summer Autumn Winter
20.0 20.0 20.0 20.0
8.5 8.0 8.0 8.0
80.7 80.4 79.9 81.5
14.7 16.3 16.5 13.5
Both the total catch and fishing effort of D. gigas in the Southeast Pacific Ocean off Peruvian offshore waters decreased from spring to autumn and then increased in winter. The catch and fishing effort varied from 20,175 tonnes to 93,614 tonnes, and from 6991 to 16,387 days, respectively. The average CPUE over the years 2011–2015 was high (> 4.5 t/d) in spring and summer but relatively low (< 4.0 t/ d) in autumn and winter. Additionally, the fishing location for Chinese squid-jigging fishing vessels was between 8°S and 20°S off Peruvian waters (Table 1). The seasonal longitudinal and latitudinal gravity centers varied from 79.9°W to 81.5°W, and from 13.5°S to 16.5°S, respectively (Table 1). Furthermore, the seasonal average SST was generally low in spring and summer. Distinct spatial variations in SST were observed in each season on the fishing ground of D. gigas off Peru (Fig. 1). The fishing ground was characterized with large cool water mass below 15 °C in the southeastern regions in spring and winter. However, during summer and autumn, the fishing ground was featured by warm SST, especially warm water with SST higher than 25 °C occupying the northwestern areas on the fishing ground of D. gigas (Fig. 1). 3.2. Habitat suitability index model Suitability index curves between the fishing effort and SST were modeled for each season, as shown in Table 2 and plotted in Fig. 2. It was observed that the SI models from spring to winter developed by 1.0 °C SST class interval were all significant (P < 0.001) with highest correlation coefficients and lowest Sum of Squares for Error through the statistical assessment (Table 2). Therefore, the SI models with 1.0 °C class interval were chosen to predict the habitat suitability of D. gigas in the following analysis. Through estimation, the environmental preference of D. gigas (SST corresponding to SI ≥ 0.6) in the Southeast Pacific Ocean varied seasonally, as shown by the SI curves (Fig. 2). In spring, the suitable range of SST varied from 16.9 °C to 18.8 °C. Suitable SI for D. gigas in summer was found to be related to temperature between 19.6 °C and 22.7 °C. The suitable temperature range for D. gigas was approximately 20.4–23.0 °C in autumn and 17.2–20.4 °C in winter (Fig. 2). Through the following methods, we evaluated the performance of the developed HSI model. The HSI values over 2011–2015 were initially estimated by the model output. The relationship between the observed catch and HSI values was evaluated based on the adjusted R2. Results indicated that the HSI model explained 94.2% (P < 0.001) of the variance of the 10-point scale complied from the catches of D. gigas off Peru (Fig. 3). We then overlaid the predicted HSI contour maps with the catches in each season (Fig. 4). It was observed that most of the catches were distributed in the suitable habitat of D. gigas (HSI ≥ 0.6) from spring to winter (Fig. 4). Catches tended to increase with the HSI values. Secondly, a large proportion of catch and fishing effort occupied the class interval with HSI ≥ 0.6, the percentages of catch and fishing effort tended to be more than 70% in spring and autumn, higher than
∑ (Longitude(i,s) × HSI(i,s) )
∑ (Latitude(i,s) × HSI(i,s) ) ∑ HSI(i,s)
Longitudinal gravity (°W)
3.1. Seasonal variation of Chinese D. gigas fishery and SST on the fishing ground
In order to examine the impacts of ocean warming on D. gigas habitat, we used five scenarios of the seasonal SST increases (SST, SST + 0.5 °C, SST + 1.0 °C, SST + 2.0 °C, SST + 4.0 °C) (IPCC, 2007), which were developed by the global climate simulation under several emission scenarios (A1FI, A1B, A1T, A2, B1, and B2) from the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC, 2007), to forecast the temporal variability in the habitat suitability and spatial changes in the gravity centers of HSI values of D. gigas in the next 100 years in the Southeast Pacific Ocean. The percentages of the suitable (HSI ≥ 0.6) and optimal (HSI ≥ 0.8) habitat of D. gigas accounting for the fishing ground were determined. The longitudinal and latitudinal gravity centers of HSI values of D. gigas from spring to winter under each scenarios (LONGHSI and LATGHSI) were calculated using the following equations (Li et al., 2016):
LATGHSI =
Northernmost location (°S)
3. Results
2.4. Examination of impacts of ocean warming on D. gigas habitat
∑ HSI(i,s)
Southernmost location (°S)
(3)
where a, b and c were the estimated model parameters using the least squares estimate to minimize the residual between SI observations and SI predictions (Yen et al., 2012, 2017); SST was the class interval value. The SI model of SST for each season was implemented by the Matlab software. Due to the SST class interval is a construct of the analysis, in order to evaluate the data variability and model uncertainty, the SST class interval was defined as 0.5 °C, 1.0 °C and 1.5 °C, respectively. The SI model developed by each SST class interval was further compared. The most suitable SI model was chosen to predict the habitat suitability of D. gigas. After developing the seasonal SI curves, we used the seasonal SST over 2011–2015 combining with the Eq. (3) to estimate the HSI values of D. gigas fishing ground. In order to explore the high HSI class intervals (HSI values higher than 0.6) varied with the ocean warming, the area with HSI ≥ 0.6 and with HSI ≥ 0.8 was defined as the suitable and optimal habitat for D. gigas, respectively (Tian et al., 2009; Yu et al., 2016). Additionally, the area with HSI ≤ 0.2 and with 0.2 < HSI < 0.6 was defined as unsuitable habitat and common habitat, respectively. Model performance was finally evaluated by comparing the percentage of catch and fishing effort in the class interval with HSI ≥ 0.6 and with HSI ≥ 0.8. To further assess the model performance for predicting the suitable and optimal habitat of D. gigas in the Southeast Pacific Ocean off Peru, the seasonal predicted maps of HSI were then overlain with fishery data (catch data) over 2011–2015. The goodness of fit of the above linear relationship between the catch and HSI values was assessed based on the adjusted R2 to validate the HSI model in predicting potential D. gigas habitats.
LONGHSI =
Season
(4)
(5)
where Longitude(i,s) was the longitude of the ith fishing grid in season s; Latitude(i,s) was the latitude of the ith fishing grid in season s; HSI (i,s) was the HSI value within the ith fishing grid in season s. 139
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Fig. 1. Spatial distribution of the seasonal averaged sea surface temperature (SST) on the fishing ground of Dosidicus gigas from spring to winter over 2011–2015 in the Southeast Pacific Ocean.
four seasons, implying that the areas of D. gigas habitat dramatically shrank. Moreover, an obvious southward movement was observed in the suitable habitat location under the above-mentioned four scenarios. The temporal and spatial variability of potential habitat of D. gigas were further quantified by examining the area of suitable and optimal habitats of D. gigas and gravity centers of HSI values on the fishing ground under different SST increase scenarios (Figs. 6 and 7). During the four fishing seasons, suitable and optimal habitats for D. gigas displayed linear decrease from recent years to the climate change scenarios of a 4.0 °C increases in SST. For example, the percentage of suitable habitat in spring was 22.2%, 20.1%, 17.5%, 11.8%, 9.69% under SST, SST + 0.5 °C, SST + 1.0 °C, SST + 2.0 °C and SST + 4.0 °C scenarios, respectively (Fig. 6). The gravity centers of the HSI values for each season showed an obvious southeastern latitudinal shift under the
90% in summer and above 60% in winter. Within the class interval with HSI ≥ 0.8, high percentages of the catch and fishing effort also occurred (Table 3). 3.3. Variability in habitat suitability and distribution of potential squid habitat in relation to ocean warming A comparing of the squid habitat under the normal SST condition in recent years during 2011–2015 (Figs. 4 and 5) showed the seasonal potential habitat of D. gigas (HSI ≥ 0.6) under the scenarios of 0.5 °C, 1.0 °C, 2.0 °C and 4.0 °C increases in SST. Due to the SST increases, the habitat suitability of D. gigas clearly decreased in each season. Potential suitable and optimal habitats occupying the fishing ground of D. gigas were gradually concentrated into narrower horizontal belt among the
Table 2 Estimation of statistical parameters for the habitat suitability index (HSI) model with three different SST class intervals for Dosidicus gigas from spring to winter in the Southeast Pacific Ocean off Peru. SSE: Sum of Squares for Error; RMSE: Root Mean Squared Error. SSE
RMSE
R2
P
Spring
HSI = 0.895 × exp( −((SST −
18.154)/1.483)2)
0.090
0.008
0.941
< 0.001
Summer
HSI = 0.597 × exp( −((SST − 21.444)/2.963)2)
0.785
0.056
0.436
> 0.050
Autumn
HSI = 0.673 × exp( −((SST − 21.883)/1.822)2)
0.270
0.019
0.776
< 0.010
Winter
HSI = 0.982 × exp( −((SST − 18.976)/2.358)2)
0.487
0.030
0.815
< 0.010
Spring
HSI = 0.991 × exp( −((SST − 17.931)/1.438)2)
0.002
0.000
0.998
< 0.001
Summer
HSI = 0.934 × exp( −((SST − 21.091)/2.472)2)
0.049
0.008
0.948
< 0.001
Autumn
HSI = 0.976 × exp( −((SST − 21.740)/1.809)2)
0.021
0.003
0.981
< 0.001
Winter
HSI = 1.100 × exp( −((SST − 18.738)/2.189)2)
0.210
0.026
0.880
< 0.001
Spring
HSI = 1.041 × exp( −((SST − 17.715)/1.672)2)
0.003
0.001
0.997
< 0.001
Summer
HSI = 0.951 × exp( −((SST − 20.677)/3.028)2)
0.071
0.024
0.887
< 0.001
Autumn
HSI = 1.003 × exp( −((SST − 21.287)/1.720)2)
0.015
0.004
0.982
< 0.001
Winter
HSI = 1.092 × exp( −((SST − 18.567)/2.218)2)
0.301
0.125
0.804
< 0.001
SST interval
Season
0.5 °C
1.0 °C
1.5 °C
Habitat suitability index model
140
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Fig. 2. Seasonal variability of habitat suitability index curve of sea surface temperature (SST) for Dosidicus gigas off Peruvian waters. HSI ≤ 0.2 (unsuitable habitat); 0.2 < HSI < 0.6 (common habitat); HSI ≥ 0.6 (suitable habitat); HSI ≥ 0.8 (optimal habitat).
gravity center of potential habitat happened between the SST + 4.0 °C scenarios and recent years in spring. The latitude moved long distance from 21.4°S to 35.0°S (Fig. 7).
4. Discussion 4.1. Habitat modeling method Habitat modeling approach is extensively and effectively applied to assess the impacts of climatic and environmental conditions on fish stocks (Yen et al., 2012). For example, Silva et al. (2016) and Yen et al. (2017) have successfully applied HSI model to evaluate the climate variability on habitat variations of anchovy fisheries off Chile and the skipjack Katsuwonus pelamis resources in the Western and Central Pacific Ocean. This study used the HSI model method, a very useful approach for fisheries research, to evaluate the impacts of ocean warming on an ommastrephid squid species D. gigas. Our findings presented evidence that ocean warming was likely to result in the shrinkage and southeastward range-shift in the potential habitats of D. gigas off Peruvian offshore waters. The SST was the only one predictor involved in
Fig. 3. Relationship between catch and estimated habitat suitability index (0–1 interval) for D. gigas fisheries in the Southeast Pacific Ocean off Peru.
five scenarios (Fig. 7). The initial HSI gravity centers (black points in Fig. 7) in each season commonly occupied the regions between 20°S and 24°S. The latitudinal distance of the southward shift increased from SST + 0.5 °C to SST + 4.0 °C scenarios. The southernmost location of
Fig. 4. Seasonal fishery data (catch, unit: tonnes) overlaid on the habitat suitability index map (suitable habitat with HSI ≥ 0.6) predicted based on sea surface temperature data from 2011 to 2015. Color patterns show habitat suitability range.(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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the model, however, it was an effective variable to predict habitat suitability of the D. gigas fishery off Peru. This was consistent with the studies of Hu et al. (2010), Wang et al. (2014) and Yi et al. (2014), all of which suggested that the SST was the most important variables contributing to the habitat conditions of D. gigas off Peruvian waters. In addition, the suitable range of the SST from spring to winter is in agreement with previous studies and corroborates empirical work showing the close relationship between SST and D. gigas habitat (Paulino et al., 2016). The HSI values from spring to winter were predicted over 2011–2015 in the Southeast Pacific Ocean off Peru (Fig. 4). We found that the location with high catches were highly consistent with the suitable and optimal habitat for D. gigas. The relationship between the observed catch and HSI values suggested that the HSI model developed
Table 3 Percentages of catch and fishing effort in the stratum with HSI ≥ 0.6 and with HSI ≥ 0.8 in the Southeast Pacific Ocean off Peru from spring to winter during 2011–2015. Season
Spring Summer Autumn Winter
HSI ≥ 0.6
HSI ≥ 0.8
Percentages of catch (%)
Percentages of fishing effort (%)
Percentages of catch (%)
Percentages of fishing effort (%)
79.8 94.8 73.9 61.6
76.5 93.9 74.3 68.2
76.5 75.0 55.3 51.9
72.3 74.9 53.1 58.3
Fig. 5. Spatial distribution of seasonal potential suitable habitats for Dosidicus gigas in the Southeast Pacific Ocean off Peru with scenarios of sea surface temperature (SST) increases of 0.5 °C, 1 °C, 2 °C, and 4 °C, respectively.
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Fig. 6. The percentage of suitable (HSI ≥ 0.6) and optimal (HSI ≥ 0.8) habitat occupying the fishing ground of Dosidicus gigas in the Southeast Pacific Ocean off Peruvian waters under different sea surface temperature (SST) increase scenarios. The sloped line on each plot was based on linear regression.
the optimal habitats for short-lived squid species. The outputs from the CPUE-based HSI models would yield large biases in predicting the distribution of squid habitat (Tian et al., 2009). Therefore, we replaced CPUE with the fishing effort data in developing the HSI model in this study. Based on the goodness of fit of the SI curves (Table 2) and a good agreement between the high HSI values and catch and fishing effort (Table 3 and Fig. 4), the HSI model in this study produced robust estimations in predicting the seasonal potential habitats of D. gigas in the Southeast Pacific Ocean off Peru. The fish habitat distribution is likely to vary with space and time, particularly for the highly migratory fish species (Goodyear, 2016). Our HSI model provided a seasonal distribution pattern of suitable and optimal habitats of D. gigas over 2011–2015, which were consistent with spatio-temporal distribution of D. gigas stock in the Southeast Pacific Ocean. These findings suggested that fishing effort from Chinese D. gigas fishery could be a reliable indicator to reflect squid aggregations.
by the factor of SST in this study could explain 94.2% of the variance of the catches of D. gigas in the Southeast Pacific Ocean off Peru, implying that the habitat quality had a very high correlation with the D. gigas fishery. Meanwhile, it was observed that squid catches by Chinese fishing vessels occupied only a fraction of the suitable and optimal habitats. Large areas of suitable habitats were not occupied by any fishing vessels. It did not imply that the predicted suitable habitats had no fish or big biases existed in the predictions. In the Southeast Pacific Ocean, fishing vessels from China, Japan, Peru and Chile exploited D. gigas. However, the fishing vessels from Peru and Chile only occupied the fishing ground within their Exclusive Economic Zone (EEZ). Outside the EEZ waters, squid fisheries were mainly from China and other countries. Most of the fishing vessels outside the EEZ were from China (about 200 vessels) with a small proportion from other countries (Chen et al., 2008). Therefore, the total fishing vessels with less than 230 numbers could not occupy the whole fishing ground in the Southeast Pacific Ocean though there were many regions with productive squid abundance. In the actual fisheries, fishing vessels from other countries exploited D. gigas in the waters with high HSI values predicted by our HSI model. But we did not include their data in our analysis due to the inaccessible data from their countries. In addition, fishermen tended to remain in the same fishing locations as long as the catch rates were high saving the fishermen from high fuel costs associated with travel between fishing grounds. If the catch rates decreased and caused financial loss, the fishing vessels tended to move to other ‘good’ areas that could yield higher catch rates. However, these “good” areas would normally not be very far away from the original grounds for purposes of cost saving. As an abundance index proxy, CPUE is frequently served as the input data into the HSI model for many studies (Chen et al., 2009; Song and Zhou, 2010; Chang et al., 2013). However, it may underestimate
4.2. The dynamics of Dosidicus gigas habitat in relation to climate change Except for the anthropogenic overfishing, another important reason caused the strong fluctuation of the abundance of pelagic fish species is climate variability (Tu et al., 2015). Climate-related changes in fish abundance have been typically characterized as the variability in habitat suitability and range shifts or displacement away from the local range (Yu et al., 2016). Prominent positive or negative impacts will be exposed to fish distribution and abundance through affecting the physiology and migration behavior (Worm and Tittensor, 2011). However, significant differences can be found in fish habitat change in response to climate change, which will be species-specific and yield variable winners and losers in the future. Regarding to D. gigas, some studies found that the dynamics of its 143
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Fig. 7. Location of gravity centers of habitat suitability index derived from sea surface temperature data for recent years (2011–2015) and with scenarios of SST increases of 0.5 °C, 1 °C, 2 °C, and 4 °C, respectively.
sometimes tended to show very strong year-to-year variations with regional limitation, habitat condition would change with the warm-tocool regime shift in short term (Heppell et al., 2013; Yu et al., 2016). While the habitat of D. gigas was established under the ocean warming climate, it could not be quickly returned to the original home range due to the unfavorable SST condition in the long term period (Figs. 6 and 7). Considering the critical role of D. gigas in the Southeast Pacific Ocean food webs, the habitat loss off Peru for D. gigas would yield significant impacts on the local marine ecosystem, not only for higher tropical level predators of commercially important species but also for principally lower tropical level prey species (Field et al., 2007; Zeidberg and Robison, 2007; Litz et al., 2011). With the continued warming condition in the future, further monitoring, supervision and prediction should be conducted. Management strategies also need to be made for the pelagic fish species including short-lived squid species based on the research predicting their persistence in the future.
habitat might be the intrinsic feedback structure of the population, such as dense-dependent effects, cannibalism, competition, predation (Lima, 2001; Pedraza-Garcia and Cubillos, 2008; Ibánez and Keyl, 2010; Ibánez et al., 2016). For example, increases in catches of D. gigas and Doryteuthis gahi in northern Peru are not related to thermal anomalies, but rather a pattern of drastic fluctuations in D. gahi catch sizes are seen (Ibánez et al., 2016). It is clearly found that the habitat distribution of D. gigas is also closely related to climate variability. In the Northern Hemisphere, some studies have reported D. gigas enlarged its distribution into the California Current System. Seasonal expansions of D. gigas could be largely explained by predator-prey interactions (such as Pacific hake) and ocean conditions (such as station depth, subsurface water temperature etc.) (Field et al., 2007; Litz et al., 2011). Observations have further shown that D. gigas individuals have occurred as far north as central California (Zeidberg and Robison, 2007) and the Gulf of Alaska (Brodeur et al., 2006). For the climate variability of ocean warming, it will become a potential climatic barrier faced by D. gigas in the Southeast Pacific Ocean off Peru in the long term. In this study, our findings suggest that ocean warming is likely to reduce the habitat suitability and the areas of highquality habitat of D. gigas. It raises great concerns over the possibility for habitat loss of D. gigas with the increasing SST. An increase in global ocean water temperature has significant impacts on other fish habitat distribution (Kibler et al., 2015). For example, in the Northwest Pacific Ocean, Pacific saury (Cololabis saira) and neon flying squid (Ommastrephes bartramii) was likely to underwent a poleward displacement and expanded their habitats to higher latitude in response to sea water temperature increase (Tseng et al., 2011; Xu et al., 2016). For D. gigas stock off Peru, the ENSO-linked episodic range shift of D. gigas
4.3. Limitations and implications Though this study provided new insights into ocean warming-driven variability in habitat suitability and spatial redistribution of suitable habitat for D. gigas by modeling approach, however, some limitations existed in the analysis. First, D. gigas stock is widely distributed from Alaska to southern Chile, and undergoes large migrations in both latitudinal and longitudinal direction. Our study only covered a relatively small spatial extent compared to the known range of this species. Considering the wide bathymetric and geographical distribution of D. gigas, it is naive to deduce that squids could not migrate northward or that they change their distribution with the increase in temperature. In 144
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predicted ocean warming through developmental plasticity in a tropical reef fish. Global Change Biol. 17 (4), 1712–1719. Field, J.C., Baltz, K., Phillips, A.J., Walker, W.A., 2007. Range expansion and trophic interactions of the jumbo squid, Dosidicus gigas, in the California Current. Calif. Cooperative Oceanic Fish. Investig. Rep. 48, 131–146. Goodyear, C.P., 2016. Modeling the time-varying density distribution of highly migratory species: Atlantic Blue Marlin as an example. Fish. Res. 183, 469–481. Heppell, S.S., Chesney, T., Montero-Styles, J., Graham, J., 2013. Interannual variability of Humboldt squid (Dosidicus gigas) off Oregon and Southern Washington. Calif. Cooperative Oceanic Fish. Investig. Rep. 54 (1), 180–191. Hu, Z.M., Chen, X.J., Zhou, Y.Q., Qian, W.G., Liu, B.L., 2010. Forecasting fishing ground of Dosidicus gigas based on habitat suitability index off Peru. Acta Oceanolog. Sin. 32 (5), 67–75. Ibánez, C.M., Argüelles, J., Yamashiro, C., Sepúlveda, R.D., Pardo-Gandarillas, M.C., Keyl, F., 2016. Population dynamics of the squids Dosidicus gigas (Oegopsida: Ommastrephidae) and Doryteuthis gahi (Myopsida: Loliginidae) in northern Peru. Fish. Res. 173, 151–158. Ibánez, C.M., Keyl, F., 2010. Cannibalism in cephalopods. Rev. Fish Biol. Fish. 20 (1), 123–136. Ichii, T., Mahapatra, K., Watanabe, T., Yatsu, A., Inagake, D., Okada, Y., 2002. Occurrence of jumbo flying squid Dosidicus gigas aggregations associated with the countercurrent ridge off the Costa Rica dome during 1997 El Niño and 1999 La Niña. Mar. Ecol. Prog. Ser. 231 (1), 151–166. IPCC, et al., 2007. Summary for policymakers. In: Solomon, S., Qin, D., Man-ning, M. (Eds.), Climate Change 2007: The Physical Science Basis. Working Group I Contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, and New York, NY, USA. Kibler, S.R., Tester, P.A., Kunkel, K.E., Moore, S.K., Litaker, R.W., 2015. Effects of ocean warming on growth and distribution of dinoflagellates associated with ciguatera fish poisoning in the Caribbean. Ecol. Modell. 316, 194–210. Li, G., Chen, X.J., Lei, L., Guan, W.J., 2014. Distribution of hotspots of chub mackerel based on remote-sensing data in coastal waters of China. Int. J. Remote Sens. 35 (11–12), 4399–4421. Li, G., Cao, J., Zou, X.R., Chen, X.J., Runnebaum, J., 2016. Modelling habitat suitability index for Chilean jack mackerel (Trachurus murphyi) in the South East Pacific. Fish. Res. 178, 47–60. Lima, M., 2001. The dynamics of natural populations: feedback structures in fluctuating environments. Rev. Chil. Hist. Nat. 74 (2), 317–329. Litz, M.N., Phillips, A.J., Brodeur, R.D., Emmett, R.L., 2011. Seasonal occurrences of Humboldt squid (Dosidicus gigas) in the northern California Current System. Calif. Cooperative Oceanic Fish. Investig. Rep. 52, 97–108. Malcolm, J.R., Liu, C., Miller, L.B., Allutt, T., Hansen, L., 2002. Habitats at risk: global warming and species loss in globally significant terrestrial ecosystems. Environmental Policy Collection. Markaida, U., Quiñónez-Velázquez, C., Sosa-Nishizaki, O., 2004. Age growth and maturation of jumbo squid Dosidicus gigas, (Cephalopoda: Ommastrephidae) from the Gulf of California, Mexico. Fish. Res. 66 (1), 31–47. Markaida, U., 2006. Food and feeding of jumbo squid Dosidicus gigas, in the Gulf of California and adjacent waters after the 1997–98 El Niño event. Fish. Res. 79 (1), 16–27. Medellín-Ortiz, A., Cadena-Cárdenas, L., Santana-Morales, O., 2016. Environmental effects on the jumbo squid fishery along Baja California’s west coast. Fish. Sci. 82, 1–11. Montero-Serra, I., Edwards, M., Genner, M.J., 2015. Warming shelf seas drive the subtropicalization of European pelagic fish communities. Global Change Biol. 21 (1), 144–153. Nevárez-Martínez, M.O., Méndez-Tenorio, F.J., Cervantes-Valle, C., López-Martínez, J., Anguiano-Carrasco, M.L., 2006. Growth, mortality, recruitment, and yield of the jumbo squid (Dosidicus gigas) off Guaymas, Mexico. Fish. Res. 79 (1), 38–47. Nigmatullin, C.M., Nesis, K.N., Arkhipkin, A.I., 2001. A review of the biology of the jumbo squid Dosidicus gigas, (Cephalopoda: Ommastrephidae). Fish. Res. 54 (1), 9–19. Pandolfi, J.M., Cohen, A.L., 2011. Projecting coral reef futures under global warming and ocean acidification. Science 333 (6041), 418. Paulino, C., Segura, M., Chacón, G., 2016. Spatial variability of jumbo flying squid (Dosidicus gigas) fishery related to remotely sensed SST and chlorophyll-a concentration (2004–2012). Fish. Res. 173, 122–127. Pedraza-Garcia, M., Cubillos, L.A., 2008. Population dynamics of two small pelagic fish in the central-south area off Chile: delayed density-dependence and biological interaction. Environ. Biol. Fishes 82 (2), 111–122. Reynolds, R.W., Marsico, D.C., 1993. An improved real-time global sea surface temperature analysis. J. Clim. 6, 114–119. Reynolds, R.W., 1988. A real-time global sea surface temperature analysis. J. Clim. 1, 75–86. Seibel, B.A., 2013. The jumbo squid, Dosidicus gigas, (Ommastrephidae), living in Oxygen Minimum Zones ii: blood–oxygen binding. Deep Sea Res. Part II Topical Stud. Oceanogr. 95 (6), 139–144. Silva, C., Andrade, I., Yáñez, E., Hormazabal, S., Barbieri, M., Aranis, A., Böhm, G., 2016. Predicting habitat suitability and geographic distribution of anchovy (Engraulis ringens) due to climate change in the coastal areas off Chile. Prog. Oceanogr. 146, 159–174. Song, L.M., Zhou, Y.Q., 2010. Developing an integrated habitat index for bigeye tuna (Thunnus obesus) in the Indian Ocean based on longline fisheries data. Fish. Res. 105 (2), 63–74. Stewart, J.S., Field, J.C., Markaida, U., Gilly, W.F., 2013. Behavioral ecology of jumbo squid (Dosidicus gigas) in relation to Oxygen Minimum Zones. Deep Sea Res. Part II 95, 197–208.
order to explore how the squid distributions changed with the SST increases in a large scale (such as the whole Eastern Pacific Ocean), we need more fishing effort data to support the HSI model construction. However, gridded fishing effort data with high spatial and temporal resolution from other countries (e.g., Peru, Chile and the data from the Northern Hemisphere) were quite difficult to obtain for this study. In future work, we need to obtain these data to conduct a more comprehensive evaluation of the variability of habitat suitability in relation to climate change. Second, habitat models in this study only considered one critical environmental driver (i.e., SST). It was clear that SST incorporated in the HSI model could not have a perfect 100% correlation with habitat quality and spatial occurrence of D. gigas. To truly understand the spatio-temporal distribution of D. gigas in the Southeast Pacific Ocean, future work should include more biotic and abiotic predictors that may be important in determining distributional changes of D. gigas such as salinity, oxygen minimum zone (Seibel, 2013; Stewart et al., 2013) and chlorophyll-a density (Paulino et al., 2016) amongst others. In conclusion, our study presented the spatial distribution pattern of seasonal habitat of D. gigas in the Southeast Pacific Ocean off Peruvian waters under ocean warming condition. In comparison to the recent SST on the fishing ground over 2011–2015, the potential habitat of D. gigas was likely to shrink and move southeastward under four scenarios of SST increases (+0.5 °C, +1.0 °C, +2.0 °C and +4.0 °C,). In order to reduce the negative impacts of ocean warming on habitat of D. gigas off Peru, more advanced science research and management measures should be conducted in future. Conclusions from the present study also provided important implications for other pelagic ommastrephid squids in worldwide oceans for better conservation and management. Acknowledgments This study was financially supported by the China Postdoctoral Science Foundation (2017M611612); the Doctoral Startup Scientific Research Foundation of Shanghai Ocean University (A2-0203-17100313); the Open Fund for Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources in Shanghai Ocean University (A1-020300-2009-5) and the Shanghai Universities First-Class Disciplines Project (Fisheries A). We would also like to thank four anonymous reviewers and the editor for their constructive suggestions to improve this manuscript. References Anderson, C.I.H., Rodhouse, P.G., 2001. Life cycles, oceanography and variability: ommastrephid squid in variable oceanographic environments. Fish. Res. 54 (1), 133–143. Argüelles, J., Rodhouse, P.G., Villegas, P., Castillo, G., 2001. Age, growth and population structure of the jumbo flying squid Dosidicus gigas in Peruvian waters. Fish. Res. 54 (1), 51–61. Bograd, S.J., Foley, D.G., 2013. Predicted habitat shifts of pacific top predators in a changing climate. Nat. Clim. Change 3 (3), 234–238. Brodeur, R.D., Ralston, S., Emmett, R.L., Trudel, M., Auth, T.D., Phillips, A.J., 2006. Anomalous pelagic nekton abundance, distribution, and apparent recruitment in the northern California current in 2004 and 2005. Geophys. Res. Lett. 33 (22), 22–28. Cao, J., Chen, X.J., Chen, Y., 2009. Influence of surface oceanographic variability on abundance of the western winter-spring cohort of neon flying squid Ommastrephes bartramii in the NW Pacific Ocean. Mar. Ecol. Prog. Ser. 381, 119–127. Carr, M.E., Strub, P.T., Thomas, A.C., Blanco, J.L., 2002. Evolution of 1996–1999 La Niña and El Niño conditions off the western coast of South America: a remote sensing perspective. J. Geophys. Res. Oceans 107 (12), 1–16. Chang, Y.J., Sun, C.L., Chen, Y., Yeh, S.Z., Dinardo, G., Su, N.J., 2013. Modelling the impacts of environmental variation on the habitat suitability of swordfish, Xiphias gladius, in the Equatorial Atlantic Ocean. ICES J. Mar. Sci. 70 (5), 1000–1012. Chen, X.J., Liu, B.L., Chen, Y., 2008. A review of the development of Chinese distantwater squid jigging fisheries. Fish. Res. 89 (3), 211–221. Chen, X.J., Li, G., Feng, B., Tian, S.Q., 2009. Habitat suitability index of chub mackerel (Scomber japonicus) from July to September in the East China Sea. J. Oceanogr. 65 (1), 93–102. Christian, J.R., Holmes, J., 2016. Changes in albacore tuna habitat in the Northeast Pacific Ocean under anthropogenic warming. Fish. Oceanogr. 25 (5), 544–554. Donelson, J.M., Munday, P.L., Mccormick, M.I., Nilsson, G.E., 2015. Acclimation to
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temperature increase on the potential habitat of Ommastrephes bartramii in the Northwest Pacific Ocean. Acta Oceanolog. Sin. 35 (2), 109–116. Xue, Y., Guan, L., Tanaka, K., Li, Z., Chen, Y., Ren, Y., 2017. Evaluating effects of rescaling and weighting data on habitat suitability modeling. Fish. Res. 188, 84–94. Yamano, H., Sugihara, K., Nomura, K., 2011. Rapid poleward range expansion of tropical reef corals in response to rising sea surface temperatures. Geophys. Res. Lett. 38 (4), 155–170. Yen, K.W., Lu, H.J., Chang, Y., Lee, M.A., 2012. Using remote-sensing data to detect habitat suitability for yellowfin tuna in the Western and Central Pacific Ocean. Int. J. Remote Sens. 33 (23), 7507–7522. Yen, K.W., Wang, G.H., Lu, H.J., 2017. Evaluating habitat suitability and relative abundance of skipjack (Katsuwonus pelamis) in the Western and Central Pacific during various El Niño events. Ocean Coastal Manage. 139, 153–160. Yi, Q., Chen, X.J., Yu, W., Liu, B.L., Li, J.H., Fang, Z., 2014. A comparison of habitat of Dosidicus gigas in the fishing ground off Chile and Peru based on information gain technique. J. Shanghai Ocean Univ. 23 (2), 272–278. Yu, W., Chen, X.J., Yi, Q., Tian, S.Q., 2015. A review of interaction between neon flying squid (Ommastrephes bartramii) and oceanographic variability in the North Pacific Ocean. J. Ocean Univ. China 14 (4), 739–748. Yu, W., Yi, Q., Chen, X.J., Chen, Y., 2016. Modelling the effects of climate variability on habitat suitability of jumbo flying squid, Dosidicus gigas, in the Southeast Pacific Ocean off Peru. ICES J. Mar. Sci. 73 (2), 239–249. Zeidberg, L.D., Robison, B.H., 2007. Invasive range expansion by the Humboldt squid, Dosidicus gigas, in the Eastern North Pacific. Proc. Natl. Acad. Sci. 104 (31), 12948–12950.
Thomasma, L.E., Drummer, T.D., Peterson, R.O., 1991. Testing the habitat suitability index model for the fisher. Wildl. Soc. Bull. 19 (3), 291–297. Tian, S.Q., Chen, X.J., Chen, Y., Xu, L.X., Dai, X.J., 2009. Evaluating habitat suitability indices derived from CPUE and fishing effort data for Ommatrephes bartramii, in the Northwestern Pacific Ocean. Fish. Res. 95 (2–3), 181–188. Tseng, C.T., Sun, C.L., Yeh, S.Z., Chen, S.C., Su, W.C., Liu, D.C., 2011. Influence of climate-driven sea surface temperature increase on potential habitats of the Pacific saury (Cololabis saira). ICES J. Mar. Sci. 68 (6), 1105–1113. Tu, Chen-Yi, Tian, Y., Hsieh, Chih-Hao., 2015. Effects of climate on temporal variation in the abundance and distribution of the demersal fish assemblage in the Tsushima warm current region of the Japan Sea. Fish. Oceanogr. 24 (2), 177–189. Veit, R.R., Pyle, P., Mcgowan, J.A., 1996. Ocean warming and long-term change in pelagic bird abundance within the California Current System. Mar. Ecol. Prog. Ser. 139 (1–3), 11–18. Waluda, C.M., Rodhouse, P.G., 2006. Remotely sensed mesoscale oceanography of the Central Eastern Pacific and recruitment variability in Dosidicus gigas. Mar. Ecol. Prog. Ser. 310, 25–32. Waluda, C.M., Yamashiro, C., Rodhouse, P.G., 2006. Influence of the ENSO cycle on the light-fishery for Dosidicus gigas, in the Peru Current: an analysis of remotely sensed data. Fish. Res. 79 (1), 56–63. Wang, J.T., Gao, F., Lei, L., Guan, W.J., Zhou, J.W., Ye, S.J., Chen, X.J., 2014. Modeling of fishing grounds for Dosidicus gigas based on BP neural network in southeast Pacific. Mar. Fish. 36 (2), 131–137. Worm, B., Tittensor, D.P., 2011. Range contraction in large pelagic predators. Proc. Natl. Acad. Sci. 108 (29), 11942–11947. Xu, J., Chen, X.J., Chen, Y., Ding, Q., Tian, S.Q., 2016. The effect of sea surface
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Ocean warming-induced range-shifting of potential habitat for jumbo flying squid Dosidicus gigas in the Southeast Pacific Ocean off Peru Wei Yua,b,c,d, Xinjun Chena,b,c,d,
T
⁎
a
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China c Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China d Collaborative Innovation Center for Distant-Water Fisheries, Shanghai 201306, China b
A R T I C L E I N F O
A B S T R A C T
Handled by B. Arara
Climate-induced ocean warming may have significant influences on abundance and geographic distribution of fish species and cause range shifts and/or expansions in their habitats. The jumbo flying squid Dosidicus gigas in the Eastern Pacific Ocean is an ecologically and commercially important species. With 1-year short lifespan, D. gigas is significantly affected by climatic and environmental variability. This study used the logbook data of the 2011–2015 Chinese squid-jigging fishery off Peruvian waters, coupled with sea surface temperature (SST) data, to explore the variations in seasonal habitat suitability and habitat distribution pattern for D. gigas in the Southeast Pacific Ocean under five scenarios: SST in recent years (2011–2015) and with 0.5 °C, 1.0 °C, 2.0 °C and 4.0 °C increases in relation to the climate variability. A fishing effort-based habitat suitability index (HSI) model was developed to estimate habitat quality of D. gigas and spatial distribution of suitable and optimal habitats in relation to ocean warming. Results indicated that obvious seasonal variations were observed in the SST on the fishing ground of D. gigas with spatial variability. HSI modeling approach in this study essentially captured habitat characteristics of D. gigas from spring to winter during 2011–2015. SST increase scenarios revealed that seasonal habitat suitability of D. gigas significantly reduced, and the percentages of suitable (HSI ≥ 0.6) and optimal (HSI ≥ 0.8) habitats occupying the fishing ground dramatically decreased due to the rising SST. Moreover, an obvious southeastward movement was observed in gravity centers of D. gigas habitat under the above-mentioned five scenarios. Our findings suggested that ocean warming was likely to result in the shrinkage and southeastward range-shift in the potential high-quality habitats of D. gigas in the Southeast Pacific Ocean off Peruvian waters.
Keywords: Dosidicus gigas Habitat suitability index Rising sea surface temperature Potential habitat Distribution shift
1. Introduction Climate-induced recent ocean warming causes significant changes in the marine species, with extremely strong impacts on potential habitat of marine species in space and time (Malcolm et al., 2002). Evidence has shown that birds, coral reefs and large marine predator species react to these ocean water temperature changes with rapid and extensive shifts in their geographical distributions and stock abundance (Veit et al., 1996; Pandolfi and Cohen, 2011; Bograd and Foley, 2013). For example, based on the 80 years of Japanese in situ sea surface temperature (SST) data, four major reef corals, the habitat-forming species in the tropical areas, have shown poleward expansions since the 1930s (Yamano et al., 2011). For fish species, sea surface water temperature increases may strongly drive in changes in their distribution and abundance due to the temperature-related variability in the habitat
⁎
quality and the occurrence of range shifts in their habitats (Donelson et al., 2015; Montero-Serra et al., 2015). However, these responses are likely to vary with species due to their different life histories. ommastrephid squid, a group with fast generation times, is particularly sensitive to climatic and environmental variability at different time scales (Anderson and Rodhouse, 2001). Adverse climatic and environmental conditions can cause mass loss in areas of suitable habitats for squids in a short time and consequently induce stock decline or fishery collapse. Squids are also temperature-dependent animals whose life cycle is closely related to ocean water surface temperature (Yu et al., 2015). The jumbo flying squid Dosidicus gigas is an ecologically important species in pelagic marine ecosystem due to its critical role in food-webs and trophic dynamics (Markaida, 2006). Dosidicus gigas exhibits northto-south and inshore-to-offshore round-trip migration patterns (Nigmatullin et al., 2001). Life history of D. gigas is complex but plastic,
Corresponding author at: College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China. E-mail address: [email protected] (X. Chen).
https://doi.org/10.1016/j.fishres.2018.02.016 Received 7 July 2017; Received in revised form 7 February 2018; Accepted 17 February 2018 Available online 20 March 2018 0165-7836/ © 2018 Elsevier B.V. All rights reserved.
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fisheries in this study were primarily distributed in the Southeast Pacific Ocean off Peruvian waters. The longitudinal and latitudinal gravity centers of fishing effort were calculated for D. gigas fishery (Li et al., 2014). Furthermore, the CPUE (catch per unit effort) within a 0.5° × 0.5° fishing grid for this squid species in each season was calculated by the following equation (Cao et al., 2009):
such as fast growth, high fecundity and strong movement capacity (Argüelles et al., 2001; Markaida et al., 2004). With huge commercial values, D. gigas supports large-scale fisheries in the Eastern Pacific Ocean (Nevárez-Martínez et al., 2006). The Peruvian offshore water is an area with high-level biological productivity. Due to its location in the Humboldt Current System combined with complicated currents and coastal wind-driven upwelling (Carr et al., 2002). Therefore, this area is a productive habitat for D. gigas and is one of the most important fishing grounds globally. The biomass of D. gigas and commercial catches account for a large proportion of all fisheries in the Humboldt Current region (Waluda et al., 2006; Yu et al., 2016). However, annual catches of D. gigas tended to be fluctuant from year to year (Chen et al., 2008). With only 1-year short lifespan, D. gigas is significantly affected by climatic and environmental variability (Medellín-Ortiz et al., 2016). Therefore, understanding the linkage between D. gigas stock and climate variability is important for better fisheries management and accurate fishing forecasts. Increasingly many studies have focused on evaluating impacts of the El Niño-Southern Oscillation (ENSO) event on spatial distribution and abundance of D. gigas (Waluda et al., 2006; Ichii et al., 2002). For instance, Waluda and Rodhouse (2006) reported that abundance of D. gigas was positively correlated with SST between 17 °C and 22 °C in July during fishing season, and with SST between 24 °C and 28 °C in September prior to fishing season. Squid abundance was also linked to mesoscale variability in oceanographic conditions (i.e., ENSO), with weaken upwelling off Peru during El Niño of 1997–1998 leading to low catches of D. gigas (Waluda and Rodhouse, 2006). Yu et al. (2016) suggested that habitat quality of D. gigas off Peru would be poor in El Niño years and favorable in La Niña years. Meanwhile, the El Niño conditions might yield enlarged unsuitable habitat and cause reduced catches. Recently, some researchers have paid increasing attentions to ocean warming-induced redistribution of potential habitat for fish species in the North Pacific Ocean (Christian and Holmes, 2016). However, there are hardly any studies investigating the influence of ocean warming on habitat quality of squids especially in the Southeast Pacific Ocean. Water temperature is usually considered as the dominant abiotic environmental factor in regulating distribution and abundance of squid species (Yu et al., 2015). Given that surface water temperature may define the limits of the habitat of squids, ocean warming events are likely to lead to individual movements and further range-shift of potential habitat of squid species like D. gigas. Climate change scenarios have predicted an average surface water temperature increase of 1.08–4.08 °C by 2100 (IPCC, 2007). We explored the possible changes of habitat of D. gigas in the Southeast Pacific Ocean off Peru in relation to this ocean warming. In this study, we used the logbook data of the 2011–2015 Chinese squid-jigging fishery off Peruvian waters, coupled with the SST data, to examine variations in seasonal habitat suitability and suitable and optimal habitat distributions for D. gigas in the Southeast Pacific Ocean under five ocean warming scenarios: SST in recent years (2011–2015) and with 0.5 °C, 1.0 °C, 2.0 °C and 4.0 °C increases. A fishing effort-based habitat suitability index (HSI) model was developed to estimate the habitat quality and spatial distribution pattern of suitable and optimal habitats in relation to above-mentioned five ocean warming scenarios.
CPUE =
∑ Catch ∑ Fishing effort
(1)
where ΣCatch was the sum of catches for all the fishing vessels within a 0.5° × 0.5° fishing grid per month; and ΣFishing effort was the sum of fishing efforts for all the fishing vessels within a 0.5° × 0.5° fishing grid per month. 2.2. Sea surface temperature data Sea surface temperature (SST) was considered as a critical environmental driver in regulating the distribution and abundance of D. gigas stock (Paulino et al., 2016; Yu et al., 2016). Thus, it was considered as a good proxy to characterize the habitat of D. gigas. In this study, the monthly SST data was sourced from the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation (OI) SST Version 2 with spatial resolution of 0.25° × 0.25° (https://www. ncdc.noaa.gov/oisst/data-access). The OI analysis was constructed by combining observations from different platforms (satellites, ships, buoys) on a regular global grid. Before the analysis was computed, the satellite data was adjusted for biases using the method from Reynolds (1988) and Reynolds and Marsico (1993). The SST data were grouped on a 0.5° × 0.5° latitude/longitude grid to match with the spatial resolution of the fishery data. 2.3. Habitat suitability modeling Habitat suitability index (HSI) model is one of the most important and effective tools to identify and explore fish habitats (Thomasma et al., 1991; Xue et al., 2017). Generally, by initially determining the suitability index (SI) as a function of one or more critical environmental factors, an empirical HSI model is then constructed by combining all input factors and further describing the suitability of a give habitat of target species (Li et al., 2016). The HSI values range from 0 to 1, indicating a poor habitat and the most environmental-favorable habitat, respectively. In this study, the HSI empirical model would present seasonal distribution of potential habitat of D. gigas in the Southeast Pacific Ocean off Peru. The season in the Southern Hemisphere was defined as follows: spring (September–November), summer (December–February), autumn (March–May), and winter (June–August). Regarding the HSI model, the first step was to quantify the statistical or parameterized functional relationships between environmental variable and the probability of fish occurrence. For short-lived squid species, CPUE-based HSI model may overestimate the ranges of optimal habitats and underestimate variations in the spatial distribution of optimal habitats (Tian et al., 2009). Therefore, fishing effort was a better proxy than CPUE to estimate the probability of squid occurrence. The SST was used as the environmental factor in the model inputs. As the response factor, fishing effort was correlated with the SST by histogram analysis. All the 3669 fishing points were divided by season. Then, the total number of fishing efforts in each SST class interval was determined for each season. Based on the above analysis, we further determined the SI values and output the environmental preference for D. gigas. The SI value at the i class interval of SST from spring to winter was calculated using the frequency of total fishing efforts at the i class interval divided by the maximum frequency of the total fishing efforts. The formula was established by the equation as follows:
2. Materials and methods 2.1. Fisheries data collection Commercial fisheries data for D. gigas grouped by 0.5° × 0.5° grid cell and by month were obtained from the Chinese Squid-jigging Science and Technology Group of Shanghai Ocean University. Data including 3669 observations from January to December during 2011–2015 were used in the analysis. The data contained fishing effort (days fished), the location of fishing ground (latitude and longitude in degrees) and catch (unit: tonnes). Fishing locations for Chinese D. gigas
SIi = 138
Fishing efforti Fishing effort max
(2)
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where i was the index representing the ith class interval of the SST; Fishing efforti was the total fishing efforts at the ith class interval; and Fishing effortmax was the maximum fishing efforts at a certain class interval. The second step was to develop the SI model, which was continuous and ranged from 0 to 1, based on the observed SI values calculated from the Eq. (2). The SI model of SST in each season was fitted in the form of:
SI = a× exp ⎡−⎛ ⎢ ⎣ ⎝
SST − b ⎞ 2⎤ c ⎠ ⎥ ⎦
Table 1 Location of Chinese squid-jigging vessels for Dosidicus gigas fishery from spring to winter in the Southeast Pacific Ocean off Peru from 2011 to 2015. Latitudinal gravity (°S)
Spring Summer Autumn Winter
20.0 20.0 20.0 20.0
8.5 8.0 8.0 8.0
80.7 80.4 79.9 81.5
14.7 16.3 16.5 13.5
Both the total catch and fishing effort of D. gigas in the Southeast Pacific Ocean off Peruvian offshore waters decreased from spring to autumn and then increased in winter. The catch and fishing effort varied from 20,175 tonnes to 93,614 tonnes, and from 6991 to 16,387 days, respectively. The average CPUE over the years 2011–2015 was high (> 4.5 t/d) in spring and summer but relatively low (< 4.0 t/ d) in autumn and winter. Additionally, the fishing location for Chinese squid-jigging fishing vessels was between 8°S and 20°S off Peruvian waters (Table 1). The seasonal longitudinal and latitudinal gravity centers varied from 79.9°W to 81.5°W, and from 13.5°S to 16.5°S, respectively (Table 1). Furthermore, the seasonal average SST was generally low in spring and summer. Distinct spatial variations in SST were observed in each season on the fishing ground of D. gigas off Peru (Fig. 1). The fishing ground was characterized with large cool water mass below 15 °C in the southeastern regions in spring and winter. However, during summer and autumn, the fishing ground was featured by warm SST, especially warm water with SST higher than 25 °C occupying the northwestern areas on the fishing ground of D. gigas (Fig. 1). 3.2. Habitat suitability index model Suitability index curves between the fishing effort and SST were modeled for each season, as shown in Table 2 and plotted in Fig. 2. It was observed that the SI models from spring to winter developed by 1.0 °C SST class interval were all significant (P < 0.001) with highest correlation coefficients and lowest Sum of Squares for Error through the statistical assessment (Table 2). Therefore, the SI models with 1.0 °C class interval were chosen to predict the habitat suitability of D. gigas in the following analysis. Through estimation, the environmental preference of D. gigas (SST corresponding to SI ≥ 0.6) in the Southeast Pacific Ocean varied seasonally, as shown by the SI curves (Fig. 2). In spring, the suitable range of SST varied from 16.9 °C to 18.8 °C. Suitable SI for D. gigas in summer was found to be related to temperature between 19.6 °C and 22.7 °C. The suitable temperature range for D. gigas was approximately 20.4–23.0 °C in autumn and 17.2–20.4 °C in winter (Fig. 2). Through the following methods, we evaluated the performance of the developed HSI model. The HSI values over 2011–2015 were initially estimated by the model output. The relationship between the observed catch and HSI values was evaluated based on the adjusted R2. Results indicated that the HSI model explained 94.2% (P < 0.001) of the variance of the 10-point scale complied from the catches of D. gigas off Peru (Fig. 3). We then overlaid the predicted HSI contour maps with the catches in each season (Fig. 4). It was observed that most of the catches were distributed in the suitable habitat of D. gigas (HSI ≥ 0.6) from spring to winter (Fig. 4). Catches tended to increase with the HSI values. Secondly, a large proportion of catch and fishing effort occupied the class interval with HSI ≥ 0.6, the percentages of catch and fishing effort tended to be more than 70% in spring and autumn, higher than
∑ (Longitude(i,s) × HSI(i,s) )
∑ (Latitude(i,s) × HSI(i,s) ) ∑ HSI(i,s)
Longitudinal gravity (°W)
3.1. Seasonal variation of Chinese D. gigas fishery and SST on the fishing ground
In order to examine the impacts of ocean warming on D. gigas habitat, we used five scenarios of the seasonal SST increases (SST, SST + 0.5 °C, SST + 1.0 °C, SST + 2.0 °C, SST + 4.0 °C) (IPCC, 2007), which were developed by the global climate simulation under several emission scenarios (A1FI, A1B, A1T, A2, B1, and B2) from the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC, 2007), to forecast the temporal variability in the habitat suitability and spatial changes in the gravity centers of HSI values of D. gigas in the next 100 years in the Southeast Pacific Ocean. The percentages of the suitable (HSI ≥ 0.6) and optimal (HSI ≥ 0.8) habitat of D. gigas accounting for the fishing ground were determined. The longitudinal and latitudinal gravity centers of HSI values of D. gigas from spring to winter under each scenarios (LONGHSI and LATGHSI) were calculated using the following equations (Li et al., 2016):
LATGHSI =
Northernmost location (°S)
3. Results
2.4. Examination of impacts of ocean warming on D. gigas habitat
∑ HSI(i,s)
Southernmost location (°S)
(3)
where a, b and c were the estimated model parameters using the least squares estimate to minimize the residual between SI observations and SI predictions (Yen et al., 2012, 2017); SST was the class interval value. The SI model of SST for each season was implemented by the Matlab software. Due to the SST class interval is a construct of the analysis, in order to evaluate the data variability and model uncertainty, the SST class interval was defined as 0.5 °C, 1.0 °C and 1.5 °C, respectively. The SI model developed by each SST class interval was further compared. The most suitable SI model was chosen to predict the habitat suitability of D. gigas. After developing the seasonal SI curves, we used the seasonal SST over 2011–2015 combining with the Eq. (3) to estimate the HSI values of D. gigas fishing ground. In order to explore the high HSI class intervals (HSI values higher than 0.6) varied with the ocean warming, the area with HSI ≥ 0.6 and with HSI ≥ 0.8 was defined as the suitable and optimal habitat for D. gigas, respectively (Tian et al., 2009; Yu et al., 2016). Additionally, the area with HSI ≤ 0.2 and with 0.2 < HSI < 0.6 was defined as unsuitable habitat and common habitat, respectively. Model performance was finally evaluated by comparing the percentage of catch and fishing effort in the class interval with HSI ≥ 0.6 and with HSI ≥ 0.8. To further assess the model performance for predicting the suitable and optimal habitat of D. gigas in the Southeast Pacific Ocean off Peru, the seasonal predicted maps of HSI were then overlain with fishery data (catch data) over 2011–2015. The goodness of fit of the above linear relationship between the catch and HSI values was assessed based on the adjusted R2 to validate the HSI model in predicting potential D. gigas habitats.
LONGHSI =
Season
(4)
(5)
where Longitude(i,s) was the longitude of the ith fishing grid in season s; Latitude(i,s) was the latitude of the ith fishing grid in season s; HSI (i,s) was the HSI value within the ith fishing grid in season s. 139
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Fig. 1. Spatial distribution of the seasonal averaged sea surface temperature (SST) on the fishing ground of Dosidicus gigas from spring to winter over 2011–2015 in the Southeast Pacific Ocean.
four seasons, implying that the areas of D. gigas habitat dramatically shrank. Moreover, an obvious southward movement was observed in the suitable habitat location under the above-mentioned four scenarios. The temporal and spatial variability of potential habitat of D. gigas were further quantified by examining the area of suitable and optimal habitats of D. gigas and gravity centers of HSI values on the fishing ground under different SST increase scenarios (Figs. 6 and 7). During the four fishing seasons, suitable and optimal habitats for D. gigas displayed linear decrease from recent years to the climate change scenarios of a 4.0 °C increases in SST. For example, the percentage of suitable habitat in spring was 22.2%, 20.1%, 17.5%, 11.8%, 9.69% under SST, SST + 0.5 °C, SST + 1.0 °C, SST + 2.0 °C and SST + 4.0 °C scenarios, respectively (Fig. 6). The gravity centers of the HSI values for each season showed an obvious southeastern latitudinal shift under the
90% in summer and above 60% in winter. Within the class interval with HSI ≥ 0.8, high percentages of the catch and fishing effort also occurred (Table 3). 3.3. Variability in habitat suitability and distribution of potential squid habitat in relation to ocean warming A comparing of the squid habitat under the normal SST condition in recent years during 2011–2015 (Figs. 4 and 5) showed the seasonal potential habitat of D. gigas (HSI ≥ 0.6) under the scenarios of 0.5 °C, 1.0 °C, 2.0 °C and 4.0 °C increases in SST. Due to the SST increases, the habitat suitability of D. gigas clearly decreased in each season. Potential suitable and optimal habitats occupying the fishing ground of D. gigas were gradually concentrated into narrower horizontal belt among the
Table 2 Estimation of statistical parameters for the habitat suitability index (HSI) model with three different SST class intervals for Dosidicus gigas from spring to winter in the Southeast Pacific Ocean off Peru. SSE: Sum of Squares for Error; RMSE: Root Mean Squared Error. SSE
RMSE
R2
P
Spring
HSI = 0.895 × exp( −((SST −
18.154)/1.483)2)
0.090
0.008
0.941
< 0.001
Summer
HSI = 0.597 × exp( −((SST − 21.444)/2.963)2)
0.785
0.056
0.436
> 0.050
Autumn
HSI = 0.673 × exp( −((SST − 21.883)/1.822)2)
0.270
0.019
0.776
< 0.010
Winter
HSI = 0.982 × exp( −((SST − 18.976)/2.358)2)
0.487
0.030
0.815
< 0.010
Spring
HSI = 0.991 × exp( −((SST − 17.931)/1.438)2)
0.002
0.000
0.998
< 0.001
Summer
HSI = 0.934 × exp( −((SST − 21.091)/2.472)2)
0.049
0.008
0.948
< 0.001
Autumn
HSI = 0.976 × exp( −((SST − 21.740)/1.809)2)
0.021
0.003
0.981
< 0.001
Winter
HSI = 1.100 × exp( −((SST − 18.738)/2.189)2)
0.210
0.026
0.880
< 0.001
Spring
HSI = 1.041 × exp( −((SST − 17.715)/1.672)2)
0.003
0.001
0.997
< 0.001
Summer
HSI = 0.951 × exp( −((SST − 20.677)/3.028)2)
0.071
0.024
0.887
< 0.001
Autumn
HSI = 1.003 × exp( −((SST − 21.287)/1.720)2)
0.015
0.004
0.982
< 0.001
Winter
HSI = 1.092 × exp( −((SST − 18.567)/2.218)2)
0.301
0.125
0.804
< 0.001
SST interval
Season
0.5 °C
1.0 °C
1.5 °C
Habitat suitability index model
140
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Fig. 2. Seasonal variability of habitat suitability index curve of sea surface temperature (SST) for Dosidicus gigas off Peruvian waters. HSI ≤ 0.2 (unsuitable habitat); 0.2 < HSI < 0.6 (common habitat); HSI ≥ 0.6 (suitable habitat); HSI ≥ 0.8 (optimal habitat).
gravity center of potential habitat happened between the SST + 4.0 °C scenarios and recent years in spring. The latitude moved long distance from 21.4°S to 35.0°S (Fig. 7).
4. Discussion 4.1. Habitat modeling method Habitat modeling approach is extensively and effectively applied to assess the impacts of climatic and environmental conditions on fish stocks (Yen et al., 2012). For example, Silva et al. (2016) and Yen et al. (2017) have successfully applied HSI model to evaluate the climate variability on habitat variations of anchovy fisheries off Chile and the skipjack Katsuwonus pelamis resources in the Western and Central Pacific Ocean. This study used the HSI model method, a very useful approach for fisheries research, to evaluate the impacts of ocean warming on an ommastrephid squid species D. gigas. Our findings presented evidence that ocean warming was likely to result in the shrinkage and southeastward range-shift in the potential habitats of D. gigas off Peruvian offshore waters. The SST was the only one predictor involved in
Fig. 3. Relationship between catch and estimated habitat suitability index (0–1 interval) for D. gigas fisheries in the Southeast Pacific Ocean off Peru.
five scenarios (Fig. 7). The initial HSI gravity centers (black points in Fig. 7) in each season commonly occupied the regions between 20°S and 24°S. The latitudinal distance of the southward shift increased from SST + 0.5 °C to SST + 4.0 °C scenarios. The southernmost location of
Fig. 4. Seasonal fishery data (catch, unit: tonnes) overlaid on the habitat suitability index map (suitable habitat with HSI ≥ 0.6) predicted based on sea surface temperature data from 2011 to 2015. Color patterns show habitat suitability range.(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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the model, however, it was an effective variable to predict habitat suitability of the D. gigas fishery off Peru. This was consistent with the studies of Hu et al. (2010), Wang et al. (2014) and Yi et al. (2014), all of which suggested that the SST was the most important variables contributing to the habitat conditions of D. gigas off Peruvian waters. In addition, the suitable range of the SST from spring to winter is in agreement with previous studies and corroborates empirical work showing the close relationship between SST and D. gigas habitat (Paulino et al., 2016). The HSI values from spring to winter were predicted over 2011–2015 in the Southeast Pacific Ocean off Peru (Fig. 4). We found that the location with high catches were highly consistent with the suitable and optimal habitat for D. gigas. The relationship between the observed catch and HSI values suggested that the HSI model developed
Table 3 Percentages of catch and fishing effort in the stratum with HSI ≥ 0.6 and with HSI ≥ 0.8 in the Southeast Pacific Ocean off Peru from spring to winter during 2011–2015. Season
Spring Summer Autumn Winter
HSI ≥ 0.6
HSI ≥ 0.8
Percentages of catch (%)
Percentages of fishing effort (%)
Percentages of catch (%)
Percentages of fishing effort (%)
79.8 94.8 73.9 61.6
76.5 93.9 74.3 68.2
76.5 75.0 55.3 51.9
72.3 74.9 53.1 58.3
Fig. 5. Spatial distribution of seasonal potential suitable habitats for Dosidicus gigas in the Southeast Pacific Ocean off Peru with scenarios of sea surface temperature (SST) increases of 0.5 °C, 1 °C, 2 °C, and 4 °C, respectively.
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Fig. 6. The percentage of suitable (HSI ≥ 0.6) and optimal (HSI ≥ 0.8) habitat occupying the fishing ground of Dosidicus gigas in the Southeast Pacific Ocean off Peruvian waters under different sea surface temperature (SST) increase scenarios. The sloped line on each plot was based on linear regression.
the optimal habitats for short-lived squid species. The outputs from the CPUE-based HSI models would yield large biases in predicting the distribution of squid habitat (Tian et al., 2009). Therefore, we replaced CPUE with the fishing effort data in developing the HSI model in this study. Based on the goodness of fit of the SI curves (Table 2) and a good agreement between the high HSI values and catch and fishing effort (Table 3 and Fig. 4), the HSI model in this study produced robust estimations in predicting the seasonal potential habitats of D. gigas in the Southeast Pacific Ocean off Peru. The fish habitat distribution is likely to vary with space and time, particularly for the highly migratory fish species (Goodyear, 2016). Our HSI model provided a seasonal distribution pattern of suitable and optimal habitats of D. gigas over 2011–2015, which were consistent with spatio-temporal distribution of D. gigas stock in the Southeast Pacific Ocean. These findings suggested that fishing effort from Chinese D. gigas fishery could be a reliable indicator to reflect squid aggregations.
by the factor of SST in this study could explain 94.2% of the variance of the catches of D. gigas in the Southeast Pacific Ocean off Peru, implying that the habitat quality had a very high correlation with the D. gigas fishery. Meanwhile, it was observed that squid catches by Chinese fishing vessels occupied only a fraction of the suitable and optimal habitats. Large areas of suitable habitats were not occupied by any fishing vessels. It did not imply that the predicted suitable habitats had no fish or big biases existed in the predictions. In the Southeast Pacific Ocean, fishing vessels from China, Japan, Peru and Chile exploited D. gigas. However, the fishing vessels from Peru and Chile only occupied the fishing ground within their Exclusive Economic Zone (EEZ). Outside the EEZ waters, squid fisheries were mainly from China and other countries. Most of the fishing vessels outside the EEZ were from China (about 200 vessels) with a small proportion from other countries (Chen et al., 2008). Therefore, the total fishing vessels with less than 230 numbers could not occupy the whole fishing ground in the Southeast Pacific Ocean though there were many regions with productive squid abundance. In the actual fisheries, fishing vessels from other countries exploited D. gigas in the waters with high HSI values predicted by our HSI model. But we did not include their data in our analysis due to the inaccessible data from their countries. In addition, fishermen tended to remain in the same fishing locations as long as the catch rates were high saving the fishermen from high fuel costs associated with travel between fishing grounds. If the catch rates decreased and caused financial loss, the fishing vessels tended to move to other ‘good’ areas that could yield higher catch rates. However, these “good” areas would normally not be very far away from the original grounds for purposes of cost saving. As an abundance index proxy, CPUE is frequently served as the input data into the HSI model for many studies (Chen et al., 2009; Song and Zhou, 2010; Chang et al., 2013). However, it may underestimate
4.2. The dynamics of Dosidicus gigas habitat in relation to climate change Except for the anthropogenic overfishing, another important reason caused the strong fluctuation of the abundance of pelagic fish species is climate variability (Tu et al., 2015). Climate-related changes in fish abundance have been typically characterized as the variability in habitat suitability and range shifts or displacement away from the local range (Yu et al., 2016). Prominent positive or negative impacts will be exposed to fish distribution and abundance through affecting the physiology and migration behavior (Worm and Tittensor, 2011). However, significant differences can be found in fish habitat change in response to climate change, which will be species-specific and yield variable winners and losers in the future. Regarding to D. gigas, some studies found that the dynamics of its 143
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Fig. 7. Location of gravity centers of habitat suitability index derived from sea surface temperature data for recent years (2011–2015) and with scenarios of SST increases of 0.5 °C, 1 °C, 2 °C, and 4 °C, respectively.
sometimes tended to show very strong year-to-year variations with regional limitation, habitat condition would change with the warm-tocool regime shift in short term (Heppell et al., 2013; Yu et al., 2016). While the habitat of D. gigas was established under the ocean warming climate, it could not be quickly returned to the original home range due to the unfavorable SST condition in the long term period (Figs. 6 and 7). Considering the critical role of D. gigas in the Southeast Pacific Ocean food webs, the habitat loss off Peru for D. gigas would yield significant impacts on the local marine ecosystem, not only for higher tropical level predators of commercially important species but also for principally lower tropical level prey species (Field et al., 2007; Zeidberg and Robison, 2007; Litz et al., 2011). With the continued warming condition in the future, further monitoring, supervision and prediction should be conducted. Management strategies also need to be made for the pelagic fish species including short-lived squid species based on the research predicting their persistence in the future.
habitat might be the intrinsic feedback structure of the population, such as dense-dependent effects, cannibalism, competition, predation (Lima, 2001; Pedraza-Garcia and Cubillos, 2008; Ibánez and Keyl, 2010; Ibánez et al., 2016). For example, increases in catches of D. gigas and Doryteuthis gahi in northern Peru are not related to thermal anomalies, but rather a pattern of drastic fluctuations in D. gahi catch sizes are seen (Ibánez et al., 2016). It is clearly found that the habitat distribution of D. gigas is also closely related to climate variability. In the Northern Hemisphere, some studies have reported D. gigas enlarged its distribution into the California Current System. Seasonal expansions of D. gigas could be largely explained by predator-prey interactions (such as Pacific hake) and ocean conditions (such as station depth, subsurface water temperature etc.) (Field et al., 2007; Litz et al., 2011). Observations have further shown that D. gigas individuals have occurred as far north as central California (Zeidberg and Robison, 2007) and the Gulf of Alaska (Brodeur et al., 2006). For the climate variability of ocean warming, it will become a potential climatic barrier faced by D. gigas in the Southeast Pacific Ocean off Peru in the long term. In this study, our findings suggest that ocean warming is likely to reduce the habitat suitability and the areas of highquality habitat of D. gigas. It raises great concerns over the possibility for habitat loss of D. gigas with the increasing SST. An increase in global ocean water temperature has significant impacts on other fish habitat distribution (Kibler et al., 2015). For example, in the Northwest Pacific Ocean, Pacific saury (Cololabis saira) and neon flying squid (Ommastrephes bartramii) was likely to underwent a poleward displacement and expanded their habitats to higher latitude in response to sea water temperature increase (Tseng et al., 2011; Xu et al., 2016). For D. gigas stock off Peru, the ENSO-linked episodic range shift of D. gigas
4.3. Limitations and implications Though this study provided new insights into ocean warming-driven variability in habitat suitability and spatial redistribution of suitable habitat for D. gigas by modeling approach, however, some limitations existed in the analysis. First, D. gigas stock is widely distributed from Alaska to southern Chile, and undergoes large migrations in both latitudinal and longitudinal direction. Our study only covered a relatively small spatial extent compared to the known range of this species. Considering the wide bathymetric and geographical distribution of D. gigas, it is naive to deduce that squids could not migrate northward or that they change their distribution with the increase in temperature. In 144
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order to explore how the squid distributions changed with the SST increases in a large scale (such as the whole Eastern Pacific Ocean), we need more fishing effort data to support the HSI model construction. However, gridded fishing effort data with high spatial and temporal resolution from other countries (e.g., Peru, Chile and the data from the Northern Hemisphere) were quite difficult to obtain for this study. In future work, we need to obtain these data to conduct a more comprehensive evaluation of the variability of habitat suitability in relation to climate change. Second, habitat models in this study only considered one critical environmental driver (i.e., SST). It was clear that SST incorporated in the HSI model could not have a perfect 100% correlation with habitat quality and spatial occurrence of D. gigas. To truly understand the spatio-temporal distribution of D. gigas in the Southeast Pacific Ocean, future work should include more biotic and abiotic predictors that may be important in determining distributional changes of D. gigas such as salinity, oxygen minimum zone (Seibel, 2013; Stewart et al., 2013) and chlorophyll-a density (Paulino et al., 2016) amongst others. In conclusion, our study presented the spatial distribution pattern of seasonal habitat of D. gigas in the Southeast Pacific Ocean off Peruvian waters under ocean warming condition. In comparison to the recent SST on the fishing ground over 2011–2015, the potential habitat of D. gigas was likely to shrink and move southeastward under four scenarios of SST increases (+0.5 °C, +1.0 °C, +2.0 °C and +4.0 °C,). In order to reduce the negative impacts of ocean warming on habitat of D. gigas off Peru, more advanced science research and management measures should be conducted in future. Conclusions from the present study also provided important implications for other pelagic ommastrephid squids in worldwide oceans for better conservation and management. Acknowledgments This study was financially supported by the China Postdoctoral Science Foundation (2017M611612); the Doctoral Startup Scientific Research Foundation of Shanghai Ocean University (A2-0203-17100313); the Open Fund for Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources in Shanghai Ocean University (A1-020300-2009-5) and the Shanghai Universities First-Class Disciplines Project (Fisheries A). We would also like to thank four anonymous reviewers and the editor for their constructive suggestions to improve this manuscript. References Anderson, C.I.H., Rodhouse, P.G., 2001. Life cycles, oceanography and variability: ommastrephid squid in variable oceanographic environments. Fish. Res. 54 (1), 133–143. Argüelles, J., Rodhouse, P.G., Villegas, P., Castillo, G., 2001. Age, growth and population structure of the jumbo flying squid Dosidicus gigas in Peruvian waters. Fish. Res. 54 (1), 51–61. Bograd, S.J., Foley, D.G., 2013. Predicted habitat shifts of pacific top predators in a changing climate. Nat. Clim. Change 3 (3), 234–238. Brodeur, R.D., Ralston, S., Emmett, R.L., Trudel, M., Auth, T.D., Phillips, A.J., 2006. Anomalous pelagic nekton abundance, distribution, and apparent recruitment in the northern California current in 2004 and 2005. Geophys. Res. Lett. 33 (22), 22–28. Cao, J., Chen, X.J., Chen, Y., 2009. Influence of surface oceanographic variability on abundance of the western winter-spring cohort of neon flying squid Ommastrephes bartramii in the NW Pacific Ocean. Mar. Ecol. Prog. Ser. 381, 119–127. Carr, M.E., Strub, P.T., Thomas, A.C., Blanco, J.L., 2002. Evolution of 1996–1999 La Niña and El Niño conditions off the western coast of South America: a remote sensing perspective. J. Geophys. Res. Oceans 107 (12), 1–16. Chang, Y.J., Sun, C.L., Chen, Y., Yeh, S.Z., Dinardo, G., Su, N.J., 2013. Modelling the impacts of environmental variation on the habitat suitability of swordfish, Xiphias gladius, in the Equatorial Atlantic Ocean. ICES J. Mar. Sci. 70 (5), 1000–1012. Chen, X.J., Liu, B.L., Chen, Y., 2008. A review of the development of Chinese distantwater squid jigging fisheries. Fish. Res. 89 (3), 211–221. Chen, X.J., Li, G., Feng, B., Tian, S.Q., 2009. Habitat suitability index of chub mackerel (Scomber japonicus) from July to September in the East China Sea. J. Oceanogr. 65 (1), 93–102. Christian, J.R., Holmes, J., 2016. Changes in albacore tuna habitat in the Northeast Pacific Ocean under anthropogenic warming. Fish. Oceanogr. 25 (5), 544–554. Donelson, J.M., Munday, P.L., Mccormick, M.I., Nilsson, G.E., 2015. Acclimation to
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