- Email: [email protected]
Influence of oceanographic and climatic variability on the catch rate of yellowfin tuna (Thunnus albacares) cohorts in the Indian Ocean
Deep–Sea Research II xxx (xxxx) xxx
Contents lists available at ScienceDirect
Deep-Sea Research Part II journal homepage: http://www.elsevier.com/locate/dsr2
Influence of oceanographic and climatic variability on the catch rate of yellowfin tuna (Thunnus albacares) cohorts in the Indian Ocean Kuo-Wei Lan a, b, *, Yi-Jay Chang c, Yan-Lun Wu a a
Department of Environmental Biology and Fisheries Science, National Taiwan Ocean University, 2 Pei-Ning Rd., Keelung, 20224, Taiwan, R.O.C Center of Excellence for Oceans, National Taiwan Ocean University, 2 Pei-Ning Rd., Keelung, 20224, Taiwan, R.O.C c Institute of Oceanography, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan, R.O.C b
A R T I C L E I N F O
A B S T R A C T
Keywords: Yellowfin tuna cohorts Oceanographic variability Indian ocean dipole Oceanic ni~ no index Geographical localization Empirical orthogonal function
Using Taiwanese longline fishery data, this study investigated the influence of climate variability and environ mental conditions in the Indian Ocean on the catch rates and distribution of immature and mature cohorts of yellowfin tuna. The variations in the seasonal distribution of immature and mature cohorts suggest that yellowfin tuna move extensively from the Arabian Sea and Bay of Bengal to the coastal waters of Somalia and around Madagascar in the Indian Ocean. The high recruitments of the immature cohort were found in 1998–2002, and the catch rate of the immature and mature cohorts revealed positive associations with periodicities of approx imately 3–4 years. We found that the distributions and catch rates of the two cohorts were sensitive to variations in climatic and marine environments. Sea surface temperature was the most influential environmental variable for both cohorts, and Chl-a was not statistically significant for the immature cohort. There was a significant negative correlation between the catch rate of the immature and mature cohort and Indian Ocean Dipoles (IODs), with periodicities of approximately 3 years during the study period, and had periodicities of approximately 1–3 years with El Ni~ no/Southern Oscillation (ENSO) events. Furthermore, the influence of IODs exhibited greater variance than that of ENSO events, and the influence of ENSO was only evident near the Arabian Sea. The in fluence of concurrent positive IOD and El Ni~ no events led to lower catch rates for the mature cohort in 2008–2009 in the northwestern Indian Ocean.
1. Introduction Climatic oscillations, anomalies, and changes affect numerous ecological processes in marine ecosystems. Consequently, they play an essential role in controlling the distribution and abundance of top predators, such as tuna, in the ocean (Langley et al., 2009; Syamsuddin ~ i et al., 2015; Lan et al., 2018). The El Nin ~ o/Southern et al., 2013; Gon Oscillation (ENSO) is a well-known and dominant form of interannual climate variability that develops from air–sea interactions in the tropical Pacific Ocean and affects weather patterns worldwide (McPhaden et al., 2006). Large-scale climate fluctuations, known collectively as the Indian Ocean Dipoles (IODs), also occur in the Indian Ocean. These are driven by patterns of interannual variability in sea surface temperatures (SSTs) that result in accompanying wind and precipitation anomalies (Saji et al., 1999; Feng and Meyers, 2003; Meyers et al., 2007). Tele connections associated with El Ni~ no result in an overall warming of the Indian Ocean due to changing cloud cover and wind patterns associated
with changes in the ascending and descending branches of Walker cir culation (Venzke et al., 2000; Xie and Arkin, 1997; Currie et al., 2013). ~ o events, they can also Although IODs tend to be triggered by El Nin occur independently (Meyers et al., 2007). The biological impacts of IOD and ENSO events in the Indian Ocean have been associated with ecosystems, fishery resources, and carbon sequestration. For instance, shifts in the distributions and catch rates of yellowfin tuna in the Indian Ocean have been associated with IOD events (Menard et al., 2007; Marsac, 2008; Lan et al., 2013). A significant negative association between IODs and the catch rates of yellowfin tuna with a periodicity of approximately 4 years was observed (Lan et al., 2013). During positive IOD events, shifting trade winds increasingly converge in the west, reducing wind speeds and convection. This causes a general increase in SST, reduction in coastal upwelling, and decrease in productivity (Saji et al., 1999; Marsac, 2008). The 1997 positive IOD/El Ni~ no event also substantially affected oceanographic conditions and had a positive effect on the catch rates of bigeye tuna in the eastern
* Corresponding author. Department of Environmental Biology and Fisheries Science, National Taiwan Ocean University, Keelung, 20224, Taiwan, R.O.C. E-mail addresses: [email protected] (K.-W. Lan), [email protected] (Y.-J. Chang), [email protected] (Y.-L. Wu). https://doi.org/10.1016/j.dsr2.2019.104681 Received 15 March 2019; Accepted 23 October 2019 Available online 24 October 2019 0967-0645/© 2019 Published by Elsevier Ltd.
Please cite this article as: Kuo-Wei Lan, Deep–Sea Research II, https://doi.org/10.1016/j.dsr2.2019.104681
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
Indian Ocean (Syamsuddin et al., 2013). The intense 1997 positive ~ o event was characterized by a strong phytoplankton bloom IOD/El Nin in the eastern equatorial Indian Ocean, an area normally characterized by low bioproductivity (Murtugudde et al., 1999; Susanto and Marra, 2005). These contrasts reveal the mercurial nature of biological impacts arising from different climatic events. In the Indian Ocean, catches of yellowfin tuna are distributed in all the tropical waters and primarily concentrated in western tropical wa ters, the Arabian Sea, and the north of the Mozambique Channel (Fu et al., 2018). Tag recoveries provide evidence of large movements of yellowfin tuna within the western equatorial region; however, few ob servations of large-scale transverse movements in the Indian Ocean have been reported (Gaertner and Hallier, 2015). This may indicate that the western and eastern regions of the Indian Ocean support relatively distinct subpopulations of yellowfin tuna. Studies of yellowfin tuna stock structure using DNA techniques suggest genetically discrete sub populations in the northwestern Indian Ocean (Dammannagoda et al., 2008) and the waters off the coast of India (Kunal et al., 2013). However, no comprehensive study of yellowfin tuna has analyzed the entire ocean basin. The movement patterns and their variability of tuna are controlled by environmental conditions because the time and area strata favorable for spawning and feeding are limited and vary (Fonteneau and Soubrier, 1996). Research indicates that yellowfin tuna prefer warm waters and are found in regions with a high concentration of primary productivity (Langley et al., 2009; Dell et al., 2011). In the Atlantic Ocean, preadult yellowfin tuna (65–110 cm) migrate toward higher latitudes and trop ical waters by following cyclical seasonal migration patterns; most specimens return to the spawning areas when they have reached sexual maturity (Fonteneau and Soubrier, 1996; Fonteneau et al., 2017). Adult trophic migration toward higher latitudes occur during the summer, and genetic migration across the ocean (Bard et al., 1991; Fonteneau et al., 2017). Archival tag data on mature yellowfin tuna in the Pacific Ocean also show cyclical movement between suitable spawning and feeding habitats. Most yellowfin tuna remain at shallow depths (<50 m) at night and do not dive to greater depths (>100 m) during the day (Schaefer et al., 2007; Gaertner and Hallier, 2015). Variation in the population abundance and distribution of yellowfin tuna has been linked to large-scale climate phenomena (Marsac, 2008; Lan et al., 2012a; Senina et al., 2015). However, the relationship be tween the distribution of yellowfin tuna cohorts and environmental variation is unclear. Estimating the preferred environmental conditions of each cohort is important for determining the migratory patterns and potential interactions of fish located in various areas. This study inves tigated the temporal and spatial variations in fishing grounds and the catch rates of immature and mature yellowfin tuna cohorts associated with oceanographic conditions using Taiwanese longline fishery data. Additionally, this study explored the processes underlying the climatic events (IOD and ENSO) influencing the interannual catch rates and distribution of yellowfin tuna cohorts.
To estimate the mean values of fork length by age, the parameters of the von Bertalanffy growth equation in the Indian Ocean were used (Fonteneau, 2008). The fork length of 50% of the mature (approximately 2 y) female yellowfin tuna population in the Indian Ocean was estimated to be 102 cm (Zudaire et al., 2013). Mature tuna cohorts also showed geographical segregation between their original spawning and feeding grounds (Fonteneau and Soubrier, 1996). We separated immature (<2 y) and mature (�2 y) cohorts and calculated the monthly catch rates for each as the number of individuals captured per 1000 hooks (ind/103 hooks). 2.2. Environmental variables and climatic variability indices Monthly environmental data for the period from January 2003 through December 2012 to match the period of Chlorophyll-a (Chl-a) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) were sourced as follows: (1) SST pathfinder monthly composite fields with a 4-km spatial resolution were obtained from the National Oceanographic Data Center. (2) Sea surface height deviation (SSHD) was obtained from the Archiving, Validation and Interpretation of Sat ellite Oceanographic data program with 0.25� resolution. (3) Mixedlayer depth (MLD) data with 1� spatial resolution were downloaded from the European Centre for Medium-Range Weather Forecast Ocean Analysis System. (4) Chl-a data were obtained from the MODIS with a 9km spatial resolution and downloaded from the NASA Ocean Color Web. The environmental variables were then calculated as monthly means on a spatial grid with 5� resolutions to fit the fishery data. Environmental variations in positive and negative anomalies asso ciated with IODs were represented by the Dipole Modular Index (DMI), which is constructed from monthly differences between SST anomalies in the western equatorial (50� E–70� E, 10� S–10� N) and southeastern equatorial (90� E–110� E, 10S� –0� N) Indian Ocean. The ENSO was rep ~ o Index (ONI) and estimated by a 3-month resented by the Oceanic Nin ~ o 3.4 region (5� N–5� S, running mean of SST anomalies in the Nin 120� W–170� W) during 1971–2000. 2.3. Generalized additive models We used generalized additive models (GAMs) to investigate the re lationships between environmental variations and the catch rates of immature and mature cohorts. Fishery data from 2003 to 2012 were used in the GAM to match the period of Chl-a data. GAMs were con structed in R (Version 2.15.0) using the GAM function of the mgcv package; catch rate was used as the response variable, variables expressing time (year and month), location (longitude and latitude), and environmental factors (SST, Chl-a, SSHD, MLD, DMI, and ONI) were used as the predictor variables. GAMs were construed as: log(CPUE þ c) ¼ a0 þ s(x1) þ s(x2) þ s(x3) þ …. s(xn);
(1)
where a0 is a constant, and s(xi) is a spline smoothing function for each covariate xi or the interaction between two covariates. All covariates were considered to be continuous, and the effective degrees of freedom were estimated for each main factor. Because the log-link function cannot handle zeros, we added a constant value of 0.1 (c) to all catch rates. A constant value of 0.1 (c) is commonly used in catch rate stan dardizations (e.g., Maunder and Punt, 2004; Wood, 2006; Lan et al., 2018). Time and location were treated as interaction terms to account for possible interannual variability—possibly driven by environmental variations—in the spatial distribution of yellowfin tuna cohorts. A model with optimal conformation was selected using a stepwise pro cedure based on the lowest Akaike information criterion (AIC).
2. Materials and methods 2.1. Yellowfin tuna fishery and fork length data Since the 1990s, Taiwanese longline fishing has been the main source of size data used by longline fleets. This has been important for in ferences about the population dynamics in areas where longline catches account for up to 85% of the total catch of tropical and temperate tuna species in the Indian Ocean (Greehan and Hoyle, 2013). The fishery data in this study were collected from 1998 to 2012 and compiled from the logbooks of Taiwanese longline fleets provided by the Overseas Fisheries Development Council of the Republic of China. The fishery data for 5� spatial grids included area coordinates, fishing effort (number of hooks), fishing date, number of catches, and randomly sampled length mea surement data (fork length in cm).
2.4. Wavelet analysis and empirical orthogonal function To confirm the effect of environmental variations caused by climate 2
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
variability on the spatial and temporal catch rates of immature and mature cohorts, wavelet analysis and empirical orthogonal functions (EOFs) were utilized. Wavelet analysis is a common tool for analyzing time series data; it involves decomposing a time series in a time –frequency space, facilitating identification of the dominant periodic components and determination of how these vary over time (Torrence and Campo, 1998). The Morlet wavelet is the most popular complex wavelet used in practice and is defined as
ψ 0 ðηÞ ¼ π
1=4 iϖo
e
e
η2 =2
greatly in time and space. T’ðx; tÞ ¼ Tðx; tÞ
N 1 X Tðx; tÞ N t¼1
(3)
Using the singular-value decomposition method, amplitude scores of T0 (x,t) catch rate data were used to decompose the data matrix to elucidate spatial patterns and time series variations. 3. Results
(2)
3.1. Catch rate patterns of immature and mature cohorts in the Indian Ocean
where η is a dimensionless time parameter and ω0 is a dimensionless frequency; this is intended to balance the localization of time and fre quency. A 95% significance level was selected based on bootstrap sim ulations run as a first-order autoregressive process. The autoregression coefficient was empirically obtained from the time series data. Crosswavelet coherence (Grinsted et al., 2004) and phase analyses were then used to investigate the causal relationships between climate events and catch rates. EOF analysis provides a concise description of the spatial variability of time series data in terms of orthogonal functions or statistical modes. It decomposes a time series dataset into its orthogonal component modes; this was used to reveal the dominant patterns of annual catch rate variability for both immature and mature cohorts. Catch rate data in the EOF analysis were arranged in a two-dimensional M � N matrix, T(x, t), where M is the number of elements in the spatial dimension (in this case, the number of 5� spatial degrees in the Indian Ocean), and N is the number of elements in the temporal dimension (1998–2012). In line with the suggestions of Paden et al. (1991) and Emery and Thomson (2001), the temporal means of the data matrix were removed before EOF analysis by finding the mean over the time series for each degree and subtracting it from each pixel (Eq. (3)). This revealed features that vary
The catch percentage of the Taiwanese longline fisheries in the In dian Ocean showed that the major populations were mature cohorts aged �3–5 years (approximately 85%, Fig. 1a). These were concentrated in the tropical Indian Ocean and Arabian Sea (Fig. 2a,c). The immature cohort comprised 25–40% of the total catch in 1998–2000 and decreased to approximately 8% after 2003 (Fig. 1a). A high catch per centage of immature cohort occurred south of Madagascar, Bay of Bengal and in the central Indian Ocean near 60� E–90� E and 10� N–20� S (Fig. 2a,b). The catch rates of the immature and mature cohorts exhibited interannual variation (Fig. 1b). High catch rates of the immature cohort occurred in 1998–2002 (>0.5 inds/103 hooks), whereas high catch rates of the mature cohort occurred only in 2004–2006 (>4.0 inds/103 hooks). Seasonal spatial distributions showed that the immature cohort was concentrated to the north of Madagascar, in the Arabian Sea, and in the Bay of Bengal in the first and second quarters (Fig. 3a and b) and then moved to the central equatorial Indian Ocean in the fourth quarter (Fig. 3d). The mature cohorts were concentrated in the Arabian Sea and Bay of Bengal throughout the year (Fig. 3e–h) and in the Somalian coastal waters in the first and fourth
Fig. 1. Annual trends for the (a) catch percentage of yellowfin tuna at <2 to >5 years and (b) catch rates for immature and mature cohorts caught by Taiwanese longliners in the Indian Ocean in 1998–2012. 3
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
Fig. 2. Spatial distribution of the catch percentage of (a) yellowfin tuna at <2 to >5 years, (b) the immature cohort, and (c) the mature cohort for the entire study period (1998–2012).
Fig. 3. Quarterly mean catch rates for immature (a–d) and mature (e–h) cohorts in the Indian Ocean. 4
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
quarter (Fig. 3e,h), gradually moving to the water north of Madagascar in the second and third quarters (Fig. 3f,g).
series data of the climatic indices and catch rate dataset. Wavelet analysis identified a significant positive correlation between the catch rate of the immature and mature cohorts, with periodicities of approx imately 3–4 years during the study period (Fig. 6a). A significant negative correlation was also identified between the catch rate of both cohorts and DMI, with periodicities of approximately 3 years during 2003–2008 (Figs. 6b) and 2003 to 2012 (Fig. 6d), respectively. A sig nificant negative correlation was found between the catch rates of the immature cohort and ONI with periodicities of approximately 1–3 years (Fig. 6c), and with a periodicity of approximately 2 years during 2004–2008 for the mature cohort (Fig. 6e). It is therefore important to obtain a better understanding of the re lationships between the climatic indices and the underlying processes associated with variability in catch rates. The first two modes in the EOF analysis accounted for 36.63% and 13.86% of the variance in the catch rate for the immature cohort and 40.06% and 18.27% of the variance in the catch rate of the mature cohort (Figs. 7 and 8). In the first mode for the immature cohort, the spatial amplitude showed positive values in the Bay of Bengal and northern Arabian Sea, especially in the Bay of Bengal (Fig. 7a). The second mode revealed inverse distribution trends in the Arabian Sea and Bay of Bengal (Fig. 7b). For the mature cohort, the spatial amplitude showed highest positive values in the Bay of Bengal and northeastern Arabian Sea (Fig. 8a). The second mode revealed positive values in the northwestern Indian Ocean and drop in the eastern Indian Ocean (Fig. 8b). The spatial amplitude results showed a significant spatial pattern in the Arabian Sea and Bay of Bengal for the immature and mature cohorts; the correlations of SST, Chl-a, MLD in the Arabian Sea and Bay of Bengal, and climatic indices (ONI and DMI) with eigenvectors were further analyzed using Pearson’s test. The annual variation of the eigenvectors was negatively correlated with DMI (r ¼ -0.48) and positively correlated with MODIS Chl-a (r ¼ 0.56) in the Bay of Bengal in the first mode, and with ONI (r ¼ 0.48) and Arabian SST (r ¼ 0.74) in the second mode of the immature group (Fig. 7c,d). For the mature cohort, the DMI (r ¼ 0.52) and Arabian Sea Chl-a (r ¼ -0.42) were positive correlated in the first mode. ONI (r ¼ -0.51) and Arabian Sea SST (r ¼ -0.42) were negatively correlated in the second mode (Fig. 8 c,d).
3.2. Relationships between catch rates and environmental variables The model selection process for GAM analysis is shown in Table 1; the deviance explained by the selected GAMs was 33.40% and 48.10% for the immature and mature cohorts (Table 1), respectively. Interaction terms of year with latitude (22.8%) and longitude (12.3%) accounted for a large part of the variation for the immature cohorts. High catch rates occurred in the north and central western Indian Ocean (Fig. 4f,g; 10� S–20� S, 40� E–80� E) in the first half of the year (Fig. 4h) but decreased after 2006 in the central western areas. The SST and climate indices (ONI and DMI) were the most important environmental vari ables; however, Chl-a had no statistically significant predictors (p < 0.05). A positive association was observed between the catch rates and SSTs of approximately 26� C–29 � C. The MLDs of >150 m and SSHDs of >0.4 m were negatively associated with catch rates, respectively (Fig. 4a–c). The effect of ONI and DMI on catch rates showed a slightly negative but not obvious trends (Fig. 4d,e). For the mature cohort, interaction terms of latitude with longitude (34.3%) and year with month (14.4%) accounted for a large part of the variation. The interaction terms showed that the high catch rates of the mature cohorts were distributed in the northwestern Indian Ocean (10� S–25� N, 40� E–80� E) and Bay of Bengal (10� N–25� N, 80� E–100� E) (Fig. 5g). These increased in the first half of years from 2004 to 2007 but decreased after 2008 (Fig. 5h). All the environmental variables had statistically significant predictors (p < 0.05), of which SST was strongest (9.24%). A positive association was observed between the catch rates and SSTs of approximately 27� C–30 � C (Fig. 5a). The SSHDs revealed dome-shaped and had a positive association of 0.2–0.5 m (Fig. 5c). The MLDs of >150 m and Chl-a of >0.5 mg/m3 were negatively associated with catch rates, respectively (Fig. 5b,d). The ONI and DMI also showed a negative association (Fig. 5d,e). 3.3. Spatial and time series analysis The GAM results showed negative associations of ONI and DMI with the catch rates of the mature cohort and not obvious patterns of the immature cohort. Using a state-space time series analysis, a single decomposition procedure effectively removed seasonality from the time
4. Discussion An analysis of the catch rate and distribution of immature and
Table 1 Construction of the GAM, residual deviance, approximate AIC, and p values of immature and mature cohorts of yellowfin tuna in the Indian Ocean. Residual deviance
One variable model explained
AIC
p value
Immature Null þs(SST) þs(MLD) þs(SSHD) þs(ONI) þs(DMI) þs(year*Latitude) þs(year*Longitude) þs(Month*Longitude) Total deviance explained
7452.00 6874.00 6048.00 3036.00 8432.00 6685.00 28663.00 27814.00 27973.00 33.40%
3.63% 0.83% 1.75% 2.33% 2.96% 22.80% 12.30% 5.56%
15620.24 15536.28 15504.65 15484.48 15444.48 15400.09 15275.33 15116.45 15035.14
<0.05 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05
Mature Null þs(SST) þs(Chl-a) þs(MLD) þs(SSHD) þs(ONI) þs(DMI) þs(year*Month) þs(Latitude*Longitude) Total deviance explained
7452.00 6960.00 4862.00 6645.00 7152.00 6043.00 7833.00 26389.00 24510.00 48.10%
9.24% 1.92% 2.39% 3.98% 2.95% 3.00% 14.40% 34.30%
12043.67 11995.61 11979.28 11896.35 11833.40 11824.97 11790.01 11605.29 11431.35
<0.05 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05
5
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
Fig. 4. Modeled effects of the (a) SST, (b) MLD, (c) SSHD, (d) ONI, and (e) DMI and interaction terms of (f) year and latitude, (g) year and longitude, and (h) month and longitude on catch rates for immature cohorts in the Indian Ocean. The solid and black-dotted lines show the fitted GAM function and 95% confidence intervals, respectively. The relative density of data points is indicated by the rug plot on the X-axis.
mature cohorts in relation to environmental variations in the Indian Ocean resulted in disparate estimates. The results of spatial distribution patterns and GAM analysis revealed important fishing grounds off the coast of Somalia and around Madagascar for immature and mature co horts, with seasonal variations. Yellowfin tuna exhibit more geograph ical segregation between their original spawning and feeding grounds (Fonteneau and Soubrier, 1996; Zudaire et al., 2013). Spawning usually takes place in specific equatorial areas during limited periods when mature fish congregate for spawning (Fonteneau and Soubrier, 1996). Zudaire et al. (2013) observed that spawning of yellowfin tuna mainly occurs in the equatorial area (0� S–10� S) and west of 75� E in the Indian Ocean, and that the main spawning season was November–February, with a second peak occurring in June. High catch rates for the mature cohort were also observed off the coast of Somalia in the first and fourth quarter and in the water around Madagascar in the second and third quarter. These movement patterns were similar to those observed by Zudaire et al. (2013). Large-scale migration often takes place between spawning and feeding grounds and covers hundreds or thousands of miles, as has been clearly demonstrated in Atlantic yellowfin tuna (Fonteneau and Soub rier, 1996; Schaefer et al., 2011). Thus, yellowfin tuna may spend a large part of their life cycle outside their spawning zone. Similar extensive movement can also be observed in the Indian Ocean. High catch rates for immature and mature cohorts were observed in the Arabian Sea and Bay of Bengal, which are important feeding grounds for yellowfin tuna. Fishing grounds and fishing periods in the Arabian Sea are associated
with upwelling and high primary productivity which attract large con centrations of crustaceans and small mesopelagic fish (Lan et al., 2012b; Fu et al., 2018). When they reach sexual maturity, most specimens re turn to the spawning areas in tropical regions (Fonteneau and Soubrier, 1996). Thus, our results suggest extensive movement of yellowfin tuna from the Arabian Sea and Bay of Bengal (feeding grounds) to the coastal waters of Somalia and Madagascar (spawning grounds) in the Indian Ocean. The spawning events of yellowfin tuna occur where SST >24 � C, which seems to regulate spawning activity; the optimal temperature for the feeding habitat of adults was estimated to be 21.9� C–26.6 � C (Schaefer et al., 2007; 2011; Boyce et al., 2008). Our results show that the immature and mature cohorts had a similar range of optimal envi ronments, although this was wider for the mature cohort than for the immature cohort. SST was the most influential environmental variable for both cohorts (26� C–29 � C for immature and 27� C–30 � C for mature). Chl-a was not statistically significant for the immature cohort, implying that shifts in the distribution of the immature cohort might be due to cyclical seasonal migrations rather than reflecting a search for prey. Yellowfin tuna are capital and income breeders (Alonso-Fern� andez and Saborido-Rey, 2012); this means that the energy stored before repro duction is insufficient to offset the cost of reproduction, and energy allocation from feeding is necessary for successful reproduction (Hen derson and Morgan, 2002). Based on our results, we suggest that, like other tropical species, when the immature yellowfin tuna reach their feeding grounds, they require energy from feeding as well as stored 6
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
Fig. 5. Modeled effects of the (a) SST, (b) MLD, (c) SSHD, (d) Chl-a, (e) ONI, and (f) DMI and interaction terms of (f) latitude and longitude, and (g) year and month on catch rates for mature cohorts in the Indian Ocean. See Fig. 4 for legends.
energy to facilitate ovarian development. Our results indicate that the catch rate of immature and mature co horts was positively correlated with SSHA values of approximately 0.2–0.5 m and implied a preference for areas closely associated with edges of eddies. The mesoscale oceanographic features would lead to the aggregation of tuna forage both by entrainment and by encouraging vertical mixing and introducing nutrients into the photic zone, thus promoting phytoplankton growth, which may subsequently attract predators from higher trophic levels (Sournia, 1994; Bertrand et al., 2002). The MLD of >150 m were negatively associated with catch rates of immature and mature cohorts. Bard et al. (1999) found the yellowfin tuna can reach depths of 350 m; however, both juvenile and adult yel lowfin tuna spend most of their time in the surface layer above 100 m. The older cohorts (�3.5 y) had a higher tolerance for low temperatures and oxygen concentrations, and enables them to spend more time searching for prey at depths below the mixed layer (Schaefer et al., 2007, 2011). The high recruitments of the immature cohort were found in 1998–2002, and the catch rate of the immature and mature cohorts revealed positive associations with periodicities of approximately 3–4 years. The higher catch rates of the mature cohort also occurred in 2003–2006. Lan et al (2012a) and Fu et al (2018) also revealed the high catch rates of mature cohorts observed in 2004–2006 were due to high recruitment during the late 1990s–early 2000s. The spatial amplitudes of first EOF mode for immature cohort suggested the highest re cruitments occurred in the Bay of Bengal and had positive associations with the annual variations of MODIS Chl-a in 2003–2012. The SeaWiFS
Chl-a were further used to observe the trends of Chl-a in 1998–2006 in Fig. 7a, and revealed the decreased trend since 1998. Currie et al. (2013) suggested that anomalous surface and euphotic layer chlorophyll blooms in the eastern equatorial Indian Ocean in fall, and southern Bay of Bengal in winter, are primarily related to IOD forcing. The first EOF mode of spatial amplitude also showed that IOD events influence the distributions of mature cohorts, and that the mature cohort is distributed more widely than the immature cohort (including in the Bay of Bengal, Arabian Sea, and around northern Madagascar). The first EOF mode of the mature cohort also indicated that the catch rates are associated with higher Chl-a in negative IOD events. The cool, steady, and strong southwestern monsoon associated with negative IOD events generates extensive coastal upwelling over the margins of continental areas in the northwestern Indian Ocean. This drives an increase in pri mary production, which would be expected to have flow-on effects on higher order productivity in the region (Menard et al., 2007; Corbineau et al., 2008). The second EOF mode revealed that the catch rates of the ~ a events (e.g., immature and mature cohorts increased during La Nin 1999, 2008 and 2011), but only around the Arabian Sea. Currie et al. (2013) used a biophysical general ocean circulation model to identify patterns of Chl-a anomalies driven by IOD and ENSO events. They found that ENSO has a weaker and lagged effect on ther mocline and chlorophyll anomalies in comparison with IOD. The only region where chlorophyll signals are predominantly related to ENSO variability is the western Arabian Sea, Oman coastal waters, and So ~ o events peaking in malia upwelling regions (Currie et al., 2013). El Nin the eastern Pacific Ocean are related to north-easterly wind anomalies, 7
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
Fig. 6. Cross-wavelet coherence between catch rates of (a) immature and mature cohorts (b–c) immature and (d–e) mature cohorts with DMI and ONI from 1998 to 2012. The solid-black contour encloses regions of >95% confidence, and the black line indicates where edge effects become important. High variability is represented by red, and weak variability by blue. Arrows indicate the phase relationship, with in-phase arrows pointing to the right and out-of-phase arrows pointing to the left. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 7. Spatial amplitude patterns (a–b) and interannual eigenvectors (c–d) of the first and second EOF modes for immature cohorts in the Indian Ocean. The annual trends for environmental factors (c–d) indicate that the phases have significant correlations with the interannual eigenvectors according to Pearson’s test.
and warmer SST in the western Arabian Sea is related to decreased Chl-a (Kao and Yu, 2009; Currie et al., 2013). Our investigation using EOF modes also showed similar patterns in that (1) the influence of IOD
exhibited greater variance (immature: 33.63%, mature: 40.06%) than that of ENSO events (immature: 13.86%, mature: 18.27%), and (2) the influence of ENSO events occurred only around the Arabian Sea and Bay 8
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
Fig. 8. Spatial amplitude patterns (a–b) and interannual eigenvectors (c–d) of the first and second EOF modes for mature cohorts in the Indian Ocean. See Fig. 7 for legends.
of Bengal. Therefore, the impact of climatic variability on the distribu tion and abundance of yellowfin tuna is also likely to depend on the balance between bottom-up effects (primary production) driven by nutrient availability. ~ o events yielded The influence of concurrent positive IOD and El Nin the highest eigenvector in 2008–2009 in the first EOF mode of the mature cohort, indicating lower catch rates in the northwestern Indian Ocean. The strongest SST anomaly patterns occurred in positive IODs ~ o and negative IODs with La Nin ~ a (Meyers et al., 2007). with El Nin Wiggert et al. (2009) found a profound redistribution of carbon uptake, with a large primary production increase in the eastern Indian Ocean. ~o This was roughly balanced by a decrease in positive IOD and El Nin events in western regions in 1997 and 2006. However, the results of wavelet analysis and EOF modes showed no clear periodicities between climatic indices and the catch rates of the mature cohort. The threat of piracy caused the bulk of the industrial purse seine and longline fleets to relocate from the coastal and off-shore waters of Somalia, Kenya, and Tanzania in 2008–2012. The Taiwanese longline data also lack effort and catch data in the northwestern Indian Ocean in 2010–2011. This may have influenced our results regarding the processes associated with climatic events that drive the distribution of yellowfin tuna cohorts.
mature cohorts suggest that yellowfin tuna move extensively from the Arabian Sea and Bay of Bengal to the coastal waters of Somalia and around Madagascar in the Indian Ocean. Furthermore, the influence on IODs exhibited a higher variance than that of ENSO events, whereas ~ o led to lower catch rates for the concurrent positive IODs and El Nin mature cohort in 2008–2009 in the northwestern Indian Ocean. Taiwanese longline fishery accounts for most of the samples collected from longline vessels operating in the Indian Ocean, 80%– 100% from the early 1990s–2000s (Greehan and Hoyle, 2013). How ever, the catch percentage of Taiwanese longline vessels in our results showed that the major fish populations were mature cohorts aged 2–3 years and that the immature cohort constituted only approximately 15% of the total catch. Newly recruited fish are primarily caught through purse seine fishery on floating objects and pole-and-line fishery in the Maldives (Fu et al., 2018). Moreover, comparisons of the size data collected from Taiwanese vessels by observers and logbooks since 2003 revealed that vessel masters reported considerably larger fish (Greehan and Hoyle, 2013). Greehan and Hoyle (2013) suggest that substantial changes in the Taiwanese mean sizes are likely to be due to sampling problems rather than changes in the size composition of the population. However, our results show that immature and mature cohorts exhibit different temporal and spatial distribution patterns related to environ mental features. Regarding the influence of the climatic indices on marine environments, Currie et al. (2013) found that the combined explanatory power of IOD and ENSO events in predicting depth-integrated Chl-a anomalies is relatively low in the Indian Ocean, and the response to concurrent events seems to be weak. Future studies must extend the time series of fishery data from interannual to decadal scales by including diverse varieties of gear types and fishing strategies to simulate animal responses to the spatial heterogeneity of biotic and abiotic conditions in a dynamic marine landscape.
5. Conclusions and remarks This study provides preliminary insight into some of the key envi ronmental features driving the distribution of yellowfin tuna cohorts and associated variability in fishery catches as well as the estimated preferred habitat and environmental conditions of immature and mature cohorts in terms of climatic indices of the IOD and ENSO. Individuals from a number of species made repeated use of specific areas with welldefined migration corridors between high-use areas (Fonteneau and Soubrier, 1996; Fonteneau et al., 2017); however, we found significant variability in movement from one year to the next within the same species. The variations in the seasonal distribution of immature and 9
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
Acknowledgements
Kunal, S.P., Kumar, G., Menezes, M.R., Meena, R.M., 2013. Mitochondrial DNA analysis reveals three stocks of yellowfin tuna Thunnus albacares (Bonnaterre, 1788) in Indian waters. Conserv. Genet. 14, 205–213. https://doi.org/10.1007/s10592-013-0445-3. Lan, K.W., Lee, M.A., Nishida, T., Lu, H.J., Weng, J.S., Chang, Y., 2012. Environmental effects on yellowfin tuna catch by the Taiwan longline fishery in the Arabian Sea. Int. J. Remote Sens. 33 (23), 7491–7506. https://doi.org/10.1080/ 01431161.2012.685971. Lan, K.W., Nishida, T., Lee, M.A., Lu, H.J., Huang, H.W., Chang, S.K., Lan, Y.C., 2012. Influence of the marine environment variability of the yellowfin tuna (Thunnus albacares) catch rate by the Taiwanese longline fishery in the Arabian Sea, with special reference to the high catch in 2004. J. Mar. Sci. Technol. 20 (5), 514–524. https://doi.org/10.6119/JMST-011-0506-1. Lan, K.W., Evans, K., Lee, M.A., 2013. Effects of climate variability on the distribution and fishing conditions of yellowfin tuna (Thunnus albacares) in the western Indian Ocean. Clim. Change 119, 63–77. https://doi.org/10.1007/s10584-012-0637-8. Lan, K.W., Chou, C.P., Lee, M.A., Vayghan, A.H., 2018. Association between the interannual variation in the oceanic environment and catch rates of bigeye tuna (Thunnus obesus) in the Atlantic Ocean. Fish. Oceanogr. 27 (4), 1–13. https://doi. org/10.1111/fog.12259. Langley, A., Briand, K., Kirby, D.S., 2009. Murtugudde, R. Influence of oceanographic variability on recruitment of yellowfin tuna (Thunnus albacares) in the western and central Pacific Ocean. Can. J. Fish. Aquat. Sci. 66, 1462–1477. https://doi.org/ 10.1139/F09-096. Marsac, F., 2008. Outlook of Ocean Climate Variability in the West Tropical Indian Ocean, pp. 1997–2008 (Working document for IOTC Indian Ocean Tuna Commission). McPhaden, M.J., Zhang, X., Hendon, H.H., Wheeler, M.C., 2006. Large scale dynamics and MJO forcing of ENSO variability. Geophys. Res. Lett. 33 (16) https://doi.org/ 10.1029/2006GL026786. Menard, F., Marsac, F., Bellier, E., Cazelles, B., 2007. Climatic Oscillations and tuna catch rates in the Indian Ocean: a wavelet approach of time series analysis. Fish. Oceanogr. 16, 95–104. https://doi.org/10.1111/j.1365-2419.2006.00415.x. Meyers, G., McIntosh, P., Pigot, L., Pook, M., 2007. The Years of El Ni~ no, La Ni~ na, and interactions with the Tropical Indian Ocean. J. Clim. 20, 2872–2880. Maunder, M.N., Punt, A.E., 2004. Standardizing catch and effort data: a review of recent approaches. Fish. Res. 70, 141–159. https://doi.org/10.1016/j.fishres.2004.08.002. Murtugudde, R.G., Signorini, S.R., Vhristian, J.R., Busalacchi, A.J., McClain, C.R., Picaut, J., 1999. Ocean colour variability of the tropical Indo-Pacific basin observed by SeaWiFS during 1997-1998. J. Geophys. Res. 104 (C8), 18351–18366. https:// doi.org/10.1029/1999JC900135. Paden, C.A., Abbot, M.R., Winant, C.D., 1991. Tidal and atmospheric forcing of the upper Ocean in the gulf of California, 1: sea surface temperature variability. J. Geophys. Res. 96 (C10), 18337–18359. https://doi.org/10.1029/91JC01597. Saji, N.H., Goswami, B.N., Vinayachandran, P.N., Yamagata, T., 1999. A dipole mode in the tropical Indian Ocean. Nature 401, 360–363. https://doi.org/10.1038/43854. Schaefer, K.M., Fuller, D.W., Block, B.A., 2007. Movements, behavior, and habitat utilization of yellowfin tuna (Thunnus albacares) in the northeastern Pacific Ocean, ascertained through archival tag data. Mar. Biol. 152, 503–525. https://doi.org/ 10.1007/s00227-007-0689-x. Schaefer, K.M., Fuller, D.W., Block, B.A., 2011. Movements, behavior, and habitat utilization of yellowfin tuna (Thunnus albacares) in the Pacific Ocean off Baja California, Mexico, determined from archival tag data analyses, including unscented Kalman filtering. Fish. Res. 112 (1–2), 22–37. https://doi.org/10.1016/j. fishres.2011.08.006. Senina, I., Lehodey, P., Calmettes, B., Nicol, S., Caillot, S., Hampton, J., Williams, P., 2015. SEAPODYM Application for Yellow Tuna in the Pacific Ocean. WCPFC-SC112015/EB-IP-01. Sournia, A., 1994. Pelagic biogeography and fronts. Prog. Oceanogr. 34, 109–120. https://doi.org/10.1016/0079-6611(94)90004-3. Susanto, R.D., Marra, J., 2005. Effects of the 1997/98 El Ni~ no on chlorophyll a variability along the southern coasts of java and sumatra. Oceanography 18, 124–127. https:// doi.org/10.5670/oceanog.2005.13. Syamsuddin, M.L., Saitoh, S.I., Hirawake, T., Bachri, S., Harto, A.B., 2013. Effects of El ni~ no–southern oscillation events on catches of bigeye tuna (Thunnus obesus) in the eastern Indian ocean off java. Fish. Bull. 111 (2), 175–188. https://doi.org/10.7755/ FB.111.2.5. Torrence, C., Compo, G.P., 1998. A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 79, 61–78. https://doi.org/10.1175/1520-0477. Venzke, S., Latif, M., Villwock, A., 2000. Coupled GCM ECHO-2. Part II: Indian ocean response to ENSO. J. Clim. 13, 1371–1383. https://doi.org/10.1175/1520-0442. Wiggert, J.D., Vialard, J., Behrenfeld, M.J., 2009. Basinwide modification of dynamical and biogeochemical processes by the positive phase of the Indian Ocean Dipole during the SeaWiFS era. Indian Ocean biogeochemical processes and ecological variability 185, 350. https://doi.org/10.1029/2008GM000776. Wood, S.M., 2006. Generalized Additive Models, an Introduction with R. Chapman and Hall, London, p. 392pp. Xie, P., Arkin, P.A., 1997. Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Am. Meteorol. Soc. 78, 2539–2558. https://doi.org/10.1175/1520-0477. Zudaire, I., Murua, H., Grande, M., Bodin, N., 2013. Reproductive potential of yellowfin tuna (Thunnus albacares) in the western Indian Ocean. Fish. Bull. 111, 252–264. https://doi.org/10.7755/FB.111.3.4.
This study was financially supported by the Council of Agriculture (107AS-9.1.5-FA-F1 and 108AS-9.2.1-FA-F1) and the National Science Council (MOST 106-2611-M-019-012 and MOST 107-2611-M-019-017). We are grateful to the Overseas Fisheries Development Council (OFDC) of Taiwan for providing data from the Taiwanese longline fishery. This manuscript was edited by Wallace Academic Editing. The first author especially thanks the conveners of the 4th CLIOTOP symposium and issues discussed in this paper are motivated in part by the CLIOTOP program. References Alonso-Fern� andez, A., Saborido-Rey, F., 2012. Relationship between energy allocation and reproductive strategy in Trisopterus luscus. J. Exp. Mar. Biol. Ecol. 416–417, 8–16. https://doi.org/10.1016/j.jembe.2012.02.001. Bard, F.X., Chabanet, C., Caouder, N., 1991. Croissance du thon albacore (Thunnus albacares) en oc� ean Atlantique estim�ee par marquages. Col. Vol. Sci. Pap., ICCAT 36, 182–204. Bard, F.X., Yen, S., Stein, A., 1999. Habitat of deep swimming tuna (Thunnus obesus, T. albacares, T. alalunga) in Central South Pacific. Collect. Vol. Sci. Pap, ICCAT 49 (3), 309–317. Bertrand, A., Josse, E., Bach, P., Gros, P., Dagorn, L., 2002. Hydrological and trophic characteristics of tuna habitat: consequences on tuna distribution and longline catchability. Can. J. Fish. Aquat. Sci. 59 (6), 1002–1013. https://doi.org/10.1139/ f02-073. Boyce, D.G., Tittensor, D.P., Worm, B., 2008. Effects of temperature on global patterns of tuna and billfish richness. Mar. Ecol. Prog. Ser. 355, 267–276. https://doi.org/ 10.3354/meps07237. Corbineau, A., Rouyer, T., Cazelles, B., Fromentin, J.M., Fonteneau, A., Menard, F., 2008. Time series analysis of tuna and swordfish catches and climate variability in the Indian Ocean (1968–2003). Aquat. Living Resour. 21, 277–285. https://doi.org/ 10.1051/alr:2008045. Currie, J.C., Lengaigne, M., Vialard, J., Kaplan, D.M., Aumont, O., Naqvi, S.W.A., Maury, O., 2013. Indian Ocean dipole and El Nino/southern oscillation impacts on regional chlorophyll anomalies in the Indian Ocean. Biogeosciences 10 (10), 6677–6698. https://doi.org/10.5194/bg-10-6677-2013. Dammannagoda, S.T., Hurwood, D.A., Mather, P.B., 2008. Evidence for fine geographical scale heterogeneity in gene frequencies in yellowfin tuna (Thunnus albacares) from the north Indian Ocean around Sri Lanka. Fish. Res. 90, 147–157. https://doi.org/ 10.1016/j.fishres.2007.10.006. Dell, J., Wilcox, C., Hobday, A.J., 2011. Estimation of yellowfin tuna (Thunnus albacares) habitat in waters adjacent to Australia’s East Coast: making the most of commercial catch data. Fish. Oceanogr. 20, 383–396. https://doi.org/10.1111/j.13652419.2011.00591.x. Emery, W.J., Thomson, R.E., 2001. Empirical orthogonal functions. In: Data Analysis Methods in Physical Oceanography, pp. 319–343. https://doi.org/10.1016/B978-0444-50756-3.X5000-X. Feng, M., Meyers, G., 2003. Interannual variability in the tropical Indian Ocean: a twoyear time scale of Indian Ocean Diople. Deep Sea Res. Part II Top. Stud. Oceanogr. 50, 2263–2284. https://doi.org/10.1016/S0967-0645(03)00056-0. Fonteneau, A., Soubrier, P., 1996. Interactions between Tuna Fisheries: a Global Review with Specific Examples from the Atlantic Ocean, pp. 84–123. FAO Fisheries Technical Paper. Fonteneau, A., 2008. A Working Proposal for a Yellowfin Growth Curve to Be Used during the 2008 Yellowfin Stock Assessment. IOTC-2008-WPTT-4. Fonteneau, A., Alay� on, P.J.P., Marsac, F., 2017. Exploitation of large yellowfin tuna caught in free schools concentrations during the 2013 spawning season (December 2012-May 2013). Collect. Vol. Sci. Pap. ICCAT 73 (2), 868–882. Fu, D., Langley, A., Merino, G., Urtizberea, A., 2018. Preliminary Indian Ocean Yellowfin Tuna Stock Assessment 1950-2017 (Stock Synthesis). IOTC-2018-WPTT20-33. Greehan, J., Hoyle, S., 2013. Review of Length Frequency Data of the Taiwanese Distant Water Longline Fleet. IOTC-2013-WPDCS09-12 Rev_2. Grinsted, A., Moore, J.C., Jeverejeva, S., 2004. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process Geophys. 11, 561–566. https://doi.org/10.5194/npg-11-561-2004. Gaertner, D., Hallier, J.P., 2015. Tag shedding by tropical tunas in the Indian Ocean and other factors affecting the shedding rate. Fish. Res. 163, 98–105. Go~ ni, N., Didouan, C., Arrizabalaga, H., Chifflet, M., Arregui, I., Goikoetxea, N., Santiago, J., 2015. Effect of oceanographic parameters on daily albacore catches in the Northeast Atlantic. Deep-Sea Res. Pt. II 113, 73–80. https://doi.org/10.1016/j. dsr2.2015.01.012. Henderson, B.A., Morgan, G.E., 2002. Maturation of walleye by age, size and surplus energy. J. Fish Biol. 61, 999–1011. https://doi.org/10.1111/j.1095-8649.2002. tb01858.x. Kao, H.Y., Yu, J.Y., 2009. Contrasting eastern-Pacific and central-Pacific types of ENSO. J. Clim. 22, 615–632. https://doi.org/10.1175/2008JCLI2309.1.
10
Contents lists available at ScienceDirect
Deep-Sea Research Part II journal homepage: http://www.elsevier.com/locate/dsr2
Influence of oceanographic and climatic variability on the catch rate of yellowfin tuna (Thunnus albacares) cohorts in the Indian Ocean Kuo-Wei Lan a, b, *, Yi-Jay Chang c, Yan-Lun Wu a a
Department of Environmental Biology and Fisheries Science, National Taiwan Ocean University, 2 Pei-Ning Rd., Keelung, 20224, Taiwan, R.O.C Center of Excellence for Oceans, National Taiwan Ocean University, 2 Pei-Ning Rd., Keelung, 20224, Taiwan, R.O.C c Institute of Oceanography, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan, R.O.C b
A R T I C L E I N F O
A B S T R A C T
Keywords: Yellowfin tuna cohorts Oceanographic variability Indian ocean dipole Oceanic ni~ no index Geographical localization Empirical orthogonal function
Using Taiwanese longline fishery data, this study investigated the influence of climate variability and environ mental conditions in the Indian Ocean on the catch rates and distribution of immature and mature cohorts of yellowfin tuna. The variations in the seasonal distribution of immature and mature cohorts suggest that yellowfin tuna move extensively from the Arabian Sea and Bay of Bengal to the coastal waters of Somalia and around Madagascar in the Indian Ocean. The high recruitments of the immature cohort were found in 1998–2002, and the catch rate of the immature and mature cohorts revealed positive associations with periodicities of approx imately 3–4 years. We found that the distributions and catch rates of the two cohorts were sensitive to variations in climatic and marine environments. Sea surface temperature was the most influential environmental variable for both cohorts, and Chl-a was not statistically significant for the immature cohort. There was a significant negative correlation between the catch rate of the immature and mature cohort and Indian Ocean Dipoles (IODs), with periodicities of approximately 3 years during the study period, and had periodicities of approximately 1–3 years with El Ni~ no/Southern Oscillation (ENSO) events. Furthermore, the influence of IODs exhibited greater variance than that of ENSO events, and the influence of ENSO was only evident near the Arabian Sea. The in fluence of concurrent positive IOD and El Ni~ no events led to lower catch rates for the mature cohort in 2008–2009 in the northwestern Indian Ocean.
1. Introduction Climatic oscillations, anomalies, and changes affect numerous ecological processes in marine ecosystems. Consequently, they play an essential role in controlling the distribution and abundance of top predators, such as tuna, in the ocean (Langley et al., 2009; Syamsuddin ~ i et al., 2015; Lan et al., 2018). The El Nin ~ o/Southern et al., 2013; Gon Oscillation (ENSO) is a well-known and dominant form of interannual climate variability that develops from air–sea interactions in the tropical Pacific Ocean and affects weather patterns worldwide (McPhaden et al., 2006). Large-scale climate fluctuations, known collectively as the Indian Ocean Dipoles (IODs), also occur in the Indian Ocean. These are driven by patterns of interannual variability in sea surface temperatures (SSTs) that result in accompanying wind and precipitation anomalies (Saji et al., 1999; Feng and Meyers, 2003; Meyers et al., 2007). Tele connections associated with El Ni~ no result in an overall warming of the Indian Ocean due to changing cloud cover and wind patterns associated
with changes in the ascending and descending branches of Walker cir culation (Venzke et al., 2000; Xie and Arkin, 1997; Currie et al., 2013). ~ o events, they can also Although IODs tend to be triggered by El Nin occur independently (Meyers et al., 2007). The biological impacts of IOD and ENSO events in the Indian Ocean have been associated with ecosystems, fishery resources, and carbon sequestration. For instance, shifts in the distributions and catch rates of yellowfin tuna in the Indian Ocean have been associated with IOD events (Menard et al., 2007; Marsac, 2008; Lan et al., 2013). A significant negative association between IODs and the catch rates of yellowfin tuna with a periodicity of approximately 4 years was observed (Lan et al., 2013). During positive IOD events, shifting trade winds increasingly converge in the west, reducing wind speeds and convection. This causes a general increase in SST, reduction in coastal upwelling, and decrease in productivity (Saji et al., 1999; Marsac, 2008). The 1997 positive IOD/El Ni~ no event also substantially affected oceanographic conditions and had a positive effect on the catch rates of bigeye tuna in the eastern
* Corresponding author. Department of Environmental Biology and Fisheries Science, National Taiwan Ocean University, Keelung, 20224, Taiwan, R.O.C. E-mail addresses: [email protected] (K.-W. Lan), [email protected] (Y.-J. Chang), [email protected] (Y.-L. Wu). https://doi.org/10.1016/j.dsr2.2019.104681 Received 15 March 2019; Accepted 23 October 2019 Available online 24 October 2019 0967-0645/© 2019 Published by Elsevier Ltd.
Please cite this article as: Kuo-Wei Lan, Deep–Sea Research II, https://doi.org/10.1016/j.dsr2.2019.104681
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
Indian Ocean (Syamsuddin et al., 2013). The intense 1997 positive ~ o event was characterized by a strong phytoplankton bloom IOD/El Nin in the eastern equatorial Indian Ocean, an area normally characterized by low bioproductivity (Murtugudde et al., 1999; Susanto and Marra, 2005). These contrasts reveal the mercurial nature of biological impacts arising from different climatic events. In the Indian Ocean, catches of yellowfin tuna are distributed in all the tropical waters and primarily concentrated in western tropical wa ters, the Arabian Sea, and the north of the Mozambique Channel (Fu et al., 2018). Tag recoveries provide evidence of large movements of yellowfin tuna within the western equatorial region; however, few ob servations of large-scale transverse movements in the Indian Ocean have been reported (Gaertner and Hallier, 2015). This may indicate that the western and eastern regions of the Indian Ocean support relatively distinct subpopulations of yellowfin tuna. Studies of yellowfin tuna stock structure using DNA techniques suggest genetically discrete sub populations in the northwestern Indian Ocean (Dammannagoda et al., 2008) and the waters off the coast of India (Kunal et al., 2013). However, no comprehensive study of yellowfin tuna has analyzed the entire ocean basin. The movement patterns and their variability of tuna are controlled by environmental conditions because the time and area strata favorable for spawning and feeding are limited and vary (Fonteneau and Soubrier, 1996). Research indicates that yellowfin tuna prefer warm waters and are found in regions with a high concentration of primary productivity (Langley et al., 2009; Dell et al., 2011). In the Atlantic Ocean, preadult yellowfin tuna (65–110 cm) migrate toward higher latitudes and trop ical waters by following cyclical seasonal migration patterns; most specimens return to the spawning areas when they have reached sexual maturity (Fonteneau and Soubrier, 1996; Fonteneau et al., 2017). Adult trophic migration toward higher latitudes occur during the summer, and genetic migration across the ocean (Bard et al., 1991; Fonteneau et al., 2017). Archival tag data on mature yellowfin tuna in the Pacific Ocean also show cyclical movement between suitable spawning and feeding habitats. Most yellowfin tuna remain at shallow depths (<50 m) at night and do not dive to greater depths (>100 m) during the day (Schaefer et al., 2007; Gaertner and Hallier, 2015). Variation in the population abundance and distribution of yellowfin tuna has been linked to large-scale climate phenomena (Marsac, 2008; Lan et al., 2012a; Senina et al., 2015). However, the relationship be tween the distribution of yellowfin tuna cohorts and environmental variation is unclear. Estimating the preferred environmental conditions of each cohort is important for determining the migratory patterns and potential interactions of fish located in various areas. This study inves tigated the temporal and spatial variations in fishing grounds and the catch rates of immature and mature yellowfin tuna cohorts associated with oceanographic conditions using Taiwanese longline fishery data. Additionally, this study explored the processes underlying the climatic events (IOD and ENSO) influencing the interannual catch rates and distribution of yellowfin tuna cohorts.
To estimate the mean values of fork length by age, the parameters of the von Bertalanffy growth equation in the Indian Ocean were used (Fonteneau, 2008). The fork length of 50% of the mature (approximately 2 y) female yellowfin tuna population in the Indian Ocean was estimated to be 102 cm (Zudaire et al., 2013). Mature tuna cohorts also showed geographical segregation between their original spawning and feeding grounds (Fonteneau and Soubrier, 1996). We separated immature (<2 y) and mature (�2 y) cohorts and calculated the monthly catch rates for each as the number of individuals captured per 1000 hooks (ind/103 hooks). 2.2. Environmental variables and climatic variability indices Monthly environmental data for the period from January 2003 through December 2012 to match the period of Chlorophyll-a (Chl-a) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) were sourced as follows: (1) SST pathfinder monthly composite fields with a 4-km spatial resolution were obtained from the National Oceanographic Data Center. (2) Sea surface height deviation (SSHD) was obtained from the Archiving, Validation and Interpretation of Sat ellite Oceanographic data program with 0.25� resolution. (3) Mixedlayer depth (MLD) data with 1� spatial resolution were downloaded from the European Centre for Medium-Range Weather Forecast Ocean Analysis System. (4) Chl-a data were obtained from the MODIS with a 9km spatial resolution and downloaded from the NASA Ocean Color Web. The environmental variables were then calculated as monthly means on a spatial grid with 5� resolutions to fit the fishery data. Environmental variations in positive and negative anomalies asso ciated with IODs were represented by the Dipole Modular Index (DMI), which is constructed from monthly differences between SST anomalies in the western equatorial (50� E–70� E, 10� S–10� N) and southeastern equatorial (90� E–110� E, 10S� –0� N) Indian Ocean. The ENSO was rep ~ o Index (ONI) and estimated by a 3-month resented by the Oceanic Nin ~ o 3.4 region (5� N–5� S, running mean of SST anomalies in the Nin 120� W–170� W) during 1971–2000. 2.3. Generalized additive models We used generalized additive models (GAMs) to investigate the re lationships between environmental variations and the catch rates of immature and mature cohorts. Fishery data from 2003 to 2012 were used in the GAM to match the period of Chl-a data. GAMs were con structed in R (Version 2.15.0) using the GAM function of the mgcv package; catch rate was used as the response variable, variables expressing time (year and month), location (longitude and latitude), and environmental factors (SST, Chl-a, SSHD, MLD, DMI, and ONI) were used as the predictor variables. GAMs were construed as: log(CPUE þ c) ¼ a0 þ s(x1) þ s(x2) þ s(x3) þ …. s(xn);
(1)
where a0 is a constant, and s(xi) is a spline smoothing function for each covariate xi or the interaction between two covariates. All covariates were considered to be continuous, and the effective degrees of freedom were estimated for each main factor. Because the log-link function cannot handle zeros, we added a constant value of 0.1 (c) to all catch rates. A constant value of 0.1 (c) is commonly used in catch rate stan dardizations (e.g., Maunder and Punt, 2004; Wood, 2006; Lan et al., 2018). Time and location were treated as interaction terms to account for possible interannual variability—possibly driven by environmental variations—in the spatial distribution of yellowfin tuna cohorts. A model with optimal conformation was selected using a stepwise pro cedure based on the lowest Akaike information criterion (AIC).
2. Materials and methods 2.1. Yellowfin tuna fishery and fork length data Since the 1990s, Taiwanese longline fishing has been the main source of size data used by longline fleets. This has been important for in ferences about the population dynamics in areas where longline catches account for up to 85% of the total catch of tropical and temperate tuna species in the Indian Ocean (Greehan and Hoyle, 2013). The fishery data in this study were collected from 1998 to 2012 and compiled from the logbooks of Taiwanese longline fleets provided by the Overseas Fisheries Development Council of the Republic of China. The fishery data for 5� spatial grids included area coordinates, fishing effort (number of hooks), fishing date, number of catches, and randomly sampled length mea surement data (fork length in cm).
2.4. Wavelet analysis and empirical orthogonal function To confirm the effect of environmental variations caused by climate 2
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
variability on the spatial and temporal catch rates of immature and mature cohorts, wavelet analysis and empirical orthogonal functions (EOFs) were utilized. Wavelet analysis is a common tool for analyzing time series data; it involves decomposing a time series in a time –frequency space, facilitating identification of the dominant periodic components and determination of how these vary over time (Torrence and Campo, 1998). The Morlet wavelet is the most popular complex wavelet used in practice and is defined as
ψ 0 ðηÞ ¼ π
1=4 iϖo
e
e
η2 =2
greatly in time and space. T’ðx; tÞ ¼ Tðx; tÞ
N 1 X Tðx; tÞ N t¼1
(3)
Using the singular-value decomposition method, amplitude scores of T0 (x,t) catch rate data were used to decompose the data matrix to elucidate spatial patterns and time series variations. 3. Results
(2)
3.1. Catch rate patterns of immature and mature cohorts in the Indian Ocean
where η is a dimensionless time parameter and ω0 is a dimensionless frequency; this is intended to balance the localization of time and fre quency. A 95% significance level was selected based on bootstrap sim ulations run as a first-order autoregressive process. The autoregression coefficient was empirically obtained from the time series data. Crosswavelet coherence (Grinsted et al., 2004) and phase analyses were then used to investigate the causal relationships between climate events and catch rates. EOF analysis provides a concise description of the spatial variability of time series data in terms of orthogonal functions or statistical modes. It decomposes a time series dataset into its orthogonal component modes; this was used to reveal the dominant patterns of annual catch rate variability for both immature and mature cohorts. Catch rate data in the EOF analysis were arranged in a two-dimensional M � N matrix, T(x, t), where M is the number of elements in the spatial dimension (in this case, the number of 5� spatial degrees in the Indian Ocean), and N is the number of elements in the temporal dimension (1998–2012). In line with the suggestions of Paden et al. (1991) and Emery and Thomson (2001), the temporal means of the data matrix were removed before EOF analysis by finding the mean over the time series for each degree and subtracting it from each pixel (Eq. (3)). This revealed features that vary
The catch percentage of the Taiwanese longline fisheries in the In dian Ocean showed that the major populations were mature cohorts aged �3–5 years (approximately 85%, Fig. 1a). These were concentrated in the tropical Indian Ocean and Arabian Sea (Fig. 2a,c). The immature cohort comprised 25–40% of the total catch in 1998–2000 and decreased to approximately 8% after 2003 (Fig. 1a). A high catch per centage of immature cohort occurred south of Madagascar, Bay of Bengal and in the central Indian Ocean near 60� E–90� E and 10� N–20� S (Fig. 2a,b). The catch rates of the immature and mature cohorts exhibited interannual variation (Fig. 1b). High catch rates of the immature cohort occurred in 1998–2002 (>0.5 inds/103 hooks), whereas high catch rates of the mature cohort occurred only in 2004–2006 (>4.0 inds/103 hooks). Seasonal spatial distributions showed that the immature cohort was concentrated to the north of Madagascar, in the Arabian Sea, and in the Bay of Bengal in the first and second quarters (Fig. 3a and b) and then moved to the central equatorial Indian Ocean in the fourth quarter (Fig. 3d). The mature cohorts were concentrated in the Arabian Sea and Bay of Bengal throughout the year (Fig. 3e–h) and in the Somalian coastal waters in the first and fourth
Fig. 1. Annual trends for the (a) catch percentage of yellowfin tuna at <2 to >5 years and (b) catch rates for immature and mature cohorts caught by Taiwanese longliners in the Indian Ocean in 1998–2012. 3
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
Fig. 2. Spatial distribution of the catch percentage of (a) yellowfin tuna at <2 to >5 years, (b) the immature cohort, and (c) the mature cohort for the entire study period (1998–2012).
Fig. 3. Quarterly mean catch rates for immature (a–d) and mature (e–h) cohorts in the Indian Ocean. 4
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
quarter (Fig. 3e,h), gradually moving to the water north of Madagascar in the second and third quarters (Fig. 3f,g).
series data of the climatic indices and catch rate dataset. Wavelet analysis identified a significant positive correlation between the catch rate of the immature and mature cohorts, with periodicities of approx imately 3–4 years during the study period (Fig. 6a). A significant negative correlation was also identified between the catch rate of both cohorts and DMI, with periodicities of approximately 3 years during 2003–2008 (Figs. 6b) and 2003 to 2012 (Fig. 6d), respectively. A sig nificant negative correlation was found between the catch rates of the immature cohort and ONI with periodicities of approximately 1–3 years (Fig. 6c), and with a periodicity of approximately 2 years during 2004–2008 for the mature cohort (Fig. 6e). It is therefore important to obtain a better understanding of the re lationships between the climatic indices and the underlying processes associated with variability in catch rates. The first two modes in the EOF analysis accounted for 36.63% and 13.86% of the variance in the catch rate for the immature cohort and 40.06% and 18.27% of the variance in the catch rate of the mature cohort (Figs. 7 and 8). In the first mode for the immature cohort, the spatial amplitude showed positive values in the Bay of Bengal and northern Arabian Sea, especially in the Bay of Bengal (Fig. 7a). The second mode revealed inverse distribution trends in the Arabian Sea and Bay of Bengal (Fig. 7b). For the mature cohort, the spatial amplitude showed highest positive values in the Bay of Bengal and northeastern Arabian Sea (Fig. 8a). The second mode revealed positive values in the northwestern Indian Ocean and drop in the eastern Indian Ocean (Fig. 8b). The spatial amplitude results showed a significant spatial pattern in the Arabian Sea and Bay of Bengal for the immature and mature cohorts; the correlations of SST, Chl-a, MLD in the Arabian Sea and Bay of Bengal, and climatic indices (ONI and DMI) with eigenvectors were further analyzed using Pearson’s test. The annual variation of the eigenvectors was negatively correlated with DMI (r ¼ -0.48) and positively correlated with MODIS Chl-a (r ¼ 0.56) in the Bay of Bengal in the first mode, and with ONI (r ¼ 0.48) and Arabian SST (r ¼ 0.74) in the second mode of the immature group (Fig. 7c,d). For the mature cohort, the DMI (r ¼ 0.52) and Arabian Sea Chl-a (r ¼ -0.42) were positive correlated in the first mode. ONI (r ¼ -0.51) and Arabian Sea SST (r ¼ -0.42) were negatively correlated in the second mode (Fig. 8 c,d).
3.2. Relationships between catch rates and environmental variables The model selection process for GAM analysis is shown in Table 1; the deviance explained by the selected GAMs was 33.40% and 48.10% for the immature and mature cohorts (Table 1), respectively. Interaction terms of year with latitude (22.8%) and longitude (12.3%) accounted for a large part of the variation for the immature cohorts. High catch rates occurred in the north and central western Indian Ocean (Fig. 4f,g; 10� S–20� S, 40� E–80� E) in the first half of the year (Fig. 4h) but decreased after 2006 in the central western areas. The SST and climate indices (ONI and DMI) were the most important environmental vari ables; however, Chl-a had no statistically significant predictors (p < 0.05). A positive association was observed between the catch rates and SSTs of approximately 26� C–29 � C. The MLDs of >150 m and SSHDs of >0.4 m were negatively associated with catch rates, respectively (Fig. 4a–c). The effect of ONI and DMI on catch rates showed a slightly negative but not obvious trends (Fig. 4d,e). For the mature cohort, interaction terms of latitude with longitude (34.3%) and year with month (14.4%) accounted for a large part of the variation. The interaction terms showed that the high catch rates of the mature cohorts were distributed in the northwestern Indian Ocean (10� S–25� N, 40� E–80� E) and Bay of Bengal (10� N–25� N, 80� E–100� E) (Fig. 5g). These increased in the first half of years from 2004 to 2007 but decreased after 2008 (Fig. 5h). All the environmental variables had statistically significant predictors (p < 0.05), of which SST was strongest (9.24%). A positive association was observed between the catch rates and SSTs of approximately 27� C–30 � C (Fig. 5a). The SSHDs revealed dome-shaped and had a positive association of 0.2–0.5 m (Fig. 5c). The MLDs of >150 m and Chl-a of >0.5 mg/m3 were negatively associated with catch rates, respectively (Fig. 5b,d). The ONI and DMI also showed a negative association (Fig. 5d,e). 3.3. Spatial and time series analysis The GAM results showed negative associations of ONI and DMI with the catch rates of the mature cohort and not obvious patterns of the immature cohort. Using a state-space time series analysis, a single decomposition procedure effectively removed seasonality from the time
4. Discussion An analysis of the catch rate and distribution of immature and
Table 1 Construction of the GAM, residual deviance, approximate AIC, and p values of immature and mature cohorts of yellowfin tuna in the Indian Ocean. Residual deviance
One variable model explained
AIC
p value
Immature Null þs(SST) þs(MLD) þs(SSHD) þs(ONI) þs(DMI) þs(year*Latitude) þs(year*Longitude) þs(Month*Longitude) Total deviance explained
7452.00 6874.00 6048.00 3036.00 8432.00 6685.00 28663.00 27814.00 27973.00 33.40%
3.63% 0.83% 1.75% 2.33% 2.96% 22.80% 12.30% 5.56%
15620.24 15536.28 15504.65 15484.48 15444.48 15400.09 15275.33 15116.45 15035.14
<0.05 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05
Mature Null þs(SST) þs(Chl-a) þs(MLD) þs(SSHD) þs(ONI) þs(DMI) þs(year*Month) þs(Latitude*Longitude) Total deviance explained
7452.00 6960.00 4862.00 6645.00 7152.00 6043.00 7833.00 26389.00 24510.00 48.10%
9.24% 1.92% 2.39% 3.98% 2.95% 3.00% 14.40% 34.30%
12043.67 11995.61 11979.28 11896.35 11833.40 11824.97 11790.01 11605.29 11431.35
<0.05 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05
5
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
Fig. 4. Modeled effects of the (a) SST, (b) MLD, (c) SSHD, (d) ONI, and (e) DMI and interaction terms of (f) year and latitude, (g) year and longitude, and (h) month and longitude on catch rates for immature cohorts in the Indian Ocean. The solid and black-dotted lines show the fitted GAM function and 95% confidence intervals, respectively. The relative density of data points is indicated by the rug plot on the X-axis.
mature cohorts in relation to environmental variations in the Indian Ocean resulted in disparate estimates. The results of spatial distribution patterns and GAM analysis revealed important fishing grounds off the coast of Somalia and around Madagascar for immature and mature co horts, with seasonal variations. Yellowfin tuna exhibit more geograph ical segregation between their original spawning and feeding grounds (Fonteneau and Soubrier, 1996; Zudaire et al., 2013). Spawning usually takes place in specific equatorial areas during limited periods when mature fish congregate for spawning (Fonteneau and Soubrier, 1996). Zudaire et al. (2013) observed that spawning of yellowfin tuna mainly occurs in the equatorial area (0� S–10� S) and west of 75� E in the Indian Ocean, and that the main spawning season was November–February, with a second peak occurring in June. High catch rates for the mature cohort were also observed off the coast of Somalia in the first and fourth quarter and in the water around Madagascar in the second and third quarter. These movement patterns were similar to those observed by Zudaire et al. (2013). Large-scale migration often takes place between spawning and feeding grounds and covers hundreds or thousands of miles, as has been clearly demonstrated in Atlantic yellowfin tuna (Fonteneau and Soub rier, 1996; Schaefer et al., 2011). Thus, yellowfin tuna may spend a large part of their life cycle outside their spawning zone. Similar extensive movement can also be observed in the Indian Ocean. High catch rates for immature and mature cohorts were observed in the Arabian Sea and Bay of Bengal, which are important feeding grounds for yellowfin tuna. Fishing grounds and fishing periods in the Arabian Sea are associated
with upwelling and high primary productivity which attract large con centrations of crustaceans and small mesopelagic fish (Lan et al., 2012b; Fu et al., 2018). When they reach sexual maturity, most specimens re turn to the spawning areas in tropical regions (Fonteneau and Soubrier, 1996). Thus, our results suggest extensive movement of yellowfin tuna from the Arabian Sea and Bay of Bengal (feeding grounds) to the coastal waters of Somalia and Madagascar (spawning grounds) in the Indian Ocean. The spawning events of yellowfin tuna occur where SST >24 � C, which seems to regulate spawning activity; the optimal temperature for the feeding habitat of adults was estimated to be 21.9� C–26.6 � C (Schaefer et al., 2007; 2011; Boyce et al., 2008). Our results show that the immature and mature cohorts had a similar range of optimal envi ronments, although this was wider for the mature cohort than for the immature cohort. SST was the most influential environmental variable for both cohorts (26� C–29 � C for immature and 27� C–30 � C for mature). Chl-a was not statistically significant for the immature cohort, implying that shifts in the distribution of the immature cohort might be due to cyclical seasonal migrations rather than reflecting a search for prey. Yellowfin tuna are capital and income breeders (Alonso-Fern� andez and Saborido-Rey, 2012); this means that the energy stored before repro duction is insufficient to offset the cost of reproduction, and energy allocation from feeding is necessary for successful reproduction (Hen derson and Morgan, 2002). Based on our results, we suggest that, like other tropical species, when the immature yellowfin tuna reach their feeding grounds, they require energy from feeding as well as stored 6
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
Fig. 5. Modeled effects of the (a) SST, (b) MLD, (c) SSHD, (d) Chl-a, (e) ONI, and (f) DMI and interaction terms of (f) latitude and longitude, and (g) year and month on catch rates for mature cohorts in the Indian Ocean. See Fig. 4 for legends.
energy to facilitate ovarian development. Our results indicate that the catch rate of immature and mature co horts was positively correlated with SSHA values of approximately 0.2–0.5 m and implied a preference for areas closely associated with edges of eddies. The mesoscale oceanographic features would lead to the aggregation of tuna forage both by entrainment and by encouraging vertical mixing and introducing nutrients into the photic zone, thus promoting phytoplankton growth, which may subsequently attract predators from higher trophic levels (Sournia, 1994; Bertrand et al., 2002). The MLD of >150 m were negatively associated with catch rates of immature and mature cohorts. Bard et al. (1999) found the yellowfin tuna can reach depths of 350 m; however, both juvenile and adult yel lowfin tuna spend most of their time in the surface layer above 100 m. The older cohorts (�3.5 y) had a higher tolerance for low temperatures and oxygen concentrations, and enables them to spend more time searching for prey at depths below the mixed layer (Schaefer et al., 2007, 2011). The high recruitments of the immature cohort were found in 1998–2002, and the catch rate of the immature and mature cohorts revealed positive associations with periodicities of approximately 3–4 years. The higher catch rates of the mature cohort also occurred in 2003–2006. Lan et al (2012a) and Fu et al (2018) also revealed the high catch rates of mature cohorts observed in 2004–2006 were due to high recruitment during the late 1990s–early 2000s. The spatial amplitudes of first EOF mode for immature cohort suggested the highest re cruitments occurred in the Bay of Bengal and had positive associations with the annual variations of MODIS Chl-a in 2003–2012. The SeaWiFS
Chl-a were further used to observe the trends of Chl-a in 1998–2006 in Fig. 7a, and revealed the decreased trend since 1998. Currie et al. (2013) suggested that anomalous surface and euphotic layer chlorophyll blooms in the eastern equatorial Indian Ocean in fall, and southern Bay of Bengal in winter, are primarily related to IOD forcing. The first EOF mode of spatial amplitude also showed that IOD events influence the distributions of mature cohorts, and that the mature cohort is distributed more widely than the immature cohort (including in the Bay of Bengal, Arabian Sea, and around northern Madagascar). The first EOF mode of the mature cohort also indicated that the catch rates are associated with higher Chl-a in negative IOD events. The cool, steady, and strong southwestern monsoon associated with negative IOD events generates extensive coastal upwelling over the margins of continental areas in the northwestern Indian Ocean. This drives an increase in pri mary production, which would be expected to have flow-on effects on higher order productivity in the region (Menard et al., 2007; Corbineau et al., 2008). The second EOF mode revealed that the catch rates of the ~ a events (e.g., immature and mature cohorts increased during La Nin 1999, 2008 and 2011), but only around the Arabian Sea. Currie et al. (2013) used a biophysical general ocean circulation model to identify patterns of Chl-a anomalies driven by IOD and ENSO events. They found that ENSO has a weaker and lagged effect on ther mocline and chlorophyll anomalies in comparison with IOD. The only region where chlorophyll signals are predominantly related to ENSO variability is the western Arabian Sea, Oman coastal waters, and So ~ o events peaking in malia upwelling regions (Currie et al., 2013). El Nin the eastern Pacific Ocean are related to north-easterly wind anomalies, 7
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
Fig. 6. Cross-wavelet coherence between catch rates of (a) immature and mature cohorts (b–c) immature and (d–e) mature cohorts with DMI and ONI from 1998 to 2012. The solid-black contour encloses regions of >95% confidence, and the black line indicates where edge effects become important. High variability is represented by red, and weak variability by blue. Arrows indicate the phase relationship, with in-phase arrows pointing to the right and out-of-phase arrows pointing to the left. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 7. Spatial amplitude patterns (a–b) and interannual eigenvectors (c–d) of the first and second EOF modes for immature cohorts in the Indian Ocean. The annual trends for environmental factors (c–d) indicate that the phases have significant correlations with the interannual eigenvectors according to Pearson’s test.
and warmer SST in the western Arabian Sea is related to decreased Chl-a (Kao and Yu, 2009; Currie et al., 2013). Our investigation using EOF modes also showed similar patterns in that (1) the influence of IOD
exhibited greater variance (immature: 33.63%, mature: 40.06%) than that of ENSO events (immature: 13.86%, mature: 18.27%), and (2) the influence of ENSO events occurred only around the Arabian Sea and Bay 8
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
Fig. 8. Spatial amplitude patterns (a–b) and interannual eigenvectors (c–d) of the first and second EOF modes for mature cohorts in the Indian Ocean. See Fig. 7 for legends.
of Bengal. Therefore, the impact of climatic variability on the distribu tion and abundance of yellowfin tuna is also likely to depend on the balance between bottom-up effects (primary production) driven by nutrient availability. ~ o events yielded The influence of concurrent positive IOD and El Nin the highest eigenvector in 2008–2009 in the first EOF mode of the mature cohort, indicating lower catch rates in the northwestern Indian Ocean. The strongest SST anomaly patterns occurred in positive IODs ~ o and negative IODs with La Nin ~ a (Meyers et al., 2007). with El Nin Wiggert et al. (2009) found a profound redistribution of carbon uptake, with a large primary production increase in the eastern Indian Ocean. ~o This was roughly balanced by a decrease in positive IOD and El Nin events in western regions in 1997 and 2006. However, the results of wavelet analysis and EOF modes showed no clear periodicities between climatic indices and the catch rates of the mature cohort. The threat of piracy caused the bulk of the industrial purse seine and longline fleets to relocate from the coastal and off-shore waters of Somalia, Kenya, and Tanzania in 2008–2012. The Taiwanese longline data also lack effort and catch data in the northwestern Indian Ocean in 2010–2011. This may have influenced our results regarding the processes associated with climatic events that drive the distribution of yellowfin tuna cohorts.
mature cohorts suggest that yellowfin tuna move extensively from the Arabian Sea and Bay of Bengal to the coastal waters of Somalia and around Madagascar in the Indian Ocean. Furthermore, the influence on IODs exhibited a higher variance than that of ENSO events, whereas ~ o led to lower catch rates for the concurrent positive IODs and El Nin mature cohort in 2008–2009 in the northwestern Indian Ocean. Taiwanese longline fishery accounts for most of the samples collected from longline vessels operating in the Indian Ocean, 80%– 100% from the early 1990s–2000s (Greehan and Hoyle, 2013). How ever, the catch percentage of Taiwanese longline vessels in our results showed that the major fish populations were mature cohorts aged 2–3 years and that the immature cohort constituted only approximately 15% of the total catch. Newly recruited fish are primarily caught through purse seine fishery on floating objects and pole-and-line fishery in the Maldives (Fu et al., 2018). Moreover, comparisons of the size data collected from Taiwanese vessels by observers and logbooks since 2003 revealed that vessel masters reported considerably larger fish (Greehan and Hoyle, 2013). Greehan and Hoyle (2013) suggest that substantial changes in the Taiwanese mean sizes are likely to be due to sampling problems rather than changes in the size composition of the population. However, our results show that immature and mature cohorts exhibit different temporal and spatial distribution patterns related to environ mental features. Regarding the influence of the climatic indices on marine environments, Currie et al. (2013) found that the combined explanatory power of IOD and ENSO events in predicting depth-integrated Chl-a anomalies is relatively low in the Indian Ocean, and the response to concurrent events seems to be weak. Future studies must extend the time series of fishery data from interannual to decadal scales by including diverse varieties of gear types and fishing strategies to simulate animal responses to the spatial heterogeneity of biotic and abiotic conditions in a dynamic marine landscape.
5. Conclusions and remarks This study provides preliminary insight into some of the key envi ronmental features driving the distribution of yellowfin tuna cohorts and associated variability in fishery catches as well as the estimated preferred habitat and environmental conditions of immature and mature cohorts in terms of climatic indices of the IOD and ENSO. Individuals from a number of species made repeated use of specific areas with welldefined migration corridors between high-use areas (Fonteneau and Soubrier, 1996; Fonteneau et al., 2017); however, we found significant variability in movement from one year to the next within the same species. The variations in the seasonal distribution of immature and 9
K.-W. Lan et al.
Deep-Sea Research Part II xxx (xxxx) xxx
Acknowledgements
Kunal, S.P., Kumar, G., Menezes, M.R., Meena, R.M., 2013. Mitochondrial DNA analysis reveals three stocks of yellowfin tuna Thunnus albacares (Bonnaterre, 1788) in Indian waters. Conserv. Genet. 14, 205–213. https://doi.org/10.1007/s10592-013-0445-3. Lan, K.W., Lee, M.A., Nishida, T., Lu, H.J., Weng, J.S., Chang, Y., 2012. Environmental effects on yellowfin tuna catch by the Taiwan longline fishery in the Arabian Sea. Int. J. Remote Sens. 33 (23), 7491–7506. https://doi.org/10.1080/ 01431161.2012.685971. Lan, K.W., Nishida, T., Lee, M.A., Lu, H.J., Huang, H.W., Chang, S.K., Lan, Y.C., 2012. Influence of the marine environment variability of the yellowfin tuna (Thunnus albacares) catch rate by the Taiwanese longline fishery in the Arabian Sea, with special reference to the high catch in 2004. J. Mar. Sci. Technol. 20 (5), 514–524. https://doi.org/10.6119/JMST-011-0506-1. Lan, K.W., Evans, K., Lee, M.A., 2013. Effects of climate variability on the distribution and fishing conditions of yellowfin tuna (Thunnus albacares) in the western Indian Ocean. Clim. Change 119, 63–77. https://doi.org/10.1007/s10584-012-0637-8. Lan, K.W., Chou, C.P., Lee, M.A., Vayghan, A.H., 2018. Association between the interannual variation in the oceanic environment and catch rates of bigeye tuna (Thunnus obesus) in the Atlantic Ocean. Fish. Oceanogr. 27 (4), 1–13. https://doi. org/10.1111/fog.12259. Langley, A., Briand, K., Kirby, D.S., 2009. Murtugudde, R. Influence of oceanographic variability on recruitment of yellowfin tuna (Thunnus albacares) in the western and central Pacific Ocean. Can. J. Fish. Aquat. Sci. 66, 1462–1477. https://doi.org/ 10.1139/F09-096. Marsac, F., 2008. Outlook of Ocean Climate Variability in the West Tropical Indian Ocean, pp. 1997–2008 (Working document for IOTC Indian Ocean Tuna Commission). McPhaden, M.J., Zhang, X., Hendon, H.H., Wheeler, M.C., 2006. Large scale dynamics and MJO forcing of ENSO variability. Geophys. Res. Lett. 33 (16) https://doi.org/ 10.1029/2006GL026786. Menard, F., Marsac, F., Bellier, E., Cazelles, B., 2007. Climatic Oscillations and tuna catch rates in the Indian Ocean: a wavelet approach of time series analysis. Fish. Oceanogr. 16, 95–104. https://doi.org/10.1111/j.1365-2419.2006.00415.x. Meyers, G., McIntosh, P., Pigot, L., Pook, M., 2007. The Years of El Ni~ no, La Ni~ na, and interactions with the Tropical Indian Ocean. J. Clim. 20, 2872–2880. Maunder, M.N., Punt, A.E., 2004. Standardizing catch and effort data: a review of recent approaches. Fish. Res. 70, 141–159. https://doi.org/10.1016/j.fishres.2004.08.002. Murtugudde, R.G., Signorini, S.R., Vhristian, J.R., Busalacchi, A.J., McClain, C.R., Picaut, J., 1999. Ocean colour variability of the tropical Indo-Pacific basin observed by SeaWiFS during 1997-1998. J. Geophys. Res. 104 (C8), 18351–18366. https:// doi.org/10.1029/1999JC900135. Paden, C.A., Abbot, M.R., Winant, C.D., 1991. Tidal and atmospheric forcing of the upper Ocean in the gulf of California, 1: sea surface temperature variability. J. Geophys. Res. 96 (C10), 18337–18359. https://doi.org/10.1029/91JC01597. Saji, N.H., Goswami, B.N., Vinayachandran, P.N., Yamagata, T., 1999. A dipole mode in the tropical Indian Ocean. Nature 401, 360–363. https://doi.org/10.1038/43854. Schaefer, K.M., Fuller, D.W., Block, B.A., 2007. Movements, behavior, and habitat utilization of yellowfin tuna (Thunnus albacares) in the northeastern Pacific Ocean, ascertained through archival tag data. Mar. Biol. 152, 503–525. https://doi.org/ 10.1007/s00227-007-0689-x. Schaefer, K.M., Fuller, D.W., Block, B.A., 2011. Movements, behavior, and habitat utilization of yellowfin tuna (Thunnus albacares) in the Pacific Ocean off Baja California, Mexico, determined from archival tag data analyses, including unscented Kalman filtering. Fish. Res. 112 (1–2), 22–37. https://doi.org/10.1016/j. fishres.2011.08.006. Senina, I., Lehodey, P., Calmettes, B., Nicol, S., Caillot, S., Hampton, J., Williams, P., 2015. SEAPODYM Application for Yellow Tuna in the Pacific Ocean. WCPFC-SC112015/EB-IP-01. Sournia, A., 1994. Pelagic biogeography and fronts. Prog. Oceanogr. 34, 109–120. https://doi.org/10.1016/0079-6611(94)90004-3. Susanto, R.D., Marra, J., 2005. Effects of the 1997/98 El Ni~ no on chlorophyll a variability along the southern coasts of java and sumatra. Oceanography 18, 124–127. https:// doi.org/10.5670/oceanog.2005.13. Syamsuddin, M.L., Saitoh, S.I., Hirawake, T., Bachri, S., Harto, A.B., 2013. Effects of El ni~ no–southern oscillation events on catches of bigeye tuna (Thunnus obesus) in the eastern Indian ocean off java. Fish. Bull. 111 (2), 175–188. https://doi.org/10.7755/ FB.111.2.5. Torrence, C., Compo, G.P., 1998. A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 79, 61–78. https://doi.org/10.1175/1520-0477. Venzke, S., Latif, M., Villwock, A., 2000. Coupled GCM ECHO-2. Part II: Indian ocean response to ENSO. J. Clim. 13, 1371–1383. https://doi.org/10.1175/1520-0442. Wiggert, J.D., Vialard, J., Behrenfeld, M.J., 2009. Basinwide modification of dynamical and biogeochemical processes by the positive phase of the Indian Ocean Dipole during the SeaWiFS era. Indian Ocean biogeochemical processes and ecological variability 185, 350. https://doi.org/10.1029/2008GM000776. Wood, S.M., 2006. Generalized Additive Models, an Introduction with R. Chapman and Hall, London, p. 392pp. Xie, P., Arkin, P.A., 1997. Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Am. Meteorol. Soc. 78, 2539–2558. https://doi.org/10.1175/1520-0477. Zudaire, I., Murua, H., Grande, M., Bodin, N., 2013. Reproductive potential of yellowfin tuna (Thunnus albacares) in the western Indian Ocean. Fish. Bull. 111, 252–264. https://doi.org/10.7755/FB.111.3.4.
This study was financially supported by the Council of Agriculture (107AS-9.1.5-FA-F1 and 108AS-9.2.1-FA-F1) and the National Science Council (MOST 106-2611-M-019-012 and MOST 107-2611-M-019-017). We are grateful to the Overseas Fisheries Development Council (OFDC) of Taiwan for providing data from the Taiwanese longline fishery. This manuscript was edited by Wallace Academic Editing. The first author especially thanks the conveners of the 4th CLIOTOP symposium and issues discussed in this paper are motivated in part by the CLIOTOP program. References Alonso-Fern� andez, A., Saborido-Rey, F., 2012. Relationship between energy allocation and reproductive strategy in Trisopterus luscus. J. Exp. Mar. Biol. Ecol. 416–417, 8–16. https://doi.org/10.1016/j.jembe.2012.02.001. Bard, F.X., Chabanet, C., Caouder, N., 1991. Croissance du thon albacore (Thunnus albacares) en oc� ean Atlantique estim�ee par marquages. Col. Vol. Sci. Pap., ICCAT 36, 182–204. Bard, F.X., Yen, S., Stein, A., 1999. Habitat of deep swimming tuna (Thunnus obesus, T. albacares, T. alalunga) in Central South Pacific. Collect. Vol. Sci. Pap, ICCAT 49 (3), 309–317. Bertrand, A., Josse, E., Bach, P., Gros, P., Dagorn, L., 2002. Hydrological and trophic characteristics of tuna habitat: consequences on tuna distribution and longline catchability. Can. J. Fish. Aquat. Sci. 59 (6), 1002–1013. https://doi.org/10.1139/ f02-073. Boyce, D.G., Tittensor, D.P., Worm, B., 2008. Effects of temperature on global patterns of tuna and billfish richness. Mar. Ecol. Prog. Ser. 355, 267–276. https://doi.org/ 10.3354/meps07237. Corbineau, A., Rouyer, T., Cazelles, B., Fromentin, J.M., Fonteneau, A., Menard, F., 2008. Time series analysis of tuna and swordfish catches and climate variability in the Indian Ocean (1968–2003). Aquat. Living Resour. 21, 277–285. https://doi.org/ 10.1051/alr:2008045. Currie, J.C., Lengaigne, M., Vialard, J., Kaplan, D.M., Aumont, O., Naqvi, S.W.A., Maury, O., 2013. Indian Ocean dipole and El Nino/southern oscillation impacts on regional chlorophyll anomalies in the Indian Ocean. Biogeosciences 10 (10), 6677–6698. https://doi.org/10.5194/bg-10-6677-2013. Dammannagoda, S.T., Hurwood, D.A., Mather, P.B., 2008. Evidence for fine geographical scale heterogeneity in gene frequencies in yellowfin tuna (Thunnus albacares) from the north Indian Ocean around Sri Lanka. Fish. Res. 90, 147–157. https://doi.org/ 10.1016/j.fishres.2007.10.006. Dell, J., Wilcox, C., Hobday, A.J., 2011. Estimation of yellowfin tuna (Thunnus albacares) habitat in waters adjacent to Australia’s East Coast: making the most of commercial catch data. Fish. Oceanogr. 20, 383–396. https://doi.org/10.1111/j.13652419.2011.00591.x. Emery, W.J., Thomson, R.E., 2001. Empirical orthogonal functions. In: Data Analysis Methods in Physical Oceanography, pp. 319–343. https://doi.org/10.1016/B978-0444-50756-3.X5000-X. Feng, M., Meyers, G., 2003. Interannual variability in the tropical Indian Ocean: a twoyear time scale of Indian Ocean Diople. Deep Sea Res. Part II Top. Stud. Oceanogr. 50, 2263–2284. https://doi.org/10.1016/S0967-0645(03)00056-0. Fonteneau, A., Soubrier, P., 1996. Interactions between Tuna Fisheries: a Global Review with Specific Examples from the Atlantic Ocean, pp. 84–123. FAO Fisheries Technical Paper. Fonteneau, A., 2008. A Working Proposal for a Yellowfin Growth Curve to Be Used during the 2008 Yellowfin Stock Assessment. IOTC-2008-WPTT-4. Fonteneau, A., Alay� on, P.J.P., Marsac, F., 2017. Exploitation of large yellowfin tuna caught in free schools concentrations during the 2013 spawning season (December 2012-May 2013). Collect. Vol. Sci. Pap. ICCAT 73 (2), 868–882. Fu, D., Langley, A., Merino, G., Urtizberea, A., 2018. Preliminary Indian Ocean Yellowfin Tuna Stock Assessment 1950-2017 (Stock Synthesis). IOTC-2018-WPTT20-33. Greehan, J., Hoyle, S., 2013. Review of Length Frequency Data of the Taiwanese Distant Water Longline Fleet. IOTC-2013-WPDCS09-12 Rev_2. Grinsted, A., Moore, J.C., Jeverejeva, S., 2004. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process Geophys. 11, 561–566. https://doi.org/10.5194/npg-11-561-2004. Gaertner, D., Hallier, J.P., 2015. Tag shedding by tropical tunas in the Indian Ocean and other factors affecting the shedding rate. Fish. Res. 163, 98–105. Go~ ni, N., Didouan, C., Arrizabalaga, H., Chifflet, M., Arregui, I., Goikoetxea, N., Santiago, J., 2015. Effect of oceanographic parameters on daily albacore catches in the Northeast Atlantic. Deep-Sea Res. Pt. II 113, 73–80. https://doi.org/10.1016/j. dsr2.2015.01.012. Henderson, B.A., Morgan, G.E., 2002. Maturation of walleye by age, size and surplus energy. J. Fish Biol. 61, 999–1011. https://doi.org/10.1111/j.1095-8649.2002. tb01858.x. Kao, H.Y., Yu, J.Y., 2009. Contrasting eastern-Pacific and central-Pacific types of ENSO. J. Clim. 22, 615–632. https://doi.org/10.1175/2008JCLI2309.1.
10