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Cited by 59Marine and fisheries researchClimate variability and modelsOceanographic and Atmospheric Processes

El Niño Southern Oscillation (ENSO) impact on tuna fisheries in Indian Ocean

Open access
Kumar, Palanisamy Satheesh, Pillai, Gopalakrishna N, Manjusha, Ushadevi·2014·SpringerPlus
DOI 10.1186/2193-1801-3-591PMC PMC4447736
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Abstract

El Nino Southern Oscillation (ENSO) is an important driver of interannual variations in climate and ecosystem productivity in tropical regions. However, detailed information about this important phenomenon of the Indian Ocean is scarce. Consequently, the objective of this study is to improve understanding of the impact of warm event El Nino and cool event La Nina on annual tuna landings from the Indian Ocean from 1980 to 2010. In this study, maximum tuna landings were recorded during a weak El Nino year (1456054 t in 2006) and during a weak La Nina year (1243562 t in 2000), although the lowest tuna catch was followed during the strong El Nino year (1204119 t in 2009) and during a strong La Nina year (706546 t in 1988). Validation of predicted tuna landings and SST were showing a significant positive correlation (p < 0.01) was observed all the major tuna species except Southern Bluefin Tuna. Whereas the other relationships such as sea level pressure, Wind actions; Zonal Wind (U), Meridonial Wind (V), and Scalar Wind (W) are less well-defined. In contrast with principal component analysis we find that Principal Components 1 explains 75.5% of the total variance and suggest that sea surface temperature plays a major role in determining tuna availability in the region especially during warm event El Nino years; landings in Indian Ocean tend to be optimum SST 25 to 26°C in ENSO event. Our results confirm the ENSO impact on climate, tuna abundance and production in the Indian Ocean. However, among the oceanic variables SST explained the highest deviance in generalized additive models and therefore considered the best habitat predictor in the Indian Ocean followed by sea level pressure and Winds (U, V, W).

AI snapshot

Written from the paper’s full text.

TL;DR

This study examines ENSO-related variability in Indian Ocean tuna landings from 1980 to 2010 and links landings to SST, sea level pressure, and wind variables. It concludes that SST is the strongest habitat predictor, with optimum tuna landings around 25–26°C during ENSO events.

Key findings

  • Maximum tuna landings were reported in a weak El Niño year (1,456,054 t in 2006) and a weak La Niña year (1,243,562 t in 2000); the abstract reports the lowest catch in a strong El Niño year (1,204,119 t in 2009) and a strong La Niña year (706,546 t in 1988), while the results section also reports a lower strong El Niño catch in 1982 (321,289 t).
  • During El Niño years, SST had significant positive correlations with skipjack (r = 0.842, p < 0.01), longtail tuna (r = 0.855, p < 0.01), kawakawa (r = 0.867, p < 0.01), and yellowfin (r = 0.685, p < 0.05); scalar wind was also highly significantly positively related to several tuna species, including yellowfin (r = 0.794), skipjack (r = 0.855), longtail (r = 0.939), frigate (r = 0.842), bullet (r = 0.867), FRZ (r = 0.952), and kawakawa (r = 0.95), all p < 0.01.
  • During La Niña years, SST was highly significantly related to bullet tuna (r = 0.976, p < 0.01), albacore (r = 0.786, p < 0.05), and frigate tuna (r = 0.786, p < 0.05), and negatively correlated with southern bluefin tuna (r = -0.738, p < 0.05).
  • PCA showed Principal Component 1 explaining 75.5% of total variance, with the first two components accounting for 97.1% of variance as reported; the paper concludes SST is the best habitat predictor, followed by sea level pressure and winds (U, V, W), and that landings tend to be optimum at SST 25 to 26°C in ENSO events.

Methods

  • Tuna data were Indian Ocean Tuna Commission nominal catch landings from Western Indian Ocean fishing area 51 and Eastern Indian Ocean area 57, used as a proxy for tuna abundance from 1980 to 2010.
  • Environmental data from NOAA/ICOADS included SST, sea level pressure, zonal wind (U), meridional wind (V), and scalar wind (W) at 2° resolution; monthly values were averaged yearly and standardized anomalies calculated.
  • Analyses included correlation, Principal Component Analysis, Non-Multidimensional Scale plot, and cluster analysis; data were log10-transformed and z-scale standardized, with SPSS and PAST software used.

Limitations

  • The paper states that fishing effort data were unavailable or insufficient from IOTC or other agencies, so landings were used as a proxy for tuna abundance.
  • The paper states further investigation is required to ascertain ENSO’s role in Indian Ocean tuna distribution, and future work should use longer time series and examine spatial variability of SST and food distribution.
  • Evident from the text: the generalized additive model is mentioned as showing SST explained the highest deviance, but GAM methods are not described; the text also contains internal inconsistencies, such as the lowest strong El Niño catch reported as 1982 in the results versus 2009 in the abstract, and differing signs for SST–scalar wind relationships across passages.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1186/2193-1801-3-591

Cites

40 of 40 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

7 of 39 cited sources checked · 4 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    Recent changes in the distribution of a number of fish species can be ascribed with high confidence to regional climate variability, such as ENSO and anomalies.

    “Recent changes in the distribution of a number of fish species can be ascribed with high confidence to regional climate variability, such as the El Nino Southern Oscillation (ENSO) and anomalies”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Brander 2007 source
  • Findingown data

    The temporal fluctuations of tuna catches in the Indian Ocean during El Nino years are confirmed, as reported earlier in the Panama bight (United States) Pacific coast and in the western Pacific.

    “Our results provide confirmation that the temporal fluctuations of the catches of tuna species in the Indian Ocean during El Nino years, as reported earlier in the Panama bight (United States) Pacific coast (Pedraza and Dıaz-Ochoa 2006), and in the western Pacific (Lehodey et al. 2008).”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites Pedraza and Dıaz-Ochoa 2006 source

      The Discussion reports that tuna landings at Buenaventura varied with the ENSO cycle during the 1990s, supporting the Panama Bight comparison. But the Introduction identifies the study area as the Colombian Pacific, not the United States Pacific coast. The Indian Ocean result is presented as the citing authors’ own finding, not one attributed to this source. (LLM, conf=0.88)

      Experimental — accuracy not yet measured
    • cites Lehodey et al. 2008 source
  • Premisecitation

    Surface conditions must reflect subsurface thermal conditions throughout the mechanical wind action linked to SLP anomaly patterns and gradients.

    “The surface conditions must then reflect subsurface thermal conditions throughout the mechanical wind action linked to SLP anomalies patterns and gradients”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Tourre et al. 2007 source
  • Findingown data

    Maximum tuna landings in the Indian Ocean from 1980 to 2010 were recorded during a weak El Nino year, 2006 (1,456,054 t).

    “maximum tuna landings were recorded during a weak El Nino year (1456054 t in 2006)”
    • Also stated in: § Indian ocean tuna fisheries and ENSO · plain
      “the highest tuna catch was reported during weak El Nino years in 2006 (14,56054 t)”
    Quote in the paperSubstantial claim
  • Findingown data

    Predicted tuna landings and SST showed a significant positive correlation (p < 0.01) for all major tuna species except Southern Bluefin Tuna.

    “Validation of predicted tuna landings and SST were showing a significant positive correlation (p < 0.01) was observed all the major tuna species except Southern Bluefin Tuna.”
    Quote in the paperSubstantial claim
  • Classified as probably not a substantial claim. 15
  • May combine two claims. 27
  • The quote only partly states this claim. 2

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Climate change affects the survival, growth, reproduction, recruitment and distribution of individuals within a species, and impacts can also be shown at the level of populations, communities, or entire ecosystems.

    “Climate change affects the survival, growth, reproduction, recruitment and distribution of individuals within a species, but impacts can also be shown at the level of populations, communities, or entire ecosystems”
    Quote in the paperNot a substantial claim1 of 3 sources checked
    • cites Brander 2007 source

      The “Evidence of Climate Impacts” passage (page unavailable) states nearly the entire claim, including effects on survival, growth, reproduction and distribution, and impacts at population, community and ecosystem levels. It does not include recruitment in that list; the copy discusses recruitment elsewhere in connection with fishing, not as an effect on individuals attributed to climate change. (LLM, conf=0.88)

      Experimental — accuracy not yet measured
    • cites Lehodey et al. 1997, 2003

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Pillai and Satheeshkumar 2012, 2013

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    ENSO is a primary cause of interannual climatic variability around the globe and an important driver of interannual variations in climate and ecosystem productivity in tropical regions.

    “El Nino Southern Oscillation (ENSO) is an important driver of interannual variations in climate and ecosystem productivity in tropical regions.”
    • Also stated in: § Introduction · plain
      “The ENSO phenomenon is regarded as the primary cause for interannual climatic variability around the globe.”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites Suarez et al. 2004

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Lehodey et al. 2006 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The economies of many countries, mainly in tropical regions, depend on the presence and intensity of ENSO.

    “The economies of many countries, mainly in tropical regions, depend on the presence and intensity of this phenomenon”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites Suarez et al. 2004

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Lehodey et al. 2006 source

      The section on anchovies and sardines in Peru and Chile reports that strong El Niño events reduced anchovy biomass and catches, and the tropical tuna section describes ENSO-related changes in tuna populations. These passages support effects on fisheries, but they do not establish that the economies of many countries, mainly tropical ones, depend on ENSO’s presence and intensity. (LLM, conf=0.82)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The distribution of albacore is affected by SST.

    “Previous studies have shown that the distribution of albacore is affected by SST (Chen et al. 2005).”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Chen et al. 2005 source

  • The quote only partly states this claim.

    Findingown data

    The lowest tuna catch in the Indian Ocean from 1980 to 2010 was recorded during the strong El Nino year 2009 (1,204,119 t).

    “although the lowest tuna catch was followed during the strong El Nino year (1204119 t in 2009)”
    • Also stated in: § Indian ocean tuna fisheries and ENSO · plain
      “in 2009 (12,04119 t) respectively.”
    Quote in the paperNot a substantial claim

  • The quote only partly states this claim.

    Findingown data

    Relationships between tuna landings and sea level pressure, zonal wind (U), meridional wind (V), and scalar wind (W) are less well-defined than the relationship with SST.

    “Whereas the other relationships such as sea level pressure, Wind actions; Zonal Wind (U), Meridonial Wind (V), and Scalar Wind (W) are less well-defined.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Among oceanic variables, SST explained the highest deviance in generalized additive models and is the best habitat predictor in the Indian Ocean, followed by sea level pressure and winds (U, V, W).

    “among the oceanic variables SST explained the highest deviance in generalized additive models and therefore considered the best habitat predictor in the Indian Ocean followed by sea level pressure and Winds (U, V, W).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    A significant positive correlation (r = 0.502; p < 0.01) was obtained between SST and scalar wind, indicating that SST is largely influenced by scalar wind in this region.

    “A significant positive correlation (r = 0.502; p < 0.01) obtained between SST and scalar wind indicates that SST is largely influenced by scalar wind in this region”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Maximum values of SLP anomalies in the Indian Ocean were observed during 1997 (0.140), 1987 (0.127) and 2009 (0.070), and lowest values were observed in 2007 (-0.174).

    “the maximum values of the SLP anomalies were observed during 1997 (0.140), 1987 (0.127) and later in 2009 (0.070) and lowest values were observed in 2007 (-0.174).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    During La Nina years, SST had a highly significant positive relationship with BLT, ALB, FRI, and a negative correlation with SBF.

    “Sea surface temperature (SST) indicating a highly significant relationship with the following tuna species, BLT (r = 0.976; p < 0.01), ALB (r = 0.786; p < 0.05), FRI (r = 0.786; p < 0.05), and had a negative correlation with SBF (r = -0.738; p < 0.05).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Reduced tuna catch was recorded during the moderate El Nino years 1987 (595,495 t) and 1994 (1,024,744 t), while in 2002 it increased to 1,308,704 t.

    “the reduced tuna catch was recorded during the moderate El Nino years 1987 (5,95495 t), in 1994 (10,24744 t) and in 2002 it has increased as (13,08704 t).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    During La Nina years, zonal wind (U) positively correlated with SBF and negatively correlated with FRI, BLT, FRZ, and KAW.

    “Zonal wind (U) positively correlated with SBF (r = 0.881; p < 0.01), and negatively correlated with FRI (r = -0.952; p < 0.01), BLT (r = -0.95; p < 0.01), FRZ (r = -0.74; p < 0.05), and with KAW (r = -0.738; p < 0.05) respectively.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The highest positive correlation existed between SST, SLP, and wind (U, V, W) and tuna catch during El Nino and La Nina years; zonal wind (U) negatively correlated with all tuna species except SBF.

    “The highest positive correlation existed between SST, SLP and Wind (U, V, W) and tuna catch during El Nino and La Nina years, the Zonal wind (U) negatively correlated with all the tuna species except SBF tuna (Tables 2 and 3).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Observed correlations between environmental variables and tuna catch reflect causal influence rather than coincidental or indirect relationships.

  • May combine two claims.

    Findingown data

    The highest tuna catch was recorded in the Indian Ocean during 2004 and 2006; a moderate La Nina year (2007) also recorded highest tuna catch; 1191828 t was landed in 2010 (strong La Nina year).

    “In the present study, the highest tuna catch was recorded in the Indian Ocean during 2004 and 2006, these findings are also supported by the tuna catch in a moderate La Nina year (2007) highest tuna catch was recorded in the Indian Ocean and 1191828 t in 2010 (strong La Nina year) was landed.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    During La Nina years, scalar wind (W) had a highly significant positive relationship with SKJ, ALB, FRZ, KAW, BLT, and a negative correlation with SBF.

    “Scalar wind (W) had a highly significant relationship with SKJ (r = 0.905; p < 0.01), ALB (r = 0.90; p < 0.01), FRZ (r = 0.976; p < 0.01), KAW (r = 0.98; p < 0.01), BLT (r = 0.762; p < 0.01), and indicating a negative correlation with SBF (r = -0.738; p < 0.05) respectively (Table 3).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Higher values of SST anomalies in the Indian Ocean were observed during 2005 (0.15) and 1988 (0.07), and the lowest value was observed in 1984 (-0.11).

    “Figure 4 represented the higher values of SST anomalies were observed during 2005 (0.15) and 1988 (0.07) and lowest value was observed in 1984 (-0.11).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Sea surface temperature explained the highest deviance in generalized additive models and was therefore considered the best habitat predictor in the Indian Ocean, followed by SLP and winds (U, V, W).

    “Sea surface temperature explained the highest deviance in generalized additive models and was therefore considered the best habitat predictor in the Indian Ocean followed by SLP and Winds (U, V, W).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Observed correlations between environmental variables and tuna catch reflect causal influence rather than coincidental or indirect relationships.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Scientific evidence supports the conclusion that climate change is already altering marine ecosystems.

    “Scientific evidence supports the conclusion that climate change is already altering marine ecosystems”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Lehodey et al. 1997, 2003

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Nicol et al. 2013 source

  • May combine two claims.

    Interpretationown data

    ENSO impacts climate, tuna abundance and production in the Indian Ocean.

    “Our results confirm the ENSO impact on climate, tuna abundance and production in the Indian Ocean.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Detailed information on climate variability and its impact on tuna resources in the Indian Ocean is scarce.

  • May combine two claims.

    Interpretationcitation

    Climate change is likely to affect regional tuna fisheries by raising average ocean surface temperatures to levels currently experienced during medium-intensity El Niños and by increasing year-to-year climate variability.

    “Climate change is likely to affect regional tuna fisheries in two major ways: by raising average ocean surface temperatures to levels currently experienced during medium-intensity El Niños (Timmermann et al. 1999) and by increasing year-to-year climate variability.”
    • Also stated in: § Multivariate statistical analysis · hedged
      “Climate change is likely to affect regional tuna fisheries in two major ways: by raising average ocean surface temperatures to levels currently experienced during medium-intensity El Niños (Timmermann et al. 1999) and by increasing year-to-year climate variability.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Timmermann et al. 1999 source

  • May combine two claims.

    Premisecitation

    Primary and secondary production and structure of marine communities are affected by increased ocean temperature, increased stratification of the water column, and changes in the intensity and timing of coastal upwelling, with consequent impacts on fish migration patterns, recruitment, growth, distribution, abundance and predator-prey relationships.

    “Primary and secondary production and structure of marine communities are affected by the increase in ocean temperature, increased stratification of the water column and changes in the intensity and timing of coastal upwelling with consequent impacts on fish migration patterns, recruitment, growth, distribution, abundance and predator and prey relationship”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Sissener and Bjorndal, 2005 source
    • cites McIlgorm et al. 2010 source

  • May combine two claims.

    Interpretationreasoning

    The positive anomaly of meridional and scalar wind triggers anomalous downwelling oceanic Rossby waves, thereby deepening the thermocline and resulting in advection of warm waters in the Indian Ocean.

    “The positive anomaly of meridional and scalar wind triggers anomalous downwelling oceanic Rossby waves, thereby deepening the thermocline and resulting in advection of warm waters in the Indian Ocean.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    During strong El Nino years (1982, 1997, 2009), tuna landings decreased in the Indian Ocean due to warm El Nino climatic variability; during weak El Nino years (2004, 2006), tuna catches may increase due to optimum SST and SLP.

    “However, from the PCA results, it may be presumed that during the strong El Nino year (1982, 1997 and 2009) the tuna landings are decreased in the Indian Ocean due to warm El Nino climatic variability and during the weak El Nino years (2004, 2006), the tuna catches may increase due to SST and SLP optimum value.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The ENSO event classifications (weak, moderate, strong El Nino/La Nina) used in the analysis are valid.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Pacific Ocean Skipjack tuna have a thermal limit for maximum abundance between 20° and 29°C.

    “Studies have indicated that Pacific Ocean tunas have thermal limits for maximum abundance of Yellowfin between 20° to 30°C and Skipjack between 20° to 29°C”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Stretta 1991 source

  • May combine two claims.

    Interpretationown data

    The time difference between maxima of SST anomalies in the Indian Ocean is approximately 8-17 years, indicating slow eastward propagation of the multi-decadal climate signal.

    “The time difference between maxima of SST anomalies in the Indian Ocean is approximately 8–17 years, indicating slow eastward propagation of the multi-decadal climate signal.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Recent observations of ENSO have shown that the Indian Ocean is consistently warm and its warm pool is expanding, particularly in recent decades.

    “Recent observations of ENSO have shown that the Indian Ocean is consistently warm and its warm pool is expanding, particularly in the recent decades”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Achuthavarier et al. 2012 source

  • May combine two claims.

    Interpretationreasoning

    The distribution of tuna and tuna-like species responds to displacement of SST, wind movements, and convergence zone, which may provide a foundation for predicting good tuna fishing grounds.

    “It is noticed from the present study that the distribution of tuna and tuna like species would respond to displacement of SST, wind movements and convergence zone that might provide a foundation for the prediction of good tuna fishing grounds.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Observed correlations between environmental variables and tuna catch reflect causal influence rather than coincidental or indirect relationships.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Premisecitation

    Understanding how the oceanic environment affects distribution of tuna species is an essential step towards ecosystem-based management of fisheries.

    “understanding how the oceanic environment affects distribution of tuna species is an essential step towards ecosystem-based management of fisheries, which is increasingly becoming a basic requirement in management policy”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Pillai and Satheeshkumar 2013

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationreasoning

    In recent moderate and strong El Nino years, maximum tuna catch was recorded compared to the 1980s to 1990s, due to increased number of fishing crafts (increased fishing effort) and human manpower.

    “The recent moderate and strong El Nino years, the maximum tuna catch was recorded compared to 1980 to 1990s, its due to increased number of fishing crafts (increased fishing effort) and human manpower.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Pacific Ocean Yellowfin tuna have a thermal limit for maximum abundance between 20° and 30°C.

    “Studies have indicated that Pacific Ocean tunas have thermal limits for maximum abundance of Yellowfin between 20° to 30°C and Skipjack between 20° to 29°C”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Stretta 1991 source

  • May combine two claims.

    Premisecitation

    CPUE of yellowfin tuna catch by the Taiwan longline fishery in the Arabian Sea showed positive correlations with SST, dipole mode index (DMI), and chl-a, especially a long-term positive correlation for the regular longline fishery in 1998-2005 with a periodicity of 2 years.

    “CPUE showed positive correlations with SST and dipole mode index (DMI) and chl-a, especially a long-term positive correlation for the regular longline fishery in 1998–2005 with a periodicity of 2 years.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Lan et al. 2012 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    SST is the limiting factor of large adult yellowfin tuna near the Hawaiian Islands.

    “Brill et al. (1999) also suggest SST to be the limiting factor of large adult yellowfin tuna near the Hawaiian Islands”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Brill et al. 1999 source

  • May combine two claims.

    Premisecitation

    The distribution of principal market tuna species, population, and recruitment of tuna stock is influenced by ENSO variability and changes in oceanic parameters such as SST, DO, and salinity; however, the relationship with ENSO is more complex in the Indian Ocean due to wider distribution and extended spawning grounds.

    “The distribution of the principal market tuna species, population, and recruitment of the tuna stock is influenced by the ENSO variability and changes in oceanic parameters such as SST, DO and salinity. However, as these species are more widely distributed and have extended spawning grounds in both east and western Indian Ocean, the relationship with ENSO is more complex (Pillai and Satheeshkumar”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Pillai and Satheeshkumar 2013

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Skipjack tuna habitat selection is significantly influenced by SST ranging from 20.5 to 26°C in the Pacific Ocean.

    “Based on the oceanic parameter model, skipjack tuna habitat selection was significantly (p < 0.01) influenced by SST ranging from 20.5 to 26°C in the Pacific Ocean (Mugo et al. 2010).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Mugo et al. 2010 source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    SST, salinity, SLP, current movement, and dissolved oxygen concentration influence spawning activities of yellowfin tuna.

    “whereas SST, salinity, SLP, current movement, and dissolved oxygen concentration influence spawning activities of yellowfin tuna (Romena 2001).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Romena 2001

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Such a scenario could expand available habitat for warm water pelagic to higher latitudes.

    “Such a Scenario could expand available habitat for warm water pelagic to higher latitudes (Cheung et al. 2009).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Cheung et al. 2009 source

  • May combine two claims.

    Premisecitation

    Catch rates of longline fisheries appear to be associated with the depth of tuna habitats; deep thermocline depth caused a high CPUE for the deep longline fishery.

    “Catch rates of longline fisheries appear to be associated with the depth of tuna habitats; deep thermocline depth caused a high CPUE for the deep longline fishery (Lan et al. 2012).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Lan et al. 2012 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Significant large scale changes of skipjack habitat in the equatorial Pacific are suggested to occur due to increased ocean temperature caused by global warming.

    “Loukos et al. (2003) suggested significant large scale changes of skipjack habitat in the equatorial Pacific due to increased ocean temperature caused by global warming.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Loukos et al. 2003 source

  • May combine two claims.

    Premisecitation

    Primary productivity in the eastern and western Indian Ocean would decline due to increased stratification between warmer surface waters and cold deep waters; decreased upwelling would lead to a decline in bigeye and adult yellowfin tuna population targeted by longline.

    “Primary productivity in the eastern and western Indian Ocean would decline due to increased stratification between warmer surface waters and cold deep waters. The decreased upwelling would lead to a decline in the bigeye and adult yellowfin tuna population, the species targeted by the longline (Lehodey et al. 1997).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Lehodey et al. 1997 source
39 of 39

  • The paper relies on this without support.

    Detailed information on climate variability and its impact on tuna resources in the Indian Ocean is scarce.

    “However, detailed information about this important phenomenon of the Indian Ocean is scarce.”
    “However, detailed information on climate variability and its impact on tuna resources in the Indian Ocean is very scanty.”

  • The paper relies on this without support.

    Tuna landings are a good indicator of tuna abundance or availability in the Indian Ocean.

    “in view of the fact that landings could be a good indicator of tuna abundance.”

  • The authors state this as an assumption.

    The first principal component is a good index of environmental effects.

    “If we accept the first principal components as a good index of environmental effects”

  • The authors state this as an assumption.

    The La Nina years from 1980 to 2010 are representative of extreme environmental conditions.

    “Tuna landings of the Indian Ocean during La Nina Year 1980 to 2010, which are considered to be representative of extreme environmental conditions, are presented in (Figure 8).”

  • The paper relies on this without support.

    Observed correlations between environmental variables and tuna catch reflect causal influence rather than coincidental or indirect relationships.

    “The length of colour bar represents the sparsity pattern of the matrix confirms the influence of SST and SST anomalies on tuna production during the El Nino year.”

  • The paper relies on this without support.

    The ENSO event classifications (weak, moderate, strong El Nino/La Nina) used in the analysis are valid.

    “during the strong El Nino year (1982, 1997 and 2009) the tuna landings are decreased in the Indian Ocean due to warm El Nino climatic variability and during the weak El Nino years (2004, 2006), the tuna catches may increase due to SST and SLP optimum value.”
6 of 6

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • The reported lowest strong-El Niño catch is not the lowest

    Major

    The abstract identifies 2009 as the lowest strong-El Niño catch at 1,204,119 t, but the results report a much lower strong-El Niño catch of 321,289 t in 1982.

  • La Niña zonal-wind-southern-bluefin correlation differs from Table 3

    Major

    The results report 0.881 at p < 0.01 for U and SBF, whereas Table 3 reports 0.78 and marks it at the 5% significance level.

  • SST-scalar-wind correlation has opposite signs in two sections

    Minor

    The sea-surface-temperature section calls the correlation between SST and scalar wind positive, whereas the sea-level-pressure section calls a correlation between those same two variables negative.

  • Figure 9 caption names the wrong ENSO event

    Minor

    The text introduces Figure 9 as an analysis of the cold La Niña event, but its caption calls it an El Niño analysis.

  • A year assigned to the La Niña PCA cluster is listed as El Niño

    Minor

    The PCA discussion describes Cluster B as La Niña years and includes 1986 in it, but Table 1 lists 1986 under moderate El Niño.

  • Table 1 includes years outside its stated range

    Minor

    The table is labeled as covering 1951-2010 but includes both 1950 and 2011.

  • La Niña scalar-wind-skipjack correlation differs from Table 3

    Minor

    The results give the W-SKJ correlation as 0.905, while Table 3 gives 0.96.

7 of 7

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. Diaz HF, Markgraf V: El Niño Historical and Paleoclimatic Aspects of the Southern Oscillation. Cambridge University Press, Campridge; 1992:476.
  2. Mullen AJ: Reaction diffusion models for dynamics distribution of yellowfin tuna, Ph.D. Thesis. University of London, London, UK; 1992:127.

Cited by

Sorted by citations

Related

Authors
Kumar, P. S., Pillai, G. N., & Manjusha, U.
Venue
SpringerPlus
Publisher
Springer Science and Business Media LLC
Published
2014-10-09
Volume
3
Issue
1
Pages
591-591
ISSN
2193-1801 (ISSN-L)
DOI
10.1186/2193-1801-3-591
PMC
PMC4447736
OpenAlex
W2150084178
Semantic Scholar
9e1f4f03f85bf207a2219dd5712ab30f63b00734
Cited by
59
References
40
Citations by year
2026: 1 · 2025: 4 · 2024: 3 · 2023: 13 · 2022: 3 · 2021: 7 · 2020: 11 · 2019: 3 · 2018: 5 · 2017: 5 · 2015: 3 · 2014: 1
FWCI
2.46
Type
journal article
Language
en
Update notices
erratum (2014-12-12)
Open access
Licence
Locations
  • SpringerPlus (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Eprints@CMFRI Open Access Institutional Repository (Central Marine Fisheries Research Institute) (acceptedVersion)
  • DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, cc-by)
Funders
Indian Council of Agricultural Research
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Marine and fisheries research
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
  • Physical Sciences › Earth and Planetary Sciences › Oceanography › Oceanographic and Atmospheric Processes
Keywords
ENSO, tuna fisheries, Indian Ocean, tuna landings, sea surface temperature, La Niña, sea level pressure, wind stress, southern bluefin tuna, fishery productivity, climate variability

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-16.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-16.

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Statistics and arithmetic are recomputed, not judged.

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