Skip to main content

Advertisement

SpringerLink
Log in

Determination of Fishing Grounds Distribution of the Indian Mackerel in Malaysia’s Exclusive Economic Zone Off South China Sea Using Boosted Regression Trees Model

  • Published:
Thalassas: An International Journal of Marine Sciences Aims and scope Submit manuscript

Abstract

With the ongoing climate change affecting the ocean, there is a need to understand and predict the future distributions of marine species in order to assess the sustainability of marine ecosystem. In this study, remotely-sensed satellite oceanographic data together with Indian mackerel (Rastrelliger kanagurta) fishery dataset were used to predict potential fishing grounds in the Exclusive Economic Zone (EEZ) of Peninsular Malaysia using boosted regression trees (BRT) model. The model was developed using three years (2008–2010) fish catch data and environmental variables of chlorophyll-a (chl-a), sea surface temperature (SST) and sea surface height (SSH). Result indicated that potential fishing grounds were closely associated with SSH, followed by SST and chl-a. The performance of the BRT model indicated acceptable fishing grounds prediction accuracy (AUC value of 0.749). Seasonal variability in fishing grounds was related to favorable environmental conditions of SSH (1.1–1.3 m), SST (29–32 °C) and chl-a (0.3–0.6 mg/m3). The projection of increases in SST due to climate change according to IPCC-AR5-RCPs was observed to influence the spatial and temporal distributions R. kanagurta. Increased temperature at 1.80 °C resulted in high potential catch areas for R. kanagurta in the EEZ. Meanwhile, elevated temperature at 2.60 °C and 3.30 °C showed decreased in potential catch areas for R. kanagurta in the EEZ. Most of the future fishing grounds area were projected to decline, and it was observed to shift outside the EEZ off South China Sea. Hence, by understanding these relationships, this analysis identifies where strategies can be adapted to face the ecological impacts under changing environmental conditions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Akhir MF (2012) Surface circulation and temperature distribution of the Southern South China Sea from Global Ocean Model (OCCAM). Sains Malaysiana 41(6):701–714

    Google Scholar 

  • Akhir MF, Chuen YJ (2011) Seasonal variation of water characteristics during inter-monsoon along the east coast of Johor. J Sustain Sci Manag 6(2):206–214

  • Akhir MF, Zakaria NZ, Tangang F (2014) Intermonsoon variation of physical characteristics and current circulation along the coast of Peninsular Malaysia. Int J Oceans Oceanogr:1–9

  • Andrade HA, Garcia AE (1999) Skipjack tuna in relation to sea surface temperature off the southern Brazilian coast, Fish. Oceanography 8:245–254

    Google Scholar 

  • Atmadipoera AS, Widyastuti PA (2015) Numerical modelling study of upwelling mechanism in Southern Makassar Strait. Jurnal Ilmu & Teknologi Kelautan Tropis 6:355–371

    Google Scholar 

  • Bardiyanto K, Wayan I (2010) Studi penangkapan ikan kembung (Rastrelliger kanagurta) dengan menggunakan data satelit di laut Jawa pada musim Tenggara dan Barat Laut. Ecotrophic 5(1):21–26

  • Bertrand A, Josse E, Bach P, Gros P, Dagorn L (2002) Hydrological and trophic characteristics of tuna habitat: Consequences on tuna distribution and long line catchability. Can J Fish Aquat Sci 59(6):1002–1013

    Article  Google Scholar 

  • Callihan JL, Takata LT, Woodland RJ, Secor DH (2008) Cohort splitting in bluefish, Pomatomus saltatrix, in the US mid- Atlantic Bight. Fish Oceanogr 17:191–205

    Article  Google Scholar 

  • Chassot E, Bonhommeau S, Reygondeau G, Nieto K, Polovina JJ, Huret M, Dulvy NK, Demarcq H (2011) Satellite remote sensing for an ecosystem approach to fisheries management. ICES J Mar Sci 68:651–666

    Article  Google Scholar 

  • Chen X, Li G, Bo F, Tian S (2009) Habitat suitability index of chub mackerel (Scomber japonicus) from July to September in the East China Sea. J Oceanogr 65:93–102

    Article  Google Scholar 

  • Chen XJ, Tian SQ, Chen Y, Liu B (2010) A modeling approach to identify optimal habitat and suitable fishing grounds for neon flying squid (Ommastrephes bartramii) in the Northwest Pacific Ocean. Fish Bull 108:1–14

    Google Scholar 

  • Cheung WL, Watson R, Pauly D (2013) Signature of ocean warming in global fisheries catch. Nature 497:365–368

    Article  Google Scholar 

  • Choudhury SB, Jena B, Rao MV et al (2007) Validation of integrated potential fishing zone (IPFZ) forecast using satellite based chlorophyll and sea surface temperature along the east coast of India. Int J Remote Sens 28(12):2,683–2,693

    Article  Google Scholar 

  • Chu PC, Edmons NL, Fan CW (1999) Dynamical mechanisms for the South China Sea seasonal circulation and thermohaline variabilities. J Phys Oceanogr 29:2971–2989

    Article  Google Scholar 

  • Collette BB, Nauen CE (1983) FAO Species Catalogue: Scombrids of the World, vol 2. FAO Fisheries Synopsis, Rome, pp 48–49

    Google Scholar 

  • Collette BB, Russo JL (1984) Morphology, systematics and biology of the Spanish mackerels (Scomberomous, Scombridae). Fish Bulletin US 82:545–692

  • De’ath G (2007) Boosted trees for ecological modelling and prediction. Ecology 88:243–251

    Article  Google Scholar 

  • Department of Fisheries Malaysia (DoFM) (2012) Annual fisheries statistics. Ministry of Agriculture and Agro-Based Industry, Putrajaya

  • Dulvy NK, Rogers SI, Jennings S et al (2008) Climate change and deepening of the North Sea fish assemblage: a biotic indicator of warming seas. J Appl Ecol 45:1029–1039

    Article  Google Scholar 

  • Edwards M, Richardson AJ (2004) Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430:881–884

    Article  Google Scholar 

  • Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77(4):802–813

    Article  Google Scholar 

  • Feng G, Xinjun C, Wenjiang G, Gang L (2016) A New model to forecast fishing ground of Scomber japonicus in the Yellow Sea and East China Sea. Acta Oceanol Sin 35(4):74–81

    Article  Google Scholar 

  • Frawley TH, Briscoe DK, Daniel PC, Britten GL, Crowder LB, Robinson CJ, Gilly WF (2019) Impacts of a shifts to a warm-water regime in the Gulf of California on jumbo squid (Dosidicus gigas). ICES J Mar Sic

  • Fulton EA (2011) Interesting times: Winners, losers, and system shifts under climate change around Australia. ICES J Mar Sci 68:1329–1342

    Article  Google Scholar 

  • Gangoso L, Márquez-Ferrando R, Ramírez F et al (2013) Understanding phenotypic responses to global change. BioEssays 35:491–495

    Article  Google Scholar 

  • Gordon A (2005) Oceanography of the Indonesian Seas and their throughflow. Oceanography 18:14–27

    Article  Google Scholar 

  • Harley CDG (2011) Climate change, keystone predation, and biodiversity loss. Science 334:1124–1127

    Article  Google Scholar 

  • Harsono G, Atmadipoera AS, Syamsudin F et al (2014) Features Observed from Multi sensor Satellite Oceanography. Asian J Sci Res 7:571–580

    Article  Google Scholar 

  • Hastie T, Tibshirani R, Friedman J (2001) The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer-Verlag, New York, pp 299–345

    Google Scholar 

  • Hughes KM, Dransfeld L, Johnson MP (2014) Changes in the spatial distribution of spawning activity by north-east Atlantic mackerel in warming seas: 1977–2010. Mar Biol 161:2563–2576

    Article  Google Scholar 

  • IPCC (2014) Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In: Barros, V.R., Field, C.B., Dokken, D.J., Mastrandrea, M.D., Mach, K.J., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., Genova, R.C., Girma, B., Kissel, E.S., Levy, A.N., MacCracken, S., Mastrandrea, P.R., White, L.L. (Eds.), Cambridge University Press, Cambridge,UK and New York, USA (688p)

  • James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning. Springer, New York, NY

  • Klemas V (2013) Fisheries Applications of Remote Sensing: An Overview. Fish Res 148:124–136

    Article  Google Scholar 

  • Krishnamurthy V, Kinter JL (2003) The Indian Monsoon and its Relation to Global Climate Variability. In: Rodó X, Comín FA (eds) Global Climate. Springer, Berlin, Heidelberg

    Google Scholar 

  • Lanz E, Manuel NM, Juana LM, Dworak JA (2009) Small pelagic fish catches in the Gulf of California associated with sea surface temperature and chlorophyll. CalCOFI Rep 50:134–146

    Google Scholar 

  • Lasram FBR, Mouillot D (2009) Increasing southern invasion enhances congruence between endemic and exotic Mediterranean fish fauna. Biol Invasions 11:697–711

    Article  Google Scholar 

  • Leathwick JR, Elith J, Francis MP et al (2006) Variation in demersal fish species richness in the oceans surrounding New Zealand: An analysis using boosted regression trees. Mar Ecol Prog Ser 321:267–281

    Article  Google Scholar 

  • Li G, Chen XJ, Lei L, Guan WJ (2014) Distribution of hotspots of chub mackerel based on remote-sensing data in coastal waters of China. Int J Remote Sens 35:4399–4421

  • Liu Z, Yang H (2000) Regional dynamics of seasonal variability in the South China Sea. J Phys Oceanogr 31:272–284

    Article  Google Scholar 

  • Loya Y, Sakai K, Yamazato K et al (2001) Coral bleaching: The winners and the losers. Ecol Lett 4:122–131

    Article  Google Scholar 

  • Lumban-Gaol J, Leben RR, Vignudelli S et al (2015) Variability of satellite-derived sea surface height anomaly, and its relationship with Bigeye tuna (Thunnus obesus) catch in the Eastern Indian Ocean. Eur J Remote Sens 48(1):465–477

    Article  Google Scholar 

  • Mansor M (1989) Tumbesaran, kematian dan corak pengrekrutan Ikan Kembung Rastrelliger kanagurta (Cuvier) di Pantai Barat Semenanjung Malaysia. Fisheries Bulletin 59 (Jabatan Perikanan: Kementerian Pertanian Malaysia), 22. [in Malay]

  • Mansor M, Abdullah S, Hamid A (1996) Population structure of small pelagic fisheries off the East Coast of Peninsular Malaysia. Fisheries Bulletin, 99 (Jabatan Perikanan: Kementerian Pertanian Malaysia), 30

  • Mazalina A, Katoh M (Comp.) (2014) Tagging of Small Pelagic Fish in the South China Sea and the Andaman Sea. Regional Project Terminal Report: Executive Summary. SEAFDEC/MFRDMD/SP/27: 35 pp.

  • Montgomery DR (1981) Commercial applications of satellite oceongraphy. Oceanus 24:56–65

    Google Scholar 

  • Nurdin S, Mustapha AM, Lihan T et al (2015) Determination of potential fishing grounds of Rastrelliger kanagurta using satellite remote sensing and GIS technique. Sains Malaysiana 44(2):225–232

    Article  Google Scholar 

  • Nurdin S, Mustapha AM, Lihan T et al (2017) Applicability of remote sensing oceanographic data in the detection of potential fishing grounds of Rastrelliger kanagurta in the archipelagic waters of Spermonde, Indonesia. Fish Res 196:1–12

    Article  Google Scholar 

  • Nuzula F, Syamsudin ML, Yuliadi LPS et al (2017) Eddies spatial variability at Makassar Strait – Flores Sea. IOP Conf Ser Earth Environ Sci 54:012079

    Article  Google Scholar 

  • Palomares M-LD, Pauly D (2019) Coastal fisheries: the past, present, and possible futures in Coasts and Estuaries (eds) Wolanski E, Day JW, Elliott M, Ramachandran R (Amsterdam: Elsevier) pp 569–576. https://doi.org/10.1016/b978-0-12-814003-1.00032-0

  • Perry AL, Low PJ, Ellis JR et al (2005) Climate change and distribution shifts in marine fishes. Science 308:1912–1915

    Article  Google Scholar 

  • Petit M (1991) Aerospatial remote sensing as catalyst of an operational marine fishery (halieutic) science. Int J Remote Sens 12:713–724

    Article  Google Scholar 

  • Polovina JJ, Howell EA (2005) Ecosystem indicators derived from satellite remotely sensed oceanographic data for the North Pacific. ICES J Mar Sci 62:31–27

  • Radiarta IN, Saitoh SI, Miyazano A (2008) GIS-based multi-criteria evaluation models for identifying suitable sites for Japanese scallop (Mizuhopecten yessoensi) aquaculture in Funka Bay, southwestern Hokkaido, Japan. Aquaculture 284:127–135

    Article  Google Scholar 

  • Rajapaksha JK, Samarakoon L, AAJK G (2013) Environmental preferences of Yellowfin Tuna in the Northeast Indian Ocean: An application of satellite data to longline Catches. Int J Fish Aquat Sci 2(4):72–80

    Google Scholar 

  • Ramírez F, Afán I, Davis L et al (2017) Climate impacts on global hot spots of marine biodiversity. Sci Adv 3:e1601198

    Article  Google Scholar 

  • Rebert JP, Donguy JR, Elidin G, Wyrtki K (1985) Relations between sea level, thermocline depth, heat content and dynamic height in the tropical Pacific Ocean. J Geophys Res 90:C611719–C611725

  • Saadon MN, Camerlengo A (1996) Interannual and seasonal variability of the mixed layer depth of the South China Sea. In: National Conference and Climate Change. Selangor, Malaysia: Universiti Putra Malaysia, 141–151

  • Sabatés A, Martín P, Raya V (2012) Changes in life-history traits in relation to climate change: Bluefish (Pomatomus saltatrix) in the northwestern Mediterranean. ICES J Mar Sci 69:1000–1009

    Article  Google Scholar 

  • Santos AMP (2000) Fisheries oceanography using satellite and air born remote sensing methods: A review. Fish Res 49:1–20

    Article  Google Scholar 

  • SEAFDEC (2007) Information collection for sustainable pelagic fisheries in the South China Sea : national country reports (ed) Raja Bidin Raja Hassan, Ku Kassim Ku Yaacob

  • Shaari NR, Mustapha AM (2018) Predicting potential Ratrelliger kanagurta Fish Habitat using MODIS satellite data and GIS modelling: A case study of Exclusive Economic Zone, Malaysia. Sains Malays 47(7):1369–1378

    Article  Google Scholar 

  • Shen S, Leptoukh GG, Acker JG et al (2008) Seasonal variations of chlorophyll - a concentration in the northern South China Sea. Geosci Remote Sens Lett 5:315–319

    Article  Google Scholar 

  • Sherman K (2008) The large marine ecosystem approach to marine resources assessment and management. In: Bianchi G, Skjoldal HR (eds) The ecosystem approach to fisheries. CABI Publishing and FAO, Rome, pp 47–75

    Chapter  Google Scholar 

  • Simpson SD, Jennings S, Johnson MP et al (2011) Continental shelf-wide response of a fish assemblage to rapid warming of the sea. Curr Biol 21:1565–1570

    Article  Google Scholar 

  • Skjoldal HR (2004) Fish stocks and fisheries in relation to climate variability and exploitation natural resource system challenge: ocean and aquatic ecosystem. Ecyclopedia of life supporting system (EOLSS), developed under the auspices of the UNESCO, Oxford, UK

  • Skjoldal HR, Misund OA (2008) Ecosystem approach to management: definitions, principles and experiences from implementation in the Sea. In: Bianchi G, Skjoldal HR (eds) The ecosystem approach to fisheries. CABI Publishing and FAO, Rome, pp 209–227

    Chapter  Google Scholar 

  • Skjoldal HR, Dalpadado P, Dommasnes A (2004) Food webs and trophic interactions. In: Skjoldal HR (ed) The Norwegian Sea ecosystem. Tapir Academic Press, Bergen pp 447–506

  • Solanki HU, Dwivedi RM, Nayak SR et al (2005) Cover: Application of remotely sensed closely coupled biological and physical process for marine fishery resources exploration. Int J Remote Sens 26(10):2029–2034

    Article  Google Scholar 

  • Solanki HU, Bhatpuria D, Chauhan P (2015) Integrative analysis of AltiKa-SSHa, MODIS-SST, and OCM-Chlorophyll signatures for fisheries applications, Marine Geodesy. 38(sup1):672–683

  • Solanki HU, Prakash P, Dwivedi RM, Nayak SR, Kulkarni A, Somvamshi VS (2010) Synergistic applications of oceanographic variables from multi-satellite sensors for forecasting potential fishing zones: methodology and validation results. Int J Remote Sens 31:775–789

  • Soykan CU, Eguchi T, Kohin S et al (2014) Prediction of fishing effort distributions using boosted regression trees. Ecol Appl 24(1):71–83

    Article  Google Scholar 

  • Stretta JM (1991) Forecasting models for tuna fishery with aerospatial remote sensing. Int J Remote Sens 12:771–779

    Article  Google Scholar 

  • Sumaila U, Pilcher T, Haggan N, Jones R (2001) Evaluating the benefits from restored ecosystems: a back to the future approach. International Institute of Fisheries Economics and Trade

  • Sumaila UR, Cheung WWL, Lam VWY et al (2011) Climate change impacts on the biophysics and economics of world fisheries. Nat Clim Chang 1:449–456

    Article  Google Scholar 

  • Supraba V, Dineshbabu AP, Thomas S, Rohit P, Rajesh KM, Zacharia PU (2016) Climate influence on oil sardine and Indian mackerel in Southeastern Arabia Sea. Int J Dev Res 6(8):9152–9157

    Google Scholar 

  • Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293

    Article  Google Scholar 

  • Tang DL, Kawamura H0, Lee MA, Dien TV, (2003) Seasonal and spatial distribution of chlorophyll-a concentrations and water conditions in the Gulf of Tonkin. South China Sea. Remote Sens Environ 8:475–483

  • Tang DL, Kawamura H, Dien TV et al (2004) Offshore phytoplankton biomass increase and its oceanographic causes in the South China Sea. Mar Ecol Prog Ser 268:31–41

  • Vivekanandan E (2011) Climate change and Indian marine fisheries. CMFRI Special Publication 105:97

  • Vivekanandan E (2013) Climate change: Challenging the sustainability of marine fisheries and ecosystems. J Aquat Biol Fish 1(1&2):54–67

    Google Scholar 

  • Weimerskirch H, Louzao M, de Grissac S et al (2012) Changes in wind pattern alter albatross distribution and life-history traits. Science 335:211–214

    Article  Google Scholar 

  • Wilson C (2011) The rocky road from research to operations for satellite ocean-color data in fishery management. ICES J Mar Sci 68(4):677–686

    Article  Google Scholar 

  • Wyrtki K (1961) Physical oceanography of the Southeast Asian waters. NAGA report. Vol. 2, Scientific Results of Marine Investigations of the South China Sea and the Gulf of Thailand, 1–195

  • Yohannan TM, Ganga U, Prathiba R et al (2002) Stock assessment of mackerel in the Indian seas. In: NGK P, Menon NG, Pillai PP, Ganga U (eds) Management of Scombroid Fisheries. Central Marine Fisheries Research Institute, Kochi, pp 101–106

    Google Scholar 

  • Yu W, Guo A, Zhang Y et al (2018) Climate-induced habitat suitability variations of chub mackerel Scomber japonicus in the East China Sea. Fish Res 207:63–73

    Article  Google Scholar 

  • Zainuddin M (2007) Mapping of potential fishing grounds of Rastrelliger kanagurta in Bantaeng waters, South Sulawesi. Jurnal Sains dan Teknologi 7(2):57–64

    Google Scholar 

  • Zainuddin M, Saitoh K, Saitoh SI (2008) Albacore (Thunnus alalunga) fishing ground in relation to oceanographic conditions in the western North Pacific Ocean using remotely sensed satellite data. Fish Oceanogr 17(2):61–73

    Article  Google Scholar 

  • Zainuddin M, Farhum A, Safruddin S et al (2017) Detection of pelagic habitat hotspots for skipjack tuna in the Gulf of Bone-Flores Sea, Southwestern Coral Triangle tuna, Indonesia. PLoS One 12(10):e0185601

    Article  Google Scholar 

  • Zhang Y, Qiu Z, Wu Y et al (2009) Predicting central fishing ground of Scomber japonicus in East China Sea based on case-based reasoning. Mar Sci (in Chinese) 33(6):8–11

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the NASA Goddard Space Flight Center for the MODIS-Aqua Level 1 data used in this study and SEAFDEC for supplying the fisheries data. Gratitude is also conveyed to Archiving Validation and Interpretation of Satellite Data (AVISO) for SSH data and QuikSCAT for the three days averaged data of ocean surface winds. The authors also gratefully acknowledge UKM for the research facilities and valuable technical assistance provided. This work was made possible by the research grant funded by the Ministry of Science, Technology and Innovation [04-01-02-SF0753].

Author information

Authors and Affiliations

  1. Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia

    Yeny Nadira Kamaruzzaman & Muzzneena Ahmad Mustapha

  2. Faculty of Fisheries and Food Science, Universiti Malaysia Terengganu, 21300, Kuala Terengganu, Terengganu, Malaysia

    Yeny Nadira Kamaruzzaman

  3. Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, 21300, Kuala Terengganu, Terengganu, Malaysia

    Mazlan Abd Ghaffar

  4. Institute of Marine Biotechnology, Universiti Malaysia Terengganu, 21300, Kuala Terengganu, Terengganu, Malaysia

    Mazlan Abd Ghaffar

Authors
  1. Yeny Nadira Kamaruzzaman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muzzneena Ahmad Mustapha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mazlan Abd Ghaffar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have contributed equally to the study design, data collection, analysis, result interpretation and drafting the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Muzzneena Ahmad Mustapha.

Ethics declarations

Conflict of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kamaruzzaman, Y.N., Mustapha, M.A. & Ghaffar, M.A. Determination of Fishing Grounds Distribution of the Indian Mackerel in Malaysia’s Exclusive Economic Zone Off South China Sea Using Boosted Regression Trees Model. Thalassas 37, 147–161 (2021). https://doi.org/10.1007/s41208-020-00282-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41208-020-00282-0

Keywords

Search

Navigation