Abstract
The commercial Indian mackerel, Rastrelliger kanagurta (R. kanagurta) is widely distributed on the east coast of the Peninsular of Malaysia. Monsoon variations influence its occurrences and abundance. Understanding the variability of oceanographic physical processes and formation of habitats is vital in exploring fish resources. Temperature differences can have an important impact on ocean circulation and mechanisms that can affect the distribution and availability of fish. This study used fishing locations data of 2008 and 2009 and derived chlorophyll-a (chl-a) and sea surface temperature (SST) from satellite data (MODIS-Aqua). The objectives of this study were to determine potential fishing grounds of R. kanagurta using a presence-absence data model, Generalized Additive Model (GAM) and presence-only data model, maximum entropy (Maxent) and to assess the impact of temperature rise on its seasonal distribution based on the IPCC-AR5-RCP temperature forecast. Results showed that the GAM model had higher prediction accuracy. The constructed model predicted larger distribution areas of R. kanagurta. Maxent model, however, predicted limited distribution range concentrated only areas surrounding the point of presence. Increase of SST projected across all RCPs resulted in a decreased extent of suitable fishing habitats. Potential habitats were observed to shift out of the EEZ. Applicability of the GAM model to understand the spatio-temporal distribution of fish habitat positively contributes to optimal fishing and sustainability in the management of marine resources.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alabia ID, Saitoh SI, Igarashi H, Ishikawa Y, Usui N, Kamachi M, Awaji T, Seito M (2016) Future projected impacts of ocean warming to potential squid habitat in western and central North Pacific. ICES J Mar Sci 73:1343–1356. https://doi.org/10.1093/icesjms/fsv203
Anderson OF, Guinotte JM, Rowden AA, Tracey DM, Mackay KA, Clark MR (2016) Habitat suitability models for predicting the occurrence of vulnerable marine ecosystems in the seas around New Zealand. Deep Sea Res 1 Oceanogr Res Pap 115:265–292. https://doi.org/10.1016/j.dsr.2016.07.006
Barange M, Bahri T, Beveridge MCM, Cochrane KL, Funge-Smith S, Poulain F (2018) Impacts of climate change on fisheries and aquaculture: synthesis of current knowledge, adaptation and mitigation options. FAO Fisheries and Aquaculture Technical Paper No. 627. FAO, Rome. https://doi.org/10.1002/2017GL072945
Barbet-Massin M, Jiguet F, Albert C, Thuiller W (2012) Selecting pseudo-absences for species distribution models: how, where and how many? Methods Ecol Evol 3:327–338. https://doi.org/10.1111/j.2041-210X.2011.00172.x
Bishop Y, Fienberg S, Holland P (1975) Discrete multivariate analysis-theory and practice. MIT Press, Cambridge, MA. https://doi.org/10.1007/978-0-387-72806-3
Bradie J, Leung B (2016) A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. J Biogeogr 44:1344–1361. https://doi.org/10.1111/jbi.12894
Carvalho S, Gonçalves J, Guisan A, Honrado J (2016) Systematic site selection for multispecies monitoring networks. J Appl Ecol 53:1305–1316. https://doi.org/10.1111/1365-2664.12505
Chambers JM, Hastie TJ (1992) Statistical models. Chapman and Hall/CRC, London. https://doi.org/10.1177/096228029200100208
Chassot E, Bonhommeau S, Reygondeau G, Nieto K, Polovina JJ, Hure M, Dulvy NK, Demarcq H (2011) Satellite remote sensing for an ecosystem approach to fisheries management. ICES J Mar Sci 68:651–666. https://doi.org/10.1093/icesjms/fsq195
Cohen J (1960) A coefficient of agreement for nominal scales. Edu Psych Meas 20:37–40. https://doi.org/10.1177/001316446002000104
Drexler M, Ainsworth CH (2013) Generalized additive models used to predict species abundance in the Gulf of Mexico: an ecosystem modelling tool. PLoS One 8:e64458. https://doi.org/10.1371/journal.pone.0064458
Duan R, Kong XQ, Huang MY, Fan WY, Wang ZG (2014) The predictive performance and stability of six species distribution models. PLoS One 9:e112764. https://doi.org/10.1371/journal.pone.0112764
Dueri S, Bopp L, Maury O (2014) Projecting the impacts of climate change on skipjack tuna abundance and spatial distribution. Glob Chang Bio 20:1–12. https://doi.org/10.1111/gcb.12460
Džeroski S (2009) Machine learning applications in habitat suitability modelling. In: Haupt AP, Pasini A, Marzban C (eds) Artificial intelligence methods in the environmental sciences. Springer, Dordrecht, pp 397–412. https://doi.org/10.1007/978-1-4020-9119-3
Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
Fisheries and Aquaculture Department (2017) About us–fisheries and aquaculture department. http://www.fao.org/fishery/. Accessed 24 February 2019
Francis MP, Morrison MA, Leathwick J, Walsh C (2011) Predicting patterns of richness, occurrence and abundance of small fish in New Zealand estuaries. Mar Freshw Res 62:1327
Ghosn D, Collins D, Baiada C, Steffe A (2012) Catch per unit effort and size composition of striped marlin caught by recreational fisheries in southeast Australian waters. Fisheries research report series 30. NSW Department of Primary Industries, Cronulla, NSW, pp 29. https://www.dpi.nsw.gov.au/
Giannoulaki M, Pyrounaki M, Bourdeix JH, Ben AL, Bonanno A, Basilone G, Iglesias M, Ventero A, Felice A, Leonori I, Valavanis V, Machias A, Saraux C (2017) Habitat suitability modelling to identify the potential nursery grounds of the Atlantic mackerel and its relation to oceanographic conditions in the Mediterranean Sea. Front Mar Sci 4:1–13. https://doi.org/10.3389/fmars.2017.00230
Goodman J (2003) Exponential priors for maximum entropy models (technical report). Microsoft Research. http://research.microsoft.com/˜joshuago/longexponentialprior.ps. Accessed 23 Ogos 2019
Groom S, Sathyendranath S, Ban Y, Bernard S, Brewin R, Brotas V, Brockmann C, Chauhan P, Choi JK, Chuprin A, Ciavatta S, Cipollini P, Donlon C, Franz B, He X, Hirata T, Jackson T, Kampel M, Krasemann H, Lavender S, Pardo-Martinez S, Mélin F, Platt T, Santoleri R, Skakala J, Schaeffer B, Smith M, Steinmetz F, Valente A, Wang M (2019) Satellite ocean colour: current status and future perspective. Front Mar Sci 6:485. https://doi.org/10.3389/fmars.2019.00485
Guillera-Arroita G, Lahoz-Monfort JJ, Elith J, Gordon A, Kujala H, Lentini PE, McCarthy MA, Tingley R, Wintle BA (2015) Is my species distribution model fit for purpose? Matching data and models to applications. Glob Ecol Biogeogr 24:276–292
Guisan A, Edwards TC Jr, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model 157:89e100–89e100. https://doi.org/10.1016/S0304-3800(02)00204-1
Guisan A, Thuiller W, Zimmermann N (2017) Overview, principles, theory, and assumptions behind habitat suitability modelling. In: Habitat suitability and distribution models: with applications in R (series: ecology, biodiversity and conservation). Cambridge University Press, Cambridge, pp 9–58. https://doi.org/10.1017/9781139028271
Halvorsen R (2012) A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling. Sommerfeltia 36:1–132. https://doi.org/10.2478/v10208-011-0016-2
Hashim M, Aziz MFHA, Hassan RB, Hossain MS (2017) Assessing target strength, abundance, and biomass for three commercial pelagic fish species along the East Coast of Peninsular Malaysia using a split-beam echo sounder. J Coast Res 33:1448–1459. https://doi.org/10.2112/JCOASTRES-D-16-00127.1
Hastie TJ, Tibshirani RJ (1990) Generalised additive models. Chapman and Hall, New York, p 352. https://doi.org/10.1201/9780203753781
Hirzel AH, Le Lay G, Helfer V, Randin C, Guisan A (2006) Evaluating the ability of habitat suitability models to predict species presences. Ecol Model 199:142–152. https://doi.org/10.1016/j.ecolmodel.2006.05.017
Hosmer DW, Lemeshow S (2000) Applied logistic regression, 2nd edn. John Wiley & Sons, Inc., New York. https://doi.org/10.1002/0471722146
IPCC (2014) Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. In: Field CB, Barros R, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, New York. https://doi.org/10.1017/CBO9781107415386
Isner A (2014) Habitat suitability modelling of Mexican spotted owl (Strix occidentalis Lucida) in Gila National Forest. University of Southern California, New Mexico. Thesis, Master of Science
Knouft JH, Anthony MM (2016) Climate and local abundance in freshwater fishes. R Soc Open Sci 3:160093. https://doi.org/10.1098/rsos.160093
Kuczynski L, Chevalier M, Laffaille P, Legrand M, Grenouillet G (2017) Indirect effect of temperature on fish population abundances through phenological changes. PLoS One 12:e0175735. https://doi.org/10.1371/journal.pone.0175735
Lewandowska A, Boyce D, Hofmann M, Matthiessen B, Sommer U, Worm B (2014) Effects of sea surface warming on marine plankton. Ecol Lett 17:614–623. https://doi.org/10.1111/ele.12265
Lezama-Ochoa N, Murua H, Chust G, Van Loon E, Ruiz J, Hall M, Chavance P, De Molina AD, Villarino E (2016) Present and future potential habitat distribution of Carcharhinus falciformis and Canthidermis maculate by-catch species in the tropical tuna purse-seine fishery under climate change. Front Mar Sci 3:34. https://doi.org/10.3389/fmars.2016.00034
Martinez AS, Willoughby JR, Christie MR (2018) Genetic diversity in fishes is influenced by habitat type and life-history variation. Ecol Evol 8:12022–12023. https://doi.org/10.1002/ece3.4661
Mcclanahan T, Schroeder RE, Friedlander A, Vigliola L, Wantiez L, Caselle J, Graham NAJ, Wilson S, Edgar GJ, Stuart-Smith RD, Oddenyo RM, Cinner JC (2019) Global baselines and benchmarks for fish biomass: comparing remote reefs and fisheries closures. Mar Ecol Prog Ser 612:167–192. https://doi.org/10.3354/meps12874
Mohammed Akib MA, Tam BM, Phumee P, Zainal Abidin M, Tamadoni S, Mather PB, Mohd Nor SA (2015) High connectivity in Rastrelliger kanagurta: influence of historical signatures and migratory behavior inferred from mtDNA cytochrome b. PLoS One 10:e0119749. https://doi.org/10.1371/journal.pone.0119749
Monnereau I, Oxenford H (2017) Impacts of climate change on fisheries in the coastal and marine environments of Caribbean Small Island developing states (SIDS). Caribbean Marine Climate Change Report Card: Science Review, United Kingdom, pp. 124–154
Nurdin S, Mustapha MA, Lihan T, Ghaffar MA (2015) Determination of potential fishing grounds of Rastrelliger Kanagurta using satellite remote sensing and GIS technique. Sains Malays 44:225–232. https://doi.org/10.17576/jsm-2015-4402-09
Nurdin S, Mustapha MA, Lihan T, Zainuddin M (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. https://doi.org/10.1016/j.fishres.2017.07.029
O’Reilly JE, Maritorena S, Mitchell BG, Siegel DA, Carder KL, Garver SA, Kahru M, McClain C (1998) Ocean color clorophyll algorithm for SEaWiFS. J Geophys Res Oceans 103:24937–24954. https://doi.org/10.1029/98JC02160
Peck MA, Reglero P, Takahashi M, Catalán IA (2013) Life cycle ecophysiology of small pelagic fish and climate-driven changes in populations. Prog Oceanogr 116:220–245
Pesce M, Critto A, Torresan S, Giubilato E, Santini M, Zirino A, Ouyang W, Marcomini A (2018) Modelling climate change impacts on nutrients and primary production in coastal waters. Sci Total Environ 628–629:919–937. https://doi.org/10.1016/j.scitotenv.2018.02.131
Peterson A, Soberón J, Pearson R, Anderson R, Martínez-Meyer E, Nakamura M, Araújo M (2011) Ecological niches and geographic distributions. Princeton University Press, New Jersey, pp 376–377. https://doi.org/10.1086/668178,87
Phillips SJ, Dudik M (2008) Modeling of species distributions with MaxEnt: new extensions and a comprehensive evaluation. Ecography 31:161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231e259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
R Core Team (2017) R: a language and environment for statistical computing. https://www.R-project.org/. Accessed 22 January 2019
Rahman MM, Hafzath A (2012) Condition, length-weight relationship, sex ratio and gonadosomatic index of Indian mackerel (Rastrelliger kanagurta) captured from Kuantan coastal water. J Bio Sci 12:426–432. https://doi.org/10.3923/jbs.2012.426.432
Rivai AA, Siregar VP, Agus SB, Yasuma H (2018) Analysis of habitat characteristics of small pelagic fish based on generalized additive models in Kepulauan Seribu waters. IOP Conf Ser: Earth Environ Sci 139:012014. https://doi.org/10.1088/1755-1315/139/1/012014
Salahuddin A, Curtis S (2011) Climate extremes in Malaysia and the equatorial South China Sea. Glob Planet Change 78:83–91. https://doi.org/10.1016/j.gloplacha.2011.05.001
Shaari NR, Mustapha MA (2018) Predicting potential R. kanagurta fish habitat using MODIS satellite data and GIS modeling: a case study of exclusive economic zone, Malaysia. Sains Malays 47:1369–1378. https://doi.org/10.17576/jsm-2018-4707-03
Smith AB, Santos MJ (2020) Testing the ability of species distribution models to infer variable importance. Ecography 43:1–13. https://doi.org/10.1111/ecog.05317
Solanki H, Bhatpuria D, Chauhan P (2016) Applications of generalized additive model (GAM) to satellite derived variables and fishery data for prediction of fishery resources distributions in the Arabian Sea. Geocarto Int 32:1–29. https://doi.org/10.1080/10106049.2015.1120357
Soley M (2014) Ecological niches, species distributions, and biogeographic processes in rodents on Neotropical Sky Islands. Academic Works, CUNY https://academicworks.cuny.edu/gc_etds/498.
Suhaila J, Deni SM, Wan Zin WZ, Jemain AA (2010) Trends in Peninsular Malaysia rainfall data during the southwest monsoon and northeast monsoon seasons: 1975–2004. Sains Malays 39:533–542
Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293. https://doi.org/10.1126/science.3287615
Tobeῆa M, Prieto R, Machete M, Silva MA (2016) Modeling the potential distribution and richness of cetaceans in the Azores from fisheries observer program data. Front Mar Sci 3:202. https://doi.org/10.3389/fmars.2016.00202
Tseng CT, Sun CL, Yeh SZ, Chen SC, Su WC, Liu DC (2011) Influence of climate-driven sea surface temperature increase on potential habitats of the Pacific Saury (Cololabis saira). ICES J Mar Sci 68:1105–1113. https://doi.org/10.1093/icesjms/fsr070
Van Vuuren DP, Stehfest E, Den Elzen MGJ, Kram T, Van Vliet J, Deetman S, Isaac M, Goldewijk KK, Hof A, Beltran AM, Van Oostenrijk, Ruijven N (2011) RCP2.6: exploring the possibility to keep global mean temperature increase below 2 °C. Clim Chang 109:95–116. https://doi.org/10.1007/s10584-011-0152-3
Vasconcelos RP, Le Pape O, Costa MJ, Cabral HN (2013) Predicting estuarine use patterns of juvenile fish with generalized linear models. Estuar Coast Shelf Sci 120:64–74. https://doi.org/10.1016/j.ecss.2013.01.018
Warren D, Matzke N, Iglesias T (2019) Evaluating species distribution models with discrimination accuracy is uninformative for many applications. J Biogeogr 47:167–180. https://doi.org/10.1111/jbi.13705
Wood SN (2006) Generalized additive models: an introduction with R. Chapman and Hall/CRC Press, Boca Raton, p 410. https://doi.org/10.1201/9781315370279
World Fishing and Aquaculture (2017) News (regional-Malaysia). http://wwwworldfishingnet. Accessed 27 December 2017
Yee TW, Mitchell ND (1991) Generalized additive models in plant ecology. J Veg Sci 2:587e602. https://doi.org/10.2307/3236170
Yeny NK, Mustapha MA, Ghaffar MA (2019) The Indian mackerel aggregation areas in relation to their oceanographic conditions. Sains Malays 48(11):2575–2581. https://doi.org/10.17576/jsm-2019-4811-27
Yi YJ, Cheng X, Yang ZF, Zhang SH (2016) MaxEnt modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan, China. Ecol Eng 92:260–269. https://doi.org/10.1016/j.ecoleng.2016.04.010
Acknowledgements
The authors express their sincere appreciation to SEAFDEC for supplying the data on fisheries. The authors are also grateful to the Distributed Active Archive Center at the Goddard Space Flight NASA for the use of the chl-a and SST AQUA-MODIS data sets. Gratitude is also forwarded to UKM for the facilities offered. This research was made possible by the Ministry of Science and Innovation grant [04-01-02-SF0753].
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have 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
About this article
Cite this article
Yusop, S.M., Mustapha, M.A. & Lihan, T. Determination of spatio-temporal distribution of Rastrelliger kanagurta using modelling techniques for optimal fishing. J Coast Conserv 25, 15 (2021). https://doi.org/10.1007/s11852-020-00796-y
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11852-020-00796-y