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Spatial Modeling of Yellowfin Tuna in the Banda Sea Based on Oceanographic Factors Using MaxEnt

ILMU KELAUTAN: Indonesian Journal of Marine Sciences · Vol. 30 No. 1 (2025) · pp. 103–114

DOI
10.14710/ik.ijms.30.1.103-114
ISSN
2406-7598, 0853-7291
Published
2025-03-08
Publisher
Marine Science Department Diponegoro University
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This study models the spatial distribution of yellowfin tuna (YFT) in the Banda Sea using the MaxEnt approach, addressing critical questions about its predictive capability, the influence of environmental variables such as sea surface temperature (SST) and chlorophyll-a concentration, and temporal patterns. MaxEnt was chosen for its ability to predict potential distribution areas based on presence data and environmental factors. Data utilized include fish catch records obtained from the fishing logbook of the Ministry of Marine Affairs and Fisheries of the Republic of Indonesia, chlorophyll-a concentration, and SST data sourced from ocean color satellite observations. Model performance was evaluated using the Area Under the Curve (AUC) metric. Study results reveal that significant spatial and temporal variations in YFT distribution are influenced by oceanographic factors, with the model performing best in July (AUC 0.72) and lowest in April, September, and December (AUC ~0.60). SST was the dominant variable in November (82.35%), while chlorophyll-a had the highest contribution in April (83.02%). These findings highlight the dynamic link between tuna distribution and environmental conditions. The spatial maps offer insights for optimizing fishing practices, reducing pressure on overexploited stocks, and supporting sustainable fisheries management through data-driven approaches like MaxEnt. However, the MaxEnt model has limitations such as sensitivity to multicollinearity, overfitting, and low transferability. Future research could enhance accuracy and robustness by using advanced methods like Spatial Maxent, Monte Carlo Variable Selection, or ensemble modeling to support adaptive fisheries management.

Keywords

Collaboration network of the authors

27 authors · 59 co-authorship links

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  • Universitas Halu Oleo
  • Hasanuddin University
  • Universitas Trunodjoyo Madura
  • IPB University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Achmad Fachruddin SyahUniversitas Trunodjoyo Madura4
Nur ArafahUniversitas Halu Oleo3
Muhammad Banda SelamatAffiliation unknown2
Sarip HidayatAffiliation unknown2
Aisjah FarhumAffiliation unknown2
Jumiadin JumiadinAffiliation unknown1
Andi Besse AmirAffiliation unknown1
Muhammad SjaifulUniversitas Halu Oleo1
SafruddinAffiliation unknown1
Amir Hamzah MuhiddinHasanuddin University1
Novia ElviantiHasanuddin University1
Muhammad RidwanHasanuddin University1
Fadli FadliUniversitas Halu Oleo1
Andi Sastria AhmadAffiliation unknown1
Sumbara SumbaraAffiliation unknown1
Rohandi BaharuddinAffiliation unknown1
Jonson Lumban GaolIPB University1
Achmad Fachruddin-SyahAffiliation unknown1
Nadela Rista ApriliyaAffiliation unknown1
Dessy BerliantyAffiliation unknown1
Dendy MahabrorAffiliation unknown1
NajamuddinAffiliation unknown1
Fijwal PatangngariAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ejournal.undip.ac.id/index.php/ijms/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Spatial Modeling of Yellowfin Tuna in the Banda Sea Based on Oceanographic Factors Using MaxEnt</dc:title>
	<dc:creator>Asuhadi, Sunarwan</dc:creator>
	<dc:creator>Zainuddin, Mukti</dc:creator>
	<dc:creator>Safruddin, Safruddin</dc:creator>
	<dc:creator>Musbir, Musbir</dc:creator>
	<dc:subject xml:lang="en-US">MaxEnt; Spatial distribution; Yellowfin tuna; Banda Sea; Sea surface temperature; Chlorophyll-a</dc:subject>
	<dc:description xml:lang="en-US">This study models the spatial distribution of yellowfin tuna (YFT) in the Banda Sea using the MaxEnt approach, addressing critical questions about its predictive capability, the influence of environmental variables such as sea surface temperature (SST) and chlorophyll-a concentration, and temporal patterns. MaxEnt was chosen for its ability to predict potential distribution areas based on presence data and environmental factors. Data utilized include fish catch records obtained from the fishing logbook of the Ministry of Marine Affairs and Fisheries of the Republic of Indonesia, chlorophyll-a concentration, and SST data sourced from ocean color satellite observations. Model performance was evaluated using the Area Under the Curve (AUC) metric. Study results reveal that significant spatial and temporal variations in YFT distribution are influenced by oceanographic factors, with the model performing best in July (AUC 0.72) and lowest in April, September, and December (AUC ~0.60). SST was the dominant variable in November (82.35%), while chlorophyll-a had the highest contribution in April (83.02%). These findings highlight the dynamic link between tuna distribution and environmental conditions. The spatial maps offer insights for optimizing fishing practices, reducing pressure on overexploited stocks, and supporting sustainable fisheries management through data-driven approaches like MaxEnt. However, the MaxEnt model has limitations such as sensitivity to multicollinearity, overfitting, and low transferability. Future research could enhance accuracy and robustness by using advanced methods like Spatial Maxent, Monte Carlo Variable Selection, or ensemble modeling to support adaptive fisheries management.</dc:description>
	<dc:publisher xml:lang="en-US">Marine Science Department Diponegoro University</dc:publisher>
	<dc:date>2025-03-08</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ejournal.undip.ac.id/index.php/ijms/article/view/67214</dc:identifier>
	<dc:identifier>10.14710/ik.ijms.30.1.103-114</dc:identifier>
	<dc:source xml:lang="en-US">ILMU KELAUTAN: Indonesian Journal of Marine Sciences; Vol 30, No 1 (2025): Ilmu Kelautan; 103-114</dc:source>
	<dc:source>2406-7598</dc:source>
	<dc:source>0853-7291</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ejournal.undip.ac.id/index.php/ijms/article/view/67214/pdf</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 ILMU KELAUTAN: Indonesian Journal of Marine Sciences</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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