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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Mar. Sci.</journal-id>
<journal-title>Frontiers in Marine Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Mar. Sci.</abbrev-journal-title>
<issn pub-type="epub">2296-7745</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fmars.2024.1391800</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Marine Science</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Habitat changes of a small endemic euryhaline fish species in the northern margin of the South China Sea under the background of global warming</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Liao</surname>
<given-names>Jian</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
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<role content-type="https://credit.niso.org/contributor-roles/investigation/"/>
<role content-type="https://credit.niso.org/contributor-roles/formal-analysis/"/>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
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<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Chen</surname>
<given-names>Hao</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Jia-Yu</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Gao-Cong</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2212284"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Guan</surname>
<given-names>Xian</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Liang</surname>
<given-names>Cai-Feng</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
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<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Guo</surname>
<given-names>Yu-Song</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/investigation/"/>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Dong</surname>
<given-names>Zhong-Dian</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1317277"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Wang</surname>
<given-names>Zhong-Duo</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1277124"/>
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</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Key Laboratory of Aquaculture in the South China Sea for Aquatic Economic Animals, Guangdong Ocean University, Fisheries College</institution>, <addr-line>Zhanjiang</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Guangdong Provincial Key Laboratory of Aquatic Animal Disease Control and Healthy Aquaculture, College of Fisheries, Guangdong Ocean University</institution>, <addr-line>Zhanjiang</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Angel Borja, Marine Research Division, Spain</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Lei Xu, Chinese Academy of Fishery Sciences (CAFS), China</p>
<p>Mario Lepage, INRAE &#x200b;&#x200b;Nouvelle-Aquitaine Bordeaux, France</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Zhong-Duo Wang, <email xlink:href="mailto:wangzd@gdou.edu.cn">wangzd@gdou.edu.cn</email>
</p>
</fn>
<fn fn-type="other" id="fn003">
<p>&#x2020;ORCID: Jian Liao, <uri xlink:href="https://orcid.org/0009-0005-0459-2060">orcid.org/0009-0005-0459-2060</uri>
</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>23</day>
<month>12</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>11</volume>
<elocation-id>1391800</elocation-id>
<history>
<date date-type="received">
<day>26</day>
<month>02</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>26</day>
<month>11</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2024 Liao, Chen, Li, Li, Guan, Liang, Guo, Dong and Wang</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Liao, Chen, Li, Li, Guan, Liang, Guo, Dong and Wang</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>Hainan medaka (<italic>Oryzias curvinotus</italic>) is a small euryhaline fish species native to the northern margin of the South China Sea. Our long-term field observations indicate a concerning decline in its wild resources. Climate change, an uncontrollable factor, has altered the species&#x2019; distribution pattern. In this study, we simulated the shifts in the species range of <italic>O. curvinotus</italic> during the Last Glacial Maximum (LGM), current, and the next one hundred year, and analyzed its habitat attributes. The results demonstrate that bio2 (mean diurnal range of temperature) is a crucial factor in shaping the species range of <italic>O. curvinotus</italic>. The simulation results reveal that the current habitats are located in the coastal areas of northern Vietnam, the northeastern Hainan Province, the coastal areas of Guangdong Province and Guangxi Zhuang Autonomous Region, and a few areas in Taiwan Province of China, covering a total area of 17.82&#xd7;10<sup>4</sup> km<sup>2</sup>. Highly suitable habitats are mainly concentrated in the coastal areas of Hai Phong, Nam Dinh, and Thanh Hoa in northern Vietnam, the central part of Leizhou Peninsula, and the west coast of the Pearl River Estuary. For the tropical species Hainan medaka, the impact of the LGM was relatively minor, and there were extensive suitable habitats during historical times, including three refugia. Currently, only Refugium 2 near Guanghai Town, Taishan County, Jiangmen City, Guangdong Province in China remains, while the other two refugia have submerged below sea level. Future climate warming under different carbon emission levels is projected to cause a short-term expansion, followed by a relief in expansion. By 2100, the potential habitat area of <italic>O. curvinotus</italic> is slightly larger than the current scenario. It is noteworthy that under future climate warming scenarios, the highly suitable habitats will not migrate northward but will expand near the 21&#xb0;N latitude. Overall, Hainan medaka is not expected to be threatened in the future. Our study provides long-term dynamic distribution data, which provides a theoretical basis for the long-term development and conservation management of Hainan medaka.</p>
</abstract>
<kwd-group>
<kwd>
<italic>Oryzias curvinotus</italic>
</kwd>
<kwd>climate change</kwd>
<kwd>habitats</kwd>
<kwd>tropical species</kwd>
<kwd>distribution</kwd>
</kwd-group>
<counts>
<fig-count count="10"/>
<table-count count="3"/>
<equation-count count="0"/>
<ref-count count="58"/>
<page-count count="13"/>
<word-count count="5597"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Marine Ecosystem Ecology</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<title>Introduction</title>
<p>Evidence that the climate is changing is indisputable (<xref ref-type="bibr" rid="B4">Berrang-Ford et&#xa0;al., 2011</xref>), and this phenomenon poses a potential threat to the planet&#x2019;s biodiversity (<xref ref-type="bibr" rid="B20">Kappelle et&#xa0;al., 1999</xref>). Over the past 100 years, the Earth&#x2019;s climate has visibly warmed, precipitation regimes have changed, and biologists have been concerned about the impact of these shifts on species distribution (<xref ref-type="bibr" rid="B2">Ara&#xfa;jo and Rahbek, 2006</xref>). Under the background of global climate change, the study of species distribution range has become the premise of biodiversity conservation strategy, especially for those endemic species with narrow distribution range. For example, the endangered endemic annonaceae species in East Africa may lose some of their original suitable habitats due to future warming (<xref ref-type="bibr" rid="B37">Mkala et&#xa0;al., 2023</xref>). In addition, climate change will drive some species to higher altitudes or latitudes (<xref ref-type="bibr" rid="B18">Hickling et&#xa0;al., 2005</xref>; <xref ref-type="bibr" rid="B5">Chen et&#xa0;al., 2011</xref>; <xref ref-type="bibr" rid="B31">Liao et&#xa0;al., 2023</xref>). Over the past few decades, more than 1,700 species have been confirmed to migrate poleward at a rate of 6.1 km/decade (<xref ref-type="bibr" rid="B40">Parmesan and Yohe, 2003</xref>), and the rate of migration has doubled or tripled in recent years to a staggering 16.9 km/decade (<xref ref-type="bibr" rid="B5">Chen et&#xa0;al., 2011</xref>). Of the species studied, the current focus is mainly on plants and various insect groups, etc., while relatively little is known about vertebrates, especially these tropical endemic fish.</p>
<p>Hainan medaka (<italic>Oryzias curvinotus</italic>) is a small euryhaline fish that is confined to the coastal waters of the South China Sea, including mangroves, brackish water confluxes in estuaries, and even freshwater streams extending inland (<xref ref-type="bibr" rid="B7">Dong et&#xa0;al., 2021</xref>). It is small in size, usually growing up to 2 inches long. Similar to freshwater Japanese medaka (<italic>Oryzias latipes</italic>), Hainan medaka is an excellent euryhaline model fish resource to be developed. As a euryhaline fish, <italic>O. curvinotus</italic> exhibits unique adaptive traits that are crucial not only for its own survival but also serve as indicators of ecological balance and environmental health in the South China Sea region. Changes in the habitat of this species directly reflect subtle variations in environmental factors such as salinity and water temperature in the region, which may further impact the stability and biodiversity of ecosystems including mangrove forests, coral reefs, and seagrass beds. However, recent fieldwork indicates that the habitat of Hainan medaka appears to be under threat, and the population resources are not optimistic. Climate change, a long-term and irreversible factor, needs to be considered in addition to the immediate effects of human activities (<xref ref-type="bibr" rid="B54">Yao et&#xa0;al., 2022</xref>). In particular, the fate of Hainan medaka, which is located in a biological hotspot (Oriental region), is a key ecological issue in the context of global climate change. By conducting thorough investigations into the habitat shifts and adaptive mechanisms of <italic>O. curvinotus</italic>, a more precise understanding of the ecological health of the South China Sea can be gained, thereby providing scientific evidence for the formulation of effective environmental protection and management strategies.</p>
<p>Effectively predicting the species range and their impacts under climate change, as well as understanding their habitat attributes, is crucial for developing appropriate strategies for species conservation (<xref ref-type="bibr" rid="B1">Alabdulhafith et&#xa0;al., 2022</xref>). Species distribution models (SDMs), also known as climate envelope-models, habitat models, and (environmental or ecological) niche-models, largely address the urgent need for ecologists to understand the habitat attributes of species and their distribution range shifts. Maximum entropy model (MaxEnt) is the most widely recognized and used SDMs, due to its high prediction accuracy, automatic assessment of important environmental variables, fast operation, and small sample size requirements (<xref ref-type="bibr" rid="B42">Phillips et&#xa0;al., 2006</xref>; <xref ref-type="bibr" rid="B32">Ma and Sun, 2018</xref>; <xref ref-type="bibr" rid="B28">Li et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B53">Xiao et&#xa0;al., 2022</xref>; <xref ref-type="bibr" rid="B31">Liao et&#xa0;al., 2023</xref>). MaxEnt is therefore widely used to predict the potential distribution of plants, birds, insects, nematodes, corals, bryophytes, and fungi (<xref ref-type="bibr" rid="B45">S&#xe9;rgio et&#xa0;al., 2007</xref>; <xref ref-type="bibr" rid="B43">Phillips and Dud&#xed;k, 2008</xref>; <xref ref-type="bibr" rid="B49">Tognelli et&#xa0;al., 2009</xref>; <xref ref-type="bibr" rid="B52">Williams et&#xa0;al., 2009</xref>; <xref ref-type="bibr" rid="B56">Young et&#xa0;al., 2009</xref>; <xref ref-type="bibr" rid="B1">Alabdulhafith et&#xa0;al., 2022</xref>), providing valuable information in biogeography, invasion biology, conservation biology, and ecology.</p>
<p>In this study, we integrated our previous field data with reported occurrence sites and climate data to analyze the historical period (the Last Glacial Maximum, LGM, dating back approximately between 25,000 and 19,000 years ago), current scenarios, and future potential habitats under different warming levels for the euryhaline fish endemic to the coast of the northern South China Sea. The objectives of this study were to (1) comprehend the habitat properties of <italic>O. curvinotus</italic>, (2) investigate its distribution and refuge locations during historical periods, and (3) assess potential habitats in the present context while considering how future climate change may impact its distribution.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<title>Materials and methods</title>
<sec id="s2_1">
<title>Field survey and occurrence data collection</title>
<p>We conducted field survey work on <italic>O. curvinotus</italic> from 2015 to 2018, and successfully recorded a total of 19 recording sites (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>), including mangrove tidal channels, estuary with a wide salinity range and some freshwater streams. In addition to our survey data, we have collected occurrence data of <italic>O. curvinotus</italic> through the Global Biodiversity Information Facility (GBIF database, <ext-link ext-link-type="uri" xlink:href="https://www.gbif.org/">https://www.gbif.org/</ext-link>, accessed on 18 June 2023) and literature (<xref ref-type="bibr" rid="B12">Hamaguchi and Sakaizumi, 1992</xref>; <xref ref-type="bibr" rid="B24">Kondo et&#xa0;al., 2001</xref>; <xref ref-type="bibr" rid="B23">Koga et&#xa0;al., 2002</xref>; <xref ref-type="bibr" rid="B35">Matsuda et&#xa0;al., 2003</xref>; <xref ref-type="bibr" rid="B13">Hamaguchi et&#xa0;al., 2004</xref>; <xref ref-type="bibr" rid="B46">Shiga and Suzuki, 2004</xref>; <xref ref-type="bibr" rid="B47">Shinomiya et&#xa0;al., 2006</xref>; <xref ref-type="bibr" rid="B19">Kamei et&#xa0;al., 2007</xref>; <xref ref-type="bibr" rid="B22">Kato et&#xa0;al., 2010</xref>, <xref ref-type="bibr" rid="B21">2011</xref>; <xref ref-type="bibr" rid="B34">Masaoka et&#xa0;al., 2012</xref>; <xref ref-type="bibr" rid="B51">Wang et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B6">Dong et&#xa0;al., 2020</xref>, <xref ref-type="bibr" rid="B7">2021</xref>; <xref ref-type="bibr" rid="B27">Li et&#xa0;al., 2022a</xref>; <xref ref-type="bibr" rid="B54">Yao et&#xa0;al., 2022</xref>; <xref ref-type="bibr" rid="B9">Gao et&#xa0;al., 2023</xref>). We have made every effort to gather all relevant published literature and publicly available data. For the occurrence data collected in the three ways, we first eliminated duplicates and outliers. In order to avoid overfitting, we created a 1km &#xd7; 1km grid in ArcGIS 10.7 (Esri, Redlands, CA, USA), and reserved only the point closest to the center in each grid. Finally, 38 valid GPS sites were obtained and used for input occurrence data to the model (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>; <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table 1</bold>
</xref>).</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Information from our comprehensive field survey conducted between 2015 to 2018 on the spatial distribution of <italic>O. curvinotus</italic>.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Sites</th>
<th valign="top" align="left">Longitude</th>
<th valign="top" align="left">Latitude</th>
<th valign="top" align="left">Date</th>
<th valign="top" align="left">Habitat type</th>
<th valign="top" align="left">Water body type</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Fucheng Town, Leizhou City,China</td>
<td valign="top" align="left">110.1500&#xb0;E</td>
<td valign="top" align="left">20.91667&#xb0;N</td>
<td valign="top" align="left">April 26, 2015</td>
<td valign="top" align="left">Mangrove</td>
<td valign="top" align="left">Saltwater</td>
</tr>
<tr>
<td valign="top" align="left">Donghaidao Mangrove, Zhanjiang City, China</td>
<td valign="top" align="left">110.3167&#xb0;E</td>
<td valign="top" align="left">21.38333&#xb0;N</td>
<td valign="top" align="left">March 20, 2016</td>
<td valign="top" align="left">Mangrove</td>
<td valign="top" align="left">Saltwater</td>
</tr>
<tr>
<td valign="top" align="left">Gaoqiao Mangrove, Lianjiang, China</td>
<td valign="top" align="left">109.7617&#xb0;E</td>
<td valign="top" align="left">21.56796&#xb0;N</td>
<td valign="top" align="left">January 08, 2016</td>
<td valign="top" align="left">Mangrove</td>
<td valign="top" align="left">Saltwater</td>
</tr>
<tr>
<td valign="top" align="left">Tuolin Town, Raoping County, China</td>
<td valign="top" align="left">117.0833&#xb0;E</td>
<td valign="top" align="left">23.56667&#xb0;N</td>
<td valign="top" align="left">March 29, 2017</td>
<td valign="top" align="left">Estuary</td>
<td valign="top" align="left">Brackish Water</td>
</tr>
<tr>
<td valign="top" align="left">Niutianyang Stream, Shantou City, China</td>
<td valign="top" align="left">116.7167&#xb0;E</td>
<td valign="top" align="left">23.31667&#xb0;N</td>
<td valign="top" align="left">March 27, 2017</td>
<td valign="top" align="left">Stream</td>
<td valign="top" align="left">Freshwater</td>
</tr>
<tr>
<td valign="top" align="left">Niutianyang Mangrove, Shantou City, China</td>
<td valign="top" align="left">116.5667&#xb0;E</td>
<td valign="top" align="left">23.31658&#xb0;N</td>
<td valign="top" align="left">March 27, 2017</td>
<td valign="top" align="left">Mangrove</td>
<td valign="top" align="left">Saltwater</td>
</tr>
<tr>
<td valign="top" align="left">Niutianyang Farm, Shantou City, China</td>
<td valign="top" align="left">116.6897&#xb0;E</td>
<td valign="top" align="left">23.35955&#xb0;N</td>
<td valign="top" align="left">March 25, 2017</td>
<td valign="top" align="left">Pond</td>
<td valign="top" align="left">Freshwater</td>
</tr>
<tr>
<td valign="top" align="left">Haojiang Estuary, Shantou City, China</td>
<td valign="top" align="left">116.6167&#xb0;E</td>
<td valign="top" align="left">23.35000&#xb0;N</td>
<td valign="top" align="left">March 26, 2017</td>
<td valign="top" align="left">Estuary</td>
<td valign="top" align="left">Brackish Water</td>
</tr>
<tr>
<td valign="top" align="left">Mangroves near the Donghai Chemical plant, Zhanjiang, China</td>
<td valign="top" align="left">110.3167&#xb0;E</td>
<td valign="top" align="left">21.38333&#xb0;N</td>
<td valign="top" align="left">March 20, 2017</td>
<td valign="top" align="left">Mangrove</td>
<td valign="top" align="left">Saltwater</td>
</tr>
<tr>
<td valign="top" align="left">Mangroves in Huguang town, Zhanjiang City, China</td>
<td valign="top" align="left">110.2833&#xb0;E</td>
<td valign="top" align="left">21.10000&#xb0;N</td>
<td valign="top" align="left">March 04, 2017</td>
<td valign="top" align="left">Mangrove</td>
<td valign="top" align="left">Saltwater</td>
</tr>
<tr>
<td valign="top" align="left">Liushawan Mangrove, Leizhou City, China</td>
<td valign="top" align="left">109.9167&#xb0;E</td>
<td valign="top" align="left">20.45000&#xb0;N</td>
<td valign="top" align="left">May 10, 2017</td>
<td valign="top" align="left">Mangrove</td>
<td valign="top" align="left">Saltwater</td>
</tr>
<tr>
<td valign="top" align="left">Huguang Stream, Zhanjiang City, China</td>
<td valign="top" align="left">110.3213&#xb0;E</td>
<td valign="top" align="left">21.10629&#xb0;N</td>
<td valign="top" align="left">March 04, 2017</td>
<td valign="top" align="left">Stream</td>
<td valign="top" align="left">Freshwater</td>
</tr>
<tr>
<td valign="top" align="left">Sanniangwan Mangrove, Qinzhou City, China</td>
<td valign="top" align="left">108.7167&#xb0;E</td>
<td valign="top" align="left">21.66667&#xb0;N</td>
<td valign="top" align="left">April 13, 2017</td>
<td valign="top" align="left">Mangrove</td>
<td valign="top" align="left">Saltwater</td>
</tr>
<tr>
<td valign="top" align="left">A stream near Sanyangwan salt farm, Qinzhou City, China</td>
<td valign="top" align="left">108.7625&#xb0;E</td>
<td valign="top" align="left">21.62845&#xb0;N</td>
<td valign="top" align="left">April 13, 2017</td>
<td valign="top" align="left">Stream</td>
<td valign="top" align="left">Freshwater</td>
</tr>
<tr>
<td valign="top" align="left">Dongzhaigang Mangrove, Haikou City, China</td>
<td valign="top" align="left">110.5833&#xb0;E</td>
<td valign="top" align="left">19.9500&#xb0;N</td>
<td valign="top" align="left">May 27, 2017</td>
<td valign="top" align="left">Mangrove</td>
<td valign="top" align="left">Saltwater</td>
</tr>
<tr>
<td valign="top" align="left">Hongshuwan Mangrove, Chengmai County, China</td>
<td valign="top" align="left">109.9833&#xb0;E</td>
<td valign="top" align="left">19.9000&#xb0;N</td>
<td valign="top" align="left">May 23, 2017</td>
<td valign="top" align="left">Mangrove</td>
<td valign="top" align="left">Saltwater</td>
</tr>
<tr>
<td valign="top" align="left">Caihong Valliage, Lingao County, China</td>
<td valign="top" align="left">109.5667&#xb0;E</td>
<td valign="top" align="left">19.86667&#xb0;N</td>
<td valign="top" align="left">May 24, 2017</td>
<td valign="top" align="left">Stream</td>
<td valign="top" align="left">Freshwater</td>
</tr>
<tr>
<td valign="top" align="left">Bamenwan mangrove, Wenchang County, China</td>
<td valign="top" align="left">110.8099&#xb0;E</td>
<td valign="top" align="left">19.86117&#xb0;N</td>
<td valign="top" align="left">May 26, 2017</td>
<td valign="top" align="left">Mangrove</td>
<td valign="top" align="left">Saltwater</td>
</tr>
<tr>
<td valign="top" align="left">Sanya Mangrove, Sanya City, China</td>
<td valign="top" align="left">109.7525&#xb0;E</td>
<td valign="top" align="left">18.40005&#xb0;N</td>
<td valign="top" align="left">March 27, 2018</td>
<td valign="top" align="left">Mangrove</td>
<td valign="top" align="left">Saltwater</td>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Current occurrence sites of <italic>O. curvinotus</italic> and its main habitats photos.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1391800-g001.tif"/>
</fig>
</sec>
<sec id="s2_2">
<title>Climate variables and processing</title>
<p>To investigate the impact of climate change on euryhaline fish distribution in tropical regions, a set of 19 bioclimatic factors (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>) with 2.5 arc-minutes spatial resolution were selected as environmental variables for the model, which were downloaded from the WORLDCLIM 2.0 database (<ext-link ext-link-type="uri" xlink:href="https://www.worldclim.org/data/bioclim.html">https://www.worldclim.org/data/bioclim.html</ext-link>, accessed on June 18, 2023). The downloaded climate variable layer was converted into ASCLL format in ArcGIS 10.7 for MaxEnt model analysis. The climate variables used in this study include historical (the LGM), current and future climates (<xref ref-type="bibr" rid="B8">Fick and Hijmans, 2017</xref>). The future climate includes four carbon emission levels (SSP1-2.6: the low-end level, 376 ppm CO<sub>2</sub>-equivalent level; SSP2-4.5: the low-moderate level, 650 ppm CO<sub>2</sub>-equivalent level; SSP3-7.0: the medium-high level, 1011 ppm CO<sub>2</sub>-equivalent level and SSP5-8.5: the high level, 1228 ppm CO<sub>2</sub>-equivalent level) (<xref ref-type="bibr" rid="B36">Meinshausen et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B30">Liao et&#xa0;al., 2022</xref>) and different future decades (2040, 2060, 2080, and 2100) (<xref ref-type="bibr" rid="B8">Fick and Hijmans, 2017</xref>).</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>The 19 climate variables used in this study.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Climate Variables</th>
<th valign="top" align="left">Unit</th>
<th valign="top" align="left">Symbol</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Annual mean temperature</td>
<td valign="top" align="left">&#xb0;C</td>
<td valign="top" align="left">bio1</td>
</tr>
<tr>
<td valign="top" align="left">Mean diurnal range of temperature</td>
<td valign="top" align="left">&#xb0;C</td>
<td valign="top" align="left">bio2</td>
</tr>
<tr>
<td valign="top" align="left">Isothermality</td>
<td valign="top" align="left">&#xd7;100</td>
<td valign="top" align="left">bio3</td>
</tr>
<tr>
<td valign="top" align="left">Temperature seasonality</td>
<td valign="top" align="left">&#xd7;100</td>
<td valign="top" align="left">bio4</td>
</tr>
<tr>
<td valign="top" align="left">Max temperature of warmest month</td>
<td valign="top" align="left">&#xb0;C</td>
<td valign="top" align="left">bio5</td>
</tr>
<tr>
<td valign="top" align="left">Min temperature of coldest month</td>
<td valign="top" align="left">&#xb0;C</td>
<td valign="top" align="left">bio6</td>
</tr>
<tr>
<td valign="top" align="left">Temperature annual range</td>
<td valign="top" align="left">&#xb0;C</td>
<td valign="top" align="left">bio7</td>
</tr>
<tr>
<td valign="top" align="left">Mean temperature of wettest quarter</td>
<td valign="top" align="left">&#xb0;C</td>
<td valign="top" align="left">bio8</td>
</tr>
<tr>
<td valign="top" align="left">Mean temperature of driest quarter</td>
<td valign="top" align="left">&#xb0;C</td>
<td valign="top" align="left">bio9</td>
</tr>
<tr>
<td valign="top" align="left">Mean temperature of warmest quarter</td>
<td valign="top" align="left">&#xb0;C</td>
<td valign="top" align="left">bio10</td>
</tr>
<tr>
<td valign="top" align="left">Mean temperature of coldest quarter</td>
<td valign="top" align="left">&#xb0;C</td>
<td valign="top" align="left">bio11</td>
</tr>
<tr>
<td valign="top" align="left">Annual precipitation</td>
<td valign="top" align="left">mm</td>
<td valign="top" align="left">bio12</td>
</tr>
<tr>
<td valign="top" align="left">Precipitation of wettest month</td>
<td valign="top" align="left">mm</td>
<td valign="top" align="left">bio13</td>
</tr>
<tr>
<td valign="top" align="left">Precipitation of driest month</td>
<td valign="top" align="left">mm</td>
<td valign="top" align="left">bio14</td>
</tr>
<tr>
<td valign="top" align="left">Precipitation seasonality</td>
<td valign="top" align="left">mm</td>
<td valign="top" align="left">bio15</td>
</tr>
<tr>
<td valign="top" align="left">Precipitation of wettest quarter</td>
<td valign="top" align="left">mm</td>
<td valign="top" align="left">bio16</td>
</tr>
<tr>
<td valign="top" align="left">Precipitation of driest quarter</td>
<td valign="top" align="left">mm</td>
<td valign="top" align="left">bio17</td>
</tr>
<tr>
<td valign="top" align="left">Precipitation of warmest quarter</td>
<td valign="top" align="left">mm</td>
<td valign="top" align="left">bio18</td>
</tr>
<tr>
<td valign="top" align="left">Precipitation of coldest quarter</td>
<td valign="top" align="left">mm</td>
<td valign="top" align="left">bio19</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>In order to avoid the multicollinearity of climate factors leading to overfitting of the model, we conducted a variance inflation factor analysis (VIF) (<xref ref-type="bibr" rid="B31">Liao et&#xa0;al., 2023</xref>). We first used ArcGIS 10.7 to convert the GPS site distribution data (<italic>csv</italic> format) into raster (<italic>shp</italic> format), then extracted 19 climate variable values at each site using the extraction function of the spatial analysis tool in the toolbox, and performed VIF analysis on 19 climate factors at all sites. We removed 12 multicollinearity climate variables (VIF &gt; 10) by using USDM version 1.1-18 package in R 4.1.3 (<xref ref-type="bibr" rid="B39">Naimi et&#xa0;al., 2014</xref>), and 7 variables were left for modeling: bio2, bio7, bio8, bio9, bio12, bio15 and bio17.</p>
</sec>
<sec id="s2_3">
<title>Potential habitat modeling and statistical analysis</title>
<p>The potential suitable habitat of <italic>O. curvinotus</italic> was simulated using the maximum entropy model by MaxEnt version 3.3.3k (<xref ref-type="bibr" rid="B42">Phillips et&#xa0;al., 2006</xref>). The input data included optimized climate variables and occurrence data. Among them, 25 percent of the occurrence data was randomly selected for the test dataset and 75 percent for the training dataset (<xref ref-type="bibr" rid="B57">Yuan et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B55">Yi et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B30">Liao et&#xa0;al., 2022</xref>, <xref ref-type="bibr" rid="B31">2023</xref>; <xref ref-type="bibr" rid="B53">Xiao et&#xa0;al., 2022</xref>). The program ran at least 1000 iterations until it converged (threshold 0.00001) (<xref ref-type="bibr" rid="B58">Zhang et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B30">Liao et&#xa0;al., 2022</xref>). The robustness of the MaxEnt model is assessed by the AUC value of the threshold-independent receiver-operating characteristic (ROC) analysis: above 0.9 is defined as excellent, between 0.8 and 0.9 is defined as good, between 0.7 and 0.8 is considered acceptable, between 0.6 and 0.7 is defined as poor, and below 0.6 is considered inadequate (<xref ref-type="bibr" rid="B42">Phillips et&#xa0;al., 2006</xref>; <xref ref-type="bibr" rid="B30">Liao et&#xa0;al., 2022</xref>). Simulating the probability of occurrence of O. curvinotus in a certain location as its suitability value to distinguish different levels of potential habitat (<xref ref-type="bibr" rid="B42">Phillips et&#xa0;al., 2006</xref>). The potential suitable habitats obtained through modeling were categorized into four levels based on their suitability values: highly suitable habitats, moderately suitable habitats, lowly suitable habitats, and unsuitable habitats. The suitability values ranged from 0.6 to 1 for highly suitable habitats, from 0.4 to 0.6 for moderately suitable habitats, from 0.2 to 0.4 for lowly suitable habitats, and from 0 to 0.2 for unsuitable habitats (<xref ref-type="bibr" rid="B58">Zhang et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B31">Liao et&#xa0;al., 2023</xref>). The area of each part was counted in the 3D analysis tool in ArcGIS, and then the area change chart was drawn by HIPLOT online server (<ext-link ext-link-type="uri" xlink:href="https://hiplot.cn/">https://hiplot.cn/</ext-link>) (<xref ref-type="bibr" rid="B29">Li et&#xa0;al., 2022b</xref>). During the LGM, habitat suitability exceeding 0.9 was defined as Refugium (<xref ref-type="bibr" rid="B10">Gathorne-Hardy et&#xa0;al., 2002</xref>). The contribution rate, permutation importance of climate variables and their response curve were completed by Jackknife test in MaxEnt software (<xref ref-type="bibr" rid="B42">Phillips et&#xa0;al., 2006</xref>).</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<title>Results</title>
<sec id="s3_1">
<title>Model performance, variable contribution, and response curves</title>
<p>The AUC values of both the training (0.999) and test (0.998) sets were greater than 0.9, indicating that the simulation results were reliable and even excellent. The results of the Jackknife test revealed relevant information about the contribution and importance of climate variables (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>). For contribution, annual precipitation (bio12, 35.32%) explained the current potential range of <italic>O. curvinotus</italic> to the greatest extent, followed by the mean diurnal range (bio2, 24.91%). Climate factors bio7 (Temperature annual range), bio8 (mean temperature of wettest quarter), bio9 (mean temperature of driest quarter), bio15 (Precipitation seasonality), and bio17 (precipitation of driest quarter) contributed a total of 39.77%. The permutation importance index identified two of the most important climate variables, bio8 (mean temperature of wettest quarter, 46.35%) and bio2 (mean diurnal range, 35.51%). Considering the contribution rate and permutation importance, bio2 is a key climate factor for the potential distribution of <italic>O. curvinotus</italic>. For the most important variables and those that contributed the most, bio2 was used for further analysis. The mean diurnal temperature range varied from 6.9 to 7.9 degrees Celsius, indicating a habitat of low suitability for <italic>O. curvinotus</italic>. When the mean diurnal temperature range falls between 6 and 6.9 degrees Celsius, the habitat suitability for <italic>O. curvinotus</italic> is considered moderate. In areas where the mean diurnal temperature range is below 6 degrees Celsius, <italic>O. curvinotus</italic> exhibits a high degree of suitability (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>).</p>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>The contribution rate, permutation importance of filtered climate factors used in the model prediction, and relevant information of the Jackknife test.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Variable</th>
<th valign="top" align="left">bio2</th>
<th valign="top" align="left">bio7</th>
<th valign="top" align="left">bio8</th>
<th valign="top" align="left">bio9</th>
<th valign="top" align="left">bio12</th>
<th valign="top" align="left">bio15</th>
<th valign="top" align="left">bio17</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Contribution (%)</td>
<td valign="top" align="left">24.909</td>
<td valign="top" align="left">0.261</td>
<td valign="top" align="left">11.618</td>
<td valign="top" align="left">4.321</td>
<td valign="top" align="left">35.317</td>
<td valign="top" align="left">14.350</td>
<td valign="top" align="left">9.226</td>
</tr>
<tr>
<td valign="top" align="left">Permutation importance (%)</td>
<td valign="top" align="left">35.506</td>
<td valign="top" align="left">7.123</td>
<td valign="top" align="left">46.345</td>
<td valign="top" align="left">10.922</td>
<td valign="top" align="left">0.008</td>
<td valign="top" align="left">0.057</td>
<td valign="top" align="left">0.040</td>
</tr>
<tr>
<td valign="top" align="left">Training gain without this variable</td>
<td valign="top" align="left">5.121</td>
<td valign="top" align="left">5.303</td>
<td valign="top" align="left">5.117</td>
<td valign="top" align="left">5.287</td>
<td valign="top" align="left">5.32</td>
<td valign="top" align="left">5.312</td>
<td valign="top" align="left">5.293</td>
</tr>
<tr>
<td valign="top" align="left">Training gain with only this variable</td>
<td valign="top" align="left">1.408</td>
<td valign="top" align="left">2.092</td>
<td valign="top" align="left">2.314</td>
<td valign="top" align="left">2.022</td>
<td valign="top" align="left">2.407</td>
<td valign="top" align="left">1.250</td>
<td valign="top" align="left">1.733</td>
</tr>
<tr>
<td valign="top" align="left">Test gain without this variable</td>
<td valign="top" align="left">5.396</td>
<td valign="top" align="left">5.442</td>
<td valign="top" align="left">5.405</td>
<td valign="top" align="left">5.449</td>
<td valign="top" align="left">5.461</td>
<td valign="top" align="left">5.465</td>
<td valign="top" align="left">5.424</td>
</tr>
<tr>
<td valign="top" align="left">Test gain with only this variable</td>
<td valign="top" align="left">1.532</td>
<td valign="top" align="left">2.445</td>
<td valign="top" align="left">2.743</td>
<td valign="top" align="left">2.355</td>
<td valign="top" align="left">2.665</td>
<td valign="top" align="left">1.599</td>
<td valign="top" align="left">1.939</td>
</tr>
<tr>
<td valign="top" align="left">AUC without this variable</td>
<td valign="top" align="left">0.998</td>
<td valign="top" align="left">0.998</td>
<td valign="top" align="left">0.998</td>
<td valign="top" align="left">0.998</td>
<td valign="top" align="left">0.998</td>
<td valign="top" align="left">0.998</td>
<td valign="top" align="left">0.998</td>
</tr>
<tr>
<td valign="top" align="left">AUC with only this variable</td>
<td valign="top" align="left">0.924</td>
<td valign="top" align="left">0.971</td>
<td valign="top" align="left">0.980</td>
<td valign="top" align="left">0.969</td>
<td valign="top" align="left">0.976</td>
<td valign="top" align="left">0.934</td>
<td valign="top" align="left">0.944</td>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Response curves of climatic suitability of bio2 [Mean diurnal range (mean of monthly max temperature-min temperature)].</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1391800-g002.tif"/>
</fig>
</sec>
<sec id="s3_2">
<title>Current habitats</title>
<p>The distribution of potential habitat in the current situation is shown in <xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>. The total potential habitat area is 17.82&#xd7;10<sup>4</sup> km<sup>2</sup>, including 1.44&#xd7;10<sup>4</sup> km<sup>2</sup> of highly suitable habitats, 3.91&#xd7;10<sup>4</sup> km<sup>2</sup> of moderately suitable habitats, and 12.47&#xd7;10<sup>4</sup> km<sup>2</sup> of lowly suitable habitats. The potential habitats are mainly distributed in the coastal areas of northern Vietnam, the northeastern Hainan Province, the coastal areas of Guangdong Province and Guangxi Zhuang Autonomous Region, and a small part of Taiwan Province in China. Highly suitable habitats are mainly distributed in Hai Phong, Nam Dinh and Thanh Hoa areas of Vietnam, the central area of Leizhou Peninsula and the west bank of the Pearl River Estuary of China. The moderately suitable habitat extends from the highly suitable habitat to Hanoi of Vietnam, the whole Leizhou Peninsula, the two sides of the Pearl River Delta, and the northeast coast of Hainan Island in China (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>). On the basis of moderately suitable habitat, the lowly suitable habitat extended further to inland areas, especially the southern Guangxi Zhuang Autonomous region has a large area of low potential habitat.</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Simulation of potential distribution areas of <italic>O. curvinotus</italic> with varying suitability in the current climate based on occurrence records.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1391800-g003.tif"/>
</fig>
</sec>
<sec id="s3_3">
<title>Habitats in the LGM</title>
<p>Our simulations revealed that during the historical period (the LGM), <italic>O. curvinotus</italic> had a wide range of habitats, which were located in tropical and subtropical regions and were therefore less affected by glacial periods. The present study reveals that during the LGM, the habitat of <italic>O. curvinotus</italic> was mainly located in the northern margin of Sundaland, including Guangdong Province, Guangxi Zhuang Autonomous Region and Hainan Province in South China, as well as the northern part of the South China Sea and the Beibu Gulf (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4</bold>
</xref>). The total potential habitat area reached 126.85&#xd7;10<sup>4</sup> km<sup>2</sup>, of which 30.83&#xd7;10<sup>4</sup> km<sup>2</sup> were highly suitable. In addition, during the LGM, there were three refuges (Refugium 1, Refugium 2 and Refugium 3). Refugium 1 was located in Beibu Gulf, Refugium 2 was located near Guanghai Town, Taishan County, Jiangmen City, Guangdong Province, and Refugium 3 was located in the east of Hong Kong (<xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5</bold>
</xref>).</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>Potential distribution areas of <italic>O. curvinotus</italic> with different suitability under paleoclimate (the LGM).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1391800-g004.tif"/>
</fig>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>Location of <italic>O. curvinotus</italic> refuges during the LGM.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1391800-g005.tif"/>
</fig>
</sec>
<sec id="s3_4">
<title>Time series analysis in the warming future</title>
<p>We simulated the potential habitat shift process of <italic>O. curvinotus</italic> under four climate warming scenarios with different carbon emission levels, as shown in <xref ref-type="fig" rid="f6">
<bold>Figures&#xa0;6</bold>
</xref>&#x2013;<xref ref-type="fig" rid="f9">
<bold>9</bold>
</xref>. Under the background of a low-end level carbon emission scenario (SSP1-2.6), the potential habitats will expand from the current to 2080 (for example, the lowly suitable habitats at the border of Guangdong Province and Guangxi Zhuang Autonomous Region in China are obviously expanding northward), but it will shrink by 2100. By 2100, the habitats in the border area between Guangdong Province and Guangxi Zhuang Autonomous Region, east Guangdong region and the coast of Fujian decreased significantly, but the highly suitable habitats in Leizhou Peninsula, northeast Hainan Island and southwest Taiwan Island increased slightly (<xref ref-type="fig" rid="f6">
<bold>Figures&#xa0;6A&#x2013;D</bold>
</xref>). Under the scenario of low-moderate level carbon emission (SSP2-4.5), there is a trend of habitat expansion from the current to 2040, and then a trend of retreat until 2100 (<xref ref-type="fig" rid="f7">
<bold>Figures&#xa0;7A&#x2013;D</bold>
</xref>). During this period, the highly suitable habitats in Leizhou Peninsula and northeast Hainan will increase (<xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7D</bold>
</xref>). Under the scenario of medium-high or high levels of carbon emissions, the potential habitats of <italic>O. curvinotus</italic> show oscillating changes, expanding from the present to 2040, then slightly shrinking in 2060, recovering in 2080, and shrinking again in 2100 (<xref ref-type="fig" rid="f8">
<bold>Figures&#xa0;8A&#x2013;D</bold>
</xref>). As in the previous cases, highly suitable habitats in Leizhou Peninsula, northeastern Hainan, and southwestern Taiwan are expected to expand by 2100 (<xref ref-type="fig" rid="f9">
<bold>Figures&#xa0;9A&#x2013;D</bold>
</xref>). In terms of area change, from the current to 2100, the four warming scenarios under carbon emission levels caused the potentially suitable habitat (including highly, moderately and lowly suitable habitats) of <italic>O. curvinotus</italic> to first expand, then stabilize, and finally retreat to a slightly higher area than the current area (<xref ref-type="fig" rid="f10">
<bold>Figures&#xa0;10A&#x2013;D</bold>
</xref>).</p>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>Potentially suitable habitat of <italic>O. curvinotus</italic> under future climate warming scenario with the low-end level carbon emission from 2040 to 2100. <bold>(A)</bold> SSP1-2.6-2040; <bold>(B)</bold> SSP1-2.6-2060; <bold>(C)</bold> SSP1-2.6-2080; <bold>(D)</bold> SSP1-2.6-2100.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1391800-g006.tif"/>
</fig>
<fig id="f7" position="float">
<label>Figure&#xa0;7</label>
<caption>
<p>Potentially suitable habitat of <italic>O. curvinotus</italic> under future climate warming scenario with the low-moderate level carbon emission from 2040 to 2100. <bold>(A)</bold> SSP2-4.5-2040; <bold>(B)</bold> SSP2-4.5-2060; <bold>(C)</bold> SSP2-4.5-2080; <bold>(D)</bold> SSP2-4.5-2100.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1391800-g007.tif"/>
</fig>
<fig id="f8" position="float">
<label>Figure&#xa0;8</label>
<caption>
<p>Potentially suitable habitat of <italic>O. curvinotus</italic> under future climate warming scenario with the medium-high level carbon emission from 2024 to 2100. <bold>(A)</bold> SSP3-7.0-2040; <bold>(B)</bold> SSP3-7.0-2060; <bold>(C)</bold> SSP3-7.0-2080; <bold>(D)</bold> SSP3-7.0-2100.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1391800-g008.tif"/>
</fig>
<fig id="f9" position="float">
<label>Figure&#xa0;9</label>
<caption>
<p>Potentially suitable habitat of <italic>O. curvinotus</italic> under future climate warming scenario with the high level carbon emission from 2040 to 2100. <bold>(A)</bold> SSP5-8.5-2040; <bold>(B)</bold> SSP5-8.5-2060; <bold>(C)</bold> SSP5-8.5-2080; <bold>(D)</bold> SSP5-8.5-2100.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1391800-g009.tif"/>
</fig>
<fig id="f10" position="float">
<label>Figure&#xa0;10</label>
<caption>
<p>Time series analysis of potential habitat areas (&#xd7;10<sup>4</sup> km<sup>2</sup>) of <italic>O. curvinotus</italic> from current to future 2100. <bold>(A)</bold> SSP5-8.5-2040; <bold>(B)</bold> SSP5-8.5-2060; <bold>(C)</bold> SSP5-8.5-2080; <bold>(D)</bold> SSP5-8.5-2100.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1391800-g010.tif"/>
</fig>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<title>Discussion</title>
<p>
<italic>O. curvinotus</italic> is a typical small tropical fish mainly found in the northern coast of the South China Sea. Generally, species with limited distributions tend to have narrower ecological niches, such as smaller temperature variations within their range, making them more susceptible to climate change (<xref ref-type="bibr" rid="B30">Liao et&#xa0;al., 2022</xref>). The habitats of species in tropical regions are typically characterized by high humidity and warmth (<xref ref-type="bibr" rid="B48">Skend&#x17e;i&#x107; et&#xa0;al., 2021</xref>). For instance, <italic>Neurobasis chinensis</italic> inhabits the hot and humid tropical regions of East Asia and is projected to expand its range northward under future warming climates (<xref ref-type="bibr" rid="B48">Skend&#x17e;i&#x107; et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B30">Liao et&#xa0;al., 2022</xref>). However, humidity is not a significant factor for underwater species throughout their lives. In this study, we determined that a mean diurnal temperature range of 8 &#xb0;C is the critical point for the distribution range of <italic>O. curvinotus</italic>. In areas where the mean diurnal temperature exceeds 8 &#xb0;C, <italic>O. curvinotus</italic> has virtually no suitable habitat. This is consistent with its status as a tropical species. Tropical species, especially tropical plants, are affected more by climate change than by topographic gradients (<xref ref-type="bibr" rid="B50">Toledo et&#xa0;al., 2012</xref>). Climate is a strong driver of species distribution, with 72% of tropical species affected by temperature (<xref ref-type="bibr" rid="B50">Toledo et&#xa0;al., 2012</xref>). In this study, the habitat of <italic>O. curvinotus</italic> has typical characteristics of tropical species, and only inhabits within the range of mean diurnal temperature of 8&#xb0;C, especially within 6&#xb0;C, which is a highly suitable habitat for it. This reveals that one of the habitat characteristics of <italic>O. curvinotus</italic> is a small range of the mean diurnal temperature. Another relatively important climatic factor shaping the distribution of <italic>O. curvinotus</italic> is the mean temperature of wettest quarter (bio8, contribution 11.62%, permutation importance 46.34%). The mean temperature of wettest quarter in the habitat area of <italic>O. curvinotus</italic> is more than 25 &#xb0;C, with the highly suitable habitat exceeding 28 &#xb0;C. These findings again confirm the habitat properties of <italic>O. curvinotus</italic> as a tropical species.</p>
<p>Our results revealed that the current distribution area is mainly concentrated in northern Vietnam, northeastern Hainan Province, the coast of Guangdong Province and Guangxi Zhuang Autonomous Region, and a small part of Taiwan Province, in total, covered ca. 17.82&#xd7;10<sup>4</sup> km<sup>2</sup>. Within these ranges, a small number of simulated habitats exist in Taiwan Province of China, which is unexpected and requires further field work to confirm. In addition, it is worth noting that our field work experience confirmed the existence of a large population of <italic>O. curvinotus</italic> in Sanya, Hainan, and we brought the population of <italic>O. curvinotus</italic> in this area back to the laboratory for morphological and DNA barcoding identification, confirming the identity of <italic>O. curvinotus</italic> (<xref ref-type="bibr" rid="B54">Yao et&#xa0;al., 2022</xref>). However, the simulation results of this study suggest that Sanya in Hainan Province is not a suitable habitat for <italic>O. curvinotus</italic>. We speculate that the Sanya population may have undergone adaptive evolution in order to meet the changing climate environment, but the evolution has not yet reached the species level. Our earlier study corroborated these results across various dimensions (<xref ref-type="bibr" rid="B7">Dong et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B54">Yao et&#xa0;al., 2022</xref>). In our previous research, we discovered that the Sanya population lacked the sex determination gene &#x201c;<italic>dmy</italic>&#x201d; when compared to other populations (<xref ref-type="bibr" rid="B7">Dong et&#xa0;al., 2021</xref>). Additionally, <xref ref-type="bibr" rid="B54">Yao et&#xa0;al. (2022)</xref> revealed a significant level of genetic differentiation between the Sanya population and others, without indicating species diversification. Currently, the Sanya population is experiencing a bottleneck phase (characterized by a small population size), confirming that it has undergone intense environmental selection (<xref ref-type="bibr" rid="B54">Yao et&#xa0;al., 2022</xref>). All of these evidences indicate that the Sanya population is a unique group, exhibiting genetic variations that have emerged as adaptations to the challenging environment of Sanya, located in the China&#x2019;s Hainan province. Regarding the relationship between the unique physiological response mechanism of <italic>O. curvinotus</italic> populations in Sanya and climate change, we have conducted a thorough analysis of the species&#x2019; resilience or vulnerability in the face of climate change. Firstly, as a euryhaline fish, <italic>O. curvinotus</italic> possesses physiological characteristics that endow it with remarkable tolerance to salinity fluctuations. Against the backdrop of climate change, the rise in sea level and fluctuations in freshwater input may lead to drastic changes in salinity levels in mangrove tidal creeks and streams. Nevertheless, through a series of unique physiological response mechanisms, such as the fine-tuned regulation of ion transport proteins, <italic>O. curvinotus</italic> effectively maintains the balance of salts within its body, ensuring the continuation of normal physiological functions even in varying salinity environments. This genetic adaptability provides significant resilience to <italic>O. curvinotus</italic> in the face of salinity fluctuations, facilitating its survival and reproduction in diverse environments.</p>
<p>However, the impact of climate change on <italic>O. curvinotus</italic> is not entirely positive. With the persistent increase in global temperatures and the frequent occurrence of extreme climate events, these environmental factors may exceed the adaptive range of <italic>O. curvinotus</italic>. For instance, high-temperature environments may significantly increase the metabolic rate of <italic>O. curvinotus</italic>, subsequently escalating its demand for food and oxygen. Under resource-limited conditions, such an elevation in metabolic rate could pose a threat to the survival of <italic>O. curvinotus</italic>. Furthermore, extreme climate events like heavy rainfall or drought can directly destroy the habitats of <italic>O. curvinotus</italic>, leading to severe degradation of its living environment. In such scenarios, the genetic adaptability of <italic>O. curvinotus</italic> may be insufficient to fully cope with these drastic changes, thus exhibiting a degree of vulnerability. However, the adaptation of <italic>O. curvinotus</italic> to this sudden climate change requires at least several generations of natural selection. Therefore, we believe that the genetic adaptability of <italic>O. curvinotus</italic> exhibits both resilience and vulnerability in response to climate change. To gain a more comprehensive understanding of this relationship, further research is needed to explore the interaction between genetic variations in <italic>O. curvinotus</italic> and climate change, as well as to delve deeper into its adaptive mechanisms. This will aid us in providing a more scientific basis for the conservation and utilization of <italic>O. curvinotus</italic>, while also serving as an important reference for addressing the biodiversity challenges posed by climate change.</p>
<p>The historical species distribution pattern in the Quaternary largely determines their current species range. The conservatism of climatic niches, combined with the cycles of glacial and interglacial periods, has forced many species to reduce their ranges in order to survive under the advance of Pleistocene ice sheets. Refugia provide geographical opportunities for species to retreat, survive, and later recolonize under favorable environmental conditions (<xref ref-type="bibr" rid="B38">Morales-Barbero et&#xa0;al., 2018</xref>). Our research has revealed three refugia, all located in the northern part of Sundaland (the northern edge of the South China Sea). Among them, the largest one, Refugium 1, is speculated to be the starting point of the dispersal of highly suitable habitats in the northern part of Vietnam and Leizhou Peninsula. While Refugium 2 and Refugium 3 are speculated to be the sources of species migration for the highly suitable habitats in the Pearl River Delta region. Typically, species range may expand or contract based on the suitability of climate change for its ecological niche (<xref ref-type="bibr" rid="B38">Morales-Barbero et&#xa0;al., 2018</xref>). Migrant species may be geographically restricted to a particular location, or continue their colonization route through a habitat matrix, depending on the suitability of climatic conditions (<xref ref-type="bibr" rid="B16">Hewitt, 2000</xref>; <xref ref-type="bibr" rid="B41">Petit et&#xa0;al., 2003</xref>; <xref ref-type="bibr" rid="B3">Barnosky, 2005</xref>). Due to the disappearance of Sundaland after the glacial period, many habitats of <italic>O. curvinotus</italic> sank into the sea, and highly suitable habitats migrated to neighboring northern Vietnam, Leizhou Peninsula, and a small part of the Pearl River Delta region. Overall, the habitats of <italic>O. curvinotus</italic> have been in a stage of retraction from the Quaternary to the present day.</p>
<p>Future warming is an undeniable reality. Over the past century, the global temperature has risen by 0.6&#xb0;C and is continuing to change at an accelerated pace. Projections indicate that by the end of the 21st century, the minimum temperature increase is expected to be between 0.3&#xb0;C and 1.7&#xb0;C, with a maximum increase of 2.6&#xb0;C to 4.8&#xb0;C (<xref ref-type="bibr" rid="B44">Root et&#xa0;al., 2003</xref>; <xref ref-type="bibr" rid="B25">Kumar and Rawat, 2022</xref>; <xref ref-type="bibr" rid="B30">Liao et&#xa0;al., 2022</xref>, <xref ref-type="bibr" rid="B31">2023</xref>). Climate warming is inevitable, and it will undoubtedly alter the current distribution patterns of various species. The impacts of warming have already been observed in numerous organisms, including plants, dragonflies, butterflies, grasshoppers, lacewings, spiders, reptiles, woodlice, ground beetles, longhorn beetles, soldier beetles, harvestmen, millipedes, aquatic bugs, freshwater fish, birds, and mammals (<xref ref-type="bibr" rid="B40">Parmesan and Yohe, 2003</xref>; <xref ref-type="bibr" rid="B44">Root et&#xa0;al., 2003</xref>; <xref ref-type="bibr" rid="B18">Hickling et&#xa0;al., 2005</xref>, <xref ref-type="bibr" rid="B17">2006</xref>; <xref ref-type="bibr" rid="B26">Lenoir and Svenning, 2015</xref>). Tropical species with narrow ecological niche are theoretically vulnerable to the stress of climate warming and migrate northward like most species. However, the subjects studied in this research did not follow this typical pattern. In our study, climate warming under four different carbon emission levels caused the current habitat of <italic>O. curvinotus</italic> to expand within a short timeframe (by 2040). After a period of adaptation (by 2100), the expansion trend was mitigated, and the expansion area retreated to slightly larger than the current climate scenario. The increase of habitats primarily occurred in northern Vietnam, southern Guangxi, Hainan Island and Taiwan Island, with a notable increase in highly suitable habitats, such as the Red River Delta in Vietnam and the central part of Leizhou Peninsula. Contrary to the typical northward migration pattern observed in other species under the influence of warming (<xref ref-type="bibr" rid="B17">Hickling et&#xa0;al., 2006</xref>), the highly suitable habitats for <italic>O. curvinotus</italic> did not shift latitudinally but instead increased their area around 21&#xb0;N. East Asia, at 21 degrees north latitude, is a global biodiversity hotspot, and the region&#x2019;s geological history has shaped a complex climate and geological environment in this area (<xref ref-type="bibr" rid="B15">Hekinian and Walker, 1987</xref>; <xref ref-type="bibr" rid="B33">Marchese, 2015</xref>), resulting in a complex network of biological and abiotic factors. Numerous species have come to rely on this hotspot habitat, making it a crucial ecosystem for various organisms (<xref ref-type="bibr" rid="B10">Gathorne-Hardy et&#xa0;al., 2002</xref>; <xref ref-type="bibr" rid="B11">Gorog et&#xa0;al., 2004</xref>). Our subject, <italic>O. curvinotus</italic>, despite its adaptability, is currently found only in a small area along the northern coast of the South China Sea, including coastal freshwater extending inland. <xref ref-type="bibr" rid="B14">Hayakawa et&#xa0;al. (2015)</xref> discovered this species in mangrove plantations and inland paddy fields in Bang La, Do Son, Vietnam, and conducted population genetics studies, recognizing it as a promising experimental model species in tropical Asia (<xref ref-type="bibr" rid="B14">Hayakawa et&#xa0;al., 2015</xref>). These provide valuable insight into the dispersal of <italic>O. curvinotus</italic> into inland freshwater habitats at the same latitude. Indeed, our simulations of <italic>O. curvinotus</italic> habitats in the context of future climate warming support this notion and underscore the need for further investigation into the species&#x2019; ecological niche and potential adaptability to changing environmental conditions.</p>
<p>Despite our optimistic findings regarding the recent survival status of this species, proactive conservation measures must still be implemented to secure its long-term survival. To this end, we propose the following conservation strategies: Firstly, monitoring and surveillance: We plan to establish a regular monitoring program to closely track the species&#x2019; population dynamics, habitat utilization, and any emerging threats. This will involve conducting surveys, monitoring breeding habitats, and collecting crucial data using remote sensing techniques. These data will not only aid in evaluating the effectiveness of our conservation efforts but also guide future actions. Secondly, habitat restoration and enhancement: We prioritize the restoration and enhancement of the species&#x2019; existing habitats, including contaminant removal, controlling invasive species, and promoting native vegetation growth. Additionally, we aim to establish new habitats in suitable areas to mitigate the potential negative impacts of predicted habitat shifts.</p>
</sec>
<sec id="s5" sec-type="conclusions">
<title>Conclusions</title>
<p>This study summarizes the habitat attributes, potential distribution areas and its long-term temporal changes of a small euryhaline fish species. Results show that <italic>O. curvinotus</italic> is currently mainly distributed along the coastal areas of northern Vietnam, northeastern Hainan, Guangdong and Guangxi Zhuang Autonomous Region, as well as in a few areas of Taiwan Province, China, covering a total area of 17.82 &#xd7;10<sup>4</sup> km<sup>2</sup>. Highly suitable habitats are mainly found in northern Vietnam, central Leizhou Peninsula, and the western coast of the Pearl River Delta. Three refugia existed in historical times, with the largest one, Refugium 1, speculated to be responsible for the dispersal and establishment of highly suitable habitats in northern Vietnam and central Leizhou Peninsula. Refugium 2 and Refugium 3 are hypothesized to be the sources of immigrant individuals with highly suitable habitats in the Pearl River Delta, with only Refugium 2 remaining to current. Climate warming in the future is not expected to pose a threat to the species&#x2019; distribution. Instead, it may lead to a rapid expansion of its habitat in a short period of time, followed by a stabilization and gradual decrease to slightly above the current habitat area. The expansion of its habitat will not shift northward as most species do, but will expand around the 21&#xb0;N latitude. This study provides valuable insights for the conservation of such small euryhaline fish species and the development of model species suitable for ecological environmental research in the region.</p>
</sec>
</body>
<back>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Material</bold>
</xref>. Further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>JL: Data curation, Investigation, Formal analysis, Conceptualization, Methodology, Software, Writing &#x2013; original draft. HC: Data curation, Investigation, Writing &#x2013; review &amp; editing. J-YL: Writing &#x2013; review &amp; editing. G-CL: Writing &#x2013; review &amp; editing. XG: Writing &#x2013; review &amp; editing. C-FL: Data curation, Writing &#x2013; review &amp; editing. Y-SG: Investigation, Methodology, Writing &#x2013; review &amp; editing. Z-DD: Writing &#x2013; review &amp; editing. Z-DW: Conceptualization, Investigation, Data curation, Supervision, Writing &#x2013; review &amp; editing.</p>
</sec>
<sec id="s8" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by the Guangdong Ocean University Scientific Research Startup Funding Project (Grant No. 060302022312), the Guangdong Provincial Ordinary University Youth Innovative Talent Project in 2024 (Grant No. 2024KQNCX134), the Guangdong Provincial Special Fund Project for Talent Development Strategy in 2024 (Grant No. 2024R3005), and the Science and Technology Planning Projects of Guangdong Province (Grant No.2021B1212050025). National key research and development program of China (2024YFD2401803).</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>We are deeply grateful to our esteemed colleagues, Shuisheng Long, Shun Zhang, Hairui Zhang, Chengqin Huang, Chun Wang, and Shunkai Huang, for their invaluable assistance during the field investigation.</p>
</ack>
<sec id="s9" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s10" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s11" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fmars.2024.1391800/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fmars.2024.1391800/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Table1.xlsx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"/>
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