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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.2024.1340522</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>Spatial&#x2013;temporal distribution characteristics of <italic>Harpadon nehereus</italic> in the Yangtze River Estuary and its relationship with environmental factors</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Yu</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2572916"/>
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<role content-type="https://credit.niso.org/contributor-roles/investigation/"/>
<role content-type="https://credit.niso.org/contributor-roles/validation/"/>
<role content-type="https://credit.niso.org/contributor-roles/visualization/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Gao</surname>
<given-names>Chunxia</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="aff" rid="aff3">
<sup>3</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
<role content-type="https://credit.niso.org/contributor-roles/formal-analysis/"/>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Chen</surname>
<given-names>Jinhui</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Qing</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Zhao</surname>
<given-names>Jing</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
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<aff id="aff1">
<sup>1</sup>
<institution>College of Marine Living Resource Sciences and Management, Shanghai Ocean University</institution>, <addr-line>Shanghai</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Key Laboratory of Sustainable Development of Oceanic Fishery Resources of Ministry of Education, Shanghai Ocean University</institution>, <addr-line>Shanghai</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Joint Laboratory for Monitoring and Conservation of Aquatic Biological Resources in the Yangtze River Estuary, Shanghai Ocean University</institution>, <addr-line>Shanghai</addr-line>, <country>China</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>Resource Monitoring Department, Shanghai Aquatic Wildlife Conservation Research Center</institution>, <addr-line>Shanghai</addr-line>, <country>China</country>
</aff>
<aff id="aff5">
<sup>5</sup>
<institution>Shanghai Academy of Environmental Sciences, Shanghai Municipal Bureau of Ecology and Environment</institution>, <addr-line>Shanghai</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Stephen J. Newman, Western Australian Fisheries and Marine Research Laboratories, Australia</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Longshan Lin, State Oceanic Administration, China</p>
<p>Zhenhua Wang, Shanghai Ocean University, China</p>
<p>Basanta Kumar Das, Central Inland Fisheries Research Institute (ICAR), India</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Jing Zhao, <email xlink:href="mailto:jzhao@shou.edu.cn">jzhao@shou.edu.cn</email>
</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>29</day>
<month>02</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>11</volume>
<elocation-id>1340522</elocation-id>
<history>
<date date-type="received">
<day>18</day>
<month>11</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>05</day>
<month>02</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2024 Li, Gao, Chen, Wang and Zhao</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Li, Gao, Chen, Wang and Zhao</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>To investigate the spatial&#x2013;temporal distribution characteristics of <italic>Harpadon nehereus</italic> in the Yangtze River Estuary (YRE) and its relationship with environmental factors, this study used the data from resource and environmental surveys conducted in the YRE and adjacent waters during August (summer) and November (autumn), 2017&#x2013;2022. Generalized additive models (GAM) were employed to analyze the relationships between the relative resources of <italic>H. nehereus</italic> and environmental factors and to predict the spatial&#x2013;temporal distribution of <italic>H. nehereus</italic> resources in 2022. Our results revealed that the best model deviance explained in summer and autumn was 64.89% and 49.90%, with average effect sizes of 0.75 and 0.70, respectively, for cross-validated regression slopes. Water temperature and salinity were identified as the key environmental factors influencing the relative resources of <italic>H. nehereus</italic> in the YRE. Overall, there were notable seasonal differences in the relationship between the relative resources of <italic>H. nehereus</italic> and environmental factors. The relative resources of <italic>H. nehereus</italic> in the YRE were higher in the summer than in autumn. In summer, both water temperature and salinity exhibited multi-wave nonlinear relationships with the relative resources of <italic>H. nehereus</italic>, while in autumn, the relative resources of <italic>H. nehereus</italic> showed a positive linear relationship with water temperature and a non-linear relationship with salinity. Additionally, the predicted and observed values of the relative resources of <italic>H. nehereus</italic> in 2022 showed similar spatial distribution patterns. The relative resources of <italic>H. nehereus</italic> was higher in the northern branch than in the southern branch and the offshore regions compared to the near-estuary regions. Altogether, our study provides a scientific basis for conservation management and sustainable utilization of <italic>H. nehereus</italic> resources in the YRE, thereby contributing to the restoration and management of fishery resources in the region.</p>
</abstract>
<kwd-group>
<kwd>
<italic>Harpadon nehereus</italic>
</kwd>
<kwd>Yangtze River Estuary (YRE)</kwd>
<kwd>spatial-temporal distribution</kwd>
<kwd>generalized additive models (GAM)</kwd>
<kwd>environmental factors</kwd>
</kwd-group>
<counts>
<fig-count count="6"/>
<table-count count="4"/>
<equation-count count="3"/>
<ref-count count="51"/>
<page-count count="10"/>
<word-count count="4674"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Marine Fisheries, Aquaculture and Living Resources</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>The Yangtze River Estuary (YRE) is located in the subtropical monsoon climate zone of eastern Eurasian and has a unique geographical location and environmental conditions (<xref ref-type="bibr" rid="B13">Kindong et&#xa0;al., 2020</xref>). Owing to the runoff of the Yangtze River, the YRE provides a rich bait base for the growth of a variety of economic fish and their juveniles. Moreover, YRE is the largest migratory corridor for saltwater and freshwater fish as well as a breeding and hatchery site in China (<xref ref-type="bibr" rid="B2">Chen et&#xa0;al., 2021</xref>). However, anthropogenic factors, such as overfishing, water pollution, and wetland reclamation, has altered the structure of aquatic communities in the YRE (<xref ref-type="bibr" rid="B42">Yang et&#xa0;al., 2020</xref>), reduced the stability of the ecosystem, and decreased the resources of some large fishes with long lifespans, and increased the resources of small fishes, such as <italic>Coilya nasus</italic> and <italic>Harpadon nehereus</italic> (<xref ref-type="bibr" rid="B41">Yang et&#xa0;al., 2022</xref>). <italic>H. nehereus</italic> has become a dominant species in the YRE (<xref ref-type="bibr" rid="B34">Sun et&#xa0;al., 2015</xref>).</p>
<p>Being a carnivorous fish, <italic>H. nehereus</italic> occupies a high trophic level and an important niche in the ecosystem (<xref ref-type="bibr" rid="B24">Pan and Cheng, 2011</xref>). <italic>H. nehereus</italic> has experienced rapid population growth along the coast of China in recent decades, compressing the ecological niches of other marine species and disrupting existing food webs (<xref ref-type="bibr" rid="B38">Wang et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B12">Jiang et&#xa0;al., 2022</xref>). <xref ref-type="bibr" rid="B38">Wang et&#xa0;al. (2021)</xref> pointed to a northward shift in overall suitable habitat for <italic>H. nehereus</italic> as the climate changes, which may further affect the stability of the ecological community. Therefore, understanding the variability of the spatial&#x2013;temporal distribution of <italic>H. nehereus</italic> can provide theoretical references and a scientific basis for the effective management and conservation of fishery resources (<xref ref-type="bibr" rid="B49">Zhao et&#xa0;al., 2014</xref>; <xref ref-type="bibr" rid="B8">Huang et&#xa0;al., 2015</xref>). Variations in the distribution of fish stocks are closely associated with environmental factors (<xref ref-type="bibr" rid="B22">Ma et&#xa0;al., 2020</xref>). Thus far, several studies have been conducted on the relationship between spatial&#x2013;temporal distribution and environmental factors. For instance, <xref ref-type="bibr" rid="B16">Li et&#xa0;al. (2021)</xref> found that changes in water temperature affect the relative abundance of <italic>Larimichthys polyactis</italic>, and <xref ref-type="bibr" rid="B21">Ma et&#xa0;al. (2022a)</xref> demonstrated that temperature and salinity have a significant impact on the relative abundance of <italic>Engraulis japonicus.</italic>
</p>
<p>Being a short-distance migratory fish, the population of <italic>H. nehereus</italic> has obvious dynamic-change characteristics. Additionally, its spatial&#x2013;temporal distribution varies with environmental conditions, and there is a non-linear relationship with the environmental factors (<xref ref-type="bibr" rid="B3">Du, 2018</xref>). Among the common species distribution models, the generalized additive model (GAM) is better able to analyze the complex connections between the data and is commonly used to construct relationships between a response variable and multiple explanatory variables, which can reflect the effect of each environmental factor on the abundance of fish stocks. Therefore, GAM has been widely used in analyzing the spatial&#x2013;temporal distribution of fishery resources and their relationship with environmental factors (<xref ref-type="bibr" rid="B40">Wu et&#xa0;al., 2019</xref>).</p>
<p>Thus far, studies on <italic>H. nehereus</italic> have primarily focused on its biological characteristics (<xref ref-type="bibr" rid="B19">Luo, 2012</xref>), morphological features (<xref ref-type="bibr" rid="B37">Wang et&#xa0;al., 2020</xref>), feeding habits (<xref ref-type="bibr" rid="B17">Liu et&#xa0;al., 2021</xref>), and aquatic product processing (<xref ref-type="bibr" rid="B1">Cao et&#xa0;al., 2020</xref>). Among these, studies on <italic>H. nehereus</italic> in the YRE are limited, with most of them focusing on trophic ecology (<xref ref-type="bibr" rid="B24">Pan and Cheng, 2011</xref>). Moreover, there are no reports on the spatial&#x2013;temporal distribution of <italic>H. nehereus</italic> and its relationship with environmental factors. Therefore, this study utilized fish resource survey data collected from the YRE and its adjacent waters during the period from 2017 to 2022. The data was analyzed using the GAM to investigate the relationship between the resource distribution characteristics of the YRE and environmental factors. The results of our study provide a scientific basis for the sustainable use and management of the aquatic biological resources of the YRE.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<label>2</label>
<title>Materials and methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Data sources</title>
<p>Since <italic>H. nehereus</italic> rarely occur in the waters of the YRE in May (spring) and February (winter), the stock and environmental data for <italic>H. nehereus</italic> in this paper were obtained from the fishery resource surveys conducted in August (summer) and November (autumn) from 2017 to 2022 in the YRE and adjacent waters (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>). The survey area consisted of the southern and northern branches of the YRE and adjacent waters. The sampling is fixed-point sampling. The netting gear used is a truss rod trawl, with an opening length of 6 m, an opening width of 1.8 m, a body length of 11m, a body mesh of 0.02 m, a number of 2 bladder nets, and a bladder mesh of 0.01 m. The survey vessel was operated at a speed of 2 knots for 0.5 h. A total of 18 survey stations were set up per season using the fixed-point sampling method. However, some of the survey stations were adjusted in 2021 (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>). The samples were taken to the laboratory for the measurement of biological data, such as body length, weight, and gonadal maturity. Environmental data were measured simultaneously at each station during the survey. Water samples were taken from each station at high and low tides, and the sampling method was determined according to the requirements of Part III of the Specification for Marine Monitoring (GB 17378.3-2007). Surface water temperature (&#xb0;C), salinity (S, &#x2030;), pH, and dissolved oxygen (DO, mg/L) were measured using a WTW Multi 3430 water quality meter.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Data on the resources and environmental factors of <italic>Harpadon nehereus</italic> in the YRE.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Season</th>
<th valign="middle" align="center">Relative resources and environment factors</th>
<th valign="middle" align="center">Average</th>
<th valign="middle" align="center">Minimum</th>
<th valign="middle" align="center">Maximum</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" rowspan="8" align="center">Summer</td>
<td valign="middle" align="center">Relative resources/(g/h-net)</td>
<td valign="middle" align="center">47.55</td>
<td valign="middle" align="center">0.00</td>
<td valign="middle" align="center">741.20</td>
</tr>
<tr>
<td valign="middle" align="center">T/(&#xb0;C)</td>
<td valign="middle" align="center">29.64</td>
<td valign="middle" align="center">25.30</td>
<td valign="middle" align="center">33.90</td>
</tr>
<tr>
<td valign="middle" align="center">S/(&#x2030;)</td>
<td valign="middle" align="center">5.54</td>
<td valign="middle" align="center">0.10</td>
<td valign="middle" align="center">30.20</td>
</tr>
<tr>
<td valign="middle" align="center">pH</td>
<td valign="middle" align="center">8.04</td>
<td valign="middle" align="center">7.68</td>
<td valign="middle" align="center">8.82</td>
</tr>
<tr>
<td valign="middle" align="center">DO/(mg/L)</td>
<td valign="middle" align="center">7.28</td>
<td valign="middle" align="center">5.94</td>
<td valign="middle" align="center">9.16</td>
</tr>
<tr>
<td valign="middle" align="center">Dep/(m)</td>
<td valign="middle" align="center">7.98</td>
<td valign="middle" align="center">1.40</td>
<td valign="middle" align="center">17.00</td>
</tr>
<tr>
<td valign="middle" align="center">Lon/(&#xb0;)</td>
<td valign="middle" align="center">121.90</td>
<td valign="middle" align="center">121.21</td>
<td valign="middle" align="center">122.18</td>
</tr>
<tr>
<td valign="middle" align="center">Lat/(&#xb0;)</td>
<td valign="middle" align="center">31.48</td>
<td valign="middle" align="center">31.30</td>
<td valign="middle" align="center">31.74</td>
</tr>
<tr>
<td valign="middle" rowspan="8" align="center">Autumn</td>
<td valign="middle" align="center">Relative resources/(g/h-net)</td>
<td valign="middle" align="center">20.02</td>
<td valign="middle" align="center">0.00</td>
<td valign="middle" align="center">819.20</td>
</tr>
<tr>
<td valign="middle" align="center">T/(&#xb0;C)</td>
<td valign="middle" align="center">16.23</td>
<td valign="middle" align="center">9.40</td>
<td valign="middle" align="center">21.10</td>
</tr>
<tr>
<td valign="middle" align="center">S/(&#x2030;)</td>
<td valign="middle" align="center">9.69</td>
<td valign="middle" align="center">0.10</td>
<td valign="middle" align="center">28.60</td>
</tr>
<tr>
<td valign="middle" align="center">pH</td>
<td valign="middle" align="center">8.24</td>
<td valign="middle" align="center">7.31</td>
<td valign="middle" align="center">10.47</td>
</tr>
<tr>
<td valign="middle" align="center">DO/(mg/L)</td>
<td valign="middle" align="center">9.68</td>
<td valign="middle" align="center">8.56</td>
<td valign="middle" align="center">13.30</td>
</tr>
<tr>
<td valign="middle" align="center">Dep/(m)</td>
<td valign="middle" align="center">8.72</td>
<td valign="middle" align="center">2.00</td>
<td valign="middle" align="center">33.50</td>
</tr>
<tr>
<td valign="middle" align="center">Lon/(&#xb0;)</td>
<td valign="middle" align="center">121.90</td>
<td valign="middle" align="center">121.21</td>
<td valign="middle" align="center">122.18</td>
</tr>
<tr>
<td valign="middle" align="center">Lat/(&#xb0;)</td>
<td valign="middle" align="center">31.49</td>
<td valign="middle" align="center">31.30</td>
<td valign="middle" align="center">31.73</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>T, water temperature; S, salinity; DO, dissolved oxygen; and Dep, water depth; Lon, longitude; Lat, latitude.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Distribution of the survey stations in the Yangtze River Estuary.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1340522-g001.tif"/>
</fig>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Modeling</title>
<p>The number of <italic>H. nehereus</italic> caught per unit of time per net (g/h-net) was used as the response variable of the GAM to reflect the relative resources of <italic>H. nehereus</italic> in the region. During processing, a constant (1) was added to the response variables to avoid the conversion error of the zero value (<xref ref-type="bibr" rid="B35">Tian et&#xa0;al., 2009</xref>) and to enable the application of GAM with identity as the link function. Considering the short-distance migration of <italic>H. nehereus</italic> (<xref ref-type="bibr" rid="B32">Sun, 2022</xref>), longitude and latitude were selected as the influencing factors of the spatial distribution of <italic>H. nehereus</italic>. Water temperature influences the rate of gonadal development in <italic>H. nehereus</italic> (<xref ref-type="bibr" rid="B32">Sun, 2022</xref>), salinity affects fish growth and development (<xref ref-type="bibr" rid="B28">Ran et&#xa0;al., 2020</xref>), water depth influences fish distribution (<xref ref-type="bibr" rid="B37">Wang et&#xa0;al., 2020</xref>), and DO and pH drive the spatial&#x2013;temporal variability of the fish community across seasons (<xref ref-type="bibr" rid="B46">Zhang, 2012</xref>); therefore, longitude, latitude, water temperature, salinity, water depth, pH, and DO were chosen as the explanatory variables of the model. The full factorial expression of GAM was as follows:</p>
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<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>+</mml:mo>
<mml:mi>s</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mi>T</mml:mi>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>+</mml:mo>
<mml:mi>s</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mi>S</mml:mi>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>+</mml:mo>
<mml:mi>s</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mi>D</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>+</mml:mo>
<mml:mi>s</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mi>H</mml:mi>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>+</mml:mo>
<mml:mi>s</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mi>D</mml:mi>
<mml:mi>O</mml:mi>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>+</mml:mo>
<mml:mi>&#x3f5;</mml:mi>
<mml:mo>;</mml:mo>
<mml:mi>f</mml:mi>
<mml:mi>a</mml:mi>
<mml:mi>m</mml:mi>
<mml:mi>i</mml:mi>
<mml:mi>l</mml:mi>
<mml:mi>y</mml:mi>
<mml:mo>=</mml:mo>
<mml:mi>g</mml:mi>
<mml:mi>a</mml:mi>
<mml:mi>u</mml:mi>
<mml:mi>s</mml:mi>
<mml:mi>s</mml:mi>
<mml:mi>i</mml:mi>
<mml:mi>a</mml:mi>
<mml:mi>n</mml:mi>
<mml:mo>,</mml:mo>
</mml:mrow>
</mml:math>
</disp-formula>
<p>where y is relative resource, s is spline smoothing function, Lat is latitude, Lon is longitude, T is water temperature, S is salinity, Dep is water depth, DO is dissolved oxygen, &#x190; is relative error, and family is the distribution mode set to Gaussian distribution.</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Model selection</title>
<p>The variance inflation factor (VIF) was used to test for collinearity of explanatory variables by season (<xref ref-type="bibr" rid="B7">Hou et&#xa0;al., 2021</xref>). In this study, the threshold value of the VIF test was set to 5, and the collinearity was considered to exist when the VIF threshold value was &gt; 5 (<xref ref-type="bibr" rid="B21">Ma et&#xa0;al., 2022a</xref>). When the VIF of only one factor was &gt; 5, the factor was removed and the collinearity test was conducted for the other factors with VIF&lt; 5; however, when the VIF of multiple environmental factors was &gt; 5, the influence factor with the largest variance expansion coefficient was removed and the collinearity test was conducted again.</p>
<p>Akaike information criterion (AIC) can be used to measure the goodness-of-fit of multi-group models, and it is generally believed that the lower the AIC value, the better the model fit (<xref ref-type="bibr" rid="B26">Planque et&#xa0;al., 2007</xref>). After screening, all the explanatory variables were added stepwise to the GAM for both seasons, and the model with the smallest AIC value was selected as the best-fitting model for each season. The AIC was calculated as follows:</p>
<disp-formula>
<mml:math display="block" id="M2">
<mml:mrow>
<mml:mtext>AIC</mml:mtext>
<mml:mo>=</mml:mo>
<mml:mn>2</mml:mn>
<mml:mtext>k</mml:mtext>
<mml:mo>&#x2212;</mml:mo>
<mml:mn>2</mml:mn>
<mml:mi>ln</mml:mi>
<mml:mtext>L</mml:mtext>
<mml:mo>,</mml:mo>
</mml:mrow>
</mml:math>
</disp-formula>
<p>where k is the number of parameters and L is the likelihood function.</p>
<p>The predictive abilities of the selected models were cross-validated with a 5-fold cross-validation method described by <xref ref-type="bibr" rid="B29">Rodr&#xed;guez et&#xa0;al. (2010)</xref> and by constructing a linear relationship between the predicted values (lnY) and the observed values (lny), using the following equation:</p>
<disp-formula>
<mml:math display="block" id="M3">
<mml:mrow>
<mml:mi>ln</mml:mi>
<mml:mtext>y</mml:mtext>
<mml:mo>=</mml:mo>
<mml:mi>a</mml:mi>
<mml:mo>+</mml:mo>
<mml:mi>b</mml:mi>
<mml:mo>&#xd7;</mml:mo>
<mml:mi>ln</mml:mi>
<mml:mi>Y</mml:mi>
<mml:mo>,</mml:mo>
</mml:mrow>
</mml:math>
</disp-formula>
<p>This process is repeated 100 times and the mean values of a and b reflect the prediction bias. For instance, a = 0 and b = 1 indicate the best model prediction performance, with no deviation between the predicted and observed values (<xref ref-type="bibr" rid="B15">Li et&#xa0;al., 2015</xref>).</p>
</sec>
<sec id="s2_4">
<label>2.4</label>
<title>Projections of the distribution of relative resources of <italic>H. nehereus</italic>
</title>
<p>Based on the environmental survey data of each site in each season of 2022, the survey area was divided into 0.025&#xb0; &#xd7; 0.025&#xb0; grids, and the center point of each grid was determined. Thereafter, the inverse distance weighting method was applied to obtain the environmental data of the center point of each grid (<xref ref-type="bibr" rid="B25">Philip and Watson, 1982</xref>). The optimal model for each season was applied to predict the distribution of <italic>H. nehereus</italic> for the corresponding season in 2022, which was then compared with the measured resource distribution.</p>
<p>Model construction, cross-validation, and prediction were performed in the R software V4.1.2, and the model was implemented through the &#x201c;mgcv&#x201d; package (<xref ref-type="bibr" rid="B39">Wood et&#xa0;al., 2016</xref>). The survey stations and resource distribution maps were drawn in Arcmap 10.8.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<sec id="s3_1">
<label>3.1</label>
<title>Model screening</title>
<p>The results revealed that in summer, the VIF values of all the environmental factors were&lt; 5, while in autumn, the VIF values of water temperature and DO were &gt; 5; however, after removing the VIF value of DO (highest), the VIF values of all the influencing factors were&lt; 5 (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>).</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Test for collinearity of model explanatory variables.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center" rowspan="2">Season</th>
<th valign="middle" colspan="7" align="center">Environmental factors</th>
</tr>
<tr>
<th valign="middle" align="center">Lon</th>
<th valign="middle" align="center">Lat</th>
<th valign="middle" align="center">T</th>
<th valign="middle" align="center">S</th>
<th valign="middle" align="center">pH</th>
<th valign="middle" align="center">DO</th>
<th valign="middle" align="center">Dep</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">Summer</td>
<td valign="middle" align="center">2.26</td>
<td valign="middle" align="center">2.97</td>
<td valign="middle" align="center">1.51</td>
<td valign="middle" align="center">3.15</td>
<td valign="middle" align="center">1.31</td>
<td valign="middle" align="center">1.70</td>
<td valign="middle" align="center">1.02</td>
</tr>
<tr>
<td valign="middle" align="center">Autumn</td>
<td valign="middle" align="center">2.98 (2.96)</td>
<td valign="middle" align="center">3.51 (3.27)</td>
<td valign="middle" align="center">10.67 (1.31)</td>
<td valign="middle" align="center">4.29 (3.62)</td>
<td valign="middle" align="center">1.92 (1.10)</td>
<td valign="middle" align="center">11.30</td>
<td valign="middle" align="center">1.31 (1.17)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Lat, latitude; Lon, longitude; T, water temperature; S, salinity; DO, dissolved oxygen; and Dep, water depth. Values in parentheses are the variance inflation factor (VIF) values of each factor after the removal of DO.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>Based on the minimum AIC principle, the variable combination of the best model in summer was T + S + Lon + Lat (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>), with a deviance explained of 64.89%. Among these, salinity had the largest contribution (34.12%), followed by longitude (22.74%), latitude (6.99%), and water temperature (1.04%). Salinity and longitude had an extremely significant effect on the abundance (<italic>P</italic>&lt; 0.001, <xref ref-type="table" rid="T4">
<bold>Table&#xa0;4</bold>
</xref>), while water temperature had a significant effect on the abundance of <italic>H. nehereus</italic> (<italic>P</italic>&lt; 0.01, <xref ref-type="table" rid="T4">
<bold>Table&#xa0;4</bold>
</xref>). The variable combination of the optimal model for autumn was consistent with that of the summer (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>), and the model deviance explained was 49.90%. Among the variables, the contribution of water temperature and salinity was very significant, accounting for 21.31% and 10.86%, respectively. The water temperature had an extremely significant effect on the relative resources of <italic>H. nehereus</italic> in the YRE in autumn (<italic>P</italic>&lt; 0.001, <xref ref-type="table" rid="T4">
<bold>Table&#xa0;4</bold>
</xref>).</p>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>The variables selecting process for GAM.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Season</th>
<th valign="middle" align="center">Environment factors</th>
<th valign="middle" align="center">AIC</th>
<th valign="middle" align="center">R<sup>2</sup>
</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" rowspan="4" align="center">Summer</td>
<td valign="middle" align="center">S</td>
<td valign="middle" align="center">351.29</td>
<td valign="middle" align="center">0.32</td>
</tr>
<tr>
<td valign="middle" align="center">S+Lon</td>
<td valign="middle" align="center">340.07</td>
<td valign="middle" align="center">0.41</td>
</tr>
<tr>
<td valign="middle" align="center">S+Lon+T</td>
<td valign="middle" align="center">322.70</td>
<td valign="middle" align="center">0.56</td>
</tr>
<tr>
<td valign="middle" align="center">S+Lon+T+Lat</td>
<td valign="middle" align="center">321.60</td>
<td valign="middle" align="center">0.57</td>
</tr>
<tr>
<td valign="middle" rowspan="4" align="center">Autumn</td>
<td valign="middle" align="center">Lat</td>
<td valign="middle" align="center">335.62</td>
<td valign="middle" align="center">0.16</td>
</tr>
<tr>
<td valign="middle" align="center">Lat+T</td>
<td valign="middle" align="center">327.86</td>
<td valign="middle" align="center">0.24</td>
</tr>
<tr>
<td valign="middle" align="center">Lat+T+Lon</td>
<td valign="middle" align="center">309.28</td>
<td valign="middle" align="center">0.40</td>
</tr>
<tr>
<td valign="middle" align="center">Lat+T+Lon+S</td>
<td valign="middle" align="center">306.65</td>
<td valign="middle" align="center">0.43</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>AIC, Akaike information criterion; T, water temperature; S, salinity; Lon, longitude; and Lat, latitude.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<table-wrap id="T4" position="float">
<label>Table&#xa0;4</label>
<caption>
<p>Parameters associated with the best-fit model for the relative resources of <italic>Harpadon nehereus</italic> in the Yangtze River Estuary in summer and autumn (2017&#x2013;2022).</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Season</th>
<th valign="middle" align="center">Optimal model</th>
<th valign="middle" align="center">
<italic>P</italic>-value</th>
<th valign="middle" align="center">AIC</th>
<th valign="middle" align="center">Deviance explanation (%)</th>
<th valign="middle" align="center">Accumulation of deviance explanation (%)</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" rowspan="4" align="center">Summer</td>
<td valign="middle" align="center">T</td>
<td valign="middle" align="center">&lt; 0.01**</td>
<td valign="middle" rowspan="4" align="center">321.60</td>
<td valign="middle" align="center">1.04</td>
<td valign="top" align="center">1.04</td>
</tr>
<tr>
<td valign="middle" align="center">S</td>
<td valign="middle" align="center">&lt; 0.001***</td>
<td valign="middle" align="center">34.12</td>
<td valign="top" align="center">35.16</td>
</tr>
<tr>
<td valign="middle" align="center">Lon</td>
<td valign="middle" align="center">&lt; 0.001***</td>
<td valign="middle" align="center">22.74</td>
<td valign="top" align="center">57.90</td>
</tr>
<tr>
<td valign="middle" align="center">Lat</td>
<td valign="middle" align="center">0.16</td>
<td valign="middle" align="center">6.99</td>
<td valign="top" align="center">64.89</td>
</tr>
<tr>
<td valign="middle" rowspan="4" align="center">Autumn</td>
<td valign="middle" align="center">T</td>
<td valign="middle" align="center">&lt; 0.001***</td>
<td valign="middle" rowspan="4" align="center">306.65</td>
<td valign="middle" align="center">21.31</td>
<td valign="top" align="center">21.31</td>
</tr>
<tr>
<td valign="middle" align="center">S</td>
<td valign="middle" align="center">0.06</td>
<td valign="middle" align="center">10.86</td>
<td valign="top" align="center">32.17</td>
</tr>
<tr>
<td valign="middle" align="center">Lon</td>
<td valign="middle" align="center">&lt; 0.05*</td>
<td valign="middle" align="center">15.40</td>
<td valign="top" align="center">47.57</td>
</tr>
<tr>
<td valign="middle" align="center">Lat</td>
<td valign="middle" align="center">&lt; 0.05*</td>
<td valign="middle" align="center">2.33</td>
<td valign="top" align="center">49.90</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>AIC, Akaike information criterion; T, water temperature; S, salinity; Lon, longitude; and Lat, latitude. *<italic>P</italic>&lt; 0.05, **<italic>P</italic>&lt; 0.01, ***<italic>P</italic>&lt; 0.001.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Influence of different factors on the relative resources of <italic>H. nehereus</italic> in the YRE</title>
<p>In summer, the relative resources of <italic>H. nehereus</italic> in the YRE showed a non-linear relationship with water temperature, salinity, and longitude (<xref ref-type="fig" rid="f2">
<bold>Figures&#xa0;2A&#x2013;C</bold>
</xref>). It showed a wavy non-linear relationship with water temperature and fluctuated greatly with an increase in water temperature, reaching the maximum value at approximately 30.4 &#xb0;C (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2A</bold>
</xref>). Meanwhile, it showed a more complicated multi-wave non-linear relationship with salinity in the range of 0&#x2013;30&#x2030;, reaching the maximum value at approximately 26 (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2B</bold>
</xref>). Additionally, it showed a non-linear relationship with longitude, which increased and then decreased with an increase in longitude in the range of 121.2&#x2013;122.2&#xb0;E (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2C</bold>
</xref>). Lastly, it showed a negative linear relationship with latitude, which decreased with an increase in latitude (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2D</bold>
</xref>).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Relationship between the relative resources of <italic>Harpadon nehereus</italic> in the Yangtze River Estuary and the environmental factors in summer. (<bold>A</bold>: Temperature. <bold>B</bold>: Salinity. <bold>C</bold>: Longitude. <bold>D</bold>: Latitude.)</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1340522-g002.tif"/>
</fig>
<p>In autumn, the relative resources of <italic>H. nehereus</italic> in the YRE demonstrated a positive linear relationship with water temperature. Additionally, non-linear relationships were observed between the relative resources of <italic>H. nehereus</italic> and factors such as salinity, longitude, and latitude (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>). It increased with an increase in water temperature in the range of 10&#x2013;20&#xb0;C (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3A</bold>
</xref>). Additionally, it increased with an increase in salinity at&lt; 20, but gradually decreased with an increase in salinity at &gt; 20 (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3B</bold>
</xref>). Furthermore, it showed an overall positive correlation with longitude in the range of 121.2&#x2013;122.2&#xb0;E (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3C</bold>
</xref>) and a multi-wave non-linear relationship with latitude in the range of 31.3&#x2013;31.7&#xb0;N, attaining a maximum value at 31.6&#xb0;N (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3D</bold>
</xref>).</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Relationship between the relative resources of <italic>Harpadon nehereus</italic> in the Yangtze River Estuary and the environmental factors in autumn. (<bold>A</bold>:Temperature. <bold>B</bold>: Salinity. <bold>C</bold>: Longitude. <bold>D</bold>: Latitude.)</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1340522-g003.tif"/>
</fig>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Predictive performance of the model</title>
<p>The results of the cross-validation of the best GAM showed (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4</bold>
</xref>) that the mean cross-validated linear regression slope, mean intercept, and mean coefficient of determination (r<sup>2</sup>) were 0.75, 1.79, and 0.29 for summer and 0.70, 1.81, and 0.27 for autumn, respectively.</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>Linear regression of cross-validation test for the best generalized additive models for summer and autumn.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1340522-g004.tif"/>
</fig>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>Spatial&#x2013;temporal distribution of <italic>H. nehereus</italic> in the YRE</title>
<p>In terms of temporal distribution, there were inter-annual and seasonal variations in the relative resource of <italic>H. nehereus</italic> in the YRE. The inter-annual variations ranged between 625.95&#x2013;3,780.80 g/h-net during 2017&#x2013;2022 and showed an increasing trend from 2017 to 2021, with a significant increase in 2021. However, the relative resource decreased in 2022, although it was relatively higher than that during 2017&#x2013;2020. Seasonally, the relative resource of <italic>H. nehereus</italic> was significantly greater in the summer than in the autumn (<xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5</bold>
</xref>).</p>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>Relative resources of <italic>Harpadon nehereus</italic> in the Yangtze River Estuary during 2017&#x2013;2022.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1340522-g005.tif"/>
</fig>
<p>In terms of spatial distribution, the <italic>H. nehereus</italic> showed an overall trend of higher relative resources in the northern branch than in the southern branch, and higher relative resources in the offshore than in the near-estuary, a feature that was even more pronounced in the autumn. The distribution of predicted values of relative resources of <italic>H. nehereus</italic> in the YRE in 2022 showed the similar distribution of true values, and the range of sizes of predicted values was similar to that of true values (<xref ref-type="fig" rid="f6">
<bold>Figure&#xa0;6</bold>
</xref>).</p>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>Predicted and observed spatial distribution of <italic>Harpadon nehereus</italic> in the Yangtze River Estuary in 2022.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1340522-g006.tif"/>
</fig>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<sec id="s4_1">
<label>4.1</label>
<title>Analysis of GAM results</title>
<p>GAM is one of the most common regression models (<xref ref-type="bibr" rid="B44">Yin et&#xa0;al., 2019</xref>) that is commonly used to study the spatial and temporal distribution of fish populations (<xref ref-type="bibr" rid="B11">Ji et&#xa0;al., 2020</xref>). In this study, we constructed two seasonal (summer and autumn) models for <italic>H. nehereus</italic> resources in the YRE during 2017&#x2013;2022 based on seven environmental factors, namely water temperature, salinity, DO, pH, water depth, longitude, and latitude. Our results revealed that the optimal model combinations for both the summer and autumn seasons were T + S + Lon + Lat, indicating that water temperature, salinity, longitude, and latitude are the primary environmental factors influencing the relative resources of <italic>H. nehereus</italic> in the YRE. Cross-validation is commonly used to verify the predictive ability of a model (<xref ref-type="bibr" rid="B10">Jensen et&#xa0;al., 2005</xref>). The results of linear regression analysis of 100 cross-validations of both the best-fit models showed the mean effect of 100 linear regressions was systematically biased from the orthogonal line of the 1:1 regression line, which may be caused by insufficient data or the absence of important influencing variables (<xref ref-type="bibr" rid="B50">Zhu et&#xa0;al., 2012</xref>). However, there was a clear positive correlation between the predicted and observed values of the models, suggesting a relatively high accuracy of the models in predicting spatial&#x2013;temporal distributions of <italic>H. nehereus</italic> in the YRE. In addition, the model deviance explained was lower in both summer and autumn, which may be due to the higher influence of other factors, such as bait, on resource distribution compared to environmental factors. Relevant studies have shown (<xref ref-type="bibr" rid="B17">Liu et&#xa0;al., 2021</xref>) that <italic>H. nehereus</italic> has a wide feeding range of over 40 species, primarily consisting of fish and shrimp, which also undergo seasonal variations and further affect the seasonal distribution and migration of <italic>H. nehereus</italic>. The combined effect of environmental factors may also affect the model deviation rate. For example, high water temperature and low DO synergistically affect the metabolic rate of fish, suggesting that DO may also influence <italic>H. nehereus</italic> distribution (<xref ref-type="bibr" rid="B45">Yu and Xian, 2009</xref>; <xref ref-type="bibr" rid="B6">Hajisamae and Yeesin, 2010</xref>).</p>
</sec>
<sec id="s4_2">
<label>4.2</label>
<title>Relationships between relative resources of <italic>H. nehereus</italic> in the YRE and environmental factors</title>
<p>The results of our study showed that environmental factors, such as water temperature and salinity, considerably influence the distribution of <italic>H. nehereus</italic> in the YRE. Water temperature is considered to be the most important influencing factor on the distribution of fish stocks (<xref ref-type="bibr" rid="B48">Zhao et&#xa0;al., 2020</xref>), which affects not only the feeding, growth, reproduction, and metabolism of fish (<xref ref-type="bibr" rid="B18">Liu et&#xa0;al., 2020</xref>) but also the abundance and distribution of their natural bait (<xref ref-type="bibr" rid="B14">Lewin et&#xa0;al., 2014</xref>), which further affect the migratory activities of the fish (<xref ref-type="bibr" rid="B22">Ma et&#xa0;al., 2020</xref>). Our results showed that the relative resources of <italic>H. nehereus</italic> in the YRE showed a wavy non-linear relationship with water temperature in summer and a positive linear relationship with the water temperature in autumn. Thus, the higher the water temperature the greater the resource of <italic>H. nehereus</italic> in this water temperature range (<xref ref-type="bibr" rid="B32">Sun, 2022</xref>). In addition, there were more pronounced seasonal variations in the relationship between <italic>H. nehereus</italic> distribution and water temperature, which may be associated with seasonal variations in the runoff from the YRE (<xref ref-type="bibr" rid="B47">Zhang, 2015</xref>). The Yangtze River runoff affects the distribution of temperature and salinity, location of spawning ground, and nutrient transport, which further affected the structure of the fish community (<xref ref-type="bibr" rid="B27">Rakocinski et&#xa0;al., 1996</xref>; <xref ref-type="bibr" rid="B30">Shan et&#xa0;al., 2004</xref>), resulting in a more pronounced relationship between fishery resources and water temperature.</p>
<p>Salinity influences all the life stages of fish (<xref ref-type="bibr" rid="B23">Ma et&#xa0;al., 2022b</xref>), and its tolerance varies among different developmental stages. <italic>H. nehereus</italic> adults migrate to high-salinity environments during the reproductive stage, while <italic>H. nehereus</italic> juveniles thrive in low-salinity environments due to their low tolerance to high-salinity (<xref ref-type="bibr" rid="B20">Luo et&#xa0;al., 2012</xref>). For instance, <xref ref-type="bibr" rid="B32">Sun (2022)</xref> found that in the coastal waters of Zhejiang province, <italic>H. nehereus</italic> adults tend to migrate to high-salinity environments (&#x2265; 30) before the spawning and reproductive period, while <italic>H. nehereus</italic> juveniles are abundant in low-salinity environments (&lt; 27). These findings are consistent with the influence of salinity on the distribution of <italic>H. nehereus</italic> observed in this study. The geographical location of the YRE is unique as it constitutes both freshwater and saltwater. Consequently, the salinity distribution in this study area spans a wide range and its effect on the relative resources of <italic>H. nehereus</italic> is more complex, with obvious seasonal variations between summer and autumn. The relative resources of <italic>H. nehereus</italic> in the YRE showed a multi-wave non-linear relationship with salinity in the summer and a non-linear relationship with salinity in autumn. This may be due to the spawning bloom of <italic>H. nehereus</italic> in summer (<xref ref-type="bibr" rid="B51">Zhuang et&#xa0;al., 2018</xref>), where fish at different developmental stages are present and have different salinities suitable for survival, while most of the <italic>H. nehereus</italic> in autumn are in the juvenile stage and prefer low-salinity environments for optimal growth and development, and so the relative resources of <italic>H. nehereus</italic> showed a decreasing trend as the salinity increased to a certain salinity level.</p>
</sec>
<sec id="s4_3">
<label>4.3</label>
<title>Spatial&#x2013;temporal distribution characteristics of <italic>H. nehereus</italic> in the YRE</title>
<p>Our study found that the relative resources of <italic>H. nehereus</italic> in the YRE showed a significant upward trend in both summer and autumn during 2017&#x2013;2021. This may be due to the increase in seasonal or migratory fish in the YRE in recent years due to anthropogenic factors such as fishing (<xref ref-type="bibr" rid="B41">Yang et&#xa0;al., 2022</xref>). China has been protecting the key waters of the Yangtze River, including the YRE, since 2003 and implemented a ten-year fishing ban policy as of January 2021. <italic>H. nehereus</italic> grows rapidly and generally reaches sexual maturity at the age of one (<xref ref-type="bibr" rid="B51">Zhuang et&#xa0;al., 2018</xref>), which may explain the sharp increase in the resources of <italic>H. nehereus</italic> in the YRE in 2021. Moreover, relative to 2021, the resources of <italic>H. nehereus</italic> decreased in 2022, but was comparatively higher than during 2017&#x2013;2020. This decrease in the relative resources of <italic>H. nehereus</italic> may be due to the invasion of salty tidal waves in the YRE in the summer of 2022 (<xref ref-type="bibr" rid="B36">Wang et&#xa0;al., 2023</xref>). The predicted and observed values of <italic>H. nehereus</italic> distribution in the YRE in 2022 were similar in range and presented similar spatial distribution characteristics. The relative resources of <italic>H. nehereus</italic> were higher in the northern branch compared to the southern branch and in the offshore regions than in the near-estuary regions, which indicates a good prediction and fitting effect of the selected models.</p>
<p>Seasonal variations in the relative resources of <italic>H. nehereus</italic> in the YRE were obvious, with the relative resources in the summer being significantly greater than that in the autumn. These results were consistent with a previous report by <xref ref-type="bibr" rid="B4">Guo et&#xa0;al. (2019)</xref> on the resources of <italic>H. nehereus</italic> in the Min River Estuary. The unique ecological environment of the YRE is affected by runoff and hydrodynamic factors. In summer, the YRE has abundant water and the hydrodynamics of the north and south branches are stronger, enabling better growth and survival of the marine migratory fishes, especially in the peak reproductive stage, and reducing intraspecies struggles and interspecies competitions (<xref ref-type="bibr" rid="B31">Shen et&#xa0;al., 2011</xref>). Therefore, the estuarine area can be used as a good baiting and fattening ground for marine migratory fishes (<xref ref-type="bibr" rid="B31">Shen et&#xa0;al., 2011</xref>). <italic>H. nehereus</italic> exhibits migratory activity from July to September, during which time they move towards the coast for spawning migration and bait solicitation (<xref ref-type="bibr" rid="B33">Sun and Chen, 1986</xref>; <xref ref-type="bibr" rid="B20">Luo et&#xa0;al., 2012</xref>). After October, when the water temperature decreases, <italic>H. nehereus</italic> gradually migrate offshore to deeper waters for overwintering (<xref ref-type="bibr" rid="B4">Guo et&#xa0;al., 2019</xref>), which explains the relatively high resources of <italic>H. nehereus</italic> in the YRE during summer and in the seaward side during autumn.</p>
<p>Salinity is an important factor influencing the spatial distribution of fish communities in the YRE, which constitutes both saltwater and freshwater (<xref ref-type="bibr" rid="B43">Ye et&#xa0;al., 2023</xref>). In terms of spatial distribution, the relative resources of <italic>H. nehereus</italic> in the YRE are higher in the northern branch compared to the southern branch and offshore compared to the near-estuary regions. This is possibly due to the significantly high salinity in the northern and offshore waters compared to the southern and near-estuary waters, respectively (<xref ref-type="bibr" rid="B5">Guo, 2022</xref>), which is suitable for <italic>H. nehereus</italic> as an oceanic migratory fish. Additionally, the southern branch has a navigation channel with frequent vessel traffic, which indirectly destroys the ecology of the watershed (<xref ref-type="bibr" rid="B14">Lewin et&#xa0;al., 2014</xref>). This may explain the low relative resources of <italic>H. nehereus</italic> in these waters.</p>
</sec>
</sec>
<sec id="s5" sec-type="conclusions">
<label>5</label>
<title>Conclusion</title>
<p>In this study, the GAM model was used to analyze the resource distribution of <italic>H. nehereus</italic> on in the YRE and its relationship with environmental factors from 2017 to 2022. The study found several resource distribution characteristics of <italic>H. nehereus</italic>: The resource distribution of <italic>H. nehereus</italic> was higher in summer compared to autumn. The resource distribution of <italic>H. nehereus</italic> was higher in the waters of the northern branch of the YRE compared to the waters of the southern branch. Additionally, the waters of the far shore exhibited higher resource distribution than the waters of the near shore. The main environmental factors identified to affect the resource distribution characteristics of <italic>H. nehereus</italic> were water temperature and salinity. However, in addition to the environmental factors selected in this paper, other environmental factors and their combined effects also influence the resource distribution of estuarine fishes (<xref ref-type="bibr" rid="B9">Huang et&#xa0;al., 2013</xref>). Therefore, in future studies, more environmental factors will be selected, biological factors will be added as well as the joint effects among environmental factors will be considered. In this way, the resource distribution characteristics of <italic>H. nehereus</italic> in the YRE and their influencing factors will be more accurately investigated. These studies will be of great significance to the resource management, protection, and sustainable use of <italic>H. nehereus</italic> and the YRE resources.</p>
</sec>
<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/supplementary material. Further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>YL: Data curation, Investigation, Validation, Visualization, Writing &#x2013; original draft. CG: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Project administration, Writing &#x2013; review &amp; editing. JC: Data curation, Funding acquisition, Investigation, Project administration, Writing &#x2013; review &amp; editing. QW: Conceptualization, Formal analysis, Funding acquisition, Writing &#x2013; review &amp; editing. JZ: Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Writing &#x2013; review &amp; editing.</p>
</sec>
</body>
<back>
<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 Shanghai Municipal Science and Technology Commission Local Capacity Building Program for Universities (23010502500; 20dz1204703).</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>The authors of this research would like to thank the teachers and students from the Research Laboratory of Quantitative Assessment and Management of Fisheries Resources and Ecosystems, Shanghai Ocean University and Shanghai Aquatic Wildlife Conservation Research Center.</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>
<p>The reviewer, ZW, declared a shared parent affiliation with the authors, YL, CG, and JZ.</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>
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