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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Mar. Sci.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Marine Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Mar. Sci.</abbrev-journal-title>
</journal-title-group>
<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.2026.1769197</article-id>
<article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>A study on spatial network construction and ecological governance of the Bohai Rim coastal zone from a land-sea coordination perspective</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Yin</surname><given-names>Shusheng</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="corresp" rid="c001"><sup>*</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1965335/overview"/>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; review &amp; editing" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing &#x2013; review &amp; editing</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="methodology" vocab-term-identifier="https://credit.niso.org/contributor-roles/methodology/">Methodology</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; original draft" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing &#x2013; original draft</role>
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<contrib contrib-type="author">
<name><surname>Hu</surname><given-names>Huajin</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="validation" vocab-term-identifier="https://credit.niso.org/contributor-roles/validation/">Validation</role>
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<aff id="aff1"><label>1</label><institution>School of Economics and Humanities, Department of Cultural Tourism, Jiangsu Vocational College of Agriculture and Forestry</institution>, <city>Zhenjiang</city>, <state>Jiangsu</state>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff2"><label>2</label><institution>Key Research Base of Humanities and Social Sciences of the Ministry Education, Center for Studies of Marine Economy and Sustainable Development, Liaoning Normal University</institution>, <city>Dalian</city>, <state>Liaoning</state>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff3"><label>3</label><institution>College of Humanities, Jinling Institute of Technology</institution>, <city>Nanjing</city>, <state>Jiangsu</state>,&#xa0;<country country="cn">China</country></aff>
<author-notes>
<corresp id="c001"><label>*</label>Correspondence: Shusheng Yin, <email xlink:href="mailto:yinshusheng01@gmail.com">yinshusheng01@gmail.com</email></corresp>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-02-24">
<day>24</day>
<month>02</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2026</year>
</pub-date>
<volume>13</volume>
<elocation-id>1769197</elocation-id>
<history>
<date date-type="received">
<day>16</day>
<month>12</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>30</day>
<month>01</month>
<year>2026</year>
</date>
<date date-type="rev-recd">
<day>19</day>
<month>01</month>
<year>2026</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2026 Yin and Hu.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Yin and Hu</copyright-holder>
<license>
<ali:license_ref start_date="2026-02-24">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. 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.</license-p>
</license>
</permissions>
<abstract>
<p>The scientifically grounded construction of ecological networks constitutes a strategic and evidence-based approach to achieving functional connectivity between terrestrial and marine habitat patches in coastal zones. Such connectivity is indispensable for conserving coastal biodiversity and sustaining regional ecological security. Focusing on the Bohai Rim Economic Zone&#x2014;a rapidly developing coastal megaregion facing intensifying land&#x2013;sea interface pressures&#x2014;this study develops and refines an integrated land&#x2013;sea ecological network through spatially explicit, process-informed analysis. We employed morphological spatial pattern analysis (MSPA) to enhance the spatial resolution and ecological realism of existing ecological red line delineations. Ecological sources were systematically identified using a multi-criteria assessment integrating habitat quality (derived from land cover, vegetation indices, and ecosystem service capacity), landscape connectivity (quantified via graph-theoretic metrics), and ecological sensitivity (based on geomorphology, hydrology, and conservation priority designations). A land&#x2013;sea coupled resistance surface was constructed by weighting both natural constraints (e.g., slope, wetland distribution, marine bathymetry) and anthropogenic pressures (e.g., urban imperviousness, port infrastructure density, fishing intensity). Ecological corridors were then modeled using circuit theory (via Circuitscape), which accounts for multiple pathways and directional flow dynamics across heterogeneous landscapes and seascapes. Finally, targeted optimization strategies&#x2014;including corridor reinforcement, pinch-point mitigation, and cross-boundary governance recommendations&#x2014;were formulated based on structural and functional vulnerability assessments.   Ecological sources were predominantly concentrated within nationally designated ecological function zones and high-sensitivity areas, including the Taihang Mountains, Yanshan Mountain range, Liaodong Hills, and Shuangtaizi River Estuary National Nature Reserve&#x2014;regions characterized by high habitat integrity and low anthropogenic disturbance. Ecological corridors exhibited three dominant spatial configurations: (1) the inland&#x2013;coastal Liaoxi Corridor, linking the Songliao Plain to the Bohai Sea; (2) the mountain&#x2013;plain transition zone between the Taihang Mountains and the North China Plain; and (3) the fluvial&#x2013;deltaic corridors along the lower Yellow River. Coastal corridors were most pronounced at the estuaries of the Liao River, Yellow River, and Yalu River&#x2014;key biogeographic gateways where freshwater, sediment, and nutrient fluxes converge. Critically, these corridors consistently aligned parallel to the coastline and demonstrated high current density and low effective resistance, indicating strong potential for ecological flow and species movement. This study reveals a spatially coherent yet structurally vulnerable ecological network in the Bohai Rim, wherein core sources remain relatively intact but connecting corridors are increasingly fragmented by linear infrastructure, port expansion, and intensive aquaculture. The observed corridor alignment with major estuaries and geomorphic gradients underscores their role as natural conduits for biotic and abiotic exchange across the land&#x2013;sea interface. However, our circuit-theory&#x2013;based assessment highlights acute pinch points&#x2014;particularly near Dalian, Tianjin, and the Yellow River Delta&#x2014;where cumulative human pressures severely constrain ecological flow. These findings advance coastal ecological network science by explicitly coupling terrestrial and marine resistance processes, and provide actionable, spatially explicit guidance for transboundary ecological planning, ecological red line refinement, and integrated land&#x2013;sea environmental governance.</p>
</abstract>
<kwd-group>
<kwd>coastal zone</kwd>
<kwd>ecological corridor</kwd>
<kwd>ecological network</kwd>
<kwd>land-sea coordination</kwd>
<kwd>MSPA</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. The 2024 Jiangsu Province Social Science Applied Research Excellence Project (244).</funding-statement>
</funding-group>
<counts>
<fig-count count="6"/>
<table-count count="6"/>
<equation-count count="6"/>
<ref-count count="45"/>
<page-count count="15"/>
<word-count count="7807"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Solutions for Ocean and Coastal Systems</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Coastal zones are transitional regions where land extends into the ocean. As complex and dynamic components of the Earth&#x2019;s surface, they are spatial units subject to intense human activity (<xref ref-type="bibr" rid="B14">Li et al., 2022</xref>; <xref ref-type="bibr" rid="B22">Luo, 2016</xref>) and among the most productive ecosystems worldwide (<xref ref-type="bibr" rid="B19">Liu et&#xa0;al., 2023</xref>). As coastal urbanization advances, coastal zones face a series of ecological and environmental challenges, including reductions in the habitats of flora and fauna (<xref ref-type="bibr" rid="B30">Sallustio et&#xa0;al., 2017</xref>), biodiversity loss (<xref ref-type="bibr" rid="B40">Yan et&#xa0;al., 2021</xref>), and increasing fragmentation of landscape patterns (<xref ref-type="bibr" rid="B38">Xia and Wu, 2021</xref>), shaped by interactions between terrestrial and marine ecological risks over long periods (<xref ref-type="bibr" rid="B14">Li et al., 2022</xref>; <xref ref-type="bibr" rid="B24">Ma et&#xa0;al., 2024</xref>). On the one hand, under coastal squeeze, human activities create impervious surfaces that alter hydrological connectivity within coastal wetlands. This disrupts material exchange and energy flows among ecological landscape patches, constraining the migration pathways and processes of wetland species (<xref ref-type="bibr" rid="B8">Guo and Hou, 2024</xref>). On the other hand, terrestrial economic activities discharge pollutants into the ocean, intensifying ecological pressures on coastal-zone systems and compressing the spatial scope for marine economic development (<xref ref-type="bibr" rid="B4">Chi et al., 2015</xref>; <xref ref-type="bibr" rid="B3">Chen et&#xa0;al., 2024</xref>). This produces a structural imbalance characterized by &#x201c;strong land, weak sea,&#x201d; which hampers the overall resilience of coastal zones (<xref ref-type="bibr" rid="B17">Liu et&#xa0;al., 2024a</xref>). Hence, scientifically identifying ecological risks in coastal zones and strengthening spatially precise governance are key tasks for promoting land-sea coordination in coastal zones (<xref ref-type="bibr" rid="B35">Wang and Cheng, 2022</xref>).</p>
<p>Current research on coastal zones devotes limited attention to ecological assessment, constructing ecological security patterns, and identifying ecological networks from a land-sea coordination perspective. Existing studies have focused on issues such as the spatial delimitation of coastal zones (<xref ref-type="bibr" rid="B23">Lyu et&#xa0;al., 2023</xref>), planning and policy formulation (<xref ref-type="bibr" rid="B20">Liu et&#xa0;al., 2022</xref>), and biological invasions (<xref ref-type="bibr" rid="B44">Zhao et&#xa0;al., 2018</xref>). Most works examine urban and regional ecological security, land-use change, characteristics of functional transformation, driving mechanisms, and ecological strategies from a terrestrial perspective, primarily through econometric models and spatial analysis. Ecological network studies define such networks as &#x201c;spatial systems&#x201d; composed of key elements that sustain ecological processes, namely, ecological sources, corridors, and strategic nodes (<xref ref-type="bibr" rid="B15">Lian et&#xa0;al., 2025</xref>). By linking fragmented and isolated habitat patches through corridors, these networks form a continuous system that enhances the functions of natural ecosystem services and landscape connectivity within the region, supporting sustainable development of regional ecological environments (<xref ref-type="bibr" rid="B36">Wei et&#xa0;al., 2025</xref>). Methodologically, mainstream approaches often combine future land-use simulations with the minimum cumulative resistance (MCR) model (<xref ref-type="bibr" rid="B43">Zhang et&#xa0;al., 2022</xref>). Ecological sources may be identified based on nature reserves, scenic areas, forest parks, and ecological redlines, or composite indicator systems based on ecological sensitivity, landscape connectivity, and the importance of landscape habitats (<xref ref-type="bibr" rid="B5">Gong et&#xa0;al., 2024</xref>). However, recent works have refined resistance surfaces by incorporating human disturbance factors, such as building density indices and nighttime light data (<xref ref-type="bibr" rid="B11">Jiang and Peng, 2024</xref>).Although nighttime light intensity and building density are commonly used proxy indicators for human activities, this study, based on the connectivity characteristics of land-sea ecological processes, selects more spatially explicit and easily accessible factors (e.g., land-use type, distance to roads, and marine functional zones) to construct a comprehensive resistance surface, thereby maintaining the comparability and integrability of land and sea factors.</p>
<p>Since the 13th Five-Year Plan, land-sea coordination has been a core component of China&#x2019;s national spatial governance strategy, explicitly calling for &#x201c;establishing an integrated pattern of territorial space development and protection for both land and sea&#x201d; (<xref ref-type="bibr" rid="B26">Ministry of Natural Resources, 2021b</xref>). The Coastal Zone Protection and Utilization Plan (2021&#x2013;2035) further emphasizes that &#x201c;ecological networks serve as a nexus to coordinate land-sea ecological conservation and restoration&#x201d; (<xref ref-type="bibr" rid="B25">Ministry of Natural Resources, 2021a</xref>). Against this policy backdrop, the Bohai Rim region stands out as a critical and representative area for examining land-sea ecological interactions. As a convergence zone of major national strategies such as the &#x201c;Beijing&#x2013;Tianjin&#x2013;Hebei Coordinated Development&#x201d; and the &#x201c;Northeast Revitalization,&#x201d; it functions as the economic core of northern China and a vital node along global migratory bird flyways. However, precisely because of its intense socioeconomic activity and strategic location, the region exhibits acute land-sea systemic tensions, making it a typical case of coastal ecological conflict and fragmentation. Terrestrial urbanization has exceeded 70%, while nearshore waters endure a pollutant load nearly twice the national average (<xref ref-type="bibr" rid="B32">State Oceanic Administration, 2023</xref>). Key ecological corridors, especially in estuary and coastal wetland areas, have become increasingly fragmented due to competing land uses and marine exploitation. These overlapping pressures&#x2014;between economic development and ecological protection, and between terrestrial expansion and marine environmental carrying capacity&#x2014;render the Bohai Rim an ideal and urgent study area for developing and testing integrated land-sea ecological network frameworks that align with national strategic priorities.</p>
<p>Most existing studies on ecological networks focus on either terrestrial or marine systems separately, resulting in a &#x201c;land-sea divide&#x201d; that limits systematic understanding of cross-system interactions (<xref ref-type="bibr" rid="B45">Zhou et&#xa0;al., 2024</xref>; <xref ref-type="bibr" rid="B18">Liu et&#xa0;al., 2019</xref>). From a land-sea coordinated perspective, ecological networks can serve as critical bridges that reconnect fragmented habitats across terrestrial and nearshore marine areas. To address this gap, this study develops an integrated ecological network for the Bohai Rim region by explicitly considering multidimensional land-sea interactions. We identify ecological sources across terrestrial and marine environments by combining morphological spatial pattern analysis, landscape connectivity evaluation, comprehensive habitat quality assessment, and marine ecological sensitivity modeling. Drawing on circuit theory, we then extract land-sea ecological corridors to enhance spatial connectivity. Methodologically, this study advances the field in two key ways: first, by integrating terrestrial and marine resistance factors into a unified land-sea resistance surface, thus overcoming the limitations of single-system perspectives; and second, by coupling circuit theory with the minimum cumulative resistance model to simulate multi-path ecological corridors. Using the Bohai Rim&#x2014;a large-scale coastal economic zone with distinct ecological and socio-economic characteristics&#x2014;as an empirical case, this research enriches the growing body of work on land-sea coordinated ecological networks and complements existing studies conducted in smaller coastal regions such as the Min Delta. The resulting framework provides a scientific basis for ecological restoration and sustainable spatial planning in coastal zones under the objectives of land-sea coordination.</p>
</sec>
<sec id="s2">
<label>2</label>
<title>Research data and methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Study area and data</title>
<sec id="s2_1_1">
<label>2.1.1</label>
<title>Overview of the study area</title>
<p>The Bohai Economic Rim is located at the junction of eastern, northern, and northeastern China, encompassing Beijing, Tianjin, Hebei, Liaoning, and Shandong (three provinces and two municipalities) (<xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1</bold></xref>). The region is characterized by fertile land, abundant sunlight, and rich mineral resources. Its resources, such as fisheries, ports, petroleum, and sea salt, play an important role in China&#x2019;s economic development. Owing to its favorable geographic location and convenient transportation conditions, the Bohai Rim has become the economic core of northern China. As of 2023, it reached a gross domestic product of 22.67205 trillion yuan and a population of 252.478 million, accounting for 17.98% and 17.91% of the national totals, respectively, highlighting its enormous growth potential (<xref ref-type="bibr" rid="B25">Ministry of Natural Resources, 2021a</xref>). However, it should be emphasized that intensive anthropogenic activities within the Bohai Rim region have induced substantial pressures on local ecological systems, resulting in exacerbated landscape fragmentation (Zheng et&#xa0;al., 2024). The overall ecological quality remains suboptimal, characterized by inefficient spatial land utilization patterns (Han et&#xa0;al., 2024). Notably, ecological spaces have demonstrated a fluctuating yet persistently declining trajectory (Hu et&#xa0;al., 2023). These emerging ecological challenges require immediate and sustained attention from policymakers and stakeholders. Since the definition of &#x201c;coastal zones&#x201d; varies widely, this study follows existing literature (<xref ref-type="bibr" rid="B16">Liu et&#xa0;al., 2025</xref>; <xref ref-type="bibr" rid="B21">Liu et&#xa0;al., 2024b</xref>; <xref ref-type="bibr" rid="B34">Tian et&#xa0;al., 2023</xref>) for delineating the coastal zone of the study area as follows: on the marine side, the boundaries are determined according to each province&#x2019;s marine functional zoning plan; on the terrestrial side, the boundaries follow provincial administrative divisions (<xref ref-type="bibr" rid="B26">Ministry of Natural Resources, 2021b</xref>). Together, these delineations define the coastal zone of the study area (<xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1</bold></xref>).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Geographic location of the study area (base map sourced from the Standard Map Service System of the Ministry of Natural Resources of China, with no modifications to the original. Review number: GS(2019)1823).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-13-1769197-g001.tif">
<alt-text content-type="machine-generated">Map of China highlighting the Bohai Sea region, with a detailed inset showing Liaoning, Hebei, and Shandong Provinces, Beijing, Tianjin, and marine areas. Color gradient indicates altitude, with marine areas in blue. Scale bars provided.</alt-text>
</graphic></fig>
</sec>
<sec id="s2_1_2">
<label>2.1.2</label>
<title>Research data</title>
<p>The data used in this study primarily include administrative boundary data (2020), land-use data (2020), road vector data (2020), water-system data (2020), digital elevation model (DEM) data, and normalized difference vegetation index (NDVI) data for the Bohai Rim, along with marine functional zoning data for Liaoning, Hebei, Tianjin, and Shandong. The administrative boundary data were obtained from the National Geomatics Center of China (<ext-link ext-link-type="uri" xlink:href="http://www.ngcc.cn">http://www.ngcc.cn</ext-link>). The road and water-system data were obtained from China&#x2019;s National Catalogue Service for Geographic Information (<ext-link ext-link-type="uri" xlink:href="http://www.ngcc.cn">http://www.ngcc.cn</ext-link>). The DEM data were acquired from the Geospatial Data Cloud (<ext-link ext-link-type="uri" xlink:href="http://www.gscloud.cn">http://www.gscloud.cn</ext-link>), using ASTER GDEM V3 data at 30 m resolution, with slope extracted. The NDVI data were downloaded from the Google Earth Engine platform, using the MOD13Q1 dataset. The land-use data, with a resolution of 30 m, was sourced from the Resource and Environmental Science and Data Platform (<ext-link ext-link-type="uri" xlink:href="https://www.resdc.cn/">https://www.resdc.cn/</ext-link>). To perform spatial overlay analysis with other datasets, such as NDVI and DEM, following <xref ref-type="bibr" rid="B42">Yu et&#xa0;al. (2023)</xref> and <xref ref-type="bibr" rid="B1">Cai et&#xa0;al. (2025)</xref>, the land-use data were resampled to a resolution of 100 m to ensure spatial alignment for regional analysis. Additionally, the land-use data were reclassified in ArcGIS. According to the land-use classification system of the Chinese Academy of Sciences, secondary classes were merged into primary categories, namely, cropland, forest land, grassland, water bodies, built-up land, and unused land. The marine functional zoning data (2011&#x2013;2020) for Liaoning, Hebei, Tianjin, and Shandong were obtained from the respective provincial governments (<xref ref-type="table" rid="T1"><bold>Table&#xa0;1</bold></xref>).</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Sources of research data.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Data name</th>
<th valign="top" align="left">Data type</th>
<th valign="top" align="left">Data source</th>
<th valign="top" align="left">Spatial resolution</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Administrative boundary data</td>
<td valign="top" align="left">Vector</td>
<td valign="top" align="left"><ext-link ext-link-type="uri" xlink:href="http://bzdt.ch.mnr.gov.cn/">http://bzdt.ch.mnr.gov.cn/</ext-link></td>
<td valign="top" align="left">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">Land-use data</td>
<td valign="top" align="left">Raster</td>
<td valign="top" align="left"><ext-link ext-link-type="uri" xlink:href="https://www.resdc.cn/">https://www.resdc.cn/</ext-link></td>
<td valign="top" align="left">30m</td>
</tr>
<tr>
<td valign="top" align="left">Road data</td>
<td valign="top" align="left">Vector</td>
<td valign="top" align="left"><ext-link ext-link-type="uri" xlink:href="https://map.tianditu.gov.cn/">https://map.tianditu.gov.cn/</ext-link></td>
<td valign="top" align="left">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">Water-system data</td>
<td valign="top" align="left">Vector</td>
<td valign="top" align="left"><ext-link ext-link-type="uri" xlink:href="https://map.tianditu.gov.cn/">https://map.tianditu.gov.cn/</ext-link></td>
<td valign="top" align="left">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">DEM data</td>
<td valign="top" align="left">Raster</td>
<td valign="top" align="left"><ext-link ext-link-type="uri" xlink:href="http://www.gscloud.cn/">http://www.gscloud.cn/</ext-link></td>
<td valign="top" align="left">30m</td>
</tr>
<tr>
<td valign="top" align="left">NDVI data</td>
<td valign="top" align="left">Raster</td>
<td valign="top" align="left">Google Earth Engine</td>
<td valign="top" align="left">100m</td>
</tr>
<tr>
<td valign="top" align="left">Marine functional zoning data</td>
<td valign="top" align="left">Vector</td>
<td valign="top" align="left"><ext-link ext-link-type="uri" xlink:href="http://files.in5.cn/202005/ztgnq/201405-&#x8fbd;&#x5b81;-&#x8fbd;&#x5b81;&#x7701;&#x4e3b;&#x4f53;&#x529f;&#x80fd;&#x533a;&#x89c4;&#x5212;.pdf">http://files.in5.cn/202005/ztgnq/201405-&#x8fbd;&#x5b81;-&#x8fbd;&#x5b81;&#x7701;&#x4e3b;&#x4f53;&#x529f;&#x80fd;&#x533a;&#x89c4;&#x5212;S.pdf</ext-link>  (Liaoning Province)<break/><ext-link ext-link-type="uri" xlink:href="https://zrzy.hebei.gov.cn/cms/ewebeditor/uploadfile/2025052616020676046.pdf">https://zrzy.hebei.gov.cn/cms/ewebeditor/uploadfile/2025052616020676046.pdf</ext-link> (Hebei Province)<break/><ext-link ext-link-type="uri" xlink:href="https://view.officeapps.live.com/op/view.aspx?src=http%3A%2F%2Fwww.shandong.gov.cn%2Foldhyj%2FP020141222404113548593.doc&amp;wdOrigin=BROWSELINK">https://view.officeapps.live.com/op/view.aspx?src=http%3A%2F%2Fwww.shandong.gov.cn%2Foldhyj%2FP020141222404113548593.doc&amp;wdOrigin=BROWSELINK</ext-link> (Shandong Province)<break/><ext-link ext-link-type="uri" xlink:href="https://sthj.tj.gov.cn/ZWGK4828/ZCWJ6738/sthjjwj/202012/W020250923562433346333.pdf">https://sthj.tj.gov.cn/ZWGK4828/ZCWJ6738/sthjjwj/202012/W020250923562433346333.pdf</ext-link> (Tianjin)</td>
<td valign="top" align="left">&#x2013;</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Research methods</title>
<sec id="s2_2_1">
<label>2.2.1</label>
<title>Identification of terrestrial ecological sources</title>
<p>Ecological sources are the foundation for constructing coastal ecological networks and represent critical areas for sustaining ecological functions. This study followed the &#x201c;source identification&#x2013;resistance surface construction&#x2013;corridor extraction&#x201d; model (<xref ref-type="bibr" rid="B23">Lyu et&#xa0;al., 2023</xref>; <xref ref-type="bibr" rid="B5">Gong et&#xa0;al., 2024</xref>). Areas of relatively high habitat quality were identified through a comprehensive habitat quality assessment, with marine ecological reserves and unused land designated as basic ecological sources (<xref ref-type="bibr" rid="B36">Wei et&#xa0;al., 2025</xref>). The GuidosToolbox was employed to conduct morphological spatial pattern analysis (MSPA) and calculate landscape metrics, which, in combination with the specific conditions of the study area, were used to identify ecological sources. An ecological resistance surface was generated through spatial analysis, and ecological corridors were extracted using the MCR model together with the gravity model, resulting in the construction of an ecological network (<xref ref-type="fig" rid="f2"><bold>Figure&#xa0;2</bold></xref>).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Methodological framework for studying ecological security patterns.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-13-1769197-g002.tif">
<alt-text content-type="machine-generated">Flowchart summarizing an ecological analysis process with four main sections: data sources, data processing, methods, and results. Data sources include land-use, road, DEM, and ecological redline data. Visual representations display different data types such as maps and charts. Data processing involves land use type, buffers, elevation, slope, extraction, and reclassification. Methods feature morphological spatial pattern analysis, habitat quality, circuit theory, and landscape connectivity analysis with formulas, leading to terrestrial and marine ecological sources and resistance analysis. Results and analysis show maps and diagrams of ecological corridors and landscape connectivity outcomes.</alt-text>
</graphic></fig>
<sec id="s2_2_1_1">
<label>2.2.1.1</label>
<title>MSPA</title>
<p>MSPA, grounded in morphological principles, was used to identify and segment landscapes in the study area (<xref ref-type="table" rid="T2"><bold>Table&#xa0;2</bold></xref>), with foreground distinguished from background (<xref ref-type="bibr" rid="B42">Yu et&#xa0;al., 2023</xref>; <xref ref-type="bibr" rid="B1">Cai et&#xa0;al., 2025</xref>). This study used the Guidos software to designate forests, grasslands, wetlands, and water bodies as foreground, each with an assigned value of 2, and other land-cover types as background, each with an assigned value of 1. The binarized raster data were imported into GuidosToolbox, where the eight-neighborhood thinning algorithm was applied to conduct MSPA and generate the processed landscape-type data for the study area. Since very small patches reduce connectivity among landscapes and hinder the construction of ecological networks, this study identified core areas with an importance index greater than 0.5 km<sup>2</sup> as ecological sources through subsequent core region analysis (<xref ref-type="bibr" rid="B27">Peng et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B1">Cai et&#xa0;al., 2025</xref>), ensuring a robust foundation for regional ecological network planning.</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Ecological implications of landscape types in MSPA.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Landscape type</th>
<th valign="top" align="left">Ecological implications</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Core</td>
<td valign="top" align="left">Ecological spaces with high ecological benefits and relatively large areas</td>
</tr>
<tr>
<td valign="top" align="left">Islet</td>
<td valign="top" align="left">Small, isolated patches lacking connectivity and performing limited ecological functions</td>
</tr>
<tr>
<td valign="top" align="left">Bridge</td>
<td valign="top" align="left">Bridges linking foreground green patches, facilitating the flow of materials between them</td>
</tr>
<tr>
<td valign="top" align="left">Loop</td>
<td valign="top" align="left">Areas that facilitate ecological interactions among landscape elements and provide buffering for long-distance species migration</td>
</tr>
<tr>
<td valign="top" align="left">Edge</td>
<td valign="top" align="left">Transitional zones between cores and non-habitat patches that safeguard the functions of cores</td>
</tr>
<tr>
<td valign="top" align="left">Perforation</td>
<td valign="top" align="left">Non-habitat patches within cores, serving as transitions between core and non-core zones, yet providing no ecological benefit</td>
</tr>
<tr>
<td valign="top" align="left">Branch</td>
<td valign="top" align="left">Patches connected to the landscape at only one end</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s2_2_1_2">
<label>2.2.1.2</label>
<title>Comprehensive evaluation of habitat quality</title>
<p>Habitat quality reflects the capacity of species to survive and develop under a given environment or condition. In this study, habitat quality was assessed by integrating habitat quality values derived from stress factors with NDVI data. The evaluation followed the comprehensive assessment model developed by <xref ref-type="bibr" rid="B39">Xie et&#xa0;al. (2015)</xref> and employed the InVEST model to calculate the composite habitat quality value (<inline-formula>
<mml:math display="inline" id="im1"><mml:mi>Q</mml:mi></mml:math></inline-formula>) of each core patch, using habitat quality values calculated from stress factors (<inline-formula>
<mml:math display="inline" id="im2"><mml:mrow><mml:msub><mml:mi>q</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>) with NDVI values (<inline-formula>
<mml:math display="inline" id="im3"><mml:mrow><mml:msub><mml:mi>q</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>). The specific formula was (<xref ref-type="disp-formula" rid="eq1"><bold>Equation 1</bold></xref>):</p>
<disp-formula id="eq1"><label>(1)</label>
<mml:math display="block" id="M1"><mml:mrow><mml:mo>{</mml:mo><mml:mtable equalrows="true" equalcolumns="true"><mml:mtr><mml:mtd><mml:mrow><mml:mi>Q</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mi>q</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>q</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>q</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>H</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>[</mml:mo><mml:mn>1</mml:mn><mml:mo>&#x2212;</mml:mo><mml:mo>(</mml:mo><mml:mfrac><mml:mrow><mml:msubsup><mml:mi>D</mml:mi><mml:mrow><mml:mi>x</mml:mi><mml:mi>j</mml:mi></mml:mrow><mml:mi>Z</mml:mi></mml:msubsup></mml:mrow><mml:mrow><mml:msubsup><mml:mi>D</mml:mi><mml:mrow><mml:mi>x</mml:mi><mml:mi>j</mml:mi></mml:mrow><mml:mi>Z</mml:mi></mml:msubsup><mml:mo>+</mml:mo><mml:msup><mml:mi>K</mml:mi><mml:mi>Z</mml:mi></mml:msup></mml:mrow></mml:mfrac><mml:mo>)</mml:mo><mml:mo>]</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mrow><mml:msub><mml:mi>q</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mi>N</mml:mi><mml:mi>D</mml:mi><mml:mi>V</mml:mi><mml:mi>I</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>N</mml:mi><mml:mi>I</mml:mi><mml:mi>R</mml:mi><mml:mo>&#x2212;</mml:mo><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi><mml:mi>I</mml:mi><mml:mi>R</mml:mi><mml:mo>+</mml:mo><mml:mi>R</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math>
</disp-formula>
<p>where <inline-formula>
<mml:math display="inline" id="im4"><mml:mrow><mml:msub><mml:mi>q</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> was the habitat quality of a land-use patch, <inline-formula>
<mml:math display="inline" id="im5"><mml:mrow><mml:msub><mml:mi>H</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> was the habitat suitability of land-use type j, <inline-formula>
<mml:math display="inline" id="im6"><mml:mrow><mml:msubsup><mml:mi>D</mml:mi><mml:mrow><mml:mi>x</mml:mi><mml:mi>j</mml:mi></mml:mrow><mml:mi>Z</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula> was the degree of habitat degradation (stress factor), K was the half-saturation constant, Z was the normalization constant, <inline-formula>
<mml:math display="inline" id="im7"><mml:mrow><mml:mi>N</mml:mi><mml:mi>I</mml:mi><mml:mi>R</mml:mi></mml:mrow></mml:math></inline-formula> was the near-infrared-band reflectance, and <inline-formula>
<mml:math display="inline" id="im8"><mml:mi>R</mml:mi></mml:math></inline-formula> was the red-band reflectance. Using the InVEST model in combination with the method used by <xref ref-type="bibr" rid="B28">Peng et&#xa0;al. (2018)</xref> and the conditions of the study area, cropland, built-up land, and unused land were selected as stress factors. Habitat suitability values of each land-use type were set for the stress factors (<xref ref-type="table" rid="T3"><bold>Table&#xa0;3</bold></xref>). Additionally, the relative weight, maximum impact distance, and spatial decay type, along with the habitat suitability values of different land-use types, were set for each stress factor (<xref ref-type="table" rid="T4"><bold>Table&#xa0;4</bold></xref>). <inline-formula>
<mml:math display="inline" id="im9"><mml:mrow><mml:msub><mml:mi>q</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> reflected vegetation cover and growth conditions in the study area. Its value ranged from -1 to 1, with 0 representing bare soil or rock and negative values indicating surfaces covered by water, cloud, or snow. To avoid the influence of water quality on the results, <inline-formula>
<mml:math display="inline" id="im10"><mml:mrow><mml:msub><mml:mi>q</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> for water bodies in the study area was set to 1. The <inline-formula>
<mml:math display="inline" id="im11"><mml:mi>q</mml:mi></mml:math></inline-formula> values were classified into three levels, using the natural breaks method in ArcGIS, with higher values indicating a more suitable core for ecological network construction.</p>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>Sensitivity of different land-use types to stress factors.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Land-use code</th>
<th valign="middle" align="left">Land-use type</th>
<th valign="middle" align="left">Habitat suitability</th>
<th valign="middle" align="left">Cropland</th>
<th valign="middle" align="left">Built-up land</th>
<th valign="middle" align="left">Unused land</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">10</td>
<td valign="middle" align="left">Cropland</td>
<td valign="middle" align="left">0.6</td>
<td valign="middle" align="left">0</td>
<td valign="middle" align="left">0.9</td>
<td valign="middle" align="left">0.5</td>
</tr>
<tr>
<td valign="middle" align="left">20</td>
<td valign="middle" align="left">Forest land</td>
<td valign="middle" align="left">1</td>
<td valign="middle" align="left">0.5</td>
<td valign="middle" align="left">0.8</td>
<td valign="middle" align="left">0.2</td>
</tr>
<tr>
<td valign="middle" align="left">30</td>
<td valign="middle" align="left">Grassland</td>
<td valign="middle" align="left">0.7</td>
<td valign="middle" align="left">0.2</td>
<td valign="middle" align="left">0.5</td>
<td valign="middle" align="left">0.3</td>
</tr>
<tr>
<td valign="middle" align="left">50</td>
<td valign="middle" align="left">Wetland</td>
<td valign="middle" align="left">0.7</td>
<td valign="middle" align="left">0.2</td>
<td valign="middle" align="left">0.7</td>
<td valign="middle" align="left">0.3</td>
</tr>
<tr>
<td valign="middle" align="left">60</td>
<td valign="middle" align="left">Water body</td>
<td valign="middle" align="left">0.9</td>
<td valign="middle" align="left">0.4</td>
<td valign="middle" align="left">0.6</td>
<td valign="middle" align="left">0.5</td>
</tr>
<tr>
<td valign="middle" align="left">80</td>
<td valign="middle" align="left">Built-up land</td>
<td valign="middle" align="left">0</td>
<td valign="middle" align="left">0</td>
<td valign="middle" align="left">0</td>
<td valign="middle" align="left">0.1</td>
</tr>
<tr>
<td valign="middle" align="left">90</td>
<td valign="middle" align="left">Unused land</td>
<td valign="middle" align="left">0.3</td>
<td valign="middle" align="left">1</td>
<td valign="middle" align="left">0.3</td>
<td valign="middle" align="left">0</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="T4" position="float">
<label>Table&#xa0;4</label>
<caption>
<p>Assignment of stress factors for habitat quality.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Stress factor</th>
<th valign="middle" align="left">Maximum distance</th>
<th valign="middle" align="left">Weight</th>
<th valign="middle" align="left">Spatial decay type</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Cropland</td>
<td valign="middle" align="left">3</td>
<td valign="middle" align="left">0.5</td>
<td valign="middle" align="left">Linear</td>
</tr>
<tr>
<td valign="middle" align="left">Built-up land</td>
<td valign="middle" align="left">8</td>
<td valign="middle" align="left">1</td>
<td valign="middle" align="left">Exponential</td>
</tr>
<tr>
<td valign="middle" align="left">Unused land</td>
<td valign="middle" align="left">5</td>
<td valign="middle" align="left">0.6</td>
<td valign="middle" align="left">Linear</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s2_2_1_3">
<label>2.2.1.3</label>
<title>Landscape connectivity evaluation</title>
<p>Landscape connectivity indices reflect the level of connectivity for information exchange. Commonly used measures include the integral index of connectivity (<italic>IIC</italic>), the probability of connectivity (<italic>PC</italic>), and the delta probability of connectivity (<italic>DPC</italic>) (<xref ref-type="bibr" rid="B10">Huang et&#xa0;al., 2022</xref>). The specific calculation formulas are as follows (<xref ref-type="disp-formula" rid="eq2"><bold>Equations 2</bold></xref>&#x2013;<xref ref-type="disp-formula" rid="eq4"><bold>4</bold></xref>):</p>
<disp-formula id="eq2"><label>(2)</label>
<mml:math display="block" id="M2"><mml:mrow><mml:mtable equalrows="true" equalcolumns="true"><mml:mtr><mml:mtd><mml:mrow><mml:mi>I</mml:mi><mml:mi>I</mml:mi><mml:mi>C</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msubsup><mml:mo>&#x2211;</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:msubsup><mml:msubsup><mml:mo>&#x2211;</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:msubsup><mml:mfrac><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:msub><mml:mi>a</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>+</mml:mo><mml:msubsup><mml:mi>n</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup></mml:mrow></mml:mfrac></mml:mrow><mml:mrow><mml:msubsup><mml:mi>A</mml:mi><mml:mi>L</mml:mi><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:mfrac></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math>
</disp-formula>
<disp-formula id="eq3"><label>(3)</label>
<mml:math display="block" id="M3"><mml:mrow><mml:mtable equalrows="true" equalcolumns="true"><mml:mtr><mml:mtd><mml:mrow><mml:mi>P</mml:mi><mml:mi>C</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msubsup><mml:mo>&#x2211;</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:msubsup><mml:msubsup><mml:mo>&#x2211;</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:msubsup><mml:msub><mml:mi>a</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:msub><mml:mi>a</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:msubsup><mml:mi>P</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow><mml:mo>*</mml:mo></mml:msubsup></mml:mrow><mml:mrow><mml:msubsup><mml:mi>A</mml:mi><mml:mi>L</mml:mi><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:mfrac></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math>
</disp-formula>
<disp-formula id="eq4"><label>(4)</label>
<mml:math display="block" id="M4"><mml:mrow><mml:mtable equalrows="true" equalcolumns="true"><mml:mtr><mml:mtd><mml:mrow><mml:mi>D</mml:mi><mml:mi>P</mml:mi><mml:mi>C</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>P</mml:mi><mml:mi>C</mml:mi><mml:mo>&#x2212;</mml:mo><mml:mi>P</mml:mi><mml:msub><mml:mi>C</mml:mi><mml:mrow><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>m</mml:mi><mml:mi>o</mml:mi><mml:mi>v</mml:mi><mml:mi>e</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi>P</mml:mi><mml:mi>C</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math>
</disp-formula>
<p>where <italic>IIC</italic> referred to the integral index of connectivity, <italic>n</italic> was the total number of patches in the region, <italic>a<sub>i</sub></italic> was the area of patch <italic>i</italic>, <italic>a<sub>j</sub></italic> was the area of patch <italic>j</italic>, <inline-formula>
<mml:math display="inline" id="im12"><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mi>L</mml:mi></mml:msub><mml:mo>&#xa0;</mml:mo></mml:mrow></mml:math></inline-formula> was the total landscape area of the study region, <inline-formula>
<mml:math display="inline" id="im13"><mml:mrow><mml:msubsup><mml:mi>n</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula> was the number of links along the shortest path between patches <italic>i</italic> and <italic>j</italic>, <italic>PC</italic> referred to the probability of connectivity, <inline-formula>
<mml:math display="inline" id="im14"><mml:mrow><mml:msubsup><mml:mi>P</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow><mml:mo>*</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula> was the maximum product of path probabilities between patches <italic>i</italic> and <italic>j</italic>, <italic>DPC</italic> measured the importance of removing a patch for maintaining overall landscape connectivity, with larger values indicating a greater contribution of the patch, and <italic>PC<sub>remove</sub></italic> was the probability of connectivity after a patch was removed from the landscape (<xref ref-type="bibr" rid="B29">Qin et&#xa0;al., 2023</xref>).</p>
<p>In this study, <italic>DPC</italic> was calculated using Conefor software. For core patches identified through habitat quality screening, the connectivity distance was set to 2,500 m and the connection probability to 0.5. The resulting <italic>DPC</italic> values indicated patch importance, with higher values reflecting greater importance and suitability for connectivity. Patches with <italic>DPC</italic> values above 1 were retained for further analysis.</p>
</sec>
</sec>
<sec id="s2_2_2">
<label>2.2.2</label>
<title>Identification of marine ecological sources</title>
<p>Ecological sensitivity assessment provides an effective measure of the ecological baseline of a study area and serves as an important basis for planning ecological security patterns. In this study, areas of high ecological sensitivity were designated as marine ecological sources. Drawing on marine functional zoning data and ecological redline data, an evaluation system was established for marine ecological sensitivity in the study area, considering the sensitivity of current marine uses and of special habitats (<xref ref-type="table" rid="T5"><bold>Table&#xa0;5</bold></xref>). The results were derived through weighted overlay analysis in ArcGIS and classified into five levels using the natural breaks method. The top three levels were identified as marine ecological sources (<xref ref-type="bibr" rid="B41">Yin et&#xa0;al., 2024</xref>; <xref ref-type="bibr" rid="B37">Wu et&#xa0;al., 2022</xref>).</p>
<table-wrap id="T5" position="float">
<label>Table&#xa0;5</label>
<caption>
<p>Assessment system for marine ecological sensitivity.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="left">Ecological sensitivity type</th>
<th valign="middle" align="left">Evaluation indicator</th>
<th valign="middle" align="left">Not sensitive</th>
<th valign="middle" align="left">Slightly sensitive</th>
<th valign="middle" align="left">Moderately sensitive</th>
<th valign="middle" align="left">Highly sensitive</th>
<th valign="middle" rowspan="2" align="left">Weight</th>
</tr>
<tr>
<th valign="middle" align="left">Graded assignment</th>
<th valign="middle" align="left">1</th>
<th valign="middle" align="left">3</th>
<th valign="middle" align="left">5</th>
<th valign="middle" align="left">7</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" rowspan="2" align="left">Marine use</td>
<td valign="middle" align="left">Current marine use</td>
<td valign="middle" align="left">Other marine areas</td>
<td valign="middle" align="left">Marine use for industry, mining, communication, and transportation</td>
<td valign="middle" align="left">Marine use for fisheries and recreation</td>
<td valign="middle" align="left">Special marine use</td>
<td valign="middle" align="left">0.6</td>
</tr>
<tr>
<td valign="middle" align="left">Type of protected area</td>
<td valign="middle" align="left">Other types</td>
<td valign="middle" align="left">Important ecosystems</td>
<td valign="middle" align="left">Nature parks</td>
<td valign="middle" align="left">Nature reserves</td>
<td valign="middle" align="left">0.2</td>
</tr>
<tr>
<td valign="middle" align="left">Special habitats</td>
<td valign="middle" align="left">Protection level</td>
<td valign="middle" align="left">Unclassified</td>
<td valign="middle" align="left">Municipal/county level</td>
<td valign="middle" align="left">Provincial level</td>
<td valign="middle" align="left">National level</td>
<td valign="middle" align="left">0.2</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s2_2_3">
<label>2.2.3</label>
<title>Construction of an ecological resistance surface</title>
<p>Ecological resistance refers to the degree of impediment that species encounter when moving across different landscape units or habitat patches. Constructing an ecological resistance surface is crucial in identifying regional ecological corridors. Following <xref ref-type="bibr" rid="B12">Jin et&#xa0;al. (2021)</xref> and taking into account the natural environment and socioeconomic conditions of the study area, six resistance factors were selected, namely, land-use type, distance to roads, distance to water systems, elevation, slope, and marine functional zones, which were assigned values (<xref ref-type="table" rid="T6"><bold>Table&#xa0;6</bold></xref>). Weights were determined using the analytic hierarchy process (AHP), and a coastal ecological resistance surface was generated through weighted overlay analysis in GIS.</p>
<table-wrap id="T6" position="float">
<label>Table&#xa0;6</label>
<caption>
<p>Resistance factors and resistance values.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="left">Coastal zone</th>
<th valign="middle" rowspan="2" align="left">Resistance factor</th>
<th valign="middle" colspan="5" align="left">Resistance value</th>
<th valign="middle" rowspan="2" align="left">Weight</th>
</tr>
<tr>
<th valign="middle" align="left">1</th>
<th valign="middle" align="left">2</th>
<th valign="middle" align="left">3</th>
<th valign="middle" align="left">4</th>
<th valign="middle" align="left">5</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" rowspan="5" align="left">Terrestrial area</td>
<td valign="middle" align="left">Land-use type</td>
<td valign="middle" align="left">Water bodies</td>
<td valign="middle" align="left">Forest land, grassland, and wetlands</td>
<td valign="middle" align="left">Cropland</td>
<td valign="middle" align="left">Unused land</td>
<td valign="middle" align="left">Built-up land</td>
<td valign="middle" align="left">0.4</td>
</tr>
<tr>
<td valign="middle" align="left">Distance to roads</td>
<td valign="middle" align="left">&lt;100m</td>
<td valign="middle" align="left">100-200m</td>
<td valign="middle" align="left">200-500m</td>
<td valign="middle" align="left">500-1000m</td>
<td valign="middle" align="left">&gt;1000m</td>
<td valign="middle" align="left">0.15</td>
</tr>
<tr>
<td valign="middle" align="left">Distance to water systems</td>
<td valign="middle" align="left">&lt;50m</td>
<td valign="middle" align="left">50-100m</td>
<td valign="middle" align="left">100-300m</td>
<td valign="middle" align="left">300-500m</td>
<td valign="middle" align="left">&gt;500m</td>
<td valign="middle" align="left">0.15</td>
</tr>
<tr>
<td valign="middle" align="left">Elevation</td>
<td valign="middle" align="left">&lt;100m</td>
<td valign="middle" align="left">100-150m</td>
<td valign="middle" align="left">150-200m</td>
<td valign="middle" align="left">200-250m</td>
<td valign="middle" align="left">&gt;250m</td>
<td valign="middle" align="left">0.15</td>
</tr>
<tr>
<td valign="middle" align="left">Slope</td>
<td valign="middle" align="left">&lt;5&#xb0;</td>
<td valign="middle" align="left">5&#xb0;-10&#xb0;</td>
<td valign="middle" align="left">10&#xb0;-15&#xb0;</td>
<td valign="middle" align="left">15&#xb0;-25&#xb0;</td>
<td valign="middle" align="left">&gt;25&#xb0;</td>
<td valign="middle" align="left">0.15</td>
</tr>
<tr>
<td valign="middle" align="left">Marine area</td>
<td valign="middle" align="left">Marine functional zones</td>
<td valign="middle" align="left">Marine protected/conservation areas</td>
<td valign="middle" align="left">Special-use/aquaculture areas</td>
<td valign="middle" align="left">Tourism, leisure, and recreation/mineral and energy areas</td>
<td valign="middle" align="left">Industrial and urban-use marine areas</td>
<td valign="middle" align="left">Port and shipping areas</td>
<td valign="middle" align="left">1</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Building on the principle of land-sea coordination proposed by <xref ref-type="bibr" rid="B7">Guo et&#xa0;al. (2023)</xref>, the coastal ecological resistance surface was identified using a land-sea coordination matrix that incorporated coastal spatial use types. When the terrestrial side consists of built-up land that adjoins a marine protected or conservation area on the marine side, land-sea incompatibility arises. Since built-up land has largely lost its ecological function (<xref ref-type="bibr" rid="B28">Peng et&#xa0;al., 2018</xref>), its resistance value was set to the maximum, while a lower resistance value was assigned for the marine side. When built-up land on the terrestrial side adjoins a marine area designated for industrial and urban use on the marine side, these activities, being concentrated near the shoreline, with a limited impact on marine species migration. However, they significantly hinder the exchange of materials and energy between land and sea. Accordingly, both the terrestrial and marine areas were assigned relatively high resistance values.</p>
<p>When the terrestrial side consists of ecological spaces, such as forests and grasslands, and the marine side is designated as port, shipping, or aquaculture areas, the two are classified as incompatible. Port operations fragment the natural environment through human activity and obstruct species migration. Therefore, these areas were assigned high resistance values. Within terrestrial areas, land types, such as unused land, bare soil, and exposed rock, have weak ecological functions and contribute little to species migration or biodiversity conservation. Hence, based on previous studies (<xref ref-type="bibr" rid="B42">Yu et&#xa0;al., 2023</xref>; <xref ref-type="bibr" rid="B29">Qin et&#xa0;al., 2023</xref>), they were assigned relatively high resistance values. Furthermore, weak in ecological function, cropland poses limited barriers to material exchange and was given a moderate value.</p>
<p>The rivers, sea, and other water bodies within the study area are important ecological spaces and pose little barrier to the exchange of materials. Accordingly, they were assigned low resistance values. Resistance values for marine functional zones were determined based on their designated functions and use intensity. Current special-use zones and aquaculture areas, where utilization intensity was limited and the original ecological environment was largely preserved, were given low values. Marine protected and conservation areas, subject to minimal human disturbance, were assigned the lowest values.</p>
</sec>
<sec id="s2_2_4">
<label>2.2.4</label>
<title>Extraction of ecological corridors</title>
<p>The circuit theory simulates the migration and dispersal of species across landscapes by drawing on the principle of electrons moving randomly through an electrical circuit. In this model, species are treated as electrons, landscape resistance as a conductive surface, and habitat patches of high quality (ecological sources) as nodes (<xref ref-type="bibr" rid="B6">Gou et&#xa0;al., 2024</xref>). Ecological nodes are key areas that play an effective or facilitative role in regional ecological processes and act as points of connection between ecological sources. The ecological corridors identified using the circuit theory represent areas most likely to be traversed by multiple species, thus they better reflect the actual movement patterns of organisms. In this study, the Linkage Pathways tool in the Linkage Mapper toolbox of ArcGIS was used, with ecological sources and the ecological resistance surface as inputs, to generate least-cost path ecological corridors, which were taken as the optimal ecological corridors (<xref ref-type="disp-formula" rid="eq5">Equation 5</xref>).</p>
<disp-formula id="eq5"><label>(5)</label>
<mml:math display="block" id="M5"><mml:mrow><mml:mtable equalrows="true" equalcolumns="true"><mml:mtr><mml:mtd><mml:mrow><mml:mi>M</mml:mi><mml:mi>C</mml:mi><mml:mi>R</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mi>f</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:munderover><mml:mo>&#x2211;</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mi>m</mml:mi></mml:mrow></mml:munderover><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mi>R</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math>
</disp-formula>
<p>where <inline-formula>
<mml:math display="inline" id="im15"><mml:mrow><mml:mi>M</mml:mi><mml:mi>C</mml:mi><mml:mi>R</mml:mi></mml:mrow></mml:math></inline-formula> was the minimum cumulative resistance value for ecological source <italic>j</italic> across all raster cells, <inline-formula>
<mml:math display="inline" id="im16"><mml:mi>f</mml:mi></mml:math></inline-formula> was the positive correlation of <inline-formula>
<mml:math display="inline" id="im17"><mml:mrow><mml:mi>M</mml:mi><mml:mi>C</mml:mi><mml:mi>R</mml:mi></mml:mrow></mml:math></inline-formula> with <inline-formula>
<mml:math display="inline" id="im18"><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> and <inline-formula>
<mml:math display="inline" id="im19"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, <inline-formula>
<mml:math display="inline" id="im20"><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> was the spatial distance traversed by organisms from ecological source <italic>j</italic> across landscape surface <italic>i</italic>, and <inline-formula>
<mml:math display="inline" id="im21"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> was the resistance of landscape cell <italic>i</italic> to species migration.</p>
<p>This study employed the gravity model to assess the strength of connections between ecological sources, describing the effectiveness and importance of ecological corridors (<xref ref-type="disp-formula" rid="eq6">Equation 6</xref>). Ecological patches with gravity strength &#x2265; 800 were classified as important corridors, while those between 0 and 800 were classified as general ecological corridors. According to the natural breaks method, the corridor widths were set to 600 m and 100 m, respectively (<xref ref-type="bibr" rid="B31">Shangguan et&#xa0;al., 2024</xref>; <xref ref-type="bibr" rid="B33">Tang et&#xa0;al., 2025</xref>).</p>
<disp-formula id="eq6"><label>(6)</label>
<mml:math display="block" id="M6"><mml:mrow><mml:msub><mml:mi>G</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:msub><mml:mi>N</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:msubsup><mml:mi>D</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msubsup><mml:mi>L</mml:mi><mml:mrow><mml:mtext>max</mml:mtext></mml:mrow><mml:mn>2</mml:mn></mml:msubsup><mml:mtext>ln</mml:mtext><mml:mo stretchy="false">(</mml:mo><mml:msub><mml:mi>S</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo stretchy="false">)</mml:mo><mml:mtext>ln</mml:mtext><mml:mo stretchy="false">(</mml:mo><mml:msub><mml:mi>S</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mrow><mml:msubsup><mml:mi>L</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msubsup><mml:msub><mml:mi>P</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:msub><mml:mi>P</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:math>
</disp-formula>
<p>where <italic>G<sub>ij</sub></italic> was the interaction force between patches <italic>i</italic> and <italic>j</italic>, <italic>N<sub>i</sub></italic> and <italic>N<sub>j</sub></italic> were the weights of the two patches, respectively, <italic>D<sub>ij</sub></italic> was the standardized potential corridor resistance between patches <italic>i</italic> and <italic>j</italic>, <italic>P<sub>i</sub></italic> and <italic>P<sub>j</sub></italic> were the resistance values of ecological source patches <italic>i</italic> and <italic>j</italic>, obtained by assigning resistance coefficients to different land-use types or ecological factors and calculating them with spatial analysis tools (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3f</bold></xref>), <italic>S<sub>i</sub></italic> and <italic>S<sub>j</sub></italic> were the areas of the two patches, respectively, <italic>L<sub>ij</sub></italic> was the cumulative resistance value of the corridor between patches <italic>i</italic> and <italic>j</italic>, and <italic>L<sub>max</sub></italic> was the maximum resistance value among all corridors.</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Spatial differentiation of ecological resistance surfaces in the Bohai Rim coastal zone (base map sourced from the standard map service system of the Ministry of Natural Resources of China, with no modifications to the original. Review number: GS(2019)1823).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-13-1769197-g003.tif">
<alt-text content-type="machine-generated">Six color-coded thematic maps display a geographic study area showing spatial resistance patterns for different factors: land use and cover change (LUCC), roads, rivers, DEM, slope, and an integrated resistance map. Each map uses five resistance classes, with colors ranging from blue (lowest resistance) to red (highest resistance), and includes a legend and scale bar for reference.</alt-text>
</graphic></fig>
</sec>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Result</title>
<sec id="s3_1">
<label>3.1</label>
<title>Analysis of ecological sources</title>
<p>MSPA was used to analyze the spatial patterns of terrestrial landscapes, producing a map of landscape types for the study area (<xref ref-type="fig" rid="f4"><bold>Figure&#xa0;4</bold></xref>). The terrestrial cores covered 66,441.81 km<sup>2</sup>, accounting for 12.83% of the total terrestrial area. They were concentrated in the northern Taihang Mountains, the Yanshan Mountains, and the Liaodong Hills, where topography limited human activity, resulting in large patch sizes and strong overall connectivity. However, in the Tai Mountains and the Jiaodong Peninsula, the number of landscape patches increased while patch size decreased, giving ecological sources a more scattered distribution.</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>MSPA classification results of terrestrial landscapes in the Bohai Rim (base map sourced from the standard map service system of the Ministry of Natural Resources of China, with no modifications to the original. Review number: GS(2019)1823).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-13-1769197-g004.tif">
<alt-text content-type="machine-generated">Map illustration showing landscape pattern classification within a study area in East Asia, using colors to indicate core areas (green), islets (brown), loops (yellow), bridges (red), perforations (blue), edges (black), branches (orange), and background or administrative boundaries (grey tones); legend provided for reference.</alt-text>
</graphic></fig>
<p>At river estuaries, natural landscapes, such as extensive tidal flats and wetlands, dominated, with fewer internal patches. However, these patches exhibited high <italic>DPC</italic> values and elevated <italic>IIC</italic> scores and formed a belt-like distribution near the Yellow River estuary. The bridges covered 1,885.97 km<sup>2</sup>, accounting for about 0.36% of the total area. Loops, branches, and islets together covered 274,137.47 km<sup>2</sup>, or 27.41% of the terrestrial area, and served as components in the construction of the ecological network, indicating that ecological sources were relatively fragmented. Edges, situated between cores and major non-green landscapes, covered approximately 13,259.14 km<sup>2</sup>, representing 19.96% of the core area. This suggested that the cores were relatively stable and had strong resistance to external disturbances.</p>
<p>Marine ecological sources were identified using marine functional zoning data and marine ecological redline data. These were overlaid with the terrestrial ecological sources. As shown in <xref ref-type="fig" rid="f5"><bold>Figure&#xa0;5</bold></xref>, marine ecological sources covered 24,814.31 km<sup>2</sup>. They primarily included the Dabijia Mountain Special Protected Island, the Shuangtaizi River Estuary Reserve, the Jinzhou Baishawan Tourism Area, the offshore fishery grounds at the top of Liaodong Bay, the Yalu River estuary, and the Dalian Spotted Seal Reserve.</p>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>Spatial distribution of ecological sources in the Bohai Rim region from the perspective of land-sea coordination (base map sourced from the standard map service system of the Ministry of Natural Resources of China, with no modifications to the original. Review number: GS(2019)1823).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-13-1769197-g005.tif">
<alt-text content-type="machine-generated">Map illustrating the distribution of marine and terrestrial ecological sources in a region, with marine areas shaded light blue and terrestrial areas shaded light green, accompanied by a legend and a distance scale in kilometers.</alt-text>
</graphic></fig>
<p>Nearshore ecological sources were relatively limited, concentrated mainly on the northern and southern sides of the Yellow River estuary. These marine ecological sources provided a relatively favorable habitat for marine species. Across the study area, ecological sources were primarily distributed in forests characterized by high habitat quality and strong connectivity, and in areas with abundant water resources and high biodiversity. The identified ecological sources encompassed most of the region&#x2019;s critical ecological functional zones, such as the Taihang Mountains, the Yanshan Mountains, the Yellow River, and the Liao River, indicating that the delineation of ecological sources in this study was consistent with actual terrestrial conditions, thus scientifically sound.</p>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Ecological resistance surface</title>
<p>In this study, six resistance factors &#x2013; land-use type, distance to roads, distance to rivers, elevation, slope, and marine functional zones &#x2013; were integrated through weighted overlay to generate the spatial distribution of individual factors and a comprehensive land-sea resistance surface. The ecological resistance values ranged from 1 to 5. As shown in <xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3</bold></xref>, areas of high resistance were concentrated primarily in the northwestern mountains of the study area, including the outlying ranges of the Taihang Mountains and the Yanshan Mountains. Additional high-resistance zones appeared in scattered clusters across the North China Plain, the Liaohe Plain, the Shandong Peninsula, and the Liaodong Peninsula, and in the marine port and shipping areas, indicating that elevation and human activity were the dominant resistance factors. Low-resistance areas were distributed among plains, rivers, estuaries, and tidal flats, underscoring the critical role of rivers and water bodies in sustaining biodiversity and enhancing landscape connectivity.</p>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Ecological corridor analysis</title>
<p>Using the MCR model in combination with the circuit theory and graph-theoretic tools, land-sea ecological corridors were delineated, resulting in the ecological network of the study area (<xref ref-type="fig" rid="f6"><bold>Figure&#xa0;6</bold></xref>). Terrestrial ecological corridors were primarily concentrated in the Liaoxi Corridor and the Taihang Mountains&#x2013;North China Plain, and along the lower reaches of the Yellow River. Most occurred in forests, grassland, and other areas with strong natural ecological attributes, indicating that these regions provided multiple migration pathways capable of supporting diverse species habitats. Marine ecological corridors were mainly distributed in areas such as Lushun&#x2013;Penglai, the Yalu River estuary, the Yellow River estuary, the Shuangtaizi River Estuary Marine Protected Area, and the aquaculture zone of Liaodong Bay. Although fewer in number, these marine corridors ran parallel to the coastline, linking major marine protected areas. Due to the influence of marine spatial utilization patterns, they were characterized by relatively simple functional structures and weak ecological connectivity. Future planning should incorporate additional ecological nodes and corridors to enhance the security and integrity of the regional marine ecological environment.</p>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>Ecological network of the study area (base map sourced from the standard map service system of the Ministry of Natural Resources of China, with no modifications to the original. Review number: GS(2019)1823).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-13-1769197-g006.tif">
<alt-text content-type="machine-generated">Side-by-side maps illustrate ecological corridors in the Bohai Rim Region. The left map shows ecological threshold levels with corridors colored by threshold value, while the right map identifies marine and terrestrial corridors, nodes, and sources using distinct colors and inset regional details.</alt-text>
</graphic></fig>
<p>For coastal ecological corridors, river estuaries acted as overlapping and transitional zones between terrestrial and marine ecosystems, with distinctive biological, physical, and chemical characteristics. In the study area, rivers, such as the Yellow River, the Hai River, the Daling River, and the Xiaoling River, were key in linking coastal ecological sources. Specifically, the coastal ecological corridors of the Bohai Rim formed a C-shaped pattern, extending southward along the coastline, from Wafangdian (Dalian) through the Liaoxi Corridor to the Yellow River estuary in Shandong. The Liao River estuary corridor cluster was jointly formed by the Daling River and the Daqing River, while dense intersections of corridors occurred in the municipal area of Jinzhou and Linghai (<xref ref-type="fig" rid="f6"><bold>Figure&#xa0;6b</bold></xref>). On land, major corridors extended northwestward, from the Daqing River to the Daliao River, and from the Beiputuo Mountain in Jinzhou eastward to Nanshan and to the Daling River, facilitating terrestrial species migration. At sea, corridors extended southwestward, from the coastal waters near the Liao River estuary to the coastal waters off western Liaoning, connecting the Daling River estuary, the Shuangtai River estuary, the Shihe River estuary, and the Xingcheng Taili Marine Nature Reserve and Conservation Area. These corridors provided flood discharge pathways for the Liao River estuary (the Red Beach Wetland) and supported the migration of fish species, such as so-iuy mullet, Chinese tapertail anchovy, and flathead grey mullet.</p>
<p>At the Yellow River estuary, coastal ecological corridors consisted of terrestrial and marine components (<xref ref-type="fig" rid="f6"><bold>Figure&#xa0;6b</bold></xref>). On land, extensive Chinese tamarisk forests, coastal wetlands, and tidal flats formed one of the most complete warm-temperate wetland ecosystems, extending southward from the old Yellow River course through the Yellow River Delta National Geopark to the estuary. At sea, the corridors linked the National Aquatic Germplasm Resources Conservation Zone in the Taoer River estuary waters of Binzhou with the waters of Dongying&#x2019;s Kenli and Dongying Districts, forming a continuous coastal corridor. Along the northern Yellow Sea coast (<xref ref-type="fig" rid="f6"><bold>Figure&#xa0;6b</bold></xref>), the ecological corridors extended from the Yalu River estuary to Dalu Island and further to Changhai County, consisting mainly of coastal islands that connected the major river estuaries of the northern Yellow Sea. With gentle shorelines and rich species diversity, this area served as an important stopover site for migratory waterbirds and held significant ecological potential. Nevertheless, ecological corridors at the land-sea interface traversed areas of intense human activity, where species migration faced greater resistance, with a high risk of fragmentation.</p>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<sec id="s4_1">
<label>4.1</label>
<title>Methods for extracting ecological sources</title>
<p>Building on the&#x201d;source identification&#x2013;resistance surface construction&#x2013;corridor extraction &#x201c;paradigm, this study systematically optimized the technical framework for coastal ecological network construction through targeted innovations designed to address key limitations in existing research. For ecological source identification, multiple influencing factors&#x2014;including land-use type, patch size, habitat quality, landscape connectivity, and spatial distribution&#x2014;were integrated, with terrestrial ecological redline data incorporated to delineate terrestrial ecological sources. Drawing on <xref ref-type="bibr" rid="B13">Ke et&#xa0;al. (2024)</xref> and <xref ref-type="bibr" rid="B2">Chen et&#xa0;al. (2023)</xref>, the study further strengthened the identification of marine ecological sources&#x2014;a significantly underrepresented aspect in prior studies&#x2014;by integrating marine functional zoning and marine ecological redline data to establish a marine ecological sensitivity evaluation system. Marine ecological sources were defined as areas characterized by rich species habitats, critical importance for ecological security and sustainable development, and high marine ecological sensitivity. This approach aligns with established principles of ecological source selection, ensuring scientific rigor, rationality, and objectivity in source delineation.</p>
<p>For resistance surface construction, natural and anthropogenic factors were jointly integrated to develop a land&#x2013;sea unified resistance surface. Additionally, small patches exhibiting high habitat quality and strategic locations were included as supplementary ecological sources to enhance the completeness and robustness of the network structure. Beyond these methodological refinements, the study achieved a fundamental shift from a &#x201c;land-centric&#x201d; to a &#x201c;land&#x2013;sea balanced integration&#x201d; approach in both the scope and composition of ecological sources. While earlier studies (<xref ref-type="bibr" rid="B12">Jin et&#xa0;al., 2021</xref>) primarily designated national or provincial nature reserves and large forest patches in mountainous and hilly regions such as the Yanshan and Taihang ranges as core sources, this study systematically identifies marine ecological redline zones, key estuarine wetlands, and special protected islands (the Dalian Spotted Seal Reserve) as marine ecological sources for the first time, which account for 27.2% of the total ecological source area (<xref ref-type="fig" rid="f5"><bold>Figure&#xa0;5</bold></xref>). These marine sources not only serve as essential habitats for marine biodiversity but also function as critical hubs linking terrestrial ecological processes with nearshore marine ecosystems, thereby addressing the long-standing oversight in coastal ecological planning where marine spaces were often treated as passive receptors or background elements.</p>
<p>Furthermore, regarding the construction logic and spatial configuration of ecological corridors, this study advances from a &#x201c;single optimal path&#x201d; to a &#x201c;multi-path probabilistic network.&#x201d; Traditional approaches based on the Minimum Cumulative Resistance (MCR) model (<xref ref-type="bibr" rid="B40">Yan et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B5">Gong et&#xa0;al., 2024</xref>) typically generate a single least-cost corridor, which in the highly urbanized plains and coastal zones of the Bohai Rim is vulnerable to fragmentation due to built-up expansion and other anthropogenic pressures. By applying circuit theory&#x2014;widely recognized for its ability to reflect realistic species movement patterns&#x2014;and using the Linkage Mapper tool, this study models multiple probable dispersal pathways for species (<xref ref-type="fig" rid="f6"><bold>Figure&#xa0;6</bold></xref>). Comparative analysis reveals that in key human&#x2013;ecological conflict zones such as the Liaohe Plain and the Yellow River Delta, MCR-derived corridors often appear as narrow, linear features confined to residual natural vegetation. In contrast, the corridors identified in this study exhibit more complex, network-like, and parallel structures, uncovering previously overlooked &#x201c;stepping stone&#x201d; patches and alternative routes. This multi-path configuration enhances the resilience and redundancy of the ecological network, improves its stability under local disturbances, and better accommodates the fragmented landscape dynamics characteristic of the Bohai Rim region.</p>
</sec>
<sec id="s4_2">
<label>4.2</label>
<title>Land-sea coordination from an ecological network perspective</title>
<p>Coastal zones, as integrated spatial-geographical systems comprising the two core components of land and sea, host diverse natural resources, complex ecosystems, and multifunctional spatial uses. They serve as vital areas for both ecological conservation and socioeconomic development. However, accelerated coastal urbanization has led to increasingly severe ecological challenges, including marine environmental pollution, degradation of ecological functions, resource scarcity, and intensified fragmentation of ecological patches. These issues are particularly acute at the estuaries of major rivers such as the Liaohe, Haihe, and Yellow Rivers, where intense human activities intersect with critical ecological networks. Compounded by the persistent mindset of &#x201c;valuing land over sea&#x201d; and the entrenched model of &#x201c;separate land-sea governance,&#x201d; current management practices and research often focus on optimizing isolated elements. This approach fails to achieve the holistic integration of terrestrial ecological spaces and marine protected areas within unified planning concepts and technical standards.</p>
<p>The constructed Bohai Rim Coastal Ecological Network&#x2014;featuring integrated land&#x2013;sea ecological sources (terrestrial: 66,441.81 km<sup>2</sup>; marine: 24,814.31 km<sup>2</sup>), a hierarchical corridor system, and key interaction nodes&#x2014;provides actionable scientific support for integrated land&#x2013;sea governance. Its core contribution lies in bridging the traditional &#x201c;land&#x2013;sea divide&#x201d; in ecological network research by establishing functional connectivity between fragmented terrestrial and marine habitats. This includes ecologically critical areas such as the Taihang&#x2013;Yanshan mountain ranges, the Dalian Spotted Seal Reserve, and the Yellow River Estuary. By incorporating both natural and anthropogenic resistance factors into a unified land&#x2013;sea resistance surface and applying circuit theory to delineate multi-path corridors, the network realistically captures species dispersal dynamics. It directly addresses the region&#x2019;s most pressing ecological challenges, notably habitat fragmentation driven by intensive human activities&#x2014;exemplified by coastal urbanization rates exceeding 70% and nearshore pollutant loads approaching twice the national average. This integrated structural and methodological framework ensures the network&#x2019;s scientific rigor and enhances its operational relevance for biodiversity conservation and ecosystem management.</p>
<p>Beyond its structural innovation, the network delivers specific value for ecological protection and coordinated spatial planning. The identified ecological sources and corridors encompass approximately 90% of the key habitats for endangered species in the Bohai Rim, offering clearly defined spatial guidance for upgrading the regional protected area system. Core terrestrial patches in high-value mountainous and wetland regions&#x2014;such as the Shuangtaizi River Estuary Reserve, which serves dual terrestrial and marine ecological functions&#x2014;can be designated as &#x201c;ecological protection redline core areas.&#x201d; Within these zones, strict prohibitions on reclamation, industrial development, and other habitat-disrupting activities are essential to maintain ecological integrity. Marine ecological sources provide a strategic basis for expanding marine protected area coverage and formulating species-specific conservation plans, including the protection of spawning grounds for anadromous fish such as the so-iuy mullet. In spatial planning, the network&#x2019;s &#x201c;C-shaped&#x201d; coastal corridor pattern and land&#x2013;sea integrated nodes (the Yellow River Estuary and Jinzhou Linghai) offer practical guidance for siting coastal development projects (e.g., ports and industrial zones) away from ecologically sensitive corridors. This spatial optimization helps preserve the continuity of material and energy flows between land and sea, thereby aligning with the objectives of the Coastal Zone Protection and Utilization Plan (2021&#x2013;2035) and improving the feasibility of land&#x2013;sea coordinated governance.</p>
<p>Furthermore, the network strengthens the implementation of land&#x2013;sea coordinated governance policies and provides precise, evidence-based guidance for ecological restoration. From a policy perspective, its outputs supply measurable indicators that respond directly to national strategic priorities for integrated coastal management. For instance, the finding that marine ecological sources constitute only 27.2% of the total source area highlights the systemic underrepresentation and inadequate protection of marine ecosystems within the current governance framework. This offers empirical justification for revising the Bohai Rim Ecological Environment Protection Plan to elevate the priority of marine habitat conservation. The hierarchical classification of corridors&#x2014;based on gravity strength (&#x2265;800 for important corridors; 0&#x2013;800 for general corridors)&#x2014;enables differentiated governance: high-priority corridors require mandatory legal protection, while general corridors can be managed through incentive-based ecological compensation mechanisms that encourage restoration actions by local governments and enterprises. Moreover, the network pinpoints human disturbance hotspots, such as the coastal zones of Bohai Bay and Liaodong Bay where corridor fragmentation is severe. This enables targeted interventions, including the regulation of aquaculture practices and promotion of ecological relocation for industrial facilities, to reduce resistance to ecological flows and improve landscape connectivity. For ecological restoration, the network clarifies intervention priorities to enhance investment efficiency. Fragmented terrestrial corridors in the Taihang&#x2013;North China Plain can be rehabilitated using &#x201c;stepping stone&#x201d; tactics, such as restoring small wetland patches and forest belts, to improve functional connectivity. Meanwhile, weakly connected marine corridors (within Liaodong Bay&#x2019;s aquaculture zones) require focused measures like aquaculture zoning, water quality remediation, and tidal flat habitat conservation. Integrated restoration projects&#x2014;such as the Yellow River Estuary wetland initiative, which links terrestrial Chinese tamarisk forests with offshore germplasm resource conservation zones&#x2014;can maximize the cost-effectiveness of restoration investments and accelerate the recovery of integrated land&#x2013;sea ecological functions, thereby supporting the long-term sustainable development of the Bohai Rim coastal region.</p>
</sec>
<sec id="s4_3">
<label>4.3</label>
<title>Limitations of the study</title>
<p>This study offers a feasible reference framework for constructing an ecological network in the Bohai Rim. Nevertheless, limited data availability and gaps in management systems prevented a comprehensive assessment of the region&#x2019;s overall land-sea environment, internal ecological conditions, or the ecological networks of biological communities and species at finer spatial scales. Future research should take into account geographical variations across the study area, differences among ecological communities, and the socioeconomic conditions and ecological needs of terrestrial areas. This would help identify the factors influencing the complexity of ecological networks across linkages and interspecific relationships, select resistance factors appropriate to different communities and species, assign resistance values with greater scientific rigor, and build ecological networks at multiple scales. This, in turn, would enable detailed classification and targeted analysis, improving the feasibility and practical applicability of coastal ecological networks.</p>
<p>In assessing the importance of ecological patches, this study considered a limited set of factors, namely, morphology, area, positional importance, and overall habitat quality, leaving the evaluation incomplete. The choice of area thresholds involved a degree of subjectivity (<xref ref-type="bibr" rid="B9">Hou et&#xa0;al., 2022</xref>). Additionally, the resistance values assigned to ecological sources were composite values, which may not fully reflect the movement characteristics of particular species populations (e.g., amphibians), introducing potential errors into the simulation results. While ecological corridors were extracted using the circuit theory, their widths require careful consideration. Future research should account for species composition, traits, distribution, and actual conservation needs in the study area, enabling detailed classification and targeted analysis. Field surveys and observation data should be integrated to support parameter settings, and strategies for determining corridor width warrant a comprehensive study.</p>
</sec>
</sec>
<sec id="s5" sec-type="conclusions">
<label>5</label>
<title>Conclusions</title>
<p>Using the Bohai Rim coastal zone as a case study, this research employed ecological redline data with MSPA, landscape connectivity evaluation, comprehensive habitat quality assessment, ecological sensitivity analysis, and the circuit theory to construct an ecological network. It proposed an integrated approach to building coastal ecological networks that addressed the inherent tensions between land and sea in coastal zones, balancing the developmental characteristics of terrestrial economies with the ecological protection requirements of marine environments, and maintaining the integrity of coastal ecosystems.</p>
<p>The results showed that the ecological network of the Bohai Rim was composed primarily of ecological sources and corridors associated primarily with forests, grasslands, water bodies, and marine protected areas. Terrestrial ecological sources covered 66,441.81 km<sup>2</sup>, while marine sources covered 24,814.31 km<sup>2</sup>. Ecological corridors were mainly concentrated in the Liaoxi Corridor, the Taihang Mountains&#x2013;North China Plain, and along the lower reaches of the Yellow River. In the coastal zone, ecological corridors ran parallel to the coastline and were concentrated around the Liao, Yellow, and Yalu River estuaries and the northern Yellow Sea coast, where they performed vital ecological functions and formed dense intersections in the Liaoxi Corridor. These findings provided a scientific basis for establishing ecological reserves in the study area and offered new perspectives for biodiversity conservation in similar coastal and bay areas.</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/supplementary material. Further inquiries can be directed to the corresponding author/s.</p></sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>SY: Writing &#x2013; review &amp; editing, Methodology, Writing &#x2013; original draft, Software, Formal Analysis. HH: Validation, Visualization, Writing &#x2013; original draft.</p></sec>
<ack>
<title>Acknowledgments</title>
<p>We would like to thank Editage (<ext-link ext-link-type="uri" xlink:href="http://www.editage.cn">www.editage.cn</ext-link>) for English language editing.</p>
</ack>
<sec id="s9" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The author(s) declared that this work 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="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declared that generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p></sec>
<sec id="s11" 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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<title>Glossary</title><def-list><def-item><term>MSPA (Morphological Spatial Pattern Analysis)</term><def>
<p>A method used to identify and classify the spatial patterns of landscape elements (such as forests, wetlands, etc.) based on image processing principles. It helps delineate core areas, bridges, loops, and other structural components in ecological networks</p></def></def-item><def-item><term>Ecological Source Areas</term><def>
<p>Regions characterized by high ecosystem integrity and biodiversity, which serve as critical starting points for species dispersal and ecological processes. These areas are essential for maintaining regional ecological security</p></def></def-item><def-item><term>Landscape Resistance Factors</term><def>
<p>Environmental variables (e.g., land use type, topography, human infrastructure) that impede species movement and ecological flows across a landscape. Resistance values are assigned to reflect the difficulty of traversal, influencing connectivity modeling</p></def></def-item><def-item><term>Ecological Network</term><def>
<p>An interconnected system of ecological components (e.g., core habitats, corridors, and nodes) designed to maintain or restore ecological connectivity. It facilitates species migration, gene flow, and ecosystem stability within fragmented landscapes</p></def></def-item></def-list></glossary>
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<fn id="n1" fn-type="custom" custom-type="edited-by">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1555923">Jinman Wang</ext-link>, China University of Geosciences, China</p></fn>
<fn id="n2" fn-type="custom" custom-type="reviewed-by">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3329173">Biao Liu</ext-link>, North University of China, China</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3329435">Zhaorui Jing</ext-link>, Shandong Agricultural University, China</p></fn>
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