Understanding spatiotemporal changes and influencing factors in the habitat quality of coastal waters: A case study of Jiangsu Province, China (2006–2020)
Habitat quality of coastal waters is under threat from frequent human activities (HA) and pressures from various sources. However, there were few effective and rational explorations of spatiotemporal changes and influencing factors in coastal waters habitat quality. By integrating coastal waters eco...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25000548 |
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| author | Zhou Chen Yanjing Chen Haifeng Zhang Hong Zhang Min Xu |
| author_facet | Zhou Chen Yanjing Chen Haifeng Zhang Hong Zhang Min Xu |
| author_sort | Zhou Chen |
| collection | DOAJ |
| description | Habitat quality of coastal waters is under threat from frequent human activities (HA) and pressures from various sources. However, there were few effective and rational explorations of spatiotemporal changes and influencing factors in coastal waters habitat quality. By integrating coastal waters ecology, environment, and HA, an optimized InVEST model was used to assess coastal waters habitat quality which incorporated the benthic biodiversity index. The optimized model was verified through both practical and theoretical assessments. Geographic spatial analysis, Hierarchical Partitioning (HP), and GeoDetector methods were used to explore spatiotemporal changes and influencing factors of habitat quality in the coastal waters of Jiangsu Province over three periods, namely 2006–2010, 2011–2015, and 2016–2020. During the 15-year period, habitat quality declined in 67.63% of Jiangsu Province’s coastal waters. The Plankton Diversity Index (PDI) had the strongest positive impact on coastal waters habitat quality between 2006 and 2010, whereas environmental pollution-related factors, and HA had the least impact. In contrast, the most significant decline in habitat quality between 2011 and 2015 coincided with intense human development and associated environmental challenges. The effects of HA and environmental pollution factors declined, while those of PDI increased between 2016 and 2020. However, potential effects of accumulated environmental pollution were observed, leading to uniform changes in habitat quality. The results indicated that the habitat quality of coastal waters was comprehensively influenced by a combination of factors, including ecological, environmental, and HA. Single factors and multiple factors affected habitat quality in 40.76% and 46.5% of the study area, respectively. This study presents an innovative and scientifically robust method for assessing habitat quality in coastal waters, providing decision-making support for integrated coastal management and sustainable marine development. |
| format | Article |
| id | doaj-art-02c8bbb9d6c04a8f8627aa66e357c084 |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-02c8bbb9d6c04a8f8627aa66e357c0842025-01-31T05:10:55ZengElsevierEcological Indicators1470-160X2025-01-01170113125Understanding spatiotemporal changes and influencing factors in the habitat quality of coastal waters: A case study of Jiangsu Province, China (2006–2020)Zhou Chen0Yanjing Chen1Haifeng Zhang2Hong Zhang3Min Xu4Nanjing Normal University, School of Marine Science and Engineering, Nanjing, 210023, China; Jiangsu Provincial Engineering Centre for Integrated Marine Development and Ecological Construction, Nanjing, 210023, ChinaNanjing Normal University, School of Marine Science and Engineering, Nanjing, 210023, China; Jiangsu Provincial Engineering Centre for Integrated Marine Development and Ecological Construction, Nanjing, 210023, ChinaIsland Research Center, Ministry of Natural Resources of the People's Republic of China, Fuzhou, 350400, ChinaNanjing Normal University, School of Marine Science and Engineering, Nanjing, 210023, China; Jiangsu Provincial Engineering Centre for Integrated Marine Development and Ecological Construction, Nanjing, 210023, ChinaNanjing Normal University, School of Marine Science and Engineering, Nanjing, 210023, China; Jiangsu Provincial Engineering Centre for Integrated Marine Development and Ecological Construction, Nanjing, 210023, China; Corresponding author.Habitat quality of coastal waters is under threat from frequent human activities (HA) and pressures from various sources. However, there were few effective and rational explorations of spatiotemporal changes and influencing factors in coastal waters habitat quality. By integrating coastal waters ecology, environment, and HA, an optimized InVEST model was used to assess coastal waters habitat quality which incorporated the benthic biodiversity index. The optimized model was verified through both practical and theoretical assessments. Geographic spatial analysis, Hierarchical Partitioning (HP), and GeoDetector methods were used to explore spatiotemporal changes and influencing factors of habitat quality in the coastal waters of Jiangsu Province over three periods, namely 2006–2010, 2011–2015, and 2016–2020. During the 15-year period, habitat quality declined in 67.63% of Jiangsu Province’s coastal waters. The Plankton Diversity Index (PDI) had the strongest positive impact on coastal waters habitat quality between 2006 and 2010, whereas environmental pollution-related factors, and HA had the least impact. In contrast, the most significant decline in habitat quality between 2011 and 2015 coincided with intense human development and associated environmental challenges. The effects of HA and environmental pollution factors declined, while those of PDI increased between 2016 and 2020. However, potential effects of accumulated environmental pollution were observed, leading to uniform changes in habitat quality. The results indicated that the habitat quality of coastal waters was comprehensively influenced by a combination of factors, including ecological, environmental, and HA. Single factors and multiple factors affected habitat quality in 40.76% and 46.5% of the study area, respectively. This study presents an innovative and scientifically robust method for assessing habitat quality in coastal waters, providing decision-making support for integrated coastal management and sustainable marine development.http://www.sciencedirect.com/science/article/pii/S1470160X25000548Optimized InVEST ModelBenthic BiodiversityHierarchical PartitioningSpatiotemporal ChangesInfluencing Factors |
| spellingShingle | Zhou Chen Yanjing Chen Haifeng Zhang Hong Zhang Min Xu Understanding spatiotemporal changes and influencing factors in the habitat quality of coastal waters: A case study of Jiangsu Province, China (2006–2020) Ecological Indicators Optimized InVEST Model Benthic Biodiversity Hierarchical Partitioning Spatiotemporal Changes Influencing Factors |
| title | Understanding spatiotemporal changes and influencing factors in the habitat quality of coastal waters: A case study of Jiangsu Province, China (2006–2020) |
| title_full | Understanding spatiotemporal changes and influencing factors in the habitat quality of coastal waters: A case study of Jiangsu Province, China (2006–2020) |
| title_fullStr | Understanding spatiotemporal changes and influencing factors in the habitat quality of coastal waters: A case study of Jiangsu Province, China (2006–2020) |
| title_full_unstemmed | Understanding spatiotemporal changes and influencing factors in the habitat quality of coastal waters: A case study of Jiangsu Province, China (2006–2020) |
| title_short | Understanding spatiotemporal changes and influencing factors in the habitat quality of coastal waters: A case study of Jiangsu Province, China (2006–2020) |
| title_sort | understanding spatiotemporal changes and influencing factors in the habitat quality of coastal waters a case study of jiangsu province china 2006 2020 |
| topic | Optimized InVEST Model Benthic Biodiversity Hierarchical Partitioning Spatiotemporal Changes Influencing Factors |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25000548 |
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