Predicting the spatial and temporal variations in habitat characteristics of <i>Harpadon nehereus</i> in the Changjiang River Estuary based on ensemble modeling

GUO Tingwen; WANG Lin; GAO Chunxia; WANG Xuefang; WU Jianhui

doi:10.12284/hyxb2025086

Article Contents

Article Navigation > Haiyang Xuebao > 2025 >

GUO Tingwen，WANG Lin，GAO Chunxia, et al. Predicting the spatial and temporal variations in habitat characteristics of Harpadon nehereus in the Changjiang River Estuary based on ensemble modeling[J]. Haiyang Xuebao，2025, 47(7)：1–12 doi: 10.12284/hyxb2025086

Citation:

GUO Tingwen，WANG Lin，GAO Chunxia, et al. Predicting the spatial and temporal variations in habitat characteristics of Harpadon nehereus in the Changjiang River Estuary based on ensemble modeling[J]. Haiyang Xuebao，2025, 47(7)：1–12 doi: 10.12284/hyxb2025086

Citation:

PDF( 2318 KB)

Predicting the spatial and temporal variations in habitat characteristics of Harpadon nehereus in the Changjiang River Estuary based on ensemble modeling

doi: 10.12284/hyxb2025086

GUO Tingwen^1
,,
WANG Lin^{2, 3},
GAO Chunxia^{1, 3},
WANG Xuefang^{1, 3
,
,},
WU Jianhui^{2, 3
,
,}

1.
College of Marine Living Resources and Management, Shanghai Ocean University, Shanghai 201306, China
2.
Shanghai Aquatic Wildlife Conservation Research Center, Shanghai 200092, China
3.
Joint Laboratory for Monitoring and Conservation of Aquatic Living Resources in the Yangtze Estuary, Shanghai 201306, China

Received Date: 2024-11-16
Rev Recd Date: 2025-04-01

Available Online: 2025-05-08

Abstract

Abstract

To evaluate whether the “ensemble approach” can enhance the predictive performance of Species Distribution Models (SDMs) in dynamically changing estuarine environments, this study utilized eight single models based on different algorithms to construct an Ensemble Model (EM) for the habitat of Harpadon nehereus, a dominant species in the Changjiang River Estuary. The data used for modeling were derived from marine biological resource surveys conducted in the Changjiang River Estuary from 2013 to 2021. The results showed that: (1) All single models outperformed the random distribution model, with the EM demonstrating the highest predictive accuracy and robustness (Area Under receiver operating character Curve, AUC = 0.875; True skill statistic, TSS = 0.650; KAPPA = 0.560; Overall accuracy, OA = 0.867). (2) The EM accurately identified both presence and absence stations of H. nehereus, clearly differentiated suitability levels in unsampled regions, and predicted areas of high suitability shared by different models. (3) Finally, the EM accurately identified the key environmental requirements of H. nehereus and reflected the central tendency across multiple models. The most suitable habitat for H. nehereus was found in waters with salinity, temperature, and chemical oxygen demand ranges of 2.754−30.300, 28.278−30.934℃, and 4.605−8.080 mg/L, respectively. This study provides a more reliable research method for the sustainable utilization and habitat protection of H. nehereus resources in the Changjiang River Estuary.
- Changjiang River Estuary,
- Harpadon nehereus,
- habitat suitability,
- key environmental requirements,
- species distribution models,
- ensemble modeling

FullText(HTML)

References(50)

References

[1]	潘邵媛, 王学昉, 田思泉, 等. 长江口中华鲟保护区海洋环境监测浮标站点的优化设计[J]. 海洋学报, 2021, 43(4): 55−64. Pan Shaoyuan, Wang Xuefang, Tian Siquan, et al. The design of the stations of marine environmental monitoring buoys in the Chinese sturgeon nature reserve in the Changjiang River Estuary[J]. Haiyang Xuebao, 2021, 43(4): 55−64.
[2]	杨钧渊, 陈锦辉, 钟俊生, 等. 长江口崇明东滩水域仔稚鱼种类组成及多样性[J]. 上海海洋大学学报, 2023, 32(4): 829−840. doi: 10.12024/jsou.20230204101 Yang Junyuan, Chen Jinhui, Zhong Junsheng, et al. Species composition and diversity of fish larvae and juveniles in the water area of Chongming Dongtan, Yangtze River Estuary[J]. Journal of Shanghai Ocean University, 2023, 32(4): 829−840. doi: 10.12024/jsou.20230204101
[3]	潘绪伟, 程家骅. 长江口外海域龙头鱼营养生态学特征[J]. 中国水产科学, 2011, 18(5): 1132−1140. Pan Xuwei, Cheng Jiahua. Feeding ecology of Harpadon nehereus in areas adjacent to Changjiang River estuary[J]. Journal of Fishery Sciences of China, 2011, 18(5): 1132−1140.
[4]	Li Yu, Gao Chunxia, Chen Jinhui, et al. Spatial–temporal distribution characteristics of Harpadon nehereus in the Yangtze River Estuary and its relationship with environmental factors[J]. Frontiers in Marine Science, 2024, 11: 1340522. doi: 10.3389/fmars.2024.1340522
[5]	唐未, 王学昉, 吴峰, 等. 基于最大熵模型模拟西印度洋剑鱼栖息地的时空分布[J]. 海洋学报, 2022, 44(10): 100−108. doi: 10.12284/j.issn.0253-4193.2022.10.hyxb202210009 Tang Wei, Wang Xuefang, Wu Feng, et al. Simulation of spatio-temporal distribution of swordfish habitat in the western Indian Ocean based on maximum entropy model[J]. Haiyang Xuebao, 2022, 44(10): 100−108. doi: 10.12284/j.issn.0253-4193.2022.10.hyxb202210009
[6]	Le Pape O, Baulier L, Cloarec A, et al. Habitat suitability for juvenile common sole (Solea solea, L. ) in the Bay of Biscay (France): A quantitative description using indicators based on epibenthic fauna[J]. Journal of Sea Research, 2007, 57(2/3): 126−136.
[7]	França S, Cabral H N. Predicting fish species distribution in estuaries: Influence of species’ ecology in model accuracy[J]. Estuarine, Coastal and Shelf Science, 2016, 180: 11−20. doi: 10.1016/j.ecss.2016.06.010
[8]	Austin M. Species distribution models and ecological theory: A critical assessment and some possible new approaches[J]. Ecological Modelling, 2007, 200(1/2): 1−19.
[9]	Jiang Rijin, Sun Haoqi, Li Xiafang, et al. Habitat suitability evaluation of Harpadon nehereus in nearshore of Zhejiang province, China[J]. Frontiers in Marine Science, 2022, 9: 961735. doi: 10.3389/fmars.2022.961735
[10]	Mohammadi A, Almasieh K, Nayeri D, et al. Comparison of habitat suitability and connectivity modelling for three carnivores of conservation concern in an Iranian montane landscape[J]. Landscape Ecology, 2022, 37(2): 411−430. doi: 10.1007/s10980-021-01386-5
[11]	Poulos H M, Chernoff B, Fuller P L, et al. Ensemble forecasting of potential habitat for three invasive fishes[J]. Aquatic Invasions, 2012, 7(1): 59−72. doi: 10.3391/ai.2012.7.1.007
[12]	王寇, 李博, 李爱国, 等. 夏季长江口及其邻近海域湍流特征分析[J]. 海洋学报, 2021, 43(11): 22−31. Wang Kou, Li Bo, Li Aiguo, et al. Characteristics of turbulence in the Changjiang River Estuary and its adjacent waters in summer[J]. Haiyang Xuebao, 2021, 43(11): 22−31.
[13]	史赟荣, 晁敏, 沈新强. 长江口张网鱼类群落结构特征及月相变化[J]. 海洋学报, 2014, 36(2): 81−92. doi: 10.3969/j.issn.0253-4193.2014.02.009 Shi Yunrong, Chao Min, Shen Xinqiang. Characteristics and monthly variations of set net fish community structure in the Changjiang River estuary[J]. Haiyang Xuebao, 2014, 36(2): 81−92. doi: 10.3969/j.issn.0253-4193.2014.02.009
[14]	吴晓丹, 宋金明, 李学刚. 长江口邻近海域水团特征与影响范围的季节变化[J]. 海洋科学, 2014, 38(12): 110−119. doi: 10.11759/hykx20140305001 Wu Xiaodan, Song Jinming, Li Xuegang. Ssonal variation of water mass characteristic and influence area in the Yangtze Estuary and its adjacent waters[J]. Marine Sciences, 2014, 38(12): 110−119. doi: 10.11759/hykx20140305001
[15]	周晓英. 长江口海域表层水温变化的气候特征[D]. 青岛: 中国海洋大学, 2005. Zhou Xiaoying. Climate characteristics of sea surface temperature (SST) variation in the Changjiang Estuary[D]. Qingdao: Ocean University of China, 2005.
[16]	何柄震. 长江口海域营养物质时空演变趋势及其对入海通量的响应研究[D]. 沈阳: 沈阳大学, 2024. He Bingzhen. The spatiotemporal evolution trend of nutrients in the Yangtze River Estuary and its response to the fluxes into the sea[D]. Shenyang: Shenyang University, 2024.
[17]	朱宇新, 周斌, 赵俊杰. 盐水入侵对长江口盐度分布及影响研究[J]. 环境保护前沿, 2014, 4(1): 25−33. doi: 10.12677/AEP.2014.41B005 Zhu Yuxin, Zhou Bin, Zhao Junjie. Research on the salinity distribution and the influence of saltwater intrusion in Yangtze River[J]. Advances in Environmental Protection, 2014, 4(1): 25−33. doi: 10.12677/AEP.2014.41B005
[18]	马金, 黄金玲, 陈锦辉, 等. 基于GAM的长江口鱼类资源时空分布及影响因素[J]. 水产学报, 2020, 44(6): 958−968. Ma Jin, Huang Jinling, Chen Jinhui, et al. Analysis of spatiotemporal fish density distribution and its influential factors based on generalized additive model (GAM) in the Yangtze River estuary[J]. Journal of Fisheries of China, 2020, 44(6): 958−968.
[19]	Wang Yichuan, Wu Xinghua, Zheng Leifu, et al. Modeling seasonal changes in the habitat suitability of Coilia nasus in the Yangtze River Estuary using tree-based methods[J]. Regional Studies in Marine Science, 2023, 67: 103212. doi: 10.1016/j.rsma.2023.103212
[20]	França S, Costa M J, Cabral H N. Inter- and intra-estuarine fish assemblage variability patterns along the Portuguese coast[J]. Estuarine, Coastal and Shelf Science, 2011, 91(2): 262−271. doi: 10.1016/j.ecss.2010.10.035
[21]	中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会. GB/T 12763.6−2007, 海洋调查规范第6部分: 海洋生物调查[S]. 北京: 中国标准出版社, 2008: 8. General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, Standardization Administration of the People’s Republic of China. GB/T 12763.6−2007, Specifications for oceanographic survey-Part 6: Marine biological survey[S]. Beijing: Standards Press of China, 2008: 8.
[22]	国家环境保护局. GB 11607−89, 渔业水质标准[S]. 北京: 中国标准出版社, 1990: 8. Environmental Protection Agency. GB 11607−89, Water quality standard for fisheries[S]. Beijing: Standards Press of China, 1990: 8.
[23]	环境保护部. HJ 828—2017, 水质化学需氧量的测定重铬酸盐法[S]. 北京: 中国环境出版社, 2017: 3. Ministry of Environmental Protection. HJ 828—2017, Water quality-Determination of the chemical oxygen demand-Dichromate method[S]. Beijing: China Environmental Publishing House, 2017: 3.
[24]	Bacheler N M, Paramore L M, Buckel J A, et al. Abiotic and biotic factors influence the habitat use of an estuarine fish[J]. Marine Ecology Progress Series, 2009, 377: 263−277. doi: 10.3354/meps07805
[25]	Luan Jing, Xu Binduo, Ji Yupeng, et al. Improving the spatial transferability of species distribution models to inform biological conservation of two piscivore fish species[J]. Biodiversity and Conservation, 2024, 33(14): 4215−4235. doi: 10.1007/s10531-024-02947-1
[26]	徐超, 王思凯, 赵峰, 等. 长江口水生动物食物网营养结构及其变化[J]. 水生生物学报, 2019, 43(1): 155−164. doi: 10.7541/2019.019 Xu Chao, Wang Sikai, Zhao Feng, et al. Trophic structure of food web and its variation on aquatic animals in the Yangtze Estuary[J]. Acta Hydrobiologica Sinica, 2019, 43(1): 155−164. doi: 10.7541/2019.019
[27]	Yalçın E, Gurbet R. Environmental influences on the spatio-temporal distribution of European Hake (Merluccius merluccius) in Izmir Bay, Aegean Sea[J]. Turkish Journal of Fisheries and Aquatic Sciences, 2016, 16(1): 1−14.
[28]	Dormann C F, Elith J, Bacher S, et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance[J]. Ecography, 2013, 36(1): 27−46. doi: 10.1111/j.1600-0587.2012.07348.x
[29]	Shrestha U B, Sharma K P, Devkota A, et al. Potential impact of climate change on the distribution of six invasive alien plants in Nepal[J]. Ecological Indicators, 2018, 95: 99−107. doi: 10.1016/j.ecolind.2018.07.009
[30]	Elith J, Graham C H, Anderson R P, et al. Novel methods improve prediction of species’ distributions from occurrence data[J]. Ecography, 2006, 29(2): 129−151. doi: 10.1111/j.2006.0906-7590.04596.x
[31]	Fielding A H, Bell J F. A review of methods for the assessment of prediction errors in conservation presence/absence models[J]. Environmental Conservation, 1997, 24(1): 38−49. doi: 10.1017/S0376892997000088
[32]	Liu Canran, White M, Newell G. Measuring and comparing the accuracy of species distribution models with presence-absence data[J]. Ecography, 2011, 34(2): 232−243. doi: 10.1111/j.1600-0587.2010.06354.x
[33]	Hao Tianxiao, Elith J, Guillera-Arroita G, et al. A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD[J]. Diversity and Distributions, 2019, 25(5): 839−852. doi: 10.1111/ddi.12892
[34]	Marmion M, Parviainen M, Luoto M, et al. Evaluation of consensus methods in predictive species distribution modelling[J]. Diversity and Distributions, 2009, 15(1): 59−69. doi: 10.1111/j.1472-4642.2008.00491.x
[35]	Thuiller W, Georges D, Engler R. Biomod2: Ensemble platform for species distribution modelling[Z]. 2014. (查阅网上资料, 未找到本条文献出版信息, 请确认)
[36]	Liu Zunlei, Jin Yan, Yang Linlin, et al. Improving prediction for potential spawning areas from a two-step perspective: A comparison of multi-model approaches for sparse egg distribution[J]. Journal of Sea Research, 2024, 197: 102460. doi: 10.1016/j.seares.2023.102460
[37]	Phillips N D, Reid N, Thys T, et al. Applying species distribution modelling to a data poor, pelagic fish complex: The ocean sunfishes[J]. Journal of Biogeography, 2017, 44(10): 2176−2187. doi: 10.1111/jbi.13033
[38]	França S, Cabral H N. Predicting fish species richness in estuaries: Which modelling technique to use?[J]. Environmental Modelling & Software, 2015, 66: 17−26.
[39]	Dreiseitl S, Ohno-Machado L. Logistic regression and artificial neural network classification models: a methodology review[J]. Journal of Biomedical Informatics, 2002, 35(5/6): 352−359.
[40]	Li Zengguang, Ye Zhenjiang, Wan Rong, et al. Model selection between traditional and popular methods for standardizing catch rates of target species: A case study of Japanese Spanish mackerel in the gillnet fishery[J]. Fisheries Research, 2015, 161: 312−319. doi: 10.1016/j.fishres.2014.08.021
[41]	刘尊雷, 杨林林, 袁兴伟, 等. 基于集成模型的小黄鱼越冬群体适宜生境及其环境影响因素[J]. 应用生态学报, 2020, 31(6): 2076−2086. Liu Zunlei, Yang Linlin, Yuan Xingwei, et al. Overwintering distribution and its environmental determinants of small yellow croaker based on ensemble habitat suitability modeling[J]. Chinese Journal of Applied Ecology, 2020, 31(6): 2076−2086.
[42]	Povak N A, Hessburg P F, Reynolds K M, et al. Machine learning and hurdle models for improving regional predictions of stream water acid neutralizing capacity[J]. Water Resources Research, 2013, 49(6): 3531−3546. doi: 10.1002/wrcr.20308
[43]	Dormann C F, Purschke O, Márquez J R G, et al. Components of uncertainty in species distribution analysis: a case study of the Great Grey Shrike[J]. Ecology, 2008, 89(12): 3371−3386. doi: 10.1890/07-1772.1
[44]	Yang Xiaolong, Zhang Xiumei, Zhang Peidong, et al. Ensemble habitat suitability modeling for predicting optimal sites for eelgrass (Zostera marina) in the tidal lagoon ecosystem: Implications for restoration and conservation[J]. Journal of Environmental Management, 2023, 330: 117108. doi: 10.1016/j.jenvman.2022.117108
[45]	Stohlgren T J, Ma P, Kumar S, et al. Ensemble habitat mapping of invasive plant species[J]. Risk Analysis, 2010, 30(2): 224−235. doi: 10.1111/j.1539-6924.2009.01343.x
[46]	Araújo M B, New M. Ensemble forecasting of species distributions[J]. Trends in Ecology & Evolution, 2007, 22(1): 42−47.
[47]	Araújo M B, Whittaker R J, Ladle R J, et al. Reducing uncertainty in projections of extinction risk from climate change[J]. Global Ecology and Biogeography, 2005, 14(6): 529−538. doi: 10.1111/j.1466-822X.2005.00182.x
[48]	孙浩奇. 浙江近海龙头鱼生物学特征及其时空分布与环境因子关系[D]. 舟山: 浙江海洋大学, 2022. Sun Haoqi. A study on the biological characteristics, temporal and spatial distribution, and the relationship with environmental factors of Harpadon nehereus in Zhejiang Province coastal waters[D]. Zhoushan: Zhejiang Ocean University, 2022.
[49]	Lewin W C, Mehner T, Ritterbusch D, et al. The influence of anthropogenic shoreline changes on the littoral abundance of fish species in German lowland lakes varying in depth as determined by boosted regression trees[J]. Hydrobiologia, 2014, 724(1): 293−306. doi: 10.1007/s10750-013-1746-8
[50]	Collins S D, Abbott J C, Mcintyre N E. Quantifying the degree of bias from using county-scale data in species distribution modeling: can increasing sample size or using county-averaged environmental data reduce distributional overprediction?[J]. Ecology and Evolution, 2017, 7(15): 6012−6022. doi: 10.1002/ece3.3115