基于物种分布模型分析环境因子对海州湾偶见种资源分布的影响

张涛; 赵天亚; 栾静; 张云雷; 张崇良

doi:10.12284/hyxb2023078

基于物种分布模型分析环境因子对海州湾偶见种资源分布的影响

doi: 10.12284/hyxb2023078

张涛^{1, 2,},
赵天亚^{1, 2},
栾静^{1, 2},
张云雷^{1, 2},
张崇良^{1, 2, ,}

1.
中国海洋大学水产学院，山东青岛 266003
2.
海州湾渔业生态系统教育部野外科学观测研究站，山东青岛 266003

基金项目: 国家重点研发计划（2018YFD0900904，2018YFD0900906）。

详细信息

作者简介:
张涛（1998-），男，河南省商丘市人，主要从事渔业资源研究。E-mail: zhangtao9807@163.com

通讯作者:
张崇良，副教授，主要从事渔业资源评估、生态系统模拟。E-mail: zhangclg@ouc.edu.cn

中图分类号: S932.4; P735
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出版历程
- 收稿日期: 2022-09-23
- 修回日期: 2023-02-20
- 网络出版日期: 2023-08-03
- 刊出日期: 2023-07-01

Analysis of the influence of environmental factors on the distribution of occasional species in the Haizhou Bay based on species distribution model

Zhang Tao^{1, 2
,},
Zhao Tianya^{1, 2},
Luan Jing^{1, 2},
Zhang Yunlei^{1, 2},
Zhang Chongliang^{1, 2
, ,}

1.
Fisheries College, Ocean University of China, Qingdao 266003, China
2.
Field Observation and Research Station of Haizhou Bay Fishery Ecosystem, Ministry of Education, Qingdao 266003, China

摘要

摘要: 偶见种易受环境变化和人类活动等外界威胁，在生物多样性保护中具有重要参考价值，但由于其数据量较少、分析困难，目前对分布特征的研究较少，其分布与环境因子的关系尚待探究。本研究基于2013–2019年海州湾渔业资源调查数据，分析了凤鲚（Coilia mystus）、红狼牙虾虎鱼（Odontamblyopusrubicundus）和虻鲉（Erisphex pottii）3种海州湾偶见种资源分布与环境因子的关系，并比较了广义可加模型（GAM）和随机森林（RF）模型对其资源分布的拟合效果，采用交叉验证的方法对模型的预测性能进行了评价。结果显示，水深是影响春、秋季凤鲚和红狼牙虾虎鱼资源分布的最显著因子，而底层水温仅在秋季是影响虻鲉资源分布的最重要环境因子。凤鲚分布模型的方差解释率最高，其次为红狼牙虾虎鱼，虻鲉模型方差解释率最低。凤鲚、红狼牙虾虎鱼和虻鲉分布模型在春季方差解释率均低于秋季。交叉验证表明，3个物种预测结果的曲线下面积（AUC）值在0.70～0.85之间，仅秋季凤鲚的AUC值达到0.9；同时GAM预测结果的AUC值均大于RF模型，表明对于偶见种而言，GAM的预测性能优于RF模型。本研究为今后开展偶见种研究的模型选择提供了参考，对偶见种资源保护具有指导意义。
- 海州湾 /
- 偶见种 /
- 广义可加模型（GAM） /
- 随机森林（RF）模型 /
- 交叉验证
Abstract: Occasional species are vulnerable to external threats such as environmental changes and human activities and have important values in biodiversity conservation. However, due to their limited availability of data and associated difficulties in statistical analysis, there are few studies on the spatial distribution and their relationships with environmental factors. In this study, based on the fishery resource surveys in the Haizhou Bay conducted from 2013 to 2019, we analyzed the relationships between the distribution and environmental factors for three occasional species, Coilia mystus, Odontamblyopus rubicundus and Erisphex pottii, using generalized additive model (GAM) and random forest (RF) model. The models were compared according to their goodness of fit and the predictive performances were evaluated using cross-validation. The results showed that depth was the most significant factor affecting the distribution of C. mystus and O. rubicundus in spring and autumn, the sea bottom temperature was the most important environmental factor influencing the distribution of E. pottii in autumn. The distribution model of C. mystus had the highest deviance explanation, followed by O. rubicundus, and E. pottii had the lowest deviance explanation. The deviance explanation by the distribution models of C. mystus, O. rubicundus and E. pottii were all lower in spring than in autumn. The cross-validation showed that the area under the curve (AUC) of the three species ranged from 0.70 to 0.85, and only the AUC of C. mystus reached 0.9 in autumn; meanwhile, the AUC of the GAM prediction results were larger than those of the RF model, indicating that the prediction performance of the GAM was better than that of the RF model for the occasional species. This study would provide a reference for the selection of models for future studies of occasional species, and have guiding significance for the conservation of the occasional species.
- Haizhou Bay /
- occasional species /
- generalized additive model (GAM) /
- random forest (RF) model /
- cross-validation

HTML全文

图 1 海州湾调查区域

灰色为等深线

Fig. 1 Sampling areas in the Haizhou Bay

The gray line is the isobath line

下载: 全尺寸图片幻灯片

图 2 3种偶见种GAM与RF模型交叉验证结果

ns代表差异不显著（p > 0.05）；**代表差异极显著（p < 0.01）

Fig. 2 Cross-validation of GAM and RF model for three occasional species

ns represent no significant difference (p > 0.05)；** represent extremely significant difference (p < 0.01)

下载: 全尺寸图片幻灯片

图 3 GAM中环境因子对凤鲚出现概率的影响

Fig. 3 Influence of environmental factors on the occurrence probability of Coilia mystus in the GAM

下载: 全尺寸图片幻灯片

图 4 GAM中环境因子对红狼牙虾虎鱼出现概率的影响

Fig. 4 Influence of environmental factors on the occurrence probability of Odontamblyopus rubicundus in the GAM

下载: 全尺寸图片幻灯片

图 5 GAM中环境因子对虻鲉出现概率的影响

Fig. 5 Influence of environmental factors on the occurrence probability of Erisphex pottii in the GAM

下载: 全尺寸图片幻灯片

表 1 海州湾春、秋季各环境因子的方差膨胀系数

Tab. 1 Variance inflation factor of each environmental factor during spring and autumn in the Haizhou Bay

季节	水深	底层温度	底层盐度	经度
春季	2.29	1.58	1.67	1.41
秋季	1.83	1.17	1.62	1.52

下载: 导出CSV

表 2 春、秋季3个偶见种最优模型

Tab. 2 Optimal model for three occasional species during spring and autumn

季节	物种	模型	解释变量	AIC	方差解释率/%
春季	凤鲚C. mystus	GAM	Depth+Longitude+SBS	69.61	38.9
	凤鲚C. mystus	RF	Depth+SBT+Longitude		18.0
	红狼牙虾虎鱼O. rubicundus	GAM	Depth+SBS+SBT	83.03	25.1
	红狼牙虾虎鱼O. rubicundus	RF	Depth+SBT+SBS		5.0
	虻鲉E. pottii	GAM	Depth+SBT	69.63	18.2
	虻鲉E. pottii	RF	Depth+SBS+SBT+Longitude		7.5
秋季	凤鲚C. mystus	GAM	Depth+Longitude+SBT	51.30	51.2
	凤鲚C. mystus	RF	Depth+Longitude		30.1
	红狼牙虾虎鱼O. rubicundus	GAM	Depth+Longitude+SBS	47.79	46.2
	红狼牙虾虎鱼O. rubicundus	RF	Longitude+SBS+Depth		7.7
	虻鲉E. pottii	GAM	SBT+SBS+Longitude	100.31	34.1
	虻鲉E. pottii	RF	SBT+SBS+Longitude+Depth		26.4
注：Depth、SBT、SBS和Longitude分别代表环境因子水深、底层温度、底层盐度和经度。

下载: 导出CSV

参考文献(46)

[1]	朱靖. 稀有种及其保护[J]. 野生动物保护用, 1979(1): 1−8, 28. Zhu Jing. Rare species and their protection[J]. Chinese Journal of Wildlife, 1979(1): 1−8, 28.
[2]	Trindade-Santos I, Moyes F, Magurran A E. Global patterns in functional rarity of marine fish[J]. Nature Communications, 2022, 13(1): 877. doi: 10.1038/s41467-022-28488-1
[3]	李显森, 于振海, 孙珊, 等. 长江口及其毗邻海域鱼类群落优势种的生态位宽度与重叠[J]. 应用生态学报, 2013, 24(8): 2353−2359. Li Xiansen, Yu Zhenhai, Sun Shan, et al. Ecological niche breadth and niche overlap of dominant species of fish assemblage in Changjiang River Estuary and its adjacent waters[J]. Chinese Journal of Applied Ecology, 2013, 24(8): 2353−2359.
[4]	王雪辉, 杜飞雁, 邱永松, 等. 1980−2007年大亚湾鱼类物种多样性、区系特征和数量变化[J]. 应用生态学报, 2010, 21(9): 2403−2410. Wang Xuehui, Du Feiyan, Qiu Yongsong, et al. Variations of fish species diversity, faunal assemblage, and abundances in Daya Bay in 1980−2007[J]. Chinese Journal of Applied Ecology, 2010, 21(9): 2403−2410.
[5]	刘旻霞, 李全弟, 蒋晓轩, 等. 甘南亚高寒草甸稀有种对物种多样性和物种多度分布格局的贡献[J]. 生物多样性, 2020, 28(2): 107−116. doi: 10.17520/biods.2019297 Liu Minxia, Li Quandi, Jiang Xiaoxuan, et al. Contribution of rare species to species diversity and species abundance distribution pattern in the Gannan subalpine meadow[J]. Biodiversity Science, 2020, 28(2): 107−116. doi: 10.17520/biods.2019297
[6]	周文嵩. 海南铜鼓岭热带常绿季雨矮林偶见种分布格局及其对生物多样性计算的影响研究[D]. 海口: 海南大学, 2014. Zhou Wensong. Study on distribution patterns of rare species and their impact on the estimation of the species diversity in tropical evergreen monsoon dwarf forests in Tongguling, Hainan[D]. Haikou: Hainan University, 2014.
[7]	陈正兴, 高德新, 张伟, 等. 黄土丘陵沟壑区不同坡向撂荒草地植物群落种群空间格局[J]. 应用生态学报, 2018, 29(6): 1846−1856. Chen Zhengxing, Gao Dexin, Zhang Wei, et al. Population spatial patterns of grassland plant communities in different slope aspects in the loess hilly area, China[J]. Chinese Journal of Applied Ecology, 2018, 29(6): 1846−1856.
[8]	Elith J, Leathwick J R. Species distribution models: ecological explanation and prediction across space and time[J]. Annual Review of Ecology, Evolution, and Systematics, 2009, 40: 677−697. doi: 10.1146/annurev.ecolsys.110308.120159
[9]	郭彦龙, 赵泽芳, 乔慧捷, 等. 物种分布模型面临的挑战与发展趋势[J]. 地球科学进展, 2020, 35(12): 1292−1305. Guo Yanlong, Zhao Zefang, Qiao Huijie, et al. Challenges and development trend of species distribution model[J]. Advances in Earth Science, 2020, 35(12): 1292−1305.
[10]	徐晓卉, 纪毓鹏, 薛莹, 等. 长山列岛邻近海域褐牙鲆资源丰度的时空分布及其与环境因子的关系[J]. 海洋学报, 2022, 44(2): 39−45. Xu Xiaohui, Ji Yupeng, Xue Ying, et al. Spatio-temporal distribution of Paralichthys olivaceus abundance and its relationship with environmental factors in the adjacent waters of Changshan Islands[J]. Haiyang Xuebao, 2022, 44(2): 39−45.
[11]	崔晏华, 刘淑德, 张云雷, 等. 海州湾春季短蛸的栖息分布特征及其与环境因子的关系[J]. 应用生态学报, 2022, 33(6): 1686−1692. Cui Yanhua, Liu Shude, Zhang Yunlei, et al. Habitat characteristics of Octopus ocellatus and their relationship with environmental factors during spring in Haizhou Bay, China[J]. Chinese Journal of Applied Ecology, 2022, 33(6): 1686−1692.
[12]	纪毓鹏, 李明坤, 韩东燕, 等. 山东南部近海脊腹褐虾时空分布及其与环境因子的关系[J]. 中国海洋大学学报(自然科学版), 2020, 50(7): 56−62. Ji Yupeng, Li Mingkun, Han Dongyan, et al. The relationship between spatiotemporal distribution of Crangon affinis and environmental factors in the southern coastal waters of Shandong[J]. Periodical of Ocean University of China, 2020, 50(7): 56−62.
[13]	Lomba A, Pellissier L, Randin C, et al. Overcoming the rare species modelling paradox: a novel hierarchical framework applied to an Iberian endemic plant[J]. Biological Conservation, 2010, 143(11): 2647−2657. doi: 10.1016/j.biocon.2010.07.007
[14]	Breiner F T, Guisan A, Bergamini A, et al. Overcoming limitations of modelling rare species by using ensembles of small models[J]. Methods in Ecology and Evolution, 2015, 6(10): 1210−1218. doi: 10.1111/2041-210X.12403
[15]	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. 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.
[16]	张涛, 沃佳, 刘逸文, 等. 海州湾偶见鱼类的种类组成及时空分布[J]. 应用生态学报, 2023, 34(3): 787−795. Zhang Tao, Wo Jia, Liu Yiwen, et al. Species composition and spatial and temporal distribution of occasional fishes in Haizhou Bay, Shandong, China[J]. Chinese Journal of Applied Ecology, 2023, 34(3): 787−795.
[17]	李敏, 纪毓鹏, 徐宾铎, 等. 黄河口及邻近水域小型鳀鲱鱼类数量分布及其与环境因子的关系[J]. 海洋学报, 2016, 38(10): 52−61. Li Min, Ji Yupeng, Xu Binduo, et al. Spatio-temporal distribution of small-sized fish species in Clupeidae and Engraulidae and its relationships with environmental factors in Huanghe River Estuary and its adjacent waters[J]. Haiyang Xuebao, 2016, 38(10): 52−61.
[18]	张云雷, 孙霄, 刘晓慧, 等. 气候变化对黄海中南部斑鰶产卵场适宜性的影响[J]. 水产学报, 2022, 46(2): 215−223. Zhang Yunlei, Sun Xiao, Liu Xiaohui, et al. Impacts of climate changes on the habitat suitability of spawning ground for Konosirus punctatus in the central and southern Yellow Sea[J]. Journal of Fisheries of China, 2022, 46(2): 215−223.
[19]	刘俊超, 贾明秀, 冯卫东, 等. 基于RF和GAM模型的南极磷虾资源分布与环境因子关系研究[J]. 中国海洋大学学报(自然科学版), 2021, 51(8): 20−29. Liu Junchao, Jia Mingxiu, Feng Weidong, et al. Spatial-temporal distribution of Antarctic Krill (Euphausia superba) resource and its association with environment factors revealed with RF and GAM models[J]. Periodical of Ocean University of China, 2021, 51(8): 20−29.
[20]	Guisan A, Edwards Jr T C, Hastie T. Generalized linear and generalized additive models in studies of species distributions: setting the scene[J]. Ecological Modelling, 2002, 157(2/3): 89−100.
[21]	李丽霞, 郜艳晖, 周舒冬, 等. 广义加性模型及其应用[J]. 中国卫生统计, 2007, 24(3): 243−244. Li Lixia, Gao Yanhui, Zhou Shudong, et al. Generalized additive models and its application[J]. Chinese Journal of Health Statistics, 2007, 24(3): 243−244.
[22]	Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5−32. doi: 10.1023/A:1010933404324
[23]	方匡南, 吴见彬, 朱建平, 等. 随机森林方法研究综述[J]. 统计与信息论坛, 2011, 26(3): 32−38. Fang Kuangnan, Wu Jianbin, Zhu Jianping, et al. A review of technologies on random forests[J]. Statistics & Information Forum, 2011, 26(3): 32−38.
[24]	王奕森, 夏树涛. 集成学习之随机森林算法综述[J]. 信息通信技术, 2018, 12(1): 49−55. Wang Yisen, Xia Shutao. A survey of random forests algorithms[J]. Information and Communications Technologies, 2018, 12(1): 49−55.
[25]	董师师, 黄哲学. 随机森林理论浅析[J]. 集成技术, 2013, 2(1): 1−7. Dong Shishi, Huang Zhexue. A brief theoretical overview of random forests[J]. Journal of Integration Technology, 2013, 2(1): 1−7.
[26]	Kabacoff R I. R in Action: Data Analysis and Graphics with R[M]. Greenwich: Manning Publications, 2011.
[27]	Wood S N. Stable and efficient multiple smoothing parameter estimation for generalized additive models[J]. Journal of the American Statistical Association, 2004, 99(467): 673−686. doi: 10.1198/016214504000000980
[28]	Burnham K P, Anderson D R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach[M]. 2nd ed. New York: Springer, 2002.
[29]	Bradley A P. The use of the area under the ROC curve in the evaluation of machine learning algorithms[J]. Pattern Recognition, 1997, 30(7): 1145−1159. doi: 10.1016/S0031-3203(96)00142-2
[30]	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
[31]	栾静, 张崇良, 徐宾铎, 等. 海州湾双斑蟳栖息分布特征与环境因子的关系[J]. 水产学报, 2018, 42(6): 889−901. Luan Jing, Zhang Chongliang, Xu Binduo, et al. Relationship between catch distribution of portunid crab (Charybdis bimaculata) and environmental factors based on three species distribution models in Haizhou Bay[J]. Journal of Fisheries of China, 2018, 42(6): 889−901.
[32]	倪一卓, 程和琴, 江红, 等. 鱼类栖息地模拟的比较研究—以东海鲐鱼为例[J]. 水产科学, 2009, 28(12): 726−732. Ni Yizhuo, Cheng Heqin, Jiang Hong, et al. Fish habitat modeling in the East China Sea: a case of chub mackerel[J]. Fisheries Science, 2009, 28(12): 726−732.
[33]	Shono H. Is model selection using Akaike’s information criterion appropriate for catch per unit effort standardization in large samples?[J]. Fisheries Science, 2005, 71(5): 978−986. doi: 10.1111/j.1444-2906.2005.01054.x
[34]	Swets J A. Measuring the accuracy of diagnostic systems[J]. Science, 1988, 240(4857): 1285−1293. doi: 10.1126/science.3287615
[35]	Li Min, Zhang Chongliang, Xu Binduo, et al. Evaluating the approaches of habitat suitability modelling for whitespotted conger (Conger myriaster)[J]. Fisheries Research, 2017, 195: 230−237. doi: 10.1016/j.fishres.2017.07.024
[36]	Jacobucci R, Grimm K J. Machine learning and psychological research: the unexplored effect of measurement[J]. Perspectives on Psychological Science, 2020, 15(3): 809−816. doi: 10.1177/1745691620902467
[37]	袁传宓, 秦安舲. 我国近海鲚鱼生态习性及其产量变动状况[J]. 海洋科学, 1984, 8(5): 35−37. Yuan Chuanbi, Qin Anling. Ecological habits and distribution of Coilia along the Chinese coast and its changes of output[J]. Marine Sciences, 1984, 8(5): 35−37.
[38]	徐敏. 盐碱度对3种鱼精子活力、受精率及孵化率的影响[D]. 上海: 上海海洋大学, 2015. Xu Min. Effects of salinity and alkalinity on sperm motility, fertilization rate and hatching rate of 3 fish species[D]. Shanghai: Shanghai Ocean University, 2015.
[39]	成庆泰, 周才武. 山东鱼类志[M]. 济南: 山东科学技术出版社, 1997. Cheng Qingtai, Zhou Caiwu. The Fishes of Shandong Province[M]. Ji’nan: Shandong Science and Technology Press, 1997.
[40]	Stefansdottir L, Solmundsson J, Marteinsdottir G, et al. Groundfish species diversity and assemblage structure in Icelandic waters during recent years of warming[J]. Fisheries Oceanography, 2010, 19(1): 42−62. doi: 10.1111/j.1365-2419.2009.00527.x
[41]	吴艳, 王洪峰, 穆立蔷. 物种分布模型的研究进展与展望[J]. 高师理科学刊, 2022, 42(5): 66−70. Wu Yan, Wang Hongfeng, Mu Liqiang. Research progress and prospect of species distribution models[J]. Journal of Science of Teachers’ College and University, 2022, 42(5): 66−70.
[42]	简盈, 张云雷, 宋业晖, 等. 基于GAM和GWR模型分析环境因子对鱼类分布的影响——以山东近海多鳞鱚为例[J]. 海洋学报, 2022, 44(7): 103−111. Jian Ying, Zhang Yunlei, Song Yehui, et al. Effect of environmental factors on fish distribution based on GAM and GWR model: a case study of Sillago sihama in the Shandong coastal waters[J]. Haiyang Xuebao, 2022, 44(7): 103−111.
[43]	Guo Yanlong, Li Xin, Zhao Zefang, et al. Prediction of the potential geographic distribution of the ectomycorrhizal mushroom Tricholoma matsutake under multiple climate change scenarios[J]. Scientific Reports, 2017, 7: 46221. doi: 10.1038/srep46221
[44]	Stockwell D R B, Peterson A T. Effects of sample size on accuracy of species distribution models[J]. Ecological Modelling, 2002, 148(1): 1−13. doi: 10.1016/S0304-3800(01)00388-X
[45]	尹洁, 王晶, 张崇良, 等. 利用two-stage GAM研究海州湾及其邻近海域小黄鱼鱼卵的时空分布特征[J]. 中国水产科学, 2019, 26(6): 1164−1174. Yin Jie, Wang Jing, Zhang Chongliang, et al. Spatial and temporal distribution characteristics of Larimichthys polyactis eggs in Haizhou Bay and adjacent regions based on two-stage GAM[J]. Journal of Fishery Sciences of China, 2019, 26(6): 1164−1174.
[46]	张云雷, 徐宾铎, 张崇良, 等. 基于Tweedie-GAM模型研究海州湾小黄鱼资源丰度与栖息环境的关系[J]. 海洋学报, 2019, 41(12): 78−89. Zhang Yunlei, Xu Binduo, Zhang Chongliang, et al. Relationship between the habitat factors and the abundance of small yellow croaker (Larimichthys polyactis) in Haizhou Bay based on the Tweedie-GAM model[J]. Haiyang Xuebao, 2019, 41(12): 78−89.