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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- 被引次数: 0
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

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