| 徐婷婷,滕广亮,李英瑕,吴强,单秀娟,张庆利,金显仕.基于GFM和GAMM模型分析对虾白斑综合征(WSSV)对黄海和东海北部水域虾类生物量的影响.渔业科学进展,2022,43(1):46-55 |
| 基于GFM和GAMM模型分析对虾白斑综合征(WSSV)对黄海和东海北部水域虾类生物量的影响 |
| Analysis of the Effect of White Spot Syndrome Virus (WSSV) on Shrimp Biomass in the Yellow Sea and the Northern East China Sea Based on GFM and GAMM Models |
| 投稿时间：2020-11-18 修订日期：2020-12-15 |
| DOI： |
| 中文关键词: 对虾白斑综合征病毒 黄海和东海北部 虾类 GFM模型 GAMM模型 |
| 英文关键词: White spot syndrome virus (WSSV) Yellow Sea and the northern East China Sea Shrimp Gradient random forest model (GFM) Generalized additive mixed models (GAMM) |
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| 中文摘要: |
| 虾类是海洋生态系统功能群的重要组成部分，其生物量变化受到多重因素的影响。本研究在开展黄海和东海北部水域虾类白斑综合征病毒(white spot syndrome virus, WSSV)流行病学调查的基础上，利用梯度随机森林模型(gradient random forest model, GFM)和广义加性混合模型(generalized additive mixed models, GAMM)，分析了2016—2018年间黄海和东海北部水域WSSV流行对虾类生物量的影响。分子检测结果显示，调查所获取的26种虾类中，11种被检测为WSSV阳性；2016、2017和2018年WSSV阳性采样站点的比率分别为48.40%、38.75%和21.74%，虾类样品中WSSV阳性检出比率分别为16.86%、9.60%和4.80%。GFM模型分析显示，解释变量“阳性样品数的对数(ln_posi)”对响应变量“虾类生物量的对数(ln_Abu)”的重要性最高。GAMM分析中，根据赤池信息准则(Akaike information criterion, AIC)最小原则筛选出的最优模型为：ln_Abu~WSSV阳性率(P_rate)+ln_posi+经度(Long)，该模型中ln_posi和P_rate是影响虾类生物量的极显著相关因子，ln_Abu随着P_rate的升高而降低。研究表明，WSSV在黄海和东海北部水域虾类中流行，推测对该海域的虾类生物量存在影响。 |
| 英文摘要: |
| Shrimp plays a key role in the functional group of marine ecosystems, and its biomass is affected by multiple factors. Based on an epidemiological survey of white spot syndrome virus (WSSV) in wild shrimp in the Yellow Sea and the northern East China Sea, the impact of WSSV prevalence on the biomass of shrimp in these regions from 2016 to 2018 was analyzed using the gradient random forest model (GFM) and generalized additive mixed models (GAMM). The results of molecular detection showed that 11 out of 26 species of shrimp obtained in the survey were determined to be WSSV-positive; the percentage of WSSV-positive sites in 2016, 2017, and 2018 was 48.40%, 38.75%, and 21.74%, respectively. The percentage of WSSV-positive samples was 16.86%, 9.60%, and 4.80% in 2016, 2017, and 2018, respectively. The GFM analysis results showed that the explanatory variable "logarithm of the number of positive samples (ln_posi)" showed the highest priority to the response variable "logarithm of the shrimp biomass (ln _Abu)". The GAMM model analysis results showed that the optimal model selected according to the minimum principle of Akaike information criterion (AIC) was ln_Abu ~ WSSV positive rate (P_rate) + ln_posi+longitude (Long), in which the ln_posi and P_rate were crucial factors affecting the biomass of shrimp, and the biomass of shrimp decreased with the increase in WSSV positive rate. The above results revealed that the WSSV was prevalent in the shrimp of the Yellow Sea and the northern East China Sea, and will have a potential impact on the biomass of shrimp. |
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