RELATIONSHIP BETWEEN HABITAT DISTRIBUTION CHARACTERISTICS AND ENVIRONMENTAL FACTORS OF ABRALIA MULTIHAMATA IN ZHEJIANG OFFSHORE
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摘要:
为探究春秋季多钩钩腕乌贼(Abralia multihamata)栖息分布特征与环境因子的关系, 研究采用2018—2023年春秋季浙江近海拖网调查资料及海洋环境数据, 以扫海面积法估算多钩钩腕乌贼生物量, 使用广义加性模型(Generalized additive model, GAM)进行分析。结果表明: 浙江近海多钩钩腕乌贼平均生物量具有显著的季节差异, 其在浙江近海南部主要分布于27°—29°N, 122°—124°E, 水深为40—70 m的海域。影响多钩钩腕乌贼生物量的因子为经纬度、深度、海表温度(Sea surface temperature, SST)、盐度(Sea surface salinity, SSS)和溶解氧浓度(Dissolved oxygen concentration, DO), 春季和秋季最适SST分别为14—18℃和18—22℃, 春季SSS和DO溶解氧作用不显著, 秋季最适SSS为27‰—35‰, 秋季DO最适浓度为7—11 mg/L。研究可为气候变化背景下浙江近海头足类资源养护提供科学依据。
Abstract:Abralia multihamata is an important cephalopod species in Zhejiang offshore with a high biomass in the continental shelf waters of the East China Sea. It serves as an important bait organism for marine fish. In this study, the biomass of Abralia multihamata was estimated by using the swept area method based on trawl survey data and marine environmental data collected in spring and autumn of 2018 to 2023 in Zhejiang offshore. The relationship between habitat distribution characteristics of Abralia multihamata and environmental factors in spring and autumn was analyzed using generalized additive model (GAM). The results showed that the average biomass of Abralia multihamata in the Zhejiang offshore exhibited notable seasonal variation. It was mainly distributed within the sea area 27°—29°N, 122°—124°E with a depth of 40—70 m. The factors influencing the cephalopod biomass included longitude, latitude, depth, sea surface temperature (SST), sea surface salinity (SSS), and dissolved oxygen concentration (DO). The optimum SST was 14—18℃ in spring and 18—22℃ in autumn. The effect of SSS and DO on the biomass of Abralia multihamata were not significant during the spring season. The optimum SSS was 27‰—35‰ and the optimum DO was found in the range of 7—11 ml/L in autumn. Furthermore, the findings of this study provide a scientific foundation for the conservation of cephalopod resources in Zhejiang offshore, especially in light of the challenges posed by climate change.
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图 1 浙江近海拖网调查站位设置图
Figure 1. Distribution of sample station and water depth in Zhejiang offshore
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图 3 春季各解释变量对多钩钩腕乌贼生物量的影响(虚线表示95%置信区间)
Figure 3. Impacts of explaining variables on abundance of A. multihamata in spring (the dashed line represents the 95% confidence interval)
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图 4 秋季各解释变量对多钩钩腕乌贼生物量的影响(虚线表示95%置信区间)
Figure 4. Impacts of explaining variables on abundance of A. multihamata in autumn (the dashed line represents the 95% confidence interval)
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图 5 春季浙江近海多钩钩腕乌贼生物量年际分布
Figure 5. Annual distribution of biomass for A. multihamata off Zhejiang coast in spring
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图 6 秋季浙江近海多钩钩腕乌贼生物量年际分布
Figure 6. Annual distribution of biomass for A. multihamata off Zhejiang coast in autumn
表 1 各环境因子春秋季VIF值
Table 1 VIF values of environmental factors in spring and autumn
环境因子Environmental factor 春季Spring 秋季Autumn LAT 7.7 2.7 LON 7.0 3.2 SST 1.5 1.8 SSS 3.4 3.7 Chl.a 1.0 1.3 DO 1.1 2.1 DEPTH 4.0 4.8 
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表 2 春秋季多钩钩腕乌贼与环境因子GAM模型选择结果
Table 2 The selection result of GAM for A. multihamata in spring and autumn
季节
Season模型
ModelAIC 累计解释
偏差
Cumulative interpretation deviation (%)R2 春季
Springln(D+1)~s(LAT) 1300.439 12.3 0.118 ln(D+1)~s(LAT)+s(LON) 1206.568 26.1 0.250 ln(D+1)~s(LAT)+
s(LON)+s(SST)1148.734 34.2 0.324 ln(D+1)~s(LAT)+
s(LON)+s(SSS)1128.111 36.3 0.346 ln(D+1)~s(LAT)+
s(LON)+s(SST)+s(SSS)1105.738 39.2 0.373 ln(D+1)~s(LAT)+s(LON)+s(SST)+s(SSS)+s(Chl.a) 1107.406 39.3 0.372 ln(D+1)~s(LAT)+s(LON)+s(SST)+s(SSS)+s(DO) 1025.366 41.0 0.390 ln(D+1)~s(LAT)+
s(LON)+s(SST)+s(SSS)+s(Chl.a)+s(DO)1027.174 41.1 0.389 ln(D+1)~s(LAT)+
s(LON)+s(SST)+s(SSS)+s(DO)+s(DEPTH)904.9 54 0.514 秋季Autumn ln(D+1)~s(LAT) 2601.366 2.24 0.019 ln(D+1)~s(LAT)+s(LON) 2376.841 27.3 0.269 ln(D+1)~s(LAT)+
s(LON)+s(SST)2332.934 31.8 0.312 ln(D+1)~s(LAT)+
s(LON)+s(SSS)2352.533 31.3 0.301 ln(D+1)~s(LAT)+
s(LON)+s(SST)+s(SSS)2319.727 34.5 0.331 ln(D+1)~s(LAT)+
s(LON)+s(SST)+
s(SSS)+s(Chl.a)2076.319 39.0 0.371 ln(D+1)~s(LAT)+
s(LON)+s(SST)+
s(SSS)+s(DO)2163.147 38.6 0.367 ln(D+1)~s(LAT)+
s(LON)+s(SST)+
s(SSS)+s(Chl.a)+s(DO)1914.778 43.3 0.412 ln(D+1)~s(LAT)+
s(LON)+s(SST)+
s(SSS)+s(Chl.a)+
s(DO)+s(DEPTH)1891.6 44.8 0.429
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表 3 GAM各因子统计结果
Table 3 Analysis results of GAM for factors
季节
Season环境因子
Environmental factorF P 春季Spring LAT 1.182 0.28860 LON 11.709 <2e-16*** SST 3.279 0.00472** SSS 0.132 0.71606 DO 1.410 0.19046 DEPTH 20.062 <2e-16*** 秋季Autumn LAT 6.094 1.95e-5*** LON 1.784 0.076671 SST 14.644 1.44e-4*** SSS 2.476 0.017101* Chl.a 0.212 0.667278 DO 8.060 5.12e-5*** DEPTH 13.325 1.39e-6*** 注: *P<0.05, **P<0.01, ***P<0.001
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表 4 2018—2023年1—12月厄尔尼诺指数
Table 4 Oceanic Niño Index (ONI) from January to December of 2018—2023
年份Year 1 2 3 4 5 6 7 8 9 10 11 12 2018 –0.9 –0.9 –0.7 –0.5 –0.2 0 0.1 0.2 0.5 0.8 0.9 0.8 2019 0.7 0.7 0.7 0.7 0.5 0.5 0.3 0.1 0.2 0.3 0.5 0.5 2020 0.5 0.5 0.4 0.2 –0.1 –0.3 –0.4 –0.6 –0.9 –1.2 –1.3 –1.2 2021 –1 –0.9 –0.8 –0.7 –0.5 –0.4 –0.4 –0.5 –0.7 –0.8 –1 –1 2022 –1 –0.9 –1 –1.1 –1 –0.9 –0.8 –0.9 –1 –1 –0.9 –0.8 2023 –0.7 –0.4 –0.1 0.2 0.5 0.8 1.1 1.3 1.6 1.8 1.9 2 注: ONI大于0.5的月份被定义为厄尔尼诺发生期, 小于-0.5的月份被定义为拉尼娜发生期Note: Months with ONI greater than 0.5 are defined as El Niño occurrence periods, while months with ONI less than -0.5 are defined as La Niña occurrence periods
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