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Distribution characteristics of Liza haematocheila and its relationship with environmental factors in Furongdao artificial reef zones, Laizhou Bay, China

  • Corresponding author: Yanli TANG, tangyanli@ouc.edu.cn
  • Received Date: 2019-05-31
    Accepted Date: 2019-07-10
  • Abundance of Liza haematocheila has declined rapidly since the 1980 s in Bohai Sea, due to the increasing fishing pressure caused by the change of fishery methods. Furongdao artificial reefs (ARs), located in Laizhou Bay, has provided a good habitat for L. haematocheila, making it one of the major fish resources in the AR area. In order to explore the response of L. haematocheila to environmental factors, the effect of environmental factors, artificial reefs, food abundance on CPUE and mean standard length of L. haematocheila were analyzed using GAM model. L. haematocheila were collected by trammels survey of Furongdao ARs during 2014-2017. The results showed that there was no significant difference between average CPUE values of artificial reef area and natural area in spring (P = 0.362), however, the CPUE in AR area is 3.53 times CPUE in the control area in the autumn. Three-way ANOVA showed that CPUE is significantly affected by year, month, artificial reefs and interaction effect of year and month (P < 0.01). Average length was affected by year (P < 0.01). Results of Delta GAM showed that the probability that L. haematocheila occurs was significantly influenced by temperature and whether the site is in the AR area. The probability of presence is higher in AR area than in the natural area. As temperature increases, probability of presence decreases. CPUE increases when temperature is below 8 °C and then decreases. Mean standard length of L. haematocheila is influenced by abundance of Bacillariophyta and zooplankton. When abundance of Bacillariophyta is smaller than 3×105 cells/m3, mean standard length decreases with the increase of the abundance of Bacillariophyta, and then tends to stabilize change. Mean standard length decreases with abundance of zooplankton rises. In conclusion, L. haematocheila is mainly distributed in the AR area at low temperature, and large fish is mainly distributed in the area with low abundance of Bacillariophyta and zooplankton.
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Distribution characteristics of Liza haematocheila and its relationship with environmental factors in Furongdao artificial reef zones, Laizhou Bay, China

    Corresponding author: Yanli TANG, tangyanli@ouc.edu.cn
  • 1. College of Fisheries, Ocean University of China, Qingdao    266003, China
  • 2. Shandong Hydrobios Resources Conservation and Management Center, Yantai    264003, China
  • 3. Marine College, Shandong University, Weihai    264209, China

Abstract: Abundance of Liza haematocheila has declined rapidly since the 1980 s in Bohai Sea, due to the increasing fishing pressure caused by the change of fishery methods. Furongdao artificial reefs (ARs), located in Laizhou Bay, has provided a good habitat for L. haematocheila, making it one of the major fish resources in the AR area. In order to explore the response of L. haematocheila to environmental factors, the effect of environmental factors, artificial reefs, food abundance on CPUE and mean standard length of L. haematocheila were analyzed using GAM model. L. haematocheila were collected by trammels survey of Furongdao ARs during 2014-2017. The results showed that there was no significant difference between average CPUE values of artificial reef area and natural area in spring (P = 0.362), however, the CPUE in AR area is 3.53 times CPUE in the control area in the autumn. Three-way ANOVA showed that CPUE is significantly affected by year, month, artificial reefs and interaction effect of year and month (P < 0.01). Average length was affected by year (P < 0.01). Results of Delta GAM showed that the probability that L. haematocheila occurs was significantly influenced by temperature and whether the site is in the AR area. The probability of presence is higher in AR area than in the natural area. As temperature increases, probability of presence decreases. CPUE increases when temperature is below 8 °C and then decreases. Mean standard length of L. haematocheila is influenced by abundance of Bacillariophyta and zooplankton. When abundance of Bacillariophyta is smaller than 3×105 cells/m3, mean standard length decreases with the increase of the abundance of Bacillariophyta, and then tends to stabilize change. Mean standard length decreases with abundance of zooplankton rises. In conclusion, L. haematocheila is mainly distributed in the AR area at low temperature, and large fish is mainly distributed in the area with low abundance of Bacillariophyta and zooplankton.

  • 鰉(Liza haematocheila)隶属脊索动物门(Chordata)、辐鳍鱼纲(Actinopterygii)、鲻科(Mugilidae)、鰉属(Liza),是广泛分布于西北太平洋沿岸的经济鱼类[1]。在20世纪70—80年代,鰉曾被引入亚速海,并逐渐扩散到黑海,由于其旺盛的繁殖能力,也逐步成为当地经济鱼类之一[2]。在我国,鰉自辽东半岛至广东沿岸均有分布[3],主要栖息于水深不超过10 m的近岸水域或河口咸淡水交界处,是一种近海广盐性鱼类[1, 4]。20世纪80年代以来,渤海渔业作业方式由拖网转为定置网[5],增加了对地方性物种花鲈(Lateolabrax maculatus)、鰉、三疣梭子蟹(Portunus trituberculatus)、口虾蛄(Oratosquilla oratoria)和日本蟳(Charybdis japonica)等渔业资源的捕捞压力,近海的鰉资源由此衰退[5-7]。2010—2011年,通过对莱州湾游泳动物底拖网调查发现,鰉资源有所恢复,但效果并不显著[8]。位于莱州湾的芙蓉岛人工鱼礁区建于2013—2014年,水深为5~10 m。莱州湾沿岸多分布径流,为鰉产卵、索饵、繁殖提供了良好的环境条件[9],使得鰉成为了礁区主要经济鱼类之一。

    人工鱼礁区单一鱼种的研究主要有鱼类的摄食生态和行为特征[10-12]、鱼类活动区域和近礁距离关系[13-17]、鱼类栖息地利用模式[16-17]。Herbig等[14]研究了北墨西哥湾灰鳞鲀(Balistes capriscus)在人工鱼礁附近的活动范围以及昼夜行为特征,Lowry等[15]对澳洲黑鲷(Acanthopagrus australis)在自然岩礁区和人工鱼礁区栖息和运动特征进行了比较研究。目前,鰉资源的研究主要涉及不同地理群体间的遗传差异及形态差异等方面[3, 18-19],在人工鱼礁区的资源分布特征尚未见报道。

    本实验以2014—2017年春季和秋季芙蓉岛人工鱼礁区三重刺网调查资料为基础,运用GAM模型方法,研究了鰉资源丰度和平均体长对水温、盐度、溶解氧、人工鱼礁、饵料生物丰度等重要环境参数的响应机制,以期为掌握莱州湾人工鱼礁区鰉分布特征,合理开发利用人工鱼礁区渔业资源提供参考。

1.   材料与方法
  • 芙蓉岛人工鱼礁区海底平坦、底质以粗砂为主。礁区面积为66.861 0 hm2,2013年开始,共投放石块礁和混凝土构件礁13.875万 空m3。调查站位共设置7个,其中,站位1、2、3、4、5位于礁区内,站位6、7位于礁区外,根据鱼礁对礁区鱼类影响范围的研究结果[20-21],确定站位6、7在礁区外200 m处(图1)。2014—2017年每年春季(5月)和秋季(11月)对礁区渔业资源和海水环境进行调查,但由于风浪、天气等因素,调查的日期并不一致。

    Figure 1.  Location of Furongdao artificial reefs and investigation sites

  • 调查渔具为三重刺网,主尺度为61 m×1.35 m,其内网目尺寸为7 cm,外网目尺寸为45 cm。每次调查设置1张三重刺网,固定在距离海底0.5 m的位置,放置时间为26 h。鰉单位捕捞努力量渔获量(CPUE)定义为每网每天捕获鰉的重量(g/net×d),公式为:

    式中,c为每次调查获得鰉的重量(g),n为每次调查使用网具的数量,h为每次调查的时间(d)。

    各调查站位的水温、溶解氧、盐度和酸碱度由YSI多参数水质测量仪(Proplus)测得。根据《海洋调查规范》(GB/T 12763-2007)以及《海洋监测规范》(GB 17378-2007),分别使用浅水III型浮游生物网和浅水I型浮游生物网采集浮游植物和浮游动物。实验室分析依据《中国海洋浮游生物图谱》[22],除浮游幼虫,浮游植物和浮游动物鉴定到种或属。

  • 应用幂函数拟合鰉的体长−体质量关系,计算礁区鰉的条件因子和生长指数:

    式中,W为鱼体质量(g),L为鱼体长(mm),a为条件因子,b为生长指数。

  • 鰉成鱼主要摄食浮游植物,并摄食少量浮游动物[1, 4],浮游植物主要为硅藻[23-24]。本实验将浮游植物、浮游动物、水温、盐度、pH、溶解氧等作为可能对鰉分布产生影响的环境参数进行分析。

    采用GAM模型分析鰉的CPUE、平均体长与各因子的关系。GAM模型的表达式如下:

    式中,g为连接函数,Y为响应变量,α为截距,Xi为第i个自变量,si为第i个自变量的立方样条平滑函数,ε为残差;自变量包括底层水温(T,°C)、盐度(S)、溶解氧(DO,mg/L)、pH、硅藻(Bacillariophyta)丰度(个/m3)、浮游植物(Phytoplankton)丰度(个/m3)、浮游动物丰度(Zooplankton)(个/m3)、浮游动物生物量(mg/m3)、人工鱼礁(调查站位位于礁区为1,位于非礁区则为0)等环境因子和年份、月份。

    由于CPUE数据中含有部分0值,本实验采用Delta方法对CPUE数据进行分析[25-28],分别应用二项分布的GAM模型(Binomial GAM)和高斯分布的GAM模型(Gaussian GAM)分析CPUE为非零值的概率和CPUE非0值与各因子之间的关系。Binomial GAM模型中g为Logit连接函数,Y代表CPUE为非零值的概率,当CPUE为0时,Y为0,否则为1。Gaussian GAM模型中,g为恒等连接函数,Y为log10(1+CPUE)。

    为避免模型的过度拟合,本实验采用逐步回归的方法筛选对响应变量具有显著影响的自变量,并通过比较模型的AICc值选择最优模型[26]。GAM模型均使用R软件“mgcv”、“AICcmodavg”程序包运行。

2.   结果
  • 鰉为芙蓉岛人工鱼礁区主要的生物资源,2014—2017年共捕获494尾,占各站位渔获物重量平均比例为31.9%。礁区春季鰉平均CPUE为287.84±254.95 g/(net×d),秋季为3 600.22±1 816.96 g/(net×d),非礁区春季平均CPUE为363.82±630.16 g/(net×d),秋季为1 017.43±1 205.38 g/(net×d)。单因素方差分析表明,春季礁区和非礁区CPUE差异不显著(F = 0.862,P = 0.362),秋季礁区CPUE显著高于非礁区(F = 12.660,P < 0.001),是非礁区的3.53倍(图2)。礁区鰉的体长范围为205~500 mm,体长优势组为270~275 mm,体质量范围为110.84~1 755.53 g,体质量优势组为210.84~260.84 g(图3)。非礁区鰉体长范围为225~465 mm,体质量范围为148.00-1 455.31 g(图4)。礁区和非礁区体长差异不显著(F = 0.076,P = 0.785),但体长超过350 mm的鰉主要出现在礁区(图4)。礁区鰉条件因子a = 2.450×10−5,生长指数b = 2.878(相关系数R2 = 0.910),为异速生长(图5)。

    Figure 2.  Seasonal variation of CPUE

    Figure 3.  Standard length and body weight frequency distribution of L. haematocheila in Furongdao artificial reef area

    Figure 4.  Boxplot of standard length of L. haematocheila in Furongdao artificial reef area and the natural area

    Figure 5.  Relationship between standard length and body weight of L. haematocheila in Furongdao artificial reef area

    应用三因素方差分析以及Pearson相关性分析初步探索各因子与鰉CPUE、平均体长的关系。三因素方差分析表明,鰉CPUE主要受到年份、月份、人工鱼礁、年份和月份的交互作用影响(P < 0.01,表1),鰉平均体长则主要受到年份的影响(P < 0.001,表1),人工鱼礁、月份对平均体长的影响并不显著(P > 0.05,表1)。Pearson分析表明,CPUE与水温、溶解氧、硅藻丰度、浮游植物丰度、浮游动物生物量显著相关(P < 0.05,表2),平均体长与溶解氧、硅藻丰度、浮游植物丰度显著相关。

    响应变量
    response variable
    自变量
    independent variables
    自由度
    degree of freedom
    均方
    mean Sq.
    F valueP value
    CPUE 年份 year 3 12 289 017 6.819 <0.001***
    月份 month 1 91 230 128 50.622 <0.001***
    人工鱼礁 artificial reefs 1 26 409 143 14.654 <0.001***
    年份×月份 year×month 3 9 768 734 5.420 0.002**
    平均体长
    mean standard length
    年份 year 3 23 432 14.124 <0.001***
    月份 month 1 5 084 3.064 0.094
    人工鱼礁 artificial reefs 1 1 337 0.806 0.349
    年份×月份 year×month 2 4 877 2.940 0.074
    注:***为P<0.001,**为0.001<P<0.01
    Notes: *** represents P<0.001; ** represents 0.001<P<0.01.

    Table 1.  Results of three-way ANOVA

    响应变量
    response variable
    环境因子 environmental factors
    水温
    temperature
    盐度
    Salinity
    溶解氧
    DO
    pH硅藻丰度
    Bacillariophyta
    浮游植物丰度
    Phytoplankton
    浮游动物丰度
    Zooplankton
    浮游动物生物量
    Zooplankton
    CPUE −0.552*** 0.029 0.313* −0.116 0.670*** 0.669*** −0.249 0.519***
    平均体长
    mean standard length
    0.211 0.210 −0.401* −0.001 −0.452* −0.454* −0.124 −0.197
    注:数值代表Pearson相关系数;*为0.01< P<0.05,***为P<0.001
    Notes: Figures represent Pearson correlation coefficients. * represents 0.01< P<0.05; *** represents P<0.001

    Table 2.  Results of Pearson correlation test

  • Binomial GAM最优模型结果表明,鰉出现的概率主要受水温与人工鱼礁的影响(表3图6),鰉出现的概率随水温升高而下降,且在礁区出现的概率高于非礁区(图5)。

    样本量
    number of samples
    残差解释量/%
    deviance explained
    校正决定系数
    R-sq. (adjusted)
    AICc驱动因子
    driving factors
    P value
    Binomial GAM 56 58.50 0.621 43.235 水温 s(temperature) 0.017*
    人工鱼礁 artificial reef 0.008**
    截距项 intercept 0.263
    Gaussian GAM 29 63.90 0.550 519.707 水温 s(temperature) <0.001***
    截距项 intercept <0.001***
    注:s为立方样条平滑函数,*为0.01< P<0.05,**为0.001<P<0.01,***为P<0.001。
    Notes: s represents cubic regression spline, * represents 0.01< P<0.05; ** represents 0.001<P<0.01; *** represents P<0.001.

    Table 3.  Optimal Delta GAM for CPUE

    Figure 6.  Influence of predictor factors on CPUE

    Gaussian GAM 模型的自变量为水温和盐度时,AICc值最低,但校正决定系数为0.419,残差解释量为51.0%,低于自变量仅包含水温的模型(表3),因此选择自变量仅包含水温的模型作为Gaussian GAM最优模型。Gaussian GAM最优模型表明,CPUE受水温影响显著(P < 0.01,表3),当水温低于8 °C时,CPUE随水温的升高而增加,当水温大于8 °C时,CPUE随水温的升高而降低(图6)。

    应用正态Q-Q图和Shapiro-Wilk检验验证模型残差的正态性,诊断GAM模型。正态Q-Q图表明Binomial GAM模型和Gaussian GAM 模型残差基本符合正态分布(图7图8)。Shapiro-Wilk检验表明,模型残差符合正态分布(Binomial GAM,w = 0.970,P = 0.177 > 0.05;Gaussian GAM,w = 0.982,P = 0.901 > 0.05)。

    Figure 7.  Normal Q-Q plot of Binomial GAM for CPUE

    Figure 8.  Normal Q-Q plot of Gaussian GAM for CPUE

  • 应用Gaussian GAM 模型分析平均体长与各因子的响应关系,最优模型结果显示鰉平均体长受到硅藻丰度和浮游动物丰度的影响(表4图9)。当硅藻丰度小于3×105 个/m3时,鰉平均体长随硅藻丰度上升而下降,随后趋于稳定;鰉平均体长随浮游动物丰度增加而呈现降低趋势(图9),这与鰉的摄食强度以及食性偏好有关。Q-Q图表明模型残差基本符合正态分布(图10),Shapiro-Wilk检验表明残差符合正态分布(w = 0.963,P = 0.552)。

    样本量
    number of samples
    残差解释量/%
    deviance explained
    校正决定系数
    R-sq. (adjusted)
    AICc驱动因子
    driving factors
    P value
    29 86.20 0.793 306.088 硅藻丰度 s(Bacillariophyta) <0.001***
    浮游动物丰度 s(Zooplankton) <0.001***
    截距项 intercept <0.001***
    注:s为立方样条平滑函数,***为P<0.001。
    Note: s represents cubic regression spline; *** represents P<0.001.

    Table 4.  Optimal GAM for mean standard length

    Figure 9.  Influence of predictor factors on mean standard length

    Figure 10.  Normal Q-Q plot of GAM for mean standard length

3.   讨论
  • 水温是影响鱼类分布的重要环境要素,刘长东等[29]应用GAM模型分析海州湾小黄鱼分布特征发现,月份、位置、水温对小黄鱼CPUE影响显著。邢磊等[30]应用GAM模型分析海州湾大泷六线鱼(Hexagrammos otakii)的分布特征,证明月份、水温和位置是影响其分布的主要环境因子,盐度并非是对其产生影响的关键因子。人工鱼礁区渔业资源研究表明,礁区的渔获物组成结构受到水温、盐度、pH、DO的影响[31-33]。鰉属于近海广盐性鱼类[1, 4],栖息于近岸水域或河口咸淡水交界处,对盐度适应范围广,因此,盐度变化对鰉丰度影响并不显著。

    李凡等[34]于2010—2011年对莱州湾游泳动物群落的季节调查发现,鰉仅为2010年冬季拖网渔获物的优势种,在春季和秋季的调查中并非优势种。王娇等[35]对黄河口及临近水域渔业资源调查表明,鰉冬季丰度最高,春季最低。芙蓉岛人工鱼礁区鰉CPUE表现出秋季高于春季的特征,且GAM模型结果表明随着水温的升高,鰉出现的概率降低,CPUE呈现先升高后降低的趋势,这可能与鰉的生活习性相关。

    鰉达到成熟年龄后一年一次产卵,渤海区鰉繁殖期通常为4月底至6月下旬,产卵盛期为5月初至5月中旬,不同年份存在变动[36-37]。王爱勇[38]于2008年5月和6月采用大型浮游生物拖网对莱州湾春季鱼卵、仔稚鱼进行调查,发现鰉鱼卵主要出现在5月份,密集分布在小清河口附近和莱州湾东北部长岛附近海域,6月没有鱼卵出现;仔稚鱼主要出现在5月,6月数量较少,分布在内湾河口区域。芙蓉岛人工鱼礁区离鰉产卵区域较远,因此春季水温较高时鰉出现概率较小,且CPUE较低。李明德等[39]发现鰉产卵后分散在沿岸及海湾索饵,于10月至翌年3月游向渤海较深海区12~16 m水深处越冬。

    人工鱼礁对鰉出现的概率影响显著。人工鱼礁投放后会对渔获物群落结构和主要鱼类的分布产生影响[13, 31-33]。唐衍力等[13]研究发现近礁距离对人工鱼礁区许氏平鲉(Sebastes schlegelii)的丰度、体长影响显著,许氏平鲉CPUE与近礁距离呈负相关,全长与近礁距离呈负相关。Dos Santos等[17]对巴西东南海岸人工鱼礁区鱼类与近礁距离关系研究证明,鱼类在礁区数量远高于距离礁区50 m处。Topping等[20]对红笛鲷(Lutjanus campechanus)在人工鱼礁区活动范围和运动特征研究表明,30%的鱼类主要分布在距离礁区30 m范围内,并且全长与近礁距离呈正相关。鰉集中分布在礁区范围内,礁区的CPUE远高于非礁区,且2015年秋季、2016年秋季非礁区均未捕获到鰉。虽然礁区与非礁区平均体长差异不显著,但体长超过350 mm的鰉主要出现在礁区。

    硅藻丰度是影响鰉平均体长的关键因子,这可能是由鰉的摄食强度和食性引起的[4, 37, 39-40]。张立言等[23]、李明德等[24]对鰉食性研究发现,鰉成鱼主要摄食硅藻,对硅藻无摄食选择性。芙蓉岛人工鱼礁区和非礁区调查共发现浮游植物72种,其中硅藻门63种,因此本实验将硅藻作为鰉的主要饵料进行分析。李明德等[24]对不同体长组鰉的食物数量和胃盲囊饱满度研究发现,同一时期同一地点的鰉,不同体长组间食性差异虽小,但摄食强度存在差异,体长为200~249 mm的鰉摄食强度最大,体长250~399 mm的鰉摄食强度较200~249 mm略低,体长大于400 mm的鰉摄食强度明显降低,与鰉生长速度相对应。鰉丰满度和附脂系数在II龄达到最高值,此后随年龄增长而降低[24]。II龄鰉平均体长为210 mm,摄食强度最旺盛,胃盲囊饱满度最高,脂肪累积较多,丰满度最高[24]。礁区和非礁区捕获的鰉体长变化范围为205~500 mm,这可能是造成鰉平均体长随硅藻丰度升高而降低的原因。

    鰉体长的GAM模型结果表明,浮游动物丰度是影响体长的关键因子,浮游动物生物量虽然与CPUE具有一定相关性,但并非影响体长的关键因子。随着体长的增加,鰉的主要饵料生物由浮游动物转变为浮游植物,体长低于15 mm的鰉饵料生物以浮游动物为主,体长15~45 mm的鰉则以浮游动物和浮游植物为主,体长大于200 mm的鰉饵料生物主要以浮游植物为主,浮游动物出现的频率和数量均低于幼鱼[4, 40]。鰉生长过程中食性的转变可能导致体长较大的成鱼倾向分布于浮游动物丰度较低的水域中。本研究发现,平均体长大于400 mm的站位,浮游动物丰度均小于260 个/m3,而浮游动物丰度高于800 个/m3的站位,鰉平均体长均低于270 mm。

4.   结语
  • 本研究分析了莱州湾芙蓉岛人工鱼礁区鰉CPUE及平均体长随环境要素的变化,人工鱼礁的建设对鰉资源具有一定影响。2014—2017年春季和秋季,鰉在礁区的分布主要受到人工鱼礁、水温、浮游动物、浮游植物的影响,盐度、溶解氧、pH并非影响鰉分布的关键因素。鰉在人工鱼礁区出现的概率大于非礁区,随着水温的升高,鰉出现的概率降低;CPUE随水温的升高呈现先升高后降低的趋势,并在8 °C达到最高值;硅藻丰度小于3×105 个/m3时,鰉平均体长随硅藻丰度上升而下降,随后趋于稳定;鰉平均体长随浮游动物丰度增加而呈现降低趋势。

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