长江口及邻近海域浮游植物生长的多环境效应因子影响解析研究
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摘要
我国东海长江口及杭州湾附近海域,大约在北纬27-32oN之间,等深线约30-50m之间,盐度26-30范围内,浮游植物的生物量较其邻近海域要高。这一方面造成了某些经济鱼类产卵和索饵场的形成,另一方面也造成了该海域成为我国的赤潮多发区和底层水缺氧区。而海洋浮游植物的生长除了与浮游藻类种群的生态学特点有关外,海水的温度、盐度、营养盐、光照、微量元素等诸多环境因子也会对其生长产生影响。对东海长江口及邻近海域而言,营养盐、温度以及由于由于泥沙含量造成的水体透光性不同等因素似乎是造成该海区浮游植物生物量较高的关键原因。但是,这些环境因子的综合影响尚没有多航次、多季节的大量调查数据来分析总结,尤其是各因子在浮游植物生长的相对影响程度没有直接的相关报道。因此,本论文针对长江口及邻近海域浮游植物生长的多环境效应因子影响程度不甚明确的问题,通过大面调查与培养实验相结合的方式,利用2002-2007年共14个航次的调查数据,应用主成分分析方法,结合多元线性回归分析和效应因子模拟计算方法,解析了营养盐、温度、浊度、盐度、石油烃、Cu、Pb、Zn、Cd等对浮游植物生长的影响程度。论文的研究成果对于揭示长江口及邻近海域赤潮发生的生态学机制具有重要意义。主要工作及结论如下:
1、总结并分析了20世纪80年代以来长江口及邻近海域浮叶绿素的平面分布特征。
长江口及邻近海域叶绿素浓度总体上呈现由河口、沿岸向外海先逐渐升高,然后再逐渐降低的趋势。叶绿素高值区既没有出现在营养盐较高的近岸海区,也没有出现在透光性较高的外海海区,其高值区大约在盐度(29.8±1.3)范围出现。在该范围内,叶绿素一般是呈斑块状分布的特点,总体上出现若干个高值中心,但高值中心的个数、数值及位置随季节和年代变化而略有差异。季节变化上,春、夏季较高,秋、冬季较低,且春、夏季的空间分布变异较大,局部海域的叶绿素浓度较高,冬季有向河口回缩的趋势。目前,研究海域的chla浓度整体上处于较高水平,尤其是局部海域的浓度比历史资料要高。
2、利用2002-2003年共5个航次的现场调查数据,应用主成分分析方法,筛选出了影响长江口及邻近海域浮游植物生长的主要环境因子。
在提取的5个主成分中,第一主成分的方差贡献率达30.2%,主要反映了长江径流等陆源输入对海区的影响,影响的环境因子主要包括营养盐浓度和水体透光性。第二主成分的方差贡献率达17.4%,主要反映了温度和石油烃的污染状况。结合特征向量矩阵及各主成分的贡献率,可以看出营养盐效应、水体光照及温度可能是影响长江口及邻近海域浮游植物生长的主要环境因子,而重金属等其他污染要素相对影响较小。
3、利用2002-2007年共14个航次的现场调查数据的主成分分析,进一步证明了营养盐、光照和温度是影响长江口及邻近海域浮游植物生长的主要环境因子,
在提取的两个主成分中,第一主成分主要反映了长江径流等陆源输入对该海区的影响,解释了方差的50.9%,其中,营养盐和浊度是同源的,且均与盐度的符号相反。第二主成分主要反映了温度的影响,解释了方差的19.7%。因此,长江径流等陆源输入的影响(包括营养盐效应和水体透光性)是浮游植物生长的首要影响因素,其次温度效应的影响。
4、在非线性转化为线性的基础上,应用多元线性回归方法,进一步分析了各主要环境因子对浮游植物生长的相对重要性。
根据通径系数(path coefficient,Pi)的大小可以看出,假如不考虑长江径流等陆源输入的影响,各环境因子对研究海域浮游植物生长的影响的相对重要性的顺序依次是:磷酸盐(PO4-P),硅酸盐(SiO3-Si),海水浊度(Turbidity),溶解无机氮(DIN)和温度(ST)。
5、应用生长效应因子原理模拟计算了长江口及邻近海域叶绿素的平面分布情况。并分析了长江口及邻近海域浮游植物生物量时空分布的主要控制因子。
在未考虑营养盐输入的情况下,模拟结果与实测结果的平面分布规律的相似性系数SI为(0.67±0.11),从数值上分析,两者在0.01水平上呈显著的正相关,Pearson相关系数为(0.56±0.21)。说明选择的效应因子计算公式及相应的参数基本合理。在影响浮游植物生长的多个环境因子中,营养盐效应、光照效应和温度效应是最重要的影响因子,其中,在不考虑营养盐补充的情况下,营养盐浓度效应和光照效应对浮游植物生长的贡献率相当,分别为45%和46%,温度的贡献率最小,约为9%。进一步分析表明,长江口及邻近海域浮游植物生物量的季节变化规律,主要受营养盐和温度共同控制,而空间分布的差异则主要由光照因子控制。
In the vicinity of the Changjiang Estuary and Hangzhou Bay exists a sea area between 27°N and 32°N where the phytoplankton biomass is higher than its adjacent area. The water depth of this area is of 30-50m and the salinity is of 26-30. Consequently, this leads to the formation of the famous Zhoushan fishery ground, however, this also leads to the frequently occurrence of harmful algal blooms (HABs) and hypoxia area in the bottom water there. In addition to its own ecological characters, the environmental factors such as seawater temperature, salinity, nutrients, solar radiation, trace elements and so on jointly influence the growth of phytoplankton. For the Changjiang Estuary and its adjacent area, it has been widely accepted that the phytoplankton growth is basically controlled by three key factors: nutrients, temperature and the light penetration owing to the concentration of turbidity. However, the combined effect of these environment factors has not been studied through sufficient in situ observations derived from multi-surveys, especially, to which extent of the influence of respective factors on the growth of phytoplankton is seldom reported. To answer this question, based on the observations of all the cruises from 2002 to 2007 the contribution of nutrients, seawater temperature, turbidity, salinity, petroleum carbons, Cu, Pb, Zn and Cd was analyzed in this dissertation using the method of multivariate statistical analysis and simulation of effective factors. This work definitely provides a valuable foundation for revealing the occurrence mechanism of HABs in Changjiang estuary and its adjacent area. The main work and key results are listed bellow:
1. The horizontal pattern of phytoplankton biomass since 1980s was analyzed and summarized.
The concentration of chlorophyll a (Chla) increased from coast to certain distance then decreased in further offshore direction. Higher Chla concentrations were observed neither in the inshore waters with higher nutrient concentrations, nor in the offshore waters with higher transparences. It occurred in the sea area with the saliniy of about 29.8psu±1.3psu. The Chla distribution there was normally in patchiness and generally has several higher-concentration centers. The numbers, the value and the location of these centers varied seasonally and annually. For the seasonal scale, the monthly average Chla concentration tended to show a“double-cycled”, higher in spring and summer whereas lower in autumn and winte. In addition, the spatial variation in spring and summer was larger than that of other seasons and the higher Chla concentration water mass withdrew to the estuary in winter. Nowdays, the Chla concentration was generally in a higher level; especially some certain areas are at their historically high level.
2. Based on the data retrieved from 5 cruises around Changjiang Estuary and its adjacent area during 2002-2003, the main environmental factors that controlling the phytoplankton growth were fixed by means of principal component analysis.
The first two principal components were retained for further interpretation among the five components extracted. The first principal component (PC1) accounted for 30.2% of the variance and clearly identified as the influence of terrigenous input of Changjiang River et al., which changed the nutrients concentration and water transparence. The second principal component (PC2) explained 17.4% of the variance and identified as the impact of temperature and petroleum hydrocarbons. From the eigenvector matrix and explained variance, the nutrients, light and temperature status of seawater were the main environmental factors that controlling the phytoplankton growth in the study area, while other polluted factors such as heavy metals had relatively minor effect.
3. Based on the data of 14 field cruises during 2002-2007, it further confirmed that the most important factors that affected the phytoplankton growth are nutrients, light and temperature.
Two principle components were extracted. The PC1 represented the impact of land runoffs such as Changjiang River and explained 50.9% of the variance. The component matrix showed that the source of nutrients and turbidity was homogenous and both were negatively correlated with salinity. The PC2 represented the impact of seawater temperature and explained 19.7% of the variance. To summarize, the growth of phytoplankton was controlled basically by terrigenous input, which changed the nutrient concentration and light penetration of the Changjiang Estuary and its adjacent area. the seawater temperature played a second role in this respect.
4. When the relationship between the Chla and the environmental factors changed from the nonlinear to linear, the relative importance of these factors was evaluated by means of multiple linear regressions.
The quantitative relationship between Chla and the environmental factors was established and the path coefficient (Pi) was calculated. The path analysis showed that phosphorus (PO4-P) had the biggest direct effect on the Chla. Based on the importance of their relationship with phytoplankton growth, the order of the five main growth parameters was concluded as: PO4-P, silicate (SiO3-Si), turbidity, dissolved inorganic nitrogen (DIN) and seawater temperature (ST).
5. The horizontal pattern of Chla in the Changjiang Estuary and its adjacent area was simulated using the growth–effect-factor theory. Moreover, the controlling factors of the spatial and temporal variation of the Chla concentration were analyzed.
Without nutrients input, the similarity index (SI) between the simulated distributions and the measured distributions of the Chla was (0.65±0.13). The Pearson correlation coefficients was (0.56±0.21) with P<0.01. This result showed that the calculation of the growth–effect-factor and the parameters chosen was reasonable. Based the analysis of the multiple effect factors, it showed that the contribution of the nutrients and the irradiation to the phytoplankton growth was similar, 45% and 46% respectively. The contribution of the ST was minimum as 9%. Further study showed that the nutrients and seawater temperature controlled the seasonal variation of the Chla, while the irradiation controlled the spatial variation of the Chla.
1、总结并分析了20世纪80年代以来长江口及邻近海域浮叶绿素的平面分布特征。
长江口及邻近海域叶绿素浓度总体上呈现由河口、沿岸向外海先逐渐升高,然后再逐渐降低的趋势。叶绿素高值区既没有出现在营养盐较高的近岸海区,也没有出现在透光性较高的外海海区,其高值区大约在盐度(29.8±1.3)范围出现。在该范围内,叶绿素一般是呈斑块状分布的特点,总体上出现若干个高值中心,但高值中心的个数、数值及位置随季节和年代变化而略有差异。季节变化上,春、夏季较高,秋、冬季较低,且春、夏季的空间分布变异较大,局部海域的叶绿素浓度较高,冬季有向河口回缩的趋势。目前,研究海域的chla浓度整体上处于较高水平,尤其是局部海域的浓度比历史资料要高。
2、利用2002-2003年共5个航次的现场调查数据,应用主成分分析方法,筛选出了影响长江口及邻近海域浮游植物生长的主要环境因子。
在提取的5个主成分中,第一主成分的方差贡献率达30.2%,主要反映了长江径流等陆源输入对海区的影响,影响的环境因子主要包括营养盐浓度和水体透光性。第二主成分的方差贡献率达17.4%,主要反映了温度和石油烃的污染状况。结合特征向量矩阵及各主成分的贡献率,可以看出营养盐效应、水体光照及温度可能是影响长江口及邻近海域浮游植物生长的主要环境因子,而重金属等其他污染要素相对影响较小。
3、利用2002-2007年共14个航次的现场调查数据的主成分分析,进一步证明了营养盐、光照和温度是影响长江口及邻近海域浮游植物生长的主要环境因子,
在提取的两个主成分中,第一主成分主要反映了长江径流等陆源输入对该海区的影响,解释了方差的50.9%,其中,营养盐和浊度是同源的,且均与盐度的符号相反。第二主成分主要反映了温度的影响,解释了方差的19.7%。因此,长江径流等陆源输入的影响(包括营养盐效应和水体透光性)是浮游植物生长的首要影响因素,其次温度效应的影响。
4、在非线性转化为线性的基础上,应用多元线性回归方法,进一步分析了各主要环境因子对浮游植物生长的相对重要性。
根据通径系数(path coefficient,Pi)的大小可以看出,假如不考虑长江径流等陆源输入的影响,各环境因子对研究海域浮游植物生长的影响的相对重要性的顺序依次是:磷酸盐(PO4-P),硅酸盐(SiO3-Si),海水浊度(Turbidity),溶解无机氮(DIN)和温度(ST)。
5、应用生长效应因子原理模拟计算了长江口及邻近海域叶绿素的平面分布情况。并分析了长江口及邻近海域浮游植物生物量时空分布的主要控制因子。
在未考虑营养盐输入的情况下,模拟结果与实测结果的平面分布规律的相似性系数SI为(0.67±0.11),从数值上分析,两者在0.01水平上呈显著的正相关,Pearson相关系数为(0.56±0.21)。说明选择的效应因子计算公式及相应的参数基本合理。在影响浮游植物生长的多个环境因子中,营养盐效应、光照效应和温度效应是最重要的影响因子,其中,在不考虑营养盐补充的情况下,营养盐浓度效应和光照效应对浮游植物生长的贡献率相当,分别为45%和46%,温度的贡献率最小,约为9%。进一步分析表明,长江口及邻近海域浮游植物生物量的季节变化规律,主要受营养盐和温度共同控制,而空间分布的差异则主要由光照因子控制。
In the vicinity of the Changjiang Estuary and Hangzhou Bay exists a sea area between 27°N and 32°N where the phytoplankton biomass is higher than its adjacent area. The water depth of this area is of 30-50m and the salinity is of 26-30. Consequently, this leads to the formation of the famous Zhoushan fishery ground, however, this also leads to the frequently occurrence of harmful algal blooms (HABs) and hypoxia area in the bottom water there. In addition to its own ecological characters, the environmental factors such as seawater temperature, salinity, nutrients, solar radiation, trace elements and so on jointly influence the growth of phytoplankton. For the Changjiang Estuary and its adjacent area, it has been widely accepted that the phytoplankton growth is basically controlled by three key factors: nutrients, temperature and the light penetration owing to the concentration of turbidity. However, the combined effect of these environment factors has not been studied through sufficient in situ observations derived from multi-surveys, especially, to which extent of the influence of respective factors on the growth of phytoplankton is seldom reported. To answer this question, based on the observations of all the cruises from 2002 to 2007 the contribution of nutrients, seawater temperature, turbidity, salinity, petroleum carbons, Cu, Pb, Zn and Cd was analyzed in this dissertation using the method of multivariate statistical analysis and simulation of effective factors. This work definitely provides a valuable foundation for revealing the occurrence mechanism of HABs in Changjiang estuary and its adjacent area. The main work and key results are listed bellow:
1. The horizontal pattern of phytoplankton biomass since 1980s was analyzed and summarized.
The concentration of chlorophyll a (Chla) increased from coast to certain distance then decreased in further offshore direction. Higher Chla concentrations were observed neither in the inshore waters with higher nutrient concentrations, nor in the offshore waters with higher transparences. It occurred in the sea area with the saliniy of about 29.8psu±1.3psu. The Chla distribution there was normally in patchiness and generally has several higher-concentration centers. The numbers, the value and the location of these centers varied seasonally and annually. For the seasonal scale, the monthly average Chla concentration tended to show a“double-cycled”, higher in spring and summer whereas lower in autumn and winte. In addition, the spatial variation in spring and summer was larger than that of other seasons and the higher Chla concentration water mass withdrew to the estuary in winter. Nowdays, the Chla concentration was generally in a higher level; especially some certain areas are at their historically high level.
2. Based on the data retrieved from 5 cruises around Changjiang Estuary and its adjacent area during 2002-2003, the main environmental factors that controlling the phytoplankton growth were fixed by means of principal component analysis.
The first two principal components were retained for further interpretation among the five components extracted. The first principal component (PC1) accounted for 30.2% of the variance and clearly identified as the influence of terrigenous input of Changjiang River et al., which changed the nutrients concentration and water transparence. The second principal component (PC2) explained 17.4% of the variance and identified as the impact of temperature and petroleum hydrocarbons. From the eigenvector matrix and explained variance, the nutrients, light and temperature status of seawater were the main environmental factors that controlling the phytoplankton growth in the study area, while other polluted factors such as heavy metals had relatively minor effect.
3. Based on the data of 14 field cruises during 2002-2007, it further confirmed that the most important factors that affected the phytoplankton growth are nutrients, light and temperature.
Two principle components were extracted. The PC1 represented the impact of land runoffs such as Changjiang River and explained 50.9% of the variance. The component matrix showed that the source of nutrients and turbidity was homogenous and both were negatively correlated with salinity. The PC2 represented the impact of seawater temperature and explained 19.7% of the variance. To summarize, the growth of phytoplankton was controlled basically by terrigenous input, which changed the nutrient concentration and light penetration of the Changjiang Estuary and its adjacent area. the seawater temperature played a second role in this respect.
4. When the relationship between the Chla and the environmental factors changed from the nonlinear to linear, the relative importance of these factors was evaluated by means of multiple linear regressions.
The quantitative relationship between Chla and the environmental factors was established and the path coefficient (Pi) was calculated. The path analysis showed that phosphorus (PO4-P) had the biggest direct effect on the Chla. Based on the importance of their relationship with phytoplankton growth, the order of the five main growth parameters was concluded as: PO4-P, silicate (SiO3-Si), turbidity, dissolved inorganic nitrogen (DIN) and seawater temperature (ST).
5. The horizontal pattern of Chla in the Changjiang Estuary and its adjacent area was simulated using the growth–effect-factor theory. Moreover, the controlling factors of the spatial and temporal variation of the Chla concentration were analyzed.
Without nutrients input, the similarity index (SI) between the simulated distributions and the measured distributions of the Chla was (0.65±0.13). The Pearson correlation coefficients was (0.56±0.21) with P<0.01. This result showed that the calculation of the growth–effect-factor and the parameters chosen was reasonable. Based the analysis of the multiple effect factors, it showed that the contribution of the nutrients and the irradiation to the phytoplankton growth was similar, 45% and 46% respectively. The contribution of the ST was minimum as 9%. Further study showed that the nutrients and seawater temperature controlled the seasonal variation of the Chla, while the irradiation controlled the spatial variation of the Chla.
引文
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