海岸带沉水植被丰度年际变化及影响因子研究
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摘要
沉水植被(Submerged Aquatic Vegetation)是一类植物体全部生长于水层下面的水生植物，为大型水生、草本类植物。沉水植被为水生生物提供了生存的环境；对于抑制生物性和非生物性悬浮物质、改善水下光照条件、溶解氧和净化水质等具有重要的作用，是健康河口是否退化的一个重要的指示器。流域的经济社会发展可以改变河口的物种多样性、改变水生生态系统中的食物网结构以及底栖生物群落组成结构。海岸带污染源主要通过影响河口水质来影响沉水植被丰度，一些因子对海岸带沉水植被丰度的影响主要通过河口特征来实现的。
本文通过对北美切斯比克海湾99个河口海岸带1984-2009年间沉水植被丰度的年际变化和主要影响因子（流域土地利用类型、盐度分区、降雨年份、平均波浪高度、沉积物和沉水植被平均生长深度等）进行分析，得到如下结果：
1、在建设用地影响下，沉水植被的丰度整体上呈现上升趋势，2002年丰度值达到顶峰，约为2.4%，2009年下降到1.6%。在农业用地影响下，沉水植被丰度的年际间呈现上升与下降的波动变化。在林地和混合用地影响下，沉水植被丰度于1984-1998年呈现上升趋势，1998-2009年出现上升与下降交替的现象。
2、1984-2009年间，不同盐度区沉水植被丰度呈现在波动中整体上升的趋势。其中：淡水区从0%上升到2.0%，在1999-2000年间出现较大的下降现象（从1.6%下降到0.4%）；低盐度地区于1984-2001年呈现上升趋势（从0.2%上升到3.1%），2001-2009年出现小范围下降（从3.1%下降到1.8%）；中盐度区1984-1998年呈现逐渐上升趋势（由0%上升到1.7%），2003-2009年丰度值逐渐降低（从2.0%下降到0.7%）；高盐度区沉水植被丰度值整体较高，1984-1998年间从1.1%上升到1.8%，2002-2003年呈现大的降低（从2.0%下降到0.6%），2007-2009年又逐渐上升（从0.6%上升到1.3%）。
3、三种降雨年份分区对沉水植被丰度无规律性影响。
4、沉水植被丰度与平均波浪高度呈极显著正相关关系（r=0.306，p=0.002）、与河口宽度呈极显著正相关关系(r=0.442，p=0.001)、与海岸线分形维度呈微弱正相关关系（r=0.290，p=0.004）、与潮汐也呈微弱正相关关系（r=0.272，p=0.007）。
5、分类回归树模型分析表明，各因子对沉水植被丰度的影响大小依次为流域与河口面比、海岸线分形维度、盐度和平均波浪高度，共可解释沉水植被丰度空间变异的63%。
Submerged aquatic vegetation（SAV）consists of a taxonomically diverse groupof plants that lives entirely beneath the water surface. SAV provides habitat andsupplies food for aquatic life, absorbs excess nutrients, and helps purify the water.Since the1960s, SAV coverage has experienced dramatic decline worldwide due toserious deterioration of water quality in coastal ecosystems.
In recent years, landscape analyses have been used to predict direct or indirecteffects of geographic characteristics on aquatic organisms. In this study, we analyzedthe inter-annual change and effects of influence factors on the spatial variation of SAVabundance based on the long-term dataset (2004-2009) from99sub-estuaries andtheir linked coastal sub-watersheds of the Chesapeake Bay in the United States.Coastal watershed land use is an important factor influencing inputs of nutrient andsediment to its associated sub-estuary, and thus indirectly affects SAV abundance. Ourresults showed that:
（1）SAV abundance generally arises in1984-2002in developed land, peaked at2.4%in2002, but drops to1.6%in2009. The inter-annual variation of SAV abundance inagricultural land turns on the trend of increasing from0.1-0.9%in1984-2002, andfluctuated up and down in2002-2009. SAV abundance in mixed land rise from0.6%to2.0%in1984-1998, and fluctuated up and down in1998-2009. The inter-annualvariation pattern of SAV abundance in forested land is similar to mixed land,increasing in1984-1998and fluctuated in1998-2009.
（2）SAV abundance in different salinity shows an trend of increasing in the overallwith fluctuations in1984-2009.The trend of inter-annual variation of SAV abundancein tidal fresh goes up from0to2.0%in1984-2009; SAV abundance drops greatlyfrom1.6%to0.4%between1999and2000. The trend in mesohaline goes up from0.2%to3.1%in1984-2001, declining weatly from3.1%to1.8%in2001-2009. Thetrend in oligohaline gose up from0to1.7%in1984-1998, declines to0.6%in2001, then declines from2.0%to0.7%in2003-2009. The SAV abundance of polyhalineis generally high in1984-1998，which is different from areas of other salinity, arisingfrom1.1to1.8; it drops from2.0%to0.6%in2002-2003, and then goes up from0.6%to1.3%.
（3）The three types of rainfall years have no regularity partition on the abundance ofsubmerged aquatic vegetation.
（4）SAV abundance have significantly positive correlations with average waveheight（r=0.306，p=0.002）, SAV abundance increases as the average wave heightincrease; SAV abundance have significantly positive correlations with mouth width（r=0.442，p=0.001）, SAV abundance increases as the mouth width increase; SAVabundance have weak positive correlations with fractal demension（r=0.290，p=0.004）,SAV abundance increases as the fractal demension increase; SAV abundance alsohave weak positive correlations with tidal range（r=0.272，p=0.007）, SAV abundanceincreases as the tidal range increase.
（5）Using the classification and regression tree (CART) model, I predicted that ratioof watershed area/sub-estuary area had the greatest impact on SAV abundance thatappeared at the highest level of the tree, followed by shoreline fractal dimension,salinity regime, and average wave height. These four geographical variables explained63%of the total spatial variation in SAV abundance across the Chesapeake Bay.
本文通过对北美切斯比克海湾99个河口海岸带1984-2009年间沉水植被丰度的年际变化和主要影响因子（流域土地利用类型、盐度分区、降雨年份、平均波浪高度、沉积物和沉水植被平均生长深度等）进行分析，得到如下结果：
1、在建设用地影响下，沉水植被的丰度整体上呈现上升趋势，2002年丰度值达到顶峰，约为2.4%，2009年下降到1.6%。在农业用地影响下，沉水植被丰度的年际间呈现上升与下降的波动变化。在林地和混合用地影响下，沉水植被丰度于1984-1998年呈现上升趋势，1998-2009年出现上升与下降交替的现象。
2、1984-2009年间，不同盐度区沉水植被丰度呈现在波动中整体上升的趋势。其中：淡水区从0%上升到2.0%，在1999-2000年间出现较大的下降现象（从1.6%下降到0.4%）；低盐度地区于1984-2001年呈现上升趋势（从0.2%上升到3.1%），2001-2009年出现小范围下降（从3.1%下降到1.8%）；中盐度区1984-1998年呈现逐渐上升趋势（由0%上升到1.7%），2003-2009年丰度值逐渐降低（从2.0%下降到0.7%）；高盐度区沉水植被丰度值整体较高，1984-1998年间从1.1%上升到1.8%，2002-2003年呈现大的降低（从2.0%下降到0.6%），2007-2009年又逐渐上升（从0.6%上升到1.3%）。
3、三种降雨年份分区对沉水植被丰度无规律性影响。
4、沉水植被丰度与平均波浪高度呈极显著正相关关系（r=0.306，p=0.002）、与河口宽度呈极显著正相关关系(r=0.442，p=0.001)、与海岸线分形维度呈微弱正相关关系（r=0.290，p=0.004）、与潮汐也呈微弱正相关关系（r=0.272，p=0.007）。
5、分类回归树模型分析表明，各因子对沉水植被丰度的影响大小依次为流域与河口面比、海岸线分形维度、盐度和平均波浪高度，共可解释沉水植被丰度空间变异的63%。
Submerged aquatic vegetation（SAV）consists of a taxonomically diverse groupof plants that lives entirely beneath the water surface. SAV provides habitat andsupplies food for aquatic life, absorbs excess nutrients, and helps purify the water.Since the1960s, SAV coverage has experienced dramatic decline worldwide due toserious deterioration of water quality in coastal ecosystems.
In recent years, landscape analyses have been used to predict direct or indirecteffects of geographic characteristics on aquatic organisms. In this study, we analyzedthe inter-annual change and effects of influence factors on the spatial variation of SAVabundance based on the long-term dataset (2004-2009) from99sub-estuaries andtheir linked coastal sub-watersheds of the Chesapeake Bay in the United States.Coastal watershed land use is an important factor influencing inputs of nutrient andsediment to its associated sub-estuary, and thus indirectly affects SAV abundance. Ourresults showed that:
（1）SAV abundance generally arises in1984-2002in developed land, peaked at2.4%in2002, but drops to1.6%in2009. The inter-annual variation of SAV abundance inagricultural land turns on the trend of increasing from0.1-0.9%in1984-2002, andfluctuated up and down in2002-2009. SAV abundance in mixed land rise from0.6%to2.0%in1984-1998, and fluctuated up and down in1998-2009. The inter-annualvariation pattern of SAV abundance in forested land is similar to mixed land,increasing in1984-1998and fluctuated in1998-2009.
（2）SAV abundance in different salinity shows an trend of increasing in the overallwith fluctuations in1984-2009.The trend of inter-annual variation of SAV abundancein tidal fresh goes up from0to2.0%in1984-2009; SAV abundance drops greatlyfrom1.6%to0.4%between1999and2000. The trend in mesohaline goes up from0.2%to3.1%in1984-2001, declining weatly from3.1%to1.8%in2001-2009. Thetrend in oligohaline gose up from0to1.7%in1984-1998, declines to0.6%in2001, then declines from2.0%to0.7%in2003-2009. The SAV abundance of polyhalineis generally high in1984-1998，which is different from areas of other salinity, arisingfrom1.1to1.8; it drops from2.0%to0.6%in2002-2003, and then goes up from0.6%to1.3%.
（3）The three types of rainfall years have no regularity partition on the abundance ofsubmerged aquatic vegetation.
（4）SAV abundance have significantly positive correlations with average waveheight（r=0.306，p=0.002）, SAV abundance increases as the average wave heightincrease; SAV abundance have significantly positive correlations with mouth width（r=0.442，p=0.001）, SAV abundance increases as the mouth width increase; SAVabundance have weak positive correlations with fractal demension（r=0.290，p=0.004）,SAV abundance increases as the fractal demension increase; SAV abundance alsohave weak positive correlations with tidal range（r=0.272，p=0.007）, SAV abundanceincreases as the tidal range increase.
（5）Using the classification and regression tree (CART) model, I predicted that ratioof watershed area/sub-estuary area had the greatest impact on SAV abundance thatappeared at the highest level of the tree, followed by shoreline fractal dimension,salinity regime, and average wave height. These four geographical variables explained63%of the total spatial variation in SAV abundance across the Chesapeake Bay.
引文
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