Predictive modelling of eelgrass (Zostera marina) depth limits
Predictive modelling of eelgrass (Zostera marina) depth limits
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大叶藻（Zostera marina）深度限制的预测模型
DOI：
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发表时间：
2005
期刊：
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通讯作者：
D. Krause‐Jensen
中科院分区：
文献类型：
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作者：
T. M. Greve;D. Krause‐Jensen
大叶藻（Zostera marina L.）可以描述在显示出大的塞奇深度差异的区域或时间段之间的深度极限的基本差异。然而，这些模型无法预测在任何特定时间特定地点的精确深度限制。在这项研究中，我们的目标是提高回归模型的能力，预测最大深度限制：（1）假设鳗鱼深度限制响应的变化，在塞奇深度的时间延迟1-2年，（2）包括其他水质变量，除了塞奇深度，（3）考虑到调节深度限制的因素可能会有所不同年之间和网站之间。我们无法通过在深度限制对塞奇深度变化的响应中引入系统延迟来改进模型。失败的原因可能是我们方法的系统性，因为一些网站的响应延迟，而另一些网站则没有。当在多元回归模型中加入额外的水质变量时，模型的解释力增加。其中，单独的塞奇深度解释了58%的深度限制的变化，除了冬季[NH 4 +]和最大水深作为自变量的解释力增加到71%。这些模型适用于某一特定年份的数据，但如果包括若干年份（1989-1998年）的数据，则只有35%的深度界限变化可由这三个因素解释。更详细的分析表明，该条例的鳗鱼草深度限制有很大的不同年份之间和网站之间，并考虑到这些信息的模型得到进一步改进。我们的研究结果证实了以前的研究表明，光是最重要的参数，在调控的鳗草的深度限制，但也揭示了复杂的深度限制的调控没有在早期的研究中表达。有限的定殖潜力可能会延迟改善光照条件的反应，缺氧/缺氧和营养物质的间接影响可能会阻止鳗草达到光照水平所允许的深度限制。因此，可以通过考虑特定于站点的信息，考虑到鳗鱼草的生长条件的基础上，预测深度限制的电源。
Empirical models relating secchi depths to maximum depth limits of eelgrass (Zostera marina L.) can describe basic differences in depth limits between areas or time periods exhibiting large differences in secchi depth. However, these models cannot predict the precise depth limit at a particular site at any specific time. In this study we aim to improve the ability of regression models to predict maximum depth limits by: (1) assuming that eelgrass depth limits respond to changes in secchi depth with a temporal delay of 1–2 years, (2) including other water-quality variables in addition to secchi depth, and (3) taking into account that factors regulating depth limits may vary between years and between sites. We were not able to improve the models by introducing a systematic delay in the response of depth limits to changes in secchi depths. The reason for this failure is likely to have been the systematic nature of our approach, since some sites responded with a delay, while others did not. The explanatory power of the models increased when additional water-quality variables were added in a multiple regression model. Where secchi depth alone explained 58% of the variations in depth limits, addition of winter [NH4+] and maximum water depth as independent variables increased the explanatory power to 71%. These models applied to data from one specific year, but when data from several years (1989–1998) were included, only 35% of the variation in depth limits could be explained by the three factors. More detailed analyses showed that the regulation of eelgrass depth limits varied considerably between years and between sites, and the models were further improved by taking this information into account. Our results confirmed previous studies by showing light to be the most important parameter in the regulation of eelgrass depth limits, but also revealed a complexity in the regulation of depth limits not expressed in earlier studies. Limited colonisation potentials may delay the response to improved light conditions, and hypoxia/anoxia and indirect effects of nutrients may prevent eelgrass from attaining the depth limit that light levels would allow. The power to predict depth limits on the basis of secchi depths can therefore be improved by taking site-specific information on eelgrass growth conditions into account.