The determinants of algal trait dynamics in phytoplankton communities over vertical, seasonal, and inter-annual gradients

浮游生物群落可以是非常动态的，并受到非生物因素（物理和化学）和生物相互作用的高度影响。因此，这些因素选择藻类性状值的特定组合，其在相应条件下提供最佳生长和/或最低损失率。了解哪些因素和相互作用选择了哪些性状组合，可以提供对系统的深刻理解和可预测性。然而，到目前为止，大多数分析藻类特征值及其驱动因素的研究通常忽略了中上层系统内的变异性。从基于性状的角度来看，特别有趣的是沿着垂直轴的变异性沿着。沿着这个轴，在许多情况下，光与养分有效性的相反垂直梯度提供了垂直性状分离的可能性。本研究的目的是分析非生物因素和生物相互作用对藻类性状垂直分布的影响，以及相应的年内和年间动态。我们强调，垂直维度是藻类群落动态的一个主要决定因素，需要考虑到这些复杂系统的透彻理解。我们将统计分析来自Rappbode水库的长期数据集，Rappbode水库是位于Harz山区的一个大型深层饮用水水库。该数据集提供了40多年来浮游植物群落的垂直分辨率数据和相应的水质数据。此外，我们将实验测试的光和营养物质的可用性对浮游植物群落的综合和单独的影响，通过孵化水样品来自三个不同的深度在一个相互移植的设计。为了解开光和营养物的影响，我们将使用不同的处理方法来培养水：透析管，富含营养物的瓶子和没有营养物的瓶子。我们将在季节更替的三个不同日期进行这项实验。最后，我们将使用浮游生物动态（浮游植物和浮游动物）的垂直解析数学模型，以模拟基于性状选择的浮游植物群落组成。该模型将与Rappbode水库的物理模型相结合，可以对季节性温度分层和混合进行逼真的模拟。该模型可以概括从我们的统计和实验工作中发现的结果，并将提供一般的和可检验的预测藻类群落中的性状分布的空间和时间模式。

Planktonic communities can be very dynamic and are highly influenced by abiotic factors (physical and chemical) and by biotic interactions. Thereby, these factors select for specific combinations of algal trait values, which offer the best growth and/or lowest loss rates at the respective conditions. A knowledge of which factors and interactions select for which trait combinations provides a deep understanding and predictability of systems. So far, however, most studies analysing the combination of algal trait values and their drivers usually neglect the variability within pelagic systems. Particularly interesting from a trait based perspective is the variability along the vertical axis. Along this axis, opposing vertical gradients of light versus nutrient availability offer in many cases the possibility for a vertical trait separation. The aim of this study is to analyse the influence of abiotic factors and biotic interactions on the vertical algal trait distribution and the corresponding dynamics within and between years. We stress that the vertical dimension is a major determinant of algal community dynamics, which needs to be taken into account for a thorough understanding of these complex systems. We will statistically analyse a long-term dataset from the Rappbode Reservoir, a large and deep drinking water reservoir located in the Harz mountain area. This dataset provides vertically resolved data of the phytoplankton community and the corresponding water quality data for a period of more than forty years. Additionally, we will experimentally test the combined and separate effects of light and nutrient availability on the phytoplankton community by incubating water samples originating from three different depths in a reciprocal transplant design. To disentangle the effects of light and nutrients we will incubate the water using different treatments: dialysis tubes, bottles enriched with nutrients, and bottles without nutrients. We will conduct this experiment at three different dates during seasonal succession. Finally, we will use a vertically resolved mathematical model of the plankton dynamics (phytoplankton and zooplankton) in order to simulate the phytoplankton community composition based on trait selection. The model will be coupled with a physical model of Rappbode Reservoir and allows a realistic simulation of seasonal temperature stratification and mixing. This model allows to generalize the results found from our statistical and experimental work and will provide general and testable predictions about spatial and temporal patterns of trait distributions in algal communities.