Climate induced changes in phenology of lake plankton communities: Implications for the match / mismatch of species interactions

长期研究表明，温带地区水生生态系统的季节性演替正在加快。这些更改很可能产生复杂的，并且可能有时间滞后的响应，从而导致到目前为止紧密耦合（匹配）的过程的解耦（不匹配）。以前的研究基本上集中在单个物种对变暖的反应上，而忽略了种间的相互作用。在AQUASHIFT中，我们的目标是确定植物和浮游动物群落过去的相移和滞后响应，以及观测到的和预测的气候变暖引起的物种相互作用的后续变化。我们的方法方法集中在统计数据探索，时间序列分析和建模，基于浮游生物的长期记录（24年），物理和化学数据来自浅层，多菌，富营养化m<s:1> gelsee（柏林）。我们期望通过热状态和物种相互作用的变化将直接温度驱动的响应与间接响应分开。将建立一个随机和/或确定性模型来描述冬季和春季气象条件与m<s:1>盖尔湖春季浮游植物发育之间的联系。模型的发展建立在以前的统计分析的基础上，并将由平行时间序列分析产生的随机项加以补充。该模型将与现有的湖泊物理模型相耦合。这种离线耦合模式系统将用于预测在一系列气候变化情景下浮游植物春季繁殖的时间和强度的变化。

Long term studies suggest that seasonal succession in aquatic ecosystems is currently advancing in temperate latitudes. Those changes are likely to generate complex, and possibly time lagged responses leading to a decoupling (mismatch) of so far tightly coupled (matched) processes. Previous studies have basically focussed on individual species' responses to warming, while neglecting inter-specific interactions. Within AQUASHIFT we aim to identify past phase shifts and time-lagged responses in phyto- and zooplankton communities, and subsequent changes in species interaction induced by observed and projected climate warming. Our methodological approach is focussed on statistical data exploration, time series analysis, and modelling, based upon long-term records (24 years) of plankton, physical and chemical data from shallow, polymictic, eutrophic Müggelsee (Berlin). We anticipate to separate direct temperature driven responses from indirect responses through changes in thermal regime and species interaction. A stochastic and/or deterministic model will be created to describe the linkage between winter and spring meteorological conditions and vernal phytoplankton development in Müggelsee. Model development builds on previous statistical analysis and will be complemented by stochastic terms resulting from the parallel time series analysis. The model will be coupled to an existing lake physics model. This offline-coupled model system will be used to project changes in the timing and intensity of the phytoplankton spring blooms under a range of climate change scenarios.