MTA-BME Water Research Group, Műegyetem rkp. 3, 1111 Budapest, Hungary.
MTA-BME Water Research Group, Műegyetem rkp. 3, 1111 Budapest, Hungary.
Sci Total Environ. 2021 Apr 10;764:143636. doi: 10.1016/j.scitotenv.2020.143636. Epub 2020 Dec 17.
During the past decades, on-line monitoring of freshwater lakes has developed rapidly. To use high frequency time-series in lake management, novel models are needed that are simple and provide insight into the complexity of phytoplankton dynamics. Chlorophyll a (Chl), a proxy for phytoplankton biomass and environmental drivers were monitored on-line in large, shallow Lake Balaton during the vegetation periods between 2001 and 2018. Growth and non-growth (G and non-G) states of algae were deduced from daily change in Chl. Random forests (RF) were used to find stochastic response rules of phytoplankton to growth-supporting environmental habitat templates. The stochastic G/non-G state was translated into long-term daily biomass dynamics by a deterministic biomass model to assess uncertainty and to distinguish between inevitable and unpredictable blooms. A biomass peak was qualified as inevitable or unpredictable if the lower 95% confidence limit of simulations exceeded or remained at the baseline Chl level, respectively. Compared to a stochastic null model based on monthly Markovian transition probabilities, RF-based models captured wax and wane of biomass realistically. Timing of peaks could be better simulated than their magnitude, likely because habitat templates were primarily determined by light whereas peak sizes might depend on unmeasured processes, such as phosphorus availability. In general, algal growth was favored by wind-induced sediment resuspension that decreased light availability but simultaneously enhanced the P supply. Seasonal temperature and an integral of departures from the "normal" seasonal temperature over 2 to 3 generations were important drivers of phytoplankton growth, whereas short-term (diel and day to day) changes in water temperature appeared to be irrelevant. Four types of years could be distinguished during the study period with respect to algal growth conditions. The present modeling approach can reasonably be used even in highly variable aquatic environments when 3 to 4 years of daily data are available.
在过去的几十年中，淡水湖泊的在线监测得到了迅速发展。为了在湖泊管理中使用高频时间序列，需要开发简单且能深入了解浮游植物动态复杂性的新型模型。叶绿素 a（Chl）是浮游植物生物量的一个指标，同时也是环境驱动因素的一个指标，本研究在 2001 年至 2018 年期间，对大而浅的巴拉顿湖（Balaton）的植被期进行了在线监测。藻类的生长和非生长（G 和 non-G）状态是根据 Chl 的日变化推断出来的。随机森林（RF）用于寻找浮游植物对支持生长的环境生境模板的随机响应规则。通过确定性生物量模型将随机 G/non-G 状态转化为长期每日生物量动态，以评估不确定性并区分必然和不可预测的水华。如果模拟的下限 95%置信区间超过或保持在基线 Chl 水平，则将生物量峰值定性为必然或不可预测。与基于每月马尔可夫转移概率的随机 null 模型相比，基于 RF 的模型更真实地捕捉到了生物量的涨落。与模拟峰值幅度相比，其时间可以更好地模拟，这可能是因为生境模板主要由光决定，而峰值大小可能取决于未测量的过程，例如磷的可用性。总的来说，藻类的生长受到风引起的底泥再悬浮的影响，底泥再悬浮会降低光的可用性，但同时会增强磷的供应。季节性温度以及 2 到 3 代期间偏离“正常”季节性温度的积分是浮游植物生长的重要驱动因素，而短期（昼夜和每天）水温变化似乎无关紧要。在研究期间，可以根据藻类生长条件将年份分为四种类型。当有 3 到 4 年的每日数据可用时，本研究中的这种建模方法即使在高度变化的水生环境中也可以合理使用。
