Ecol Appl. 2015 Jan;25(1):186-99. doi: 10.1890/13-1677.1.
A Bayesian network model was developed to assess the combined influence of nutrient conditions and climate on the occurrence of cyanobacterial blooms within lakes of diverse hydrology and nutrient supply. Physicochemical, biological, and meteorological observations were collated from 20 lakes located at different latitudes and characterized by a range of sizes and trophic states. Using these data, we built a Bayesian network to (1) analyze the sensitivity of cyanobacterial bloom development to different environmental factors and (2) determine the probability that cyanobacterial blooms would occur. Blooms were classified in three categories of hazard (low, moderate, and high) based on cell abundances. The most important factors determining cyanobacterial bloom occurrence were water temperature, nutrient availability, and the ratio of mixing depth to euphotic depth. The probability of cyanobacterial blooms was evaluated under different combinations of total phosphorus and water temperature. The Bayesian network was then applied to quantify the probability of blooms under a future climate warming scenario. The probability of the "high hazardous" category of cyanobacterial blooms increased 5% in response to either an increase in water temperature of 0.8°C (initial water temperature above 24°C) or an increase in total phosphorus from 0.01 mg/L to 0.02 mg/L. Mesotrophic lakes were particularly vulnerable to warming. Reducing nutrient concentrations counteracts the increased cyanobacterial risk associated with higher temperatures.
建立了一个贝叶斯网络模型，以评估营养条件和气候对不同水文和营养供应的湖泊中蓝藻水华发生的综合影响。从位于不同纬度、大小和营养状态范围不同的 20 个湖泊中收集了理化、生物和气象观测数据。利用这些数据，我们构建了一个贝叶斯网络，（1）分析蓝藻水华发展对不同环境因素的敏感性，（2）确定蓝藻水华发生的概率。根据细胞丰度，水华分为低、中和高三个危害类别。决定蓝藻水华发生的最重要因素是水温、养分供应和混合深度与光深的比值。根据总磷和水温的不同组合，评估了蓝藻水华发生的概率。然后，将贝叶斯网络应用于量化未来气候变暖情景下水华发生的概率。水温和总磷的增加都会导致“高危害”类别的蓝藻水华发生概率增加 5%。水温升高 0.8°C（初始水温高于 24°C）或总磷从 0.01mg/L 增加到 0.02mg/L 都会导致这种情况。中营养湖泊尤其容易受到变暖的影响。降低营养浓度可以抵消与较高温度相关的蓝藻风险增加。
