Department of Botany, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
Aquatic Contaminants Research Division, Environment and Climate Change Canada, Montreal, Canada.
Harmful Algae. 2020 Jul;97:101859. doi: 10.1016/j.hal.2020.101859. Epub 2020 Jun 29.
There is growing evidence that cyanobacterial blooms are becoming more common in different parts of the world; within this context, predictive cyanobacteria models have an essential role in lake management. Several models have been successfully used in temperate systems to describe the main drivers of cyanobacterial blooms, but relatively less work has been conducted in the Tropics. We analyzed data from six Brazilian reservoirs and from five Canadian lakes using a combination of regression tree analyses and variation partitioning to evaluate the similarities and differences between regions. Our results, together with a synthesis of the literature from different latitudes, showed that trophic state (i.e. nutrients), climatic variables (e.g., temperature and/or precipitation) and hydrodynamic regimes (i.e. water residence time) are significant drivers of cyanobacteria biomass over several scales. Nutrients came out as the primary predictor in both regions, followed by climate, but when all systems were pooled together, water residence time came out as most important. The consistency in variables identified between regions suggests that these drivers are widely important and cyanobacteria responded quite similarly in different geographical settings and waterbody types (i.e. lakes or reservoirs). However, more work is needed to identify key thresholds across latitudinal gradients. Taken together, these results suggest that multi-region syntheses can help identify drivers that predict broad-scale patterns of cyanobacteria biomass.
越来越多的证据表明，蓝藻水华在世界不同地区变得越来越普遍；在这种情况下，预测蓝藻模型在湖泊管理中具有重要作用。已经有几种模型在温带系统中成功用于描述蓝藻水华的主要驱动因素，但在热带地区的相关工作相对较少。我们使用回归树分析和变异划分组合分析了来自六个巴西水库和五个加拿大湖泊的数据，以评估不同地区之间的相似性和差异。我们的结果，以及来自不同纬度的文献综述表明，营养物质（即养分）、气候变量（例如温度和/或降水）和水动力状况（即水停留时间）是在多个尺度上影响蓝藻生物量的重要驱动因素。在两个地区中，养分都是首要预测因子，其次是气候，但当所有系统都集中在一起时，水停留时间成为最重要的因素。在不同地区之间确定的变量的一致性表明，这些驱动因素非常重要，并且蓝藻在不同的地理环境和水体类型（即湖泊或水库）中反应非常相似。然而，需要进一步的工作来确定跨纬度梯度的关键阈值。总的来说，这些结果表明，多区域综合分析可以帮助确定预测蓝藻生物量广泛模式的驱动因素。
