Abstract：
The Okavango Delta region in Botswana experienced exceptionally intense landscape-wide cyanobacterial harmful algal blooms (CyanoHABs) in 2020. In this study, the drivers behind CyanoHABs were determined from thirteen independent environmental variables, including vegetation indices, climate and meteorological parameters, and landscape variables. Annual Land Use Land Cover (LULC) maps were created from 2017 to 2020, with ∼89% accuracy to compute landscape variables such as LULC change. Generalized Additive Models (GAM) and Structural Equation Models (SEM) were used to determine the most important drivers behind the CyanoHABs. Normalized Difference Chlorophyll Index (NDCI) and Green Line Height (GLH) algorithms served as proxies for chlorophyll-a (green algae) and phycocyanin (cyanobacteria) concentrations. GAM models showed that seven out of the thirteen variables explained 89.9% of the variance for GLH. The models showcased that climate variables, including monthly precipitation (8.8%) and Palmer Severity Drought Index- PDSI (3.2%), along with landscape variables such as changes in Wetlands area (7.5%), and Normalized Difference Vegetation Index (NDVI) (5.4%) were the determining drivers behind the increased cyanobacterial activity within the Delta. Both PDSI and NDVI showed negative correlations with GLH, indicating that increased drought conditions could have led to large increases in toxic CyanoHAB activity within the region. This study provides new information about environmental drivers which can help monitor and predict regions at risk of future severe CyanoHABs outbreaks in the Okavango Delta, Botswana, and other similar data-scarce and ecologically sensitive areas in Africa. Plain Language Summary: The waters of the Okavango Delta in Northern Botswana experienced an exceptional increase in toxic cyanobacterial activity in recent years. Cyanobacterial blooms have been shown to affect local communities and wildlife in the past. To determine the drivers behind this increased bloom activity, we analyzed the effects of thirteen independent environmental variables using two different statistical models. Within this research, we focused on vegetation indices, meteorological, and landscape variables, as previous studies have shown their effect on cyanobacterial activity in other parts of the world. While driver determination for cyanobacteria has been done before, the environmental conditions most important for cyanobacterial growth can be specific to the geographic setting of a study site. The statistical analysis indicated that the increases in cyanobacterial bloom activity within the region were mainly driven by persistent drier conditions. To our knowledge, this is the first study to determine the driving factors behind cyanobacterial activity in this region of the world. Our findings will help to predict and monitor areas at risk of future severe cyanobacterial blooms in the Okavango Delta and other similar African ecosystems.
摘要：
博茨瓦纳的奥卡万戈三角洲地区在2020年经历了异常强烈的景观范围内的蓝细菌有害藻华（CyanoHAB）。在这项研究中,CyanoHABs背后的驱动因素是由13个独立的环境变量确定的，包括植被指数，气候和气象参数，和景观变量。2017年至2020年创建了年度土地利用土地覆盖（LULC）地图，计算LULC变化等景观变量的准确率为89%。广义加法模型（GAM）和结构方程模型（SEM）用于确定CyanoHAB背后最重要的驱动因素。归一化叶绿素指数（NDCI）和绿线高度（GLH）算法用作叶绿素a（绿藻）和藻蓝蛋白（蓝藻）浓度的代理。GAM模型显示，13个变量中有7个解释了GLH的89.9％的方差。模型展示了气候变量，包括月降水量(8.8%)和帕尔默严重干旱指数-PDSI(3.2%)，连同景观变量，如湿地面积的变化(7.5%)，和归一化植被指数（NDVI）（5.4％）是三角洲内蓝藻活动增加的决定性驱动因素。PDSI和NDVI均与GLH呈负相关，表明干旱条件的增加可能导致该地区有毒的CyanoHAB活性大幅增加。这项研究提供了有关环境驱动因素的新信息，这些信息可以帮助监测和预测奥卡万戈三角洲未来有严重CyanoHABs爆发风险的地区。博茨瓦纳,以及非洲其他类似数据稀缺和生态敏感的地区。简明扼要的语言摘要：近年来，博茨瓦纳北部的奥卡万戈三角洲水域的有毒蓝细菌活动异常增加。过去，蓝藻水华已被证明会影响当地社区和野生动植物。为了确定这种增加的开花活动背后的驱动因素，我们使用两种不同的统计模型分析了13个独立环境变量的影响。在这项研究中，我们专注于植被指数，气象,和景观变量，正如以前的研究表明它们对世界其他地区的蓝藻活动的影响。虽然以前已经做过蓝藻的驾驶员确定，对蓝藻生长最重要的环境条件可能特定于研究地点的地理环境。统计分析表明，该地区蓝藻水华活性的增加主要是由持续的干燥条件驱动的。据我们所知,这是第一项确定世界该地区蓝藻活动背后驱动因素的研究。我们的发现将有助于预测和监测奥卡万戈三角洲和其他类似非洲生态系统中未来有严重蓝藻水华风险的地区。
