Meriggi Carlotta, Mehrshad Maliheh, Johnson Richard K, Laugen Ane T, Drakare Stina
Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Uppsala, 750 07, Sweden.
Department of Natural Sciences, University of Agder, Kristiansand, Norway.
ISME Commun. 2023 Jun 6;3(1):56. doi: 10.1038/s43705-023-00264-2.
Species distribution models (SDMs) calibrated with bioclimatic variables revealed a high probability for range expansion of the invasive toxin producing cyanobacterium, Raphidiopsis raciborskii to Sweden, where no reports of its presence have hitherto been recorded. While predictions focused on the importance of climate variables for possible invasion, other barriers to dispersal and successful colonization need to be overcome by the species for successful invasion. In this study, we combine field-based surveys of R. raciborskii (microscopy and molecular analysis using species-specific primers) of 11 Swedish lakes and in-silico screening of environmental DNA using 153 metagenomic datasets from lakes across Europe to validate the SDMs prediction. Field-based studies in lakes with high/low predicted probability of occurrence did not detect the presence of R. raciborskii, and in-silico screening only detected hints of its presence in 5 metagenomes from lakes with probability ranging from 0.059 to 0.825. The inconsistencies between SDMs results and both field-based/in-silico monitoring could be due to either sensitivity of monitoring approaches in detecting early invasions or uncertainties in SDMs that focused solely on climate drivers. However, results highlight the necessity of proactive monitoring with high temporal and spatial frequency.
利用生物气候变量校准的物种分布模型（SDMs）显示，产生毒素的入侵蓝藻拉氏尖头藻（Raphidiopsis raciborskii）向瑞典扩散的可能性很高，此前瑞典尚无关于其存在的报告。虽然预测聚焦于气候变量对可能入侵的重要性，但该物种要成功入侵，还需克服其他扩散和成功定殖的障碍。在本研究中，我们结合了对瑞典11个湖泊的拉氏尖头藻进行的实地调查（使用物种特异性引物进行显微镜检查和分子分析），以及利用来自欧洲各地湖泊的153个宏基因组数据集对环境DNA进行的计算机模拟筛选，以验证物种分布模型的预测。在预测出现概率高/低的湖泊中进行的实地研究未检测到拉氏尖头藻的存在，而计算机模拟筛选仅在来自概率范围为0.059至0.825的湖泊的5个宏基因组中检测到其存在的线索。物种分布模型结果与实地/计算机模拟监测之间的不一致，可能是由于监测方法在检测早期入侵时的敏感性，或者是物种分布模型仅关注气候驱动因素所存在的不确定性。然而，结果凸显了以高时空频率进行主动监测的必要性。
