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Metagenome-assembled genomes were generated for two xenic cyanobacterial strains collected from aquatic sources in Kenya and sequenced by NovaSeq S4. Here, we report the classification and genome statistics of Microcystis panniformis WG22 and Limnospira fusiformis. 

Abstract The Winam Gulf (Kenya) is frequently impaired by cyanobacterial harmful algal blooms (cHABs) due to inadequate wastewater treatment and excess agricultural nutrient input. While phytoplankton in Lake Victoria have been characterized using morphological criteria, our aim is to identify potential toxin‐producing cyanobacteria using molecular approaches. The Gulf was sampled over two successive summer seasons, and 16S and 18S ribosomal RNA gene sequencing was performed. Additionally, key genes involved in production of cyanotoxins were examined by quantitative PCR. Bacterial communities were spatially variable, forming distinct clusters in line with regions of the Gulf. Taxa associated with diazotrophy were dominant near Homa Bay. On the eastern side, samples exhibited elevatedcyrAabundances, indicating genetic capability of cylindrospermopsin synthesis. Indeed, near the Nyando River mouth in 2022,cyrAexceeded 10 million copies L⁻¹where there were more than 6000Cylindrospermopsisspp. cells mL⁻¹. In contrast, the southwestern region had elevatedmcyEgene (microcystin synthesis) detections near Homa Bay whereMicrocystisandDolichospermumspp. were observed. These findings show that within a relatively small embayment, composition and toxin synthesis potential of cHABs can vary dramatically. This underscores the need for multifaceted management approaches and frequent cyanotoxin monitoring to reduce human health impacts. 

Summary Disturbances, here defined as events that directly alter microbial community composition, are commonly studied in host‐associated and engineered systems. In spite of global change both altering environmental averages and increasing extreme events, there has been relatively little research into the causes, persistence and population‐level impacts of disturbance in the dynamic coastal ocean. Here, we utilize 3 years of observations from a coastal time series to identify disturbances based on the largest week‐over‐week changes in the microbiome (i.e. identifying disturbance as events that alter the community composition). In general, these microbiome disturbances were not clearly linked to specific environmental factors and responsive taxa largely differed, aside from SAR11, which generally declined. However, several disturbance metagenomes identified increased phage‐associated genes, suggesting that unexplained community shifts might be caused by increased mortality. Furthermore, a category 1 hurricane, the only event that would likely be classifieda priorias an environmental disturbance, was not an outlier in microbiome composition, but did enhance a bloom in seasonally abundant phytoplankton. Thus, as extreme environmental changes intensify, assumptions of what constitutes a disturbance should be re‐examined in the context of ecological history and microbiome responses.