Using metabarcoding and droplet digital PCR to investigate drivers of historical shifts in cyanobacteria from six contrasting lakes

A paleoecological investigation of recent cyanobacterial blooms and their drivers in two contrasting lakes

Cyanobacterial blooms are one of the most significant threats to global water security and freshwater biodiversity. Interactions among multiple stressors, including habitat degradation, species invasions, increased nutrient runoff, and climate change, are key drivers. However, assessing the role of anthropogenic activity on the onset of cyanobacterial blooms and exploring response variation amongst lakes of varying size and depth is usually limited by lack of historical records. In the present study we applied molecular, paleolimnological (trace metal, Itrax-µ-XRF and hyperspectral scanning, chronology), paleobotanical (pollen) and historical data to reconstruct cyanobacterial abundance and community composition and anthropogenic impacts in two dune lakes over a period of up to 1200 years. Metabarcoding and droplet digital PCR results showed very low levels of picocyanobacteria present in the lakes prior to about CE 1854 (1839-1870 CE) in the smaller shallow Lake Alice and CE 1970 (1963-1875 CE) in the larger deeper Lake Wiritoa. Hereafter bloom-forming cyanobacteria were detected and increased notably in abundance post CE 1984 (1982-1985 CE) in Lake Alice and CE 1997 (1990-2007 CE) in Lake Wiritoa. Currently, the magnitude of blooms is more pronounced in Lake Wiritoa, potentially attributable to hypoxia-induced release of phosphorus from sediment, introducing an additional source of nutrients. Generalized linear modelling was used to investigate the contribution of nutrients (proxy = bacterial functions), temperature, redox conditions (Mn:Fe), and erosion (Ti:Inc) in driving the abundance of cyanobacteria (ddPCR). In Lake Alice nutrients and erosion had a statistically significant effect, while in Lake Wiritoa nutrients and redox conditions were significant.

100 years of anthropogenic impact causes changes in freshwater functional biodiversity

Despite efforts from scientists and regulators, biodiversity is declining at an alarming rate. Unless we find transformative solutions to preserve biodiversity, future generations may not be able to enjoy nature's services. We have developed a conceptual framework that establishes the links between biodiversity dynamics and abiotic change through time and space using artificial intelligence. Here, we apply this framework to a freshwater ecosystem with a known history of human impact and study 100 years of community-level biodiversity, climate change and chemical pollution trends. We apply explainable network models with multimodal learning to community-level functional biodiversity measured with multilocus metabarcoding, to establish correlations with biocides and climate change records. We observed that the freshwater community assemblage and functionality changed over time without returning to its original state, even if the lake partially recovered in recent times. Insecticides and fungicides, combined with extreme temperature events and precipitation, explained up to 90% of the functional biodiversity changes. The community-level biodiversity approach used here reliably explained freshwater ecosystem shifts. These shifts were not observed when using traditional quality indices (e.g. Trophic Diatom Index). Our study advocates the use of high-throughput systemic approaches on long-term trends over species-focused ecological surveys to identify the environmental factors that cause loss of biodiversity and disrupt ecosystem functions.

Reconstructing historical time-series of cyanobacteria in lake sediments: Integrating technological innovation to enhance cyanobacterial management

The global rise of cyanobacterial blooms emphasizes the need to develop tools to manage water bodies prone to cyanobacterial dominance. Reconstructing cyanobacterial baselines and identifying environmental drivers that favour cyanobacterial dominance are important to guide management decisions. Conventional techniques for estimating cyanobacteria in lake sediments require considerable resources, creating a barrier to routine reconstructions of cyanobacterial time-series. Here, we compare a relatively simple technique based on spectral inferences of cyanobacteria using visible near-infrared reflectance spectroscopy (VNIRS) with a molecular technique based on real-time PCR quantification (qPCR) of the 16S rRNA gene conserved in cyanobacteria in 30 lakes across a broad geographic gradient. We examined the sedimentary record from two perspectives: 1) relationships throughout the entire core (without radiometric dating); 2) relationships post-1900s with the aid of radiometric dating (i.e., ²¹⁰Pb). Our findings suggest that the VNIRS-based cyanobacteria technique is best suited for reconstructing cyanobacterial abundance in recent decades (i.e., circa 1990 onwards). The VNIRS-based cyanobacteria technique showed agreement with those generated using qPCR, with 23 (76%) lakes showing a strong or very strong positive relationship between the results of the two techniques. However, five (17%) lakes showed negligible relationships, suggesting cyanobacteria VNIRS requires further refinement to understand where VNIRS is unsuitable. This knowledge will help scientists and lake managers select alternative cyanobacterial diagnostics where appropriate. These findings demonstrate the utility of VNIRS, in most instances, as a valuable tool for reconstructing past cyanobacterial prevalence.