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Reagan Errera

Publications and source records attributed to Reagan Errera.

2 recordsLinked to original sources

Diverse cyanopeptides follow distinct temporal succession patterns in freshwater harmful algal blooms

Toxic cyanobacterial harmful algal blooms (cyanoHABs) threaten freshwater resources globally and are intensifying with increasing eutrophication. Bloom toxicity is strongly influenced by intraspecific variation in the biosynthetic repertoires of toxic cyanobacteria, yet few studies examine the diversity of cyanobacterial cyanopeptides beyond hepatotoxic microcystins . To understand the dynamics and drivers of cyanopeptide diversity in cyanoHABs, we analyzed temporal patterns of cyanobacteria, metabolites, and their biosynthetic gene clusters (BGCs) in western Lake Erie using a 7-year time series (2016–2022) of metagenomic and metabolomic data. Our findings demonstrate that shifts from Microcystis to Dolichospermum occur later in the bloom season, coinciding with lower temperatures. Modules of co-varying BGCs (biosynthesis modules) from these genera were identified with hierarchical clustering, with uncharacterized BGCs among the most abundant. Biosynthesis modules rich in nonribosomal peptide synthetases (NRPS) peaked in early August, coinciding with elevated levels of inorganic nitrogen, warmer temperatures, and high Microcystis abundance. In contrast, modules rich in polyketide synthases (PKS) and ribosomally synthesized and post-translationally modified peptides (RiPPs) peaked following the Microcystis maximum in mid-August. Metabolomic analyses confirmed that metabolites followed shared seasonal patterns with their associated biosynthesis modules, forming three phases characterized by (i) microcystins, (ii) anabaenopeptins and aeruginosins, and (iii) aerucyclamides. These phases co-varied with bottom-up and top-down pressures, with later phases coinciding with increased microbially processed organic nitrogen and reduced detection of grazers. This study demonstrates consistent seasonal patterns of cyanobacterial metabolite succession and co-occurrence beyond microcystins, suggesting tradeoffs between biosynthetic resource demands and ecological controls.

Ohio

Impact of spectral resolution on quantifying cyanobacteria in lakes and reservoirs: A machine-learning assessment

Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of best-available multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large (N = 905) database of colocated in situ radiometric spectra and PC are employed. We first examine the performance of selected widely used machine-learning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance ( R r s ) spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a full-width at half-maximum (FWHM) of < 6 nm. Results show that the multilayer perceptron (MLP) neural network applied to HICO spectral configurations (median errors < 65%) outperforms other ML models. This model is subsequently applied to R r s spectra resampled to the band configuration of existing satellite instruments and of the one proposed for the next Landsat sensor. These results confirm that employing MLP models to estimate PC from hyperspectral data delivers tangible improvements compared with retrievals from multispectral data and benchmark algorithms (with median errors between ~73% and 126%) and shows promise for developing a globally applicable cyanobacteria measurement approach.

IEEE Transactions in Geoscience and Remote Sensing