There has been limited research on the abiotic and biotic factors affecting the prevalence of phago-mixotrophy (prevMixo) among nanophytoplankton across freshwater ecosystems. In recent years, large-scale sampling campaigns like the EPA-National Lakes Assessment and the NSERC LakePulse survey have generated surface water community composition data for hundreds of lakes across North America, covering large environmental gradients. We present results from our analyses of the nanophytoplankton community data from these two surveys, focusing on a taxonomic comparison of the mixoplankton communities across ecoregions and multivariate analyses of the environmental drivers of the prevMixo. We identified potentially phago-mixotrophic taxa in the majority of sites and across all ecozones sampled. Lake trophic state was identified as the main predictor of nanophytoplankton resource-acquisition strategy assemblages, with lower prevalence and diversity of mixoplankton communities in more eutrophic lakes. Lake trophic state also controlled the composition of the mixoplankton community and increased total phosphorus levels were associated with a loss of mixoplankton diversity. This study represents the most comprehensive assessment of the prevMixo in lake nanophytoplankton communities to date spanning hundreds of sites and a dozen ecozones.

Abstract The particulate absorption coefficient is one of the fundamental inherent optical properties describing interactions of light with material in water. Its spectral properties contain important information about chemical and biological constituents. It is often partitioned into algal and non‐algal fractions which provide useful information describing phytoplankton. Particulate absorption coefficient has been routinely measured in the ocean particularly to calibrate remote sensing algorithms. However, the methods to measure marine algal and non‐algal absorbing fractions might fail in freshwaters due to difficulties extracting green‐algae pigments and cyanobacterial phycocyanin and the high organic content of the non‐algal particles, making direct bleaching biased. In this work, we describe a method with sequential extraction, bleaching, and post‐processing to obtain unbiased pigments and non‐algal absorption fractions in freshwater environments, and we compare it against the resulting fractions obtained by only extraction or bleaching, using samples collected from 649 lakes across Canada. The resulting non‐algal particles spectra from our method appear free of interfering pigments while maintaining spectral shapes, as verified by the higher correlation coefficient between the 400 and 700 nm exponential coefficient ( S , often referred to as slope) of the non‐algal particles spectra and the organic fraction of total suspended solids, and by having a better correlation between the ratio of absorption coefficient of phytoplankton at 620 and 676 nm and cyanobacterial biomass percentage. Overall, this method solves the two problems in freshwater particulate absorption partitioning associated with (1) unextracted pigments with methanol extraction methods and (2) bias introduced to non‐algal absorption spectra from NaOH bleaching.

Abstract In vivo pigment fluorescence methods allow simple real‐time detection and quantification of freshwater algae and cyanobacteria. Available models are still limited to high‐cost fluorometers, validated for single instruments or individual water bodies, preventing data comparison between multiple instruments, and thus, restricting their use in large‐scale monitoring programs. Moreover, few models include corrections for optical interference (water turbidity and colored dissolved organic matter, CDOM). In this study, we developed simple models to predict phytoplankton and cyanobacterial chlorophyll a ( Chl a ) concentrations based on Chl a and C‐phycocyanin in vivo fluorescence, using multiple low‐cost handheld fluorometers. We aimed to: (1) fit models to mixed cyanobacterial and microalgal cultures; (2) cross‐calibrate nine fluorometers of the same brand and series; (3) correct the CDOM and turbidity effects; and (4) test the algorithms’ performance with natural samples. We achieved comparable results between nine instruments after the cross‐calibration, allowing their simultaneous use. We obtained algorithms for total and cyanobacterial Chl a estimation. We developed parametric corrections to remove CDOM and turbidity interferences in the algorithms. Five sampling sites (from a lake, a stream, and an estuary) were used to test the algorithms using eight cross‐calibrated fluorometers. The models showed their best performance after CDOM and turbidity corrections (total Chl a : R 2 = 0.99, RMSE = 7.8 μ g Chl a L −1 ; cyanobacterial Chl a : R 2 = 0.98, RMSE = 9.8 μ g Chl a L −1 ). In summary, our models can quantify total phytoplankton and cyanobacterial Chl a in real time with multiple low‐cost fluorometers, allowing its implementation in large‐scale monitoring programs.