Abstract Regulators require adequate information to select best practices with less ecosystem impacts for remediation of freshwater ecosystems after oil spills. Zooplankton are valuable indicators of aquatic ecosystem health as they play pivotal roles in biochemical cycles while stabilizing food webs. Compared with morphological identification, metabarcoding holds promise for cost-effective, high-throughput, and benchmarkable biomonitoring of zooplankton communities. The objective of this study was to apply DNA and RNA metabarcoding of zooplankton for ecotoxicological assessment and compare it with traditional morphological identification in experimental shoreline enclosures in a boreal lake. These identification methods were also applied in context of assessing response of the zooplankton community exposed to simulated spills of diluted bitumen (dilbit), with experimental remediation practices (enhanced monitored natural recovery and shoreline cleaner application). Metabarcoding detected boreal zooplankton taxa up to the genus level, with a total of 24 shared genera, and while metabarcoding-based relative abundance served as an acceptable proxy for biomass inferred by morphological identification (ρ ≥ 0.52). Morphological identification determined zooplankton community composition changes due to treatments at 11 days post-spill (PERMANOVA, p = 0.0143) while metabarcoding methods indicated changes in zooplankton richness and communities at 38 days post-spill (T-test, p

Abstract Regulators require adequate information to select best practices with less ecosystem impacts for remediation of freshwater ecosystems after oil spills. Zooplankton are valuable indicators of aquatic ecosystem health as they play pivotal roles in biochemical cycles while stabilizing food webs. Compared with morphological identification, metabarcoding holds promise for cost-effective, high-throughput, and benchmarkable biomonitoring of zooplankton communities. The objective of this study was to apply DNA and RNA metabarcoding of zooplankton for ecotoxicological assessment and compare it with traditional morphological identification in experimental shoreline enclosures in a boreal lake. These identification methods were also applied in context of assessing response of the zooplankton community exposed to simulated spills of diluted bitumen (dilbit), with experimental remediation practices (enhanced monitored natural recovery and shoreline cleaner application). Metabarcoding detected boreal zooplankton taxa up to the genus level, with a total of 24 shared genera, and while metabarcoding-based relative abundance served as an acceptable proxy for biomass inferred by morphological identification (ρ ≥ 0.52). Morphological identification determined zooplankton community composition changes due to treatments at 11 days post-spill (PERMANOVA, p = 0.0143) while metabarcoding methods indicated changes in zooplankton richness and communities at 38 days post-spill (T-test, p

Molot LA, Schiff SL, Venkiteswaran JJ, Baulch HM, Higgins SN, Zastepa A, Verschoor MJ, Walters D. 2021. Low sediment redox promotes cyanobacteria blooms across a trophic range: implications for man...

Molot LA, Schiff SL, Venkiteswaran JJ, Baulch HM, Higgins SN, Zastepa A, Verschoor MJ, Walters D. 2021. Low sediment redox promotes cyanobacteria blooms across a trophic range: implications for man...

Phytoplankton blooms are a global water quality issue, and successful management depends on understanding their responses to multiple and interacting drivers, including nutrient loading and climate change. Here, we examine a long-term dataset from Lake 227, a site subject to a fertilization experiment (1969–present) with changing nitrogen:phosphorus (N:P) ratios. We applied a process-oriented model, MyLake, and updated the model structure with nutrient uptake kinetics that incorporated shifting N:P and competition among phytoplankton functional groups. We also tested different temperature and P-loading scenarios to examine the interacting effects of climate change and nutrient loading on phytoplankton blooms. The model successfully reproduced lake physics over 48 yr and the timing, overall magnitude, and shifting community structure (diazotrophs vs. non-diazotrophs) of phytoplankton blooms. Intra- and interannual variability was captured more accurately for the P-only fertilization period than for the high N:P and low N:P fertilization periods, highlighting the difficulty of modeling complex blooms even in well-studied systems. A model scenario was also run which removed climate-driven temperature trends, allowing us to disentangle concurrent drivers of blooms. Results showed that increases in water temperature in the spring led to earlier and larger phytoplankton blooms under climate change than under the effects of nutrient fertilization alone. These findings suggest that successful lake management efforts should incorporate the effects of climate change in addition to nutrient reductions, including intensifying and/or expanding monitoring periods and incorporating climate change into uncertainty estimates around future conditions.

Phytoplankton blooms are a global water quality issue, and successful management depends on understanding their responses to multiple and interacting drivers, including nutrient loading and climate change. Here, we examine a long-term dataset from Lake 227, a site subject to a fertilization experiment (1969–present) with changing nitrogen:phosphorus (N:P) ratios. We applied a process-oriented model, MyLake, and updated the model structure with nutrient uptake kinetics that incorporated shifting N:P and competition among phytoplankton functional groups. We also tested different temperature and P-loading scenarios to examine the interacting effects of climate change and nutrient loading on phytoplankton blooms. The model successfully reproduced lake physics over 48 yr and the timing, overall magnitude, and shifting community structure (diazotrophs vs. non-diazotrophs) of phytoplankton blooms. Intra- and interannual variability was captured more accurately for the P-only fertilization period than for the high N:P and low N:P fertilization periods, highlighting the difficulty of modeling complex blooms even in well-studied systems. A model scenario was also run which removed climate-driven temperature trends, allowing us to disentangle concurrent drivers of blooms. Results showed that increases in water temperature in the spring led to earlier and larger phytoplankton blooms under climate change than under the effects of nutrient fertilization alone. These findings suggest that successful lake management efforts should incorporate the effects of climate change in addition to nutrient reductions, including intensifying and/or expanding monitoring periods and incorporating climate change into uncertainty estimates around future conditions.