Long-term influence of climate and experimental eutrophication regimes on phytoplankton blooms

Phytoplankton blooms respond to multiple drivers, including climate change and nutrient loading. Here we examine a long-term dataset from Lake 227, a site exposed to a fertilization experiment (1969-present). Changes in nitrogen:phosphorus loading ratios (high N:P, low N:P, P-only) did not impact mean annual biomass, but blooms exhibited substantial inter- and intra-annual variability. We used a process-oriented lake model, MyLake, to successfully reproduce lake physics over 48 years and test if a P-limited model structure predicted blooms. The timing and magnitude of blooms was reproduced during the P-only period but not for the high and low N:P periods, perhaps due to N acquisition pathways not currently included in the model. A model scenario with no experimental fertilization confirmed P loading is the major driver of blooms, while a scenario that removed climate-driven temperature trends showed that increased spring temperatures have exacerbated blooms beyond the effects of fertilization alone.\n\nSignificance StatementHarmful algal blooms and eutrophication are key water quality issues worldwide. Managing algal blooms is often difficult because multiple drivers, such as climate change and nutrient loading, act concurrently and potentially synergistically. Long-term datasets and simulation models allow us to parse the effects of interacting drivers of blooms. The performance of our model depended on the ratio of nitrogen to phosphorus inputs, suggesting that complex biological dynamics control blooms under variable nutrient loads. We found that blooms were dampened under a \"no climate change\" scenario, suggesting that the interaction of nutrient loading and increased temperature intensifies blooms. Our results highlight successes and gaps in our ability to model blooms, helping to establish future management recommendations.\n\nData Availability StatementData and metadata will be made available in a GitHub repository (https://github.com/biogeochemistry/Lake-227). Upon manuscript acceptance, the repository will be made publicly available and a DOI will be provided. We request that data users contact the Experimental Lakes Area directly, per their data use policy (http://www.iisd.org/ela/wp-content/uploads/2016/04/Data-Terms-And-Conditions.pdf).