Couture, R.-M. | Search bioRxiv

bioRxiv2025

Interannual surface water CO2 and O2 dynamics during fall in a small headwater lake

Lake metabolism is often quantified using continuous measures of dissolved oxygen (O2), where a 1: -1 stoichiometry with carbon dioxide (CO2) is assumed because of their roles in photosynthesis and respiration, respectively. However, many other physical, chemical, and biological processes decouple dissolved O2 and CO2 concentrations in lakes. Tracking departures from 1:-1 stoichiometry may provide insights into larger scale ecosystem functioning, particularly during fall when temperatures change and destratification occurs. Using continuous measures of both dissolved O2 and CO2 in a small temperate headwater lake, we looked at the interannual gas departure signals during fall over seven years. The beginning of fall, defined here as the start of leaf colour change, differed among years but coincided well with the onset of lake destratification and a shift in surface gas concentrations. Fall surface CO2 accumulation rates varied considerably, whereas O2 depletion rates were rather similar among years. Departure signals were broadly related to interannual differences in climate: more CO2 accumulated in the surface during the hottest-wettest fall compared to the coldest-driest one (0.81 and 0.37 {micro}mol L-1 d-1, respectively), presumably from more catchment than hypolimnetic inputs. Lower CO2 accumulation occurred during years with prolonged hypolimnetic hypoxia potentially through enhanced CO2 consumption by methanogenesis. Other internal biological phenomena influenced fall departure signals, including a large metalimnetic oxygen peak, and higher fall surface primary production. We suggest gas departures during fall provide an integrative metabolic fingerprint for temperate stratified lakes, as well as insights into winter-priming conditions. HighlightsO_LIAnnual fall surface water CO2 accumulation rates vary more than O2 depletion rates. C_LIO_LIExternal fall inputs and internal processes influence gas departures differently. C_LIO_LIFall gas departures may act as an integrative metabolic signal in temperate lakes. C_LI

ecology↗

bioRxiv2019

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).

ecology↗