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Long-Term Influence of Climate and Experimental Eutrophication Regimes on Phytoplankton 4 Blooms 5 6 7 Kateri R

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Long-Term Influence of Climate and Experimental Eutrophication Regimes on Phytoplankton 4 Blooms 5 6 7 Kateri R bioRxiv preprint doi: https://doi.org/10.1101/658799; this version posted June 3, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Article type: Letter 2 3 Long-term influence of climate and experimental eutrophication regimes on phytoplankton 4 blooms 5 6 7 Kateri R. Salk1, Jason J. Venkiteswaran2, Raoul-Marie Couture3, Scott N. Higgins4, Michael J. 8 Paterson4, and Sherry L. Schiff5 9 10 1Nicholas School of the Environment, Duke University, Durham, North Carolina, USA 11 2Department of Geography and Environmental Studies, Wilfrid Laurier University, Ontario, 12 Canada 13 3Department of Chemistry, Université Laval, Quebec, Canada 14 4IISD Experimental Lakes Area Inc., Manitoba, Canada 15 5Department of Earth and Environmental Sciences, University of Waterloo, Ontario, Canada 16 17 18 Corresponding author: 19 Kateri Salk 20 [email protected] 21 Nicholas School of the Environment 22 Duke University 23 Durham, NC 27708 24 USA 25 26 Author Contribution Statement 27 KRS conducted modeling and analysis efforts. RMC provided expertise on model application 28 and validation. SLS and JJV provided guidance on the research questions. KRS, RMC, JJV, and 29 SLS analyzed model fit, guided scenario analysis, and provided interpretations of results. SNH 30 and MJP provided guidance on historical data analyses, model input data, and postprocessing 31 analyses. KRS wrote the manuscript, and all coauthors contributed edits to the manuscript. 32 1 bioRxiv preprint doi: https://doi.org/10.1101/658799; this version posted June 3, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 33 Significance Statement 34 Harmful algal blooms and eutrophication are key water quality issues worldwide. 35 Managing algal blooms is often difficult because multiple drivers, such as climate change and 36 nutrient loading, act concurrently and potentially synergistically. Long-term datasets and 37 simulation models allow us to parse the effects of interacting drivers of blooms. The 38 performance of our model depended on the ratio of nitrogen to phosphorus inputs, suggesting 39 that complex biological dynamics control blooms under variable nutrient loads. We found that 40 blooms were dampened under a “no climate change” scenario, suggesting that the interaction of 41 nutrient loading and increased temperature intensifies blooms. Our results highlight successes 42 and gaps in our ability to model blooms, helping to establish future management 43 recommendations. 44 45 Data Availability Statement 46 Data and metadata will be made available in a GitHub repository 47 (https://github.com/biogeochemistry/Lake-227). Upon manuscript acceptance, the repository will 48 be made publicly available and a DOI will be provided. We request that data users contact the 49 Experimental Lakes Area directly, per their data use policy (http://www.iisd.org/ela/wp- 50 content/uploads/2016/04/Data-Terms-And-Conditions.pdf). 51 52 53 Abstract 54 Phytoplankton blooms respond to multiple drivers, including climate change and nutrient 55 loading. Here we examine a long-term dataset from Lake 227, a site exposed to a fertilization 56 experiment (1969–present). Changes in nitrogen:phosphorus loading ratios (high N:P, low N:P, 57 P-only) did not impact mean annual biomass, but blooms exhibited substantial inter- and intra- 58 annual variability. We used a process-oriented lake model, MyLake, to successfully reproduce 59 lake physics over 48 years and test if a P-limited model structure predicted blooms. The timing 60 and magnitude of blooms was reproduced during the P-only period but not for the high and low 61 N:P periods, perhaps due to N acquisition pathways not currently included in the model. A 62 model scenario with no experimental fertilization confirmed P loading is the major driver of 63 blooms, while a scenario that removed climate-driven temperature trends showed that increased 64 spring temperatures have exacerbated blooms beyond the effects of fertilization alone. 65 66 Keywords 67 Eutrophication, phytoplankton blooms, climate change, nutrient loading, ecosystem modeling 68 2 bioRxiv preprint doi: https://doi.org/10.1101/658799; this version posted June 3, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 69 Introduction 70 Eutrophication and harmful algal blooms (HABs) are global water quality issues, 71 increasing in occurrence and intensity in lakes worldwide (Heisler et al. 2008; Paerl et al. 2018). 72 Anthropogenic nutrient loading is the major driver of excess phytoplankton growth (Carpenter et 73 al. 1998), and climate-related drivers including temperature and precipitation are additional 74 influencing factors (Paerl et al. 2011; Sinha et al. 2017). Changes in these external drivers can 75 induce regime shifts in lakes, which often occur abruptly (Ratajczak et al. 2018). Mitigating the 76 negative effects of eutrophication and HABs requires a nuanced understanding of the interacting 77 effects of external drivers (Elliott 2012). 78 Whole-ecosystem experiments have demonstrated their potential to reveal the effect of 79 nutrient loading on lake eutrophication (Schindler 1998). Lake 227 at the IISD-Experimental 80 Lakes Area (IISD-ELA; Ontario, Canada) is a well-known example of one such whole- 81 ecosystem manipulation. The experiment has been conducted for five decades, during which 82 artificial N and P loadings were added in molar ratios of 27:1 (1969-1974), 9:1 (1975-1989), and 83 0:1 (P-only, 1990-present) while P loads were held constant. These abrupt transitions in external 84 nutrient loads and stoichiometric ratios enable examination of phytoplankton responses to 85 changing fertilization. Decreases in N loading in Lake 227 resulted in the dominance of 86 diazotrophic cyanobacteria during the mid-summer phytoplankton bloom (Hendzel et al. 1994), 87 whereas annual biomass remained unchanged due to a constant P supply (Schindler et al. 2008; 88 Paterson et al. 2011; Higgins et al. 2017). Lake 227 offers a unique opportunity to leverage a 89 five-decade fertilization experiment to test our ability to characterize the drivers of 90 phytoplankton blooms and examine the role of climate on interannual variation in biomass. 3 bioRxiv preprint doi: https://doi.org/10.1101/658799; this version posted June 3, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 91 The response of lakes to concurrent drivers of eutrophication can be explored using 92 process-oriented models (Couture et al. 2018; Page et al. 2018; Janssen et al. 2019). A variety of 93 lake ecosystem models exist that include physical processes and nutrient dynamics, varying in 94 modeling approach, spatial dimensions, and complexity of process representation (Robson 95 2014). Models using a one-dimensional (1D) physical driver have been successfully applied to 96 small lakes where horizontal mixing is rapid (Saloranta and Andersen 2007). Given the tradeoffs 97 in choosing an appropriate model for a given system, 1D models strike a balance between 98 computation effort, data requirements, and ability to determine the extent to which physical 99 drivers will influence the nutrient cycling and phytoplankton growth over multi-year timescales. 100 Here, we explore inter- and intra-annual bloom dynamics, using statistical approaches 101 and process-oriented modeling to integrate climatic variables and fertilization regime as drivers 102 of phytoplankton growth. Specifically, we aim to (1) determine if the hypothesis of P-limitation 103 appropriately reproduces bloom dynamics across different fertilization regimes, and (2) 104 disentangle the concurrent effects of climate and nutrient loading on phytoplankton blooms over 105 five decades. 106 107 Methods 108 I. Historical monitoring data 109 Lake 227 is a small (5 ha) headwater lake in northwest Ontario, Canada. The hydrology 110 of Lake 227 is relatively simple, with a bowl-shaped morphometry (mean depth 4.4 m, 111 maximum depth 10 m) and negligible groundwater inputs. Starting in 1969 and continuing 112 through present, physical, chemical, and biological data were collected biweekly in Lake 227 113 during the ice-free season and 2-4 times during the ice-covered period. Meteorological data, 4 bioRxiv preprint doi: https://doi.org/10.1101/658799; this version posted June 3, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 114 including air temperature, wind speed, precipitation, and radiation, were collected at a station 115 jointly operated with Environment and Climate Change Canada, located 4.1 km from Lake 227. 116 Loads of nutrients from the Lake 227 watershed were estimated using data from streams flowing 117 into a nearby reference lake, Lake 239, which were monitored continuously for flow and weekly 118 for nutrient chemistry during the ice-free season. Measurements of wind speed, radiation, ice 119 phenology, and inflow volume and temperature were scaled to the conditions at Lake 227 120 (Supplemental Information). 121 Statistical analyses of climate, nutrient, and phytoplankton data were conducted to 122 determine trends in drivers and effects over time. Meteorological datasets included year-round 123 measurements, whereas lake and stream datasets included measurements only from the ice-free 124 season.
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