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Thermal diversity within marine phytoplankton communities

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Thermal diversity within marine phytoplankton communities

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Content Copyright 2020 Joshua D. Kling Thermal diversity within marine phytoplankton communities by Joshua D. Kling A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirement for the Degree DOCTOR OF PHILOSOPHY MARINE BIOLOGY AND BIOLOGICAL OCEANOGRAPHY August 2020 ii Table of Contents Dissertation Abstract: ............................................................................................................. iv Dissertation Introduction ........................................................................................................ 1 Rationale ............................................................................................... Error! Bookmark not defined. Understanding a changing global ocean ............................................................................................ 2 Specific Phytoplankton Responses .................................................................................................... 5 Global Change and Microbial Diversity ............................................................................................. 6 Thesis Motivation ............................................................................................................................. 8 References ..................................................................................................................................... 10 Chapter One: Transient exposure to novel high temperatures reshapes coastal phytoplankton communities .......................................................................................................................... 17 Abstract ......................................................................................................................................... 18 Introduction ................................................................................................................................... 18 Methods: ........................................................................................................................................ 19 Results ........................................................................................................................................... 22 Discussion ...................................................................................................................................... 25 References ..................................................................................................................................... 28 Chapter Two: Interactions between irradiance and thermal niche in a novel low-light, cold- adapted nano-diatom from a wintertime temperate estuary ................................................. 30 Abstract ......................................................................................................................................... 30 Introduction ................................................................................................................................... 32 Methods: ........................................................................................................................................ 34 Results ........................................................................................................................................... 39 Discussion ...................................................................................................................................... 46 Main Figures and Tables ................................................................................................................. 53 References ..................................................................................................................................... 60 Chapter Three: Dual thermal ecotypes co-exist within a nearly genetically-identical population of the unicellular marine cyanobacterium Synechococcus ...................................................... 68 Abstract: ........................................................................................................................................ 68 Introduction: .................................................................................................................................. 70 Methods: ........................................................................................................................................ 73 iii Results ........................................................................................................................................... 77 Discussion: ..................................................................................................................................... 82 Main Figures and Tables ................................................................................................................. 88 References: .................................................................................................................................... 94 Dissertation Conclusion & Future Work ............................................................................... 102 References: .................................................................................................................................. 108 Appendix ............................................................................................................................. 111 Chapter 1 Supplemental Figures and Tables ................................................................................. 112 Chapter 2 Supplemental Figures and Tables ................................................................................. 164 Chapter 3 Supplemental Figures and Tables ................................................................................. 172 iv Dissertation Abstract: Marine photosynthetic carbon fixation in the sunlit upper reaches of the ocean is almost entirely carried out by chlorophyll-containing, single-celled microorganisms, and is responsible for half of the net primary production on the planet. Because of this connection to the marine carbon cycle, it is essential to assess the responses of marine phytoplankton to global change. However, this work is challenged by the dazzling diversity of both eukaryotic and prokaryotic lineages which coexist in complex phytoplankton assemblages. My dissertation contributes to this effort by investigating how the diversity of phytoplankton influences their resilience to rising temperatures. In my first study, I used natural California coastal communities collected across three seasons to show that the phytoplankton assemblage as a whole was able to maintain growth well above typical temperature ranges. However, either steady or fluctuating temperatures exceeding the maximum threshold recorded in a decade-long observational dataset caused drastic rearrangements in the phytoplankton community, including the appearance of novel dominant species. My dissertation work also highlights that there are still unrecognized but environmentally-important taxa with bizarre and unexpected life histories and thermal responses, even in the most well-studied environments. In my second study, I characterized a recently isolated nanoplanktonic diatom from the Narragansett Bay Time Series that occupies a distinct low-light, low-temperature niche. This isolate demonstrated an unusual sensitivity to light, whereby its ability to respond to what should be favorable increases in temperature is constrained by light intensity. Six years of amplicon sequencing data from the time series site suggest that this diatom is a temperate wintertime/early spring specialist, and will likely not fare well in a warmer and more stratified future ocean. In addition to expanding knowledge of functional diversity at the species level, my work also examines the potential of intra-specific diversity to v house hidden adaptations to rising temperatures. Natural microbial populations are composed of distinct individual strains, whose relative abilities to contribute to the success of the whole population in a changing environment have not been well-studied. In my third study, I compared the thermal responses of 11 strains of the marine unicellular cyanobacterium Synechococcus simultaneously isolated from a single estuarine water sample to explore this cryptic intra-specific diversity. Surprisingly, these nearly genetically-identical strains showed distinct low and high temperature phenotypes. This study indicates that strain-level variation could be a key yet understudied element in the responses of phytoplankton to global change. Together, these studies highlight that the diversity of marine phytoplankton at the species and individual level includes both functional variability and redundancy relative to temperature. We can expect community composition to change over time in a warming ocean, reflecting the increasing abundance of preadapted groups or individual strains; however, wherever there are winners there are also losers. Besides providing new insights into the contribution of diversity to climate resilience, this dissertation also highlights the need to expand our knowledge of functional thermal traits, especially for typically under-studied pico- and nanoplankton which are often only known from sequence data. 1 Dissertation Introduction Rationale Widespread burning of fossil fuels since the Industrial Revolution has changed the planet’s atmosphere by increasing greenhouse gas concentrations. For instance, the current concentration of atmospheric CO2 is >400 ppm, nearly double what it was in the 18 th century (Pachauri et al. 2014). CO2 levels this high have not been seen for almost 12 million years, and in the past, these events were accompanied by substantial warming and sea ice loss (Hansen et al. 2006; Tripati et al. 2009). The high specific heat of seawater has meant that warming of the ocean’s surface has happened more slowly than warming of the land. Regardless, ocean heat content has increased 14 x10 22 J since 1975, and global mean Sea Surface Temperature (SST) has gone up 0.7°C in the last three decades, which is expected to lead to an average SST increase of ~4 °C by 2100 (Hansen et al. 2006; Levitus et al. 2009; Pachauri et al. 2014). This expected temperature increase has the potential to impact carbon cycling in the ocean, especially in regard to marine phytoplankton. Their activity in the sunlit upper portions of the ocean acts as a major source of CO2 removal through carbon fixation. This accounts for nearly half of global primary production (Field et al. 1998). As these phytoplankton sink out of the sunlit euphotic zone into deep ocean waters, much of their biomass is remineralized at mid- depth by heterotrophic bacteria, and eventually re-injected back into the atmosphere as CO2. However, a small portion of this organic material reaches the bottom where it can be buried in marine sediments, removing it from the atmosphere for geological time scales (Billett et al. 1983). Because this process is largely mediated by the activity of phytoplankton, investigations of how phytoplankton respond to rising temperatures will facilitate more accurate predictions about how the marine carbon cycle will be affected by anthropogenic climate change. This 2 dissertation examines to what extent adaptations to predicted warmer temperatures currently exist within phytoplankton communities, and reveals a surprising diversity of functional thermal traits within phytoplankton communities, and even within species. Understanding a changing global ocean The mechanisms of temperature impacts on marine phytoplankton are multitudinous, but can be broadly categorized as being either indirect or direct effects (Sarmiento et al. 2004; Riebesell et al. 2009). Indirect effects are those caused by impacts to the non-living environment which then affect the biota, such as changes in seawater stratification or oxygen content. These effects are highly variable across the ocean’s surface, changing based on season, latitude and proximity to land masses. Direct processes are those that act on the organisms themselves, causing changes in metabolism and growth which ultimately affect the organism’s fitness, survival, and contribution to biogeochemical cycles (Norberg 2004). Because marine phytoplankton assemblages contain very distantly-related phylogenies, direct effects often vary across taxonomic groups. Indirect and direct effects can also have interactions that may either mitigate or compound the overall impacts of rising temperatures (Feng et al. 2008; Hutchins and Fu 2017; Qu et al. 2018; Wang et al. 2019). Indirect processes One of the primary indirect impacts of warmer temperatures on marine environments will be the decrease in surface water density through expansion due to heating and freshening from ice melt (Dickson et al. 2002). Surface waters are typically depleted in limiting nutrients such as nitrate and phosphate, and photosynthetic growth relies on injections of nutrient-rich deep ocean waters through mixing and upwelling. Decreasing the density of surface waters increases stratification of the water column, which means that more energy is required for deep water to move into the surface (Behrenfeld et al. 2006). Increased stratification due to climate change is 3 thought to drive the observed expansion of low chlorophyll oceanic gyres (Polovina et al. 2008). However, it has been suggested that in some oligotrophic regions a more stable, stratified water column will not change overall net primary productivity, and may instead be counteracted by stronger Ekman transport (Dave and Lozier 2010). In coastal upwelling areas like California, Ekman transport plays a huge role in phytoplankton dynamics. Here, seasonal equatorward winds drive surface waters offshore, bringing up cold, nutrient-rich underlying waters. As winds slacken or seasons change, warmer oligotrophic conditions take over. During active upwelling, vertically-advected nutrients can stimulate massive phytoplankton blooms. These upwelling regions make up only 2% of the global ocean surface area, but provide 20% of global fisheries (Pauly and Christensen 1995). Currently, several conflicting models exist, describing how these important ecosystems will fare in a warmer climate. Roemmich and McGowan (1995) and Behrenfeld et al. (2006) both suggest that similar to oligotrophic regions, upwelling in these regimes will also be reduced with higher temperatures due to stratification, and result in a similar decline in primary production. However, Bakun (1990) and Sydeman et al. (2014) predict warming will have the opposite effect. They suggest that nearby terrestrial warming will create a more intense heat differential between the land and sea that will increase upwelling-favorable winds. Whether future upwelling intensifies or decreases, though, it is clear that either way there will be large implications for microbial biology and biogeochemistry in these productive coastal regimes (Capone and Hutchins 2013). Indirect temperature impacts are different at high latitudes. Powerful storms at these latitudes during the winter and spring drive mixing, and phytoplankton here typically experience higher nutrient concentrations than at lower latitudes. Consequently, these regions of the ocean 4 have some of the most productive waters on the planet. Light can be limiting however, due to low solar elevation and a deep mixed layer (Harrison and Li 2007; Riebesell et al. 2009). Stratification of the water column here can cause a shoaling of the thermocline, which could ameliorate some of this light limitation by keeping phytoplankton near the surface (Taylor and Ferrari 2011). In this way, structuring of the water column caused by warming has been hypothesized by some to increase primary production at these latitudes (Riebesell et al. 2009). Direct effects Temperature has system-wide impacts on ectothermic organisms at the biochemical level through changes to protein folding, enzyme efficiency, membrane fluidity, and nutrient incorporation (to name a few) and hence is one of the key drivers of phytoplankton growth (Eppley 1972; Raven and Geider 1988). Growth responses to temperature are often unequally skewed. This means rising temperatures are expected to gradually increase phytoplankton growth rates across most of their thermal niche up to an optimum temperature, but then growth rates decrease rapidly above that optimal temperature (Huey and Kingsolver 1989; Boyd et al. 2013). This pattern is thought to be driven by the difference in thermal sensitivity between photosynthetic and respiratory machinery (Yvon-Durocher et al. 2010). By upregulating respiration and downregulating carbon fixation, higher temperatures decrease the amount of carbon available for growth (Padfield et al. 2016). This translates into overall lower growth rates at temperatures above optimal levels. Comparisons of thermal response curves (which measure growth rates across a spectrum of temperatures) for isolated phytoplankton species suggest changes in SST will impact species differently, depending on taxonomy and location (Thomas et al. 2012; Boyd et al. 2013). For example, tropical and some temperate species have thermal optima very close to ambient temperatures, whereas at high latitudes, low temperatures may be growth-limiting (Boyd et al. 5 2013; Fu et al. 2014). One of the implications is that SST changes of only a few degrees could be lethal to many species currently living near their upper thermal limits at low latitudes. Already, there are numerous reports of poleward expansion of ranges for marine phytoplankton species in response to warming (Thomas et al. 2012). Specific Phytoplankton Responses In reality, indirect and direct temperature effects are not isolated from each other, and studies attempting to predict the phytoplankton response to climate change have to consider both. Lower nutrient availability (an indirect effect) can depress the thermal responses in marine phytoplankton, thereby limiting their ability to survive and adapt to stressful high temperatures (Marañón et al. 2018; Aranguren-Gassis et al. 2019). In a centric marine diatom even at lower temperatures where warming should increase growth rates, low nutrient concentrations precluded an increased growth rate in response to favorably increasing temperatures (Qu et al. 2018). In the marine diazotrophic cyanobacterium Trichodesmium, phosphorus limitation depressed growth rates at all temperatures and even decreased its thermal niche width (Qu et al., 2019). It should be recognized though, that there are nuances to the connection between nutrients and temperature. For instance, rising temperatures have been predicted to decrease the iron demand in marine diazotrophs and thus increase growth and nitrogen fixation rates (Jiang et al. 2018). When thinking about how phytoplankton respond to environmental challenges, such as rising SST, it must be recognized that there is a strong temporal component. Acclimation happens over short time periods, and is the ability to respond rapidly to changes in the environment physiologically, without any genetic changes. It is also referred to as phenotypic plasticity, whereby a single genotype can produce different phenotypes in response to variable environmental stressors (Ghalambor et al. 2015). Adaptation, or a change in phenotype caused by underlying mutations or other genetic changes, typically happens over many generations. 6 Long-term evolution experiments have shown that microbes are capable of significant adaptive responses to rising temperatures. Long-term culturing of the coccolithophore Emiliana huxleyii (Listmann et al. 2016) and the diatom Thalassiosira pseudonana (O’Donnell et al. 2018) resulted in higher thermal optima after hundreds of generations at high temperatures. Likewise, the freshwater chlorophyte Chlorella vulgaris was able to adapt to temperatures that originally inhibited growth after ~100 generations (Padfield et al. 2016). Acclimation and adaptation can also be connected. For instance, plastic responses can allow phytoplankton to better cope with environmental changes, making them more likely to survive into the next generation. This ultimately increases their potential to have long-term fixed adaptive responses (Ghalambor et al. 2015), as has been shown experimentally in marine organisms using the model picophytoplankton Ostreococcus tauri. When multiple strains were grown under elevated future CO2 concentrations for 400 generations, a positive correlation was observed between large initial plastic responses and the ability to evolve fixed adaptive responses (Schaum et al. 2013, 2016; Schaum and Collins 2014). Global Change and Microbial Diversity Biological oceanography has made a great deal of progress in defining the myriad ways that global change is reshaping marine ecosystems, and in learning how marine phytoplankton will respond to these changes. However, much of our understanding of phytoplankton responses relies on work that used a relatively small number of model organisms functioning as proxies for large swaths of biological diversity. These methods can be helpful for highlighting important general trends, such as changes in dominant taxa. For instance, shifts away from diatoms and towards much smaller nano- or picophytoplankton and cyanobacteria would have drastic consequences for carbon export and trophic relationships (Hare et al. 2007; Marinov et al. 2013). However, natural communities of photosynthetic microbes are dynamic and massively diverse 7 (Engelen et al. 2015; Delmont and Eren 2018; Lee et al. 2019), and it is still largely unknown how much functional diversity exists across the breadth of observed phylogenetic diversity. Relatively little work has been done to explore the present range of thermal responses currently existing in natural phytoplankton populations, and few data exist describing how resilient current phytoplankton assemblages are to thermal stress (Shade et al. 2012; Irwin et al. 2015). For instance, there are fundamental uncertainties about threshold temperatures that can cause large shifts in community composition. Also unknown are the scales at which diversity is ecologically relevant. Communities contain multiple functional groups (diatoms, cyanobacteria, etc,), and diversity at this level is obviously important, with rearrangements of these groups having major ecological consequences. However, multiple coexisting species (interspecific diversity) exist within these functional groups. In particular, many pico- or nanoplankton have not been directly studied in the lab, or are just now being recognized for their important roles in the marine environment (Ichinomiya et al. 2016; Leblanc et al. 2018). Recently, differences between lineages or strains within a species complex (intraspecific diversity) have been suggested to also be relevant to functional diversity, despite a high degree of genomic similarity. This area has been largely understudied, because of methodological limitations. At this taxonomic level morphology usually cannot be used to differentiate between strains, and even molecular techniques may have difficulty detecting this fine scale diversity. Recently though, some studies resolving amplified marker genes into amplicon sequence variants (ASV) have been able to detect distinct ecotypes between sequences differing by as little as a single base pair (Eren et al. 2013). Such single base pair differences in conserved marker genes can however mask a great deal of genomic dissimilarity in functional genes. Whole-DNA shotgun sequencing approaches such as metagenomics can reconstruct whole genomes from 8 environmental DNA, but these metagenomically assembled genomes (MAGs) represent a consensus sequence for a population rather than a distinct organism. Recent variations in metagenomics such as the use of long read sequencing technologies or Hi-C linkage have claimed to be able to resolve fine-scale diversity (Bickhart et al. 2019), but these techniques have yet to be widely used. Single-cell genomics has been able to resolve coexisting strains in the environment (Kashtan et al. 2014), but a limitation of this technique is that it often has difficulty reconstructing complete genomes. Despite these methodological challenges, intraspecific diversity has been observed to differentiate strains into temperature-defined ecotypes as seen in picocyanobacteria (Kashtan et al. 2014; Sohm et al. 2016; Lee et al. 2019), diatoms (Canesi and Rynearson 2016; Rynearson et al. 2020), and picoeukaryotes (Schaum et al. 2013). There is also evidence that fine scale diversity could be an intermediate step in major speciation events, as radiations into numerous thermal niches have been suggested to play a key role in the evolutionary history of marine phytoplankton. For instance, it has been hypothesized that just a handful of mutations in the photosynthetic machinery of the marine picocyanobacterium Synechococcus are responsible for its expansion into relatively cold, high latitude waters (Pittera et al. 2014, 2017). In a global change context, fine-scale species- and strain-level rearrangements likely won’t affect biogeochemistry as significantly as changes in interspecific diversity. However, intraspecific microdiversity could instead be a source of potential thermal resilience if particular strains in a population are already adapted to warmer temperatures. Thesis Motivation Anthropogenic CO2 is actively reshaping the world ocean. Rising temperatures, falling pH, and new weather patterns are challenging life everywhere. A key question for scientists studying marine biological systems is how communities of organisms will respond. This has 9 been a difficult question to answer, because scientists still have only an incomplete understanding of the breath of diversity that exists in ocean assemblages. This is especially true of planktonic microbial communities, which are key elements of marine ecosystems. My dissertation seeks to add to our knowledge about temperature responses of phytoplankton communities. In particular, I aim to explore the extent to which these communities already contain functional diversity capable of responding to warming. This work assesses these pre- adaptations to high temperature within natural communities, and utilizes recent isolates to explore unrecognized, cryptic species possessing unusual thermal adaptations. Finally, it delves into intraspecific diversity to explore how different strains within a population can have different responses to rising temperatures. 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Supplemental information can be found in the appendix to this dissertation. 18 The ISME Journal https://doi.org/10.1038/s41396-019-0525-6 ARTICLE Transient exposure to novel high temperatures reshapes coastal phytoplankton communities Joshua D. Kling 1 ● Michael D. Lee 2 ● Feixue Fu 1 ● Megan D. Phan 1 ● Xinwei Wang 1,3 ● Pingping Qu 1 ● David A. Hutchins 1 Received: 29 March 2019 / Revised: 4 September 2019 / Accepted: 13 September 2019 © The Author(s), under exclusive licence to International Society for Microbial Ecology 2019 Abstract Average sea surface temperatures are expected to rise 4° this century, and marine phytoplankton and bacterial community composition, biogeochemical rates, and trophic interactions are all expected to change in a future warmer ocean. Thermal experiments typically use constant temperatures; however, weather and hydrography cause marine temperatures to fluctuate ondiel cycles and overmultipledays.Weincubatednaturalcommunities ofphytoplankton collected from California coastal waters during spring, summer, and fall under present-day and future mean temperatures, using thermal treatments that were either constant or fluctuated on a 48h cycle. As assayed by marker-gene sequencing, the emergent microbial communities were consistent within each season, except when culture temperatures exceeded the highest temperature recorded in a 10- year local thermal dataset. When temperature treatments exceeded the 10-year maximum the phytoplankton community shifted, becoming dominated by diatom amplicon sequence variants (ASVs) not seen at lower temperatures. When mean temperatures were above the 10-year maximum, constant and fluctuating regimes each selected for different ASVs. These findings suggest coastal microbial communities are largely adapted to the current range of temperatures they experience. Theyalsosuggestageneral hypothesiswherebymultiyear uppertemperature limitsmay represent thresholds,beyond which large community restructurings may occur. Now inevitable future temperature increases that exceed these environmental thresholds, even temporarily, may fundamentally reshape marine microbial communities and therefore the biogeochemical cycles that they mediate. Introduction Marine phytoplankton draw down atmospheric CO 2 , support marine food webs,andprovide long-term carbon storage in underlying deep waters [1]. Currently, anthropogenic CO 2 inputs are inducing changes in phytoplankton communities by changing temperature and climate regimes [2]. Present-day atmospheric CO 2 con- centrations of >400ppm have not been seen for almost 12 million years, and these past elevated CO 2 events were accompanied by substantial warming [3, 4]. The high specificheatofseawaterhasmeantthatwarmingofthe ocean’ssurfacehashappenedmoreslowlythanwarming of land. Even so, global mean sea surface temperature (SST) has gone up 0.7°C in the last three decades, and is expected to rise an additional 4°C this century [3, 5, 6]. Warming influences phytoplankton growth and physiol- ogy [7, 8]. Growth rates across temperatures for phyto- plankton typically have thermal performance curves (TPC) that increase gradually with rising temperatures to an opti- mal maximum, then decrease rapidly at higher temperatures [9]. Thermal optima and limits for species are typically connected to in situ thermal regimes. For instance, phyto- plankton at lower latitudes often live near their optimal temperatures, whereas temperate species are temperature- limited [10–12]. Because of the rapid decrease in growth * David A. Hutchins dahutch@usc.edu 1 Department of Biological Sciences, University of Southern California, Los Angeles, CA 90007, USA 2 ExobiologyBranch,NASA AmesResearchCenter,Moffett Blvd., Mountain View, CA 94035, USA 3 School of Life Sciences, Xiamen University, 361005 Xiamen, China Supplementary informationTheonlineversionofthisarticle(https:// doi.org/10.1038/s41396-019-0525-6) contains supplementary material, which is available to authorized users. 1234567890();,: 1234567890();,: 19 rates beyond the thermal optimum, rising temperatures can lead to changes in phytoplankton phenology [13, 14], standing photosynthetic biomass [15], metabolic rates, and stoichiometry [16], affecting phytoplankton-mediated bio- geochemistry and higher trophic levels. For example, some studies predict diatoms will become less abundant, with cyanobacteria or pico- and nanoeukaryotic taxa being favored [17–19], while others suggest the opposite [20, 21]. Shifts within these taxonomic groups are also likely, with warmer conditions selecting for more high-temperature- adapted species or strains [12, 22–24]. Typically, studies assessingtheimpactsofwarmingon phytoplankton examine shifts in physiological and bio- geochemical responses under constant-temperature condi- tions in a laboratory incubator. In reality, the sea surface is adynamicthermalenvironmentwithtemperatures fluctu- ating over varying time scales from changing weather, diel cycles, vertical and horizontal advection, seasonal changes, and ocean-atmosphere oscillations [25, 26]. Moreover, rising mean atmospheric temperatures are pre- dicted to increase both the frequency and magnitude of future thermal fluctuations in the surface ocean [27, 28]. Because TPCs of phytoplankton and other marine organ- isms decline quickly past their optimal temperature, ther- mal fluctuations at higher temperatures may mean periodically experiencing temperatures thatare detrimental to growth [29]. Despite the growing recognition that temperature varia- tions and transitory extreme thermal events need to be considered when making predictions about biological responsesto climatechange,fewstudieshavelookedathow fluctuating temperatures impactmicrobialcommunities.One of these studies found that growth rates could either increase ordecreaseunderfluctuatingtemperatures,dependingwhere they occurred on the phytoplankton’s TPC [30]. Commu- nities in freshwater lakes have shown shifts towards smaller, faster-growing taxa [31]. Variable temperature regimes also can help maintain crucial diversity by supporting multiple thermal niches [32]. Long-term evolution of the cosmopo- litan marine phytoplankton Ostreococcus tauri under fluc- tuating pCO 2 favored greater phenotypic plasticity, which was then found to be predictive of future evolutionary suc- cess [33]. Similar results were seen in diatoms maintained for >100 generations under fluctuating temperatures [34]. Nutrient availability can also interact with environmental variability. Fluctuating temperatures decrease growth of the nitrogen-fixing cyanobacterium Trichodesmium under nutrient-replete conditions, but growth rates are lower and similar in variable and constant thermal treatments under phosphorus limitation [35]. We examined how seasonally changing California coastal plankton microbial communities responded to fluc- tuating temperatures over 7–14 generations in a series of incubation experiments. These included both constant and fluctuating temperatures, at both present-day and projected- future means. Phytoplankton assemblages in coastal Cali- fornia experience both seasonal and short-term temperature fluctuations, with daily fluctuations of up to 4°C being common [25, 36, 37], suggesting they may be well-adapted to thermal variability. Additionally, microbial communities at our coastal sample collection site (San Pedro Ocean Time-series; SPOT) have well documented seasonal and annually recurring patterns [37–39], allowing us to interpret our temperature manipulations in the context of 20 years of microbial community and environmental data. Methods Sampling site Surface water for the experiment was collected from the Southern California Bight at the San Pedro Ocean Time- series (SPOT) station (33˚33′ N, 118˚24′ W). Seasonal sampling in September 2016 (summer), November 2016 (fall), and May 2017 (spring) examined microbial commu- nities collected at ambient surface water temperatures of 20.6°,16.5°and16.1°,respectively(Table1).Seawaterwas collected in carboys from 3m depth, with 100µm mesh prefiltration to remove zooplankton, and was then taken back to the University of Southern California, where it was stored overnight at collection temperature. Initial samples and the incubation-experiment setup used water combined fromallthe collection carboys.Theremainingsurface water was filtered through a 0.2µm gravity filter and used for subsequent culture dilutions. Incubation experiments Because of the oligotrophic conditions and low biomass at SPOT, nutrients were added to stimulate Table 1 Temperature treatments were defined based on the in situ temperature when experimental water was collected. This became the mean temperature used for both present treatments, which were split into a constant treatment (control) that was held at that temperature, andatreatmentthatfluctuated4°aboveorbelowthatmeanevery24h. We then set constant and fluctuating temperatures with a mean that was 4° (spring and fall) and 5° (summer) warmer than the mean of our present treatments, in order to simulate warmer, future conditions Temperature treatment Spring Summer Fall Present-constant 16° 21° 16° Present-fluctuating 12°–20° 17°–25° 12°–20° Future-constant 20° 26° 20° Future-fluctuating 16°–24° 22°–30° 16°–24° J. D. Kling et al. 20 photoautotrophic growth and so enable measurements of the effects of temperature on microbial communities. Each incubation experiment used triplicate one-liter flasks enri- ched with nitrate, silicate and phosphate added to final concentrations of 30, 30 and 2µM, respectively, represent- ing values often observed during strong upwelling events along the California coast [40]. Iron, other trace metals, and vitamins were added at replete concentrations equivalent to Aquil medium [41] to avoid micronutrient limitation. Wemeasuredtheoverall temperatureresponsesof each seasonal phytoplankton community by generating thermal performance curves (TPCs). Initial seawater was split into 8−10 temperature treatments, ranging from 10 to 32°C. Individual water baths with their own heating elements and thermostats were used to ensure accurate temperature treatments. Due to these being mixed communities, in vivo Chl a fluorescence was used to calculate bulk phytoplankton community growth rates for all tempera- tures using the formula: μ¼ ln F1 " F0 ðÞ t1 % t0 .Inthisequation, µ is divisions per day, F 0 and F 1 are the raw fluorescence unit values measured on a Turner AU-10 fluorometer (Turner Designs Inc., Sunnyvale, CA, USA) at the beginning and end of each dilution period respectively, and t is the number of days between measurements. We fitted the TPC growth data to the Eppley−Norberg tem- perature model to make predictions of each community’s thermal limits and optimal growth temperature [10]. We also used a modified version of this model that takes into account thermal fluctuations to estimate growth rates in variable thermal environments [42]to compare our experimental variable thermal treatments (±4°C) to 10 years of daily maximum SST data obtained from the National Ocean and Atmospheric Administration’s National Data Buoy Center using a mooring station located 10.8km from SPOT. Additionally, we split enriched SPOT water into tem- perature treatments intended to simulate present and predicted-future surface-water temperatures at both constant and fluctuating temperatures (Table 1). Present temperatures were set to match the temperature at SPOT at the time of collection. The present-constant treatments acted as our experimental control. Future temperatures were increased 4°C (spring and fall) or 5°C (summer) from the present temperature (Table 1), following predicted increases in SST [6]. The two fluctuating temperature treatments had the same means as the constanttreatments, but they alternatedbetween awarmphaseabove(+4°C)andacoldphasebelow(−4°C) the mean value sequentially every 24h, yielding a 48h complete thermal cycle (Fig. S1). This time scale was based on previously published high-resolution temperature data from the Southern California Bight [25]. Hereafter we will refer to pre-nutrient addition samples as “initial” and each seasonal temperature study as “experimental”. Bulk growth was monitored using daily in vivo fluor- escence measurements, and one-part culture was transferred to ten-parts filtered, enriched seawater every 2 days to keep cultures in logarithmic growth and maintain a relatively constant nutrient environment [10, 43]. All experiments were run for at least seven transfers (~7−14 doublings over at least 14 days, depending on growth rate). After seven dilutions, experiments were sampled daily for 3 days to look for responses specific to either cold or warm phase of fluctuation treatments (Fig. S1). Due to high growth rates duringthe summerexperiment,anadditional dilutionhadto be made between cold and warm fluctuations to avoid cells entering stationary phase. For experiments we calculated growth rates using the same equation given above, but in addition to in vivo Chl a fluorescence, growth rates were also calculated based on particulate organic carbon (POC), intended to represent the whole assemblage, and biogenic silica (BSi) as a metric for diatom-specific rates. Biogeochemical assays To measure POC and particulate organic nitrogen (PON), samples were filtered onto precombusted GF/F filters (2h at 450°C) and analyzed using a Costech Elemental Combustion system (Valencia, CA, USA) [44]. POC was used to estimate bulk assemblage growth rates by recording the change in POC over 2 days, capturing growth during both cool and warm periods. To measure BSi, samples were filtered onto 3µm polycarbonate filters and measured as in [45]to estimate diatom biomass. Similar to POC-derived growth rates, changes in BSi over 2dayswereusedtoderivediatom-specificgrowthrates in our treatments. Cells were filtered onto precombusted GF/F filters for particulate organic phosphorus (POP) measurements [46]. In addition to indirectly measuring Chl a using in vivo fluorescence, we measured total chlorophyll by filtering onto GF/F filters and extracting in 90% acetone for 24h. Extracted Chl aand in vivo fluor- escence were measured on a Turner AU-10 (Turner Designs Inc., Sunnyvale, CA). During spring and fall experiments, carbon fixation rates were measured by spiking30mlofeachenrichmentwith50µlof 14 Clabeled sodium bicarbonate, then incubated for 3h, filtered onto GF/F filters, and placed in 4.5ml of scintillation solution. Total radioactivity (TA) wasmeasured using triplicate solutions of combined isotope and scintillation solution spiked with 100µl of phenyethlamine. We accounted for filter absorption using 10ml from each replicate- enrichment spiked with identical amounts of isotopes and filtered immediately. Samples were incubated in the dark overnight and radioactivity was measured with a Tri- Carb 2500TR liquid scintillation counter after 24h (Beckman Coulter Inc., Brea, CA) [47]. Transient exposure to novel high temperatures reshapes coastal phytoplankton communities 21 Amplicon sequencing Microbial diversity was sampled before nutrients were added, and at the end of the final temperature fluctuation cycle. Cells were filtered (1.2µm polycarbonate) and stored in liquid nitrogen. Extractions used the DNeasy Power Soil kit(Qiagen,Hilden,Germany)modifiedtoincludea10-min 65°C incubation before vortexing. Amplification and sequencing of the V4-V5 hypervariable region of the 16S rRNA gene was done using the primers 515F-Y (5′- GTGYCAGCMGCCGCGGTAA-3′) and 926R (5′- CCGYCAATTYMTTTRAGTTT-3′), as described in [48]. These primers successfully amplify large proportions of known prokaryotes, and chloroplasts (via the 16S rRNA gene),aswellaseukaryotes(viathe18SrRNA)[48].These primers have been previously used to describe microbial communities at this study site [49]. Library prep and sequencingwasdoneatMolecularResearchDNAlabs(MR DNA; Shallowater, TX, USA) on the Illumina Miseq plat- form producing 2×300bp paired-end reads. DNA samples from the spring experiment were treated the same way as summer and fall DNA samples, but were sequenced on a different date. To avoid potential sequencing run-specific batch effects, each season was analyzed individually. The quality of DNA from one replicate in the spring future- constant treatment was low and consequently contained few reads. This replicate was excluded from sequence analysis. Sequence data analysis Our processing workflow is shown in Fig. S2. In short, raw sequence reads were demultiplexed using Sabre (github.com/najoshi/sabre, version 1.0) and primers removed with usearch’sfastx_truncate (version 9.2) command to cut the first 20 bases from forward and first 19 from reverse reads. These were analyzed with the DADA2 pipeline, version 1.6.0 [50]. Default settings were used except where noted following DADA2’sstan- dard workflow, version 1.8 (https://benjjneb.github.io/da da2/tutorial.html). The 18S rRNA gene region that is amplifiedbythese primersis typically longerthanour 2× 300 paired-end sequencing protocol spans, and hence would be discarded by the merge step our analysis pipe- line [48]. We gathered all nonmerged reads and separated out putative 18S rRNA gene sequences from reads rejected due to quality issues using the number of mis- matched base pairs. We found that sequences with >35 mismatches typically matched eukaryotic 18S rRNA gene sequences when BLASTed against NCBI’snonredundant nucleotide (nr/nt) database (Table S1). Because of this correlation, all reads with >35 mismatches were assumed to be eukaryotic 18S rRNA gene sequences. These were concatenated with ten ambiguous, “n” bases and pro- cessed with the merged, 16S rRNA reads. Amplicon sequence variants (ASVs) identified by the Silva132 database[51]aschloroplastswereremovedandseparately assigned taxonomy using PhytoREF, a curated database of phytoplankton chloroplast 16S rRNA sequences [52]. ASVs derived from putative 18S rRNA gene sequences were identified using the Protist Ribosomal Reference database, PR2 [53]. Previous work with these primers at SPOT observed that estimates of phytoplankton diversity were typically similar using reads assigned to either 16S or 18S rRNA amplicons [49]. For this study we chose to rely on the 16S rRNA plastid gene sequences to describe theeukaryoticphytoplanktoncommunity.18SrRNAgene copy number varies considerably between taxa based on genomesize[54],whereasplastidnumberscanvarybased on environmental variables (e.g. nutrient availability) which we control for in our experiments. Putative-18S assigned taxonomy was used to check for the presence of metazoan grazers (which should have been largely removed with our prefiltering during sampling), and for dinoflagellates, whose plastid 16S rRNA gene sequences are highly divergent [55]andnotamplified with these primers. For the highest level of taxonomic resolution, we BLASTed all bacterial ASVs that comprised >10% and eukaryotic ASVs comprising >5% of recovered reads in any given sample against the NCBI’snr/nt database, excluding uncultured sample sequences. We used percent similarity thresholds to assign taxonomic rank following published values for primers amplifying the V4 rRNA region [56]. For dominant phytoplankton ASVs we con- firmed their taxonomic identity by also BLASTing the corresponding 18S rRNA sequences. Because these pri- mers produce relatively fewer 18S rRNA amplicons and the natural differences in copy numbers of each gene, we paired sequences by comparing the relative abundance of recovered 18S and plastid 16S rRNA sequences. ASVs of each gene that had the strongest correlation (highest Spearman’scorrelationalcoefficient) were considered to belong to the same organism. Analysis of read counts including calculations of diversity, ecological distance, and all ordinations was done in R [57]andRStudio[58]usingthePhyloseq[59] and Vegan [60]packages.Forordinations,theASVcount matrix was first transformed using the variance stabilizing transformation within DESeq2 [61], Euclidean distances were calculated (Vegan), and we tested for significance between groups using a permutational ANOVA (Vegan), after confirming an equal amount of variance between groups using the betadisper function (Vegan). Sig- nificance of temperature treatment effects on major taxo- nomic groups was tested using both one-way ANOVA and t test, and DESeq2 was used to test for differential J. D. Kling et al. 22 abundance in individual ASVs by comparing them to our control treatment. Results During each sampling effort, conditions at SPOT were oligotrophic. Total chlorophyll levels were <0.5µg/l, with little available nitrogen (mean=0.026 µM, SD=0.01) or phosphorus (mean=0.17 µM, SD=0.03) (Table S2). Thermal response curves showed the seasonal assemblages in all three experiments were able to grow at least 6°C above the 10-year record maximum temperature for each season at SPOT (Fig. 1). Bulk growth rates of the spring communities did not drop to zero until 32°C was reached (Fig. 1a), and growth rates in the summer (Fig. 1b) and fall (Fig. 1c) were still maximal at the highest temperature tested (30°C). Maximum growth rates for each seasonal community were measured at or above 26°C (Fig. 1). For each experiment, we compared the impact of our ±4°C experimental fluctuations to daily temperature fluc- tuations in our 10-year temperature dataset from SPOT (Table S3). The modified Eppley−Norberg model of Bernhardt et al. that accounts for temperature fluctuations [42] predicted no difference between in situ temperature fluctuations and our ±4°C fluctuations. This suggests that from the perspective of phytoplankton growth rates, our temperature fluctuations were similar to what they experi- ence in situ. In the spring experiment, growth rates calculated using POC were lower in the present-fluctuating (12–20°C) than the present-constant (Fig. 2a, p=0.03), but this difference was not reflected in either the BSi-derived diatom growth rate (Fig. 2d) or the BSi:POC ratio (Fig. 2g). Future- fluctuating temperatures in the summer (22–30°C) had POC-derived growth rates (Fig. 2b) significantly higher than the present-constant (21°C, p=0.004) and present- fluctuating treatment (17–25°C, p=0.05). In the summer, BSi-derived growth rates were higher in the future-constant treatment than other treatments (p<0.05). In the fall, treatment had no effect on POC or BSi-derived growth rate (Fig. 2c, f), but the future-constant treatment (20°C) had a significantly higher BSi:POC ratio than the other treatments (Fig. 2i, p<0.05). Other biogeochemical and bulk bio- chemical parameters were relatively unaffected by the temperature manipulation experiments. Elemental ratios of particulate C, N, P, Chlacontent, and carbon fixation rates were variable, but not statistically different for treatments within each seasonal experiment (Fig. S3). rRNA-gene amplicon sequencing was used to assess changes in the phytoplankton communities associated with different temperature treatments. Ordinations clearly sepa- rated initial and experimental samples, based on nutrient level (Fig. S4A). When initial samples were removed, collection-season clearly correlated with sample groupings (Fig. S4B). Within each experiment, the first two axes of a principle coordinates analysis (PCoA) were able to explain between 27.7% and 32.4% of the variation, and each sea- sonal study had a significant difference between at least two treatments as detected by a permutational ANOVA (Fig. 3). In all treatments, bacterial sequences made up the majority of recovered amplicons (Fig. 4a). Chloroplast 16S rRNA gene sequences belonged mostly to picoeukaryotes in initial samples, with diatoms being dominant in spring and fall experiments (Fig. 4a, b). The majority of recovered sequences in all experiments belonged to the Alpha and Gammaproteobacteria and the Bacteroidetes phyla (Fig. 4b). Within these larger taxo- nomic groups, 132 bacterial ASVs were observed that comprised >1% of the relative abundance of amplicons recoveredfrom atleastone sample(Table S4).Themajority of these belonged to the Bacteroidetes (61 ASVs) Fig. 1 Chlorophyll afluorescence-based thermal performance curves calculated for the seasonal mixed phytoplankton community at SPOT during a spring (n=13, R 2 =0.74), b summer (n=8, R 2 =0.77), c fall (n=10, R 2 =0.76). Fitted line shows the Eppley−Norberg tem- perature model. Vertical lines summarize 10 years of seasonal tem- perature data,wherethe darksolidline isthe meanwith dashedlines ± one standard deviation, and the solid red line is the highest recorded temperature Transient exposure to novel high temperatures reshapes coastal phytoplankton communities 23 with Saprospiraceae (22 ASVs) and the Flavobacteriaceae (18 ASVs) being the most diverse families. Alphaproteo- bacteria included 44 ASVs, which were largely made up by the Rhodobacteraceae (21 ASVs). Of the 15 Gam- maproteobacteria comprising >1%, ten were in the genus Alteromonas. Three ASVs were placed into Gammapro- teobacteria; however, SILVA was only able to identify two of them to class. The other ASV was placed into the family Oligoflexaceae. Other groups crossing the 1% threshold were the Planctomycetes (three ASVs), Verrucomictrobia (two ASVs), and the Actinobacteria (one ASV). We did not detect differences in community composi- tion between constant vs. fluctuating treatments at broad taxonomic levels in the spring experiment (Fig. 4b), but did for Alphaproteobacteria and Cyanobacteria in the fall (p<0.05). The summer experiment demonstrated much greater variation between the constant temperature and fluctuating treatments. For example, summer-derived communities demonstrated a decrease in the relative abundance of Alphaproteobacteria recovered sequences (p<0.05)inthefuture-fluctuating treatment as compared to both present-constant and present-fluctuating treatments. These were mostly made up of copiotrophic heterotrophs in the Hyphomonadaceae and Rhodobacter- aceae (Table S4), and there was also an increase in the relative abundance of Planctomycetes (p<0.05) sequen- ces in the future-fluctuating treatment. The shift in the Planctomycetes was from an increase in a single ASV (ASV20), which when BLASTed most closely matched Candidatus Brocadiales fulgida,a knownanammox bacteria [62]. Gammaproteobacteria also decreased in relative abundance (p<0.05) during the summer experi- ment in the present-fluctuating treatment relative to the present-day constant treatment. The largest change came Fig. 3 Ordination of a principle coordinate analysis (PCoA) using Euclidean distance calculated on 16s rRNA gene amplicon data from a spring, b summer, and c fall. Listed p values are the result of a permutational ANOVA Fig. 2 Community growth rates determined as changes in particulate organic carbon (POC, a–c), and the diatom-specific growth measured with changing BSI (d–f), both measured over 2 days at the end of 14 days of growth. g−i depict BSi to POC ratios as an indicator of the relative abundance of diatoms in each treatment. Statistical sig- nificance between two treatments (p<0.05) is shown with a star. p values for nearly significant treatments (p<0.1) are shown with their respective brackets, and error bars represent standard deviation J. D. Kling et al. 24 from a single ASV (asv3) identified with BLAST as an Alteromonas sp.(Fig.S5).Inthespring,twobacterial ASVs that made up more than 10% of the recovered bacterial amplicons belonging to the genus Phaeobacter and an unknown Flavobacteriaceae declined in relative abundance in all treatments, compared to the present- constant control (Fig. S5A). Similarly, in the summer, any temperature manipulation caused two dominant bacterial ASVs (one Alteromonas sp.andanotherunknownFla- vobacteriaceae) to decrease their relative abundance. Bacillariophyta were the most abundant eukaryotic phytoplankton in experiments, although some prymne- siophytes were detected (Fig. 4b). 18S sequences were screened for dinoflagellates and for the presence of any metazoan grazers not removed by prefiltering (Fig. S6). Neither was seen except in initial samples, particularly in the spring experiments, where copepods (likely nauplii) made up as much as 35% of total initial sequences recovered (Fig. S6). In the summer experiment, prymne- siophyceae sequences were most abundantly recovered in the present-constant (2.1%, SD=0.7) compared to other treatments. The phytoplankton community in each incubation experiment had one or two dominant ASVs making up the majority of the photoautotrophic community (Fig. 5, Table S5). In the spring experiment an uncharacterized Pseudo-nitzschia sp. (asv9) and Minidiscus trioculatus (a small, unicellular, centric diatom; asv2) were the most abundant in all samples (Fig. 5). The Pseudo-nitzschia 18S sequence recovered (asv105; Fig. S7A; Table S6) was 100% identical to Pseudo-nitzschia americana strain UNC1412 (NCBI accession number KX229689.1), which was isolated from the California upwelling zone in 2014. On a phylogenetic tree of top BLAST hits, the two nearest referencessequenceswerenotknownproducersofthetoxin domoic acid (Fig. S8), which is produced by many other members of this genus. The same Minidiscus trioculatus ASV (asv2) was the most abundant phytoplankton in each of the fall enrichments (Fig. 5b, Fig. S7B). In the summer experiment, both present-constant and present-fluctuating treatments were largely dominated by an ASV matching the picoeukaryotic phytoplankton (asv1) belonging to the group basal to the diatoms, the bolido- phytes; however, the recovered 18S rRNA amplicon was a 100% match to the diatom Leptocylindrus convexus, part of an early branching group of diatoms [63, 64]. Microscopy revealed large numbers of cells morphologically identical to Leptocylindrus, so asv1 was assigned taxonomy based on its 18S rRNA gene. In the warmer, future-constant treat- ment this diatom is supplanted by a different diatom ASV Chaetoceros simplex (asv8), which was in turn replaced by another diatom, Arcocellulus mammifer (asv4), when those Fig. 4 aBarscomparingthepercentageofrecoveredrRNAsequences, largely representing heterotrophic bacteria and two major functional groups of autotrophic phytoplankton. Values are expressed as the mean percent amplicons recruited for bacteria (black), diatoms (gray), and picoeukaryotic phytoplankton (light gray). Error bars represent standard deviation. b Phylum and class level (proteobacteria) abundance using 16S rRNA gene sequence identity. The size of each bubble corresponds to percent amplicons recovered. Closed circles show taxa that are significantly more or less abundant by a one-way ANOVA (p<0.05). Brackets show specific significant differences between groups. Plastid 16S rRNA gene sequences were used for taxonomic assignments of eukaryotic phytoplankton Transient exposure to novel high temperatures reshapes coastal phytoplankton communities 25 warmer temperatures fluctuated (Figs. 5b, S5). The match- ing 18S rRNA sequence recovered for asv4 belonged to the genus Minutocellus (Fig. S7C, Table S6). Because Arco- cellulus and Minutocellus are very closely related [63], we used the plastid 16S rRNA sequence identity to assign its taxonomy. Testing of differential abundance using DESeq2 [61] compared all treatments to the present-day, constant control treatment. Some statistically significant shifts in abundance were measured in every experiment (Table S7); however, in general most of these shifts between treatments did not involve dominant ASVs (>10% for bacteria and >5% for eukaryotic phytoplankton). Those that did were the increase in ASVs matching the genus Rugeria (asv31) in the spring future-fluctuating treatment (Fig. 5a), and the shift from a Leptosylindrus-dominated to a Chaetoceros-domi- nated (future-constant) and Arcocellulus-dominated (future- fluctuating) phytoplankton community in the summer (Fig. 5b). Discussion Enrichments showed positive growth rates even at tem- peratures higher than those ever experienced at the coastal California site where they were collected (>25°). In addi- tion, thebulk community growth rates wererelativelystable across temperatures and did not show the typical negative skewness of phytoplankton isolate thermal response curves [11]. This broad optimum range across temperatures is likely the result of thermal functional redundancy in these natural communities. In other words, different members likely have different limits defining their optimum and stressful temperatures, thus providing redundancy and robustness to bulk growth rates, even with a shifting underlying community. Careful examination of the com- munities in our comparison of warming and fluctuating conditions showed that incubations largely maintained the same dominant taxa until they were exposed transiently to unusually high temperatures exceeding the summer experiment upper temperature limit. In our experiments, no consistent difference was observed in our POC- and BSi-determined growth rates between constant and fluctuating treatments. Thermal fluc- tuations and nutrient inputs are often linked through vertical mixing and advection events in the coastal regime where SPOT is located, so the phytoplankton we enriched for may already be well-adapted to fluctuating temperatures. At future mean temperatures in both the summer and fall (26 and 20°C respectively), we did observe significant and near significant differences between constant and fluctuating conditions in the ratio of diatom frustule mass (BSi) relative to the POC present. Plotting these ratios during the final dilution series (3 days) shows that BSi accumulated faster relative to POC in the future-constant treatment in the summer and fall experiments, than in future-fluctuating treatments (Fig. S9B, C). This difference was particularly pronounced in the summer experiment, when the slope of a line fitted to the data was 6.5 times higher in the future- constant vs. the future-fluctuating treatment (Fig. S9B). Fig. 5 Heatmaps of amplicon sequence variants (ASVs) that comprised: a >10% of total reads within a sample for bacteria, and >5% of the total reads for b diatoms and c picoeukaryotic phytoplankton. Triangles represent treatments where a given ASV was differentially abundant compared to the control (present-constant treatment outlined with dashed lines) J. D. Kling et al. 26 Our experimental design was intended to simulate cli- mate change impacts on primary producers within a coastal zone. Outside of a brief spring bloom, SPOT is typically oligotrophic, with new primary production often relying on episodic and ephemeral Ekman upwelling [36]. The low chlorophyll, picoeukaryote-dominated initial community shifted to a high chlorophyll, largely diatom-dominated community in our experiment, which is what we would expect following a typical transitory upwelling or mixing event at the SPOT time series station. Because of the strong influence of the experimental nutrient additions, initial communities were excluded from any statistical testing. The communities we observed post nutrient addition were distinct to each season. The differences in the out- comes of fall and spring incubations, despite similar tem- peratures, are likely due to differences in the communities when the samples werecollected. The microbialcommunity atSPOT when sampled monthly overmultipleyears is most dissimilar to the microbial community 6 months before or after the sample is taken [38]. Our fall and spring samples were taken exactly 6 months apart, so it is not surprising that after the incubations we got different results. Of course, as also suggested by our results, these distinct spring and fall community structures may be largely a function of their differing recent thermal histories. Bacterial communities in all three experiments were consistent with those often associated with diatom blooms. For instance, ASVs from genera such as Pseudophaeo- bacter (asv16), Phaeobacter (asv18), and Rugeria (asv31, Alphaproteobacteria) as well as Alteromonas (asv3, Gam- maproteobacteria) and Lewinella (asv32 and asv26, Bac- teroidetes)allmadeup10%ormoreoftheampliconsinany one sample. These genera are copiotrophic heterotrophs, and so are frequently reported in diatom cultures and in situ blooms [65]. Interestingly, a Bacteroidetes ASV matching Kordia jejudonensis(asv11)wasonlyrelativelyabundantin the spring present-fluctuating treatment, in which diatom levels were the lowest within that experiment. This genus contains species that produce allelopathic compounds known to be lethal to phytoplankton, which could explain the relatively low diatom counts [66]. Phytoplankton species that emerged following nutrient additions also were typical of eutrophic periods in the Cali- fornia Current. Pseudo-nitzschia, Minidiscus, Leptocylindrus, and Chaetoceros are common bloom-forming diatom genera in the California Current System [67, 68]. The dominant diatom from the future-fluctuating treatment in the summer, Arcocellulus mammifer, is not mentioned in the literature at thissite.Instead,bloomsofthisspecieshavebeenrecordedin aquaculture ponds in the tropical South Pacific[69]. The apparent thermophilic niche of this species is consistent with the fact that it was only observed in our treatment inter- mittently exposed to extreme high temperatures. Leptocylindrusconvexussequenceswereonlyrecoveredin relative abundance at present-constant and present-future temperatures. In the future-constant treatment from the same experiment (mean temperature=26°C), this organism seemed to be supplanted by the chain forming diatom Chaetoceros simplex. Chaetoceros spp. are heavily silicified, potentially explaining the significantly higher BSi:C ratio in thefuture-constanttreatment.Thisspecieswasonlydominant in the future-constant treatment, however, and in the future- fluctuating treatment the dominant phytoplankter changed again to the diatom Arcocellulus mammifer.Thisshiftwas also seen in declining diatom-specific growth rates and BSi concentrations relative to the future-constant treatment. In our experiment the phytoplankton bloom from each seasonal enrichment was distinct from those collected during the other seasons. Past work at SPOT has showed that microbialassemblagesfromagivenmontharemostsimilarto other months from the same season, even across years (378). Further, we compared the abundance of the dominant phy- toplankton taxa from our spring (ASV9 Pseudo-nitzschia sp.) and summer (asv1 Leptocylindrus convexus, asv8 Chaeto- ceros simplex, and asv4 Arcocellulus mammifer) incubation experiments with previously published data from Needham and Fuhrman (2016; 49), who followed the response to a spring-time nutrient pulse over the course of 6 months (Fig. S10). All three dominant ASVs from our spring incu- bations were detected in nearly every in situ sample from this prior study. In March when temperatures were low and available nitrogen high (>4µM), amplicons matching ASV9 (>99% similarity across the entire length of the sequence) became more abundant, while amplicons matching ASV1 or ASV8 remained barely detectable. In mid-May in the Need- ham and Fuhrman (2016; 49) study as temperatures began to rise, a modest increase in available nitrogen (~1µM) stimu- latedchlorophyllaproductionandresultedinasmallincrease in amplicons matching asv1 and asv8 that were dominant in our summer experiment, while those matching ASV9 remained low. In addition, asv4 that was dominant in the summer future-fluctuating treatment, where it experienced much higher temperatures than those observed at SPOT, remained the same throughout this dataset. The consistency between our experimental community structure and in situ observations suggests that it is the seasonal thermal environ- ment that dictates which species are able to respond to ephemeralnutrientinputs,andthatourexperimentsaccurately simulated these seasonal patterns. With these experiments, we repeatedly observed that incubation temperatures (whetherfluctuating or constant) that fell within present-day norms stimulated dense phytoplankton blooms that were largely taxonomically indistinguishable across all treatments. Similarities in composition also suggest a functional redundancy that maintained biogeochemical and bulk biochemical processes within the envelope of historic Transient exposure to novel high temperatures reshapes coastal phytoplankton communities 27 temperatures. Even when communities were grown under conditions that periodically exposed them to temperatures close to this maximum (17–25°C), the dominant primary producersdidnotchange.Onlywhenconsistentlyculturingat high temperatures (26°C) that exceeded historic maximum temperature (25.3°C as measured over the past 10 years) did the community of primary producers shift. Further, periodi- cally exposing the community to 30°C (4.7°C above the 10- yearmaximum)pushedtheChaetoceros simplexbeyondtheir thermal maximum, and resulted in enriched abundance of Arcocellulus mammifer regardless of the brevity of exposure and the shift back to lower temperatures (22°C) the following day. Thermal impacts such as shifts in dominant taxa or declining phytoplankton abundance could happen at even lower temperatures in situ. For instance, some grazers are lesssusceptibletothermalstressthaneukaryoticphototrophs [70,71].Becauseweremovedthesefromoursystem,weare unable to assess the interaction of temperature and grazing pressure in shaping community composition. Nutrient levels could also be confounding, as recent work suggests an interactive effect between nutrient concentrations and tem- peratures, with less thermal resilience under oligotrophic conditions [72], although limiting nutrients can increase thermal tolerance in some marine diazotrophs [73]. Our nutrient concentrations were kept replete through frequent dilutions with nutrient-amended seawater, masking any potential temperature/nutrient availability interactions in our future treatments. It is possible that in situ grazing pressures and nutrient limitation could interact strongly with warming to allow impacts on phytoplankton communities with smaller temperature perturbations. These data suggest that temperatures that match or slightly exceed historic high temperatures even briefly(on atimescaleofweeks)cancause major shifts in dominant phytoplankton, even under nutrient-replete conditions. This is in addition to the impacts of long-term elevated mean temperatures such as those that have been predicted by ocean/atmosphere models [6]. Our observations sug- gest these new communities are stable, and still capable of maintaining their role in marine ecosystems. This is also consistent with observations from other marine ecosys- tems, where short-term heat waves have had ecological consequences over and above those of more modest, longer-term warming. A temperature anomaly off Aus- tralia’swestcoastin2010/2011increasedtemperatures 2.5°C above seasonal norms, and for a brief period (~1 week) exceeded the normal seasonal temperatures by 5°C [74]. The effects of these anomalous conditions seemingly irreversibly shifted the ecosystem from a kelp- dominated community with an abundance of temperate fish to a benthic, turf algal assemblage with tropical fish species that were not present before the heatwave. Recent studies that experimentally manipulated thermal regimes with the marine copepod Tigriopus californicus suggested how this process might happen, hypothesizing that prior exposure to sub-lethal warm temperatures made indivi- duals more vulnerable to short-term extreme heat events [75]. This makes sense given the typical shape of micro- bial thermal curves and the unequal impacts that thermal fluctuations have on growth rates at higher temperature, where they can often decrease optimal and lethal thermal limits. As marine microbial ecosystems continue to experience warming, it is likely that these scenarios combining warming with fluctuating, short-term heat waves could become more common. This work suggests that encounters with unprece- dented high-temperatures could lead to broad shifts in dominant phytoplankton taxa. By simulating the bloom- forming conditions that periodically occur in this coastal regime following upwelling, we observed that the onset of high temperatures not previously experienced in situ may serve to delineate a threshold where warming affects the composition of the microbial community. This threshold is likely modulated by other co-stressors such as nutrient availability and grazing, by the duration of high-temperature exposure, and potentially by the range of temperatures in a given regions of the ocean. Here we offer a testable hypoth- esis that we believe can act as a starting point for testing the limits of present-day community structure and function in the context of a warming ocean. Data Availability All scripts used for quality control, analysis of sequence data, and figure preparation can be found at: https://doi.org/ 10.6084/m9.figshare.7603790.v2. Sequence data have been uploaded to NCBI under the Bioproject ID PRJNA512541. SRA accession numbers and associated metadata are found in Table S8. Data from San Pedro Ocean Time-series (SPOT) monthly sampling can be found at https://dornsife. usc.edu/spot/data/, and daily temperature data is from the National Data Buoy Center (https://www.ndbc.noaa.gov/), station 46222. Acknowledgements Thanks to Troy Gunderson, Elaina Graham, Babak Hassanzadeh and the USC Wrigley Institute for Environmental Studies for help with logistics and analyses. Funding was provided by NationalScienceFoundationawardsOCE1538525andOCE1638804 to FF and DAH. Compliance with ethical standards Conflict of interest The authors declare that they have no conflict of interest. J. D. Kling et al. 28 Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Falkowski P, Barber R, Smetacek V. Biogeochemical controls and feedbacks on ocean primary production. Science. 1998;281:200–7. 2. Hutchins DA, Fu F. Microorganisms and ocean global change. Nat Microbiol. 2017;2:17058. 3. Hansen J, Sato M, Ruedy R, Lo K, Lea DW, Medina-Elizade M. Global temperature change. Proc Natl Acad Sci USA. 2006;103:14288–93. 4. Tripati AK, Roberts CD, Eagle RA. 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Hutchins 1 1 Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA 2 Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, USA Abstract Diatoms have well-recognized roles in fixing and exporting carbon and supplying energy to marine ecosystems, so it is essential to understand how their physiology and community dynamics will respond to both current and future temperatures. Recently, our knowledge of the diversity and importance of historically-understudied nano- and picodiatoms has been expanding. We describe a small (~5 µm) diatom from the genus Chaetoceros isolated from temperate Narragansett Bay, Rhode Island at wintertime temperatures (2 °C). This isolate has an obligate specialization for a low-light environment and cannot survive irradiance > 120 µmol photons / m 2 * sec -1 , while light levels either above or below a very narrow optimum decrease its maximum potential growth rate. It also exhibits a striking interaction between irradiance and thermal responses whereby as temperatures increase, lethal levels of light decrease. Its 18S rRNA gene sequence is similar to a unique branch of Chaetoceros described from the Baltic Sea and the Arctic Ocean, suggesting it could be distributed beyond this temperate estuary. Historical 18S rRNA amplicon data from our study site show this isolate was abundant throughout a six year period, and its presence strongly correlates with winter and early spring 31 months when light and temperature are low. The unusual low-light, psychrophilic ecological niche of this species may be threatened by future higher temperatures and light exposure in the surface ocean due to climate change. 32 Introduction Phytoplankton photosynthesis in the ocean is responsible for 50% of the global conversion of inorganic CO 2 to organic biomass (Field et al. 1998). This process provides energy for higher trophic levels (Smith and Hollibaugh 1993) and shuttles atmospheric CO 2 into deep ocean waters or marine sediments, storing carbon on geological time scales (Lyle 1988). These fundamental ecosystem services make marine phytoplankton essential to study in the context of global climate change, particularly given their ability to offset atmospheric CO 2 emissions (Hutchins et al. 2019). Current projections suggest a rise in mean sea surface temperature (SST) of 4 °C by 2100 (Pachauri et al. 2014) which could have large impacts on the physiology and composition of these microbial photosynthetic communities, and their ability to sequester carbon (Hutchins and Fu 2017). Some studies predict a decrease in net marine primary productivity (Behrenfeld et al. 2006). However, making predictions about warming effects on phytoplankton can be difficult because of the still relatively underexplored biological diversity of these microorganisms, and their regionally-specific responses. For instance, in some regimes rising SST may lead to an increase in stratification, which can decrease the supply of available nutrients for photosynthetic growth (Capotondi et al. 2012). This could be particularly problematic for phytoplankton in the already nutrient-poor subtropical gyres; however, in high latitudes where mixed layers are deep and nutrients are often relatively abundant, warming-induced stratification may relieve light limitation, and so contribute to growth (Riebesell et al. 2009). Phytoplankton communities are also incredibly diverse, containing numerous coexisting photosynthetic bacterial and protistan lineages. Despite nearly a century of research, new and 33 environmentally relevant phytoplankton species are still being discovered. This is especially true of small (< 5 µm) nano and picoplankton, which prior to the advent of next-generation sequencing have been routinely under-sampled by microscopic techniques (Abad et al. 2016; Leblanc et al. 2018). For example, the smallest known diatom genus (Minidiscus) was recently shown to be capable of forming dense blooms, and similar very small diatom groups are now recognized as being globally abundant (Leblanc et al. 2018). Although our knowledge of phytoplankton diversity is expanding, it is an open question how much functional thermal diversity exists within this observed phylogenetic diversity. For instance, phytoplankton communities can typically sustain growth well beyond current mean temperatures, but excursions above historical thermal maximum thresholds can cause major community restructuring (Kling et al. 2019). These changes in community structure can affect ecosystem roles and biogeochemistry. For example, diatoms have dense silica frustules and a propensity to form blooms when nutrients are abundant, which makes them efficient at exporting carbon (Guidi et al. 2016). Shifts away from diatoms and towards non-silicified species that have been observed in natural phytoplankton communities challenged by warming could thus have large consequences for biogeochemical cycles (Hare et al. 2007; Feng et al. 2010). Predictions of such lineage-specific temperature responses have often been based on just a handful of cultured model isolates, such as the diatom Thalassiosira pseudonana, (Berges et al. 2002), the coccolithophore Emiliania huxleyii (Feng et al. 2008), and the diazotrophic cyanobacterium Trichodesmium erythraeum (Mulholland and Bernhardt 2005; Fu et al. 2014). However, the model organism approach to understanding resilience to rising temperatures in 34 marine phytoplankton undoubtedly under-samples the potential range of thermal responses. This is especially true for nano- or picoplankton, which are relatively poorly represented in culture collections, and whose full importance we are just beginning to appreciate. Furthermore, lab-derived growth rates and other proxies for fitness can be uncertain predictors of ecological success. In situ variations in temperature, light, and nutrients and interspecific interactions (e.g. competitive or trophic interactions) can make it difficult to scale up conclusions from lab-based studies to natural communities. In this study we expand our knowledge of nanoplankton diversity relative to temperature and light, by characterizing a previously unrecognized nanodiatom from a temperate estuary belonging to the genus Chaetoceros. We place its physiology and life history into an environmental context by combining laboratory experiments with a wealth of taxonomic and ecological time series data spanning six years. We report that this Chaetoceros isolate exhibits a unique physiological relationship between light and temperature, which skews its abundance strongly towards periods of low light and cold temperatures. This wintertime specialist is thus potentially vulnerable to the warmer conditions expected at mid- and high- latitudes with continuing climate change. Methods: Isolation and Culturing This diatom was isolated from water collected at the Narragansett Bay Time Series site (Hitchcock and Smayda, 1977; Rynearson et al., 2020b, latitude 41.47, longitude -71.40) in March, 2018. SST was 2 °C at the time of collection. Surface water collections were prefiltered through a 100 µm mesh to remove large grazers, and then sorted at the University of Rhode 35 Island Graduate School of Oceanography cell sorting facility using a BD FACSCalibur (San Jose, CA, USA). Cells < 5 µm with chlorophyll a fluorescence were sorted into 96 well plates containing natural seawater amended with nutrients following the recipe for F media diluted to F/20 (Guillard 1975). The well plates were monitored over time, and when fluorescence was detected in a well it was transferred to new media while gradually increasing nutrients to F/2 concentrations. Isolates were then switched to artificial seawater made according to the recipe for Aquil medium (Sunda, et al. 2005). Stock cultures were maintained long-term at 4 °C under 30 µmol photons / m 2 * sec -1 . of cool-white fluorescent light, and diluted biweekly with fresh medium. Temperature and Light Assays All culture work was done in climate controlled walk-in incubators on shelves holding banks of cool white fluorescent lights. Light levels were verified with daily measurements using a LI-250A light meter (LI-COR Biosciences, Lincoln, NE, USA) to ensure accurate and uniform irradiance. Cultures were kept in triplicate 15ml polystyrene culture vials and temperature for all experiments was set using a series of water baths, each with its own heating and cooling element and thermostat. Replicates were kept in exponential phase by diluting cultures with sterile media when biomass reached a predetermined threshold. Cultures were acclimated to each combination of irradiance and temperature for two weeks. After this initial acclimation period, growth rates were determined using daily measurements of in vivo fluorescence on a Turner AU-10 fluorometer (Turner Designs Inc., Sunnyvale, CA, USA) for an additional seven to ten days. In vivo fluorescence was used as a proxy for photosynthetic biomass, because it efficiently allowed us to make daily 36 measurements of the large number of simultaneously maintained cultures in this study (as many as 90 at a time). All well-acclimated replicates were kept in the same nutrient conditions, and growth rate calculations were based on in-vivo data from the fluorescence of each individual replicate measured relative to itself over time (Gilstad and Sakshaug, 1990; Chen and Durbin, 1994; Wood et al., 2005; Ichimi et al., 2012, Kling et al. 2019). In-vivo fluorescence was never used as a proxy to compare biomass across different light and temperature treatments; however, in a pilot study with this isolate we did observe that fluorescence and cell count increased linearly even across different light and temperature treatments (Figure S1). These data were then used to calculate specific growth rates using GrowthTools (DOI:10.5281/zenodo.3634918), an R package for calculating exponential growth rates for large experiments. This package calculates growth rate as the slope of a regression line fit to the log of these data (Wood et al. 2005). In order to assess growth rate response to light intensity for this isolate, cultures acclimated to 16 °C were further acclimated to seven light intensities (15, 30, 50, 60, 70, 100, and 120 µmol photons / m 2 * sec -1 ), followed by growth rate measurements. In addition, we measured growth rate responses across a range of temperatures in cultures acclimated to one of three light conditions (15, 30, and 50 µmol photons / m 2 * sec -1 ). For each light level, cultures were grown at 10 temperatures (2, 8, 12, 14, 16, 18, 20, 22, 24, 26 °C). GrowthTools (DOI:10.5281/zenodo.3634918) was used again to calculate thermal performance curves (TPCs) using the Eppley-Norberg model (Norberg 2004; Thomas et al. 2012). For the thermal curve done under 15 µmol photons / m 2 * sec -1 , 4 °C was used as the lowest temperature instead of 2 °C. 37 In addition to acclimated growth experiments, we exposed the cultures to short-term doses of extreme light levels on the order of hundreds of µmol photons / m 2 * sec -1 , similar to previously published diatom light-stress experiments (Zhu and Green 2010; Dong et al. 2016). For these light stress experiments we used ~638 µmol photons / m 2 * sec -1 , which approximated the highest value measured in a 50-year dataset of surface irradiance measurements at the Narragansett Bay Time Series station isolation site. Triplicate cultures acclimated to either 4 or 16 °C and 30 µmol photons / m 2 * sec -1 were exposed to this extreme light level for one, three, or six hours and compared to triplicate cultures that were not exposed (negative control) or were continuously exposed (positive control). After the exposure period, cultures were moved back to 30 µmol photons / m 2 * sec -1 and fluorescence was recorded twice daily over three days of 12/12 hour light/dark cycles following exposure. Sequencing For sequencing, 200 ml of dense culture was filtered onto a 0.22 µm polyethersulfone Whatman Nuclepore filter (GE Healthcare, Chicago, IL, USA) which was then flash frozen with liquid nitrogen and stored at -80 °C. DNA was extracted using a DNEasy Power Water kit (Qiagen, German Town, MD, USA). DNA was prepared for sequencing using the Nextera DNA Flex Library Prep kit (Illumina, San Diego, CA, USA). Sequencing was done at the University of Southern California’s Genome Core on a Illumina Nextseq 550. Raw sequence data was quality checked using Fastqc (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ , v. 0.11.8) and Multiqc (Ewels et al. 2016, v. 1.6) and reads were trimmed to remove low quality bases with Trimmomatic (Bolger, Lohse, and Usadel 2014, v. 0.38). To recover 18S rRNA gene sequences from these sequence data, we mapped all reads to a dataset of 200 complete 38 or nearly complete (>1000 bp) Chaetoceros 18S rRNA gene sequences downloaded from NCBI using Bowtie2 (Langmead and Salzberg 2012, v. 2.3.5). All reads that mapped even once were recovered using Seqtk (https://github.com/lh3/seqtk, v. 1.3) and assembled with SPAdes (Bankevich et al. 2012, v. 3.11). To identify our isolate using it’s 18S rRNA gene sequence, full length copies of this gene were downloaded from NCBI for 25 distinctly named species of Chaetoceros. The pennate diatom Pseudo-nitzschia australis was included as an outgroup. All sequences were aligned using Muscle (Edgar 2004, v. 3.8.31), the alignment trimmed using trimAL (Capella-Gutiérrez, Silla-Martínez, and Gabaldón 2009, v. 1.4.15), and FastTree (Price, Dehal, and Arkin 2009, v. 2.1.10) was used to construct a phylogenetic tree. Six years of amplicon sequencing data from our study site using diatom specific primers (Zimmermann et al. 2011) matching the V4 hypervariable region of the 18S rRNA gene were obtained from Rynearson et al., 2020. Raw sequence data was accessed on NCBI (Bioproject accession number PRJNA327394) and quality filtered using the same tools as described for our whole genome Illumina sequencing (WGS). Quality-controlled reads were merged and denoised into Amplicon Sequence Variants (ASVs) using DADA2 (Callahan et al. 2016, v. 1.14.0). BLAST (McGinnis and Madden 2004, v. 2.9.0) was used to look for ASVs that matched the V4 rRNA gene sequence from the full length sequence assembled from our WGS data. We also mined years of observational data to put the occurrence of our isolate ASV in Narragansett Bay into a long-term temperature and irradiance context. Sea surface temperature (SST) data matching the amplicon sequencing data were downloaded from the Narragansett Bay Time-series website (https://web.uri.edu/gso/research/plankton/data/). Dates where no SST measurements from the time-series dataset were available were 39 supplemented by SST data from the National Data Buoy Centers station QPTR1 – 8454049 at nearby Quonset Point (https://www.ndbc.noaa.gov/station_page.php?station=qptr1). Irradiance data for Narragansett Bay were downloaded from the National Research Reserve System’s Central Data Management Office website (https://cdmo.baruch.sc.edu/dges/) for station NARPCMET. In order to understand the distribution of this isolate beyond Narragansett Bay, we utilized the Tara Oceans V9 amplicon dataset (De Vargas et al. 2015). Sequence data from low- and mid- latitudes have been previously analyzed and resolved into ASVs using DADA2 which were screened for the presence of this isolate diatom using BLAST (McGinnis and Madden 2004; Callahan et al. 2017). In addition, we downloaded the Tara Polar dataset and analyzed all amplicon sequencing data for these Arctic Ocean surface samples in size fractions with a lower limit < 5 µm. Reads were processed following the methods of Callahan et al. (2017). Statistics and Data Availability All statistics for analyzing these data and graphic visualizations were done using R (Team 2019, v. 3.6.1) and Rstudio (Team 2016, v. 1.13.83). Differences between light treatments were determined using a one-way ANOVA and the Tukey test while differences between TPCs were assessed using a repeated measure ANOVA. In both cases, significance was determined at the p < 0.05 level. All environmental data compiled in this study, the output from DADA2, scripts used to download the Tara Polar Samples, and scripts used in analysis have been made publicly available at https://doi.org/10.6084/m9.figshare.11907879.v1. Raw WGS data can be found on NCBI under the SRA accession PRJNA608686. Results 40 Light Curve When grown at 16 °C, this diatom isolate had an asymmetric response to increasing light levels, skewed towards low irradiance (Figure 1). Although at the lowest light level tested (15 µmol photons / m 2 * sec -1 ) the specific growth rate was only 0.13 day -1 (±0.01), when irradiance was increased to 30 µmol photons / m 2 * sec -1 , the growth rate nearly tripled, to 0.33 day -1 (±0.03). At this light level the specific growth rate was significantly higher than at all of the other irradiance levels tested (p < 0.05). Increasing the light level beyond 30 µmol photons / m 2 * sec -1 caused the growth rate to rapidly decrease again. For 50, 60, 70 µmol photons / m 2 * sec -1 the specific growth rates were 0.19 (±0.04), 0.20 (±0.02), and 0.18 day -1 (±0.04) respectively, and they were statistically indistinguishable from each other (p > 0.05). Growth rates of our Chaetoceros isolate dropped significantly to only 0.10 ±0.01 day -1 at 100 µmol photons / m 2 * sec -1 (p < 0.05), and experienced negative growth (-0.06 day -1 , ±0.01) when light levels were increased to 120 µmol photons / m 2 * sec -1 , leading to eventual cell death (Figure 1). Thermal Curves Interactive effects between light and thermal niche for this Chaetoceros isolate from the thermal performance curves (TPCs) at three different irradiance levels (15, 30, and 50 µmol photons / m 2 * sec -1 ) are shown in Figure 2a and Table 1. Here we abbreviate the light treatments as low, optimal, and high light. Using a repeat measures ANOVA, each of these TPCs was significantly different from each other (Figure 2a, p < 0.001). The full range of temperatures this isolate was able to grow under (the thermal niche width) was highest at optimal and low light (27.0 and 25.7 °C, respectively), while the high light niche width was 3.8 °C lower than at 41 the optimal temperature (Table 1). The difference in niche width was manifested as a decrease in the upper temperature limit (Tmax), under low light, which was 1.3 °C (23.7 °C) lower than under optimal light, and as a 3.5 °C (1.5 °C) increase in the lower temperature limit (Tmin), under high light, compared to optimal light (Table 1). The temperature at which the specific growth rate was highest, or Topt, was highest at the optimal light level (17.2 °C ±0.86, Fig 2b, Table 1). The Topt decreased under both low and high light, and although it fell farther in low light, both low and high light treatments had Topt’s within one standard deviation of each other (13.7 ±0.87 and 15.2 ±1.17 °C respectively). The maximum specific growth rate, or µMax, across these TPC models was highest under optimal light (0.28 day -1 ±0.02) and high light (0.25 day -1 ±0.02, Figure 2c, Table 1). Response to Extreme Light Stress In addition to considering how this diatom responded when allowed to acclimate to constant low- mid range light and temperature conditions, we also assessed how it responded to pulses of extreme light at two temperatures, 4 and 16 °C. At both temperatures, constant exposure to the upper limit of irradiance recorded for the site where this isolate was collected (638 µmol photons / m 2 * sec -1 ) was lethal. In the constant extreme light control treatment at 4 °C, fluorescence decreased steadily until it reached the lower limit of detectable fluorescence at the very end of this experiment (Figure 3a). However, at 16 °C fluorescence reached the lower limit after just 24 hours (Figure 3c), 3.3x faster than at 4 °C (Figure 3b & d). At the lower temperature this Chaetoceros isolate was able to maintain positive growth even after being exposed to extreme light for six hours, although exposure for both three and six hours significantly decreased the growth rate compared to control cultures never exposed to extreme 42 light (p < 0.05). Similarly, growth rates were significantly lower in the three- and six-hour exposure treatments compared with the unexposed control. Cells acclimated to 16 °C also had significantly lower growth rates compared to unexposed cultures (p < 0.05); however, unlike at the colder temperature, exposure to extreme light for six hours was lethal for cells acclimated to the higher temperature. 18S rRNA resolved taxonomy Short read sequencing produced nine million 150bp paired-end Illumina reads. Mapping reads to 200 full length Chaetoceros spp.18S sequences and assembling all reads that mapped at least once produced a single contig 1812 bp long. When BLASTed against the nt database and excluding all but cultured isolates, this assembled 18S rRNA gene sequence was the closest match to C. cf. wighamii strain BH65_48, with 99.8% identity across 92% of the sequence (accession KY980353.1). Unfortunately, this strain had no isolation information attached; however, the next closest match was to another strain of C. cf. wighamii, RCC3008 at 100% identity across 90% of the query (KT860959.1). Isolation information obtained from the Roscoff Culture Collection (RCC) showed that this isolate originated from the coastal Baltic Sea in 2010 at 4 °C, and was maintained under low-light conditions similar to those in our experiment, at 50 µmol photons / m 2 * sec -1 . Aligning 25 full length Chaetoceros spp.18S sequences (Table S1) representing the majority of named Chaetoceros species allowed us to construct a high quality phylogenetic tree (average maximum likelihood = 0.91) of this genus (Figure 4). Similar to the BLAST results, the Baltic isolate C. cf. wighamii was the closest branching sequence, followed by temperate isolates C. throndsenii from the Gulf of Naples (93.6% ID and 96% coverage), and C. 43 lorenzianus (94.1% ID and 90% coverage) and C. constrictus (93% ID and 94.3% coverage) from Las Cruces, Chile. Environmental Amplicon Data Before using this 18S rRNA sequence to look for this isolate in the available amplicon sequencing data for the Narragansett Bay Times Series site, we first confirmed that there was enough diversity within the V4 hypervariable region across the genus Chaetoceros to distinguish this isolate from other members of the same genus. Using aligned V4 regions of the same species and sequences shown in Supplementary Table 1, we constructed a tree with the same branching pattern as Figure 4 (shown in Figure S2). This means that using primers that amplify the V4 region is an acceptable means of searching for this isolate in environmental sequencing datasets that rely on this hypervariable region of the 18S rRNA gene. Processing the publicly-available diatom sequences from Narragansett Bay resulted in 5170 distinct amplicon sequence variants (ASVs); however only 20 of these ASVs had an average relative abundance greater than one percent of recovered amplicons across the data set, suggesting there are a large number of relatively rare diatom ASVs in this environment. When BLASTed, the most common diatom genera were Thalassiosira (eight), Skeletonema (four), and Minidiscus (two)( Table S2). These are consistent with previous observations at Narragansett Bay, where Thalassiosira and Skeletonema often dominate the diatom community (Canesi and Rynearson 2016; Rynearson et al. 2020b). Of these 20 most abundant recovered ASVs, one was a perfect match (100% ID and 100% coverage across the V4 hypervariable region) to the 18S rRNA gene sequence we recovered from WGS data. 44 Because the primer set used to amplify 18S rRNA gene sequences is specific to diatom sequences, these primers have the limitation that they only report shifting relative abundances within the diatom community, and cannot show us changes in the absolute abundance of diatoms in Narragansett Bay. To avoid over-inflating potential correlations between changing relative abundance within the diatom community and environmental factors, we used one percent relative abundance as a threshold and looked at the changing probability of an observation being above this threshold under different conditions. For instance, this ASV was greater than one percent of the total recovered amplicons in 28 of the 80 samples. It had a ~0.5 probability of detection in samples in January, February, and March (Figure 5a). This probability peaked at 0.86 in April, before decreasing to 0.5 again in May. In June through August the probability of detection was ~0.2 and dropped to zero in September and October, before rising again to 0.5 in November and December (Figure 5a). Although not a statistically-robust way of describing this isolate’s abundance in situ, it can be noted that across all 80 samples it makes up on average 4.1% of the relative diatom abundance per sample. It can also make up as much as 76.8% of all recovered amplicons (Figure S3); however, from the available observational data we were not able to associate this isolate with major phytoplankton blooms Narragansett Bay. In this dataset, chlorophyll a concentrations were greater than 10 µg/L in 10 different samples. The ASV matching our Chaetoceros isolate was only detected above the one percent relative abundance in three of these high chlorophyll a events, but in each of these samples where it was detected it never made up more than 1.5% of the recovered amplicons. In order to compare the probability of detection with surface temperature, we split temperatures into 13 two-degree bins from 0 to 26 °C. We observed a strong association with 45 the detection of the ASV matching our isolate with low temperatures (Figure 5b). At temperatures < 12° C, the probability of observing this ASV was on average 0.52 (±0.19). At temperatures between 12 and 26 °C, the probability of observing this ASV was 0.11 (±0.11) on average, almost five times lower than at cooler temperatures. The distribution of light exposure readings throughout the dataset was mostly between 10 and 40 moles photons / m 2 * day -1 , so measurements within this range were grouped in increments of 5 moles photons / m 2 * day, with readings outside this distribution recorded as < 10 and > 40 moles photons / m 2 * day -1 , respectively. The probability of detection was approximately equal at ~0.3 for all total irradiance levels >10 moles photons / m 2 * day -1 ; however, when total irradiance was < 10 moles photons / m 2 * day -1 , the probability of detection nearly tripled to 0.9 (Figure 5c). Beyond Narragansett Bay, this isolate was not detected in any previously published amplicon data covering open ocean stations at tropical and temperate latitudes (Callahan et al. 2017). Prior to analysis, a phylogenetic tree was made using the V9 region of the 18S rRNA gene for the complete 18s sequences used to generate Figure 4, which showed this hypervariable region was able to differentiate this isolate from other Chaetoceros spp. (Figure S4). Two ASVs that matched our isolate with 100% BLAST ID across >90% of the sequence were detected in surface waters for the high-latitude Tara Polar samples at 8 of 16 Arctic Ocean stations (Figure 6). Both ASVs were found at the same stations with approximately equal relative abundance, and thus their results are reported together. The combined relative abundance for these ASVs was low, making up only >0.2% of the total 18s rRNA amplicon pool at stations where they were detected. Furthermore, they were mostly detected at stations where the relative abundance of diatom amplicons was comparatively low (Figure S5). Despite their seemingly low 46 relative abundance, both of these isolates are in the 98 th percentile of abundance for resolved ASVs. In addition, these samples were only collected between May and October. Based on the seasonality depicted in data from Narragansett Bay, it is likely these numbers underestimate its overall abundance in polar waters, especially during unsampled colder months. Discussion A preference for low light levels for growth is not necessarily uncommon in marine phytoplankton. For instance, Prochlorococcus, a dominant unicellular marine picocyanobacterium in the oligotrophic gyres, has well-defined low- and high-light ecotypes occupying deep and surface layers of the euphotic zone, respectively (Moore et al. 1998; Goericke et al. 2000; Johnson et al. 2006). Studies on low light Prochlorococcus have reported upper light limits that are similar to those we describe here for our diatom isolate (Goericke et al. 2000). Even among diatoms, adaptations to low light levels have been reported in species living in benthic environments (Admiraal 1976). It is possible that this is a benthic diatom that is being resuspended in the water column in this shallow estuary. However, centric diatoms are not typically considered active in the benthos (although dormant resting stages can be observed), as opposed to pennate species that have gliding motility and thus are more adapted for surface-associated environments (Conley et al. 1989; Ligowski 2000; McQuoid and Godhe 2004). For this reason, we think this explanation is unlikely. It could be that the low-light physiology of our Chaetoceros isolate is an adaptation to deeper layers of the photic zone, similar to low-light Prochlorococcus. However, Narragansett Bay is shallow (8m) and unlikely to support distinct deep photic zone ecotypes. In the literature, diatoms living under sea ice have 47 been reported to maintain active photosynthesis below 5 µmol photons / m 2 * sec -1 (Seckbach 2007), but Narragansett Bay typically does not freeze over during the winter (Pineda et al. 2005). Thus, although there is precedent among diatoms for adaptation to extreme low light in other environments, this specialization has not been reported in planktonic diatoms. In fact, this diatom from a planktonic environment was incapable of growing at light levels > 120 µmoles photons / m 2 * sec -1 . We also showed that at sub- or supraoptimal light levels, the maximum thermally-determined growth rate and the thermal optimum decreases. Similar observations have been made in a metanalysis of phytoplankton temperature-light effects, however at much higher light levels (Edwards et al. 2016). In temperate latitude regimes such as Narragansett Bay temperature and light levels increase simultaneously as spring progresses into summer, in the form of increased day length and angle of solar incidence. This suggests that the realized cold season thermal niche of this diatom is defined by the negative interactive effect of both temperature and light. This also may explain why observations of this diatom in situ typically occur at temperatures well below the range of optimal growth temperatures predicted by our TPC models (13.7-17.2 °C). This also poses the question of whether our diatom persists in this estuary year-round. In five years of amplicon data it was detected in between ~45-80% of the samples taken between November and May, when temperatures are low and the days are short. Although it was infrequently detected during some of the summer months, it was never detected during either September or October. It could be that this Chaetoceros sp. persists in the estuary through the warmer months, but in extremely low abundances below our detection limit. 48 Another possibility is that the species may form resting stages during adverse conditions, and then emerge in the late fall when conditions improve. Chaetoceros spp. are known to form resting stages when nutrients are exhausted, or conditions are otherwise adverse. These resting stages can germinate and repopulate the water column when conditions improve (McQuoid and Godhe 2004). It is also possible that it is not present at all during the warmer months, and the increase in detection in November is the result of annual re-recruitment from the nearby North Atlantic Ocean. Its broad distribution in the Tara Polar samples in addition to its temperature and light preferences suggest this diatom could be adapted to the cold and low-light levels found during winter and early spring months in the North Atlantic (Siegel et al. 2002; Boss and Behrenfeld 2010). Future application of molecular sampling methods may show that this diatom substantially contributes to early spring bloom community primary production at high latitudes. Although this study documents this diatom’s interesting preference for low light, more work will be needed to investigate the mechanism causing this sensitivity to irradiances of >120 µmol photons / m 2 * day -1 . For photosynthetic organisms, an accumulation of deleterious reactive oxygen species (ROS) in the cell is often seen following exposure to extreme irradiance (Wilhelm et al. 2014 and references therein). ROS buildup is typically localized to the chloroplast, and is associated with cell death in the model diatom Phaeodactylum tricornatum (Mizrachi et al. 2019). It is possible that there is a fitness cost associated with being able to photosynthesize in low light environments, whereby this diatom is inefficient at or unable to regulate excess light energy coming into its photosystems, causing the accumulation of ROS. For instance under low light conditions, many diatoms photo-acclimate by increasing the size of 49 their chloroplasts and the number of photosystems and antenna pigments they contain, in order to increase photon capture (Rosen and Lowe 1984; Lepetit et al. 2012). It could be that our low-light Chaetoceros has a limited ability to adjust its photosynthetic energy acquisition systems when exposed to high light. Another possibility could be interference in the xanthophyll cycle, which regulates non- photochemical quenching (NPQ) by shuttling excess light energy into heat and fluorescence before it can contribute to damaging ROS production (Ruban et al. 2004). Experimentally disrupting NPQ in benthic diatoms exposed to extreme light reduced turnover of PSII subunits (Cartaxana et al. 2013), a primary means of limiting the impact of ROS in phytoplankton (Wu et al. 2012). Alternately, the diatom described in this study may have a reduced capacity to produce ROS-scavenging antioxidants such as glutathione or ascorbate (Cartaxana et al. 2013). These various mechanisms are not mutually exclusive, and should be considered in further work. Molecular techniques such as transcriptomics or proteomics could provide insights into how photosynthesis can be efficient at very low light, by comparing how the regulation of photoprotective mechanisms differs in this diatom compared with diatoms accustomed to higher light levels. Future work should also consider the effect of light spectral quality on the irradiance and temperature interactions we describe here. All culturing in this study was done using white light; however, in aquatic environments not only is the total irradiance variable, but also the availability of specific wavelengths. Shorter wavelength blue light has more energy, and thus penetrates farther into the water column than longer red wavelengths. Phytoplankton associated with low light environments such as deep water or beneath sea ice are often 50 specialized for utilizing these higher energy wavelengths (Gosselin et al. 1990; Shimada et al. 1996). Blue light has also been suggested to trigger germination in the resting stages of marine diatoms in coastal ecosystems, suggesting it could have a regulatory effect in addition to supplying energy for carbon fixation (Shikata et al. 2009). In the diatom P. tricornutum, blue light has also been associated with photoprotection. Cultures maintained under blue light upregulated the production of proteins associated with photoprotection and the xanthophyll cycle, and were better able to take advantage of increases in available light than cultures grown under red light, which had a limited ability to mount a photoprotective response to increased light (Schellenberger Costa et al. 2013). The role of blue light is also interesting when considering that this Chaetoceros sp. favors cold temperatures, suggesting it is adapted to wintertime conditions and high latitudes. At higher latitudes and during the winter, solar elevation is lower compared to low latitudes or during the summer. This results in a lower angle of incidence, which causes more light to be reflected from the ocean’s surface; however, this process is skewed towards longer wavelengths, which are preferentially reflected (Campbell and Aarup 1989). The implication is that phytoplankton at higher latitudes or during the winter season experience more blue light relative to red light. It would be interesting to test whether these diatoms experience the same light sensitivity when grown under blue light as white light. The interactive effects of light and temperature on this diatom’s growth in the lab and pattern of abundance in situ raise interesting questions about how marine phytoplankton will respond to rising temperatures associated with climate change. It is broadly suggested that organisms at high latitudes exist at temperatures well below their thermal optima, and 51 therefore rising temperatures will be advantageous, increasing their growth rate (Thomas et al. 2012; Boyd et al. 2013). The average temperature at Narragansett Bay in the five years of temperature data accompanying this amplicon dataset is 12.4 °C, below the optimal temperatures predicted by our three TPC models. However, because of the strong regulation of thermal niche by light level in this Chaetoceros sp. it could be that this isolate will not fare better with rising temperatures, as warmer conditions increase its susceptibility to light stress. This interaction between light and temperature may in fact shrink the range of months where this diatom is detectable. For instance, it is frequently observed as late in the year as April and May, where day length is longer and solar elevation higher than during the winter months. Rising temperatures during those months may be harmful, increasing the diatom’s susceptibility to light stress and effectively excluding it from the estuary in the subsequent summer and early fall seasons. It is also possible that the opposite could happen during winter months (e.g. December to March) when days are short, and solar elevation is low. Future warming during these months could potentially increase growth rates, making this isolate more abundant in the winter. Our study highlights one facet of the largely unrecognized but almost limitless diversity that exists in marine phytoplankton communities. It is fascinating that a planktonic diatom with such a specialized light and temperature niche was discovered at the longest running phytoplankton timeseries in existence, and that despite its specialization it is one of the more common diatoms at this well-studied site. It is entirely possible that this species or others with similar preferences for cold, dark conditions are cryptically abundant at other temperate and high latitude sites. 52 This work also shows that light and temperature can interact to define a thermal niche. Even in species that thrive at comparatively high light levels, changes in light could similarly impact their response to changes in temperature and influence how they will fare in a warming ocean. Future studies should consider high light as an interactive variable along with other co- stressors such as elevated temperatures and CO 2 when predicting how phytoplankton will respond to global change. Acknowledgements We would like to thank Meghan Phan and Roxanna Andrade for assistance with preliminary library prep and Illumina sequencing. Funding was provided by National Science Foundation grants OCE1538525 and OCE1638804 to DAH, and OCE1638834 to TR. 53 Main Figures and Tables Figure 1: Growth rates at 16 °C across a range of seven light levels for a novel Chaetoceros sp. isolate. Error bars represent ±1 standard deviation. Stars show treatments that are statistically significant (p < 0.05) compared to all other treatments via one-way ANOVA. Brackets indicate statistical significance between specific samples. 15 30 50 60 70 100 120 Irradiance (µmol photons / m 2 *sec -1 ) Specific Gr o wth Rate (day -1 ) -0.1 0.0 0.1 0.2 0.3 0.4 54 Figure 2: a) Thermal performance curves across three light levels. For all best-fit curves, r 2 > 0.7 and all are significantly different from each other using repeat measure ANOVA (p < 0.001). Differences between light treatments are shown for the b) thermal optimum (Topt) and c) maximum growth rate (µMax). Error bars show ±1 standard deviation within the modelled optimal temperatures and growth rates for each. 0 10 20 -0.1 0.0 0.1 0.2 0.3 0.4 0 10 20 °C 0 10 20 Specific Gr o wth Rate (day -1 ) a 0 5 10 15 µmol Photons / m 2 *sec -1 30 50 15 20 °C 0.0 0.1 0.2 0.3 c b Specific Gr o wth Rate (day -1 ) 55 Figure 3: Effects of temperature and light exposure time on cultures of the novel Chaetoceros sp. isolate exposed to 638 µE / m 2 * sec., the highest incident light level recorded in 41 years of data from its isolation location. a) Fluorescence (RCF) and b) specific growth rates (d -1 ) of cultures grown at 4 °C under extreme light exposure. c) and d) show the same parameters for cultures grown under extreme light at 16 °C. In panels a & c, periods of darkness are shown as grey bands. b & d depict the growth rates for each exposure treatment after three days. All error bars indicate ±1 standard deviation. Stars and brackets show treatments that are significantly different by one-way ANOVA (p < 0.05). 2 4 6 RCF Hours Exposure 0.00 0.25 -0.25 -1.75 -1.50 0.50 Specific Gr o wth Rate (day -1 ) 1 2 3 -0.75 -0.50 -0.25 0.00 0.25 0.50 Exposure (hours) 0 1 6 36 3 a b c d 0 24 48 56 Figure 4: Phylogenetic tree representing the diversity of the diatom genus Chaetoceros constructed using full-length 18S rRNA gene sequences from NCBI. A sequence from the pennate diatom species Pseudo-nitzschia australis is included as an outgroup. The isolate described in this study, DM53, is highlighted in bold. C. lorenzianus DM53_this_study C. cf. socialis C. danicus C. cf. neogracilus C. constrictus C. throndsenii C. curvisetus C. debilus C. cf. convolutus C. setoense C. dichaeta C. atlanticus C. rastrus C. wighamii C. muelleri C. teres Pseudo-nitzschia australis C. didymus Chaetoceros protuberens C. cf. wighamii C. tortissimus C. lauderi C. eibenii C. calcitrans C. peruvianus C. pseudo-curvisetus 1 0.951 0.877 0.995 0.895 0.889 1 0.99 0.961 0.998 0.991 1 0.987 1 1 0.997 1 0.983 0.993 0.71 0.005 0.693 Tree scale: 0.01 0.947 57 Figure 5: Probability of detection for the ASV matching the isolate described in this study in five years of 18S rRNA gene amplicon data by a) month, b) temperature, and c) seven-day average of photons received per square meter. The number of samples falling within each category is shown along the top of each graph. ● ● ● ● ● ● ● ● ● ● ● ● 0.00 0.25 0.50 0.75 1.00 1 2 3 4 5 6 Month 7 8 910 11 12 ● ● ● ● ● ● ● ● ● ● ● ● ● 0.25 0.00 0.50 0.75 1.00 0- 2 2- 4 4- 6 6- 8 8-10 10-12 12-14 14-16 16-18 18-20 20-22 22-24 24-26 °C Detection Frequency ● ● ● ● ● ● ● ● 0.00 0.25 0.50 0.75 1.00 <10 10-15 15-20 20-25 25-30 30-35 35-40 >40 moles photons / m 2 *day a b c n n 7 8 9 9 3 7 3 2 5 6 7 8 11 2 n 7 17 10 7 6 14 8 11 6 6 6 7 7 6 7 7 7 7 7 58 Figure 6: Combined relative abundance of two ASVs in the Tara polar ocean stations that were 100% match to the V9 region of the 18S rRNA gene sequence recovered from the diatom isolate presented in this study. The red dot shows the isolation location of a diatom in the Roscoff Culture Collection whose 18S rRNA gene is a close match to this isolate. ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 60° N 0° 180° 90° W 90° E %Amplicons ● 0.01 ● 0.05 ● 0.10 ● 0.15 ● Not Detected ● RCC3008 59 Table 1: Thermal performance curve (TPC) parameters calculated at three light intensities. Standard deviations are shown where available, within parenthesis. µmol photons / m 2 * sec -1 Width Tmin Tmax Topt µMax r 2 15 25.7 -2.0 23.7 13.7 (±0.87) 0.16 (±0.01) 0.87 30 27.0 -2.0 25.0 17.2 (±0.86) 0.28 (±0.02) 0.75 50 23.2 1.5 24.7 15.2 (±1.17) 0.25 (±0.02) 0.71 60 References Abad, D., A. Albaina, M. Aguirre, A. Laza-Martínez, I. Uriarte, A. Iriarte, F. Villate, and A. Estonba. 2016. Is metabarcoding suitable for estuarine plankton monitoring? A comparative study with microscopy. Mar. Biol. 163: 149. Admiraal, W. 1976. Influence of light and temperature on the growth rate of estuarine benthic diatoms in culture. Mar. 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Hutchins 1 1 Department of Biological Sciences, University of Southern California, Los Angeles, CA 90007, USA 2 Exobiology Branch, NASA Ames Research Center, Moffett Blvd., Mountain View, CA 94035, USA 3 Blue Marble Space Institute of Science, 1001 4th Ave, Seattle, WA 98154, USA 4 Kellogg Biological Station, Michigan State University, Hickory Corners, MI 49060, USA 5 Graduate School of Oceanography, The University of Rhode Island, Kingston, RI 02881, USA 6 Third Institute of Oceanography, Xiamen University, Xiamen, 361005, Fujian, China Abstract: The extent and ecological significance of intraspecific diversity within marine microbial populations is still poorly understood, and it remains unclear if such strain-level microdiversity will contribute to fitness and persistence in a rapidly changing ocean environment. In this study, we cultured 11 sympatric strains of the ubiquitous marine picocyanobacterium Synechococcus isolated from a thermal selection incubation experiment using a phytoplankton community from Narragansett Bay (Rhode Island, USA). Thermal phenotypes were determined for each isolate using thermal performance curves, demonstrating that this single population was subdivided into distinct ‘cool’ and ‘warm’ thermotypes that correlated strongly with their 69 respective experimental isolation temperatures. Differences in maximum growth temperatures (Tmax) were especially prominent between high and low temperature isolates. Despite the segregation of this Synechococcus population into these two contrasting thermotypes, all 11 isolates were nearly genetically identical with an average nucleotide identity (ANI) >99.9%. Within this tiny amount of diversity, we detected variation at a locus containing genes for the phycobilisome antenna complex that correlated with the two divergent thermal ecotypes. Our study demonstrates that marine microbial populations can contain cryptic microdiversity in the form of environmentally-relevant thermotypes that will potentially increase their resilience to future rising temperatures. 70 Introduction: Marine bacteria control most marine biogeochemical cycles (Fuhrman and Azam 1982; Capone et al. 2005) and are both hyperabundant and hyperdiverse, with commonly reported records of 10 8 cells per liter representing thousands of taxonomically distinct units (Venter et al. 2004). Within this level of macrodiversity there is also a great deal of intraspecific microdiversity, such as bacterial species complexes that include numerous strains or ecotypes (Needham et al. 2017; Chafee et al. 2018; Delmont et al. 2019). Much of the work documenting the ecological relevance of intraspecific microdiversity has used amplified marker genes such as the 16S rRNA gene, resolved to single base pair differences (García-García et al. 2019). However, there is still much work to be done to describe the potentially substantial genomic diversity that exists within groups identical at the 16S rRNA level. For instance, it remains unclear to what extent intraspecific microdiversity contributes meaningfully to the biogeochemical roles of a bacterial species, or to its ability to survive in a changing environment. Efforts to understand the interactions of microbes with the marine environment have often relied on approaches that underestimate or mask intraspecific diversity. For instance, culture-based methods are usually limited to a handful of strains that are amenable to cultivation, and are currently available in culture collections. These are then used to make generalizations about the activity of a broader taxonomic group (Hutchins et al. 2013; Fu et al. 2014; Zimmerman et al. 2014). Sequencing approaches avoid this culturing bottleneck but lack the ability to provide rate measurements, and many commonly used sequencing methods also have difficulty detecting microdiversity. For instance, metagenomic or metatranscriptomic data assembly pipelines often are unable to discern sequencing errors from rare genotypes or strains 71 (Olson et al. 2017). For both culture-based and sequencing studies there are ways of addressing intraspecific diversity within a microbial population by utilizing high-throughput culturing (Henson et al. 2018) or new technologies such long-read sequencing (Warwick-Dugdale et al. 2019) or high-C linkage (Bickhart et al. 2019). However, these are not yet in widespread use by much of the environmental microbiology community. Currently, single cell genomics gets closest to addressing these questions by examining individual cells in situ. For example, Kashtan et al. (2014) sequenced 90 Prochlorococcus cells from three samplings (~30 co-occurring individuals) at the Bermuda Atlantic Timeseries and detected multiple subpopulations defined by a shared genomic backbone. Individuals within these subpopulations differed from each other by only 1.2% to 4.7% of the available genome, but still appeared to represent environmentally meaningful ecotypes (Kashtan et al. 2014). Although single cell sequencing studies like this are able to detect ecotypes, their detection of intraspecific microdiversity is limited by the fact that they seldom produce complete genomes (Kashtan et al. 2014; Utami et al. 2018; Martinez-Hernandez et al. 2019). In addition, purely sequence-based in situ approaches are also limited in the amount of ecotype microdiversity they can reveal, simply because detection relies on observed correlations between relative abundance and ambient environmental parameters (e.g. temperature, nutrients, light). Thus, rare ecotypes with optimal niches that lie outside of current conditions will remain cryptic. For example, most marine microbial communities will undergo future selection by temperatures exceeding those that they currently experience (Hutchins and Fu 2017; Kling et al. 2019), as current climate models predict that anthropogenic carbon emissions will raise sea surface temperatures ~4°C by the year 2100 (Pachauri et al. 72 2014). In order to understand the ability of microbial populations to persist and maintain their functional roles under changing thermal regimes, it is important to understand if unrecognized microdiversity relevant to future warmer temperatures currently exists within microbial species (Thomas et al. 2012; Cavicchioli et al. 2019; Hutchins et al. 2019). The unicellular marine cyanobacterium Synechococcus is a major microbial functional and taxonomic group that is found from low latitude equatorial waters to high polar latitudes (Sohm et al. 2016; Lee et al. 2019). This widespread and diverse genus is commonly expected to increase in both abundance and distribution as result of climate warming (Flombaum et al. 2013). Together with the other major genus of marine picocyanobacteria Prochlorococcus, they account for ~25% of global carbon fixation (Flombaum et al. 2013). Synechococcus in particular has been strongly correlated with carbon export to the deep ocean (Guidi et al. 2016), making this genus an important component of the marine carbon cycle. In this study, we first used multiple temperature incubations of a natural coastal assemblage to enrich for intraspecific ‘thermal specialist’ strains of Synechococcus. We then isolated multiple sympatric strains of Synechococcus from the contrasting temperature incubations and characterized their thermal niches, allowing us to recover two coexisting but divergent thermal phenotypes from this single population. Finally, we used high-coverage, short read sequencing to obtain complete genomes for all of the isolates (Lee et al., 2019). Surprisingly, when comparing assemblies of their genomes the two sets of thermally-distinct isolates are nearly identical. We do detect potential variation that correlates with their measured thermal niche in a locus coding for the photosynthetic accessory pigment C- 73 phycocyanin, a seemingly minor trait that is nevertheless strongly correlated with thermal niche specialization in this Synechococcus population. Methods: Sampling and Cell Isolation Surface water at a temperature of 22°C and salinity of 28.48 was collected from the Narragansett Bay Time Series site (latitude 41.47, longitude -71.40) on July 18 th , 2017 (Canesi and Rynearson 2016). Collected surface water was initially pre-filtered using 100µm mesh to remove debris and large grazers. Initially, some of the collected water was sorted with a BD FACSCalibur flow cytometer (San Jose, CA, USA). Cells that were <1.5 µm in diameter and had measurable phycocyanin fluorescence were assumed to be Synechococcus, and were sorted into 96-well plates containing F/20 media (Guillard 1975). In order to select for multiple temperature phenotypes, we split the collected seawater into four temperature treatments with triplicate bottles at 18°, 22°, 26°, and 30°. Incubations were performed in 2L polycarbonate bottles, and were amended with nutrients to match F/40 media (Guillard 1975). We used semi-continuous dilutions with 0.2 um-filtered seawater medium to prevent nutrients in these bottles from becoming depleted and the cells entering stationary phase. After 10 days, Synechococcus cells were again sorted into 96 well plates containing F/20 medium. Wells showing growth over time were transferred into artificial seawater (Sunda, Price, & Morel, 2005), and nutrient concentrations were gradually increased up to F/2 levels (Guillard 1975). All temperature-sorted isolates were maintained long term at 22° C at a light intensity of 150 µmol photons/m 2 *sec -1 , with weekly transfers into fresh culture medium. Thermal Performance Assays 74 Thermal niches were calculated by measuring each strain’s thermal performance curve. This was done by acclimating aliquots of each culture for two weeks to a broad range of temperatures between 9° and 33°. Temperatures >33° were added as needed for strains able to grow at these levels. This temperature range was chosen because it exceeds the current summer high and low temperatures in Narragansett Bay, and encompasses the projected warmer temperatures expected in coming decades. Strains were grown at each temperature in triplicate 8 ml borosilicate vials containing 5ml of F/2 medium. Biomass was recorded every two days using in vivo chlorophyll a fluorescence measured on a Turner AU-10 fluorometer (Turner Designs Inc., Sunnyvale, CA, USA), and growth rates and Eppley-Norberg thermal performance curves (Norberg 2004) were calculated in R (Team 2019) using the package growthTools (DOI:10.5281/zenodo.3634918). Rare cultures containing contaminants (verified using fluorescence microscopy) were excluded from the dataset. In two strains for low temperature treatments, LA20 and LA27, after two weeks of acclimation no Synechococcus cells were observed in the culture, so the growth rate was set to zero for these cultures. The results of these thermal performance curves are hereafter referred to as a strain’s phenotype. We verified growth rates for two strains isolated from high and low temperature treatments using changes in particular organic carbon (POC). Strains were grown in triplicate in 1L polycarbonate bottles for two weeks with dilutions every three days at 22 °C. In addition to POC, carbon fixation was also measured for both strains. Analysis for both POC and carbon fixation were done as in Qu, Fu, & Hutchins (2018) and references therein. At the end of two weeks, cultures were diluted to equal biomass and the temperature was increased to 28 °C. Both isolates were sampled at the beginning, after two days, and after four days. 75 Sequencing and Analysis 250ml of dense culture for each strain was filtered onto 0.2 µm Polyethersulfone (PES) membrane filters and DNA was extracted using the DNeasy PowerSoil kit (Qiagen, Germantown, MD, USA). Sequencing was done on an Illumina Hiseq at 2x150 with ~10 million reads per sample at Novogene Inc. (Beijing, China). The quality of base calls was assured using fastqc (Andrews 2010) and assembled with SPAdes version 3.13 (Bankevich et al. 2012). To assist in genome curation, reads were assigned taxonomy using Centrifuge (Kim et al. 2016) trained on the p compressed+h+v index (downloaded July 2019), and raw reads were mapped to their assembled genomes using bowtie2 (Langmead and Salzberg 2012). The resulting assemblies were curated to remove associated heterotrophs using Anvi’o version 5.5 (Eren et al. 2015) based on tetranucleotide frequency, differential coverage, and read taxonomy. Gene calls for curated assemblies were generated using Prodigal (Hyatt et al. 2010) which were then annotated using kofamScan (Aramaki et al. 2019) and imported into Anvi’o. In addition to short read sequencing, DNA was extracted from one low temperature and one high temperature isolate for long read sequencing using an Oxford Nanopore Minion (Oxford, UK). 200ml of dense culture was concentrated using centrifugation (27,000 x g for 17 minutes) and extracted with the GenElute Bacterial Genomic DNA Kit (Millipore Sigma, Burlington, MA, USA). Basecalling was done using Guppy version 2.2.3 and long reads were filtered using filtLong version 0.2.0 (https://github.com/rrwick/Filtlong). Filtered long reads were mapped to their respective draft assemblies using Minimap2 version 2.17 (Li 2018), and the same was done for the short reads using bowtie2 (Langmead and Salzberg 2012). Mapped long and short reads were then assembled together using Unicycler version 0.4.8 (Wick et al. 76 2017) with gene calling and annotation in a manner similar to genomes assembled only with short reads. In order to place our isolates in the context of the broader diversity of Synechococcus, we pulled all Synechococcus genomes (a total of 78 as of July 2019) from Refseq (Table S1) and created a phylogenetic tree using concatenated amino acid sequences for 239 single copy core genes identified as being native to all of these genomes using GtoTree (Lee 2019). All trees were visualized using the interactive Tree of Life webpage (Letunic and Bork 2006), and calculated average nucleotide identity (ANI) using fastANI (Jain et al. 2018) for finer taxonomic resolution. The Anvi’o pangenomic pipeline (Delmont and Eren 2018) was used to identify genes correlating with the original incubation temperature from which each strain was isolated, hereafter referred to as genotype. In addition, we looked for sequence variants at loci of interest by mapping reads to the nearest phylogenetic neighbor with a complete genome and profiling single-codon-variants (SCV), following the Anvi’o population genetics protocol. Photophysiology Measurements To analyze differences in photosynthetic accessory pigment function we concentrated 200ml of dense culture by centrifuging for 15 minutes at 27,000 x g. Cell pellets were then resuspended in 5ml of sterile media, and the fluorescence and absorption spectra measured on a SpectraMax m2 (Molecular Devices, San Jose, CA, USA). In order to detect changes in efficiency in the light gathering mechanisms with temperature, we incubated the cell concentrate for 10 minutes at temperatures increasing every three degrees from 22°-57° following the methods of Pittera, Partensky, & Six, (2017). Fluorescence was then excited at 530nm and the resulting emission measured from 600-700nm, matching the profile of the 77 allophycocyanin/phycocyanin pigment complex (Six et al. 2007). In addition, we measured the photosynthetic efficiency of photosystem II (Fv/Fm) for these two strains when acclimated to 28° C using a PHYTO-PAM with an excitation wavelength set to 645 nm for C-phycocyanin and allophycocyanin (Heinz Walz, Effeltrich, Germany). Fv/Fm measurements were made using triplicate cultures and three technical replicates each that were dark acclimated for 20 minutes, as in McParland et al. (2019). Data availability Curated genomes are available from SRA under the Bioproject ID PRJNA566206. Isolate information and individual Biosample accession numbers can be found in Supplemental Table 2. Scripts used in the analysis and generation of all figures as well as all physiological data are available at https://figshare.com/projects/Kling_et_al_2020/66188. Phenotypic data are also available at: www.bco-dmo.org/award/712792. Results Out of the 11 strains of Synechococcus isolated in this study, one originated from our initial sorting of the collected seawater, before nutrients were added (Table S2). The other ten isolates were collected from the enrichment experiments, five from the 18° C enrichments and five from 30°. Hereafter, strains collected from 18° or 22° C are referred to as “cool temperature” while strains from 30° C are called “warm temperature”. Despite these considerable differences in temperature, all of the cool and warm temperature isolates shared virtually identical morphologies (Table S2). Synechococcus was detected in the other temperature treatments, but cell sorting did not produce any culturable isolates from these incubations. 78 After growing each isolate across multiple temperatures, our thermal niche models generated high quality thermal performance curves for each isolate, with an average R 2 of 0.81 (±0.14 SD, Figure 1A). The average thermal maximum (Tmax) was highest for warm temperature isolates (35.6 °C, ±0.5 SD) compared to cool temperature isolates (33.5 °C, ±0.9). This difference in Tmax between warm and cool temperature isolates was significant (T-test, p = 0.005, Figure 1B). The optimal growth temperature (Topt) was also higher for strains from warm temperatures, with a mean of 29.8° (±1.8 SD), than for cool temperature (27.6 °C, ±1.2 SD); however, this difference was only significant at the p = 0.06 level (Figure 1B). Minimum growth temperature (Tmin) and niche width (Tmax – Tmin) did not significantly differ from each other for strains isolated from any temperature. In addition to differences in growth observed using in vivo fluorescence measurements, a comparison of one high- and one low-temperature isolate showed that the warm-adapted strain accumulated ~2x more volume-normalized POC after the temperature was increased from 22 to 28 °C (p = 0.002, Figure S1A). The high temperature isolate also maintained a higher growth rate (although only significant at the p < 0.07 level) than the low temperature isolate (Figure S1B). To compare genomic differences between low and high temperature strains, sequence data for each isolate collected from this study (n=11) was assembled and manually curated to produce 100% complete draft genomes (Table S3). Recovered short read genomes (hereafter called draft assemblies) were 2.74 Mbp long (±0.01 SD) split between 22.1 (±6 SD) contigs with a mean GC content of 63.3% (±0.00) and mean gene content of 2976.0 (±11 SD) for each isolate. We generated long reads for one cool (LA31) and one warm temperature isolate (LA127), and were able to close the genome of the warm temperature strain (one contig 2.75 79 Mbp long). Including long reads significantly improved the assembly of the cool temperature isolate, reducing the number of contigs from 18 to six (Table S3). When placed on a phylogenetic tree with all presently available Synechococcus genomes (Figure S2), the isolates from this study fell on the same branch within marine subcluster 5.2 (Figure 2A). The most closely related isolate in Genbank was CB0101 from Chesapeake Bay, a geographically proximate estuary (Fucich et al. 2019). With this approach, using concatenated AA sequences of 239 single-copy core genes, the 11 isolates from this study were indistinguishable from each other. This high relatedness was even more apparent using ANI, with strains having 99.99% (±0.003 SD) average nucleotide identity (Figure 2B). This suggests that our assemblies differ on the order of only hundreds of base pairs. Despite the high degree of similarity, when comparing draft assemblies 62 gene clusters were identified by Anvi’o (Figure S3, Table S4) as having less than 100% functional (e.g. differences in sequence) or structural (e.g. insertions, deletions) homogeneity between one or more assemblies. Manually examining these gene clusters revealed that much of this detected variation in structural homogeneity was driven by poor alignments (Figure S4A). Actual differences in functional homogeneity were typically caused by a transition in one or a few isolates (Figure S4B). Only two gene clusters examined had patterns of variation that correlated with the temperature treatments that these strains were isolated from. These two gene clusters contain genes coding for the 𝛼 and β subunits of the photosynthetic accessory pigment C-phycocyanin (cpcA and cpcB respectively). Cool temperature assemblies contained a complete copy of cpcA, while warm temperature assemblies lacked a complete copy of the 𝛼 phycocyanin subunit 80 (Figure S5A). On the other hand, all but one warm temperature isolate (LA126) had a complete copy of cpcB. In contrast, the isolate assemblies from cool temperatures only had the first 25 and the last 56 amino acids from cpcB on different contigs (Figure S5B). Because these differences in assemblies between cool and warm temperature isolates are for genes coding for accessory pigments, we compared photosystem function between phenotypically distinct warm and cool temperature isolates. We measured whole cell fluorescence spectra matching C-phycocyanin across rising temperatures for cool temperature strain LA31 (Topt = 27.5° C, Tmax = 31.7° C) and warm temperature strain LA127 (Topt = 29.7° C, Tmax = 35.3° C). The warm temperature isolate had a lower change in fluorescence at physiologically-relevant temperatures below 45 °C (Figure 3A). Fluorescence for both isolates increased exponentially between 48-54 °C, before abruptly crashing to zero at 57 °C. This suggests that light-harvesting energetic losses to fluorescence increase as the photosynthetic antenna complex becomes stressed by warming temperatures, before completely disassociating at a critical high temperature and losing all fluorescence. Although only significant at the p = 0.07 level, the warm temperature isolate had lower fluorescence at the peak fluorescence for both isolates at 54 °C, suggesting it is better able to maintain its functionality under extreme thermal stress. Furthermore, both isolates had differing photophysiologies when comparing Fv/Fm (Figure 3B). When acclimated to 28° C the warm temperature isolate had significantly higher values (p < 0.01), suggesting its photosystem II had a greater photochemical efficiency than the cool temperature strain. The values reported here are analogous to Fv/Fm measurements reported for other Synechococcus spp. (Mackey et al. 2013). 81 A closer look at the genes associated with C-phycocyanin was unable to discern the exact genetic mechanism causing these measured differences in photosynthetic function. We compared the closed warm temperature isolate genome and the closed genome of the closely related Chesapeake Bay strain CB0101 (Figure 4A). In both closed genomes the majority of C- phycocyanin genes are found at a single locus. In all draft assemblies, assembly failed at this exact locus (Figure 4B & C). Interestingly, the pattern of contig breaks is conserved for all draft assemblies of isolates from cool temperatures (Figure 4B), and a distinct pattern of contig breaks is consistent for the warm temperature isolates (Figure 4C). These conserved contig breaks for strains isolated from different temperatures match measured differences in thermal performance curves and photophysiology, suggesting these genes are involved in these phenotypic traits. Although long read sequencing was able to span this difficult-to-assemble region for the warm temperature isolate LA127, it was not able to for the cool temperature isolate LA31 (Figure 3D), preventing a direct comparison. For the five genes at this locus that did not assemble in our draft genomes, no single nucleotide variants (SNVs) were detected compared to the closed genome from LA127 (Supplemental Note 1 and Figure S6). Although minor sequence differences were detected between the complete copies of these genes recovered in the closed hybrid genome (Table S5 & S6), they were identical to the completely assembled copies in the draft assemblies (Table S7). Mapping rates were used to attempt to detect different gene copy numbers at this locus which might explain this systematic pattern of assembly failure (Supplemental Note 2); however, these data were inconclusive. Copy numbers of cpcA and cpcB vary across Subcluster 5.2 genomes (Table S8) and these isolates appear to have multiple copies of genes (Figures S7- 82 S9); however, these data were not able to tell whether or not copy number differs between temperatures. Although the exact genomic differences causing these assembly results are as yet unclear, these contig breaks occur systematically between cool and warm temperature isolates. As this robustly correlates with the observed temperature responses (Figure 1), it suggests that genes associated with accessory pigment C-phycocyanin production could be involved in thermal adaptation. Discussion: Our study demonstrates that a seemingly homogenous population of coastal cyanobacteria in fact conceals a great deal of cryptic thermal diversity. The division of this estuarine Synechococcus population into contrasting cool- and warm-adapted thermotypes was only revealed through the strain-sorting that occurred during the extended incubation experiments at 18 o C and 30 o C, followed by culture isolations and thermal phenotype determinations in the laboratory. By using high coverage short reads as well as long reads to reconstruct these isolate’s genomes, we observed that this striking phenotypic divergence between the two thermotypes appears to be tied to very minor genetic differences. Although previous work has established that distinct functionally-relevant ecotypes coexist within populations of picocyanobacteria (Kashtan et al. 2014; Thompson and Kouba 2019), this is the first study to report coexisting ecotypes at this high level of genomic resolution. Although an exact molecular mechanism explaining the difference between these phenotypes was not determined, variation in assembly of the genomic region containing genes coding for C-phycocyanin correlated with isolation temperature and measured thermal phenotype. In addition, we measured distinct differences in both thermal resilience of the phycobilisome accessory pigment complex and photosynthetic efficiency (Fv/Fm) between the 83 two thermal ecotypes isolated from different temperatures. These genomic and photophysiological differences are consistent with previous work examining thermal adaptation in marine Synechococcus. For instance, it has been suggested that differences in light harvesting machinery can explain the global distribution of Synechococcus clades across large temperature differences (Pittera et al. 2014; Grébert et al. 2018). In particular variation in R-phycocyanin, an ortholog of C-phycocyanin, implicated the alpha and beta subunits of this protein (rpcA and rpcB respectively) in Synechococcus thermal adaptation. Pittera et al. (2017) found that at elevated temperatures a tropical, low latitude strain had lower fluorescence of the antenna pigment complex (indicating more efficient photosynthetic energy capture) than a sub-polar strain. This is similar to the trend we observed between high and low temperature phenotypes in our study. When comparing amino acid sequences of rpcA and rpcB, Pittera et al. (2017) also observed a single substitution in each protein that correlated with the isolation temperature of each strain. These were predicted to make the phycobilisome complex more rigid in high temperatures, and more flexible in cold temperatures (Pittera et al. 2017). In addition, it has also been observed that intracellular concentrations of phycobilisome proteins are increased under high temperatures (Mackey et al. 2013). It should also be noted that there could be additional, non-genomic factors such as epigenetic effects not tested for in this study that can increase phenotypic heterogeneity within a population, even at a high degree of relatedness. Epigenetic differences have sometimes been found to be associated with bacterial stress responses (Beaulaurier et al. 2015; Hu et al. 2018). Although our experimental setup does not permit us to look at the relative abundance of each ecotype in the original population, it is noteworthy that the one isolate collected 84 directly from the environment was the low temperature ecotype. In an environmental context, the average summertime surface water temperature at our sample site for the period from 1957 to 2019 was 20.6 °C, with a maximum of 26.5 °C (Figure 5A). This distribution of temperatures falls below the Topt for both ecotypes but is closer to that of cool temperature ecotype strains, suggesting they would be favored compared to warm temperature strains. Warm temperature isolates were also collected from 30 °C enrichments, 3.5 °C above the highest measured temperature at this site. Average summer SST at this site has been increasing at a rate of 0.03 °C per year since 1959 (Figure 5A), meaning that the average summertime SST in Narragansett Bay will likely increase to ~23 °C by the end of the century. Assuming a similar distribution of temperatures in the year 2100, there will be periods when SST is above the average Topt of the low temperature ecotype, and conditions will favor the high temperature ecotype (Figure 5B). Any continued trend of rising temperatures beyond 2100 will continue to further expand the niche of the warm temperature ecotype. Although this cryptic intraspecific diversity increases the resilience of this population to long term warming trends, it is also interesting to consider why adaptations to temperatures exceeding current thermal maxima are currently maintained in this population. The higher growth rates of cool temperature isolates under typical summer conditions suggest that having the warm temperature phenotype has a fitness cost, and in theory selection should remove this phenotype from the population (Innan & Kondrashov, 2010 and references therein). Given the temperature trends at this site, it seems unlikely these are seasonal ecotypes, as the shape of thermal curves suggest that the warm temperature phenotype only has a growth advantage above the maximum observed temperature. 85 An intriguing explanation for this cryptic thermal diversity is that these microbes originated in warmer low latitude waters, and were advected into this relatively cooler region as part of the northerly flow of the nearby Gulf Stream. It has been estimated that microbes entrained in the Gulf Stream may experience a range of temperatures as a result of advection that is larger than changes due to seasonal patterns (Doblin and Van Sebille 2016). Although Narragansett Bay is a narrow coastal estuary, wind-driven circulation during summer months facilitates persistent exchange between estuarine waters in the Bay and oceanic waters in Rhode Island Sound (Kincaid et al. 2003). This has led to the conjecture that allochthonous inputs of sub-tropical phytoplankton could occur, although this has not been directly observed (Borkman and Smayda 2009). These coexisting temperature phenotypes are interesting in the context of marine Synechococcus evolution, as temperature is thought to be a key driver of diversity between clades within this group (Pittera et al. 2014; Sohm et al. 2016). It is possible that intraspecific microdiversity of thermal phenotypes could have been a mechanism in the diversification of Synechococcus into the distinct lineages observed today. When a population consisting of multiple thermotypes encounters a novel thermal environment, one phenotype may be selected over another, potentially leading to genetic divergence and speciation. Further studies will be needed on intraspecific microdiversity at this level, between nearly identical strains, to assess the potential role of such hidden diversity in the evolution of Synechococcus and marine microbes in general. This level of microdiversity also has implications for our general understanding of biological responses to rising temperatures. It has been shown that there can be a greater 86 diversity of responses to ocean acidification between ecotypes within phytoplankton taxonomic and functional groups, than between groups (Schaum et al. 2013, Hutchins et al. 2013). Our findings show a similar trend for thermal diversity, in that ecotypes with distinct responses to climate warming can coexist within a population. This microdiversity in thermal traits also has been detected in other marine phytoplankton. In a similar study conducted within Narragansett Bay, thermal performance curves of recently isolated strains of the diatom genus Skeletonema were compared and showed a similar high degree of intraspecific diversity of thermal traits (Anderson and Rynearson 2020). This study also observed a similar significant difference in thermal maxima (Tmax) across strains, and suggested that variability at such thermal limits plays an important role in both ecological and biogeochemical dynamics. Because of the high degree of genetic similarity between these isolates, metabarcoding or metagenomic microbial surveys would not be able to detect this level of functional microdiversity. This prior diatom study suggests that findings such as those we observed in this temperate estuarine cyanobacteria population could be widespread among marine microbial taxa. In the case of our estuarine Synechococcus, this cryptic thermal microdiversity could allow this population to continue to occupy its picoplanktonic niche even in the face of considerable shifts in environmental temperatures. Another important implication is that culture studies using a single isolate or strain from a population can potentially underestimate that population’s resilience to warming. A better understanding of the existing functional thermal diversity within populations is needed to correctly model the impact that future elevated temperatures will have on microbial communities, and on the biogeochemical cycles that they regulate. 87 Acknowledgements: This work was funded by National Science Foundation Dimensions of Biodiversity grants OCE1638804 to DAH, OCE1638834 to TR, and OCE1638958 to EL, and by OCE 1851222 to DAH and EAW. Thanks to David Kehoe (Indiana University) for helpful insights into Synechococcus photophysiology, and to J. Cameron Thrash (University of Southern California) and Ben Temperton (University of Exeter) for helping generate and perform base calling on Minion long reads. 88 Main Figures and Tables Figure 1: Thermal growth rate responses of Synechococcus isolates used in this study, depending on whether the isolate came from cool (blue) or warm (red) incubation experimental temperatures. A Specific growth rates versus temperature, modelled using the Eppley-Norberg approach to calculate Thermal Performance Curves (tpc) for all isolates. B Boxplots showing the maximum temperature limit (Tmax) and optimal temperature (Topt) for the two sets of isolates. Error bars represent the standard deviations of the means, and the star indicates significance at the p < 0.05 level. 24 28 32 36 Tmax Topt T emperature (°C) p= 0.06 0.00 0.25 0.50 0.75 10 15 20 25 30 35 T empe r ature (°C) Gr o wth r ate (day -1 ) Cool Warm Figure 1: A 89 Figure 2: A Maximum likelihood tree showing relatedness of all three subclusters comprising marine Synechococcus, using strain Synechococcus lacustris Tous to root the tree. Phylogenies were established by concatenating AA sequences of 239 single copy core genes. B Average nucleotide identity (ANI) between all 11 Narragansett Bay isolates from this study. CB0101 from the Chesapeake Bay is included for comparison, as the most closely-related genome present in Genbank. 90 Figure 3: A Mean change in whole cell integrated fluorescence spectra from 600-700nm across temperatures and (B) Photochemical efficiency (Fv/Fm) of PSII at an excitation wavelength matching phycocyanin and allophycocyanin (645 nm). Blue colors indicate the cool temperature (18°-22°) isolate LA31, while red shows warm temperature (30° C) isolate LA127. The star indicates significant differences at the p = 0.05 level, and error bars represent ± 1 SD. Figure 3: T empe r ature (°C) 0 100 200 300 30 40 50 Integrated fluorescence (%T0) p= 0.07 0.00 0.25 0.50 0.75 1.00 LA31 LA127 Fv/Fm Strain A 91 Figure 4: Assembly and coverage information of genes within the locus containing the primary C-phycocyanin genes, cpcA (blue arrows) and cpcB (orange arrows). A C-Phycocyanin locus in the closest related genome available on NCBI, CB0101. B Structure of draft assemblies of isolates recovered from 18° or 22° (C) and 30° C (D). Same locus in a low and high temperature strain incorporating long reads to close assembly gaps in a hybrid assembly approach. 2kb 4kb 6kb LA3 CB0101 Phycocyanin αchain( cpcA ) Phycocyanobilin lyase α Phycocyanobilin lyase β Phycocyanin βchain( cpcB ) Breaks within genes Phycocyanin assoc. rod LA20 LA27 LA21 LA29 LA31 LA101 LA103 LA1 17 LA126 LA127 LA127 Hybrid 30° Isolates 18°-22° Isolates Phycoerythrin assoc. linker Genes: A 2kb 4kb 6kb B C D LA31 Hybrid 92 Figure 5: A Boxplot of summertime sea surface temperature (SST) increases at the Narragansett Bay Time Series from 1957 to 2019. Trendline shows the output of a linear model fit to the data. The slope of this model and the p value are shown below the data. B Hypothetical normal seasonal temperature distributions created using the mean of the recent data shown in panel A (solid line), and the predicted distribution of these data in the year 2100 (dashed line) using the slope of the linear model. A blue vertical line shows the average Topt for all cool temperature isolates. Temperatures above this line, which will likely favor warm temperature ecotypes, are shown in orange. 12 16 20 24 1960 1980 2000 2020 Y ear T empe r ature ( ° C) 15 20 25 30 Daily High T empe r ature ( ° C) A Current 2100 Figure 5: 93 Temperature n Parameter Mean SD Max Min 22° (Initial) 1 Tmax 33.8 Topt 27.9 Tmin 11.2 Width 21.8 18° 5 Tmax 33.4 1 34.3 31.7 Topt 27.6 1.4 29.8 26 Tmin 12.4 3.1 14.8 9 Width 21 2.5 24.5 18.9 30° 5 Tmax 35.6 0.5 36.5 35.3 Topt 29.8 1.8 31.3 26.8 Tmin 14.2 4.4 19.1 9 Width 21.4 4.7 26.3 16.1 Table 1: Results of calculating the thermal performance curve (TPC) for 11 Synechococcus isolates.TPC parameters are reported in °C. 94 References: Anderson, S. I., and T. A. Rynearson. 2020. Variability approaching the thermal limits can drive diatom community dynamics. Limnol. Oceanogr. 1–13. doi:10.1002/lno.11430 Andrews, S. 2010. 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These changes are likely to continue with the unabated combustion of fossil fuels, potentially doubling current CO2 concentrations (Pachauri et al. 2014). At the same time, only in the last three decades has the technology become available to sample the diversity of the microbes that play key roles in the marine carbon cycle. The advent of ‘omic techniques combined with global molecular sampling efforts such as the Tara Oceans Project are enabling unprecedented measurements of the diversity and distribution of microbial communities across ocean basins (Engelen et al. 2015; Lee et al. 2019). This means that we are now accurately describing microbial ecosystems in the midst of a period of great environmental change. Current estimates of microbial diversity suggest there are 10 12 microbial species on the planet (Locey and Lennon 2016) with the majority being rare species. Because of the reliance on abundant or easily cultured model organisms for studying growth responses to warming, it is likely that current projections for marine ecosystems do not take into account the full breadth of microbial diversity. As an example, it has been theorized that rare species act as a reservoir for adaptions to a range of environmental stressors (Coveley et al. 2015). Broadly, this concept is referred to as the insurance hypothesis, where a higher diversity of species increases that likelihood some members of the community will survive when the environment changes, even if more abundant species will not (Yachi and Loreau 1999). This suggests that species richness can act as a buffer, but also contributes to stabilizing overall ecosystem functioning (Yachi and 103 Loreau 1999; Valone and Barber 2008; Leary and Petchey 2009). Studies using experimental models and simplified artificial communities support this hypothesis for phytoplankton, suggesting a linear relationship between phytoplankton diversity and primary production at high temperatures (Vallina et al. 2017; Bestion et al. 2020). This dissertation highlights understudied areas of phytoplankton thermal biodiversity, and describes their importance in predicting the impacts of rising temperatures in marine ecosystems. In chapter 1, I measured the effect temperature has on community growth and composition using a diverse, natural phytoplankton community sampled over three seasonal timepoints. I show that enough thermal diversity exists within the community at this location throughout the year to maintain photosynthetic growth well beyond the highest temperature recorded in a decade-long observational dataset. It is possible, though, that this thermal functional redundancy is not necessarily representative of all phytoplankton communities. It could be that this resilience is a product of living in a nearshore environment, where rapid temperature changes are common (Leinweber et al. 2009) and thus support high functional diversity (Kremer and Klausmeier 2017). If this functional resilience in terms of photosynthetic growth is consistent across ecosystems, it suggests that there is no physiological reason that phytoplankton assemblages will not be able to grow at predicted future temperatures. This finding implies that indirect effects such as changing nutrient availability may have a larger impact on primary production than direct effects on physiology. Chapter 1 also shows that underlying this functional resilience to warming is a shifting community composition. In particular, I demonstrate that current phytoplankton assemblages at SPOT are maintained when temperatures are within the envelope of previously experienced temperatures, but crossing the maximum historic temperature threshold causes the community 104 structure to change. This could provide a testable hypothesis for future studies, by suggesting that historic temperature maximums can act as tipping-points for major shifts in the phytoplankton community. Although the changes we observed were constrained to within the diatom group, genus- or species-level rearrangements could have impacts on marine carbon cycling. Diatoms can have species-specific sinking rates (Passow 1991; Litchman et al. 2009), nutritional values as prey (Kawamura et al. 1998; Jones and Flynn 2005), and susceptibility to grazing (Martin-Cereceda et al. 2003; Zhang et al. 2017); however, these values are not well known for many diatom species (or for other phytoplankton groups). Future work should consider ways to test the hypothesis that historic temperature maxima can be thresholds for community turnover, and assess how such thermally-driven changes in dominant diatoms affect carbon export and grazing. This also highlights the need to expand the number of model species and strains used to make inferences about thermal responses in phytoplankton communities. Although it is impractical to expect a thorough understanding of all the diversity revealed by large-scale ‘omic studies, it cannot be denied that the functional diversity that exists within observed phylogenetic diversity is largely unknown. As an example, in Chapter 2 I describe an unusual physiology recorded in a diatom recently isolated from a wintertime temperate coastal estuary. In culture this isolate showed a strong preference for low light levels, similar to those used for cultivating obligately low light ecotypes of Prochlorococcus. However, such low-light specialists have not been previously described for planktonic diatoms. Furthermore, I demonstrated that for this diatom, light and temperature negatively interact with each other to determine this diatom’s realized ecological niche. For instance, in culture this diatom is capable of growing from below zero to nearly 26 °C; however, in a six-year 18S rRNA amplicon dataset from the isolation 105 location, this diatom is nearly twice as likely to be found at temperatures below 12 °C (well below its predicted optimal temperature). Also, it is nearly three times as likely to be found during periods with the lowest light levels measured over 6 years, than at any other time. This preference for low light and low temperatures is unexpected, and suggests other similarly bizarre life histories could be discovered by broadening our knowledge of functional diversity among marine phytoplankton. This demonstrates that traditional cell isolation and culturing methods followed by physiological characterization can sometimes provide a depth of new understanding that would be difficult to obtain using environmental ‘omics sampling alone. The isolate I describe in Chapter 2 also challenges the general hypothesis that at high latitudes phytoplankton are light-limited, and that the predicted increase in light availability through decreasing high albedo sea ice and shoaling of the thermocline will stimulate production (Gosselin et al. 1990; Riebesell et al. 2009; Yun et al. 2012). My findings suggest that although bulk primary production may increase, the increased light availability may not be beneficial for all members of the phytoplankton community, even in putatively light-limited environments such as temperate wintertime ecosystems. More broadly, it is worth exploring further whether light and temperature have similar negative interactions (albeit at higher irradiance levels) for other phytoplankton. There are currently few data on how light and temperature interact in marine phytoplankton, but this study suggests that irradiance can have a powerful effect on thermal niche. Although interspecific phylogenetic diversity is likely an important source of functional diversity, my third chapter suggests that considerable functional diversity can also reside within a single population of a species. In Chapter 3 I explored how individual strains from the same species can functionally differ from each other. For this study, I isolated strains of the unicellular 106 cyanobacterium Synechococcus from a single original water sample that was enriched for photosynthetic growth under high and low temperatures. Interestingly, these strains showed distinct high- or low-temperature adapted phenotypes corresponding with their isolation temperature. Underlying these phenotypic differences was an exceptional degree of relatedness, where each strain had an average nucleotide identity of 99.9% to every other strain. Comparing their genomes, it appears that variation in the loci associated with production of the accessory pigment C-phycocyanin is responsible for these phenotypic differences. This is further supported by the literature, which suggests minor variation in orthologs of these genes are strongly correlated with thermal adaptation across Synechococcus clades (Pittera et al. 2014, 2017; Grébert et al. 2018). This level of fine-scale variation between closely related strains involving a single locus is difficult to detect in microbial communities, and highlights an under-sampled reservoir of relevant microbial diversity. For instance, 16S rRNA amplicon sequencing would not have the resolution to detect differences between these Synechococcus strains, and metagenomic approaches using short-read shotgun sequencing tend to collapse strain-level diversity during assembly and binning. The methods described in Chapter 3 of using enrichments to select for a diversity of temperature responses followed by high throughput cultivation, phenotyping, and subsequent whole genome sequencing could be applied to other microbial communities in order assess the prevalence and importance of cryptic, strain-level thermal diversity. Functional diversity within phytoplankton communities exists across organizational scales: between functional groups (e.g. diatoms, haptophytes, cyanobacteria), between genera and their component species, and between individual strains. In this dissertation, I show that the responses of phytoplankton communities to changing climate are nuanced, and diversity at each 107 of these levels of community organization can contribute to the emergent response. I also show that much of this diversity, both inter- and intra-specifc, is still undescribed. In order to address these knowledge gaps, modification of commonly applied tools and methods for predicting phytoplankton temperature responses is required. For instance, molecular methods lack the resolution to confidently predict how changing community structure affects growth or biogeochemical cycling and also have difficulty resolving strain level variation. Although culture-based methods are able to directly measure the hidden details and complexities of thermal traits, they are typically limited to a handful of representative isolates. This dissertation suggests that combing these methods can yield unique results that could not be obtained by either in isolation. Future work could consider using enrichments for photosynthetic microbes under simulated future temperatures (Chapters 1 & 3), then applying molecular methods to identify phytoplankton in the community capable of taking advantage of a warmer climate, while at the same time targeting them for subsequent isolation and culture-based temperature studies (Chapters 2 & 3). This and similar approaches that systematically attempt to describe the breadth of temperature responses currently existing with phytoplankton communities will be essential for accurately predicting the impacts of anthropogenic carbon pollution on the phytoplankton, and the consequences for the biological and biogeochemical functioning of the future marine environment. 108 References: Bestion, E., S. Barton, F. C. García, R. Warfield, and G. Yvon‐Durocher. 2020. Abrupt declines in marine phytoplankton production driven by warming and biodiversity loss in a microcosm experiment. Ecol. 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Sci. 4. doi:10.3389/fmars.2017.00202 111 Appendix The following pages contain all supplemental information for the chapters presented in this dissertation. 112 Chapter 1 Supplemental Figures and Tables Figure S1: For each seasonal experiment, temperature treatments were split into either A) present or B) future (present + 4°C) mean temperatures. These were then either kept the same throughout the study (constant) or fluctuated 4°C above and below the mean temperature every 24 hours. Treatments for the spring experiment are shown here, wherein the present-constant was kept at 16°C (water temperature at our collection site), present-fluctuating changed between 12°-20°C, future-constant was kept at 20°C, and future-fluctuating changed between 16°-24°C. 0 10 20 30 0 10 20 30 T empe r ature (C°) A) B) 0 1 2 3 4 5 6 F u t u r e - co n st a n t F u t u r e - flu ct u a t i n g P r e se n t - co n st a n t D a ys P r e se n t - flu ct u a t i n g 113 Figure S2: A) Workflow summary for quality filtering and analyzing raw amplicon sequence data. Pertinent R commands within the DADA2 package are listed below each box. B) 18s reads were separated from reads that failed to merge from quality issues by the number of mismatches. Reads with more than 35 mismatches (red horizontal line) belonged to eukaryotic 18s sequences when analyzed with BLAST. Non-merged reads fewer mismatches than this threshold were discarded. C) QC'd, primerless, demultiplexed reads Raw fastq files 4,097 Amplicon sequence variants (ASVs) Merged (16s) & concatenated reads (18s) Concatenated reads Error corrected, trimmed, and dereplicated read file Chimera filtered Assigned taxonomy Fastqc (0.1 1.7): check for illumina adapters Sabre (1.0): demultiplex reads Usearch (9.2): remove primers (fastx_truncate) DADA2 (1.8.0): -filterAndT rim() -learnErrors() -derepFastq() -dada() -mergePairs() -mergePairs(justConcatenate=T) -removeBimeraDenovo() -assignT axonomy() --Bacteria = Silva SSU132 --18s = PR2 (4.10.0) -- Chloroplast 16s = Phytoref A) B) Figure S2 Percentile Number of mismatches 90.0 30 50 70 90 92.5 95.0 97.5 100 114 Figure S3: Results of biogeochemical and bulk biochemical parameters measured at the end of each seasonal experiment. Differences between each enrichment within a seasonal study were statistically insignificant A) for carbon to nitrogen, B) carbon to phosphorus, and C) nitrogen to phosphorus stoichiometries. When chlorophyll a was normalized to carbon (D) we observed that significant differences did exist between treatments in the fall although post-hoc testing did not show significance in pairwise comparisons (*). E) Carbon fixation rates were not measured during the summer and no significant differences were seen between treatments in either spring or summer. C : N 6 7 8 9 10 100 150 200 C : P N : P µg ca r b o n f i xe d / µg POC * hou r -1 A) B) C) D) E) µ g ch l o r o p h yl l a / µg P O C 0.2 0.3 0.1 0.04 0.06 0.02 0.0 25 30 20 15 10 16° 20° 12°-20° 12°-20° 16° 20° 12°-20° 12°-20° 21° 26° 17°-25° 22°-30° Present- Constant Present- Fluctuating Futu r e- Constant Futu r e- Fluctuating p =0.04* p =0.06 Fa l l Summer Spring p =0.42 p =0.09 p =0.16 p =0.16 p =0.25 p =0.87 p =0.08 p =0.11 p =0.12 p =0.58 p =0.09 p =0.59 Treatment 115 Figure S4: Principle coordinate analysis (PCOA) using Euclidean distance calculated on 16S amplicon sequencing. A) All treatments show a distinct separation between the communities observed in situ prior to nutrient addition and those when nutrients were abundant. B) When initial samples were removed from the data set, season became the separator. -150 -100 -50 0 50 100 0 100 200 Axis 1 [ 23.9%] Initia l W ith nutrien t s Ax i s 2 [1 2 .6%] Figure S4: Present-Constant Present-Fluctuatin g Future-Constant Future-Fluctuatin g A) B) 0 50 - 5 0 -100 - 5 0 0 50 Ax i s 1 [21.6 % ] Sprin g Sprin g Fall Fall Summer Summer Sprin g Summer Fall Axis 2 [13.3%] 116 Figure S5: Heatmaps of Amplicon Sequence Variants (ASVs) that comprised: A) >10% of total reads within a sample for Bacteria, and > 5% of the total reads for B) diatoms and C) picoeukaryotic phytoplankton. Coloring shows the difference in relative abundance (percent of total reads) compared to the present-constant treatment, the control in this study. Triangles represent treatments where a given ASV was differentially abundant compared to the control (present-constant treatment). Present-constant Present-fluctuating Future-constant Future-fluctuating Present-constant Present-fluctuating Future-constant Future-fluctuating Present-constant Present-fluctuating Future-constant Future-fluctuating Ralstonia sp.(asv44) Alteromonas sp.(asv3) Kordia jejdonensis(asv1 1) Flavobacteriaceae 3 (asv38) Lewinella sp. 1 (asv32) Lewinella sp. 2 (asv56) Candidatus Brocadiales(asv20) Flavobacteriaceae 2 (asv37) Flavobacteriaceae 1 (asv15) Pseudophaeobacter sp. (asv16) Phaeobacter sp. (asv18) Rugeria sp. (asv31) A) Pseudo-nitzschia sp.(asv9) Navicula lanceolata(asv71) Minidiscus trioculatus(asv2) Cylindrotheca closterium(asv1 14) Arcocellulus mammifer(asv4) Chaetoceros simplex(asv8) B) Imantonia rotunda(asv45) Aureococcus sp.(asv84) C) More Abundant Less Abundant p < 0.01 -40% 0 40% Dif ference in relative abundance Figure S5: Spring Fall Summer Triparma mediterranea(asv1) 117 Figure S6: Phyla and class (Proteobacteria) level composition of microbial communities observed for spring, summer, fall using 18s reads for eukaryotic taxa. Taxonomic identity was assigned using 16s rRNA (Bacteria) and 18s rRNA (Eukaryota). All taxa are presented as the percent of all amplicons recovered from a given sample. T otal Bacteria Gammaproteobacteria T otal Eukaryota % Amplicon s Recovere d Spring Summer Fall Unassigne d 1 % 10 % 50 % 90 % Present-constant Initial Initial Initial Arthropoda Present-fluctuating Future-constant Future-fluctuating Present-constant Present-fluctuating Future-constant Future-fluctuating Present-constant Present-fluctuating Future-constant Future-fluctuating Other (each <1%) Verrucomicrobia Cyanobacteria Other (each <1%) Arthropoda Bacillariophyta Bacteroidetes Planctomycetes Deltaproteobacteria Alphaproteobacteria 118 Figure S7: Comparison of the dominant plastid 16S amplicon recovered with the dominant 18S sequence matching the most similar abundance pattern for A) spring, B) fall, and C) summer (ASVs dominant in present-constant and present-fluctuating, future- constant, and future-fluctuating treatments respectively). Asterisks mark ASVs identified by BLASTing against the NCBI nt database that do not have plastid 16S sequences. ● ● ● ● ● ● ● ● ● ● ● 0 25 50 75 100 0 25 50 75 100 asv105- Pseudo-nitzschia americana (% 18S amplicons) asv9- Pseudo-nitzschia sp. (% plastid 16S amplicons) ● ● ● ● ● ● ● ● ● ● ● ● 0 25 50 75 100 0 25 50 75 100 asv134- Minidiscus trioculatus (% 18S amplicons) asv2- Minidiscus trioculatus (% plastid 16S amplicons) ● ● ● ● ● ● ● ● ● ● ● ● 0 25 50 75 100 0 25 50 75 asv1- Triparma mediterranea (% plastid 16S amplicons) ● ● ● ● ● ● ● ● ● ● ● ● 0 25 50 75 100 0 25 50 75 100 asv70- Chaetoceros tenuissimus* (% 18S amplicons) asv8- Chaetoceros simplex (% plastid 16S amplicons) ● ● ● ● ● ● ● ● ● ● ● ● 0 25 50 75 100 0 25 50 75 100 asv138- Minutocellus sp.* (% 18S amplicons) asv4- Arcocellulus mammifer (% plastid 16S amplicons) A) C) B) r s =0.47 r s =0.75 r s =0.95 r s =0.85 r s =0.87 Figure S6: Comparing the dominant plastid 16S amplicon recovered with the dominant 18S seqeunce matching the most similar abundance pattern for A) spring, B) fall, and C) summer (ASVs dominant in present-constant and present-fluctuating, future-constant, and future-fluctuating treatments respectively). Asterisks mark ASVs identified by BLAST ing against the NCBI nt database that do not have plastid 16S sequences. 100 asv57- Leptocylindrus convexus* (% 18S amplicons) 119 Figure S8: Phylogenetic tree showing all Psuedo-nitzschia 18S rRNA sequences recovered from NCBI after blasting the partial 18S rRNA sequence recovered in this study (asv9). Known domoic acid producers are marked here with an asterisk. Our sequence was a 100% match to Pseudo-nitzschia americana strain UNC1412, a strain not recognized as a domoic acid producer. JN 0 9 1 7 2 1 . 1 P s eu d o - n i t z s c hi a f r a ud u l e n t a ( i s o l a t e N W F S C196 ) K J 6 08 0 7 8 . 1 P s e u do - ni t z s c h i a g a l a x i a e ( s t r a i n S Z N - B 6 06 ) K P 7 0 8 9 9 6 . 1 P s e u d o - n it z s c h i a d e c ip i e n s KM 4 0 1 8 54 . 1 C ha et oce r o s d a y a e n s i s ( s t r a i n M C 1 0 7 S ) GU 3 7 3 964 . 1 P s e u d o - ni t z sch i a m u l t i s er i e s KX 2 2 9 6 9 0 . 1 P s e u d o - nit z s ch i a f raud u lent a ( s t r ai n U N C 141 3 ) K P 7 0 8 9 9 7 . 1 P s e u d o - ni t z sc h i a f uku y o i K P 7 0 8 9 9 4 . 1 P s e u d o - n i t z s c h i a c i r c u m p o r a G U 3 7 3 9 6 6 . 1 P s e ud o - n i t z s c h i a p u n g e n s K X 25 3 9 5 2 . 1 P s e u d o - n i t z s c h i a s u b c u r v a t a A Y 22 19 4 7 . 1 P s e u d o- ni t z s c h i a m u l t is erie s G U 3 7 3 9 6 8 . 1 P s e ud o - n i t z s c h i a p u n g e n s A M2 3 538 2 . 1 P s e u do-ni t z s ch i a m u l t i s e r ie s K P 7 0 9 0 0 1 . 1 P s e u d o - n i t z s c h i a l u n d h o l m i a e A M2 3 538 1 . 1 P s e u do-ni t z s ch i a m u l t i s e r ie s K P 7 0 8 9 9 8 . 1 P s e u d o - ni t z sc h i a f uku y o i JN 0 9 1 7 2 6 . 1 P s e u d o - n it z sc h i a a u s t r a l i s KP 7 0 8 9 8 7. 1 P s e u d o - n i tzs ch i a a r e n y s e n s i s JN 0 9 1 7 1 7 . 1 P s e u d o - n i t z s ch i a l i n e o l a ( i s o l a t e _ N W F S C1 8 8 ) KJ 8 6 6 9 0 7 . 1 P s e u d o- n i t z s ch i a g r a n i i ( s t r a i n U N C 1 1 0 2 ) KP 7 0 8 9 8 8. 1 P s e u d o - n i tzs ch i a a r e n y s e n s i s GU 3 7 3 9 6 5 . 1 P s e u d o - n i t z s c hi a p s e u d o d e li c a ti s s i m a G U 3 7 3 9 6 7 . 1 P s e ud o - n i t z s c h i a p u n g e n s K J 6 0 8080 . 1 P s eud o - nit z s c hi a m ann i i KJ 6 0 8 0 7 3. 1 P s e u d o - n i t z s c h i a a r e n yse n s i s ( s tr a i n S Z N-B67 5 ) KM 4 0 1 8 53. 1 C h ae t o ce r o s d a y a e n si s ( s t r a i n M C 1 0 7L ) K P 7 0 8 9 9 9 . 1 P s e u d o - ni t z sc h i a f uku y o i K P 7 0 8 9 9 5 . 1 P s eud o - n it z s c h i a c u s p i d at a KP 7 0 9 0 0 0. 1 P s e u d o - n i t z s c h i a k o da m a e KJ 6 0 8 0 8 1 . 1 P s e udo - n i t z s c h i a m u l t is t r i at a ( s t ra i n E 1 5 7 5 ) G U 3 7 3 9 6 9 . 1 P s e u d o - n i t z s c h i a s eri a t a KX 2 2 9 68 9 . 1 P s e u d o-ni t z s c h i a a m er ica n a ( s t r a i n UN C1 4 1 2 ) K P 7 0 9 0 0 3 . 1 P s e u d o - n i t z s c h i a m i c r o p o r a AM 2 3 5 3 8 4 . 1 P s e u d o - n it z s c hi a a u s t r a l i s U1 8 24 1 . 1 P s e u d o - n i t z s c h i a m u l t i s e r i e s JN 0 9 1 7 0 9 . 1 P s e u d o - n i t z s ch i a d e l i c a t i s s i m a JN 0 9 1 7 2 7 . 1 P s e u d o - n i t z s c hi a h e i m i i (i s o l a t e N W F S C 2 0 5 ) KP 7 0 9 0 0 6 . 1 P s eu do - ni tz s c h i a s a b i t KP7 0 8 9 90 . 1 P se u d o - ni t z sc h i a b r asil i a n a KP 7 0 8 9 9 2 . 1 P s e u d o -nitz s c h i a c a c i a n t h a KP 7 0 9 0 0 5 . 1 P s eu do - ni tz s c h i a s a b i t K T 8 6 1 1 4 7 . 1 P s e u d o - n it z s c h i a s p . ( R C C 2 6 1 9 ) K T 8 6 1 1 7 6 . 1 P s e u do - n i t z s c h i a g r a n i i ( R C C 2 0 0 6 ) K T 8 6 1 169. 1 P s e u d o - n i t z s c h i a s p . (R C C 227 3 ) G U 3 7 3 9 6 0 . 1 P s e u d o - n i t z s c h i a c u sp i d a t a KJ 6 0 8 0 7 2. 1 P s e u d o - n i t z s c h i a a r e n yse n s i s ( s tr a i n _ S Z N -B 6 4 8 ) GU 3 7 3 9 7 0. 1 P s e u d o - n i t z s c h i a s p ._ C C M P 1 3 0 9 K Y 8 5 2 2 5 7 . 1 C h a e t o c e r o s t e n u i s s i m u s ( s t r a i n n e w C A 3 ) KP 7 0 8 9 9 1 . 1 P s e u do - n i t z s ch i a b r a sil i a n a K J 6 0 8 0 7 7 . 1 P s e u d o - n i t z s c h i a f r a u d u l e n t a ( s t r a i n S Z N - B 6 7 0 ) * A M2 3 538 0 . 1 P s e u do-ni t z s ch i a m u l t i s e r ie s J N 5 9 9 1 6 6 . 1 P s e u d o- ni tz s c h i a a u s tr a l i s KP7 0 8 9 8 9 . 1 P s e ud o - n i tzs c h i a b a t e s i a n a K P 7 0 9 0 0 4 . 1 P s e u d o - n i t z s c h i a p un g en s * K n ow n d om oi c ac i d p r od u c e r * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * K M3 8 687 4 . 1 P s eu d o - n itz sc hi a b r a s il i a n a ( str a i n_ C C MA4 0 5 ) JN 0 9 1 7 2 4 . 1 P s e u d o - n i t z s c h i a p u n g e n s AY 4 8 5 4 9 0 . 1 P s e u d o - n i t z schi a s p . ( C CMM P 1 3 0 9 ) JN 0 9 1 7 1 9 . 1 P s e u d o - n i t z s c h i a c u s p i d a t a ( i s o l a t e N W F S C 1 9 2 ) JN 9 3 46 7 1 . 1 P s e u d o - n i t z s c hi a s p . ( R C C2 0 0 8 ) JN 0 9 1 7 0 8 . 1 P s e u d o -n it z s c h i a p s e u d o d e l i c a t i s s i m a ( i s o l at e N W FSC0 8 1 ) JK J 6 0 8 0 7 5 . 1 P s e u d o - n i t z s c hi a d e li c a t i s s i m a ( s t r a i n S Z N - B 6 5 3 ) JK J 6 0 8 0 7 4 . 1 P s e u d o - n i t z s c hi a d e li c a t i s s i m a ( s t r a i n S Z N - B 6 4 9 ) FJ 2 2 2 7 5 2 . 1 P s e u do-ni t z s c h i a tu r g i d u l a ( i s o l a t e N W F S C 2 2 0 ) EF 4 3 2 5 2 1 . 1 B a c i l l a riop h y t a s p . K J 6 0 8 0 8 2 . 1 P s e u d o - n i t z s c h i a p s e u d o d el i c a t i s s i m a ( s t r a i n S Z N - B 6 5 6 ) K J 6 0 8 0 8 2 . 1 P s eu d o - n i t z s c h i a l u n d h o l mi a e JN 0 9 1 7 1 8 . 1 P s e u d o - n i t z s c h i a c u s p i d a t a ( i s o l a t e N W F S C 1 9 0 ) U1 8 2 4 0 . 1 P s e u d o - ni t z s chi a p u n g e n s KX 2 1 5 9 1 7 . 1 P s e u d o - n itz s c h i a m a nni i ( s t ra i n C I M _ D - 4 ) K P 7 0 8 9 9 3 . 1 P s e u d o - n i t z s c h i a c i r c u m p o r a JF 7 9 4 0 4 6 . 1 P s e u d o - n i t z s c h i a s p . _ R C C 20 0 4 K J 6 0 8 0 7 9 . 1 P s e u do - n i t z s c h i a g a l a x i a e ( S Z N - B6 1 7 ) K J 6 0 8 0 7 6 . 1 P s e ud o - ni t z s c h i a d e l i c a t i s si m a ( s t r a i n S Z N - B 63 1 ) G U3 7 3 9 6 1 . 1 P s e u d o -n i t z s c h i a a u s t r a l i s a sv9 ( T h i s st u d y) * F i gur e S 8: 120 Figure S9: BSi plotted as a function of POC during the last dilution of the A) spring, B) summer, and C) fall experiments. “m” refers to the slope of each line fit to the data for a general linear model calculated with base R. “rs“ is the spearman’s coefficient for each model. Correlations were strongest in the spring and fall (rs < 0.65), but weakest in the summer. The Future-constant treatment had the highest slopes in both the summer and fall and the lowest slope or greatest disconnect from carbon accumulation was in the summer future-fluctuating treatments. Present-constan t Present-fluctuatin g Future-constan t Future-fluctuatin g Spring Summe r Fal l mols Biogenic Silica mols Particulate O r ganic Carbon 40 30 20 10 0 100 200 300 100 200 300 100 200 300 A) B) C) m=0.02 r s =0.48 m=0.04 r s =0.93 m=0.07 r s =0.85 m=0.08 r s =0.47 m=0.05 r s =0.74 m=0.07 r s =0.89 m=0.13 r s =0.60 m=0.04 r s =0.66 m=0.13 r s =0.92 m=0.05 r s =0.26 m=0.01 r s =0.85 m=0.07 r s =0.85 Figure S7: 121 Figure S10: A comparison of the data from Needham and Fuhrman (2016) with the dominant ASVs from the Spring (asv1) and Summer (asv1, 8, and 4) experiments from this study. A-C shows physical parameters from the SPOT: temperature, nitrate and nitrate concentrations, and chlorophyll a concentration. D-G) show the abundance of amplicons matching dominant taxa from this study. 14 16 18 20 Temperature (C°) 0 1 2 3 4 µM NO 2 - +NO 3 - 0.0 2.5 5.0 7.5 10.0 µg/L Chl a 0 5 10 15 20 25 0 5 10 15 20 25 Percent Amplicons Recovered 0 5 10 15 20 25 0 5 10 15 20 25 April M a y J une J uly A ugust Date A) B) C) D) ASV9 P se u d o - n i t zsch i a sp . E ) a sv1 L e p t o cyl i n d r u s co n ve xu s F ) a sv8 C h a e t o ce r o s si m p l e x G ) a sv4 A r co ce l l u l u s m a m m i f e r Figure S8: 122 Season Treatment Temperature QC'd Reads Filtered Reads Merged Reads (16s) 18s Non- merged Reads Discarded non-merged Chimera removed reads (16S+18S) Spring Initial 16.1° 59394 49506 29880 2390 17236 28714 Spring Initial 16.1° 66458 55397 37056 2319 16022 35793 Spring Initial 16.1° 58851 49375 29660 2603 17112 28436 Spring Present-constant 16° 59719 51424 46568 1458 3398 42945 Spring Present-constant 16° 50026 42869 39935 1364 1570 38607 Spring Present-constant 16° 86638 76174 69520 1879 4775 63617 Spring Future-constant 20° 64223 55519 48860 1126 5533 43150 Spring Future-constant 20° 49566 42754 38349 1434 2971 37011 Spring Future-constant 20° 62606 52792 44697 1574 6521 42747 Spring Present-fluctuating 12°-20° 76401 66710 63151 1267 2292 59478 Spring Present-fluctuating 12°-20° 67256 57661 51351 1689 4621 47174 Spring Future-fluctuating 16°-24° 64767 55288 51272 1493 2523 48964 Spring Future-fluctuating 16°-24° 64760 56349 52437 1568 2344 48814 Spring Future-fluctuating 16°-24° 65406 55796 52268 1705 1823 48336 Summer Initial 20.6° 73742 68778 59326 5778 3674 55995 Summer Initial 20.6° 55331 51319 45115 3526 2678 43728 Summer Initial 20.6° 46009 42858 37583 2851 2424 36654 Summer Present-constant 21° 41588 39237 36593 1290 1354 35504 Summer Present-constant 21° 61077 57134 52992 2009 2133 50587 Summer Present-constant 21° 47078 44427 41403 1039 1985 40484 Summer Future-constant 26° 62414 58214 52801 1478 3935 49647 Summer Future-constant 26° 50227 47251 43378 1390 2483 41344 Summer Future-constant 26° 50001 47044 43128 1403 2513 40769 Summer Present-fluctuating 17°-25° 65637 61542 57621 1443 2478 54834 Summer Present-fluctuating 17°-25° 67192 63552 60132 990 2430 58372 Summer Present-fluctuating 17°-25° 51437 48275 45207 975 2093 44001 Table S1: The number of reads at each processing step being used with DADA2 following demultiplexing and primer removal (QC'd reads). Filtered reads are those that were returned from filtureAndTrim(). 16S and 18S reads were split during the merge step and then recombined prior to the chimera removal step. 123 Summer Future-fluctuating 22°-30° 50582 46942 43418 1408 2116 40054 Summer Future-fluctuating 22°-30° 54891 51235 46976 1239 3020 43805 Summer Future-fluctuating 22°-30° 50207 46894 43299 1183 2412 40055 Fall Initial 16.5° 44539 41566 33166 3208 5192 30842 Fall Initial 16.5° 39311 36806 29666 3060 4080 27423 Fall Initial 16.5° 66345 61297 50878 4631 5788 47304 Fall Present-constant 16° 74407 69502 63727 3020 2755 54720 Fall Present-constant 16° 51187 47786 43755 2166 1865 38350 Fall Present-constant 16° 62803 58234 53350 2329 2555 48132 Fall Future-constant 20° 75188 69651 63900 2867 2884 58352 Fall Future-constant 20° 69213 64804 59780 2572 2452 54843 Fall Future-constant 20° 58760 55064 49766 2318 2980 47192 Fall Present-fluctuating 12°-20° 56937 53044 48485 2092 2467 45471 Fall Present-fluctuating 12°-20° 51210 48252 44257 1901 2094 39116 Fall Present-fluctuating 12°-20° 62033 58000 53500 2272 2228 50892 Fall Future-fluctuating 16°-24° 44875 41683 37953 1785 1945 35022 Fall Future-fluctuating 16°-24° 49158 45883 41591 1942 2350 38917 Fall Future-fluctuating 16°-24° 47131 44186 40157 1966 2063 37083 Mean 58558.7 53138.1 47225.2 2045.5 3867.4 44165.4 SD 10447.5 9080.2 9629.3 957.3 3725.0 8679.2 124 May '17 September '16 November '16 Detection Limit SD within standards Salinity (ppt) 33.5 33.4 33.3 NA NA Chlorophyll a (µg/ml) 0.2 0.3 0.1 NA NA NO 3 - (µM) 0.0 0.0 0.0 0.2 0.09 PO 4 -3 (µM) 0.2 0.2 0.2 0.1 0.03 Biogenic SiO 4 NA 0.3 0.5 0.001 0.10 Table S2: Surface water characteristics at the San Pedro Ocean time-series. Salinity and chlorophyll a were measured in situ on the RV Yellowfin during monthly sampling at SPOT. Nutrient data were measured in the analytical lab at the University of Santa Barbara’s Marine Science Institute (biogenic SiO 4 done by authors following protocols outlined in the methods) and were consistent with oligotrophic conditions in the Southern California Bight. 125 Season Constant In Situ Variation Thermal Variation In this Experiement Constant In Situ Variation Thermal Variation In this Experiment Spring 1.22 1.22 1.22 1.37 1.37 1.37 Summer 1.41 1.41 1.41 1.59 1.58 1.57 Fall 1.22 1.22 1.22 1.37 1.37 1.37 Predicted Present-Day Growth Rates Predicted Future Growth Rates Table S3: Growth rates predicted at the present-day and future temperatures used in each experiment. Predicted growth rate were generated using models that hold temperatures constant, use 10 years of in situ daily temperature changes, and use the experimental level of variation used in our fluctuating temperature treatments. 126 ASV Kingdom Phylum Class Order Family Genus asv206 Bacteria Proteobacteria Alphaproteobacteria Caulobacterales Hyphomonadaceae Litorimonas asv209 Bacteria Proteobacteria Alphaproteobacteria Caulobacterales Hyphomonadaceae Maricaulis asv58 Bacteria Proteobacteria Alphaproteobacteria Caulobacterales Hyphomonadaceae Maricaulis asv35 Bacteria Proteobacteria Alphaproteobacteria Caulobacterales Hyphomonadaceae asv49 Bacteria Proteobacteria Alphaproteobacteria Caulobacterales Hyphomonadaceae asv54 Bacteria Proteobacteria Alphaproteobacteria Caulobacterales Hyphomonadaceae asv67 Bacteria Proteobacteria Alphaproteobacteria Caulobacterales Hyphomonadaceae asv73 Bacteria Proteobacteria Alphaproteobacteria Micavibrionales asv78 Bacteria Proteobacteria Alphaproteobacteria NRL2 asv183 Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Hoeflea asv48 Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Lentilitoribacter asv120 Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Rhizobiaceae Pseudahrensia asv28 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Aliiroseovarius asv148 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Donghicola asv143 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Jannaschia asv23 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Jindonia asv184 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Marivita asv30 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Marivita asv99 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Marivita asv351 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Pelagicola asv10 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Phaeobacter asv213 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Planktotalea asv16 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Pseudophaeobacter asv164 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Pseudophaeobacter asv18 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Pseudophaeobacter asv40 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Shimia asv403 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Sulfitobacter asv46 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Sulfitobacter Silva Taxonomy Table S4: All bacterial ASVs that make up more than 1% of the reads in any sample. The taxonomy that is reported here was determined using the SILVA database. We also present the mean relative abundance (mean percent total reads) for each treatment within seasonal experiments. 127 asv81 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Sulfitobacter asv190 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Tateyamaria asv208 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Thalassobius asv228 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Thalassobius asv69 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Thalassobius asv158 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae asv177 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae asv22 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae asv221 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae asv236 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae asv290 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae asv31 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae asv350 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae asv50 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae asv515 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae asv230 Bacteria Proteobacteria Alphaproteobacteria SphingomonadalesSphingomonadaceae Erythrobacter asv3 Bacteria Proteobacteria GammaproteobacteriaAlteromonadales Alteromonadaceae Aestuariibacter asv110 Bacteria Proteobacteria GammaproteobacteriaAlteromonadales Alteromonadaceae Alteromonas asv113 Bacteria Proteobacteria GammaproteobacteriaAlteromonadales Alteromonadaceae Alteromonas asv115 Bacteria Proteobacteria GammaproteobacteriaAlteromonadales Alteromonadaceae Alteromonas asv175 Bacteria Proteobacteria GammaproteobacteriaAlteromonadales Alteromonadaceae Alteromonas asv187 Bacteria Proteobacteria GammaproteobacteriaAlteromonadales Alteromonadaceae Alteromonas asv195 Bacteria Proteobacteria GammaproteobacteriaAlteromonadales Alteromonadaceae Alteromonas asv26 Bacteria Proteobacteria GammaproteobacteriaAlteromonadales Alteromonadaceae Alteromonas asv287 Bacteria Proteobacteria GammaproteobacteriaAlteromonadales Alteromonadaceae Alteromonas asv6 Bacteria Proteobacteria GammaproteobacteriaAlteromonadales Alteromonadaceae Alteromonas asv82 Bacteria Proteobacteria GammaproteobacteriaAlteromonadales Alteromonadaceae Alteromonas asv64 Bacteria Proteobacteria GammaproteobacteriaAlteromonadales Alteromonadaceae asv21 Bacteria Proteobacteria GammaproteobacteriaAlteromonadales Marinobacteraceae Marinobacter asv224 Bacteria Proteobacteria GammaproteobacteriaOceanospirillales Nitrincolaceae asv62 Bacteria Proteobacteria GammaproteobacteriaOceanospirillales Oleiphilaceae Oleiphilus asv155 Bacteria Proteobacteria Deltaproteobacteria Bradymonadales asv96 Bacteria Proteobacteria Deltaproteobacteria Bradymonadales 128 asv149 Bacteria Proteobacteria Deltaproteobacteria Oligoflexales Oligoflexaceae asv388 Bacteria Proteobacteria asv41 Bacteria Proteobacteria asv297 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae Aureispira asv317 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae Aureispira asv19 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae Lewinella asv234 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae Lewinella asv32 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae Lewinella asv56 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae Lewinella asv121 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae asv129 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae asv145 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae asv147 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae asv15 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae asv180 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae asv182 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae asv188 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae asv199 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae asv266 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae asv27 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae asv37 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae asv55 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae asv68 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae asv85 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae asv97 Bacteria Bacteroidetes Bacteroidia Chitinophagales Saprospiraceae asv103 Bacteria Bacteroidetes Bacteroidia Chitinophagales asv217 Bacteria Bacteroidetes Bacteroidia Chitinophagales asv38 Bacteria Bacteroidetes Bacteroidia Chitinophagales asv156 Bacteria Bacteroidetes Bacteroidia Cytophagales Cyclobacteriaceae Fabibacter asv44 Bacteria Bacteroidetes Bacteroidia Cytophagales Cyclobacteriaceae Fulvivirga asv170 Bacteria Bacteroidetes Bacteroidia Cytophagales Flammeovirgaceae 129 asv89 Bacteria Bacteroidetes Bacteroidia Cytophagales Flammeovirgaceae asv107 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Crocinitomicaceae Fluviicola asv128 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Crocinitomicaceae Fluviicola asv342 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Crocinitomicaceae Fluviicola asv88 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Crocinitomicaceae Fluviicola asv124 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Cryomorphaceae Owenweeksia asv150 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Cryomorphaceae Owenweeksia asv225 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Cryomorphaceae Owenweeksia asv100 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Cryomorphaceae asv197 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Cryomorphaceae asv254 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Cryomorphaceae asv212 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae Dokdonia asv242 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae Dokdonia asv202 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae Gilvibacter asv11 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae Kordia asv161 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae Kordia asv167 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae Kordia asv42 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae Polaribacter_4 asv75 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae Psychroserpens asv122 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae Tenacibaculum asv133 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae Tenacibaculum asv222 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae Tenacibaculum asv47 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae Tenacibaculum asv191 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae Ulvibacter asv140 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae Winogradskyella asv347 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae Winogradskyella asv74 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae Winogradskyella asv87 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae Winogradskyella asv189 Bacteria Bacteroidetes Bacteroidia Flavobacteriales Flavobacteriaceae asv95 Bacteria Bacteroidetes Bacteroidia Flavobacteriales NS9_marine_group asv396 Bacteria Bacteroidetes Bacteroidia SphingobacterialesNS11-12_marine_group asv176 Bacteria Bacteroidetes Rhodothermia Balneolales Balneolaceae Balneola 130 asv260 Bacteria Bacteroidetes Rhodothermia Balneolales Balneolaceae Balneola asv29 Bacteria Firmicutes Bacilli Bacillales Sporolactobacillaceae Salipaludibacillus asv215 Bacteria PlanctomycetesPhycisphaerae Phycisphaerales Phycisphaeraceae Phycisphaera asv249 Bacteria PlanctomycetesPhycisphaerae Phycisphaerales Phycisphaeraceae SM1A02 asv20 Bacteria Planctomycetes asv130 Bacteria VerrucomicrobiaVerrucomicrobiae VerrucomicrobialesRubritaleaceae Luteolibacter asv478 Bacteria VerrucomicrobiaVerrucomicrobiae VerrucomicrobialesRubritaleaceae Luteolibacter asv72 Bacteria Actinobacteria Actinobacteria PropionibacterialesNocardioidaceae Nocardioides 131 % Present Constant sd % Present Fluctuating sd % Future Constant sd % Future Fluctuating 0.23 0.24 0.15 0.22 0.31 0.25 0.43 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.33 0.09 0.54 0.33 1.14 1.00 3.03 0.06 0.01 0.08 0.06 0.00 0.00 0.06 0.03 0.05 0.00 0.00 0.35 0.41 0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.18 0.84 0.57 0.10 0.48 0.09 0.87 0.15 0.14 0.13 0.04 0.75 0.75 0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.78 2.41 0.03 0.53 0.41 0.21 0.21 0.46 0.33 0.80 3.02 2.55 4.10 0.84 1.27 0.32 1.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.08 1.74 0.67 0.56 0.04 0.00 4.90 0.00 0.00 0.01 0.00 0.16 0.22 0.19 0.00 0.00 0.04 0.05 0.00 0.00 0.45 3.02 1.36 4.93 3.91 3.36 1.09 1.35 0.09 0.04 0.83 1.17 0.00 0.00 0.22 0.75 1.00 1.41 0.41 0.12 0.05 10.68 0.22 0.10 0.46 0.49 0.63 0.89 0.35 6.67 5.49 1.23 0.21 3.69 4.99 2.24 0.12 0.15 0.03 0.03 0.00 0.01 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.36 0.67 0.78 0.39 0.28 2.39 1.54 0.21 Spring 132 0.99 0.72 1.89 2.58 0.59 0.71 0.19 0.77 0.57 0.16 0.18 0.16 0.12 0.06 0.38 0.55 0.00 0.00 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.05 0.00 0.00 0.11 0.15 0.00 0.00 0.00 0.13 0.19 0.00 0.00 0.00 0.82 0.56 0.11 0.10 0.09 0.12 0.02 0.16 0.28 0.00 0.00 0.70 0.98 0.14 0.06 0.04 0.11 0.08 0.06 0.01 7.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.52 0.73 0.00 0.04 0.06 0.10 0.12 0.08 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.03 0.05 0.17 0.25 0.70 0.75 0.13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.64 0.63 0.20 0.01 1.87 0.38 0.99 0.00 0.00 0.00 0.00 1.04 1.47 0.06 1.76 1.34 0.32 0.02 4.77 3.18 2.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.96 0.46 3.80 1.61 1.97 1.08 2.55 0.01 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.09 0.01 0.01 0.01 0.62 1.08 0.00 0.00 0.00 0.00 0.57 0.51 0.22 0.72 0.38 0.75 1.02 0.91 133 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.57 0.81 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.83 1.16 0.98 1.32 0.12 0.01 3.19 0.00 0.00 0.01 0.01 0.00 0.00 0.92 0.00 0.00 0.00 0.00 0.29 0.40 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.83 1.65 0.01 0.01 0.15 0.17 0.04 0.07 0.12 0.00 0.00 0.00 0.00 0.00 0.06 0.10 0.00 0.00 0.00 0.00 0.00 12.39 3.73 0.69 0.84 2.13 2.84 1.19 0.00 0.00 0.04 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 1.24 1.75 0.12 1.04 1.11 1.04 1.39 0.69 0.89 3.94 0.00 0.00 0.07 0.11 0.02 0.02 1.34 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.02 0.03 0.05 0.08 0.12 0.07 0.03 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.03 0.08 0.04 0.12 0.05 5.21 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 1.98 2.80 0.00 0.00 0.00 134 0.00 0.01 0.00 0.01 0.91 1.26 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.04 1.18 0.03 0.00 1.45 2.01 0.13 0.02 0.04 0.00 0.00 0.00 0.00 1.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.02 0.01 0.01 2.18 3.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.61 0.36 28.82 40.60 0.30 0.22 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.07 0.00 0.00 0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.63 0.76 0.04 0.05 0.35 0.50 0.12 1.14 0.89 0.00 0.00 0.15 0.11 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.15 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.56 0.43 0.21 0.27 0.04 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.02 0.03 0.03 0.79 0.69 0.04 0.02 0.99 1.00 1.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 135 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.75 2.39 2.36 1.95 1.18 0.24 1.80 0.01 0.01 0.00 0.00 0.00 0.00 0.65 0.00 0.00 0.00 0.00 1.19 1.52 0.18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.05 0.06 0.00 0.00 1.88 0.37 0.64 0.00 0.00 0.00 0.00 0.00 0.67 1.11 0.11 0.16 0.15 0.13 0.86 136 sd % Present Constant sd % Present Fluctuating sd % Future Constant sd 0.58 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.02 0.04 0.36 0.62 0.00 0.03 0.03 0.02 0.02 0.01 0.02 4.85 0.01 0.01 0.04 0.06 0.18 0.27 0.05 0.04 0.01 0.01 0.02 0.00 0.00 0.23 0.07 0.07 0.03 0.03 0.56 0.88 0.05 2.08 1.84 0.36 0.58 0.68 0.89 0.00 0.02 0.00 0.02 0.02 0.01 0.01 0.00 0.28 0.04 0.22 0.13 0.23 0.12 0.00 0.00 0.00 0.43 0.75 0.65 0.82 0.50 0.25 0.24 0.06 0.07 0.02 0.02 0.20 0.01 0.01 0.00 0.01 0.02 0.03 0.00 0.00 0.00 0.02 0.03 0.06 0.06 0.03 0.00 0.00 0.00 0.00 0.00 0.00 1.13 0.00 0.00 0.00 0.00 0.00 0.00 0.37 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.43 0.01 0.01 0.01 0.01 0.88 0.87 0.10 0.03 0.06 0.04 0.07 1.80 1.41 0.56 0.00 0.00 0.00 0.00 0.00 0.00 1.03 0.13 0.04 0.09 0.01 0.31 0.37 0.38 0.00 0.00 0.00 0.00 0.00 0.00 9.65 0.29 0.41 0.02 0.03 0.06 0.10 0.28 0.00 0.00 0.00 0.00 0.00 0.00 1.91 0.00 0.00 0.03 0.05 0.00 0.00 0.03 0.19 0.11 0.20 0.13 0.29 0.42 0.62 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.00 0.00 Spring Summer 137 0.07 0.00 0.00 0.00 0.00 0.07 0.13 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.08 0.15 0.04 0.07 0.43 0.54 0.00 0.34 0.08 0.00 0.00 0.28 0.43 0.00 0.00 0.00 0.00 0.00 0.03 0.02 0.00 0.00 0.00 0.00 0.00 0.06 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.01 0.06 0.01 0.07 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.13 0.00 0.00 0.00 0.00 0.00 0.00 9.99 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.69 0.33 0.11 0.04 0.42 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 7.55 5.07 0.85 1.02 1.81 1.77 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.31 0.02 0.06 0.11 0.18 0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.04 0.68 0.85 0.35 0.42 0.37 1.46 0.68 0.11 0.00 0.00 0.00 0.00 0.00 0.00 1.36 3.44 1.12 1.65 1.55 5.90 2.81 0.00 0.11 0.04 0.05 0.05 0.13 0.13 0.00 0.12 0.03 0.05 0.06 0.02 0.01 2.40 0.02 0.04 0.02 0.03 0.15 0.13 0.00 0.72 0.63 0.13 0.21 0.24 0.19 0.01 0.01 0.02 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.38 0.38 0.20 0.01 0.01 0.00 0.00 138 0.00 0.25 0.43 1.13 1.19 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.02 0.05 0.04 6.12 2.70 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.99 0.39 0.29 0.05 0.02 0.04 0.01 1.59 0.00 0.00 0.00 0.01 0.00 0.00 0.02 0.14 0.13 0.08 0.05 4.41 6.64 0.22 0.00 0.00 0.01 0.01 0.01 0.02 0.00 0.87 1.46 0.05 0.08 0.52 0.89 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.73 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.17 0.19 0.09 0.06 0.03 0.01 0.00 1.19 2.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.10 0.09 0.16 0.00 1.05 1.82 0.00 0.00 0.00 0.00 0.21 0.00 0.00 0.00 0.00 0.00 0.00 4.93 0.24 0.09 0.04 0.03 1.36 1.73 2.32 4.37 7.50 0.03 0.01 0.03 0.00 0.00 2.38 1.99 1.40 1.26 0.32 0.56 0.00 0.98 0.86 0.75 0.65 0.60 0.95 0.04 0.76 0.26 0.13 0.05 0.57 0.64 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.34 0.20 0.06 0.03 0.29 0.11 0.00 0.01 0.01 0.00 0.00 0.93 1.46 7.02 0.90 1.55 0.00 0.00 0.00 0.00 0.14 0.00 0.01 0.01 0.01 1.24 0.71 0.00 0.33 0.18 0.26 0.15 0.14 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 139 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.01 0.02 0.03 0.00 0.00 0.13 0.01 0.01 0.01 0.01 0.01 0.02 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.13 0.00 0.00 0.00 0.00 0.00 0.00 2.91 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.15 0.17 0.01 0.02 1.06 1.84 0.00 0.03 0.05 0.02 0.03 0.83 0.83 0.00 0.01 0.01 0.01 0.01 2.82 2.18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.26 0.26 0.00 0.00 0.39 0.58 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.02 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.01 0.00 0.02 0.02 0.03 0.01 0.01 0.02 0.00 0.02 0.02 0.01 0.01 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.03 0.40 0.69 0.07 0.06 0.05 0.03 0.03 0.03 0.02 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.03 0.05 0.00 0.00 0.02 0.03 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.10 0.04 0.00 0.05 0.05 0.01 0.03 0.03 0.00 0.00 0.00 0.01 1.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.05 0.08 1.10 1.88 140 0.00 0.00 0.00 0.11 0.18 0.77 1.31 0.42 0.03 0.04 0.13 0.11 0.04 0.04 0.63 0.01 0.01 0.03 0.04 0.01 0.01 0.24 0.00 0.00 0.00 0.00 0.00 0.01 0.00 1.42 0.02 0.88 0.97 2.67 1.94 3.23 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.05 0.24 0.00 0.11 0.02 0.17 0.06 141 % Future Fluctuating sd % Present Constant sd % Present Fluctuating sd % Future Constant 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.16 0.15 0.41 0.40 0.06 0.10 0.08 0.07 0.12 0.46 0.16 1.76 1.25 0.63 0.00 0.00 0.27 0.07 0.40 0.28 0.36 0.01 0.02 0.97 1.00 2.35 0.12 0.80 0.02 0.04 0.73 0.35 0.91 0.69 1.11 0.26 0.25 0.08 0.07 0.16 0.16 0.09 0.02 0.02 0.29 0.16 1.25 0.66 0.84 0.66 0.52 0.03 0.00 0.03 0.00 0.03 0.19 0.19 0.00 0.00 0.02 0.03 0.00 0.00 0.00 0.51 0.13 0.63 0.21 0.66 0.00 0.00 0.09 0.05 0.25 0.11 0.55 0.06 0.11 1.80 1.98 2.00 0.53 2.79 0.00 0.00 0.00 0.00 0.07 0.12 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.21 0.96 0.68 0.59 0.23 0.00 0.00 0.05 0.04 0.13 0.13 0.76 0.17 0.22 0.02 0.01 0.03 0.02 0.04 0.29 0.14 0.04 0.01 0.02 0.02 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.14 0.02 3.59 1.12 5.76 3.03 2.69 0.00 0.00 0.02 0.02 0.00 0.00 0.01 0.00 0.00 0.52 0.07 1.31 0.74 0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.09 0.11 0.04 0.23 0.25 0.16 1.29 0.49 1.14 1.28 1.41 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.19 0.18 0.30 0.28 0.85 Summer Fall 142 0.00 0.00 0.00 0.00 0.01 0.02 0.01 0.00 0.00 0.01 0.02 0.00 0.00 0.00 0.03 0.06 0.00 0.00 0.00 0.00 0.00 0.38 0.65 0.00 0.00 0.01 0.02 0.05 0.00 0.00 0.00 0.00 0.01 0.02 0.17 0.04 0.08 0.02 0.02 0.63 0.43 0.36 0.00 0.00 0.00 0.00 0.86 0.87 0.49 0.04 0.03 3.49 0.69 3.88 0.28 1.48 0.00 0.00 0.07 0.06 0.04 0.04 0.65 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.06 0.11 0.07 0.10 0.09 0.60 0.48 0.45 0.33 1.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.19 0.21 0.25 0.03 0.91 0.83 9.70 1.21 8.50 5.57 12.09 0.00 0.00 0.68 0.23 0.42 0.22 0.56 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.61 0.90 0.07 0.06 0.32 0.10 0.22 0.00 0.00 0.18 0.11 0.17 0.07 0.34 0.00 0.00 0.15 0.12 0.16 0.06 0.31 0.00 0.00 0.11 0.11 0.16 0.06 0.30 0.62 0.20 0.22 0.13 0.40 0.10 0.38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.80 0.38 0.97 0.66 1.52 0.61 2.15 0.08 0.05 0.37 0.23 0.41 0.16 0.68 0.02 0.00 0.86 0.40 0.72 0.80 0.73 0.02 0.02 0.01 0.02 0.00 0.00 0.01 0.02 0.02 0.00 0.01 0.00 0.00 0.02 0.01 0.02 0.15 0.08 0.57 0.42 1.77 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 143 0.00 0.01 0.00 0.00 0.01 0.01 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.43 0.29 0.02 0.02 0.02 0.02 0.02 0.00 0.00 0.65 0.75 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.55 0.95 0.00 0.07 0.03 2.26 0.06 2.20 1.72 0.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.43 0.49 0.06 0.01 0.56 0.88 1.41 0.00 0.00 0.00 0.00 3.89 6.73 0.00 0.67 0.24 0.01 0.01 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.57 0.66 0.34 0.37 0.23 0.00 0.00 0.00 0.00 0.00 0.00 1.26 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.12 0.13 0.02 0.01 0.01 0.01 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.25 1.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.07 0.00 0.00 0.01 0.01 0.00 0.23 0.14 0.02 0.02 0.04 0.01 0.05 0.74 1.18 0.02 0.03 0.00 0.00 0.02 1.36 2.35 0.00 0.00 0.02 0.03 0.02 1.65 1.77 0.01 0.01 0.00 0.00 0.02 0.00 0.00 0.84 1.07 0.65 0.94 0.34 0.08 0.15 0.01 0.01 0.06 0.07 1.28 0.04 0.05 0.00 0.00 0.00 0.00 0.00 0.09 0.08 0.01 0.01 0.00 0.00 0.00 0.20 0.25 0.01 0.01 0.01 0.01 0.00 0.16 0.16 0.04 0.00 0.07 0.03 3.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 144 0.00 0.00 0.30 0.45 0.00 0.00 0.00 0.02 0.02 1.97 2.81 0.02 0.03 0.03 0.00 0.00 0.14 0.04 0.53 0.78 0.91 0.01 0.01 0.00 0.00 0.52 0.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.35 0.39 0.00 0.00 0.01 0.01 0.00 0.13 0.02 0.03 0.01 0.00 0.01 0.02 0.04 0.04 0.02 0.01 0.02 0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.50 0.64 0.35 0.51 0.00 0.00 0.00 0.52 0.60 0.06 0.06 0.03 0.00 0.00 0.00 0.00 0.01 0.01 0.77 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.29 0.50 0.01 0.02 0.90 0.00 0.00 1.19 2.06 0.06 0.11 0.05 0.02 0.02 5.52 2.20 0.07 0.00 0.10 0.01 0.01 0.46 0.63 0.52 0.54 1.11 0.00 0.00 0.18 0.19 0.00 0.00 0.00 0.00 0.00 0.60 0.77 0.10 0.17 0.02 0.30 0.11 0.27 0.46 0.00 0.00 0.00 0.00 0.00 1.41 1.33 3.38 3.69 0.31 0.00 0.00 0.39 0.12 0.45 0.62 0.21 0.00 0.00 0.00 0.00 1.49 2.58 0.01 0.55 0.55 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.65 0.32 1.22 0.45 0.34 1.29 2.13 0.22 0.07 0.99 0.40 0.19 0.00 0.00 0.04 0.05 0.01 0.02 0.26 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.25 0.10 0.02 0.01 0.00 0.01 0.01 145 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.64 0.46 0.01 0.01 0.03 0.01 0.02 0.03 0.05 0.02 0.01 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.03 8.50 3.09 0.06 0.02 0.05 0.02 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.40 0.07 0.02 0.00 0.06 0.01 0.08 146 sd % Future Fluctuating sd 0.09 0.00 0.00 0.14 0.00 0.00 0.40 1.37 1.43 0.16 0.40 0.09 0.61 0.85 0.57 0.18 1.17 0.94 0.11 0.06 0.05 0.06 0.90 0.35 0.01 0.03 0.00 0.00 0.00 0.00 0.36 0.34 0.06 0.15 0.21 0.16 3.12 1.79 1.20 0.30 0.30 0.51 0.00 0.00 0.00 0.22 0.16 0.13 0.21 0.25 0.21 0.01 0.13 0.11 0.09 0.04 0.03 0.00 0.00 0.00 1.61 4.19 3.44 0.01 0.00 0.00 0.10 1.20 1.73 0.00 0.01 0.02 0.05 0.05 0.08 0.79 0.60 0.24 0.00 0.00 0.00 0.15 1.53 1.38 Fall 147 0.01 0.12 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.29 4.10 3.60 0.19 0.35 0.45 0.79 0.00 0.00 0.49 2.54 0.32 0.74 0.13 0.13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.45 0.72 1.15 2.50 2.75 0.00 0.00 0.00 0.05 0.48 0.69 7.37 9.39 4.01 0.17 0.69 0.34 0.00 0.00 0.00 0.21 0.66 0.48 0.24 0.70 0.53 0.21 0.71 0.58 0.20 0.67 0.56 0.26 0.69 0.27 0.00 0.00 0.00 0.76 3.01 1.36 0.28 1.15 0.63 0.72 1.07 0.58 0.02 0.00 0.00 0.01 0.00 0.00 2.26 1.27 0.49 0.00 0.00 0.00 0.00 0.00 0.00 148 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.01 0.02 0.01 0.02 0.00 0.00 0.00 0.71 1.46 1.01 0.00 0.00 0.00 2.36 0.00 0.00 0.00 0.06 0.01 0.01 0.00 0.00 0.00 0.13 0.15 0.36 0.57 0.98 1.35 0.00 0.00 0.00 0.00 0.00 0.05 1.00 1.69 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.01 0.05 0.02 0.03 0.02 0.03 0.04 0.00 0.00 0.02 0.01 0.01 0.52 1.10 1.91 2.18 0.44 0.75 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.02 6.31 1.36 2.03 0.00 0.00 0.00 149 0.00 0.00 0.00 0.01 0.01 0.02 1.38 0.26 0.39 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.02 0.02 0.01 0.01 0.00 0.00 0.00 0.00 0.24 0.42 0.01 0.27 0.46 0.03 0.36 0.43 1.34 0.19 0.33 0.00 0.33 0.57 1.47 0.04 0.03 0.08 0.00 0.00 0.11 0.25 0.41 1.12 1.02 0.57 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.14 0.25 0.24 0.15 0.13 0.10 0.18 0.24 0.02 0.10 0.17 0.00 0.00 0.00 0.09 0.23 0.18 0.11 0.28 0.32 0.41 0.94 0.85 0.03 0.00 0.00 0.00 0.44 0.77 0.02 0.01 0.01 150 0.00 0.01 0.02 0.00 0.03 0.02 0.01 0.01 0.01 0.06 0.00 0.00 0.01 0.06 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.11 0.05 151 ASV KingdomPhylum* Class* Order* Family* Genus* asv16 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Pseudophaeobacter asv18 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Phaeobacter asv31 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Rugeria asv44 Bacteria Proteobacteria Betaproteobacteria Burkholderiales Burkholderiaceae Ralstonia asv3 Bacteria Proteobacteria GammaproteobacteriaAlteromonadales Alteromonadaceae Alteromonas asv11 Bacteria Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae Kordia asv15 Bacteria Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae Flavobacteriaceae 1 asv37 Bacteria Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae Flavobacteriaceae 3 asv38 Bacteria Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae Flavobacteriaceae 3 asv34 Bacteria Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae Flavobacteriaceae 4 asv32 Bacteria Bacteroidetes Saprospiria Saprospirales Lewinellaceae Lewinella asv56 Bacteria Bacteroidetes Saprospiria Saprospirales Lewinellaceae Lewinella asv20 Bacteria PlanctomycetesPlanctomycetia Candidatus Brocadiales asv9 Eukaryota Ochrophyta Bacillariophyceae Bacillariales Bacillariaceae Pseudo-nitzschia asv71 Eukaryota Ochrophyta Bacillariophyceae Bacillariales Bacillariaceae Navicula asv2 Eukaryota Ochrophyta Bacillariophyceae Bacillariales Bacillariaceae Minidiscus asv114 Eukaryota Ochrophyta Bacillariophyceae Bacillariales Bacillariaceae Cylindrotheca asv4 Eukaryota Ochrophyta Bacillariophyceae Cymatosirales Cymatosiraceae Arcocellulus asv8 Eukaryota Ochrophyta Bacillariophyceae Naviculales Naviculaceae Chaetoceros asv1 Eukaryota Ochrophyta Bolidophyceae Parmales Triparmaceae Triparma** asv45 Eukaryota Haptophyta Prymnesiophyceae Prymnesiales Prymnesiaceae Imantonia asv84 Eukaryota Ochrophyta Pelagophyceae Pelagomonadales Pelagomonadaceae Aureococcus * >98.6% = Species, >95% = Genus, >85% = Family, >80% = Order, >80% = Class, >45% Phylum ** Renamed Triparma ; see ref. 63. Table S5: Results of BLAST searches on the dominant ASV’s (>10% of amplicons recovered in any sample for Bacteria and >5% for the Eukaryota). 152 Species* Best Blast Hit Best Blast Hit ID % Similarity % Coverage Max Score sp. KX989223.1 Pseudophaeobacter sp. 100 100 688 sp. HE818197.1 Phaeobacter sp. Ph222 100 100 688 sp. KP410680.1 Ruegeria sp. MME-070 99 100 671 sp. CP011998.1 Ralstonia solanacearum strain YC45 95 100 573 sp. KT001466.1 Alteromonas sp. GB2 100 100 689 jejudonensis NR_126287.1 Kordia jejudonensis SSK3-3 100 100 684 Flavobacteriaceae 1 JQ661026.1 Gilvibacter sediminis b17 92 100 512 Flavobacteriaceae 3 JQ661026.1 Gilvibacter sediminis b17 91 100 501 Flavobacteriaceae 3 CP016269.1 Flavobacteriaceae UJ101 85 100 377 Flavobacteriaceae 4 JQ661026.1 Gilvibacter sediminis b17 93 100 542 sp. 1 EU371937.1 Lewinella cohaerens 97 100 628 sp. 2 EU371937.1 Lewinella cohaerens 97 100 617 JQ864319.1 Candidatus Brocadia fulgida 84 99 353 sp. LN735400.3 Pseudo-nitzschia sp. RCC2619 100 100 681 lanceolata LN735305.2 Navicula lanceolata 100 100 691 trioculatus FJ002231.1 Minidiscus trioculatus 100 100 691 closterium LN735462.3 Cylindrotheca closterium 99 100 686 mammifer FJ002193.1 Arcocellulus mammifer 99.73 100 686 simplex KJ958479.1 Chaetoceros simplex 100 99 686 mediterranea LN735367.3 Bolidomonas** mediterranea RCC 239 99 100 669 rotunda LN735489.3 Imantonia rotunda 100 100 682 sp. GQ231541.1 Aureococcus anophagefferens CCMP 1984 100 100 691 153 Season Treatment 16S/18S ASV Kingdom Spring All 16S asv9 Eukaryota Spring All 18S asv105 Eukaryota Summer Present-constant & -fluctuating 16S asv1 Eukaryota Summer Present-constant & -fluctuating 18S asv57 Eukaryota Summer future-constant 16S asv8 Eukaryota Summer future-constant 18S asv70 Eukaryota Summer future-fluctuating 16S asv4 Eukaryota Summer future-fluctuating 18S asv138 Eukaryota Fall All 16S asv2 Eukaryota Fall All 18S asv134 Eukaryota ** Renamed Triparma ; see ref. 63. Table S6: Results of BLAST searches on the dominant ASV’s (>10% of amplicons recovered in any sample for Bacteria and >5% for the Eukaryota). 154 Phylum* Class* Order* Family* Genus* Ochrophyta Bacillariophyceae Bacillariales Bacillariaceae Pseudo-nitzschia Ochrophyta Bacillariophyceae Bacillariales Bacillariaceae Pseudo-nitzschia Ochrophyta Bolidophyceae Parmales Triparmaceae Triparma ** Ochrophyta Bacillariophyceae Coscodicophyceae Leptocylindraceae Leptocylindrus Ochrophyta Bacillariophyceae Naviculales Naviculaceae Chaetoceros Ochrophyta Bacillariophyceae Naviculales Naviculaceae Chaetoceros Ochrophyta Bacillariophyceae Cymatosirales Cymatosiraceae Arcocellulus Ochrophyta Bacillariophyceae Cymatosirales Cymatosiraceae Miinutocellus Ochrophyta Bacillariophyceae Bacillariales Bacillariaceae Minidiscus Ochrophyta Bacillariophyceae Bacillariales Bacillariaceae Minidiscus 155 Species* Best Blast Hit Best Blast Hit ID % Similarity sp. LN735400.3 Pseudo-nitzschia sp. RCC2619 100 americana KX229689.1 Pseudo-nitzschia americana 100 mediterranea LN735367.3 Bolidomonas** mediterranea RCC 239 99 convexus KC814811.1 Leptocylindrus convexus SZN-B768 100 simplex KJ958479.1 Chaetoceros simplex 100 tenuissimus MG972315.1 Chaetoceros tenuissimus Na44A1 100 mammifer FJ002193.1 Arcocellulus mammifer 99.73 sp. MF001989.1 Minutocellus sp. P1 100 trioculatus FJ002231.1 Minidiscus trioculatus 100 trioculatus HQ912563.1 Minidiscus trioculatus CCMP495 100 156 % Coverage Max Score 100 681 97 462 100 669 97 459 99 686 97 462 100 686 97 462 100 691 97 462 157 Differential abundance + - + - + - + - + - + - + - + - + - Bacteria 10 13 9 11 28 16 15 13 23 15 13 11 25 10 29 13 17 13 Alphaproteobacteria 5 7 4 6 14 7 11 7 13 6 5 4 10 1 11 4 4 4 Gammaproteobacteria 1 2 1 1 1 2 0 1 1 2 0 1 0 2 0 1 0 1 Deltaproteobacteria 3 0 1 1 2 1 0 0 1 0 0 0 0 0 1 0 0 0 Bacteroidetes 2 3 3 1 9 5 4 4 7 6 7 4 15 7 17 8 12 8 Planctomycetes 0 0 0 0 2 0 0 0 1 0 1 1 0 0 1 0 1 0 Verrucomicrobia 0 1 0 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 Cyanobacteria 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 Eukaryota 2 0 0 0 2 0 0 2 2 1 2 2 2 0 1 1 4 1 Bacillariophyta 2 0 0 0 2 0 0 2 2 1 2 2 1 0 0 1 4 1 Bolidophyceae 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Prymnesiophyceae 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 Fall Present- Day Future Constant Future Constant Future Fluctuating Present- Day Future Constant Future Fluctuating Present- Day 22°-30° 12°-20° 20° Future Fluctuating 16°-24° 17°-25° 12°-20° 20° 16°-24° 26° Spring Summer Table S7: The number of differentially abundant ASV’s for each treatment compared to the Present-Day Constant control treatment in each season. “+” refers to the number of ASVs that are more abundant and “-“ refers to ASV’s that are less abundant. 158 Accession Study Bioproject accession Biosample accession Library ID SRR8395149 SRP175317 PRJNA512541 SAMN10690005 spgT01_SAM1_14 SRR8395175 SRP175317 PRJNA512541 SAMN10690006 spgT02_SAM1_14 SRR8395179 SRP175317 PRJNA512541 SAMN10690007 spgT03_SAM1_14 SRR8395181 SRP175317 PRJNA512541 SAMN10686168 spgSA1_SAM1_14 SRR8395182 SRP175317 PRJNA512541 SAMN10686869 spgSA2_SAM1_14 SRR8395183 SRP175317 PRJNA512541 SAMN10686870 spgSA3_SAM1_14 SRR8395184 SRP175317 PRJNA512541 SAMN10686871 spgSF1_SAM1_14 SRR8395177 SRP175317 PRJNA512541 SAMN10686872 spgSF2_SAM1_14 SRR8395178 SRP175317 PRJNA512541 SAMN10686884 spgSF3_SAM1_14 SRR8395144 SRP175317 PRJNA512541 SAMN10686873 spgVA1_SAM1_14 SRR8395180 SRP175317 PRJNA512541 SAMN10686874 spgVA2_SAM1_14 SRR8395187 SRP175317 PRJNA512541 SAMN10686876 spgVF1_SAM1_14 SRR8395176 SRP175317 PRJNA512541 SAMN10686877 spgVF2_SAM1_14 SRR8395154 SRP175317 PRJNA512541 SAMN10686878 spgVF3_SAM1_14 SRR8395163 SRP175317 PRJNA512541 SAMN10690008 smrT01_SAM1_31 SRR8395164 SRP175317 PRJNA512541 SAMN10690009 smrT02_SAM1_31 SRR8395186 SRP175317 PRJNA512541 SAMN10690010 smrT03_SAM1_31 SRR8395155 SRP175317 PRJNA512541 SAMN10686879 smrSA1_SAM1_31 SRR8395156 SRP175317 PRJNA512541 SAMN10686880 smrSA2_SAM1_31 SRR8395157 SRP175317 PRJNA512541 SAMN10686881 smrSA3_SAM1_31 SRR8395158 SRP175317 PRJNA512541 SAMN10686882 smrSF1_SAM1_31 SRR8395159 SRP175317 PRJNA512541 SAMN10686883 smrSF2_SAM1_31 SRR8395160 SRP175317 PRJNA512541 SAMN10686884 smrSF3_SAM1_31 SRR8395161 SRP175317 PRJNA512541 SAMN10686885 smrVA1_SAM1_31 SRR8395146 SRP175317 PRJNA512541 SAMN10686886 smrVA2_SAM1_31 SRR8395147 SRP175317 PRJNA512541 SAMN10686887 smrVA3_SAM1_31 SRR8395170 SRP175317 PRJNA512541 SAMN10686888 smrVF1_SAM1_31 SRR8395169 SRP175317 PRJNA512541 SAMN10686889 smrVF2_SAM1_31 SRR8395172 SRP175317 PRJNA512541 SAMN10686890 smrVF3_SAM1_31 SRR8395185 SRP175317 PRJNA512541 SAMN10690011 fallT01_SAM1_31 Table S8: Accession numbers and metadata for all demultiplexed sequence data used in this study. 159 SRR8395162 SRP175317 PRJNA512541 SAMN10690012 fallT02_SAM1_31 SRR8395145 SRP175317 PRJNA512541 SAMN10690013 fallT03_SAM1_31 SRR8395171 SRP175317 PRJNA512541 SAMN10686891 fallSA1_SAM1_31 SRR8395166 SRP175317 PRJNA512541 SAMN10686892 fallSA2_SAM1_31 SRR8395165 SRP175317 PRJNA512541 SAMN10686893 fallSA3_SAM1_31 SRR8395168 SRP175317 PRJNA512541 SAMN10686894 fallSF1_SAM1_31 SRR8395167 SRP175317 PRJNA512541 SAMN10686895 fallSF2_SAM1_31 SRR8395174 SRP175317 PRJNA512541 SAMN10686896 fallSF3_SAM1_31 SRR8395173 SRP175317 PRJNA512541 SAMN10686897 fallVA1_SAM1_31 SRR8395152 SRP175317 PRJNA512541 SAMN10686898 fallVA2_SAM1_31 SRR8395153 SRP175317 PRJNA512541 SAMN10686899 fallVA3_SAM1_31 SRR8395150 SRP175317 PRJNA512541 SAMN10686900 fallVF1_SAM1_31 SRR8395151 SRP175317 PRJNA512541 SAMN10686901 fallVF2_SAM1_31 SRR8395148 SRP175317 PRJNA512541 SAMN10686902 fallVF3_SAM1_31 160 Description Fwd reads A spring phytoplankton enrichment. Culturing temperature: prenutrient addition (T0) spg_T01_output1.fastq A spring phytoplankton enrichment. Culturing temperature: prenutrient addition (T0) spg_T02_output1.fastq A spring phytoplankton enrichment. Culturing temperature: prenutrient addition (T0) spg_T03_output1.fastq A spring phytoplankton enrichment. Culturing temperature: constant 16 C spg_SA1_output1.fastq A spring phytoplankton enrichment. Culturing temperature: constant 16 C spg_SA2_output1.fastq A spring phytoplankton enrichment. Culturing temperature: constant 16 C spg_SA3_output1.fastq A spring phytoplankton enrichment. Culturing temperature: constant 20 C spg_SF1_output1.fastq A spring phytoplankton enrichment. Culturing temperature: constant 20 C spg_SF2_output1.fastq A spring phytoplankton enrichment. Culturing temperature: constant 20 C spg_SF3_output1.fastq A spring phytoplankton enrichment. Culturing temperature: fluctuating 12-20 C spg_VA1_output1.fastq A spring phytoplankton enrichment. Culturing temperature: fluctuating 12-20 C spg_VA2_output1.fastq A spring phytoplankton enrichment. Culturing temperature: fluctuating 16-24 C spg_VF1_output1.fastq A spring phytoplankton enrichment. Culturing temperature: fluctuating 16-24 C spg_VF2_output1.fastq A spring phytoplankton enrichment. Culturing temperature: fluctuating 16-24 C spg_VF3_output1.fastq A summer phytoplankton enrichment. Culturing temperature: prenutrient addition (T0) smr_T01_output1.fastq A summer phytoplankton enrichment. Culturing temperature: prenutrient addition (T0) smr_T02_output1.fastq A summer phytoplankton enrichment. Culturing temperature: prenutrient addition (T0) smr_T03_output1.fastq A summer phytoplankton enrichment. Culturing temperature: constant 21 C smr_SA1_output1.fastq A summer phytoplankton enrichment. Culturing temperature: constant 21 C smr_SA2_output1.fastq A summer phytoplankton enrichment. Culturing temperature: constant 21 C smr_SA3_output1.fastq A summer phytoplankton enrichment. Culturing temperature: constant 26 C smr_SF1_output1.fastq A summer phytoplankton enrichment. Culturing temperature: constant 26 C smr_SF2_output1.fastq A summer phytoplankton enrichment. Culturing temperature: constant 26 C smr_SF3_output1.fastq A summer phytoplankton enrichment. Culturing temperature: fluctuating 17-25 C smr_VA1_output1.fastq A summer phytoplankton enrichment. Culturing temperature: fluctuating 17-25 C smr_VA2_output1.fastq A summer phytoplankton enrichment. Culturing temperature: fluctuating 17-25 C smr_VA3_output1.fastq A summer phytoplankton enrichment. Culturing temperature: fluctuating 22-30 C smr_VF1_output1.fastq A summer phytoplankton enrichment. Culturing temperature: fluctuating 22-30 C smr_VF2_output1.fastq A summer phytoplankton enrichment. Culturing temperature: fluctuating 22-30 C smr_VF3_output1.fastq A fall phytoplankton enrichment. Culturing temperature: prenutrient addition (T0) fall_T01_output1.fastq 161 A fall phytoplankton enrichment. Culturing temperature: prenutrient addition (T0) fall_T02_output1.fastq A fall phytoplankton enrichment. Culturing temperature: prenutrient addition (T0) fall_T03_output1.fastq A fall phytoplankton enrichment. Culturing temperature: constant 16 C fall_SA1_output1.fastq A fall phytoplankton enrichment. Culturing temperature: constant 16 C fall_SA2_output1.fastq A fall phytoplankton enrichment. Culturing temperature: constant 16 C fall_SA3_output1.fastq A fall phytoplankton enrichment. Culturing temperature: constant 20 C fall_SF1_output1.fastq A fall phytoplankton enrichment. Culturing temperature: constant 20 C fall_SF2_output1.fastq A fall phytoplankton enrichment. Culturing temperature: constant 20 C fall_SF3_output1.fastq A fall phytoplankton enrichment. Culturing temperature: fluctuating 12-20 C fall_VA1_output1.fastq A fall phytoplankton enrichment. Culturing temperature: fluctuating 12-20 C fall_VA2_output1.fastq A fall phytoplankton enrichment. Culturing temperature: fluctuating 12-20 C fall_VA3_output1.fastq A fall phytoplankton enrichment. Culturing temperature: fluctuating 16-24 C fall_VF1_output1.fastq A fall phytoplankton enrichment. Culturing temperature: fluctuating 16-24 C fall_VF2_output1.fastq A fall phytoplankton enrichment. Culturing temperature: fluctuating 16-24 C fall_VF3_output1.fastq 162 Rev reads spg_T01_output2.fastq spg_T02_output2.fastq spg_T03_output2.fastq spg_SA1_output2.fastq spg_SA2_output2.fastq spg_SA3_output2.fastq spg_SF1_output2.fastq spg_SF2_output2.fastq spg_SF3_output2.fastq spg_VA1_output2.fastq spg_VA2_output2.fastq spg_VF1_output2.fastq spg_VF2_output2.fastq spg_VF3_output2.fastq smr_T01_output2.fastq smr_T02_output2.fastq smr_T03_output2.fastq smr_SA1_output2.fastq smr_SA2_output2.fastq smr_SA3_output2.fastq smr_SF1_output2.fastq smr_SF2_output2.fastq smr_SF3_output2.fastq smr_VA1_output2.fastq smr_VA2_output2.fastq smr_VA3_output2.fastq smr_VF1_output2.fastq smr_VF2_output2.fastq smr_VF3_output2.fastq fall_T01_output2.fastq 163 fall_T02_output2.fastq fall_T03_output2.fastq fall_SA1_output2.fastq fall_SA2_output2.fastq fall_SA3_output2.fastq fall_SF1_output2.fastq fall_SF2_output2.fastq fall_SF3_output2.fastq fall_VA1_output2.fastq fall_VA2_output2.fastq fall_VA3_output2.fastq fall_VF1_output2.fastq fall_VF2_output2.fastq fall_VF3_output2.fastq 164 Chapter 2 Supplemental Figures and Tables Figure S1: Comparison of fluorescence across cell concentrations for cultures in exponential phase for held at either 16 or 20 °C or 30 or 50 µmoles photons / m 2 * sec -1 . Vertical red line shows the fluorescence all cultures were diluted to at the beginning of each semi-continuous growth cycle. Black line is the result of a simple linear regression with the r 2 shown. ● ● ● ● ● ● ● ● ● ● ● ● R 2 =0.67 50000 100000 150000 200000 0 2 4 6 Fluorescence Cells / ml ● 16° C& 30 uE ● 16° C& 50 uE ● 20° C& 30 uE ● 20° C& 50 uE 165 Figure S2: Maximum likelihood tree for the genus Chaetoceros using the V4 hypervariable region of the 18S rRNA gene. C. wighamii C. setoense C. eibenii C. cf. neogracilus C. debilus C. cf. convolutus Chaetoceros teres C. tortissimus C. calcitrans DM53 this study C. atlanticus C. curvisetus C. constrictus C. didymus C. lauderi C. muelleri C. rastrus C. dichaeta C. danicus C. peruvianus Pseudo-nitzschia australis C. cf. socialis C. cf. wighamii C. protuberens C. pseudo-curvisetus C. lorenzianus C. throndsenii 1 0.9 0.946 0.24 0.993 0.782 0.8 0.734 0.956 0.886 1 0.877 0.939 0.931 0.119 0.905 0.387 0.868 0.999 1 0.585 1 Tree scale: 0.01 166 Figure S3: Relative abundance of ASV6 (recovered ASV matching the 18S rRNA sequence from our isolate) for each month throughout six years of monthly sampling. 0 20 40 60 80 1 2 3 4 5 6 7 8 9 10 11 12 Month P ercent T otal Amplicons 167 Figure S4: Maximum likelihood tree for the genus Chaetoceros using the V9 hypervariable region of the 18S rRNA gene. DM53 this study C. wighamii C. peruvianus C. debilius C. lorenzianus C. cf. convolutus Chaetoceros protuberens Pseudo-nitzschia australis C. curvisetus C. calcitrans C. setoense C. cf. wighamii C. dichaeta C. psuedo-curvisetus C. throndsenii C. rastrus C. eibenii C. danicus C. atlanticus C. muelleri C. cf. socialis C. teres C. constrictus C. tortissimus C. lauderi C. didymus 0.867 0.807 0.931 0.96 0.859 0.765 0.89 0.996 0.833 0.794 0.757 0.756 0.794 0.896 0.882 0.966 0.789 0.895 0.761 0.682 Tree sca le : 0.01 0.473 168 Figure S5: a) The percentage of recovered V9 amplicons that belonged to diatoms, and b) the percentage of all diatom amplicons that matched the Chaetoceros sp. isolate described in this study. 0 10 20 30 Total Diatom amplicons (%) 0 10 20 30 T ARA_155 T ARA_158 T ARA_163 T ARA_168 T ARA_173 T ARA_175 T ARA_178 T ARA_180 T ARA_188 T ARA_189 T ARA_191 T ARA_193 T ARA_194 T ARA_196 T ARA_201 T ARA_205 T ARA_206 P ercent of Diatom Amplicons (%) a b 169 Accession Genus Speices Strain Notes JN599166.1 Pseudo-nitzschia australis 10249 10Ab Outgroup MG972303.1 Chaetoceros protuberens Ro1A2 MG972272.1 Chaetoceros didymus SOLB2 MG972280.1 Chaetoceros eibenii Ro1B2 MG972267.1 Chaetoceros dichaeta PA18-B1 MG972211.1 Chaetoceros atlanticus 7C1 MG972244.1 Chaetoceros danicus newCB1 MG972226.1 Chaetoceros cf. convolutus Ch5C4 MG972298.1 Chaetoceros peruvianus newEA1 MG972310.1 Chaetoceros rastrus newDA3 MG972256.1 Chaetoceros constrictus Ch13B1 MG972292.1 Chaetoceros lorenzianus Ch4C4 MG972324.1 Chaetoceros throndsenii NA45B4 KY980353.1 Chaetoceros cf. wighamii BH65 48 EU240879.1 Chaetoceros calcitrans f. pumilus MG972320.1 Chaetoceros teres CH12B1 MG972285.1 Chaetoceros lauderi Na34C3 AB847418.1 Chaetoceros setoense KX253957.1 Chaetoceros cf. socialis UNC1406 KT860958.1 Chaetoceros wighamii RCC3007 AY625896.1 Chaetoceros muelleri CCAP1010/3 JF794049.1 Chaetoceros cf. neogracilus RCC2016 MG972251.1 Chaetoceros debilius Ch13A4 MG972326.1 Chaetoceros tortissimus Na25B2 LC466962.1 Chaetoceros curvisetus El4A2 MG972305.1 Chaetoceros pseudo-curvisetus newBC2 Chaetoceros sp. DM53 This study. Table S1: 18S rRNA gene IDs and full length sequences from 25 Chaetoceros isolates for named or putative (cf.) species on NCBI. 170 asv Phylum Class Order Family Genus Species Strain asv1 Bacillariophyta Coscinodiscophyceae Thalassiosirales Thalassiosiraceae Thalassiosira sp. GF366-14 asv2 Bacillariophyta Coscinodiscophyceae Thalassiosirales Skeletonemataceae Skeletonema costatum asv3 Bacillariophyta Mediophyceae Lithodesmiales Lithodesmiaceae Ditylum brightwellii SKLMP_B001 asv4 Bacillariophyta Coscinodiscophyceae Thalassiosirales Thalassiosiraceae Minidiscus cornicus RCC5859 asv5 Bacillariophyta Coscinodiscophyceae Thalassiosirales Thalassiosiraceae Thalassiosira sp. GF366-1 asv6 Bacillariophyta Coscinodiscophyceae Chaetocerotales Chtocerales Chaetoceros cf. wighamii BH46_123 asv7 Bacillariophyta Coscinodiscophyceae Thalassiosirales Thalassiosiraceae Thalassiosira sp. GF366-22 asv8 Bacillariophyta Coscinodiscophyceae Thalassiosirales Thalassiosiraceae Thalassiosira rotula RCC841 asv9 Bacillariophyta Coscinodiscophyceae Thalassiosirales Thalassiosiraceae Thalassiosira weissflogii asv10 Bacillariophyta Coscinodiscophyceae Thalassiosirales Stephanodiscophyceae Cyclotella choctawhatcheeana asv11 Bacillariophyta Coscinodiscophyceae Thalassiosirales Thalassiosiraceae Conticribra guillardii BH46_58 asv12 Bacillariophyta Coscinodiscophyceae Thalassiosirales Skeletonemataceae Skeletonema pseudocostatum NZmm2W5 asv13 Bacillariophyta Coscinodiscophyceae Thalassiosirales Thalassiosiraceae Thalassiosira tenera asv14 Bacillariophyta Coscinodiscophyceae Thalassiosirales Thalassiosiraceae Thalassiosira pacifica asv15 Bacillariophyta Coscinodiscophyceae Thalassiosirales Skeletonemataceae Skeletonema menzelii CCMP790 asv16 Bacillariophyta Coscinodiscophyceae Thalassiosirales Stephanodiscophyceae Cyclotella sp. MNURFJ04 asv17 Bacillariophyta Coscinodiscophyceae Thalassiosirales Thalassiosiraceae Thalassiosira punctigera RCC4667 asv18 Bacillariophyta Coscinodiscophyceae Thalassiosirales Thalassiosiraceae Minidiscus spinulatus RCC5861 asv20 Bacillariophyta Coscinodiscophyceae Chaetocerotales Chtocerales Chaetoceros tenuissimus Na44A1 asv21 Bacillariophyta Coscinodiscophyceae Thalassiosirales Skeletonemataceae Skeletonema costatum CCAP1077/4 Table S2: blast results for the top 20 ASVs that exceeded one percent relative abundance of recovered amplicons. 171 Notes Best BLAST Accession Percent ID Query Coverage Total Score E value MG662230.1 100 100 747 0 Equal match with Skeletonema marinoi KY364698.1 100 100 743 0 Equal match with other strains MG890416.1 100 100 747 0 Equal match with other strains MN528623.1 100 100 743 0 Equal match with other strains MG662232.1 100 100 747 0 Equal match with RCC3008 KY980229.1 100 100 737 0 Equal match with other strains MG662228.1 100 100 747 0 Equal match with other strains KT860983.1 100 100 747 0 Equal match with other strains HM991702.1 100 100 747 0 Equal match with other Cyclotella spp. JQ217341.1 100 100 745 0 Equal match with Thalassiosira guillardii CC03-04 KY980193.1 100 100 747 0 Equal match with other strains KY054992.1 99.75 100 737 0 HM991701.1 100 100 747 0 Equal match with other strains HM991697.1 100 100 747 0 AJ535168.1 100 100 741 0 KT781056.1 100 100 741 0 Equal match with other strains MN524964.1 100 100 747 0 Equal match with other strains and one Thalassiosira sp. MN528626.1 100 100 747 0 Equal match with other strains MG972315.1 100 100 741 0 Equal match with other strains and one S. grethae. AY684946.1 100 100 743 0 172 Chapter 3 Supplemental Figures and Tables Figure S1: Large volume experiments with two isolate strains representing both cool (blue) and warm (orange) temperature isolates. A Accumulation of particulate organic carbon over the course of 4 days after increasing the temperature from 22 to 28 °C. B Growth rate from day 0 to day 4 for each strain derived from the change in POC. Error bars represent one standard deviation from the mean. p =0.002 100 200 300 400 500 0 1 2 3 4 Day A verage uM POC 0.0 0.1 0.2 0.3 Specific Growth Rate (day -1 ) p =0.07 A B Figure S1: LA31 LA127 173 Figure S2: Maximum likelihood tree for all 11 isolates collected in this study and all 78 available complete Synechococcus assemblies in Genbank. Gleobacter violaceus is included as the outgroup. T ree scale: 0.1 GCA 000485815.1 NKBG15041c GCA 000161795.2 WH8109 GCA 002760395.1 63 A Y4M1 GCA 000332275.1 PCC 7336 GCA 002760445.1 65 A Y640 GCA 000316685.1 PCC 6312 GCA 000586015.1 GCA 000010065.1 PCC 6301 GCA 000155595.1 PCC 7335 GCA 001693255.1 PCC 7003 GCA 001521855.1 PCC 73109 GCA 000013225.1 JA - 2-3Ba 2-13 GCA 900177825.1 70 0 2 GCA 000153045.1 WH 5701 GCA 000012525.1 PCC 7942 GCA 002760415.1 65 A Y6A5 GCA 005577135.1 PCC 1 1901 GCA 900473925.1 N5 GCA 001632105.1 MITS9504 GCA 002252635.1 M W 101C3 GCA 000715475.1 NK B G042902 GCA 002760375.1 60 A Y4M2 GCA 001631935.1 MITS9509 GCA 003011 125.1 T ous GCA 000515235.1 CC 9 616 GCA 00 1 182765.1 WH8103 GCA 900474295.1 UW140 GCA 002760475.1 63 A Y4M2 GCA 000012625.1 CC 9 605 GCA 002760345.1 65 A Y6Li GCA 900474015.1 UW106 GCA 900474045.1 N19 GCA 000012505.1 CC 9 902 GCA 000737575.1 KO R DI-49 GCA 001039265.1 GF B 01 GCA 001039265.1 7001 GCA 001039265.1 NIES981 GCA 002252675.1 BO8801 GCF_000316515.1 PCC6307 GCA 000153825.1 RS 9 916 GCA 000230675.2 WH8016 GCA 900473955.1 GEYO GCA 004332405.1 BS 5 6D GCA 900473895.1 N32 GCA 004332415.1 BS 5 5D GCA 001693275.1 PCC 7 1 17 GCA 003957805 .1 UTEX 3055 GCA 000063505.1 WH7803 GCA 002252625.1 1G 1 0 GCA 002721235.1 BDU 130192 GCA 002018015.1 GCA 900473965.1 UW179A GCA 000013205.1 JA - 3-3Ab GCA 001693295.1 PCC 8807 GCA 000014585.1 CC 9 3 1 1 GCA 900177365.1 O G 1 GCA 900474085.1 UW86 GCA 000063525.1 RCC307 GCA 002754935.1 PCC 6715 GCA 000019485.1 PCC 7002 GCA 000179255.1 CB 0 205 GCA 004209775.1 WH8101 GCA 001632165.1 MITS9508 GCA 000195975.1 WH8102 GCA 0000 1 1385.1 PCC 7421 GCA 002356215.1 NI E S-970 GCA 000179235.2 CB 0 101 GCA 900473935.1 UW105 GCA 002252665.1 8F6 GCA 900473975.1 N26 GCA 000737535.1 KO R DI-100 GCA 001885215.1 Sy n Ace01 GCA 001040845.1 WH8020 GCA 000153065.1 RS 9 917 GCA 000817325.1 UT EX2973 GCA 900474245.1 UW179B GCA 900047545.1 GCA 000737595.1 KO R DI-52 GCA 000153805.1 BL 1 07 GCA 900474185.1 UW69 GCA 000317085.1 PCC 7502 GCA 000153285.1 WH7805 This Study Marine Synechococcus 5.3A Marine Synechococcus 5.2 Figure S2: Marine Synechococcus 5.3B Candidatus Synechococcus spongarium Synechococcus elongatus Gleobacter violaceus Marine Synechococcus 5.1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0.7 0.5 1 0.8 0.6 1 1 0.9 1 1 1 1 1 1 0.9 0.9 0.9 0.9 0.8 1 1 1 1 0.67 174 Figure S3: Visualization of Anvi’o pangenomic analysis. From inside to outside: tree represents tetranucleotide frequency in all 11 isolates analyzed in this study, with black regions showing the presence and absence of all gene clusters, number of genomes with a given gene cluster, number of genes in a given gene cluster, number of paralogs, number of single copy genes, homogeneity within a gene cluster, and successful annotation of the gene cluster (shown in green). Gene clusters with less than 100% homogeneity that were manually examined to detect genomic differences that correlate with temperature phenotype are highlighted with purple. LA101 LA103 LA117 LA126 LA127 LA20 LA21 LA27 LA29 LA3 LA31 Num contributing genomes Num genes in GC Max num paralogs SCG Clusters Func. Homogeneity Ind. Geo. Homogeneity Ind. Comb. Homogeneity Ind. Annotation Low Comb. Homogeneity Low Comb. Homogeneity Warm T emperature Isolates Cool T emperature Isolates Figure S3: 175 Figure S4: First 100bp from two gene clusters identified by Anvi’o as having less than 100% homogeneity, but were determined to not correlate with isolation temperature or the observed high/low temperature phenotypes: A GC0000045 and B GC0000041. 1....:.... 1 00bp LA31 LA101 MPAPQKLRRGDCDAISRHLRAIGRIPLLTADEEISLGRAVQNGQRLLETAEEMKLRSGGQTPSVQAWALEVGITPRQLQRQLKAAERASERMVTANLRLV LA103 MPAPQKLRRGDCDAISRHLRAIGRIPLLTADEEISLGRAVQNGQRLLETAEEMKLRSGGQTPSVQAWALEVGITPRQLQRQLKAAERASERMVTANLRLV LA117 MPAPQKLRRGDCDAISRHLRAIGRIPLLTADEEISLGRAVQNGQRLLETAEEMKLRSGGQTPSVQAWALEVGITPRQLQRQLKAAERASERMVTANLRLV LA126 MPAPQKLRRGDCDAISRHLRAIGRIPLLTADEEISLGRAVQNGQRLLETAEEMKLRSGGQTPSVQAWALEVGITPRQLQRQLKAAERASERMVTANLRLV LA127 MPAPQKLRRGDCDAISRHLRAIGRIPLLTADEEISLGRAVQNGQRLLETAEEMKLRSGGQTPSVQAWALEVGITPRQLQRQLKAAERASERMVTANLRLV Warm Cool LA3 --------------------------------------------RLLETAEEMKLRSGGQTPSVQAWALEVGITPRQLQRQLKAAERASERMVTANLRLV LA3 MPAPQKLRRG------------------------------------------------DCDA---------------ISRHLRA---------------- LA20 MPAPQKLRRGDCDAISRHLRAIGRIPLLTADEEISLGRAVQNGQRLLETAEEMKLRSGGQTPSVQAWALEVGITPRQLQRQLKAAERASERMVTANLRLV LA21 MPAPQKLRRGDCDAISRHLRAIGRIPLLTADEEISLGRAVQNGQRLLETAEEMKLRSGGQTPSVQAWALEVGITPRQLQRQLKAAERASERMVTANLRLV LA27 MPAPQKLRRGDCDAISRHLRAIGRIPLLTADEEISLGRAVQNGQRLLETAEEMKLRSGGQTPSVQAWALEVGITPRQLQRQLKAAERASERMVTANLRLV LA29 MPAPQKLRRGDCDAISRHLRAIGRIPLLTADEEISLGRAVQNGQRLLETAEEMKLRSGGQTPSVQAWALEVGITPRQLQRQLKAAERASERMVTANLRLV A MPAPQKLRRGDCDAISRHLRAIGRIPLLTADEEISLGRAVQNGQRLLETAEEMKLRSGGQTPSVQAWALEVGITPRQLQRQLKAAERASERMVTANLRLV Warm Cool B 1 ....:.... 1 00bp LA101 ----------MSLVLDQVALTIAGRPLVKQVSLNLNPGEVVGLLGPNGAGKTTTFNLVTGLLRPDSGAVVMDEQSVAALSMPERARLGIGYLPQEASVFR LA103 ----------MSLVLDQVALTIAGRPLVKQVSLNLNPGEVVGLLGPNGAGKTTTFNLVTGLLRPDSGAVVMDEQSVAALSMPERARLGIGYLPQEASVFR LA117 ----------MSLVLDQVALTIAGRPLVKQVSLNLNPGEVVGLLGPNGAGKTTTFNLVTGLLRPDSGAVVMDEQSVAALSMPERARLGIGYLPQEASVFR LA126 ----------MSLVLDQVALTIAGRPLVKQVSLNLNPGEVVGLLGPNGAGKTTTFNLVTGLLRPDSGAVVMDEQSVAALSMPERARLGIGYLPQEASVFR LA127 ----------MSLVLDQVALTIAGRPLVKQVSLNLNPGEVVGLLGPNGAGKTTTFNLVTGLLRPDSGAVVMDEQSVAALSMPERARLGIGYLPQEASVFR LA20 ----------MSLVLDQVALTIAGRPLVKQVSLNLNPGEVVGLLGPNGAGKTTTFNLVTGLLRPDSGAVVMDEQSVAALSMPERARLGIGYLPQEASVFR LA21 ----------MSLVLDQVALTIAGRPLVKQVSLNLNPGEVVGLLGPNGAGKTTTFNLVTGLLRPDSGAVVMDEQSVAALSMPERARLGIGYLPQEASVFR LA27 ----------MSLVLDQVALTIAGRPLVKQVSLNLNPGEVVGLLGPNGAGKTTTFNLVTGLLRPDSGAVVMDEQSVAALSMPERARLGIGYLPQEASVFR LA29 ----------MSLVLDQVALTIAGRPLVKQVSLNLNPGEVVGLLGPNGAGKTTTFNLVTGLLRPDSGAVVMDEQSVAALSMPERARLGIGYLPQEASVFR LA3 ----------MSLVLDQVALTIAGRPLVKQVSLNLNPGEVVGLLGPNGAGKTTTFNLVTGLLRPDSGAVVMDEQSVAALSMPERARLGIGYLPQEASVFR LA31 ----------MSLVLDQVALTIAGRPLVKQVSLNLNPGEVVGLLGPNGAGKTTTFNLVTGLLRPDSGAVVMDEQSVAALSMPERARLGIGYLPQEASVFR LA21 MTALQSVETSSGLRVEGLEKAYRKKVVIRGVSMSLDRGEVVALLGPNGSGKTTTFYAIAGLITPERGSVRIDGRDVTYMPMYRRARLGIGYLPQEMSIFR Figure S4: 176 Figure S5: Alignments of A cpcA and B cpcB amino acid sequences recovered from assemblies using Anvi’o. Assemblies for LA20, 27, and 29 have small fragmented terminal cpcA copies that are not shown here for simplicity. In each case they perfectly align with the cpcA copies found in the 30 °C isolates. 1bp ....:....100bp LA101_cpcA_fragment ---------------------------------------------------------------------------------------------------- LA103_cpcA_fragment ---------------------------------------------------------------------------------------------------- LA126_cpcA_fragment ---------------------------------------------------------------------------------------------------- LA127_cpcA_fragment ---------------------------------------------------------------------------------------------------- LA20_cpcA_complete MKTPLTEAVAAADSQGRFLSNTELNAAFGRFERAANALAAAKALTAKADELVNGAAQAVYNKFPYTTQMQGANYASDARGKAKCARDIGYYLRQVTYCLV LA21_cpcA_complete MKTPLTEAVAAADSQGRFLSNTELNAAFGRFERAANALAAAKALTAKADELVNGAAQAVYNKFPYTTQMQGANYASDARGKAKCARDIGYYLRQVTYCLV LA27_cpcA_complete MKTPLTEAVAAADSQGRFLSNTELNAAFGRFERAANALAAAKALTAKADELVNGAAQAVYNKFPYTTQMQGANYASDARGKAKCARDIGYYLRQVTYCLV LA29_cpcA_complete MKTPLTEAVAAADSQGRFLSNTELNAAFGRFERAANALAAAKALTAKADELVNGAAQAVYNKFPYTTQMQGANYASDARGKAKCARDIGYYLRQVTYCLV LA3_cpcA_complete MKTPLTEAVAAADSQGRFLSNTELNAAFGRFERAANALAAAKALTAKADELVNGAAQAVYNKFPYTTQMQGANYASDARGKAKCARDIGYYLRQVTYCLV LA31_cpcA_complete MKTPLTEAVAAADSQGRFLSNTELNAAFGRFERAANALAAAKALTAKADELVNGAAQAVYNKFPYTTQMQGANYASDARGKAKCARDIGYYLRQVTYCLV ....:. 1 62bp LA101_cpcA_fragment ------------------------SPSWYVEALNYIKANHGISGDAGVIANNYIDYAIAALV LA103_cpcA_fragment ------------------------SPSWYVEALNYIKANHGISGDAGVIANNYIDYAIAALV LA126_cpcA_fragment ------------------------SPSWYVEALNYIKANHGISGDAGVIANNYIDYAIAALV LA127_cpcA_fragment ------------------------SPSWYVEALNYIKANHGISGDAGVIANNYIDYAIAALV LA20_cpcA_complete AGGTGPIDEYLIAGLDEINRAFELSPSWYVEALNYIKANHGISGDAGVIANNYIDYAIAALV LA21_cpcA_complete AGGTGPIDEYLIAGLDEINRAFELSPSWYVEALNYIKANHGISGDAGVIANNYIDYAIAALV LA27_cpcA_complete AGGTGPIDEYLIAGLDEINRAFELSPSWYVEALNYIKANHGISGDAGVIANNYIDYAIAALV LA29_cpcA_complete AGGTGPIDEYLIAGLDEINRAFELSPSWYVEALNYIKANHGISGDAGVIANNYIDYAIAALV LA3_cpcA_complete AGGTGPIDEYLIAGLDEINRAFELSPSWYVEALNYIKANHGISGDAGVIANNYIDYAIAALV LA31_cpcA_complete AGGTGPIDEYLIAGLDEINRAFELSPSWYVEALNYIKANHGISGDAGVIANNYIDYAIAALV A B Warm Cool Warm Cool Warm Cool Warm Cool 1bp ....:....100bp LA31_cpcB_fragment MFDAFTKVVAQADARGEFLNAGQID--------------------------------------------------------------------------- MFDAFTKVVAQADARGEFLNAGQID--------------------------------------------------------------------------- LA101_cpcB_complete MFDAFTKVVAQADARGEFLNAGQIDALSAMVAESNKRMDSVNRITSNASKIVTNAARELFDQQPALIAPGGNAYTHRRMAACLRDMEIILRYVTYAVFTG LA103_cpcB_complete MFDAFTKVVAQADARGEFLNAGQIDALSAMVAESNKRMDSVNRITSNASKIVTNAARELFDQQPALIAPGGNAYTHRRMAACLRDMEIILRYVTYAVFTG LA117_cpcB_complete MFDAFTKVVAQADARGEFLNAGQIDALSAMVAESNKRMDSVNRITSNASKIVTNAARELFDQQPALIAPGGNAYTHRRMAACLRDMEIILRYVTYAVFTG LA127_cpcB_complete MFDAFTKVVAQADARGEFLNAGQIDALSAMVAESNKRMDSVNRITSNASKIVTNAARELFDQQPALIAPGGNAYTHRRMAACLRDMEIILRYVTYAVFTG LA27_cpcB_fragment MFDAFTKVVAQADARGEFLNAGQID--------------------------------------------------------------------------- LA29_cpcB_fragment MFDAFTKVVAQADARGEFLNAGQID--------------------------------------------------------------------------- LA3_cpcB_fragment MFDAFTKVVAQADARGEFLNAGQID--------------------------------------------------------------------------- ....:.. 1 72bp LA31_cpcB_fragment ------------------------------------------------TPGDCSALMSEIGTYFDRAAAAVA LA101_cpcB_complete DASVLEDRCLNGLRETYLALGVPGASVAEGVRKMKDAAIAIANDRNGITPGDCSALMSEIGTYFDRAAAAVA LA103_cpcB_complete DASVLEDRCLNGLRETYLALGVPGASVAEGVRKMKDAAIAIANDRNGITPGDCSALMSEIGTYFDRAAAAVA LA117_cpcB_complete DASVLEDRCLNGLRETYLALGVPGASVAEGVRKMKDAAIAIANDRNGITPGDCSALMSEIGTYFDRAAAAVA LA127_cpcB_complete DASVLEDRCLNGLRETYLALGVPGASVAEGVRKMKDAAIAIANDRNGITPGDCSALMSEIGTYFDRAAAAVA LA27_cpcB_fragment ----------------YLALGVPGASVAEGVRKMKDAAIAIANDRNGITPGDCSALMSEIGTYFDRAAAAVA LA29_cpcB_fragment ----------------YLALGVPGASVAEGVRKMKDAAIAIANDRNGITPGDCSALMSEIGTYFDRAAAAVA LA3_cpcB_fragment ----------------YLALGVPGASVAEGVRKMKDAAIAIANDRNGITPGDCSALMSEIGTYFDRAAAAVA LA20_cpcB_fragment MFDAFTKVVAQADARGEFLNAGQID--------------------------------------------------------------------------- LA21_cpcB_fragment LA20_cpcB_fragment -----------------LALGVPGASVAEGVRKMKDAAIAIANDRNGITPGDCSALMSEIGTYFDRAAAAVA LA21_cpcB_fragment ----------------------------------------IANDRNGITPGDCSALMSEIGTYFDRAAAAVA LA126_cpcB_fragment MFDAFTKVVAQADARGEFLNAGQID--------------------------------------------------------------------------- LA126_cpcB_fragment ----------------YLALGVPGASVAEGVRKMKDAAIAIANDRNGITPGDCSALMSEIGTYFDRAAAAVA Figure S5: 177 Supplemental Note 1: It is possible that there could be sequence variation between these unassembled copies. For the closed genome of the warm temperature isolate LA127, variation was seen between its two copies of cpcA and cpcB (Table S5). A closer look at the read-recruitment to the complete copies of cpcA and cpcB in the draft genomes showed nucleotide positions that appear to vary between these unassembled gene copies near the 3’ end (Figure S6A). This variation was in 37.8% of the reads. For cpcB, reads that recruit inside the gene reveal 6 single-nucleotide variant sites spread across the gene. Four of the six variant bases occur with a frequency of ~30%, and the other two at ~50% (Figure S6B). For both cpcA and cpcB these SNVs only occur at the third base in codons and likely do not contribute to differences in peptide sequence. This is also true when looking at amino acid sequence for the copies of the cpcA and cpcB genes in the complete LA127 genome (Table S6B). Mapping reads for all isolates back to the complete genome using bowtie2’s “—very-sensitive” flag did not detect any SNVs between reads and the mapped sequence for these two genes. Further, when BLASTed all completely assembled copies of both C-phycocyanin genes had a 100% match to all other complete copies in the other draft assemblies and to one of the copies in the complete genome. This suggests that although some variation exists between copies of these genes within a genome, all isolates share these differences and sequence variation for cpcA and cpcB do not explain the observed phenotypes. Figure S6: Visualization of reads mapped to A cpcA for the cool temperature isolate LA20 and B cpcB for the warm temperature isolate LA127. Base by base coverage is shown using grey and green arrows show the location of the genes. Single nucleotide variation between the assembled sequence and mapped reads within each gene are shown using the vertical bars and letters. Height of bars corresponds to the percent of total mapped reads that contain the SNV, indicated with the right axis. 178 Figure S7: Results of mapping all reads to the complete genome from the warm temperature isolate LA127. Shown here is the coverage (grey shading) to the locus shown in Figure 1. The two isolates shown here are representative of all warm (top) and cool temperature isolate reads. Notably, no SNVs were shown across this locus, suggesting a high degree of sequence similarity. 500 1,000 1,500 2,000 2,500 3,000 3,500 0 200 400 600 800 1,000 0 50 100 150 LA31 LA127 Phycocyanin Beta Chain Phycocyanin Alpha Chain Phycoerythrin associated Linker Protein Length (BP) 179 Supplemental Note #2: Looking at the cpcA genes that successfully assembled from the isolates derived from cool temperatures (blue arrows in Figure S8), 1 complete copy was assembled, while there are 2 copies present in the Chesapeake Bay strain CB0101 (Figure 3A) and in the hybrid assembly of the warm temperature strain LA127 (hereafter referred to as the complete genome). It is possible that variation in the copy number could contribute to distinct pattern of assembly failure. Too much similarity between gene copies can be difficult for assemblers to distinguish and could result in assemblers collapsing genes. Examining the recruitment of reads that built the assembly reveals that the single complete copy of cpcA has 1.6x the coverage for cpcA as compared to the mean coverage of the rest of the genome (Figure S6A and Figure S8B). It is likely then that all of these isolates whose draft genomes assembled a complete cpcA copy actually had more than one copy. Figure S8: A Two loci containing genes coding for components of the accessory pigment C- phycocyanin in the closed genome for warm temperature strain LA127. B The same loci for all draft assemblies for isolates from cool temperatures with the results of mapping each isolate’s reads back to its own assembly shown as a boxplot. The dashed line shows 1x coverage and contig breaks within genes are shown as jagged vertical lines. C Loci for warm temperature isolate draft genomes and coverage. Switching focus to cpcB (orange arrows in Figure S8), the closed genome and closely related CB0101 genome possesses 3 copies across these 2 loci (Figure S8A). For the isolates recovered from the cooler temperatures, no copies of cpcB successfully assembled (Figure S8B, orange arrows). However, one cpcB gene did successfully assemble in four of the five warm temperature-derived isolates (Figure S8C, orange arrows). In these, read recruitment revealed Complete Genome 2kb 4kb 6kb Phycocyanin αchain( cpcA ) Phycocyanobilin lyase α Phycocyanobilin lyase β Phycocyanin βchain( cpcB ) Breaks within genes Phycocyanin assoc. rod No Annotation Phycoerythrin assoc. linker Genes: A 1kb Locus 2 Locus 1 Normalized Coverage Gene Gene 1x 0x 2x 1x 0x 2x B Gene Gene Normalized Coverage C 180 mean gene- coverages of about double the mean of the rest of the genome (Figure S8C), and closer inspection of the read recruitment to this gene showed this was not uniformly distributed across it, but rather 60% of the gene had about 2.3X coverage and the rest quickly dropped to 1X approaching the 5’ and 3’ end of the gene. This is likely due to the variation that exists outside of the gene-copies, preventing reads from recruiting near the ends (Figure S6B). This suggests to us that while only one copy of cpcB assembled in these warm temperature- derived isolates, they likely possess multiple copies. Mapping reads from all isolates back to the C-phycocyanin gene copies in the closed genome did not show statistically significant differences in the relative coverage for reads from isolates of different temperatures. This suggests that they may all have similar copy numbers of these two genes; however, as another means of gaining insight into the variability of these loci, we recruited our isolate reads to the nearest relative with a closed genome, CB0101. Because CB0101 and our isolates only share 85.5% ANI (±0.04 SD), genome-wide coverage was low (12% ±0.5 SD) compared to mapping reads from each isolate to its own assembly. However, mapping rates to CB0101’s copies of cpcA and cpcB were four to five times higher than to the rest of its genome, indicating these genes are more highly conserved than the rest of the genome as a whole and a good target for mapping. When mapping reads from warm and cool temperature isolates to CB0101, the combined coverage of cpcA genes is ~1.7x that of the coverage across each isolate’s genome (Figure S9). The similarity in coverage for reads coming from both sets of isolates could suggest that both have the same cpcA copy number. Combined coverage of CB0101’s cpcB gene though showed that isolates from warm temperatures (30 °C) had higher coverage relative to low temperature isolates (p = 0.004, Figure S9) which could suggest they have a higher copy number. Figure S9: Normalized coverage for reads mapping to CB0101’s copies of cpcA and cpcB. Star indicates significance using a student’s T-test (p < 0.5). 1.0x 1.25x 1.5x 1.75x 2.0x cpcA cpcB Normalized Coverage Cool Warm 181 Genbank Assembly ID Strain Name GCA_000010065.1 Synechococcus elongatus PCC6301 GCA_000012505.1 CC9902 GCA_000012525.1 PCC7942 GCA_000012625.1 CC9605 GCA_000013205.1 JA-3-3Ab GCA_000013225.1 JA-2-3Ba GCA_000014585.1 CC9311 GCA_000019485.1 PCC7002 GCA_000063505.1 WH7803 GCA_000063525.1 RCC307 GCA_000161795.2 WH8109 GCA_000179235.2 CB0101 GCA_000195975.1 WH8102 GCA_000316685.1 PC6312 GCA_000317085.1 PCC7502 GCA_000737535.1 KORDI-100 GCA_000737575.1 KORDI-49 GCA_000737595.1 KORDI-52 GCA_000817325.1 UTEX2973 GCA_001182765.1 WH8103 GCA_001521855.1 PCC73109 GCA_001693255.1 PCC7003 GCA_001693275.1 PCC7117 GCA_001693295.1 PCC8807 GCA_001885215.1 SynAce01 GCA_002356215.1 NIES-970 GCA_002754935.1 Synechococcus lividus PCC6715 GCA_003957805.1 Synechococcus elongatus UTEX3055 GCA_004209775.1 WH8101 GCA_001040845.1 WH8020 GCA_005577135.1 PCC11901 GCA_000332275.1 PCC7336 GCA_000153045.1 WH5701 GCA_000153065.1 RSS9917 GCA_000153285.1 WH7805 GCA_000153805.1 BL107 GCA_000153825.1 RS9916 GCA_000155595.1 PCC7335 GCA_000515235.1 CC9616 GCA_000179255.1 CB0205 GCA_000230675.2 WH8016 GCA_000485815.1 NKBG15041 Table S1: Accession numbers for all complete Synechococcus genomes on Refseq as of July 2019. 182 GCA_000586015.1 Synechoccus spongarium SH4 GCA_000715475.1 MKBG042902 GCA_001039265.1 GFB01 GCA_002252625.1 1G10 GCA_002252635.1 MW101C3 GCA_004332405.1 BS56D GCA_004332415.1 BS55D GCA_900047545.1 Synechococcus spongarium m9 GCA_900177365.1 OG1 GCA_900177825.1 7002 GCA_900473895.1 N32 GCA_900473925.1 N5 GCA_900473935.1 UW105 GCA_900473955.1 GEYO GCA_900473965.1 UW179A GCA_900473975.1 N26 GCA_900474015.1 UW106 GCA_900474045.1 N19 GCA_900474085.1 UW86 GCA_900474185.1 UW69 GCA_900474245.1 UW179B GCA_900474295.1 UW140 GCA_001631935.1 MITS9509 GCA_001632105.1 MITS9504 GCA_001632165.1 MITS9508 GCA_002018015.1 Synechococcus spongarium LMB GCA_002252665.1 8F6 GCA_002252675.1 BO8801 GCA_002721235.1 BDU130192 GCA_002760345.1 65AY6Li GCA_002760375.1 60AY4M2 GCA_002760395.1 63AY4M1 GCA_002760415.1 65AY6A5 GCA_002760445.1 65AY640 GCA_002760475.1 63AY4M2 GCA_003011125.1 Synechococcus lacustris Tous GCF_000316515.1 Cyanobium gracile PCC6307 GCF_900088535.1 Cyanobium sp. NIES981 GCF_000155635.1 Cyanobium sp. 7001 GCA_000011385.1** Gleobacter violaceus PCC7421 **(outgroup) 183 184 Strain ID Isolation Temperature (C°) Isolation Location Culture appearanc e Cell Size (µm) Fluorescence Color Biosample Accession LA3 22 Natural Seawater Green 1.5-2.0 Red SAMN12784244 LA20 18 Phytoplankton Enrichment Green 1.5-2.0 Red SAMN12784239 LA21 18 Phytoplankton Enrichment Green 1.5-2.0 Red SAMN12784240 LA27 18 Phytoplankton Enrichment Green 1.5-2.0 Red SAMN12784241 LA29 18 Phytoplankton Enrichment Green 1.5-2.0 Red SAMN12784242 LA31 18 Phytoplankton Enrichment Green 1.5-2.0 Red SAMN12784243 LA101 30 Phytoplankton Enrichment Green 1.5-2.0 Red SAMN12784234 LA103 30 Phytoplankton Enrichment Green 1.5-2.0 Red SAMN12784235 LA117 30 Phytoplankton Enrichment Green 1.5-2.0 Red SAMN12784236 LA126 30 Phytoplankton Enrichment Green 1.5-2.0 Red SAMN12784237 LA127 30 Phytoplankton Enrichment Green 1.5-2.0 Red SAMN12784238 Table S2: Characteristics of the 11 Synechococcus strains isolated from this study. Fluorescence color was observed under blue light excitation. 185 Strain ID Isolation Temperature (°C) # Contigs Largest Contig (bp) Total Length N50 %GC %Complete Gene Count LA3 22 20 591,139 2,727,894 334,458 63.3 100 2,972 LA20 18 20 633,279 2,727,686 333,892 63.3 100 2,970 LA21 18 23 633,282 2,748,254 334,325 63.3 100 2,989 LA27 18 21 840,723 2,734,659 334,452 63.3 100 2,973 LA29 18 25 634,005 2,735,075 207,677 63.3 100 2,980 LA31 18 18 633,281 2,722,347 333,614 63.3 100 2,968 LA31_hybrid 18 6 1,423,720 2,749,802 1,423,720 63.2 100 2,964 LA101 30 21 633,293 2,734,502 334,458 63.3 100 2,971 LA103 30 16 633,257 2,728,816 337,793 63.3 100 2,965 LA117 30 35 633,312 2,748,349 229,507 63.2 100 2,988 LA126 30 33 633,319 2,762,240 334,232 63.2 100 3,001 LA127 30 27 340,544 2,736,273 204,696 63.3 100 2,975 LA127_hybrid 30 1 2,750,736 2,750,736 2,750,736 63.2 100 2,968 Mean (short read) 22.1 688,596.2 2,737,991.4 395,235.3 63.3 100.0 2,976 SD 7.6 255,588.5 11,723.8 328,371.1 0.0 0.0 11.2 Table S3: Summary of assembly quality. Results were summarized in quast, and completeness estimated by Anvi'o during the binning process. 186 Gene Cluster ID GC_00000001 GC_00000002 GC_00000003 GC_00000004 GC_00000005 GC_00000006 GC_00000007 GC_00000008 GC_00000009 GC_00000010 GC_00000011 GC_00000012 GC_00000013 GC_00000014 GC_00000015 GC_00000016 GC_00000017 GC_00000018 GC_00000019 GC_00000020 GC_00000021 GC_00000022 GC_00000023 GC_00000024 GC_00000025 GC_00000026 GC_00000027 GC_00000028 GC_00000029 GC_00000030 GC_00000031 GC_00000032 GC_00000033 GC_00000034 GC_00000035 GC_00000036 GC_00000037 GC_00000038 GC_00000039 GC_00000040 GC_00000041 GC_00000042 Table S4: IDs for all gene clusters with combined homogeinty below 100%. 187 GC_00000043 GC_00000044 GC_00000045 GC_00000046 GC_00000047 GC_00000048 GC_00000049 GC_00000050 GC_00000051 GC_00002892 GC_00002896 GC_00002898 GC_00002901 GC_00002913 GC_00002914 GC_00002915 GC_00002927 GC_00002928 GC_00002929 GC_00002930 188 Function Query ID Subject ID Percent ID Length Mismatches Gaps Start End E-Value Bit score Phcyocyanin Alpha Chain 1431 1434 99.796 489 1 0 1 489 0 898 Phycocyanin Beta Chain 1432 1435 99.422 519 3 0 1 519 0 942 Phycocyanin Beta Chain 1432 2729 99.037 519 5 0 1 519 0 931 Phycocyanin Beta Chain 1435 2729 99.229 519 4 0 1 519 0 937 Phycoerythrin Associated Linker Protein 732 1437 75.705 461 105 7 83 540 1.26E-61 224 Table S5: Results of an all vs. all BLAST of the gene sequences not assembled in draft genomes but present in the LA127 complete genome. 189 Function Query ID Subject ID Percent ID Length Mismatches Gaps Start End E-Value Bit score Phcyocyanin Alpha Chain 1431 1434 100 162 0 0 1 162 1.24E-121 327 Phycocyanin Beta Chain 1432 2729 100 172 0 0 1 172 1.15E-129 349 Phycocyanin Beta Chain 1432 1435 100 172 0 0 1 172 1.15E-129 349 Phycocyanin Beta Chain 1435 2729 100 172 0 0 1 172 1.15E-129 349 Phycoerythrin Associated Linker Protein 732 2841 60.188 319 110 4 1 307 1.53E-127 358 Phycoerythrin Associated Linker Protein 732 1433 53.185 314 112 3 1 313 1.75E-106 305 Phycoerythrin Associated Linker Protein 732 1437 48.571 315 122 2 1 313 3.23E-102 292 Phycoerythrin Associated Linker Protein 732 2503 45.833 24 12 1 240 262 3.6 16.9 Phycoerythrin Associated Linker Protein 1256 2503 41.176 34 17 1 144 174 0.002 26.2 Phycoerythrin Associated Linker Protein 1433 1437 69.366 284 84 2 45 328 2.97E-149 410 Phycoerythrin Associated Linker Protein 1433 2841 72.772 202 54 1 45 246 2.59E-106 303 Phycoerythrin Associated Linker Protein 1437 2841 59.036 249 90 4 1 239 8.66E-99 282 Phycoerythrin Associated Linker Protein 2503 1256 41.176 34 17 1 118 151 0.002 25.8 Table S6: Results of an all vs. all BLAST of the protein sequences not assembled in draft genomes but present in the LA127 complete genome. 190 Query Gene ID Subject Gene ID %ID Length Mismatches Gaps Query Start Query End E-Value Bit score LA3_cpcA LA127_hybrid_cpcA_1431 100 489 0 0 1 489 0 904 LA3_cpcA LA29_cpcA 100 489 0 0 1 489 0 904 LA3_cpcA LA27_cpcA 100 489 0 0 1 489 0 904 LA3_cpcA LA21_cpcA 100 489 0 0 1 489 0 904 LA3_cpcA LA20_cpcA 100 489 0 0 1 489 0 904 LA3_cpcA LA31_cpcA 100 489 0 0 1 489 0 904 LA3_cpcA LA127_hybrid_cpcA_1434 99.8 489 1 0 1 489 0 898 LA31_cpcA LA127_hybrid_cpcA_1431 100 489 0 0 1 489 0 904 LA31_cpcA LA29_cpcA 100 489 0 0 1 489 0 904 LA31_cpcA LA27_cpcA 100 489 0 0 1 489 0 904 LA31_cpcA LA21_cpcA 100 489 0 0 1 489 0 904 LA31_cpcA LA20_cpcA 100 489 0 0 1 489 0 904 LA31_cpcA LA3_cpcA 100 489 0 0 1 489 0 904 LA31_cpcA LA127_hybrid_cpcA_1434 99.8 489 1 0 1 489 0 898 LA20_cpcA LA127_hybrid_cpcA_1431 100 489 0 0 1 489 0 904 LA20_cpcA LA29_cpcA 100 489 0 0 1 489 0 904 LA20_cpcA LA27_cpcA 100 489 0 0 1 489 0 904 LA20_cpcA LA21_cpcA 100 489 0 0 1 489 0 904 LA20_cpcA LA31_cpcA 100 489 0 0 1 489 0 904 LA20_cpcA LA3_cpcA 100 489 0 0 1 489 0 904 LA20_cpcA LA127_hybrid_cpcA_1434 99.8 489 1 0 1 489 0 898 LA21_cpcA LA127_hybrid_cpcA_1431 100 489 0 0 1 489 0 904 LA21_cpcA LA29_cpcA 100 489 0 0 1 489 0 904 LA21_cpcA LA27_cpcA 100 489 0 0 1 489 0 904 LA21_cpcA LA20_cpcA 100 489 0 0 1 489 0 904 LA21_cpcA LA31_cpcA 100 489 0 0 1 489 0 904 LA21_cpcA LA3_cpcA 100 489 0 0 1 489 0 904 Table S7: Results of blasting the DNA sequences of every completely assembled copy of cpcA and cpcB against every other. Sequences from the complete hybrid assembly of strain LA127 are labelled "hybrid" in the query or subject ID. 191 LA21_cpcA LA127_hybrid_cpcA_1434 99.8 489 1 0 1 489 0 898 LA27_cpcA LA127_hybrid_cpcA_1431 100 489 0 0 1 489 0 904 LA27_cpcA LA29_cpcA 100 489 0 0 1 489 0 904 LA27_cpcA LA21_cpcA 100 489 0 0 1 489 0 904 LA27_cpcA LA20_cpcA 100 489 0 0 1 489 0 904 LA27_cpcA LA31_cpcA 100 489 0 0 1 489 0 904 LA27_cpcA LA3_cpcA 100 489 0 0 1 489 0 904 LA27_cpcA LA127_hybrid_cpcA_1434 99.8 489 1 0 1 489 0 898 LA29_cpcA LA127_hybrid_cpcA_1431 100 489 0 0 1 489 0 904 LA29_cpcA LA27_cpcA 100 489 0 0 1 489 0 904 LA29_cpcA LA21_cpcA 100 489 0 0 1 489 0 904 LA29_cpcA LA20_cpcA 100 489 0 0 1 489 0 904 LA29_cpcA LA31_cpcA 100 489 0 0 1 489 0 904 LA29_cpcA LA3_cpcA 100 489 0 0 1 489 0 904 LA29_cpcA LA127_hybrid_cpcA_1434 99.8 489 1 0 1 489 0 898 LA127_hybrid_cpcA_1431 LA29_cpcA 100 489 0 0 1 489 0 904 LA127_hybrid_cpcA_1431 LA27_cpcA 100 489 0 0 1 489 0 904 LA127_hybrid_cpcA_1431 LA21_cpcA 100 489 0 0 1 489 0 904 LA127_hybrid_cpcA_1431 LA20_cpcA 100 489 0 0 1 489 0 904 LA127_hybrid_cpcA_1431 LA31_cpcA 100 489 0 0 1 489 0 904 LA127_hybrid_cpcA_1431 LA3_cpcA 100 489 0 0 1 489 0 904 LA127_hybrid_cpcA_1431 LA127_hybrid_cpcA_1434 99.8 489 1 0 1 489 0 898 LA127_hybrid_cpcA_1434 LA127_hybrid_cpcA_1431 99.8 489 1 0 1 489 0 898 LA127_hybrid_cpcA_1434 LA29_cpcA 99.8 489 1 0 1 489 0 898 LA127_hybrid_cpcA_1434 LA27_cpcA 99.8 489 1 0 1 489 0 898 LA127_hybrid_cpcA_1434 LA21_cpcA 99.8 489 1 0 1 489 0 898 LA127_hybrid_cpcA_1434 LA20_cpcA 99.8 489 1 0 1 489 0 898 LA127_hybrid_cpcA_1434 LA31_cpcA 99.8 489 1 0 1 489 0 898 LA127_hybrid_cpcA_1434 LA3_cpcA 99.8 489 1 0 1 489 0 898 LA101_cpcB LA127_hybrid_cpcB_2729 100 519 0 0 1 519 0 959 LA101_cpcB LA127_SR_cpcB 100 519 0 0 1 519 0 959 192 LA101_cpcB LA117_cpcB 100 519 0 0 1 519 0 959 LA101_cpcB LA103_cpcB 100 519 0 0 1 519 0 959 LA101_cpcB LA127_hybrid_cpcB_1435 99.2 519 4 0 1 519 0 937 LA101_cpcB LA127_hybrid_cpcB_1432 99 519 5 0 1 519 0 931 LA103_cpcB LA127_hybrid_cpcB_2729 100 519 0 0 1 519 0 959 LA103_cpcB LA127_SR_cpcB 100 519 0 0 1 519 0 959 LA103_cpcB LA117_cpcB 100 519 0 0 1 519 0 959 LA103_cpcB LA101_cpcB 100 519 0 0 1 519 0 959 LA103_cpcB LA127_hybrid_cpcB_1435 99.2 519 4 0 1 519 0 937 LA103_cpcB LA127_hybrid_cpcB_1432 99 519 5 0 1 519 0 931 LA117_cpcB LA127_hybrid_cpcB_2729 100 519 0 0 1 519 0 959 LA117_cpcB LA127_SR_cpcB 100 519 0 0 1 519 0 959 LA117_cpcB LA103_cpcB 100 519 0 0 1 519 0 959 LA117_cpcB LA101_cpcB 100 519 0 0 1 519 0 959 LA117_cpcB LA127_hybrid_cpcB_1435 99.2 519 4 0 1 519 0 937 LA117_cpcB LA127_hybrid_cpcB_1432 99 519 5 0 1 519 0 931 LA127_SR_cpcB LA127_hybrid_cpcB_2729 100 519 0 0 1 519 0 959 LA127_SR_cpcB LA117_cpcB 100 519 0 0 1 519 0 959 LA127_SR_cpcB LA103_cpcB 100 519 0 0 1 519 0 959 LA127_SR_cpcB LA101_cpcB 100 519 0 0 1 519 0 959 LA127_SR_cpcB LA127_hybrid_cpcB_1435 99.2 519 4 0 1 519 0 937 LA127_SR_cpcB LA127_hybrid_cpcB_1432 99 519 5 0 1 519 0 931 LA127_hybrid_cpcB_1432 LA127_hybrid_cpcB_1435 99.4 519 3 0 1 519 0 942 LA127_hybrid_cpcB_1432 LA127_hybrid_cpcB_2729 99 519 5 0 1 519 0 931 LA127_hybrid_cpcB_1432 LA127_SR_cpcB 99 519 5 0 1 519 0 931 LA127_hybrid_cpcB_1432 LA117_cpcB 99 519 5 0 1 519 0 931 LA127_hybrid_cpcB_1432 LA103_cpcB 99 519 5 0 1 519 0 931 LA127_hybrid_cpcB_1432 LA101_cpcB 99 519 5 0 1 519 0 931 LA127_hybrid_cpcB_1435 LA127_hybrid_cpcB_1432 99.4 519 3 0 1 519 0 942 LA127_hybrid_cpcB_1435 LA127_hybrid_cpcB_2729 99.2 519 4 0 1 519 0 937 LA127_hybrid_cpcB_1435 LA127_SR_cpcB 99.2 519 4 0 1 519 0 937 193 LA127_hybrid_cpcB_1435 LA117_cpcB 99.2 519 4 0 1 519 0 937 LA127_hybrid_cpcB_1435 LA103_cpcB 99.2 519 4 0 1 519 0 937 LA127_hybrid_cpcB_1435 LA101_cpcB 99.2 519 4 0 1 519 0 937 LA127_hybrid_cpcB_2729 LA127_SR_cpcB 100 519 0 0 1 519 0 959 LA127_hybrid_cpcB_2729 LA117_cpcB 100 519 0 0 1 519 0 959 LA127_hybrid_cpcB_2729 LA103_cpcB 100 519 0 0 1 519 0 959 LA127_hybrid_cpcB_2729 LA101_cpcB 100 519 0 0 1 519 0 959 LA127_hybrid_cpcB_2729 LA127_hybrid_cpcB_1435 99.2 519 4 0 1 519 0 937 LA127_hybrid_cpcB_2729 LA127_hybrid_cpcB_1432 99 519 5 0 1 519 0 931 194 Accession Strain cpcA copies cpcB copies Isolation Location GCA_000153045 WH5701 3 4 Milford, Connecticut, USA GCA_000179235 CB0101 2 3 Baltimore, Maryland, USA GCA_000179255 CB0205 1 2 Baltimore, Maryland, USA GCA_001039265 GFB01 4 4 Brazil GCA_001885215 SynAce01 1 2 Antarctica GCA_002252625 1G10 1 2 Fresh water lake in Argentina GCA_002252635 MW101C3 2 2 Fresh water lake in Austria GCA_002252665 8F6 1 2 Fresh water lake in Mexico GCA_002252675 BO 8801 2 3 Fresh water lake in Germany GCF_000155635 PCC7001 3 4 Long Island, New York, USA GCF_000316515 PCC6307 2 3 Fresh water lake in Wisconsin, USA GCF_900088535 NIES-981 4 4 Okinawa, Japan Table S6: Copy number of C-phycocyanin alpha ( cpcA ) and beta ( cpcB ) subunits in all complete genomes from Synechococcus marine subcluster 5.2. Strains come from brackish or coastal ecosystems unless otherwise noted.

Abstract (if available)

Abstract Marine photosynthetic carbon fixation in the sunlit upper reaches of the ocean is almost entirely carried out by chlorophyll-containing, single-celled microorganisms, and is responsible for half of the net primary production on the planet. Because of this connection to the marine carbon cycle, it is essential to assess the responses of marine phytoplankton to global change. However, this work is challenged by the dazzling diversity of both eukaryotic and prokaryotic lineages which coexist in complex phytoplankton assemblages. My dissertation contributes to this effort by investigating how the diversity of phytoplankton influences their resilience to rising temperatures. In my first study, I used natural California coastal communities collected across three seasons to show that the phytoplankton assemblage as a whole was able to maintain growth well above typical temperature ranges. However, either steady or fluctuating temperatures exceeding the maximum threshold recorded in a decade-long observational dataset caused drastic rearrangements in the phytoplankton community, including the appearance of novel dominant species. My dissertation work also highlights that there are still unrecognized but environmentally-important taxa with bizarre and unexpected life histories and thermal responses, even in the most well-studied environments. In my second study, I characterized a recently isolated nanoplanktonic diatom from the Narragansett Bay Time Series that occupies a distinct low-light, low-temperature niche. This isolate demonstrated an unusual sensitivity to light, whereby its ability to respond to what should be favorable increases in temperature is constrained by light intensity. Six years of amplicon sequencing data from the time series site suggest that this diatom is a temperate wintertime/early spring specialist, and will likely not fare well in a warmer and more stratified future ocean. In addition to expanding knowledge of functional diversity at the species level, my work also examines the potential of intra-specific diversity to house hidden adaptations to rising temperatures. Natural microbial populations are composed of distinct individual strains, whose relative abilities to contribute to the success of the whole population in a changing environment have not been well-studied. In my third study, I compared the thermal responses of 11 strains of the marine unicellular cyanobacterium Synechococcus simultaneously isolated from a single estuarine water sample to explore this cryptic intra-specific diversity. Surprisingly, these nearly genetically-identical strains showed distinct low and high temperature phenotypes. This study indicates that strain-level variation could be a key yet understudied element in the responses of phytoplankton to global change. Together, these studies highlight that the diversity of marine phytoplankton at the species and individual level includes both functional variability and redundancy relative to temperature. We can expect community composition to change over time in a warming ocean, reflecting the increasing abundance of preadapted groups or individual strains

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Kling, Joshua David (author)

Core Title Thermal diversity within marine phytoplankton communities

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Biology (Marine Biology and Biological Oceanography)

Publication Date 08/11/2020

Defense Date 08/11/2020

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag community ecology,OAI-PMH Harvest,phytoplankton,thermal response

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Hutchins, David (committee chair), Ehrenreich, Ian (committee member), Heidelberg, John (committee member), Levine, Naomi (committee member)

Creator Email Joshuakl@berkeley.edu,Joshuakl@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-363126

Unique identifier UC11666472

Identifier etd-KlingJoshu-8915.pdf (filename),usctheses-c89-363126 (legacy record id)

Legacy Identifier etd-KlingJoshu-8915.pdf

Dmrecord 363126

Document Type Dissertation

Rights Kling, Joshua David

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Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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