Long-term warming increased richness in the asymmetric mutualism between bacteria and microalgae

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Long-term warming increased richness in the

asymmetric mutualism between bacteria and microalgae

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Authors Agusti, Susana;Jin, Peng;Stanschewski, Clara;Diaz Rua, Ruben Citation Agusti, S., Jin, P., Stanschewski, C., & Diaz-Rua, R. (2022). Long-

term warming increased richness in the asymmetric mutualism between bacteria and microalgae. https://doi.org/10.22541/

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Long-term warming increased richness in the asymmetric mutualism between bacteria and microalgae

Susana Agusti

¹

, Peng Jin

²

, Clara Stanschewski

¹

, and Ruben Diaz-Rua

¹

1

King Abdullah University of Science and Technology

2

Guangzhou University November 18, 2022

Abstract

Microalgae, the ocean’s primary producers, have proven a large capacity for adaptation, but the implications for species interactions are rarely examined. In a 2-year experiment, we exposed the marine diatom Chaetoceros tenuissmus to warming and examined the responses of its mutualistic bacterial community. The diatom adapted to warming by increasing its temperature optimum and maximum growth rate, whereas the microbiota increased its maximum growth rate without changing its temperature optimum. Rhodobacteria dominated the diatom-associated communities at ambient temperatures and this dominance did not diminish under warming conditions. Extinctions occurred in low-abundance genera, but under warming conditions, new partners appeared. The warming consortium was stable when transplanted to ambient temperatures, indicating a strong association. Duration of exposure to temperature appeared relevant, highlighting the consequences of short-warming events. Our results agree with predictions that long-term evolution of asymmetric mutualistic associations increases strength and diversity, particularly under warming.

Long-term warming increased richness in the asymmetric mutualism between bacteria and microalgae

Susana Agust´ı,^1*Peng Jin,²Clara S. Stanschewski,^1,3 and Rub´en D´ıaz-R´ua¹

1King Abdullah University of Science and Technology (KAUST), Red Sea Research Center (RSRC), Thuwal, 23955-6900, Saudi Arabia

2School of Environmental Science and Engineering, Guangzhou University, Guangzhou, 510006, China

3 King Abdullah University of Science and Technology (KAUST), Center for Desert Agriculture (CDA), Thuwal, 23955-6900, Saudi Arabia

* Corresponding author: [email protected] , King Abdullah University of Science and Technology (KAUST), Red Sea Research Center (RSRC), Thuwal, 23955-6900, Saudi Arabia, phone: +966 0128082848 Peng Jin: [email protected]

Clara S. Stanschewski: [email protected] Rub´en D´ıaz-R´ua: [email protected]

Author contributions: S.A. conceived and designed the study; S.A. isolated the diatom; S.A. and C.S.S.

provided the bacterial counts; R.D.-R. analyzed the bacterial community; P.J. analyzed the phytoplankton counts; S.A., R.D.-R., and P.J. interpreted the data; S.A. and P.J. drafted the initial manuscript; all authors contributed to the writing and approved the final manuscript.

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Data statement:Data and code related to this publication can be found in the ENA database under the project accession number PRJEB56151.

The article is prepared as aLetter Number of words in the abstract: 146 Number of words: 4458

Number of Figures and Tables: 4 (three figures and one Table) Count of references: 52

Keywords: Warming, mutualism, coevolution, long-term adaptation, richness, species interaction, microalgae, bacteria, microbiome

Running title: Long-term warming increased richness in asymmetric mutualism ABSTRACT

Microalgae, the ocean’s primary producers, have proven a large capacity for adaptation, but the implications for species interactions are rarely examined. In a 2-year experiment, we exposed the marine dia- tomChaetoceros tenuissmus to warming and examined the responses of its mutualistic bacterial community.

The diatom adapted to warming by increasing its temperature optimum and maximum growth rate, whereas the microbiota increased its maximum growth rate without changing its temperature optimum.Rhodobacteria dominated the diatom-associated communities at ambient temperatures and this dominance did not diminish under warming conditions. Extinctions occurred in low-abundance genera, but under warming conditions, new partners appeared. The warming consortium was stable when transplanted to ambient temperatures, indicating a strong association. Duration of exposure to temperature appeared relevant, highlighting the consequences of short warming events. Our results agree with predictions that long-term evolution of asymmetric mutualistic associations increases strength and diversity, particularly under warming.

INTRODUCTION

There is growing evidence that microalgae, the major marine primary producers, are able to adapt to global change drivers, such as elevated CO2(Jinet al.2013; Lohbecket al.2012), pollutants (Stachowski-Haberkorn et al.2013), and increased temperature (Jin & Agust´ı 2018; Jinet al.2022; Padfieldet al.2016; Schaumet al.

2017). The short generation times and high population densities of microalgae confer a substantial capacity for rapid evolutionary responses to environmental changes (Collins 2011). In a recent study, we demonstrated that tropical diatoms from a warming sea could adapt quickly to ongoing warming, even while experiencing thermal extremes, by employing differing optimality models (Jin & Agust´ı 2018). However, the adaptation of planktonic microalgae to warming may alter species interactions, which are rarely considered. Microalgae and bacteria have cooccurred in oceans for millions of years, resulting in several types of interactions, ranging from mutualism to parasitism (Amin et al. 2009; Seyedsayamdostet al. 2011). The survival of these microalga–

bacteria mutualisms in a changing ocean will depend on coevolutionary responses (i.e., reciprocal changes between interacting species) (Thompson 2009), as the mutualistic pair have a close ecological relationship and can act as agents of natural selection for each other.

It has been reported that antagonistic coevolution acted as a driver for diversification and divergence (For- de et al. 2008; Patersonet al. 2010). Asymmetric mutualism may, however, enhance long-term coexistence and facilitate biodiversity maintenance (Bascompteet al. 2006). In a mutualistic interaction, for instance, cocultured sulfate-reducing bacteria and methanogenic archaea cooperated to perform an energy-yielding reaction; their communities stabilized and showed higher growth rates and yields after hundreds of generations (Hillesland et al.2010). However, ocean warming and other stressors related to global change could result in mutualism breakdowns (Kiers et al. 2010). For insects, warming has reportedly disrupted interactions between hosts and symbionts as well as those between hosts and parasitoids (Higashi et al. 2020). High temperatures can also eliminate certain essential microorganisms’ partners, as has been described for both

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corals (Hugheset al.2018) and ants (Wernegreen 2012). Moreover, at low latitudes, global change is driving the development of unique environmental conditions as temperatures surpass those seen over the course of most organisms’ recent evolution. These changes pose crucial questions regarding an organism’s range of evolutionary responses that need to be tackled (Collins et al.2011; Kiers et al.2010). It is also critical to understand the persistence of coevolution in such unique conditions.

In this study, we applied a long-term experimental coevolutionary approach (Brockhurst & Koskella 2013) to one microalga (Chaetoceros tenuissimus ) isolated from the subtropical Red Sea and its associated bacterial community to investigate how adaptation to ocean warming may affect their mutualistic association.

Microalgal strains isolated from the sea develop a community of associated bacteria despite axenic mani- pulation. In such a microcosm, an asymmetric nutritional mutualism exists where culture conditions are optimized for microalgal growth, and heterotrophic bacteria benefit from the organic products released by algae (Cole 1982). Although the algae have a weak dependence on bacteria, the bacteria develop a diverse community strongly dependent on the algae. Over ˜2 years, we exposed the diatom and its heterotrophic bacterial partners to two temperatures: (1) ambient 26 °C, the mean surface temperature of the Red Sea from 1982–2015 (Chaidez et al. 2017), and (2) 30 °C, the experimental warming temperature. The latter condition (mean SST + 4°C) represents the temperature projected for the turn of the next century (2100) under the high-warming emission scenario RCP SSP5-8.5 developed by IPCC (2021). We previously found that the diatomC. tenuissimusadapted to long-term warming by increasing their temperature optimum and maximum growth rate, applying a “hotter is better” strategy (Jin & Agusti 2018). In this study, we found its microbiome was dominated (42–49% of reads) by one Rhodobacteria (Roseovarius -asp.), even in the case of warming temperature adaptation, indicating a strong association between this bacterium and the alga. The bacterial community that coevolved with the diatom under warming showed no changes in their temperature optimum but did exhibit a substantially increased maximum growth rate after long-term warming. In addition, long-term warming brought significant adjustments in the bacterial community that were consistent between replicates, resulting in the appearance of new, secondary genus with higher richness and improved thermal performance. This warming consortium was stable when transferred to ambient temperature conditions during a transplant experiment, indicating a strong association after long-term coevolution. However, these new genera experienced no gain in richness after short-term warming exposure. Duration of exposure to temperature appeared to be relevant in determining the mutualistic partners’ responses, highlighting the consequences of short warming events in mutualistic associations. Our results agree with the prediction that long-term evolution of asymmetric mutualistic associations increases mutualism strength and community richness, which were maximized under warming.

METHODS

Species isolation and culture conditions

Approximately 10 clones of the diatom speciesChaetoceros tenuissimus were isolated from coastal Red Sea waters near the Al Fahal Reef (22.2528°N, 38.9612°E) (details of isolation provided in Jin & Agust´ı 2018).

The cultures were maintained as batch cultures in filtered seawater that was taken from the same location and enriched with f/4 medium and silicate. The cultures grew with a light: dark cycle of 12: 12 h under 400 μmol photons m^-2s^-1. Four independent replicated cultures (n = 4) were run semi-continuously for ˜2 years under ambient (26 °C, the mean sea surface temperature of the Red Sea during 1982–2015) and warming conditions (30°C, high-warming emission scenario RCP SSP5-8.5 projected for the turn of the next century) by renewing the medium every 3 days. This long-term selection experiment allowed the cultures to evolve in the experimental temperature environment. The initial cell concentration was set at 1,000 cells mL^-1, and the medium was partially renewed every 3 days to restore cell density to initial levels (i.e., growth batch cycle).

The cell densities were maintained within a range of about 2.0×10⁵ to 7.0×10⁵ cells mL^-1 at the time of dilution. After 2 years, the diatomC. tenuissimushad undergone approximately 2,224 and 2,280 generations under long-term ambient (LA) and long term-warming (LW) temperature selection, respectively, based on the estimations in our previous study (Jinet al. 2020). Nutrients were not limiting, as the cell abundances achieved at the end of the batch cycles were far from those expected at the stationary phase.

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The thermal responses ofC. tenuissimus and associated bacteria growing semi-continuously in cultures that evolved under LA and LW conditions for ˜2 years were determined at nine assay temperatures. At the end of the long-term selection period, cultures under LA and LW conditions were inoculated into 200 mL flasks at an initial phytoplankton cell density of 3×10⁵cells mL^-1, and then incubated at 16, 18, 22, 24, 26, 30, 34, 36 and 38°C. The cultures grew with a light: dark cycle of 12: 12 h under 400μmol photons m^-2 s^-1for 8–12 days until the stationary phase was reached. After a 1-day acclimation period in the new culture medium and temperature, cell counts were performed every 24 h with 200–400μL samples using a BD FACSCanto II flow cytometer (BD Bioscience, Oxford, UK) calibrated with CS&T beads. To each sample, 4μm of SYBR Green I solution was added, after which samples were left in the dark for ˜10 min to stain bacterial DNA, allowing for the examination of bacteria through flow cytometry. A low speed of around 10 μL min^-1 was used to run the samples through the cytometer and 200 bead events were recorded. The bacterial groups were distinguished from noise and other components of the sample based on their fluorescent signals (red and green fluorescence) as well as the 90° side light scatter (SSC) signals. Bacterial growth rates at each assay temperature were calculated as the slope of the linear regression of ln(cell concentration) as a function of experimental day in the exponential growth phase. Cell abundance of the diatom C. tenuissimus was quantified in parallel with bacterial analyses by examining the samples under an optical microscope (LEICA DMI 3000B, Germany) using a hemocytometer.

The thermal reaction norms ofC. tenuissimusand the bacteria that evolved under the LA and LW temperatures were assessed by applying the equation described by Thomaset al.(2012) and Boydet al.(2013):

f(T) =ae^bT

1−

T−z

w 2

2 , (1)

where the specific growth rate, f , depends on the temperature,T , and is defined as a function of the parameters z ,w , a, and b . w is the temperature niche width (the range of temperatures over which the growth rate is positive), whilez ,a , andb possess no explicit biological meaning but interact to influence the rate of increase in growth with temperature, the maximum growth rate, and the optimum temperature for growth. Specifically,z determines the location of the maximum of the quadratic portion of this function.

a andb are the Epply curve coefficient and Epply curve exponent, respectively. We estimated the critical thermal minimum (CTmin, the lowest temperature at which the growth of bacteria is zero), critical thermal maximum (CTmax , the highest temperature at which the growth of bacteria is zero), maximum growth rate (μmax), optimal temperature for growth (T opt, the temperature at which the growth rate is maximal), and thermal breadth (expressed asB80, 80% performance of the maximum growth rate breadth; Angilletta 2009) by numerically maximizing the equation after estimating the parameter values for each replicate. A Student’s t -test was used to test the differences inCT_min , CT_max ,T _opt, μmax, andB₈₀ between the two temperature-adapted populations.

Transplant experiment

The cultures that had undergone ˜2 years of selection under ambient (LA) and warming conditions (LW) were exposed to the reciprocal ambient (26 °C) or warming (30 °C) temperature (i.e., reciprocally transplanted) and allowed to grow for 8 days in triplicate 200 mL flasks. Other culture conditions (e.g., medium, light intensity) remained identical to those used in the long-term selection experiment. Bacterial and C.

tenuissimus cell abundance was monitored daily using methods described above.

Bacterial DNA extraction and 16S rRNA gene library preparation

We analysed the bacterial community composition of samples from the LA and LW treatments as well as samples from the 8-day reciprocal transplant experiment by high-throughput sequencing of the 16S rRNA gene. For each replicate, 200 mL phytoplankton cultures with bacteria were sampled at the end of the algae’s exponential growth. We collected free bacteria samples by performing sequential size-fractionated filtration (3- and 0.2-μm polycarbonate filters) with syringes. DNA extraction was performed with 0.2-μm polycarbonate filters using a PowerWater DNA Kit (MoBio Laboratories, Inc., Carlsbad, CA, USA). The V3 and V4 regions of the 16S rRNA gene were amplified using the primers described by Klindworthet al. (2013). Specifically,

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we used 341F (5’-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG-CCTACGGGNGGCWGCAG-3’) and 785R (5’-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-GACTACHVGGGTATCTAATCC- 3’) primers, both of which contain Illumina overhang adapter sequences at the beginning of the primer sequence.

Two replicates were run in separate PCR reactions per sample (final volume 25 μl) using Qiagen multiplex PCR master mix (QIAGEN, Valencia, CA), 2.5μl of genomic DNA as a DNA template, and a final primer concentration of 0.3μM. We used the following PCR conditions: initial denaturation at 95 °C for 15 min, followed by 30 cycles consisting of denaturation (95°C for 40 s), annealing (55°C for 30 s), and extension (72

°C for 30 s), and a final extension step at 72°C for 5 min. Duplicate PCR products were pooled and analyzed by gel electrophoresis (1.5% agarose). The 16S rRNA gene library was prepared following the Illumina 16S metagenomic sequencing library preparation guide. Briefly, the amplicons were cleaned by AMPure XP magnetic bead–based purification (Beckman Coulter, Brea, CA, USA), and MiSeq indices were added via PCR. Indexed PCR amplicons were cleaned by AMPure XP magnetic bead–based purification, quantified using a Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA, USA), and pooled equimolarly. A KAPA SYBR FAST Universal qPCR kit with Illumina Primer Premix (Kapa Biosystems Ltd., London, UK) was used for pool quantification, after which pool size was evaluated using a Bioanalyzer (Agilent Technologies, Santa Clara, USA). Then, 6 pM of the pool was sequenced on the Illumina MiSeq platform with 25% PhiX control at KAUST Bioscience core lab facilities. The 16S libraries were sequenced using 2×300 bp paired-end reads and a MiSeq reagent kit v3 (Illumina, Inc.).

Sequence analysis

Primer sequences were removed using cutadapt (Martin 2011), after which the divisive amplicon denoi- sing algorithm 2 (DADA2) pipeline (dada2 package version 1.16 in RStudio) was used to analyze the sequencing data. The DADA2 microbiome pipeline (available at https://github.com/benjjneb/dada2) descri- bes microbial communities using unique sequence variants, known as amplicon sequence variants (ASVs) (Callahan et al. 2016). All functions were run using the recommended and default parameters (https://benjjneb.github.io/dada2/tutorial.html). Taxonomic classification of the selected ASVs was performed with the Silva reference database (Silva reference files, Release 138). Singletons and reads belonging to mit- ochondria, chloroplasts, and eukaryotes were excluded from further analyses. Species-level identifications were made in the case of a 100% BLAST match; otherwise, identifications were made at the genus level.

Unique strains within the same genus were differentiated by adding a unique lowercase letter. All sequenced reads were deposited in the ENA database under the project accession number PRJEB56151.

Statistical analyses

The final ASV table was divided by treatment, after which we evaluated the distribution of bacterial ASV relative abundance in each treatment within different taxonomic levels (order, family, and genus). Downstream analyses were performed in RStudio (version 1.4.1717) using the phyloseq package and results were visualized using the ggplot2 package in the statistical software R (R Core Team, version 3.6.1). We estimated alpha diversity using the Shannon diversity index, species evenness, and richness. Canonical analysis of principal coordinates (CAP) was used to test whether bacterial community changes were associated with strain selection or assay temperature using the Bray–Curtis distance between samples. We tested the significance of the resulting ordination with a permutational MANOVA (PerMANOVA) test (adonis function from the vegan package in R) with 999 permutations. A mixed-effects model was used to examine the interactions between strain long-term evolution and assay temperature conditions. For this analysis, the response (e.g., growth rate) was considered the dependent variable, while evolved conditions and assay conditions acted as the fixed effects, and the replicate was treated as a random effect nested within treatment. Differences among sample types were tested using at -test and comparison of means by a Student’s t -test, and interactions between strain association and temperature were tested by a two-way ANOVA, using JMP 13.0 (SAS) software.

RESULTS

Growth responses

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C. tenuissimus adapted to warming by increasing its optimal growth temperature from 29 °C to 31 °C and slightly but significantly increasing its maximum growth rate by ˜5% (Fig. 1a and c). For the bacteria associated with C. tenuissimus , there were no significant changes in T _opt, corresponding to 28.7 ± 1.2

°C and 29.0 ±0.8°C for LA and LW, respectively (t -test,t = 0.307, p = 0.774) (Fig. 1b and d). Similar to the microalga (Fig. 1e), the LW bacterial populations showed significantly higher μmax than LA groups (LW: 2.41 ±0.11, LA: 2.09 ±0.15; Fig. 1f) (t -test, t = 2.961,p = 0.042). The critical thermal minimum (CT_min ) and critical thermal maximum (CT_max ) did not shift in LW bacterial populations (Table S1). No significant differences in thermal breadth (expressed asB80, in°C) between LA and LW bacteria populations were detected (Table S1).

We found a close relationship between bacterial and phytoplankton growth (Fig. S1). Bacterial growth followed that of phytoplankton after a lag phase of 3 days when evolving under LA (Fig. S1a and b) and 4 days under LW (Fig. S1c and d)—a pattern consistently observed among the replicates. When LW and LA communities were exposed to reciprocal temperatures, bacterial growth in the LW community continued to be significantly higher than that of LA populations, regardless of the change in assay temperatures (strain selection×assay temperature interaction, two-way ANOVA,F 1,8 = 7.207,p = 0.028) (Fig. S1). However, the maximum number of bacterial cells in LW communities was significantly lower than that of LA groups (Fig. S1), and this difference remained when communities were exposed to reciprocal temperatures (strain selection×assay temperature interaction, two-way ANOVA,F _1,8 = 343.174,p<0.001).

Community changes

The bacterial communities associated with the LA and LW C.tenuissimus strains were dominated by the phylum Proteobacteria, which represented>95% of the total reads (Fig. S2). At the order level, the Rho- dobacterales (consisting of the family Rhodobacteraceae) dominated the communities regardless of selection temperature (Fig. S2) but showed significantly higher abundance in the LW community (87.6±0.8%; mean

± SE) than in the LA group (60.2 ±2.2%) (t -test, t = -11.3, df = 12, p < 0.0001). The abundance of Caulobacterales, however, significantly increased in the bacterial communities associated with LA strains (23.3±0.7%) relative to that of LW strains (2.5±1.4%) (t -test,t = 12.75, df = 12,p <0.0001) (Fig. S2).

While a small proportion of Phycisphaeraceae (3.76±0.29%) was detected in the LA populations, this family was greatly reduced in the LW populations (0.01±0.009%) (Fig. S2). One member of the genusRoseovarius (identified asRoseovariussp.) dominated the community of bacteria associated with both LA (49.6±2.9%) and LW (42.8± 2.4%) strains (Fig. 2). There were several genera common to the two long-term selection strains (Fig. 2), although some genera strains (Thalassococcus, Cognatishimia-a, Maricaulis-a, Lewinella, andMarinobacter-b ; Fig. 2) appeared much more abundant in communities associated with the LW strain;

specifically, a Thalassococcus sp. and aCognatishimia-a sp. made up a considerable proportion of the LW community, with 25.7 ± 2.1% and 11.5 ± 1.45% of reads, respectively (Fig. 2c). These genera were also present after brief exposure to ambient temperatures in the transplant experiment (Fig. 2d). Other bacterial genera associated with the LA diatom strain includedHyphomonadacea (Fig. 2a and b), which represented

>20% of reads, but had low abundance in the LW community (0.8% of reads;t -test, t = 24.6, df = 6,p

<0.0001). The short-term exposure of the LA community to warming shaped bacterial community structure by reducing the number of taxa (Fig. 2a and b; Table 1) but significantly increasing the abundance ofMarinobacter-a (from ˜2% to ˜17%) (t -test, t = 9.23, df = 6,p<0.0001).

The total number of ASVs was highest in the LW group, which had a maximum of 22 and a mean of 17 ASVs, while the LA community had a mean of just 13.3 ASVs (Table 1); the two groups shared a mean of 11 ASVs. When the LA populations were exposed to warming temperatures, they experienced no change in richness, but when LW populations were exposed to ambient temperatures, there was a small reduction in richness to 15.7 ASVs. Diversity (Shannon index) and evenness were overall low in the bacterial communities, and although we observed some increase in diversity in the LW group, the differences between LW and LA community diversity were not significant. Similarly, though evenness was slightly higher in the LA community, differences in mean evenness were not significant (Table 1). Extinctions, calculated as the disappearance below the detection of ASV reads relative to the LA community, were greater in the LW

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group, which was compensated with the appearance of new partners (Table 1).

In addition, we found that theC. tenuissimus –associated bacterial communities differed significantly after long-term temperature treatments (PerMANOVA, p = 0.002). Canonical analysis of principal coordinates (CAP) indicated that there were substantial changes to community structure associated with the unique, evolved strains that explained the most variance in the model (54.3%) (Fig. 3). After the reciprocal transplant experiment, the bacterial community generally remained similar to that associated with the selected strains, and exposure to reciprocal temperatures explained a smaller variance of 14.4% (Fig. 3).

DISCUSSION

Our experiment evaluated the coevolution of a diatom (C. tenuissimus) and its associated bacterial community after long-term selection pressure imposed by warming. The adaptation ofC. tenuissimus to warming involved significant changes in its thermal responses, including an increased Topt andμmax. However, theC.

tenuissimus –associated microbiota significantly increasedμmaxwithout varying Topt. In this nutritional association, one genus dominated the bacterial community independently of the temperature and time, showing no decrease in abundance when coevolving in the face of warming temperatures. However, long-term warming resulted in the incorporation of new bacterial partners, increasing species richness and bacterial growth rate. This new mutualism was stable when briefly transplanted to an ambient-temperature environment.

C. tenuissimus adapted quickly to warming, employing a “hotter is better” strategy by shifting itsT _opt and increasing itsμmaxunder warming conditions (Jin & Agust´ı 2018). In addition, over ˜2 years of warming selection,C. tenuissimus –associated bacteria increased their maximum growth rate (μ_max) without shifting their optimal growth temperature (T _opt) or critical thermal limits (CT_minandCT_max). The result that the growth rates of both phytoplankton and bacteria significantly increased in parallel after long-term warming selection is consistent with previous studies, which reported that mutualisms often favor the evolution of trait complementarity, in which there is a high degree of trait matching between interacting partners (Nuismer et al. 1999). However, the bacterial populations that evolved under warming conditions showed significantly lower maximum abundance and a 1-day lag phase in growth. This could be due to adjustments in the community, as our study showed that the bacteria adapted to warming showed a lower abundance of Caulobacterales together with the presence ofThalassococcus, Cognatissima, Lewinella , andMarinobacter , which were absent from the community that evolved under ambient temperatures.

Diatoms co-occur with specific bacterial taxa, and the phyla Proteobacteria and Bacteroidota are the main heterotrophic bacterial phyla associated with diatoms (Amin et al. 2012). This understanding was some- what reflected in our study, as Proteobacteria represented more than 95% of theC. tenuissimus –associated communities. Planctomycetes, however, were the second-most abundant phylum, though they were reduced and replaced by Bacteriodota when the communities were transplanted to the reciprocal temperature. The Rhodobacteriales were the dominant order and are one of the major groups of marine bacteria, compris- ing up to 20% of coastal and 15% of mixed-layer ocean bacterioplankton communities (Buchan et al.2005 and references therein). This order is typically abundant in bacterial communities that are associated with algae (Eilers et al. 2000; Zubkov et al. 2001) and diatoms (Amin et al.2012; Pinhassi et al. 2004). The Rhodobacteriaceae family dominated, and this taxon is considered to have many “ecological generalists”

that sustain basic bacterial production under various environmental conditions because of their nutritional versatility in the use of phytoplankton-derived organic matter as carbon and energy sources (Buchan et al.

2005; Moranet al. 2004; Mou et al.2007). Similarly, the abundance of the order Alteromonadales increased significantly when both the long-term warming and ambient communities were exposed to reciprocal temperatures. These bacteria are a widely distributed group of Gammaproteobacteria (Garcia-Martinezet al.2002) but are believed to be sparsely present in marine environments (Eilerset al. 2000). Transcriptomics analysis of these heterotrophic bacteria revealed that Alteromonadaceae includes many “ecological specialists” that grow rapidly, expressing metabolism genes related to nitrogen assimilation, fatty acid catabolism, and the tricarboxylic acid cycle (McCarren et al. 2010). We assume that short-term temperature stress led to a significant increase in their abundance, probably because of differences in the substrates produced by the alga (Teelinget al. 2012) that should be explored further.

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Planktonic microalgae and bacteria coexist on the ocean’s surface and have evolved to engage in complicated interactions that significantly modify each other’s behaviour and ultimately impact biogeochemical cycles (Aminet al. 2012). As is true of interactions involving other organisms, the interactions between microalgae and heterotrophic bacteria can generate four broad outcomes: mutualism, parasitism, predation, or competition. Parasitic bacteria are common (Paul & Pohnert 2011), and in nutrient-deficient systems, both bacteria and phytoplankton can compete for inorganic nutrients, as has been documented for phosphate (Thingstad et al. 1993). However, most interactions between phytoplankton and bacteria are beneficial.

Autotrophic phytoplankton provide dissolved organic carbon (DOC) to heterotrophic bacteria in the ocean, and the variety of DOC produced by phytoplankton likely plays a role in shaping the diversity of their associated bacterial communities (Hayneset al. 2007). Phytoplankton, in turn, may benefit from the vitamins (Haines & Guillard 1974) or siderophores (Amin et al. 2009) that bind the ferric iron produced by many marine bacteria. Mutualism, then, is the most common interaction between bacteria and microalgae, and is an important process influencing ocean biogeochemistry (Cole 1982; Cho & Azam 1990).

For phytoplankton epibiota in controlled laboratory culture conditions, it was described recently that bacteria–diatom and bacteria–bacteria interactions select for a simplified association, and that this community can adapt over long-term associations (Crenn et al 2018). The low bacterial diversity index found in our microcosms is in accordance with such a simplified system. In addition, we found that the bacterial community was significantly related to long-term adaptation rather to temperature in this study. We also observed a larger number of extinctions within the lower-abundance bacterial ASVs, suggesting that they were more affected by temperature selection than the dominant bacterial genera. In previous work, a small proportion of species, considered super-generalists, were found to play a crucial role in organizing evolution and coevolution in species-rich assemblages (Bascompteet al. 2006; Guimar˜aeset al.2007, 2011). The more generalized nature of some mutualisms may provide insurance against the detrimental impacts of a specific environmental disturbance by increasing the chances that the mutualist assemblage contains at least some species that show natural resistance (Bascompte & Stouffer 2009). However, one danger of relying on multiple species is that what may superficially appear to be a redundant mutualism may actually be structured by niche specialization, with partners providing complementary, though not necessarily equivalent, benefits (Stachowicz & Whitlatch 2005). Likewise, the quality of services offered by redundant partner species may differ. Over multiple generations, small differences in partner quality could also have strong evolutionary consequences. The emergence of the generalist is considered a fundamental component of the maintenance of convergence at the community level within highly diversified mutualistic assemblages (Bascompteet al.

2003).

Adaptation will be necessary to avoid the sharp decline in tropical phytoplankton diversity predicted in the absence of an evolutionary response to warming (Thomaset al.2012). The increased bacterial richness present after exposure to long-term warming in the asymmetric association studied here appears consistent with studies between plants and animals; these studies have shown that asymmetries inherent in coevolutionary networks may facilitate long-term coexistence, biodiversity maintenance, and the acquisition of novel partners, and also lead to evolutionary shifts such as increased generality of interactions (Bascompteet al.2006;

Sachs & Simms 2006). Therefore, our experimental results with marine microbial communities highlight the importance of long-term coevolution for the coexistence of and shifts in coevolutionary partners occurring in asymmetric interactions under warming.

Acknowledgements

This study was funded by King Abdullah University of Science and Technology (KAUST) through baseline funding provided to S.A. and National Natural Science Foundation (41806141) funding provided to P.J.

Author contributions: S.A. conceived and designed the study; S.A., and C.S.S. provided the bacterial data; R.D.-R. analyzed the bacterial community; P.J. analyzed the phytoplankton counts; S.A., R.D.-R., and P.J. interpreted the data; S.A. and P.J. drafted the initial manuscript; all authors contributed to the writing and approved the final manuscript.

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Competing interests: The authors declare that they have no competing interests.

Data statement:Data and code related to this publication can be found in the ENA database under the project accession number PRJEB56151.

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Table 1. Mean (+- SE) values of species richness, Shannon diversity index, and evenness (EVAR) of the bacterial communities associated with Chaetoceros tenuissimus strains after long-term adaptation to ambient (LA) and warming (LW) temperatures, and when exposed to the reciprocal temperature (in italics).

Significant differences were only observed for richness (p = 0.026). Values labeled with the same letter are not significantly different (p>0.05,t -test comparison of means). SAV’s extinctions are relative to the LA community.

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Ambient strain (LA)

Warming strain (LW)

Warming strain (LW) Long-term Long-term Short-term

(exposed to warming)

Long-term Short-term (exposed to ambient)

Short-term (exposed to ambient)

Short-term (exposed to ambient) Richness Richness 13.33^b 13.33 ^b 13.33^b 17.00^a 15.67^ab

±0.88 ± 0.88 ±0.88 ±1.15 ±0.88

Shannon Shannon 1.51 1.52 1.52 1.64 1.54

±0.04 ± 0.03 ±0.03 ±0.06 ±0.05

EVAR EVAR 0.11 0.11 0.11 0.09 0.09

±0.02 ±0.01 ±0.01 ±0.01 ±0.01

Extinctions Extinctions - 4 4 6 5

Figure legends Hosted file

image1.emf available at https://authorea.com/users/525761/articles/596024-long-term-warming- increased-richness-in-the-asymmetric-mutualism-between-bacteria-and-microalgae

Fig. 1Pattern of thermal adaptation ofChaetoceros tenuissimus (a, c, e) and its associated bacterial community (b, d, f) to long-term ambient (26°C; blue) and warming (30 °C; red) temperatures. (a) Thermal reaction norms for growth rates of phytoplankton and (b) their associated bacteria across a range of temperatures. Solid lines show the thermal reaction norms based on the thermal model. (c) Box and whisker plots of the optimal temperature for growth (T opt) of phytoplankton and (d) bacteria as well as (e) the growth rate (μmax) of phytoplankton and (f) bacteria at ambient and warming temperatures (n = 3). Asterisks indicate significant differences between ambient and warming treatments based on a Student’s t -test (p<

0.05).

Fig. 2 Changes in the Chaetoceros tenuissimus –associated bacterial community. Columns represent the mean percent relative abundance of ASVs identified to the genus level after (a) long-term ambient (LA; 26

°C; blue) and (b) long-term warming (LW; 30°C; red) selection. Changes in the bacterial community after the short-term reciprocal transplant experiment are provided where (c) LA was transplanted to 30°C (blue

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fill with red border) and (d) LW transplanted to 26°C (red fill with blue border). Bacteria highlighted in grey corresponded to genera common to all strains, while those highlighted in red were found only in association with the LW strain. Blue and red asterisks indicate significantly higher percent relative abundances (Student’s t -test, p<0.0001) in the ambient and warming strains, respectively.

Hosted file

image3.emf available at https://authorea.com/users/525761/articles/596024-long-term-warming- increased-richness-in-the-asymmetric-mutualism-between-bacteria-and-microalgae

Fig. 3 Canonical analysis of principal coordinates (CAP) based on Chaetoceros tenuissimus –associated bacterial ASVs present after long-term temperature selection. Blue symbols correspond to long-term ambient temperatures (LA; 26°C; circles) and red symbols denote long-term warming conditions (LW; 30°C; circles).

Triangles correspond to theC. tenuissimus –associated bacterial communities after transplant to reciprocal temperature conditions. Each point represents a replicated sample. CAP was calculated using Bray–Curtis distances with a multivariate t-distribution.