1 Title: Genomic adaptations in information processing underpin trophic strategy in a whole- 2 ecosystem nutrient enrichment experiment 3 4 Authors: Jordan G. Okie1,2, Amisha T. Poret-Peterson3, Zarraz M.P. Lee2, Alexander Richter4, Luis D. 5 Alcaraz5, Luis E. Eguiarte6, Janet L. Siefert7, Valeria Souza6, Chris L. Dupont4, and James J. Elser2,8 6 7 Affiliations: 8 1School of Earth and Space Exploration, Arizona State University, Arizona, USA. 9 2School of Life Sciences, Arizona State University, Arizona, USA. 10 3ARS Crops Pathology and Genetic Research Unit, USDA, California, USA. 11 4 J. Craig Venter Institute, California, USA. 12 5 Departamento de Biología Celular, Facultad de Ciencias, Universidad Nacional Autónoma de 13 México, Mexico. 14 6Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de 15 México, Mexico. 16 7Department of Statistics, Rice University, Texas, USA. 17 age, University of Montana, Montana, USA. 18 Email addresses (in authorship order): [email protected], [email protected], 19 [email protected], [email protected], [email protected], [email protected], 20 [email protected], [email protected], [email protected], [email protected] 21 Running head: Genomics of r/K selection strategy 22 23 Keywords: Metagenomics, microbial communities, phosphorus fertilization, genome size, codon usage 24 bias, GC content, rRNA operon copy number, ecological stoichiometry, growth rate hypothesis, r/K 25 selection theory. 26 27 28 Corresponding author: Jordan G. Okie, School of Earth and Space Exploration, Arizona State 29 University, PO Box 871404, Tempe, AZ 85287, Phone: 505-366-1218; Fax: 480-965-8960; Email: 30 [email protected] 31 Data accessibility statement: Data has been deposited in appropriate public repositories and the DOI 32 made available at the end of the paper.

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33 34 Abstract 35 Several universal genomic traits affect trade-offs in the capacity, cost, and efficiency of biochemical 36 information processing underpinning metabolism and reproduction. We analyzed their role in 37 mediating planktonic microbial community responses to nutrient enrichment in an oligotrophic, 38 phosphorus-deficient pond in Cuatro Ciénegas, Mexico—one of the first whole-ecosystem 39 experiments involving replicated metagenomic assessment. Mean bacterial genome size, GC content, 40 total number of tRNA genes, total number of rRNA genes, and codon usage bias in ribosomal 41 protein sequences were higher in the fertilized treatment, as predicted assuming oligotrophy favors 42 lower information-processing costs while copiotrophy favors higher processing rates. Contrasting 43 changes in trait variances also suggested differences between traits in mediating assembly under 44 copiotrophic versus oligotrophic conditions. Trade-offs in information-processing traits are 45 apparently sufficiently pronounced to play a role in community assembly as the major components 46 of metabolism—information, energy, and nutrient requirements—are fine-tuned to an organism’s 47 growth and trophic strategy. 48

49

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50 Introduction

51 Traits influencing the informational underpinnings of metabolism may be crucial to performance

52 and community assembly but ecologists have largely focused on the proximal energetic and

53 stoichiometric features of metabolism (Leal et al., 2017; Sibly et al., 2012; Sterner and Elser, 2002).

54 Organisms must be able to store, copy, and translate the information contained in genetic material.

55 And, they must be able to adaptively update their transcriptome and proteome in response to altered

56 environmental conditions. To grow and reproduce rapidly, rates at every step of the metabolic

57 network must be sufficiently high such that no single step is unduly rate-limiting, including the

58 information processes that underpin biosynthesis and regulate metabolic networks. This necessary

59 integration of functions is a hallmark of all organisms.

60 The structure and size of the genome affect the rate, efficiency, and robustness of the

61 information processes supporting metabolism, growth, and reproduction (Appendix 1). There are

62 necessary tradeoffs in the costs and benefits of these features (Appendix 1; see also Smith, 1976),

63 which should consequently make individual organisms most competitive and better-suited for only

64 particular ranges of growth and trophic conditions (Roller and Schmidt, 2015). Organisms best

65 suited to compete in environments with abundant resources (copiotrophs) must have the capacity

66 for sufficiently high intracellular rates of information processing to support high rates of metabolism

67 and reproduction. However, maintaining the genomic and structural capacity for rapid growth is

68 costly, potentially placing such taxa at a disadvantage in stable, nutrient-poor environments where

69 growth rates are chronically slow (Giovannoni et al., 2014). Oligotrophic environments may thus

70 instead favor organisms (oligotrophs) that have information processing machinery that is less costly

71 to build, maintain, and operate, thereby increasing resource use efficiency and growth efficiency

72 (Koch, 2001; Roller and Schmidt, 2015).

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73 Genomic traits affecting the rates and costs of biochemical information processing within

74 cells can thus influence the degree to which an organism is optimized for oligotrophy versus

75 copiotrophy. This oligotrophic-copiotrophic strategy continuum is reminiscent of the classic slow-

76 fast life history continuum (Stearns, 1992), classical r/K selection theory (MacArthur and Wilson,

77 2001; Pianka, 1972, 1970), and their subsequent developments dealing with the evolution of

78 interspecific variation in rates of resource use, mortality, growth, and reproduction (e.g., Dobson,

79 2012; Grime and Pierce, 2012; Krause et al., 2014; Sibly and Brown, 2007). In conjunction with

80 research on the role of functional traits and niches in shaping the assembly of communities (e.g.,

81 Fukami et al., 2005; Litchman and Klausmeier, 2008; McGill et al., 2006; Okie and Brown, 2009; Roller

82 and Schmidt, 2015), this work suggests traits associated with biological rates and efficiencies of

83 resource use, growth, and reproduction play an important role in community assembly. It is unclear,

84 however, whether the traits specifically related to rates and costs of biochemical information

85 processing have sufficiently pronounced tradeoffs or physiological effects to play an important role

86 in the evolutionary ecology of organisms and assembly of communities, although there are some

87 promising indications (Dethlefsen, 2004; Roller et al., 2016).

88 Here, coupling metagenomic analysis with a trait-based framework that synthesizes theory

89 and hypotheses from genomics, ecology, and evolutionary cell biology, we investigate the role that

90 several universal genomic traits play in determining the response of a planktonic microbial

91 community to nutrient enrichment in a whole-ecosystem experiment. To date relatively few studies

92 have coupled metagenomics with a trait-based framework to clarify the drivers of community

93 assembly (Burke et al., 2011; Chen et al., 2008; Mackelprang et al., 2011; Raes et al., 2011). Even

94 fewer (indeed, none that we are aware of) have deployed such approaches in the context of whole-

95 ecosystem experimentation to test ecologically relevant hypotheses under field conditions. We focus

96 on a set of four information processing traits hypothesized to affect the ability of organisms to

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97 obtain high maximum growth rates necessary for thriving in copiotrophic environments or reduce

98 the energetic and resource requirements necessary to persist under nutrient-poor conditions:

99 1. Multiplicity of genes essential to protein biosynthesis. Copiotrophs are predicted to have higher copy

100 numbers of rRNA operons and tRNA genes, since higher numbers of these genes increase

101 their maximum overall transcription rates, helping maintain higher abundances of ribosomes

102 (which are constructed from rRNA and proteins) and tRNAs. In turn, the larger pools of

103 ribosomes and tRNAs facilitate increased translation rates of protein synthesis necessary for

104 achieving high growth rates (Condon et al., 1995; Higgs and Ran, 2008; Rocha, 2004; Roller et

105 al., 2016). However, higher numbers of these genes incur costs, potentially putting organisms

106 with more copies of rRNA and tRNA genes at a disadvantage under low resource

107 conditions. In particular, more DNA has to be synthesized, maintained, and regulated, and

108 there may be an increased risk of transcribing overly large phosphorus-rich pools of rRNA

109 and tRNA, increasing phosphorus requirements (Elser et al., 2003, 1996; Godwin et al.,

110 2017; Makino et al., 2003) and reducing growth efficiency (Roller et al., 2016).

111 2. Genome size. Organisms with smaller genomes are predicted to do better in stable and

112 oligotrophic environments, as they require fewer resources (such as phosphorus, P) to

113 maintain and replicate their genomes, have higher carbon use efficiency (Saifuddin et al.,

114 2019), and have smaller cells with increased surface-area-to-volume ratios facilitating

115 resource uptake (Giovannini 2015). In contrast, organisms with larger genomes should do

116 better in complex or copiotrophic environments, where they can take advantage of their

117 typically higher intrinsic growth rates (DeLong et al., 2006) and more diverse and flexible

118 gene and metabolic networks (e.g., Konstantinidis and Tiedje, 2004; Maslov et al., 2009;

119 Saifuddin et al., 2019) to facilitate substrate catabolism and respond more rapidly to feasts

120 following famines.

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121 3. GC content. GC content (the percentage of DNA composed of the nucleotide bases guanine

122 (G) and cytosine (C)) varies greatly among taxa but the reasons for this variation are

123 controversial, as multiple different selective and neutral forces may be operating (Bentley

124 and Parkhill, 2004; Hildebrand et al., 2010). However, researchers have proposed that G and

125 C have higher energy costs of production and more limited intracellular availability

126 compared to A and T/U (Rocha and Danchin 2002). Additionally, higher GC content DNA

127 has more nitrogen (Bragg and Hyder, 2004). Thus, lower GC content may be favored in

128 oligotrophic environments, whereas the metabolic and resource-sparing benefits of low GC

129 content should be less consequential in resource-rich environments, leading to a relaxed role

130 for GC content in the ecology and evolution of copiotrophs.

131 4. Codon usage bias. Eighteen of life’s twenty proteinogenic amino acids can be encoded in the

132 genome by more than one of life’s sixty-one different proteinogenic codons (nucleotide

133 triplets), leading to redundancy in the genetic code. However, these synonymous codons

134 have different kinetic properties, including different translation rates and probabilities of

135 mistranslation (Higgs and Ran, 2008). In highly expressed genes essential to growth (such as

136 ribosomal protein genes), there should be increased selection for biasing the usage of certain

137 synonymous codons over others in order to increase the accuracy and speed of translation,

138 especially in organisms with fast growth rates (Hershberg and Petrov, 2008; Higgs and Ran,

139 2008; Plotkin and Kudla, 2011; Vieira-Silva and Rocha, 2010). We thus predict that

140 copiotrophic environments should favor organisms with higher codon usage bias in their

141 ribosomal protein genes, whereas codon usage bias should play a relaxed role in oligotrophic

142 environments.

143 Further details and background on these traits are provided in Appendix 1.

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144 Research on the above genomic traits has revealed correlations of these traits with growth

145 and trophic strategy in a variety of eukaryotic and prokaryotic species (Vieira-Silva and Rocha, 2010),

146 starting to unravel the mechanisms by which these traits influence fitness (Appendix 1), and

147 suggesting that they may play an important role in ecology (Freilich et al., 2009; Lauro et al., 2009;

148 Weider et al., 2005). However, existing work has tended to look at only one or two of these traits at

149 a time (Allen et al., 2012; Raes et al., 2007). More work is also required to help resolve incongruities

150 in the literature, such as different views on the evolutionary ecology of bacteria genome size (e.g.,

151 DeLong et al., 2010 versus Vieira-Silva et al., 2010) (Appendix 1). It is also necessary to develop a

152 less fragmented understanding of the evolutionary and physiological ecology of these genomic traits

153 (Bentley and Parkhill, 2004; Freilich et al., 2009; Vieira-Silva and Rocha, 2010) and their role in

154 community assembly across a wide range of organisms and environments.

155 Importantly, most ecological studies of these traits have been based on studies of microbial

156 isolates, comparative analyses, or sampling across environmental gradients (Allen et al., 2012;

157 Foerstner et al., 2005; Freilich et al., 2009; Raes et al., 2007; Roller et al., 2013; Vieira-Silva and

158 Rocha, 2010). Experimental work examining their role in mediating the structure of communities

159 under natural conditions is extremely limited. Given the complexity of biotic and abiotic interactions

160 shaping communities, that most microbial taxa are uncultivable in isolation (Ho et al., 2017), and

161 that community traits can have widely different responses to temporal/experimental versus

162 geographic variation in abiotic variables (e.g., Sandel et al., 2010), direct experimentation in the field

163 with complex communities is required to better establish the validity of inferences about these

164 purported molecular adaptations for ecological dynamics in nature.

165 Our study site is Lagunita, an oligotrophic, highly phosphorus-deficient pond in Cuatro

166 Ciénegas, a biological reserve in Mexico (Lee et al., 2015, 2017). Because of its strong nutrient

167 limitation, this ecosystem offers a useful setting for a fertilization experiment to evaluate the role of

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168 information-processing traits in community assembly and the trophic strategies of organisms. Our

169 study is noteworthy as one of the first whole-ecosystem experiments involving experiment-level

170 replicated metagenomic assessments of community response. If these individual genomic features

171 indeed affect the ability of organisms to survive and reproduce as a function of nutrient availability,

172 then collective measures of these features at the metagenomic level, which aggregates the genomes

173 of all of the individuals constituting a community, should likewise exhibit these characteristics and

174 be reflected in their responses to experimental fertilization (Krause et al., 2014; Wallenstein and Hall,

175 2012).

176

177 Results

178 Biomass and chlorophyll a concentrations increased substantially in response to nutrient

179 enrichment (biomass: 198% mean increase, P = 0.009; chlorophyll a: 831% mean increase, P =

180 0.001, Appendix 1-figure 1), as did the ratio of phosphorus to carbon (P:C) in seston biomass

181 (19.5% mean increase, P = 0.014, Fig 1A). We observed changes in several components of the

182 predicted genomic signatures of growth and trophic strategy (Fig. 1). As the percentages of Bacteria,

183 Archaea, Eukarya, and viruses making up the community were not discernibly different between

184 unfertilized and fertilized treatments (P = 0.46, 0.37, 0.39, 0.21, respectively), these genomic changes

185 reflected changes in the abundances of taxa at finer phylogenetic scales, in particular of bacterial

186 taxa, which made up 94% of the metagenomes (see Appendix 1 and Appendix 1-figures 1-4 for

187 details). Furthermore, the genomic changes reflected widespread changes within the community—

188 they were not driven by just a few specific populations, since 188 genera showed changes in

189 abundance (with P < 0.05) and no single genus dominated the community (the highest relative

190 abundance of a taxon at the genus level was 4%). Consistent with predictions, mean estimated

191 genome size of bacteria was 25% higher in the fertilized treatment (P = 0.011), with nutrient

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192 enrichment explaining 75% of the variation between samples in mean genome size (Fig. 1). GC

193 content of open reading frames was 9.9% higher in the fertilized treatment: 54% compared to 49%

194 in the unfertilized treatment (P = 0.007, R2 = 86%).

195 Genomic features indicative of adaptations for maintaining high rates of transcription and

196 translation were also positively associated with fertilization. The per-sequence occurrence rate of

197 tRNA genes and the total number of tRNA genes per community were 93% and 64% higher,

198 respectively, in the fertilized treatment (P < 0.001 and P =0.065 with R2 = 53%, respectively). The

199 residuals after regressing log number of tRNA genes and log total number of reads per sample to

200 control for differences in sequencing depth were also higher in the fertilized treatment (P = 0.087,

201 R2 = 52%). Likewise, in the fertilized treatment, the per sequence occurrence rate of 16S rRNA

202 genes and total number of rRNA operons per community were 119% and 86% higher, respectively

203 (per sequence Poisson rate test: P < 0.001; number of rRNA operons: P = 0.096 with R2 = 50%).

204 The residuals after regressing log number of rRNA operons versus log total number of reads per

205 sample were also higher (P = 0.038, R2 = 52%). Fertilization explained 65% of the co-variation in

206 these two traits (number of rRNA operons and tRNA genes) along a single dimension, which was

207 quantified by principal component analysis and provides a measure of protein synthesis capacity (P

208 = 0.031).

209 Consistent with predictions, nutrient enrichment also increased codon usage bias in

210 ribosomal protein genes according to two measures of codon usage bias—the effective number of

211 codons (ENC) and ENC’ (Fig. 1E and Fig. 2). Values of ENC and ENC’ for genes vary inversely

212 with the level of codon usage bias: from 20, which signifies extreme codon usage bias in which one

213 codon is used exclusively for each of the amino acids encoded by a gene, to 61, which represents the

214 case in which the use of alternative synonymous codons is equally likely (no codon usage bias). The

215 mean ENC and ENC’ of ribosomal protein sequences detected in the metagenomes decreased with

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216 fertilization by 6.7 and 4.8%, respectively, indicating increased codon usage bias (ENC: P = 0.018,

217 R2 = 66%; ENC’: P = 0.031, R2 = 55%). Median ENC and ENC’ were also lower in the fertilized

218 treatment (P = 0.006, R2 = 75% and P = 0.010; R2 = 75%) and Kolmogorov-Smirnoff tests

219 indicated that the distributions in ENC and ENC’ of ribosomal protein sequences significantly

220 differed between treatments (all P < 0.01). In particular, as shown in Fig. 2A and B, a greater

221 frequency of sequences exhibited little-to-no codon usage bias (ENC and ENC’ values around 60-

222 62) in the unfertilized communities (Fig. 2), whereas in the fertilized treatment there were notably

223 higher numbers of ribosomal protein sequences with extremely high codon usage bias (ENC and

224 ENC’ < ~37).

225 As suggested by Figs. 1 and 2, the variance in the P:C ratio and in mean and median codon

226 usage bias of communities (quantified by ENC’, the more powerful indicator of codon usage bias)

227 substantially decreased with fertilization by around an order of magnitude or more—by factors of 8,

228 31, and 27, respectively (P:C ratio: P = 0.058; mean ENC’: P = 0.056; median ENC’: P = 0.069;

229 Appendix 1-table 1). Variance in genome size and mean and median ENC also decreased

230 substantially by factors of 10, 10, and 6, respectively, but these responses are less certain (genome

231 size: P = 0.174; mean ENC: P = 0.169; median ENC: P = 0.275; Appendix 1-table 1). In contrast,

232 variance in the log-transformed number of tRNA genes and rRNA operons substantially increased

233 with fertilization, by 846% and 655%, respectively (rRNA: P < 0.001; tRNA: P = 0.013), while GC

234 content variance did not appear to exhibit any change (P = 0.56; Appendix 1-table 1).

235 Finally, we examined how well nutrient enrichment predicted the covariation of these

236 genomic traits along a single principle component analysis (PCA) axis, which, according to our trait

237 predictions, should quantify where a community’s information-processing traits fall along an

238 oligotrophy-copiotrophy strategy continuum. The single PCA axis captured 78% of the variance in

239 the genomic traits for the communities, and nutrient treatment explained 86% of the variation in

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240 community genomic trait composition along this single axis of trophic strategy (GLM: P = 0.004;

241 Fig. 3).

242

243 Discussion

244 Our fertilization study demonstrated strong nutrient limitation in the Lagunita ecosystem, as

245 manifested by increased biomass, chlorophyll content, and P:C ratios of plankton in the fertilized

246 pond versus the unenriched mesocosms. Using metagenomics, we found strong differences in

247 information-processing traits between the fertilized pond and unfertilized internal mesocosms,

248 which agree with all five of our directional predictions . We are not aware of any obvious reasons or

249 existing theoretical work to indicate that a difference in habitat size between treatments should lead

250 to the observed trait differences. We thus interpret the observed genomic trait differences as instead

251 resulting primarily from differences in the growth and nutrient conditions of the two treatments.

252 However, a valuable future experiment could be to repeat the experiment using internal mesocosms

253 for both the fertilized and unfertilized treatments in order to help rule out potential mesocosm

254 effects. Conservationists and ecologists should also have an involved discourse on whether it is

255 worth conducting future research to verify our interpretation by performing much larger

256 experiments across multiple ponds (with wider environmental impacts).

257 We interpret these differences primarily as reflecting trait-mediated ecological dynamics —

258 the differential success of lineages present within the pond and colonization of other lineages—

259 rather than microevolutionary changes within populations. Our reasoning is that the relative short

260 time period of our experiment (32 days) encompasses a relatively low maximum number of

261 generations of replication (Appendix 1) and so limited opportunity for measurable genomic

262 evolution (e.g., Lenski and Travisano, 1994). Overall, our study suggests that ecologically-significant

263 phenotypic information about communities can be uncovered using a read-based approach

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264 (metagenomics) that leverages the relevant abundance and DNA characteristics of conserved genetic

265 elements. Importantly, this approach avoids the biases associated with the massive gaps present in

266 microbial taxonomic databases and cross-taxonomy assembly efficiencies (Kunin et al., 2010; Sogin

267 et al., 2006; Temperton and Giovannoni, 2012; Yooseph et al., 2010).

268

269 An oligotrophy-copiotrophy gradient in information-processing traits. Remarkably, nutrient enrichment

270 explained 50% or more of the variation in each trait and 88% of the co-variation of these five traits

271 along a single statistical dimension quantifying the oligotrophy-copiotrophy strategy continuum (Fig.

272 3). The congruence of all trait responses with our predictions suggests that the effects of these

273 genomics traits on rates and costs of biochemical information-processing are sufficiently

274 pronounced to play a role in community assembly.

275 We found that the fertilized community had genomic traits expected to augment

276 translational capacity whereas the unfertilized communities had genomic traits lowering the costs of

277 biochemical information processing. For instance, the fertilized community had increased codon

278 usage bias in ribosomal protein genes, which can improve the speed and accuracy of translation of

279 ribosomal proteins by increasing the rate at which tRNAs bind to mRNA codons of ribosomal

280 protein genes, as well as possibly reducing the likelihood of mistranslation (Higgs and Ran,

281 2008)(Fig. 1E and 2). The resulting increased rate of production of ribosomes supports the larger

282 ribosomal pools undergirding higher overall rates of protein synthesis. The fertilized communities

283 also had higher numbers of rRNA and tRNA genes. In conjunction with the observed increase in

284 the biomass P:C ratio, this finding supports the “Growth Rate Hypothesis”—that fast-growing cells

285 should have higher P-contents that reflect higher concentrations of P-rich ribosomal RNA that are

286 maintained by increased copies of rRNA and tRNA genes (Elser et al., 2000).

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287 Mean genome size was also higher in the fertilized pond, reflecting the need to encode larger

288 array of genes needed to support production of the expanded translational and catabolic capacity of

289 faster growing cells and possibly the benefits of maintaining a streamlined genome requiring minimal

290 resources for maintenance and replication in nutrient-poor conditions (e.g., Giovannoni et al.,

291 2005)(Fig. 1B). Consistent with our prediction that low GC-content in oligotrophic taxa is a valuable

292 resource conservation strategy, GC content was lower in the unfertilized communities (Fig. 1F).

293 These results contrast with Vieira-Silva and Rocha (2010), who in comparative studies of bacteria

294 found no significant interspecific correlation of GC content and genome size with generation time

295 (Vieira-Silva et al., 2010, 2010). The difference in results may reflect multiple factors, including (1)

296 the presence of confounding variables and pronounced statistical error (measurement and biological)

297 inherent to interspecific microbial growth and trait databases that are collated from a variety of

298 sources (e.g., ecosystems, conditions such as temperature), as in their study; (2) an

299 underrepresentation in databases of the unculturable prokaryotic species that may comprise the

300 majority of species in microbiomes such as Lagunitas and typically have different evolutionary

301 ecologies than culturable taxa (Pande and Kost, 2017; Swan et al., 2013). Such differences in results

302 highlight the value of conducting in situ experiments.

303 Overall, however, our findings are largely consistent with observational studies of

304 metagenomes and genomes along productivity gradients and comparisons across species varying in

305 growth rate (Allen et al., 2012; DeLong et al., 2010; e.g., Foerstner et al., 2005; Lauro et al., 2009;

306 Raes et al., 2007; Roller et al., 2013; Swan et al., 2013). Our experiment on communities in situ

307 suggests that much of the variation in these genomic traits along productivity gradients or across

308 species may similarly be attributed to effects of information processing costs and rates on an

309 oligotrophy-copiotrophy strategy continuum and related adaptive strategies, such as r/K selection

310 and Grime’s “C-S-R Triangle” (e.g., Grime and Pierce, 2012).

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311

312 Variance changes in the genomic traits. We also observed changes in the variances in several community-

313 level traits (Table 1). As the direction of these changes differed among the traits in response to

314 enrichment, the variance responses do not simply reflect potential for increased dispersal limitation

315 between unfertilized mesocosms compared to enriched pond samples (see Methods). Instead, all-else-

316 being equal, they suggest that the strength of a trait’s role in filtering the community is different

317 between unenriched and enriched treatments, which could reflect differences in the degree to which

318 a trait affects costs versus rates of information processing. In general, traits having much more of an

319 impact on costs of information should play more of a role in oligotrophic environments, whereas

320 traits having more of an impact on rates (e.g., probably codon usage bias) should play more of a role

321 in copiotrophic environments.

322 The substantial reduction in variance levels of codon usage bias in response to fertilization

323 are in agreement with this viewpoint: the evolution of low codon usage bias is primarily thought to

324 reflect relaxed selection rather than positive selection for neutral (random) codon usage, although

325 many complex issues remain to be resolved (Hershberg and Petrov, 2008). In contrast, the highly

326 increased variance in rRNA operon and tRNA gene numbers in response to fertilization may

327 indicate that the augmented P and N requirements of taxa having high rRNA and tRNA gene copy

328 numbers, which tend to have larger pools of ribosomes and other translation machinery, is

329 particularly detrimental in oligotrophic conditions (Stevenson and Schmidt, 2004). In nutrient-rich

330 conditions, on the other hand, some organisms may still do well even with fairly low numbers of

331 these genes, such as by having multiple genome copies (e.g., Mendell et al., 2008) or other

332 mechanisms increasing concentrations and kinetics of RNA polymerase, aminoacyl-tRNA

333 synthetase, rRNA and tRNA (e.g., Yadavalli and Ibba, 2012).

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334 We also observed a 10-fold decrease in variance implying that genome size has a more

335 variable ecological role in oligotrophic ecosystems, although this change was only suggestive (P =

336 0.17). Based on genome streamlining theory alone, which suggests small genomes should be favored

337 in oligotrophic conditions due to the favoring of cellular architectures that minimize resource

338 requirements (Giovannoni et al., 2014; Morris et al., 2012), we would expect reduced variance in

339 genome size in oligotrophic environments, in contradiction with our results. Consideration of

340 metabolic scaling may provide the explanation. Since there is presumably an interspecific increase in

341 active mass-specific metabolic rate and rmax with genome size in bacteria (DeLong et al., 2010), under

342 abruptly enriched conditions, members having larger genomes may displace the complex and diverse

343 original community by growing faster. In contrast, apparently genome size may not sufficiently

344 affect metabolic costs to play as strong a role in the evolutionary ecology and assembly of these

345 oligotrophic communities. This agrees with others who have argued that the elemental costs of

346 DNA play a negligible role in the evolutionary ecology of genome size (Lynch, 2006; Mira et al.,

347 2001; Sterner and Elser, 2002; Vieira-Silva et al., 2010)—the smaller genomes found in many

348 parasites, symbionts, and oligotrophs may instead reflect the mutational loss of genes due to relaxed

349 positive selection for keeping non-essential genes (Mira et al., 2001).

350 Unlike the other traits, we observed no change in variance in GC content between

351 treatments. Although we expected environmental filtering for low GC content in oligotrophic

352 conditions, it is less clear why there may be filtering for high GC content in response to nutrient

353 enrichment, pointing to a need for further investigations into the implications of GC content for

354 microbial physiological and community ecology.

355 .

356

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357 Concluding remarks. The genomic traits studied here affect the costs and rates of biochemical

358 information processing within cells and all of these traits responded as predicted to nutrient

359 enrichment. Cells proliferating under nutrient-enriched conditions had increased capacities for

360 transforming and storing information, whereas those persisting in oligotrophic conditions had

361 genomic traits with reduced costs in information processes underpinning metabolism and

362 reproduction. Optimizing tradeoffs in the efficiency and capacity of information processing may

363 thus play a vital role in the evolutionary specialization of a microbe’s cellular biology to the particular

364 trophic conditions of an ecosystem. Information processing traits should be included in the

365 development of trait-based theories and frameworks for microbial community ecology, as apparently

366 all three core components of metabolism—information, energy, and material requirements and

367 transformations—must be closely fine-tuned to the growth and trophic strategy of a microorganism.

368

369 Materials and Methods

370 Study site description: The whole-ecosystem fertilization experiment took place in Lagunita, a shallow

371 (< 0.33 m) pond averaging 35,000 L in volume and roughly ~12 m x 4 m. It is adjacent to a larger

372 lagoon (Laguna Intermedia from the Churince system) in the Cuatro Ciénegas basin (CCB), an

373 enclosed evaporitic valley in the Chihuahuan desert, Mexico. Despite its aridity, the CCB harbors a

374 variety of groundwater-fed springs, streams, and pools. Past research has also shown that these

375 aquatic environments harbor a high diversity of unique microbiota (Souza et al., 2018, 2006) who

376 have evolved under strong stoichiometric imbalance (high nitrogen (N):phosphorus (P) ratios) and

377 prevalent ecosystem P limitation (Corman et al., 2016; Elser et al., 2005). Lagunita waters are high in

2+ 2- 2- 378 conductivity, dominated by Ca , SO4 , and CO3 , have an average molar TN:TP ratio of 122,

379 indicative of strong P limitation, as previously demonstrated in this system during a mesocosm

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380 experiment completed in 2011 (Lee et al., 2015, 2017). During the summer season, the pond shrinks

381 substantially and the surface water temperature increases.

382 Experimental design: On May 25 2012, prior to initiation of fertilization, five replicate

383 enclosures were established in different parts of the pond; these were unenriched treatments to serve

384 as reference systems for comparison to the pond after enrichment. As in Lee et al. (2015, 2017),

385 each unenriched mesocosm consisted of a 40-cm diameter clear plastic tube enclosing around 41 L

386 (based on an average depth of 0.33 m at the time mesocosms were installed). This volume fluctuated

387 slightly during the experiment and decreased very slightly towards the end of the experiment due to

388 evaporation (which decreased the pond volume by 1.4%). The mesocosms were fully open to the

389 atmosphere and sediments. Each mesocosm’s water column was gently mixed periodically during

390 our regular sampling (described below). Thus, with exposure to both the air and the bottom

391 sediments, the unenriched mesocosms were essentially cylindrical “cross-sections” of the ecosystem.

392 The 41-L volume is a typical size for an aquatic mesocosm (e.g., see review of 350 mesocosms by

393 Petersen et al., 1999) and appropriate for microbial studies, encompassing on the order of 30 billion

394 prokaryotic cells (estimated from our cell counts). See Appendix 1 for more details.

395 The fertilization procedure was based on a previous mesocosm experiment in Lagunita (Lee

396 et al., 2015, 2017). Prior to the initiation of the experiment, a morphometric map of the pond was

397 created, allowing us to estimate the pond's water volume and adjust that volume estimate as water

3- 398 depth changed through the season. Based on the pond’s volume, we fertilized to increase PO4

399 concentration in the water by 1 uM (as KH2PO4). We also added NH4NO3 in a 16:1 (molar) N:P

400 ratio with the added P. The soluble reactive phosphorus (SRP) concentration of the pond was then

401 measured every 3-4 days, after which we added sufficient KH2PO4 to bring the pond's in situ

402 concentration back to 1 uM, along with the appropriate amount of NH4NO3 to achieve a 16:1 molar

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403 ratio. Fertilizer was added by mixing fertilizer solution with ~2 L pond water and broadcasting the

404 mixture into all regions of the pond.

405 We thus performed a sustained whole-ecosystem fertilization with replicate internal

406 unfertilized mesocosms to serve as reference systems. Whole-ecosystem manipulation assures that

407 any experimental responses are ecologically relevant as the system manipulated encompasses the full

408 scale and scope of ecosystem processes that might modulate that response (Carpenter, 1998). Such a

409 whole-ecosystem approach can be especially powerful when coupled to appropriate reference

410 systems. Although our internal unfertilized mesocosms were smaller than the surrounding fertilized

411 pond, we consider them to be pertinent references for investigating the role of genomic traits in

412 community assembly under differing nutrient conditions for several theoretical, empirical, practical,

413 and ethical reasons. While replicate whole ponds for comparison would be preferred, the availability

414 of multiple ponds at Cuatro Ciénegas for such experimentation is extremely limited given the basin’s

415 arid nature. Indeed, true replication of whole-ecosystem manipulations is very rarely achieved. We

416 thus followed the recommendations of Carpenter (1989, 1998): relying on application of a strong

417 experimental treatment applied at the ecosystem scale and informed by previous experimentation

418 (Lee et al., 2017) together with replication of internal reference mesocosms to assess impacts of

419 nutrient fertilization. In this way, we maximized the ecological realism of our perturbation by

420 applying it at the ecosystem scale, while retaining the ability to compare manipulated dynamics

421 against a benchmark. We preferred a whole-ecosystem fertilization of the pond over fertilizing

422 internal mesocosms within the pond because the smaller enclosures might have provided an artificial

423 view of how the microbial community responds to a nutrient perturbation at the ecosystem scale. In

424 addition, mesocosms cut off many sources of colonizing species (such as shore sediments/soils in

425 ponds) that can contribute to community reorganization following a perturbation (e.g., Thibault and

426 Brown, 2008). Since this study aimed to understand the role of genomic traits in the assembly of

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427 communities (not just the disassembly caused by extinctions), it was thus important to avoid

428 inhibiting community responses driven by the colonization of species.

429 Given the enormous heterogeneity between communities and water chemistry from

430 different sites within the area, as well as ability of microbes to disperse between ponds, using internal

431 reference systems rather than other whole ponds is arguably more informative as it avoids

432 introducing confounding factors related to variation between ponds (such as contrasting microbial

433 communities) and instead introduces just one potentially confounding factor, the difference in size

434 between the unfertilized mesocosms and the fertilized pond. We thus consider our approach to be

435 the scientifically appropriate one for this conservation area. The design is a natural first step from

436 experiments in small, homogenous bottles or bags. Scaling such experiments across multiple

437 ponds/lakes may be a future step for experimental metagenomic research but not a responsible

438 current step for research in the ecologically sensitive Cuatro Ciénegas basin area.

439 Field monitoring, sampling, and routine water chemistry: Following initiation of fertilization, the

440 pond and internal unfertilized mesocosms were sampled every four days to monitor basic

441 biogeochemical and ecological responses (see Appendix 1 for water chemistry sampling details). At

442 the end of the experiment (32 days), we sampled for metagenomics: five water samples from the

443 pond itself (fertilized treatment) and one water sample from inside each of the five unfertilized

444 internal mesocosms. It is worth noting that, given the substantial seasonal changes in temperatures

445 and water chemistry, we think comparing metagenomic data of the pond pre-fertilization to the

446 fertilized pond 33 days later would be an inappropriate approach for gaining insight into the effects

447 of fertilization. Thus, we focus on comparing post-fertilization metagenome data with temporally

448 matched data from the unfertilized mesocosms.

449 The water inside the mesocosm was gently stirred with a dip net prior to sampling. Sampling

450 involved submerging a 1-L polycarbonate beaker just under the surface of the water. Microbes in the

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451 water samples were filtered onto sterile GF/F filters (0.7-µm nominal pore size, Whatman,

452 Piscataway, NJ, USA), frozen immediately in liquid nitrogen, and held at <80° C until laboratory

453 DNA extraction, purification, and sequencing. Given the 0.70 µm pore size, extremely small

454 prokaryotes were not part of our metagenomes and so our results do not apply to these

455 picobacterioplankton. If anything, their inclusion would augment predicted community-level trait

456 responses to fertilization, since picoplankton are slow-growers, tend to do poorly in nutrient-rich

457 waters, have small genomes, and so likely decreased in abundance in the fertilized treatment. Routine

458 water chemistry methods were used, as in Lee et al. (2015, 2017)

459 DNA extraction, sequencing, annotation, and phylogenetics: DNA was extracted using the MO BIO

460 PowerWater DNA Isolation kit with a slight modification (increasing volume of PW1 solution to 1.5

461 mL) . DNA yield and quality were assessed by PicoGreen assay (Appendix 1) and prepared for

462 sequencing on Illumina MiSeq with 12 samples per v2 2x250bp sequencing ru. Raw reads were

463 trimmed of barcodes, quality filtered, and rarefied to 100,000 sequences per sample (Appendix 1).

464 For the quality filtering, we used the standard Qscore of 25. Two samples from the fertilized

465 treatment and one sample from the unfertilized treatment were left out of subsequent analyses

466 because they had sequencing depths less than 20% of the rest of the samples (whose sequencing

467 depth averaged 2.5 x 106 reads) and low-quality scores. Sequences were phylogenetically annotated

468 using the Automated Phylogenetic Inference System (APIS, Appendix 1) with default parameters,

469 which is designed to optimize annotation accuracy (Allen et al., 2012). PCoA was used to visualize a

470 Bray-Curtis distance matrix of the APIS annotations using the R (R Development Core Team, 2011)

471 package vegan (Oksanen et al., 2016). We also used the statistical package edgeR (Robinson et al.,

472 2010) in R to identify the taxonomic groups at the genus, class, phylum, and domain levels that

473 exhibited discernable changes in relative abundance and calculate the P-value of these changes. edgeR

474 is specifically designed for dealing with sequencing count data in which there are minimal levels of

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475 replication (Robinson et al., 2010).

476 Trait bioinformatics. We used Phylosift (Darling et al., 2014) to annotate non-sub-sampled

477 libraries, allowing for the counting of the number of bacterial and archaeal 16S rRNA genes in each

478 sample, which averaged 2607. Counting 16S rRNA genes provides a means of evaluating the copy

479 number of rRNA operons, because in prokaryotes 16S RNA genes are typically transcribed as part

480 of a rRNA operon and furthermore the typical situation, at least in the genomes of cultivated

481 organisms, is that each bacterial rRNA operon has a single 16S rRNA gene (Grigoriev et al., 2011).

482 The program tRNAscan-SE v1.4 (Lowe and Eddy, 1997) which is specifically designed for

483 recognizing tRNA genes, was used with the provided general tRNA model in order to count the

484 number of tRNA genes in non-sub-sampled libraries. Variation in the total number of tRNA genes

485 indicates variation in gene copy numbers, since the number of tRNA genes per genome is driven by

486 variation in tRNA gene copy number rather than tRNA diversity (Higgs and Ran, 2008). In Statistics,

487 we describe our method for ensuring that results were not sensitive to variation in DNA sequencing

488 depth. Although the tRNAscan-SE approach employed did not distinguish between bacteria,

489 archaea and eukaryotes, the observed rarity of Eukarya and insignificant changes in the group’s

490 relative abundance between treatments (see Results) indicate that the tRNAscan-SE results, and our

491 other bioinformatic analyses reflect variation in prokaryotes, namely bacteria (archaea are very rare in

492 CCB samples, Lee et al., 2017), rather than eukaryotes.

493 Bacterial genome sizes were estimated according to methods in Allen et al. (2012)(Appendix

494 1). Briefly, length normalized core marker gene counts identified as bacterial by APIS were used to

495 determine number of genome equivalents in a sample. Total number of predicted proteins annotated

496 as bacterial by APIS was then divided by number of genome equivalents.

497 To quantify the degree of synonymous codon usage bias of each observed bacterial and

498 archaeal ribosomal protein gene sequence, we first used Phylosfit (Darling et al., 2014) to identify

21

499 the sequences and then used the program ENCprime to calculate two commonly used metrics, the

500 effective number of codons (ENC) (Wright, 1990) and a related measure, ENC’ (Novembre, 2002),

501 for each sequence. ENC and ENC’ are relatively statistically well-behaved and insensitive to short

502 gene lengths compared to other measures of codon usage bias (Novembre, 2002; Wright, 1990).

503 ENC’ additionally accounts for departures in background nucleotide composition from a uniform

504 distribution and is therefore considered to provide a more powerful and reliable measure of codon

505 usage bias (Novembre, 2002). Background nucleotide composition for each sample was considered

506 to be the average nucleotide frequencies of all the samples’ reads.

507 Statistics: We used T-tests assuming unequal variances to evaluate mean differences between

508 treatments. For community-level genomic traits, these tests were one-tailed with the alternative

509 hypothesis based on the predicted differences described in the Introduction. General Linear Models

510 (GLM) with treatment (unfertilized vs fertilized) as a fixed factor were used to determine the

511 amount of variation (R2) in traits explained by treatment. Numbers of tRNA genes and rRNA

512 operons were log-transformed before these analyses to achieve normality. In order to account for

513 potential effects of sequencing depth on the number of rRNA operons and tRNA genes, we also

514 regressed the numbers against a sample’s log total number of reads and then performed GLM

515 analyses on the residuals (ascertaining whether or not for a given sequencing depth samples from the

516 fertilized treatment have higher numbers of these genes, that is, higher residuals).

517 We also employed single-tailed Poisson rate tests to examine differences in the numbers of

518 rRNA operons and tRNA genes. The Poisson rate test is more powerful than the T-test for

519 examining differences in the per sequence rate of occurrences of tRNA genes and rRNA operons

520 between treatments. In the Poisson rate test, the sample size for each treatment was the number of

521 DNA samples and the treatment’s observation length was the mean number of reads per

522 metagenome. Two-sided Bonett’s tests were used to test for treatment effects on variance between

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523 samples of community-level traits. Kolmogorov-Smirnoff tests were conducted to evaluate whether

524 or not within-sample distributions of ENC and ENC’ values of ribosomal protein gene sequences

525 differ between treatments.

526 Finally, we examined how well nutrient enrichment predicted the covariation (correlation) of

527 these genomic traits along a single axis quantified by Principal Components Analysis (PCA). Should

528 nutrient enrichment explain substantial variation in the communities along this single axis, then the

529 PCA values provide a measure of molecular adaptiveness to oligotrophic versus copiotrophic

530 conditions. We used median ENC’ as the measure of codon usage bias in the PCA. We calculated

531 the principal component score of each metagenomic sample along the first dimension and used

532 GLM analysis to determine how well fertilization explained variation in the communities’ scores

533 along this dimension. Before performing PCA, in order to give equal weight to each trait, variables

534 were first standardized (z-scored) by subtracting means and dividing standard deviations. Overall, we

535 aimed to avoid over-reliance on significance levels and P-values in judging scientific results (Carver,

536 1993; Halsey et al., 2015; McGill et al., 2006; Rothman, 2016), so we report P-values and effect sizes

537 and let readers judge for themselves the significance of the results.

538 We focused on the community response of genomic traits to varying nutrient conditions,

539 rather than on a detailed natural history of the phylogenetic composition of the community, not only

540 because we are interested in interrogating metagenomic changes within a trait-based framework, but

541 also for pragmatic reasons. There are multiple bioinformatic challenges to precisely resolving the

542 phylogenetic composition of entire microbial prokaryotic communities and ensuring that the

543 phylogenetic biases of various molecular methods do not differ between environments or growth

544 conditions (Kunin et al., 2010; Sunagawa et al., 2013). For instance, there are massive gaps in

545 prokaryotic taxonomic databases (Rinke et al., 2013; Temperton and Giovannoni, 2012), and

546 metagenomes generated from low and high growth communities in oceans have different levels of

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547 taxonomy blindness (Kalenitchenko et al., 2018; Sogin et al., 2006; Yooseph et al., 2010). Also, DNA

548 sequence assembly introduces a bias, as clonal populations with even low coverage assemble very

549 well while high abundance populations with strain diversity will not assemble well.

550

551 Acknowledgements

552 This study was conducted with financial support from NSF (DEB-0950175) and NASA (NAI5-

553 0018) grants awarded to JJE, WWF-FCS to VS and LEE, and NSF (1536546) and NASA NAI

554 (NNH05ZDA001C, NNH12ZDA002C, NNA08CN87A, NNA13AA93A) to JLS. This study was

555 made possible with the sampling permit from Vida Silvestre-SEMARNAT, granted to V. Souza

556 (09762). The WWF-FCS Alliance provided funds for VS and LEE. We thank J. Learned, J. Corman,

557 J. Ramos, and E. Moody for field assistance, the Cuatro Ciénegas community for its hospitality, and

558 S. Vieira-Silva for advice and sharing of the codon usage bias analysis program growthpred-v1.07. This

559 paper was written during a sabbatical leave of LEE and VSS in the University of Minnesota in Peter

560 Tiffin and Michael Travisano laboratories, respectively, with support by scholarships from PASPA,

561 DGAPA, UNAM.

562

563 Accession Codes

564 Raw sequence data and metadata have been submitted to NCBI Sequence Read Archives, accessible

565 through BioProject PRJEB22811.

566

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771 Main Article Figure Captions 772 Figure 1. Community-level trait responses to nutrient enrichment. As predicted, each information 773 processing trait was higher in the fertilized treatment (Fert) versus the unfertilized treatment (Con). 774 Because the faster growing organisms generally have more P-rich ribosomes, seston P:C ratio also 775 increased (as shown in A). Boxes show 25/75% quantiles, center horizontal lines show medians, and 776 vertical lines show the data range. In E, a community’s codon usage bias is the inverse mean 777 effective number of codons (1/ENC) of a metagenome’s ribosomal protein sequences, with higher 778 1/ENC values indicating increased codon usage bias. 779 780 Figure 2. Histograms and cumulative distributions of the codon usage biases (ENC’ values) of the 781 ribosomal protein sequences in the metagenomes, with lower ENC’ values indicating increased 782 codon usage bias and thus increased speeds and/or accuracies of translation of ribosomes. (A) 783 Histograms showing that the unfertilized communities have more sequences with nearly no 784 detectable codon usage bias (values of 60-61), higher median ENC’ values (indicated by the tall 785 vertical lines), and typically fewer sequences with high codon usage bias (ENC’ values from 20-40). 786 Bar colors and their overlapping shades indicate different samples. (B) Cumulative distributions 787 showing that overall the fertilized communities’ distributions have fatter left-tails, so higher 788 frequencies of ENC’ values <50 and thus more frequent codon usage bias. Y-axis shows the 789 proportion of ENC’ values ≤ ENC’ value designated by the plotted curves (fertilized = red curves, 790 unfertilized = black curves). 791 792 Figure 3. The principal component scores quantifying the position of metagenomes along a single 793 dimension representing the oligotrophy-copiotrophy strategy continuum for information processing 794 traits. Higher scores on the vertical axis indicate communities whose information processing traits 795 are better suited for copiotrophy (increased maximum growth rate in resource abundant 796 environments), whereas low scores indicate adaptation to oligotrophy (increased resource use 797 efficiency in low nutrient environments). Nutrient enrichment explains 86% of the variance in 798 metagenomes along this axis of growth and trophic strategy (R2 = 86%, P =0.004). Within each 799 treatment, circular symbols are spaced horizontally for visual clarity. 800

30

) 4.4 4000 -3 AB 2600 C 4.2 3800 2400

2200 4 3600 2000 3.8 3400 1800 3.6 3200 1600 3.4 1400 3000 genes tRNA Number of

3.2 1200

Mean genome size (reads) size genome Mean Phosphorous : Carbon ratio (x10 ratio Carbon : Phosphorous 2800 3 1000 2600 Cont Fert Con Fert Con Fert

0.022 DE F 0.55 2000 0.0215 0.54 0.021 0.53 1500 0.0205 0.52

0.51

1000 0.02 GC content GC 0.5 0.0195

Number of rRNA operons of Number 0.49 500 (1/ENC) bias usage Codon 0.019 0.48

Con Fert Con Fert Con Fert Treatment A 0.3 Unfertilized 0.2

Fraction 0.1

0 20 30 40 50 60 0.3 Fertilized 0.2

Fraction 0.1

0 20 30 40 50 60 B 1

0.5

0

20 30 40 50 60 Cumulativeprobability Effective number of codons' (ENC') 4

3

2

1

0

-1

Metagenomic trophic strategy score score strategy trophic Metagenomic -2 Oligotrophy Oligotrophy Copiotrophy -3 Unfertilized Fertilized 1 Appendix 1

2 Further background on the genomic traits 3 We have highlighted reasons for the choice of genomic traits used in this study in the introduction 4 of the main text. Below, we provide more details and references on the relevance of these traits for 5 rates and costs of information processing and their role in community assembly and the trophic 6 strategy of organisms. 7 Copy number of highly expressed genes essential to biosynthesis: In order to grow 8 quickly, organisms must be able to transcribe genes, translate mRNA, and synthesize proteins at a 9 sufficiently fast rate. Since ribosomes catalyze protein synthesis, increased capacity to replenish 10 diminished ribosomal pools or maintain large standing stocks facilitates growth. Thus, faster 11 growing organisms should benefit from increasing their number of rRNA operon copies (and so the 12 16S rRNA gene) in order to increase the rate of transcription of rRNAs, allowing the size and rate of 13 turnover of the ribosomal pool to be larger and thereby preventing translation by the ribosomes 14 from being the rate-limiting step in cellular growth. 15 Indeed, in bacteria and eukaryotes there are intraspecific and interspecific correlations 16 between rRNA operon copy number per genome, which can vary by over an order of magnitude in 17 bacteria (Acinas et al., 2004; Větrovský and Baldrian, 2013), and growth rate or generation time 18 (Condon et al., 1995; Gyorfy et al., 2015; Klappenbach et al., 2000; Lauro et al., 2009; Shrestha et al., 19 2007; Stevenson and Schmidt, 2004; Yano et al., 2013). Thus microbes that are competitive in fast 20 growing environments are likely to have to have a greater number of copies of the rRNA gene 21 operon. 22 Furthermore, a consequence of faster growing organisms having to have larger cellular 23 quotas of rRNA is that they should have higher phosphorus contents (Elser et al., 2003, 1996; 24 Makino et al., 2003), since rRNA is phosphorus rich, and thus require more phosphorus-rich 25 resources in order to build these rRNA pools. These observations, known as the “growth rate 26 hypothesis” (Elser et al., 2000), have received considerable empirical support from comparative 27 studies, but limited research has explored their usefulness as a predictive trait for community 28 ecology. 29 Likewise, since the concentrations of tRNAs affects the rate of protein translation, having 30 more copies of tRNA genes, as well as a greater diversity of anticodons among the tRNA genes, may 31 help a cell maintain larger pools of tRNAs, as has been observed (Higgs and Ran, 2008; Kanaya et 32 al., 1999) and thereby maintain the faster translation rates required for faster growth rates. Indeed, 33 the total number of tRNA genes (which, for example, vary from under 30 to over 120 per genome 34 in bacteria) has been shown to be negatively correlated with generation time (Higgs and Ran, 2008; 35 Rocha, 2004). 36 Genome size: Genome size is a complex trait affecting multiple aspects of an organism’s 37 ecology, physiology, and molecular biology, making it both an important trait to study as well as a 38 challenging one, since its influence on ecology may differ between environments, organisms, and 39 historical circumstances. Many microbiologists previously thought there was no relationship between 40 growth rate and genome size (Mira et al., 2001). Recent work suggests that among species growth 41 rate and genome size may be positively correlated (DeLong et al., 2010), although this remains 42 controversial and conflicts with some other studies that may not have addressed confounding 43 variables such as the effects of temperature on growth rate (Vieira-Silva et al., 2010; Vieira-Silva and 44 Rocha, 2010). Additionally, bacteria in oligotrophic zones of the oceans tend to have smaller 45 genomes than bacteria in copiotrophic zones (Allen et al., 2012; Lauro et al., 2009). Thus, organisms 46 with larger genomes may, all else being equal, do better under high growth conditions and so 47 respond favorably to nutrient fertilization. 48 There are several non-mutually exclusive reasons why genome size may be correlated with 49 growth rate and response to fertilization. For one, because DNA is P-rich, large genome-sized 50 organisms require more phosphorus to maintain and replicate their genomes and so they have 51 greater difficulty than small genome size species in obtaining sufficient amounts of P in oligotrophic, 52 P-limited environments. Larger genome-species also tend to have to have larger cells, leading to, on 53 average, decreased surface-to-area-volume ratios, which in turn puts larger cells at a disadvantage for 54 obtaining sufficient nutrients (Okie, 2013). Genome size also affects the size, structure and function 55 of metabolic networks of organisms by allowing for a greater diversity of enzymes, a higher diversity 56 of metabolic pathways, and enhanced metabolic multi-functionality, which may affect an organism’s 57 ability to take up a variety of substrates, the speed of resource uptake and transformation, and the 58 yield of resource transformation (DeLong et al., 2010; Maslov et al., 2009). Finally, larger genomes 59 allow organisms to encompass greater copy numbers of highly expressed genes and genes involved 60 in protein translation machinery, such as tRNA and rRNA genes, which in turn allow for greater 61 translation rates, as discussed above. 62 Nucleotide base composition of DNA: The percentage of a genome’s DNA composed of 63 the nucleotide bases guanine and cytosine is known as a genome’s GC content. Genome GC 64 content varies from widely across life, and there appears to be pervasive selection on the GC 65 content of bacterial genomes, leading to selection for high GC content in some bacterial genomes 66 (Hildebrand et al., 2010). The reasons for positive selection on GC content are contested, and it is 67 likely that there are multiple different selective forces on GC content related to the niches and 68 environmental conditions of different species. Because GC bonds have eight nitrogen atoms 69 whereas AT bonds contain only seven, higher GC content genomes tend to have higher nitrogen 70 content (Bragg and Hyder, 2004), making higher GC content organisms have increased nutrient 71 requirements for replication and DNA repair. 72 Rocha and Danchin (Rocha and Danchin, 2002) found that parasitic and symbiotic bacteria 73 have lower GC content than free-living bacteria and propose an explanation based on the 74 biochemical details of nucleotide metabolism and differences in the energetic expense of GTP and 75 CTP nucleotides versus ATP and UTP nucleotides. The explanation boils down to the difference 76 resulting from increased selection by competition for scarce resources in parasites and symbionts. 77 Here, we extend this explanation to include extremely oligotrophic genomes as similarly susceptible 78 to these evolutionary forces as parasites and symbionts, and thus propose that an emergent 79 consequence of these effects should be a positive association between GC content and growth rate. 80 Suggestive support for this possibility is provided by the observation that GC content tends to be higher 81 in DNA of environmental samples from complex environments, such as soils, and environments with high amount of 82 nutrients, such as whale carcasses and copiotrophic waters, compared to simple and lower productivity environments 83 such as oligotrophic pelagic communities in the Sargasso Sea (Allen et al., 2012; Foerstner et al., 2005; Raes et 84 al., 2007). 85 Codon usage bias: An amino acid can be encoded by multiple different codons (nucleotide 86 triplets), but these synonymous codons have different kinetic properties, including different 87 probabilities of mistranslation. In highly expressed genes essential to growth, such as ribosomal 88 protein genes, there should be increased selection for biasing the usage of certain synonymous 89 codons over others in order to optimize the accuracy and speed of translation, especially in 90 organisms with fast growth rates. 91 Since 18 of the 20 standard amino acids are encoded by two or more synonymous codons, 92 genomes and genes may favor certain codons over others without altering translation products. 93 Different synonymous codons have different probabilities of mistranslation. Thus, the use of more 94 accurately translated codons can be beneficial because mistranslation wastes energy, reduces the 95 translation rate (all else being equal), and/or can cause cytotoxic protein misfolding. Codon usage 96 bias (CUB) has been documented in bacteria, archaea, and eukaryotes (Satapathy et al., 2014; 97 Subramanian, 2008; Vieira-Silva and Rocha, 2010). The highly expressed genes of a genome, such as 98 ribosomal protein genes, tend to have greater CUB (Subramanian, 2008; Vieira-Silva and Rocha, 99 2010). ). As CUB can alter the accuracy and speed of translation, this greater CUB presumably 100 reflects selection for a greater translational accuracy and/or or efficiency (Hershberg and Petrov, 101 2008) which should increase rates of translation. 102 It follows from this positive effect of CUB on translation that faster-growing organisms 103 should tend to have greater CUB, especially in genes that are highly expressed and essential for 104 growth (such as ribosomal protein genes). Indeed, a negative relationship between an organism’s 105 generation time and the CUB of its ribosomal protein genes has been reported (Subramanian, 2008; 106 Vieira-Silva and Rocha, 2010). A few studies have compared the CUB of metagenomes from 107 different environments (Roller et al., 2013; Vieira-Silva and Rocha, 2010), but more work is required 108 to clarify the role of CUB in community assembly. 109 110 111 Additional methods 112 Experiment: A small number of fish and larger aquatic macroinvertebrates (~1 cm or greater) were 113 removed from the unfertilized mesocosms with a dip net before beginning the experiment. Such 114 removals were necessary to ensure that enclosures did not experience unduly large stochastic 115 disruption from animal activities resulting from differing numbers of large animals being trapped 116 inside at unnaturally high densities. This would have confounded the difference in nutrient 117 conditions between the enriched and unenriched treatments, undermining the purpose of the 118 experiment. To the extent that such consumers augmented nutrient availability outside of the 119 unenriched enclosures, then their removal from the enclosures would have amplified the nutrient 120 enrichment contrast between the fertilized pond and the unenriched enclosures. 121 122 Field monitoring and sampling: Water was sampled adjacent to and within each of the five internal 123 mesocosms. Samples were filtered onto Whatman GF/C filters for analysis of chlorophyll (chl a) 124 concentrations and passed through 0.2-µm polyethersulfone membrane filters (Pall Life Sciences,

125 Port Washington, NY) and filtrate was used for analyses of nitrate (NO3), ammonia/ammonium

126 (NH3/4), soluble reactive phosphorus (SRP), and total dissolved phosphorus (TDP). After 16 and 32 127 days of fertilization, water samples were also filtered onto pre-combusted Whatman GF/F filters for 128 analysis of concentrations of C, N, and P in suspended particulate matter (seston). Unfiltered water 129 samples were frozen for later analysis of total N (TN) and total P (TP) concentrations. 130 131 Routine water chemistry: Chl a on GF/C filters was quantified fluorometrically using a TD-700 132 fluorometer (Turner Designs,Sunnyvale, CA) after 16–24 hours of extraction in cold absolute 133 methanol (Arar and Collins, 1997). TN concentrations were measured using a Shimadzu TOC/TN 134 analyzer. TDP and TP concentrations were measured using the same colorimetric method after 135 persulfate digestion of the filtered or unfiltered samples, respectively. GF/F filters with seston were 136 thawed, dried at 60°C and then packed into tin discs (Elemental Microanalysis, U.K.) for C and N 137 analyses with a Perkin Elmer PE 2400 CHN Analyzer at the Arizona State University Goldwater 138 Environmental Laboratory. Another set of dried GF/F filters prepared from the same water 139 samples was used for estimating seston P content. These filters were digested in persulfate followed 140 by colorimetric analysis for P. 141 142 Calculations on the potential for evolutionary change during the experiment: For microorganisms with 2-h 143 minimum generation times—the approximate average minimum generation time of prokaryotic 144 isolates in many data sets (e.g., DeLong et al., 2010; Vieira-Silva and Rocha, 2010)—a maximum of 145 384 generations of replication can occur in the 32-day period of the experiment, which is barely 146 sufficient for much evolutionary change (Lenski and Travisano, 1994), especially of some of the 147 genome-scale traits like GC content and codon usage bias. Many of the microorganisms in natural 148 ecosystems (that is, the unculturable taxa underrepresented in generation time datasets and that 149 make up the majority of prokaryotic communities) likely have minimum generation times greater 150 than 2 hours and under field conditions do not achieve or sustain their minimum generation times 151 for the whole course of the experiment. Thus we expect that in our experiment, populations 152 experienced much fewer than 384 generations, providing very limited opportunity for evolutionary 153 change to affect the genomic traits. 154

155 Results – Changes in Taxonomic Composition 156 The percentages of Bacteria, Archaea, Eukarya, and viruses making up the communities were not 157 discernibly different between unfertilized and fertilized treatments (P =0.46, 0.37, 0.39, 0.21, 158 respectively). For Bacteria and Eukarya, the percentages varied relatively little between samples 159 within and across treatments (R2 = 9.6% and 13.5%, respectively). For Archaea and viruses, the 160 mean percentages were substantially different between treatments (43 and 46% lower mean 161 percentages in the fertilized treatment compared to the unfertilized treatment, respectively), but due 162 to the high within-treatment variance, treatment explained limited variation in the relative 163 abundances of these domains (R2 = 14% and 27%, respectively). The mean relative abundances in 164 the unfertilized versus fertilized treatments were, respectively, 94% versus 93% for Bacteria, 6.0% 165 versus 6.6% for Eukarya, 0.40% versus 0.23% for Archaea, and 0.05% versus 0.03% for viruses. 166 Thus, Bacteria dominated the communities in both treatments. However, as expected, nutrient 167 enrichment altered microbial community composition at a finer phylogenetic resolution, as indicated 168 by the PCoA plot of community phylogenetic composition (based on the APIS analyses; Fig. S2) 169 and supported by statistical analysis: the Multidimensional Scaling (MDS) scores from a two- 170 dimensional analysis differed between treatments (first dimension: R2 = 45.6%, P =0.096; second 171 dimension: R2 = 51.3%, P = 0.070) and several taxonomic groups changed in abundance with 172 fertilization (Fig. S3 and S4, all P < 0.01). Our results thus confirm the rarity of microbial eukaryotes 173 in the pond and show modest changes in the group’s relative abundance between treatments, 174 indicating that the tRNA counts and our other bioinformatic analyses reflect mostly the response in 175 bacteria. 176

177 Appendix 1-table 1. Differences in the variance of community-level traits between treatments.

Variance Ratio of Bonett's Community-level Trait varianc test P- Unfertilized Fertilized es value P:C ratio 2.09x10-7 2.06x10-8 0.13 0.058 mean genome size 105,315.4 10,984.8 0.1 0.174 log number of rRNA genes 0.0056 0.0422 7.55 < 0.001 log number of tRNA genes 0.0148 0.1396 9.46 0.013 GC content 0.0001 0.0002 1.9 0.56 mean ENC 3.0554 0.3034 0.1 0.169 median ENC 2.5861 0.413 0.16 0.275 mean ENC' 2.5494 0.0824 0.03 0.056 median ENC' 1.832 0.0671 0.04 0.069

178 Appendix 1 Figure Captions 179 180 Appendix 1-figure 1. Chlorophyll a concentration increased with nutrient enrichment whereas it 181 remained relatively invariant in the unfertilized treatment. 182 183 Appendix 1-figure 2. Principle coordinates analysis (PCoA) of the community phylogenetic 184 structure inferred from the metagenomes shows that the phylogenetic composition of samples, as 185 indicated by each point, is substantially different between unfertilized and fertilized treatments. 186 Microbial community phylogenetic composition also varied notably within the unfertilized 187 mesocosms, falling into two clusters driven by variation in the relative abundance of 188 Alphaproteobacterum, whereas enriched communities all shared relatively similar phylogenetic 189 composition, indicating a convergence of effects of fertilization on community composition. 190 191 Appendix 1-figure 3. Relative abundances of taxonomic phyla in the samples from the unfertilized 192 (Cont) and fertilized (Fert) treatments. 193 194 Appendix 1-figure 4. The significant relative abundance changes in taxonomic classes suggested by 195 the statistical analysis of the phylogenetic composition determined by APIS. The statistical analysis is 196 based on the statistical methods developed in the EdgeR package (Robinson et al., 2010). Note that 197 less than 50% of reads could be annotated. All P < 0.01. 198 199 200 60 ●● Control ●● Fertilized 50 ●●

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30 ●● yll A (ng/ml) ●● ●● 20

Chlorop h ● ● ●● ●● ●● 10 ●● ●● ●● ●● ● ●● ● ● ●● ●● ● ●● ●● 0 −10 −5 0 5 10 15 20 25 30 35 Days Control Fertilized Dimension 2 − 0.10 0.00 0.10 −0.2−0.1 0.0 0.1 0.2 Dimension 1

Log2 Fold Change Bacteria Less abundant with with Fertilization abundant Less with Fertilization abundant More Algae Fungi