sign in sign up
<<
Home , Bacterial phyla, Microbialite

Revista de Biología Tropical ISSN: 0034-7744 [email protected] Universidad de Costa Rica Costa Rica

Centeno, Carla M.; Mejía, Omar; Falcón, Luisa I. Habitat conditions drive phylogenetic structure of dominant bacterial phyla of microbialite communities from several locations in Mexico Revista de Biología Tropical, vol. 64, núm. 3, septiembre, 2016, pp. 1057-1065 Universidad de Costa Rica San Pedro de Montes de Oca, Costa Rica

Available in: http://www.redalyc.org/articulo.oa?id=44946472011

How to cite Complete issue Scientific Information System More information about this article Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Journal's homepage in redalyc.org Non-profit academic project, developed under the open access initiative Habitat conditions drive phylogenetic structure of dominant bacterial phyla of microbialite communities from several locations in Mexico

Carla M. Centeno 1, Omar Mejía 1 & Luisa I. Falcón 2 1. Departamento de Zoología, Escuela Nacional de Ciencias Biológicas- Instituto Politécnico Nacional , Prolongación de Carpio y Plan de Ayala s/n, Miguel Hidalgo, C.P. 11340, Distrito Federal, México; [email protected], [email protected] 2. Instituto de Ecología, Universidad Nacional Autónoma de México. Circuito exterior s/n., Ciudad Universitaria, C.P. 04510, Distrito Federal, México; [email protected]

Received 16- IV -2015. Corrected 19- II -2016. Accepted 16- III -2016.

Abstract: Community structure and composition are dictated by evolutionary and ecological assembly pro - cesses which are manifested in signals of, species diversity, species abundance and species relatedness. Analysis of species coexisting relatedness, has received attention as a tool to identify the processes that influence the com - position of a community within a particular habitat. In this study, we tested if microbialite genetic composition is dependent on random events versus biological/abiotical factors. This study was based on a large genetic data set of two hypervariable regions (V5 and V6) from previously generated barcoded 16S rRNA amplicons from nine microbialite communities distributed in Northeastern, Central and Southeastern Mexico collected in May and June of 2009. Genetic data of the most abundant phyla (, , , , and ) were investigated in order to state the phylogenetic structure of the complete communities as well as each . For the complete dataset, Webb NTI index showed positive and significant values in the nine communities analysed, where values ranged from 31.5 in Pozas Azules I to 57.2 in Bacalar Pirate Channel; meanwhile, NRI index were positive and significant in six of the nine communities analysed with values ranging from 18.1 in Pozas Azules I to 45.1 in Río Mesquites. On the other hand, when compar - ing each individual phylum, NTI index were positive and significant in all groups, except in Cyanobacteria for which positive and significant values were only found in three localities; finally, NRI index was significant in only a few of the comparisons performed. The results suggest that habitat filtering is the main process that drives phylogenetic structure in bacterial communities associated to microbialites with the exception of Cyanobacteria where different lineages can contribute to microbialite formation and growth. Rev. Biol. Trop. 64 (3): 1057- 1065. Epub 2016 September 01.

Key words: community assembly, community composition, Cyanobacteria, heterotrophic , microbial - ites, NRI, NTI.

Microbialites are organo-sedimentary which first appeared during the Archaean eon structures formed by the interaction of resident (Allwood, Walter, Kamber, Marshall, & Burch, benthic microorganisms (bacteria, and 2006). Nowadays, these biogenic fabrics devel - a few ) (Burne & Moore, 1987) and op in different kinds of aquatic systems world - surrounding environmental factors (Foster et wide, from fully marine conditions (Reid et al., 2009; Westphal, Heindel, Brandano, & al., 2000) to freshwater desert ponds (Dinger, Peckman, 2010), which allow the precipita - Hendrickson, Winsborough, & Marks, 2006). tion of carbonates and / or in situ calcification, The active growing microbial-portion is verti - with the subsequent formation of lithified hori - cally stratified and represents diverse (Bolhuis zons that shape microbialite fabric (Dupraz & & Stal, 2011; Centeno et al., 2012; Mobberley, Vischer, 2005). Microbialites are considered Ortega, & Foster, 2012) and highly produc - living representatives of ancient , tive multi-trophic microbial self-controlling

Rev. Biol. Trop. (Int. J. Trop. Biol. ISSN-0034-7744) Vol. 64 (3): 1057-1065, September 2016 1057 ecosystems (Pringault, De Wit, & Camoin, In other words, both indices tend to reveal 2005; Schneider, Arp, Reimer, Reitner, & phylogenetic clustering in two levels, NTI at Daniel, 2013), where slight changes in depth the tips of the branches in a lower taxonomical correspond to marked niche boundaries (Armit - resolution, and NRI to the base of the tree at a age, Gallagher, Youngblut, Buckley, & Zinder, deep taxonomical level (Letcher, 2010). 2012). To date, it is well known that micro - The degree of phylogenetic relatedness bial communities associated to microbialites computed by NRI and NTI allows the under - are dominated by bacterial phyla including: standing of the processes (biotic or abiotic) Proteobacteria, Bacteroidetes, Cyanobacteria, that determine community composition (Bry - Planctomycetes, Verrucomicrobia and Acti - ant et al., 2008). Hence, the goal of this study nobacteria, among others (Papineau, Walker, was to evaluate if dominant bacterial phyla that Mojzsis, & Pace, 2005; Baumgartner et al., form and maintain microbialites in different 2009, Foster et al., 2009; Centeno et al., 2012; geographic locations exhibit patterns of phylo - Saghaï et al., 2015). Nevertheless, it is unclear genetic structure and if so, to elucidate if biotic if these bacterial assemblages differ between or abiotic factors are their main causal factors. locations (Martiny et al., 2006), as well as To test this, we considered a large genetic data the factors that are responsible for their com - set of two hypervariable regions (V5 and V6), munity structure and composition (Fierer & from previously generated barcoded 16S ribo - Lennon, 2011; Chong, Pearce, Convey, Yew, somal (rDNA) amplicons from microbialite & Tan, 2012). communities distributed in different geographi - Community structure and composition are cal regions of Mexico. It has been suggested driven by ecological and evolutionary pro - that environment drives biogeographic pat - cesses (Hanson, Fuhrman, Horner-Devine, & terns at a large scale, whereas competition Martiny, 2012; Pontarp, Ripa, & Lundberg, determines diversity in small neighbourhoods 2012) that can either facilitate or inhibit that (HilleRisLambers, Adler, Harpole, Levine, & a particular taxon colonizes a local habitat; Mayfield, 2012), thus we hypothesize that and can be manifested in signals of species biological ensembles present in each location relatedness, species diversity and species abun - will be driven by abiotic factors, due that each dance (Pontarp, Sjöstedt, & Lundberg, 2013). environment has different conditions of pH, Species relatedness has received attention to temperature, conductivity and available nutri - try to identify the processes that influence the ents (Centeno et al., 2012). composition of a biological ensemble. Hanson et al., (2012) proposed that selection, drift, dis - MATERIAL AND METHODS persal and mutation, are the main processes that rule microbial biogeographic patterns. In this The abundance of each microbialite phyla phylogenetic context, the use of net relatedness in each locality has been previously described index (NRI) and nearest taxon index (NTI) in Centeno et al. (2012). (Webb, 2000) has proven useful for the analy - sis of microbial phylogenetic structure within Microbialites 16S rDNA sequence selec - different communities and environments. NRI tion: Sequences of nine localities from dif - measures the overall phylogenetic distance ferent geographical regions of Mexico where between paired taxa of a particular location rel - microbialites develop were included. A perma - ative to the total gene pool (Chong et al., 2012), nent stream (RM) and two freshwater ponds (PI whereas NTI is based on the average phyloge - and PII) in Cuatro Cienegas basin, Northeastern netic distance to the nearest neighbor, and it is Mexico; Alchichica crater-lake, an athalosaline used to determine whether closely related taxa soda-lake in Central Mexico, where two micro - in a community are more related than expected bialite morphotypes develop: a spongy type by chance (Horner-Devine & Bohannan, 2006). (AS) that is widely distributed, and a columnar

1058 Rev. Biol. Trop. (Int. J. Trop. Biol. ISSN-0034-7744) Vol. 64 (3): 1057-1065, September 2016 type (AC) with a restricted distribution in the were obtained and aligned with Clustal X v.2.1 lake; and two costal lagoons sites, Sian Ka´an (Thompson, Gibson, Plewniak, Jeanmougin, (S) and Bacalar (BP, BM and BR), Southeast - Higgins, 1997) using default parameters. Maxi - ern Mexico (Fig. 1). In each locality, a total mum likelihood phylogenetic reconstructions of six microbialites were sampled manually were analyzed for different datasets: the first in May and June (summer) of 2009. In order contained all phyla, and the rest was construct - to obtain a homogeneous mixture for each of ed for each separate phylum, using RAxML the ten localities, approximately 5 g of each T-REX web server ( http://www.trex.uqam. microbialite were grind with 30 mL of liquid ca ) (model GTR+GAMMA and Hill-climb - nitrogen and 10 mL of extraction buffer (100 ing) (Boc, Diallo, & Makarenkov, 2012). The mM Tris-HCl, 20 mM NaCl, 100 mM EDTA phylogenetic reconstructions obtained were pH 8); total environmental DNA was extracted exported into Newick format (Olsen, 1990) for according with the protocol of Zhou, Bruns subsequent analyses. and Tiedje (1996). Microbialites sequences A matrix of unique sequences including (~250 bp length) covering the V5-V6 hyper - abundance data was generated for each phy - variable region of the 16S rDNA gene were lum and for the dataset containing all phyla. generated using a parallel massive sequencing These matrices and their corresponding newick approach as described in Centeno et al. (2012). format tree were imported into phylocom 4.1 The genetic affiliation of each sequence was (Webb, Ackerley, & Kembel, 2008) to calculate determined according to the Ribosomal Data the NRI and NTI indexes using the command Project, RDP-classifier ( http://rdp.cme.msu. comstruct (community structure) and model edu ) with a cutoff of 80% of similarity. After it, 2. This model assumes the null hypothesis only the sequences of the five most abundant that microorganisms are randomly distributed phyla were used in the analysis. All sequences over space. Statistical significance was com - used in this study were deposited in Bioproject pared against the hypothesis of null structure SRX095440 of NCBI GeneBank. derived from 999 randomized matrices. High and positive values of both metrics indicate Sequence alignment and phylogenetic phylogenetic clustering, where taxa coexisting reconstruction: A total of 3 171 sequences in a local assemblage are more closely related

Fig. 1. Geographic location of the samples analyzed in this study.

Rev. Biol. Trop. (Int. J. Trop. Biol. ISSN-0034-7744) Vol. 64 (3): 1057-1065, September 2016 1059 to each other than expected by chance (Pontarp, Consequently, a majority of the communities Canbäck, Tunlid, & Lundberg, 2012), which that form microbialites analyzed in this study is consistent with the hypothesis that selec - tend to be phylogenetically clustered. On the tive filters such as environmental conditions other hand, there were no significant negative are responsible for community composition values for NRI or NTI, and thus overdispersion (Pérez-Gutiérrez et al., 2013). Negative values was not suggested as a relevant feature in any of NRI and NTI indicate overdispersion (Horn - of the assemblages studied (Table 1). er-Devine & Bohannan, 2006; Webb, Ack - In contrast, when we tested the phylo - erly, McPeeek, & Donoghue, 2002), in other genetic structure of each individual phylum words, that the taxa present in a community within the different localities we observed are less related to each other than expected by particular differences (Table 2). A significant chance, as expected if biological interactions clustering pattern was observed in Bacteroide - including parasitism, competition, predation tes and Planctomycetes measured by NTI and and facilitation are the main factors controlling NRI, whereas Proteobacteria and Verrucomi - community structure. crobia yielded positive and significant values only for NTI in the nine study localities. Finally, Cyanobacteria differed considerably RESULTS from all other phyla since only three of the All microbialites communities shared the nine communities analyzed (S, AC and AS) most abundant bacterial phyla, Proteobacte - showed phylogenetic clustering, while the rest ria, Planctomycetes, Verrucomicrobia, Bac - were randomly structured. There was no evi - teroidetes, and Cyanobacteria, that together dence of overdispersion in none of the phyla represented ~ 70 % of the original dataset. The analyzed (Table 2). phylogenetic analysis of the most abundant phyla identified in microbialites showed that DISCUSSION the net relatedness index (NRI) yielded positive and significant values in six of the nine locali - Explaining the distribution of free-living ties. Further, the nearest taxon index (NTI), microorganisms as well as understanding the showed positive and significant results for all forces that structure community composition is sites. The results of both metrics suggested that a central issue in (Hanson closely related taxa in a community were more et al., 2012; Stegen, Lin, Konopka, & Fredick - related than expected by chance (Table 1). son, 2012; Stomeo et al., 2013). It is generally

TABLE 1 NRI and NTI of microbialite forming communities from different geographic locations

Locality Key OTUs NRI NTI Southeastern Mexico Sian Ka’an Biosphere Reserve S 1 400 -29.207 41.400* Bacalar “Rapids” BR 1 553 -0.8992 52.131* Bacalar “Pirate Channel” BP 1 305 21.660* 57.257* Bacalar “Medio” BM 1 167 0.9290 56.194* Northeastern Mexico Río Mesquites RM 937 45.140* 56.202* Pozas Azules II PII 1 052 23.382* 56.679* Pozas Azules I PI 1 278 18.566* 31.550* Central Mexico Alchichica crater-lake spongy morphotype AS 721 28.606* 50.376* Alchichica crater-lake columnar morphotype AC 728 27.386* 47.196*

NRI = net relatedness index, NTI = nearest taxa index, OTU = operational taxonomic unit. Stars indicate statistically significant values (P < 0.05).

1060 Rev. Biol. Trop. (Int. J. Trop. Biol. ISSN-0034-7744) Vol. 64 (3): 1057-1065, September 2016 TABLE 2 NRI and NTI of major phyla in microbialite communities from different locations

Phylum Bacteroidetes Cyanobacteria Plactomycetes Proteobacteria Verrucomicrobia Locality OTUs NRI NTI OTUs NRI NTI OTUs NRI NTI OTUs NRI NTI OTUs NRI NTI S 122 2.94* 1.65* 73 2.60* 1.53* 231 2.77* 2.27* 820 -0.53 2.88* 154 1.04 1.41* BP 124 3.41* 3.05* 61 0.64 0.77 210 1.65 2.29* 770 -0.58 3.70* 140 1.18 1.46* BR 119 3.13* 2.24* 97 0.66 0.86 245 2.03* 1.65* 928 0.50 3.66* 164 1.07 1.49* BM 119 -1.14 2.36* 53 0.69 1.18 153 1.93* 2.72* 696 -1.50 3.69* 146 0.14 1.43* RM 57 0.61 1.35 17 0.23 0.34 158 3.51* 1.72* 625 -0.04 3.83* 80 1.23 1.77* PI 106 3.22* 2.54* 40 -0.81 1.43 216 1.71 2.91* 803 1.21 2.14* 113 1.69 1.50* PII 108 2.94* 2.79* 54 0.42 0.47 241 2.35* 3.45* 819 0.83 4.12* 125 1.71* 1.53* AS 67 0.51 2.08* 137 0.36 2.01* 80 4.46* 3.82* 417 3.95* 5.49* 29 -1.81 1.61* AC 57 1.37 1.91* 140 0.71 1.90* 73 2.53* 3.06* 916 7.99* 3.48* 158 -1.69 0.92

OTU = operational taxonomic unit, NRI = net relatedness index, NTI = nearest taxa index. Stars indicate statistically significant values (P < 0.05). S = Sian Ka’an Biosphere Reserve, BP = Bacalar “Pirate Channel”, BR = Bacalar “Rapids”, BM = Bacalar “Medio”, RM = Río Mesquites. PI = Pozas Azules I, PII = Posas Azules II, AS = Alchichica crater-lake spongy morphotype, AC = Alchichica crater-lake columnar morphotype.

assumed that community membership is gener - Bohannan, 2006). In other words, this supports ated through a mixture of stochastic processes the hypothesis that habitat characteristics or and micro-evolutionary processes (mutation, environmental stress can filter a community, selection, gene flow and genetic drift) (Hanson so that only closely related species can persist et al., 2012; Stegen et al., 2013) that include (Pontarp et al., 2013). In a previous published both environmental filtering and biotic inter - paper, Centeno et al. (2012) found that abiotical actions (Pérez-Gutiérrez et al., 2013). In this factors such as pH and conductivity explained sense, the analysis of the phylogenetic com - 33 % of the differences in community com - munity structure through NTI and NRI met - position in microbialites from the same study rics, has been an excellent tool to understand sites here included. In spite of such low value the composition of communities in relation it was higher than the 25 % of the variance to ecological interactions, especially for large explained by the same variables in prokary - data sets generated through next generation otic communities isolated from water columns sequencing approaches (Jones et al., 2009). in the same localities (Centeno, unpublished In this study, the global analysis of domi - data) and higher than the variation explained nant bacterial phyla recovered from complex by environmental variables (26 %) in other communities forming microbialites showed microbial studies (Hanson et al., 2012). The phylogenetic structuring and clustering of taxa low percentage of explained variance in genetic that are more closely related to co-occurring composition could be attributed to the compari - nearest relatives than expected by chance. sion of samples from a wide geographic gradi - This pattern can be explained by habitat filter - ent and not from a single locality. For example, ing as the main factor that drives community Mobberley et al. (2012) compared four types structure, where a group of closely related taxa of thrombolitic mats from a single locality in share a trait or set of traits, which allow them Bahamas, and found that 67 % of the variance to persist in a given habitat, making them suit - is explained by the two first principal coordi - able for those particular environmental condi - nates. Similar results were found by Schneider tions at specific localities (Horner-Devine & et al. (2013) analyzing genetic composition of

Rev. Biol. Trop. (Int. J. Trop. Biol. ISSN-0034-7744) Vol. 64 (3): 1057-1065, September 2016 1061 bacteria in a hypersaline lake at Kiritimati atoll Bacteroidetes, Verrucomicrobia and Cyano - Central Pacific, where almost 91 % of the vari - bacteria) we observed that phylogenetic struc - ance was explained by pH and depth; however, ture measured by NTI and NRI was variable. this value drops to 63 % when they compared We found that Proteobacteria, Planctomyce - their results with a similar hypersaline system tes, Bacteroidetes and Verrucomicrobia were in Guerrero Negro, Mexico. The clustering pat - phylogenetically clustered in specific sites, tern observed in microbialites considered in this whereas Cyanobacteria showed a tendency to study, would be related to micro-environmental be randomly structured. Since Cyanobacteria heterogeneity as proposed by Horner-Devine were amongst the main biological drivers of and Bohannan (2006), since these are complex microbialite formation (Reid et al., 2000), dif - biofabrics, where biogeochemical and physico - ferent species will be randomly associated to chemical gradients promote niche availability microbialitic fabrics, depending on the environ - in a few millimeters, mimicking what has been ment (Pholchan, Baptista, Davenport, Sloan, & described in microbial mat systems (Ley et al., Curtis, 2013). Similar results were found in a 2006; Harris et al., 2013). Similar clustering salt marsh microbial mat, where Armitage et patterns have been found in other microbial al. (2012) detected phylogenetic clustering of assemblages. For example, Bryant et al. (2008) bacteria in deeper layers of the mat structure, found that were phylogenetically while the clustering signal was lost in the top clustered in soils along an elevation gradient. layers where oxygenic phototrophs including Jones et al. (2009) found that acidobacterial cyanobacteria reside. Chase (2010) argued communities collected throughout North and that this tendency could be related to the func - South America, were more phylogenetically tion of the trophic consortia, where stochastic clustered as soil pH departed from neutrality, processes predominate in highly productive thus suggesting that pH is an effective habitat systems (in this case represented by the photo - filter that restricts community membership to trophic surface layer of the microbialites where progressively more narrowly defined lineages, resident cyanobacteria are located), whereas as pH deviates from neutrality. Barberán and deterministic processes predominate in lower- Casamayor (2010) used the NRI and NTI productive systems (in this case the portion metrics to evaluate processes that lead to the below the surface layer in the microbialites phylogenetic structuring of bacterioplankton, structures). The same trend has been observed and reported significant phylogenetic cluster - in terrestrial systems dominated by cyanobac - ing related to salinity and ionic composition. teria. For example, Stomeo et al. (2013) men - Amaral-Zettler et al. (2011) reported phyloge - tioned that heterotrophic assemblages could be netic clustering in microbial taxa inhabiting the explained partially by environmental factors, aquatic systems of Rio Tinto, Spain, indicating whereas the phototrophic bacterial assemblages that abiotic factors are primarily responsible exhibited strong signals related to stochasticity. for shaping community structure. Chong et Makhalanyane et al. (2013) found evidence al. (2012) found phylogenetic clustering in of random phylogenetic structure in desert soil bacterial assemblages in different sites hypolithic communities when cyanobacteria of Antarctica. Finally, Pontarp et al. (2012) and heterotrophic bacteria were analyzed sepa - analyzed the phylogenetic structure of marine rately, whereas species co-occurrence was non- bacterioplankton in coastal regions from nine random when bacterial groups were analyzed sampling points around the world, and also together. We hypothesize that the three cyano - suggested that marine bacterioplankton were bacterial ensembles that showed phylogenetic strongly structured by habitat filtering, where clustering in this study, including both micro - water temperature was the main factor. bialite types from Alchichica crater-lake and When we analyzed each bacterial phylum Sian Ka’an coastal lagoon, develop in stressful separately (Proteobacteria, Planctomycetes, environments that filter specific taxa, sharing

1062 Rev. Biol. Trop. (Int. J. Trop. Biol. ISSN-0034-7744) Vol. 64 (3): 1057-1065, September 2016 a common ancestry and common traits that sampling was conducted under permit PPF/ allow them to adapt to these systems (Pontarp DGOPA.033/2013 (LIF). et al., 2013). The samples included in this study comprised a limited latitudinal gradient RESUMEN of approximately 8º in latitude (from 18º 35’ N - 26º 55’ N), nevertheless, they were consistent Las condiciones del hábitat determinan la estruc - with those of Caruso et al. (2011), who evalu - tura filogenética de filos (phyla) bacterianos domi - ated desert microbial communities worldwide. nantes de comunidades de microbialitos de diferentes In both analyses, cyanobacteria showed evi - regiones en México . La estructura y composición de las comunidades son determinadas por procesos evolutivos dence of random colonization, while dominant y ecológicos que se manifiestan en señales de diversidad, heterotrophic bacteria showed a tendency to abundancia y la relación de especies. El análisis de la rela - phylogenetic clustering. ción de especies que coexisten ha recibido atención como The major bacterial phyla here analyzed una herramienta para identificar los procesos que influyen represent high diverse groups in terms of en la composición de una comunidad dentro de un hábitat particular. En este estudio, evaluamos si la composición genetic variation and metabolic functional - genética de microbialíticas depende de aconte - ity, the sequences proportions found were cimientos al azar vs factores biológicos/ abióticos. Este similar to data generated with more recent estudio se basa en un conjunto de datos genéticos de dos high-throughput sequencing technologies (e.g,. regiones hipervariables (V5 y V6) de gen 16S rRNA gene - Illumina) (Saghaï et al., 2015), supporting rados previamente de nueve comunidades de microbialitos distribuidos en el Noreste, Centro y Sureste de México, the importance of these taxa as members of recolectados en mayo y junio 2009. Los datos genéticos de microbialites communities. We suggested that los filos más abundantes (Proteobacteria, Planctomycetes, environmental factors are more important in Verrucomicrobia, Bacteroidetes y Cyanobacteria) fueron limiting bacterial structure and composition analizados para determinar la estructura filogenética de than biological factors in the microbialites of la comunidad y de cada filo por separado. Para el análisis conjunto, el índice NTI de Webb mostró valores positivos study, and microbialite communities in other y significativos en las nueve comunidades analizadas, en regions of the world must follow a similar phy - donde los valores oscilaron entre 31.5 en Pozas Azules I y logenetic pattern, although further studies are 57.2 en el Canal Pirata en Bacalar; en contraste, los valores necessary to validate this hypothesis. del índice NRI fueron positivos y significativos en seis de In conclusion, our results suggest that las nueve comunidades analizadas con valores oscilando desde 18.1 en Pozas Azules I hasta 45.1 en Río Mezquites. habitat filtering acts as the main structuring Por otro lado, en la comparación de cada filo individual, el process determining genetic composition of índice NTI fue positivo y significativo en todos los grupos communities forming microbialites. However, excepto en Cyanobacteria, en donde valores positivos y Cyanobacteria, which are key species in micro - significativos fueron encontrados sólo en tres localidades; bialite fabric formation (where they act as the finalmente, el índice NRI fue significativo sólo en unas cuantas de las comparaciones realizadas. Los resultados main photoautotrophs and diazotrophs), are the sugieren que el filtrado del hábitat es el proceso principal only lineage that did not show a phylogenetic que determina la estructura filogenética de las comunida - structure, suggesting that different cyanobac - des bacterianas asociadas a microbialitos con la excepción terial lineages can contribute to microbialite de las cianobacterias en donde diferentes linajes pueden formation and growth. contribuir a la formación y crecimiento del microbialito. Palabras clave: ensamble comunitario, composición ACKNOWLEDGMENTS comunitaria, cianobacterias, bacterias heterotróficas, microbialitos, NRI y NTI. This work was funded by CONACyT grant No. 151796, SIP 20150839 (OM) and UNAM- REFERENCES PAPIIT grant No. 100212 (LIF). CMC received a postdoctoral fellowship from CONACyT. Allwood, A. C., Walter, M. R., Kamber, B. S., Marshall, C. P., & Burch, I. W. (2006). reef from the Authors are grateful to Osiris Gaona and Anto - Early Archaean era of Australia. Nature, 441 (7094), nio Cruz-Peralta for technical assistance. All 714-718.

Rev. Biol. Trop. (Int. J. Trop. Biol. ISSN-0034-7744) Vol. 64 (3): 1057-1065, September 2016 1063 Amaral-Zettler, L. A., Zettler, E. R., Theorux, S. M., Pala - Dinger, E. C., Hendrickson, D. A., Winsborough, B. M., & cios, C., Aguilera, A., & Amils, R. (2011). Communi - Marks, J. C. (2006). Role of fish in structuring inver - ty structure across the in the extreme Río tebrates on stromatolites in Cuatro Cienegas, Mexico. Tinto. The ISME Journal, 5 (1), 42-50. Hydrobiology, 563 (1), 407-420.

Armitage, D. W., Gallagher, K. L., Youngblut, N. D., Buc - Dupraz, C., & Visscher, P. T. (2005) Microbial lithification kley, D. H., & Zinder, S. H. (2012). Millimeter-scale in marine stromatolites and hypersaline mats. Trends patterns of phylogenetic and trait diversity in a salt in , 13 (9), 429-438. marsh microbial mat. Frontiers in Microbiology, 3 , 293. Fierer, N., & Lennon, J. T. (2011). The generation and maintenance of diversity in microbial communities. Barberán, A., & Casamayor, E. O. (2010). Global phyloge - American Journal of Botany, 98 (3), 439-448. netic community structure and β diversity patterns in surface bacterioplankton metacommunities. Aquatic Foster, J. S., Green, S. J., Ahrendt, S. R., Golubic, S., Reid, Microbial Ecology, 59 (1), 1-10. R. P., Hetherington, K., & Bebout, L. (2009). Mole - cular and morphological characterization of cyano - Baumgartner, L. K., Spear, J. R., Buckley, D. H., Pace, N. bacterial diversity in the stromatolites of Highborne R., Reid, R. P., Dupraz, C., & Visscher, P. T. (2009). Cay, Bahamas. The ISME Journal, 3(5), 573-87. Microbial diversity in modern marine stromatolites, Highborne Cay, Bahamas. Environmental Microbio - Harris, J. K., Caporaso, J. G., Walker, J. J., Spear, J. R., logy, 11 (10), 2710-2719. Gold, N. J., Robertson, C. E., … Pace, N. R. (2013) Phylogenetic stratigraphy in the Guerrero Negro Boc, A., Diallo, A. B., & Makarenkov, V. (2012). T-REX: hypersaline microbial mat. The ISME Journal, 7(1), a web server for inferring, validating and visuali - 50-60. zing phylogenetic trees and networks. Nucleic Acids Research, 40 (W1), W573-W579. Hanson, C. A., Fuhrman, J. A., Horner-Devine, M. C., & Martiny, B. H. (2012). Beyond biogeographic Bolhuis, H., & Stal, L. J. (2011). Analysis of bacterial and patterns: processes shaping the microbial landscape. archaeal diversity in coastal microbial mats using Nature Reviews Microbiology, 10 (7), 497-506. massive parallel 16S rRNA gene tag sequencing. The ISME Journal, 5(11), 1701-1712. HilleRisLambers, J., Adler, P. B., Harpole, W. S., Levi - ne, J. M., & Mayfield, M. M. (2012). Rethinking Bryant, J. A., Lamanna, C., Morlon, H., Kerkhoff, A. J., community assembly trough the lens of coexistence Enquist, B. J., & Green, J. L. (2008). Microbes on theory. Annual Review of Ecology and Systematics, mountainsides: Contrasting elevational patterns of 43 , 227-248. bacterial and diversity. Proceedings of the National Academy of Sciences USA, 105 (Suppl. 1), Horner-Devine, M. C., & Bohannan, J. M. (2006). Phylo - 11505-11511. genetic clustering and overdispersion in bacterial Burne, R., & Moore, L. (1987). Microbialites: Organosedi - communities. Ecology, 87 (7), S100-108. mentary Deposits of Benthic Microbial Communities. Palaios, 2(3), 241-254. Jones, R. T., Robeson, M. S., Lauber, C. L., Hamady, M., Knight, R., & Fierer, N. (2009). A comprehensive Caruso, T., Chan, Y., Lacap, D. C., Lau, M., McKay, C. P., survey of soil acidobacterial diversity using pyro - & Pointing, S. B. (2011). Stochastic and deterministic sequencing and clone library analysis. The ISME processes interact in the assembly of desert microbial Journal, 3(4), 442-453. communities on a global scale. The ISME Journal, 5(9), 1406-1413. Letcher, S. G. (2010), Phylogenetic structure of angios - perm communities during tropical forest succession. Centeno, C. M., Legendre, P., Beltrán, Y., Alcántara- Proceedings of the Royal Society B, 277 (1678), Hernández, R. J., Lidström, U., Ashby, M., & Falcón, 97-104. L. I. (2012). Microbialite genetic diversity and com - position related to environmental variables. FEMS Ley, R. E., Harris, J. K., Wilcox, J., Spear, J. R., Miller, S. Microbiology Ecology, 82 (3), 724-735. R., Bebout, B. M., … Pace, N. R. (2006). Unexpec - ted diversity and complexity of the Guerrero Negro Chase, J. M. (2010). Stochastic Community Assembly hypersaline microbial mat. Applied and Environmen - Causes Higher Biodiversity in More Productive Envi - tal Microbiology, 72 (5), 3685-95. ronments. Science, 328 (5984), 1388-1391. Makhalanyane, T. P., Valverde, A., Lacap, D. C., Pointing, Chong, C. W., Pearce, D. A., Convey, P., Yew, W. C., & S. B., Tuffin, M. I., & Cowan, D. A. (2013). Evidence Tan, I. K. (2012). Patterns in the distribution of soil of species recruitment and development of hot desert bacterial 16S rRNA gene sequences from different hypolithic communities. Environmental Microbiolo - regions of Antarctica. Geoderma, 181 (2012), 45-55. gy Reports, 5(2), 219-224.

1064 Rev. Biol. Trop. (Int. J. Trop. Biol. ISSN-0034-7744) Vol. 64 (3): 1057-1065, September 2016 Martiny, J. B., Bohannan, B. J., Brown, J. H., Colwell, Saghaï, A., Zivanovic, Y., Zeyen, N., Moreira, D., Benze - R. K., Fuhrman, J. A., Green, J. L., … Staley, J. T. rara, K., Bertolino, P., … López-García, P. (2015). (2006). Microbial biogeography: putting microor - Metagenome-based diversity analyses suggest a sig - ganisms on the map. Nature Reviews Microbiology, nificant contribution of non-cyanobacterial lineages 4(2), 102-112. to carbonate precipitation in modern microbialites. Frontiers in Microbiology, 6(797). Mobberley, J. M., Ortega, M., & Foster, J. S. (2012). Com - parative diversity analyses of modern marine throm - Schneider, D., Arp, G., Reimer, A., Reitner, J., & Daniel, bolites by barcoded pyrosequencing. Environmental R. (2013). Phylogenetic Analysis of a Microbialite- Microbiology, 14 (1), 82-100. Forming Microbial Mat from a Hypersaline Lake of the Kiritimati Atoll, Central Pacific. PLoS ONE, Olsen, G. (1990). “Newick’s 8:45” Tree Format Standard. 8, e66662. Retrieved from http://evolution.genetics.washington. edu/phylip/newick_doc.html Stegen, J. C., Lin, X., Konopka, A. E., & Fredrickson, J. K. (2012). Stochastic and deterministic assembly Papineau, D., Walker, J. J., Mojzsis, S. J., & Pace, N. R. processes in subsurface microbial communities. The (2005). Composition and structure of microbial com - munities from stromatolites of Hamelin Pool in Shark ISME Journal, 6 (9), 1653-1664. Bay, Western Australia. Applied and Environmental Microbiology, 71 (8), 4822-4832. Stegen, J. C., Lin, X., Fredrickson, J. K., Chen, X., Kennedy, D. W., Murray, C. J., … Konopka, A. Pérez-Gutiérrez, R. A., López-Ramírez, V., Islas, A., Alca - (2013). Quantifying community assembly processes raz, L. D., Hernández-González, I., Olivera, B. C., … and identifying features that impose them. The ISME Olmedo-Álvarez, G. (2013). Antagonism influences Journal, 7(11), 2069-2079. assembly of a guild in a local community and is depicted as a food-chain network. The ISME Stomeo, F., Valverde, A., Pointing, S. B., McKay, C. P., Journal, 7(3), 487-497. Warren-Rhodes, K. A., Tuffin, M. I., & Cowan, D. A. (2013). Hypolithic and soil microbial community Pholchan, M. K., Baptista, J., Davenport, R. J., Sloan, assembly along an aridity gradient in the Namib W., & Curtis, T. P. (2013). Microbial community Desert. , 17 (2), 329-337. assembly, theory and rare functions. Frontiers in Microbiology, 4( 68). Thompson, J. D., Gibson, T. J., Plewniak, F., Jeanmou - gin, F., Higgins, D. G. (1997). The CLUSTAL_X Pontarp, M., Canbäck, B., Tunlid, A., & Lundberg, P. windows interface: flexible strategies for multiple (2012). Phylogenetic Analysis Suggests That Habitat sequence alignment aided by quality analysis tools. Filtering Is Structuring Marine Bacterial Commu - Nucleic Acids Reserarch, 25 (24), 4876-4882. nities Across the Globe. Microbial Ecology, 64 (1), 8-17. Webb, C. O. (2000). Exploring the Phylogenetic Structure of Ecological Communities: An Example for Rain Pontarp, M., Ripa, J., & Lundberg, P. (2012). On the origin Forest Trees. American Naturalist, 156 (2), 145-155. of phylogenetic structure in competitive metacom - munities. Evolutionary Ecology Research, 14 (3), Webb, C. O., Ackerly, D. D., McPeek, M. A., & Donoghue, 269-284. M. J. (2002). Phylogenies and community ecology. Annual Review of Ecology and Systematics, 33 , Pontarp, M., Sjöstedt, J., & Lundberg, P. (2013). Experi - 475-505. mentally induced habitat filtering in marine bacte - rial communities. Marine Ecology Progress Series, 477 (77), 77-86. Webb, C. O., Ackerly, D. A., & Kembel, S. W. (2008). Phylocom: software for the analysis of phylogenetic Pringault, O., De Wit, R., & Camoin G. (2005). Irra - community structure and trait evolution. Bioinforma - diance regulation of photosynthesis and respiration tics, 24 (18), 2098-2100. in modern marine microbialites built by benthic cyanobacteria in a tropical lagoon (New Caledonia). Westphal, H., Heindel, K., Brandano, M., & Peckmann, J. Microbial Ecology, 49 (4), 604-616. (2010). Genesis of microbialites as contemporaneous framework components of deglacial coral reefs, Tahi - Reid, R. P., Visscher, P. T., Decho, A. W., Stolz, J. F., ti (IODP 310). Facies, 56 (3) , 337-352. Bebout, B. M., Dupraz, C., … DesMarais, D. J. (2000). The role of microbes in accretion, lamination Zhou, J., Bruns, M. A., & Tiedje, J. M. (1996). DNA reco - and early lithification of modern marine stromatoli - very from soils of diverse composition. Applied and tes. Nature, 406 (6799), 989-992. Environmental Microbiology, 62 (2), 316-322.

Rev. Biol. Trop. (Int. J. Trop. Biol. ISSN-0034-7744) Vol. 64 (3): 1057-1065, September 2016 1065