OUP user menu

Benthic bacterial diversity from freshwater tufas of the Iberian Range (Spain)

Hugo Beraldi-Campesi, Concepción Arenas-Abad, Ferran Garcia-Pichel, Omar Arellano-Aguilar, Luis Auqué, Marta Vázquez-Urbez, Carlos Sancho, Cinta Osácar, Silvia Ruiz-Velasco
DOI: http://dx.doi.org/10.1111/j.1574-6941.2012.01303.x 363-379 First published online: 1 May 2012

Abstract

Aiming to characterize the bacterial diversity of modern tufa systems of the Iberian Range (Spain), we surveyed the 16S rRNA gene sequence diversity from 24 sites within three rivers (Añamaza, Mesa and Piedra). These tufas record substantial calcareous growth under different physicochemical conditions and are part of an important, regional landscape-building system. The bacterial community structure and composition, richness and diversity were quantified from denaturing gradient gel electrophoresis fingerprints. Retrieved DNA sequences could be assigned to 10 bacterial phyla and included a variety of phototrophic and heterotrophic groups. Cyanobacteria, mainly filamentous taxa, constituted 43% of all the retrieved sequences, followed by Firmicutes (11%), Gammaproteobacteria (10%), Alphaproteobacteria (7%), Acidobacteria (6%), Bacteroidetes (5%), Betaproteobacteria (4%), Planctomycetes (4%), Actinobacteria (3%) and Deltaproteobacteria (2%). Diatom and Xanthophyceae chloroplast sequences were also detected. Physicochemical variables measured at each site were modelled with multivariate statistics. Principal component analyses yielded the highest variance for salinity-related variables (conductivity; Na+, Cl and Embedded Image concentrations), which correlated negatively and significantly with diversity indices. However, the highest variance explained by individual principal components was relatively low (< 34%). Overall, we show that these young fluvial tufas are inhabited by a large variety of bacteria in diverse and widespread communities.

Keywords
  • freshwater
  • tufa
  • bacterial diversity

Introduction

Freshwater tufas are calcium carbonate (CaCO3) rocks that precipitate in low-temperature (≤ 30 °C; i.e. not hydrothermal) fluvial waters. They form in springs and rivers that drain and crosscut carbonate-rich bedrocks. Aquifers in these areas provide surface waters with abundant dissolved calcium and bicarbonate that precipitate as CaCO3. Precipitation can occur abiotically through physical CO2 degasification (Merz-Preiβ & Riding, 1999; Chen et al., 2004), but it can also be mediated biologically (Pentecost, 1988; Pedley et al., 2009; Arp et al., 2010). Tufa deposits develop sedimentary structures and fabrics depending on the bed topography, the chemical and hydrological conditions, and the presence, type and amount of associated benthic organisms. Further characteristic facies can be defined by the thickness of the deposit, and its density, lamination and overall morphology (see Arenas-Abad et al., 2010; Arenas et al., 2010; Gradzinski, 2010; Vázquez-Urbez et al., 2010).

Tufas are a common landscape feature throughout the Iberian Range in Spain (Fig. ). In this region, Mesozoic dolostones and limestones hold aquifers that feed several rivers discharging into the Ebro, Tajo and Júcar basins (Fig. ). While plants and mosses in these rivers have been described (see Martínez-Rica, 1998; Oliván et al., 2005; Arnáez et al., 2008; and references therein), the microbial biota has not. Cyanobacteria, macroscopic algae and mosses are notorious and always visible inhabitants on tufa surfaces and likely have a relevant role as primary producers sustaining complex microbial communities. Recent surveys using DNA sequencing tools (Ng et al., 2006; Cousin et al., 2007; Santos et al., 2010) have demonstrated that microbial communities in tufas can be richer and more complex than previously thought and likely have an impact on mineral precipitation and the development of characteristic textures and facies.

Geographical location of the Iberian Range in Spain, indicating the distribution of the studied rivers and the regional geology. Sampling sites are sequentially numbered for the Añamaza (inset ‘1’ A-1 through A-10) and Piedra and Mesa rivers (inset ‘2’ P-1 through P-20, and M-1 through M-10, respectively), along with the main human settlements along the rivers.

As a first approach to assess the microbial diversity in modern tufas of the Iberian Range and to detect possible correlations between diversity and physicochemical variables of these systems, we surveyed three tufa-rich rivers Añamaza, Mesa and Piedra (Ar, Mr and Pr hereafter) for 16S rRNA gene bacterial sequences, using fingerprinting techniques. Plausible correlations between the diversity and the water physicochemistry are discussed based on statistical models.

Geological and environmental context

The Iberian Range is a NW-SE-oriented, ~ 500-km-long mountain range that separates the Ebro and Central basins (Fig. ). The three studied rivers flow northwards and are indirect tributaries of the Ebro River. They cut through extensive and thick Jurassic and Upper Cretaceous marine carbonate rocks and Triassic evaporitic, sulphate-rich rocks (Gibbons & Moreno, 2002). The calcium bicarbonate–rich water is mainly derived from the Mesozoic carbonate aquifers that feed the rivers locally and regionally (Coloma et al., 1999; Pinuaga-Espejel et al., 2004; Valero-Garcés et al., 2008; Vázquez-Urbez et al., 2011), and keep the rivers flowing during dry seasons.

Dilution of the waters occurs twice a year during seasonal rains. The climate is continental Mediterranean, with strong seasonal changes in temperature and precipitation. The mean air temperature ranges between 8 and 12 °C in winter and 20–23 °C in summer, and annual precipitation varies from 270 to 470 mm, mostly occurring during spring and autumn (data provided by the Agencia Estatal de Meteorología, Spain).

Prominent Pleistocene and Holocene tufa deposits are conspicuous along the valleys of these rivers and speak for the longevity these systems on Earth (Arenas et al., 2004, 2009; Vázquez-Urbez et al., 2010, 2011). The Añamaza valley records slightly wetter and cooler conditions compared to the Piedra-Mesa region (Fig. ; Vázquez-Urbez et al., 2011). This region also includes upper Jurassic–lower Cretaceous lacustrine and fluvial units with carbonate, evaporites and fine detrital facies (i.e. Tera and Oncala Groups). Some of these contain gypsum pseudomorphs (Mas et al., 2011) and pyrite. These sulphur components would be responsible, at least in part, for the Embedded Image content of the water (Arenas et al., 2009).

Materials and methods

Sampling sites and physicochemical variables

Each of the studied rivers have been monitored for physical, chemical and biological parameters (facies, depositional rates, stable isotope composition of sediment and water, and water chemistry) for the past decade (Pr = 20 sites, 1999–2010; Mr = 10 sites, 2003–2009; and Ar = 10 sites, 2007–2010; see also Arenas et al., 2004, 2009; Vázquez-Urbez et al., 2010, 2011). This includes water temperature, conductivity, pH, dissolved ions (Embedded Image, Cl, Embedded Image, Na+, K+, Ca2+ and Mg2+), flow velocity and water depth (Table ). Depositional rates of CaCO3 have been measured every 6 months using limestone tablets attached to the underwater substrate at different sites (see Vázquez-Urbez et al., 2010). Measurements for this study were taken from December to January and at the end of June for water chemistry, water depth and velocity, and in March and September for tufa accumulation, water depth and velocity. Table summarizes the characteristics of the depositional environments and correlative sedimentary facies. Samples for microbial analyses were taken from the close surroundings of monitoring tablets during one sampling campaign at the beginning of July 2007.

View this table:

Geographic location, physical and chemical variables measured at the sampling sites of the Añamaza, Mesa and Piedra rivers at the end of June 2007

SiteLatLonVel (cm s−1)Depth (cm)CaCO3 (mm)FaciesTemp (°C)Conduct (MicroS)pHEmbedded Image ppmCl ppmEmbedded Image ppmNa+ ppmK+ ppmCa2+ ppmMg2+ ppm
A141.889583−1.96109246110E15.37927.8265.957.63220.903.181.50123.0020.67
A241.901939−1.9479613016NRB215.88118.34276.388.53213.553.531.48138.4021.22
A341.909136−1.94106115016NRC215.78318.75279.409.24207.254.091.71131.6021.32
A541.911553−1.92869714459.41C116.18148.77262.9310.90220.054.732.40108.7021.31
A641.913992−1.930697571NRA15.67968.89214.9411.40228.934.322.6199.3021.30
A741.920097−1.9313754521.91C115.87698.71202.4211.60210.034.792.3697.3021.50
A841.941606−1.9067945212NRB115.47688.66202.8814.40211.985.791.9996.0021.26
A941.941606−1.90679410180.89C115.47688.66202.8814.40211.985.791.9996.0021.26
A1041.962358−1.90465041170.68E167568.63197.3213.90211.456.052.08115.3021.44
M141.116775−1.99529211170.14C214.56567.99281.9552.8063.9529.622.2979.5020.63
M241.117331−1.994628155210.56A14.56708.08261.0854.2072.8729.192.2678.1020.75
M341.124964−1.98046765160E15.57697.93281.2582.5080.7550.892.6976.6022.71
M441.143908−1.94111990230C216.77448.18247.4080.5090.2054.722.6376.8022.63
M541.145275−1.941044101190A16.17408.27247.4080.1083.3748.252.3479.6022.59
M641.156094−1.912828118200C216.97248.30235.1180.4083.9053.932.6463.9022.49
M741.156172−1.91259217562.41C117.37248.27237.6678.4079.1753.702.4571.0022.82
M841.157525−1.90862850160.79C217.57028.30240.4478.9080.2252.122.3484.8022.32
M941.171700−1.898719141120.15C218.77038.39232.3378.9079.7040.952.7372.6022.57
M1041.190911−1.876492131110E21.57577.68299.8052.2085.1228.992.6076.1029.56
P141.091611−1.7706815371.59E16.46347.32337.8316.1027.156.902.4676.1026.38
P241.115572−1.78073110270.66E17.76988.20300.5014.7099.577.162.6383.1027.38
P341.158456−1.7871501134.52.40C117.26818.17286.5816.00100.997.112.4582.3026.95
P441.165500−1.790278004.79A17.06718.10270.3516.70106.446.822.1379.6026.78
P541.174972−1.791969981.55.48A16.96508.16249.4916.50100.706.862.3675.6027.41
P641.175603−1.79112825231.47B116.96738.13277.0815.80110.146.791.4482.6027.31
P741.190081−1.78262219126.95B119.46828.07279.6317.10101.227.513.0983.3027.47
P841.190081−1.78262211132.78C1-A19.46828.07279.6317.10101.227.513.0983.3027.47
P941.190081−1.78262219454.59C119.46828.07279.6317.10101.227.513.0983.3027.47
P1041.191256−1.7819444321NRB116.26788.11262.7015.60105.946.721.9378.2027.04
P1141.191361−1.781231005.24C116.16608.12253.4315.70106.477.161.9578.1027.17
P1241.191361−1.781231004.24C116.16608.12253.4315.70106.477.161.9578.1027.17
P1341.191361−1.781231000.23D16.16608.12253.4315.70106.477.161.9578.1027.17
P1441.191606−1.782000240118.08A16.16768.16256.6717.60102.777.222.4879.4027.15
P1541.191731−1.78262815288.91A16.26678.07253.6616.90108.046.561.9377.0027.12
P1641.191831−1.783292002.40D16.26147.90228.3916.80108.577.201.8175.1027.26
P1741.191831−1.783292001.54C116.56357.93238.3615.70109.626.291.7183.5026.47
P1841.191886−1.78360317679.65A16.46648.21251.8016.30111.726.522.0482.8027.12
P1941.192011−1.78510677195.69B116.36548.31248.3316.20113.827.382.1087.0026.52
P2041.197533−1.79309283190E16.46508.03254.8218.00111.197.632.8280.5027.06
  • NR, not recorded.

  • Sampling sites are indicated per river as A (Añamaza), M (Mesa) and P (Piedra) rivers. Increasing numbers reflect down-stream direction.

  • It refers to the total thickness of sediment accumulated on the tablets in 6 months (April–September 2007).

  • See facies codes and descriptions in Table .

  • Sites P4, P11, P12 and P17 often had a thin water lamina (mostly turbulent flow) ranging between 0.1 and 0.5 cm in depth. Approximate water velocity was estimated (with floating objects) as ranging between 30 and 90 cm s−1.

  • Sites P13 and P16 were dominated by spray and splash, rarely developing a very thin water lamina (≈ 0.1 cm).

View this table:

Characteristics of the depositional environments and correlative sedimentary facies (compiled from Vázquez-Urbez et al., 2010, 2011, and Auqué et al., in press)

Depositional subenvironmentWater velocity (cm s−1)Depth (cm)Sedimentary facies
Areas of fast-flowing water, including steep stretches along the river bed, devoid of bryophytes and macrophytes90–2207–11A, Stromatolitic, dense laminated tufa, formed of microbial bodies, preserved as μm size, tube-shaped calcite structures
Areas of slow-flowing to standing water, upstream and downstream of waterfalls and barrages20–6010–45B, Loose, commonly nonlaminated sediment: B1, Lime mud, diverse mm-cm carbonate grains. Rare oncolites. Boundstones of macrophytes in palustrine conditions. B2, Loose lime mud with clays and fine to very coarse sand-size, allochthonous and autochthonous carbonate grains
Stepped waterfalls and small falls (1–10 m high) with bryophytes30–90Water film (mm)C1, Mostly spongy tufa: mats of mosses, filamentous algae, cyanobacteria and herbaceous plants, coated with calcite. Rare and poor lamination and banding
Small, generally stepped jumps, rapids, subhorizontal platforms and irregular horizontal beds with cobbles70–1109–25C2, Mostly spongy tufa: mats of filamentous algae, mosses, cyanobacteria and diatoms, poorly coated with calcite. Lamination is commonly absent.
Spray and splash areas adjacent to waterfallsNo water laminaSprayD, Mats of cyanobacteria, mosses and filamentous algae coated and impregnated with calcite. Commonly thin, nonlaminated deposits
Areas with gravel and cobble sediment in low-angle inclination and horizontal beds; in some cases close to spring inputs0–5010–140E, Detrital, mostly nontufaceous sediment: sparse biota and little carbonate sediment. In some places, poorly calcite-coated filamentous algae and characeae, mosses and microbial films

Microbiological sampling

For each of the sites sampled for bacteria (marked with * in Table : six sites in Ar, 10 in Mr and 16 in Pr), three to five, ~ 0.5-cm3 chips of underwater, solid tufa were excised mechanically (with clean metal corers and spatulas) in areas free of mosses and macroscopic algae. Carbonate chips were placed in sterile 2-mL plastic vials and immediately frozen in liquid nitrogen, transported to the laboratory and stored at −80 °C until further processing.

DNA extraction and quantification

Under a sterile laminar flow hood, for each site, all the samples collected there were aseptically combined by crushing them together in a mortar. A measured aliquot of 0.5 mL of the slurry was transferred into a tube containing bead solution of a commercial DNA extraction kit for soils (Ultra Clean Soil DNA isolation kit, MoBio Laboratories, Solana Beach, CA). There were a total of 32 DNA vials. The DNA was further purified using a QIAquick DNA Purification Kit (Qiagen, Valencia, CA). DNA quality and quantity were determined from electrophoresis agarose gels (Rothrock & García-Pichel, 2005) using a Bio-Rad Fluor-S Multi-Imager system (Bio-Rad Laboratories, Hercules, CA) with a Bio-Rad EZ-load Precision molecular mass standard and the Quantity-One software v. 4.2.1 (Bio-Rad, Richmond, CA).

Microbial community fingerprinting analyses

Purified DNA, one vial per site, was amplified through PCR using universal bacterial primers GC-GM5F and 907R (Muyzer et al., 1995). The PCR product was then purified using a QIAquick PCR Purification Kit (Qiagen) and quantified by agarose gel electrophoresis. Approximately 300 ng of the amplified DNA was loaded into a denaturing gradient gel electrophoresis (DGGE), using a 30–60% chemical gradient of denaturants (urea-formamide) on a 6% polyacrylamide gel (Muyzer et al., 1996) to separate the 16S rRNA gene amplified fragments. Internal standards, custom-made with 16s rRNA gene fragments of three different bacteria in pure culture were used to align different gel images. DGGE gels for each river were electrophoresed for 10 h at 100 V in a Bio-Rad Dcode Universal Mutation Detection System (Bio-Rad Laboratories). Each DGGE was stained with SybrGold dye (Bio-Rad) for 30 min in the dark and imaged with a Bio-Rad Fluor-S Multi-Imager. Conspicuous bands were excised from the gels with sterile scalpels and placed in sterile vials with 50 μL of 10 mM Tris buffer. The vials were left at 4 °C for 3 days to elute the DNA, which was then purified, re-amplified by PCR and sequenced at the DNA Laboratory of the Arizona State University. The sequences were handled with Sequencing Analysis Software version 5.2 (Applied Biosystems, LLC, by Life Technologies, Carlsbad, CA) and mega 4 (Tamura et al., 2007) and compared to known 16S rRNA gene sequences from public databases using the BLASTn function (http://blast.ncbi.nlm.nih.gov). Sequences with < 80% similarity to published sequences were considered artifacts and removed from the study.

Quantification of richness and diversity

Digitized images of DGGE fingerprints were used to quantify the number of bands per lane, their crosslane correlation with neighbouring bands and their relative pixel intensity, using Quantity-One software (Bio-Rad). Richness per site was assessed as the number of bands in a lane. The cumulative richness per river was calculated as the total number of horizontal positions of bands in all the lanes of one river. Shannon–Weaver diversity indices (Shannon & Weaver, 1949) were calculated assigning to each band a relative abundance based on its pixel intensity, divided by the sum of intensities detected in all bands in its lane and normalized to the known intensity of the standards for cross comparison of DGGEs (see Nübel et al., 1999). The average diversity per river was calculated as the average of the indices in that river.

Statistical analyses

Cluster analyses (Everitt & Dunn, 1991) and principal component analysis (PCA; Manly, 1994) were used to visualize the community structure and correlate indices of microbial diversity with physicochemical variables. upgma dendrograms were built upon DGGE banding patterns using the Quantity-One software v. 4.6.3. (Bio-Rad), applying the Dice coefficient of similarity. Dendrograms of physicochemical data were built with the statistical package Minitab v.15 (Minitab Inc., PA), using a complete linkage methods and euclidean distances between data points. Imputations for ‘NR’ data (Table ) were replaced with their series mean using the statistical package spss 17.0 for Windows (SPSS Inc., Chicago, IL). PCA of the physicochemical data was performed with the statistical package spss 17.0 for Windows and graphs handled with BiPlot (Lipkovich & Smith, 2002). Five principal components (PCs), of 13, were retained, which explained 87.14% of the variance contained in the original data set. Linear regression models were fitted using the diversity index as the response variable and each one of the PC as explanatory variables.

Results

Phylogenetic assignments

A total of 94 good-quality sequences were obtained from the DGGEs (Table ). They could be assigned to ten divisions of the bacteria. Cyanobacteria constituted 43% of all the sequences, followed by Firmicutes (11%), Gammaproteobacteria (10%), Alphaproteobacteria (7%), Bacteroidetes (6%), Betaproteobacteria (5%), Acidobacteria (4%), Planctomycetes (4%), Actinobacteria (3%) and Deltaproteobacteria (2%; Fig. ). Four per cent of the sequences corresponded to chloroplasts of Xanthophyceae and diatoms (1% goes in the decimals, not shown).

Percentage of bacterial sequences within bacteria divisions retrieved from DGGEs and according to their closest GenBank relative.

View this table:

Taxonomical affinity between 16S rRNA gene sequences from this study and sequences from GenBank. Only sequences with ≥ 80% similarity using blast are presented

SiteSequence was similar to% SimilarityPhylumAccession no
A2Pleurocapsa sp.97CyanobacteriaHQ271088
A2Oscillatoria acuminata96CyanobacteriaHQ271089
A5Phormidium sp.92CyanobacteriaHQ271090
A5Uncultured Acidobacteria bacterium96AcidobacteriaHQ271091
A9Bacillus sp.91FirmicutesHQ271092
M2Bosea sp.87AlphaproteobacteriaHQ271098
M2Phormidium autumnale95CyanobacteriaHQ271099
M3Phormidium pristleyi98CyanobacteriaHQ271100
M3Synechococcus sp.97CyanobacteriaHQ271101
M3Porphyrobacter sp.98AlphaproteobacteriaHQ271102
M4Phormidium murrayi85CyanobacteriaHQ271103
M7Flavobacterium sp.87BacteroidetesHQ271104
M7Tychonema bourrellyi99CyanobacteriaHQ271105
M8Pleurocapsa sp.96CyanobacteriaHQ271106
M8Uncultured gamma proteobacterium92GammaproteobacteriaHQ271107
M8Nostoc sp.94CyanobacteriaHQ271108
M9Vaucheria litorea chloroplast100XanthophyceaeHQ271109
M9Microcoleus steenstrupii96CyanobacteriaHQ271110
M10Phormidium autumnale99CyanobacteriaHQ271093
M10Tychonema bourrellyi99CyanobacteriaHQ271094
M10Phormidium setchellianum88CyanobacteriaHQ271095
M10Phormidium autumnale98CyanobacteriaHQ271096
M10Sphingomonas sp.87AlphaproteobacteriaHQ271097
P1Solibacter usitatus98AcidobacteriaHQ271112
P1Uncultured Firmicutes bacterium94FirmicutesHQ271117
P1Uncultured Solibacter sp.82AcidobacteriaHQ271118
P1Dokdonella sp.88GammaproteobacteriaHQ271126
P1Pseudanabaena sp.93CyanobacteriaHQ271124
P1Uncultured Planctomycetes bacterium81PlanctomycetesHQ271133
P1Uncultured Planctomycetes bacterium87PlanctomycetesHQ271125
P1Uncultured Planctomycetes bacterium94PlanctomycetesHQ271134
P3Microcoleus rushforthii100CyanobacteriaHQ271148
P3Pseudomonas sp.82GammaproteobacteriaHQ271149
P3Phormodium pristleyi93CyanobacteriaHQ271150
P4Tychonema bourrellyi97CyanobacteriaHQ271151
P4Uncultured Myxococcales96DeltaproteobacteriaHQ271152
P5Vaucheria litorea chloroplast91XanthophyceaeHQ271154
P5Uncultured Beggiatoa sp.92GammaproteobacteriaHQ271155
P5Marichromatium sp.90GammaproteobacteriaHQ271156
P5Phormidium subfuscum96CyanobacteriaHQ271157
P5Sphingopyxis sp.94AlphaproteobacteriaHQ271158
P5Sphingopyxis sp.92AlphaproteobacteriaHQ271159
P5Vaucheria litorea chloroplast97XanthophyceaeHQ271153
P5Beta proteobacterium84BetaproteobacteriaHQ271160
P5Nostoc punctiforme100CyanobacteriaHQ271161
P5Lyngbya majuscula95CyanobacteriaHQ271162
P6Nostoc sp.100CyanobacteriaHQ271163
P6Phormidium tenue100CyanobacteriaHQ271164
P6Phormidium lumbricale94CyanobacteriaHQ271165
P6Exiguobacterium sp.87BacteroidetesHQ271166
P6Flavobacterium sp.85BacteroidetesHQ271167
P6Phormidium autumnale92CyanobacteriaHQ271168
P6Bacillus sp.89FirmicutesHQ271169
P8Uncultured Firmicutes bacterium97FirmicutesHQ271170
P8Uncultured Firmicutes bacterium93FirmicutesHQ271171
P8Uncultured actinobacterium98ActinobacteriaHQ271172
P8Uncultured diatom chloroplast96DiatomHQ271173
P8Uncultured Myxococcales bacterium86DeltaproteobacteriaHQ271174
P8Flavobacterium croceum92BacteroidetesHQ271175
P8Beta proteobacterium95BetaproteobacteriaHQ271176
P8Tychonema bourrellyi98CyanobacteriaHQ271177
P8Oscillatoria sp.85CyanobacteriaHQ271178
P9Microcoleus steenstrupii94CyanobacteriaHQ271179
P9Desulfotomaculum sp.84FirmicutesHQ271180
P9Uncultured Firmicutes bacterium81FirmicutesHQ271181
P10Tychonema bourrellyi98CyanobacteriaHQ271111
P11Calothrix anomala91CyanobacteriaHQ271113
P11Lysobacter pocheonensis93GammaproteobacteriaHQ271114
P11Bacillus niacini96FirmicutesHQ271115
P11Oscillatoria acuminata88CyanobacteriaHQ271116
P14Uncultured Sphingobacterium sp.96BacteroidetesHQ271119
P14Uncultured beta proteobacterium90BetaproteobacteriaHQ271120
P14Variovorax sp.88BetaproteobacteriaHQ271121
P15Dokdonella sp.92GammaproteobacteriaHQ271122
P15Oscillatoria earlei89CyanobacteriaHQ271123
P16Uncultured Rhodobacter sp.90AlphaproteobacteriaHQ271127
P16Leptolyngbya valderiana98CyanobacteriaHQ271128
P16Arthrobacter sp.100ActinobacteriaHQ271129
P16Uncultured Blastococcus sp.96ActinobacteriaHQ271130
P17Uncultured Beggiatoa sp.81GammaproteobacteriaHQ271131
P17Tychonema sp.85CyanobacteriaHQ271132
P18Bacillus sp.100FirmicutesHQ271135
P18Phormidium autumnale99CyanobacteriaHQ271136
P18Uncultured Acidobacteria bacterium99AcidobacteriaHQ271137
P18Methylophilus sp.93BetaproteobacteriaHQ271138
P18Phormidium autumnale100CyanobacteriaHQ271139
P18Lewinella cohaerens96BacteroidetesHQ271140
P18Tychonema bourrellyi94CyanobacteriaHQ271141
P18Bacillus psychrodurans98FirmicutesHQ271142
P19Phormodium pristleyi95CyanobacteriaHQ271143
P19Uncultured Pirellula sp.83PlanctomycetesHQ271144
P20Erythrobacter sp.81AlphaproteobacteriaHQ271145
P20Phormidium pristleyi97CyanobacteriaHQ271146
P20Acidithiobacillus sp.89GammaproteobacteriaHQ271147

The dominant group of the studied communities, the oxygenic, photoautotrophic Cyanobacteria, was represented by ~ 80% of nonheterocystous, filamentous taxa (Leptolyngbya sp., Lyngbya sp., Microcoleus sp., Oscillatoria sp., Phormidium sp., Pseudanabaena sp. and Tychonema sp.), well known from marine and terrestrial habitats, including tufas (Pentecost, 2005; Cousin & Stackebrandt, 2010; and references therein). Nitrogen fixers included Calothrix sp., Nostoc sp. and Pleurocapsa sp., which are also known from freshwater tufas (Cousin & Stackebrandt, 2010). Synechococcus sp. is a unicellular, cosmopolitan cyanobacterium, with species known from all aquatic habitats (e.g. Whitton & Potts, 2000). Most of the cyanobacterial sequences found here were correlated with published sequences of known freshwater, calcifying cyanobacteria (Pentecost, 2005; Krumbein & Giele, 2009; Arp et al., 2010).

The Firmicutes was dominated by Bacillus sp., heterotrophic, aerobic, endospore producers, and one Desulfotomaculum sp., a widespread sulphate-reducing bacteria (Lin et al., 2006). The Gammaproteobacteria were represented by a number of phylotypes: Acidithiobacillus sp., a sulphur–iron oxidizer known from soils and mine drainages (Waksman & Joffe, 1922); Dokdonella sp., a heterotroph also found in soils (Yoon et al., 2006); Lysobacter sp., a genus comprising chitin, starch, cellulose and lignin decomposers, with stringent exoenzymatic capabilities, often harmful for other organisms (Christensen & Cook, 1978; Roesti et al., 2005); Pseudomonas sp., Gram-negative rods, usually aerobic and capable of degrading a great variety of organic compounds (Madigan et al., 2003); Beggiatoa sp., a filamentous sulphide-oxidizing chemolithotroph, mostly autotroph or mixotroph (see Strohl & Larkin, 1978; Nelson & Castenholz, 1981); and Marichromatium sp., an anaerobic, purple sulphur phototrophic bacterium of marine habitats but with close relatives (e.g. Chromatium sp.), recently reported from tufas (Cousin & Stackebrandt, 2010). Six Alphaproteobacteria phylotypes were detected, some of which are known from other tufa systems. For example, Sphingomonas sp. is a heterotroph found in freshwater tufas (Cousin & Stackebrandt, 2010), as well as in freshwater, human-impacted habitats (Min & Rickard, 2009); Bosea sp. comprises chemolithoheterotrophs capable of oxidizing sulphur compounds in the presence of organic carbon and has been found in soil and freshwater sediments (Das et al., 1996; Pesce & Wunderlin, 2004); Eryhtrobacter sp., Porphyrobacter sp. and Rhodobacter sp. (relatives of Sphingomonas sp.) are aerobic anoxygenic phototrophs, mainly from marine environments, but also found in hot and cold freshwaters, soils and tufas, along with Sphingopyxis sp. (Koblížek et al., 2003; Rainey et al., 2003; Lee et al., 2008; Cousin & Stackebrandt, 2010). The Bacteroidetes included phylotypes allied to typical fermenters (such as Sphingobacterium sp. and Exiguobacterium sp.) and chitin decomposers (Flavovacterium sp.), occurring naturally in soils and freshwater systems (Abed et al., 2002; Cousin et al., 2007; Ali et al., 2009). Sequences within the Betaproteobacteria included relatives of Variovorax sp., a soil heterotroph (Willems et al., 1991; Jamieson et al., 2009) and Methylophilus sp., a methanol consumer also found in tufas (Cousin & Stackebrandt, 2010). The Acidobacteria contained the genus Solibacter sp., a slow-growing, soil heterotroph, known to degrade chitin, cellulose, pectin and starch, among other substrates, and capable of Embedded Image and Embedded Image reduction (Ward et al., 2009). We detected a few Planctomycetes, known from a variety of habitats, including soil and freshwater, and recently found in tufas (Cousin & Stackebrandt, 2010). Particularly, the genus Pirellula sp. has been found in flooded rice fields, where it can live under anoxic conditions upon ammonia oxidation (Miskin et al., 1999; Derakshani et al., 2001), although it also has marine and brackish water relatives (e.g. Glöckner et al., 2003). The Actinobacteria contained Blastococcus sp., an aerobic heterotroph known, among other environments, from carbonate surfaces of arid, tropical and temperate climates (Urzì et al., 2001). Although poorly resolved, the Deltaproteobacteria were represented by the Myxococcales, a group of heterotrophs with bacteriolytic and cellulolytic capabilities, apparently abundant in tufas (Cousin & Stackebrandt, 2010).

Community structure

The DGGE banding patterns, a reflection of the community composition, revealed numerous phylotypes in each site. Darker and thicker bands, interpreted as dominant phylotypes, were oftentimes distributed across sites (Fig. ). Cumulative richness was counted as 43 distinguishable phylotypes in the Pr, 32 in the Mr and 33 in the Ar. Sites 1, 2 and 5 of the Ar doubled the number of bands compared to sites 7, 9 and 10. In the Mr, richness decreased five times from site 1 to 5 and recovered through sites 6–10. In the Pr, richness was always relatively high with minor oscillations. Sites in the Mr were the least diverse in average (Shannon's diversity index = 2.41 ± 0.73; range = 1.34–3.24), followed by the Ar (diversity index = 2.69 ± 0.35; range = 2.21–3.00) and the Pr (diversity index = 3.37 ± 0.24; range = 3.02–3.83). These differences were only significant for the Pr (Kruskal–Wallis test, P ≤ 0.01). Rarefaction curves (Supporting Information, Fig. S1) based on unique band positions in the gels showed saturation of our ability to detect microbial diversity in each of the rivers, suggesting that the somewhat smaller number of samples in the Ar survey did not play a role in the differences of Shannon's diversity measured among rivers.

Composite image of DGGE gels displaying the banding pattern of bacterial phylotypes from the Añamaza, Mesa and Piedra rivers’ sites (named A, M, P, respectively). Coloured arrows indicate excised bands and bacterial phylotypes after blast (see Table ). The urea gradient used to run the gels is indicated as 30 and 60 (%). Site, richness and diversity are indicated as S, R, D, respectively.

The Ar showed a significant linear decrease in site diversity (r2 = −0.78; P = 0.046) as one moves downstream (Fig. ) while the Mr and Pr showed no significant linear patterns (r2 < 0.5; P ≥ 0.78). The geographic location of few, well-known natural springs (Fig. ), which are known to locally influence the water chemistry in other fluvial systems (Helena et al., 2000; Valero-Garcés et al., 2008), did not seem to have a direct influence on the diversity, as their distribution was not correlated with specific changes in diversity. In fact, diversity at or close to the known spring sites either increases or decreases independent to the position of the spring along a single river or even comparing the three rivers.

Variation of the diversity indices along the rivers. Water flow goes from left to right. Sampling sites are numbered (see Fig. ). Underground discharges are indicated with symbols at their approximate locations. Reference towns are indicated with arrows.

A upgma dendrogram comparing the similarity of the banding pattern in each DGGE lane clustered sites of the same river together (Fig. ). The Pr sites clustered together at a 50% similarity index. The Ar sites clustered together at a 42% similarity, and the Mr site's clusters had < 30% similarity (except for site M10, which clustered by itself). This relatively low similarity index may indicate that at least half of the phylotypes are shared across rivers (with few unique phylotypes). Yet, bootstrap values indicate that the Ar sites are well separated from the Pr and Mr sites, with individual clusters in Pr and Mr sites well separated from each other. However, low bootstrap values between major clusters of Pr and Mr suggests that both rivers have some commonalities regarding their overall community composition. In general, more similar banding patterns were shown for sites in close geographic locations. For example, downstream sites M1–M3 and M4–M5 clustered with a tight 97 bootstrap value, followed by smaller values for sites M6–M10 (Fig. ). A similar, downstream clustering trend is seen for the Pr and Ar sites (e.g. clustering of sites P1 through P8, then P9 through P15 and P16 through P20). This downstream/upstream site distinction suggests somewhat discrete changes in community composition at some point in each river. In the Ar, this change occurs approximately between sites A2 and A5; in the Mr between sites M3 and M7; and in the Pr between sites P15 and P16.

upgma cluster analysis applied to the banding pattern of individual lanes of the DGGE images. Letters A, M and P correspond to Añamaza, Mesa and Piedra rivers, respectively. Boxed areas show main clustering pattern of the sites. Bootstrap values correspond to 1000 sampling replicates.

Environmental variables

Dendrograms for physicochemical variables of all the sites (Fig. ) show four clusters distinguished at the 50% similarity, which separated sites from the Ar, and most of the Pr and Mr sites. Most of the Mr sites could be distinguished from the Pr at the 60% similarity. For this dendrogram, we observed no downstream distribution in the clustering of sites within a river, which we interpret as a heterogeneous physicochemical composition of the rivers, perhaps reflecting random inputs from urban areas, irrigation districts and natural springs.

Dendrograms of the physicochemical variables measured at the sites. Similarity index is given in percentage. Boxed areas correspond to clustered sites at the 50% similarity index (dashed vertical line). Dark vertical line indicates clustering of the M sites at the 60% similarity index. Letters A, M and P correspond to Añamaza, Mesa and Piedra rivers, respectively.

Transformation of the data to a fewer components through PCA similarly showed that the rivers could be separated by the variance of their physicochemical variables (Fig. ). The first component (PC1) explained 33.09% of the variance and gave high loadings to Embedded Image and Ca2+. The second component (PC2) explained 22.74% of the variance and gave high loading to Na+ and Cl (Table ). The PCA further showed that the Ar and Mr are affected by variables that imply salinity (Ca2+ and Embedded Image, in the Ar, and Na+ and Cl in the Mr), while the Pr sites are affected, to a lesser extent, mostly by Mg2+. Thus, salinity-related variables may have some influence on the measured bacterial diversity.

Biplot of the first two PCA components. Clustering distinguishes sites from the same river [Mesa (Mr), Piedra (Pr), and Añamaza (Ar)]. The arrows represent the original variables, their length represents the variance and the angle between arrows represents their correlation. Closer and longer vectors indicate higher loadings for the indicated variables, in this case Embedded Image and Ca2+ for the Ar sites, Na+ and Cl for the Mr sites, and Mg2+, Embedded Image, and CaCO3 for the Pr sites.

View this table:

Matrix of the first five PCA principal components. Higher loading values (bold face) imply higher variance. The total variance measured for these components was 87.14%

Component
PC1PC2PC3PC4PC5
Temperature0.5371−0.09050.40050.4065−0.5180
Conductivity−0.68130.48030.08680.4364−0.1363
pH−0.75160.26430.4133−0.0955−0.0741
HCO30.5305−0.2556−0.28190.64130.1740
Cl0.44630.85620.0349−0.13670.0329
SO40.9602−0.06270.08640.1295−0.1617
Na0.41780.86400.0235−0.12890.0284
K0.50430.27710.51800.2744−0.2529
Ca0.8407−0.0505−0.16080.4289−0.0149
Mg0.6191−0.69730.12200.0197−0.1338
Vel0.08390.29970.66240.24010.5162
Depth0.21320.3469−0.51310.46880.2515
CaCO3−0.1006−0.56780.56110.03940.4334
Eigenvalue4.302.961.751.370.95
Variance%33.0922.7413.4910.517.31
Cumulative%33.0955.8369.3279.8387.14

Linear correlations between diversity indices and components PC1 through PC13 gave a significant, negative correlation (Pearson correlation = −0.713; P < 0.001) only for PC2. All other Pearson correlations were not statistically significant (data not shown). A regression model of diversity on PC2 produced an F statistic value of 31.08 with 1 and 30 degrees of freedom (P < 0.001). The relatively high loadings of Na and Cl (0.8640 and 0.8562, respectively) in PC2 and its significant correlation with diversity indices may indicate a direct, albeit slight influence of salinity (as NaCl) on the diversity.

Discussion

Phylotypes and potential for metabolic diversity

As with most microbial communities, the diversity in the studied tufa systems contained taxa that represent different metabolisms integrated into a complex energy and carbon exchange system, with photoautotrophs (cyanobacteria) as primary producers, and a variety of chemolithotrophs and chemoheterotrophs that would breakdown and recycle the organic matter and inorganic compounds that accumulate interstitially within the first millimetres of the tufa surface. Although not quantified here, archaea, fungi, protozoa, metazoans, algae and plants (especially bryophytes) seem to be important biotic components of these systems as well.

The greatest abundance of rRNA sequences quantified here was of cyanobacteria. Most cyanobacteria were nonheterocystous filaments, although some heterocystous, filamentous taxa and minor coccal members were also detected (Table ). This coincides with field observations of these conspicuous organisms on the tufa surface (Fig. ). The utmost abundant filaments of the Phormidium type were seen arranged semi-perpendicular to the tufa surface and coated with calcite crystals (Fig. a). In some areas, noncoated filaments were seen filling microscopic crevasses that were not filled with sediment or protruding from the surface (Fig. b). This same dominance of nonheterocystous, filamentous forms has been previously recognized in drastically different environments, such as marine microbial mats and cyanobacterial communities from arid topsoils (Stal, 1994; Reid et al., 2000; Beraldi-Campesi & García-Pichel, 2011), indicating perhaps an evolutionary trend related to the phenotype and not necessarily the genotype. Cyanobacteria may also be responsible for much of the accretion of the tufa by (1) providing sites for calcite nucleation, (2) enhancing CaCO3 precipitation by depleting the surrounding, dissolved CO2, and (3) trapping and binding particles, as they do in other carbonate microbialites (Garcia-Pichel et al., 2004; Konhauser, 2007; Arp et al., 2010). As cyanobacteria constitute the bulk of the biofilm's biomass, they may also participate in developing particular microtextures within the tufa structure (Arenas et al., 2007, 2010; Gradzinski, 2010).

Tufa surface infested with filamentous cyanobacteria (Oscillatoriaceae). (a) Perspective of filaments (in orange owing to chlorophyll autofluorescence) in erect position, perpendicular to the calcified tufa surface (green), as seen with epifluorescence microscopy. Note the difference between the bright-orange (noncalcite-coated filaments), the dull-orange calcite-coated filaments (lower arrow) and the partially degraded, nonfluorescent and coated filaments (upper arrow). (b) Plan view of the tufa surface showing noncoated filaments occupying the crevasses (horizontal arrows) and already-calcified filaments in micritic micromounds (vertical arrows). (c) Dense population of cyanobacteria showing selective calcite precipitation around filaments (white arrow). Some filaments were able to migrate away (upward) from the calcification zone, leaving behind cylindrical tubes in calcite chunks (black arrow). All scale bars = 100 μm.

Anoxygenic phototrophs (purple nonsulphur bacteria) within the alphaproteobacteria were also detected and may be present below the cyanobacterial cover within stratified communities, which is where they are typically found in other aquatic systems (Madigan et al., 2003). Purple nonsulphur bacteria, also detected here, can fix dinitrogen and use organic or inorganic sources of carbon for their metabolism (Madigan et al., 2003); this metabolic plasticity may assure their presence in the community and may account for their ample distribution in aquatic environments, including freshwater tufas (Cousin & Stackebrandt, 2010).

Sequences of sulphur-related gammaproteobacteria included purple sulphur bacteria (Marichromatium sp.) and a sulphur oxidizer (Beggiatoa sp.), both usually associated with sulphide-rich microenvironments (Madigan et al., 2003). While Beggiatoa sp. can be found in soils and in saline and freshwater settings (Strohl & Larkin, 1978; Nelson & Castenholz, 1981), Marichromatium includes only marine species (Serrano et al., 2009) which are generally halophilic and alkaliphilic (Swingley et al., 2008). It is possible that the DNA sequences found for this type are related to purple bacteria (e.g. Chromatium sp.) that are known from freshwater environments, including tufas (Cousin & Stackebrandt, 2010). These bacteria may be common below the cyanobacterial cover, in areas where organic matter accumulates in pore spaces as new carbonate accretes on top of the biofilms at the tufa surface.

The presence of sulphate-reducing bacteria (i.e. Desulfotomaculum sp.) may be relevant for processes that enhance tufa lithification. Sulphate-reducing bacteria are active players in the lithification of marine, carbonate microbialites by releasing Ca2+ ions adsorbed onto the EPS sheaths they degrade and by increasing the pH locally as they remove H2SO4 to gaseous H2S (Konhauser, 2007; Dupraz et al., 2009), which results in pervasive carbonate precipitation around them. Although not proven for freshwater systems, their presence in these and other tufa systems may imply a same geochemical role as in marine microbialites.

In general, members of the Bacteroidetes, Betaproteobacteria, Deltaproteobacteria, Planctomycetes and Firmicutes found in this study were heterotrophic organisms that are common in soils and freshwaters (Cousin & Stackebrandt, 2010) and that represent the major taxa responsible for decomposing and recycling various organic materials produced by primary producers, the resilient remains of other organisms (bryophytes, fungi, algae, protozoa, metazoan, etc.) and dissolved organic compounds (Madigan et al., 2003). This heterotrophic biota would be essential for the breakdown of the bulk biomass and the mineralization of key elements that are left available for other organisms in the environment, a common trophic succession in freshwater habitats (Sigee, 2005).

Even though potential metabolisms can be detected through genomic surveys, more research is needed to assess the potential role that different phototrophs and heterotrophs exert on the formation of tufas, particularly those that are known to affect carbonate precipitation.

Diversity and environmental variables

Interestingly, the calculated diversity indices for these tufa systems are comparable to indices for bacteria in soils of dry-hot areas of the world (Gundlapally & Garcia-Pichel, 2006; Strauss et al., 2011), and much higher than those calculated, by the same molecular means, for oxygenic phototrophs in microbial mats of hypersaline lagoons (Nübel et al., 1999), which are both ‘extreme’ environments with marked differences in their physicochemical context. Nevertheless, diversity indices seem rather poorly informational, unless the identity of the community members is known and their distribution is considered within a particular context.

Multivariate analysis show that Embedded Image and Ca2+ are distinct variables for the Ar, while Na+ and Cl are distinctive for the Mr. Embedded Image, Mg2+ and CaCO3 weakly correlate with Pr sites (Fig. , Table ). The strong correlation between Embedded Image and Ca2+ (85.6% similarity), and Na+ and Cl (99.2% similarity; Fig. S2), suggests that these ions derive from dissolved gypsum and halite that enter the waters through springs and seepage. The higher content in Embedded Image and Ca2+ in the Ar, along with the slightly lower temperature of the region (as compared to the two other studied rivers), may be good reasons of such statistical separation. These levels of salinity, although significant for the Ar and Mr, are still within the range of freshwaters (as opposed to marine salinities) and, thus, may not be detractors of bacterial diversity. However, during dry seasons, when the springs feed the rivers that are not diluted by rains, salinity may increase. If salinity levels increase to a minimum of ~ 2%, it may interfere with cellular metabolism and photosynthesis (Garcia-Pichel et al., 1999; Fleming et al., 2007) and, along with luminosity (intensity), water temperature and alkalinity changes over time, and it may affect the net production of biomass (dependent essentially on oxygenic photoautotrophs) at the surface of these tufas. Nevertheless, other variables not measured here, such as concentration of Embedded Image, TOC and toxic organometals should also be considered as potential regulators of diversity. Anthropogenic sources influencing the salinity should be also evaluated.

Regarding tufa thickness, although it did not show a strong correlation with diversity, it may influence, over time, the spatial distribution of the biota, especially because carbonate sedimentation can be very fast in some places (up to 1.7 cm year−1; Vázquez-Urbez et al., 2010), implying thus a dynamic and continuous change in the substrate to which benthic bacteria have to cope with. Results from monitoring physical and chemical variables in these rivers over time (see ; Vázquez-Urbez et al., 2010, 2011; Auqué et al., in press) show a clear relationship between tufa sedimentation rates and water flow velocity for the Pr and Ar. The highest rates of tufa precipitation are recorded in fast flow conditions (facies A; e.g. site P18) and in stepped cascades with a thin lamina of turbulent flow (facies C1; e.g. site P11) and the lowest rates in spray (facies D; e.g. P13) and slow-flowing (facies B1; e.g. P19) areas (see Tables and ; Vázquez-Urbez et al., 2010, 2011). In addition, all these environments have a seasonal pattern of variation related to temperature-dependent parameters, with higher rates of deposition in warm periods (spring and summer) than in cool ones (autumn and winter). Differently, the Mr shows no clear relationship between water velocity and sedimentation rates. Moreover, its seasonal pattern is reversed with respect to the other two rivers. This may be due to strong hydrological variations linked to periods of dryness and flooding in the Mr and the influence of spring inputs along its course (Auqué et al., in press).

From these data and the information provided in this article, it would be reasonable to further analyse whether there is a relationship between the distribution of particular bacteria and the environmental factors that define each environment. However, because not all the bands in the DGGEs were sequenced, this understanding is so far limited. For instance, Phormidium sp. is present in most of the studied sites across all rivers and likely to be in all the tufa benthic environments. By contrast, Calothrix sp. is only detected in a stepped cascade (i.e. in P11; Tables and ). Nevertheless, it may be that Calothrix sp. is present in more than one site, but sequences are not available.

Conclusions

The surfaces of the actively growing, freshwater tufas of the Añamaza, Mesa and Piedra rivers contain a great diversity of bacteria. These are structured in a rich and complex ecosystem in which primary producers, consumers and recyclers occupy their respective niches at the benthic substrate. It would be expected that the photosynthetic and heterotrophic metabolisms found here display a similar pattern in their stratified distribution as it is found in other tufas, especially given the great similarity of bacterial groups and individual taxa between this and other studies (Arp et al., 2010; Cousin & Stackebrandt, 2010). Whether or not this similarity is universal or only regional requires complementary studies, for example, similar surveys in southern continents.

The bacterial diversity found here seems to be somewhat affected by the increase in water salinity, despite that these variables had low variance in our PCA and stayed within the range of freshwater. Several geological units across the Iberian Range (e.g. Keuper Facies and, additionally in the case of the Añamaza river, the Tera and Oncala Groups) likely supply the Na+, Cl and Embedded Image contents of the waters. Aquifers feeding the springs in this region may have an important role with respect to salinity.

Integral studies that look at different aspects of freshwater tufa systems are certainly welcome to further understand the origin and functioning of these reservoirs of diversity on Earth, today and in the past. It would also be suitable to characterize the imprint that microorganisms alone and in combination with macroscopic eukaryotes, leave on the tufa structures and textures, which is needed to differentiate biotic from abiotic systems.

Supporting Information

Additional Supporting Information may be found in the online version of the article:

Fig. S1. Rarefaction curves to assess sampling effort in the three rivers. Blue lines represent the 95% confidence interval.

Fig. S2. Correlation analysis of the measured physicochemical variables. Na+ and Cl correlate with 99.22% similarity, and Embedded Image and Ca2+ correlate with 85.57% similarity.

Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

Acknowledgements

We thank Dr Michal Gradzinski, Jagiellonian University; Antonio Altamira and Gerardo Zenteno, Institute of Geology, UNAM; Jessica Mohler, Scott T. Bates, Sarah Strauss, Ruth Potrafka, Edgardo Ramirez-Reinat and Susanne Neuer; Arizona State University; and Trent Lalonde, University of Colorado; and one anonymous reviewer for their contributions to the improvement of this article; and Enrique Arranz, University of Zaragoza, for fluorescence microscopy assistance. This study was funded by projects REN2002-3575CLI, CGL05063BTE and CGL2009-09216 by the Spanish Government and European Regional Development Funds. This research is part of the Continental Sedimentary Basin Analysis group (Aragón Government-University of Zaragoza).

Footnotes

  • Editor: Riks Laanbroek

References

View Abstract