L. H. Zeglin 1 *, C. D. Takacs-Vesbach 1 , C. N. Dahm 1 , J. E. Barrett 2 , and M. N. Gooseff 3

1. 1d 0-3 cm 3-6 E. coli 6-10 1 1c A B C D 2c 2d 0-3 E. coli cm 3-6 6-10 A B C D 2 3 SUMMARY Nitrate gradients are related to evapoconcentration. Total DIN is related to bacterial richness. Ammonium is important to biotic communities, but processes affecting its distribution are more complex. Bacterial communities are potentially less diverse, and certainly different in composition, at Rio Salado, the hot site. This is likely related to site differences in hydrologic variability: over long time scales (salinity) and/or short (precipitation/flood regime). Gradient structure can shift temporally (see right). Underway: completion of clone libraries. Next: quanitfication of functional genes (e.g. those coding nitrogenase and ammonia monoxygenase enzymes) across the gradients to understand bacterial metabolic capability, and relate function to aquatic - terrestrial nutrient distribution. Abiotic processes are similar at both sites; are biotic processes, also? Upper Onyx Lower Onyx E. coli Jan. 2006 Dec. 2005 3d 0-3 E. coli cm 3-6 6-10 A B C D A B C D E. coli D A B C Upper Onyx Lower Onyx Biological, physical and nutrient gradients in near-stream hydrologic margins of hot and cold deserts L. H. Zeglin1*, C. D. Takacs-Vesbach1,C. N. Dahm1, J. E. Barrett2, and M. N. Gooseff3 1Department of Biology, University of New Mexico, Albuquerque, NM USA 871312 Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 246013Department of Geology and Geological Engineering, Colorado School of Mines, Golden, CO USA 80401 Near-stream nutrient transfer is of great importance to stream and terrestrial biota. The desert stream "hydrologic margin" (HM) is a gradient of water content and solute distribution between aquatic and terrestrial zones. A ribbon of soil where the primary limiter, water, is present, the HM is a hotspot of biological activity and nutrient transformation. We expect distribution of nutrients and bacterial communities to be driven by hydrologic processes, i.e. water availability across the gradient, at both sites. Differences between sites will be related to the difference in temperature and climate. Sampling schematic. Please note: A-B-C-D notation of transect position is used for orientation of samples and results throughout the poster. Sites. Rio Salado, SEV (1); Upper and Lower Onyx River, MCM (2, 3). See below for more information. 1b 1a HM Mean values for physical parameters in 2004 samples. Conductivity at Rio Salado is +10X than Onyx River. Extractable N and temperature values also range over an order of magnitude. *mean values from long-term data (MCM, http://www.mcmlter.org) and July 2004 (SEV). Figures 1a-d. Rio Salado, NM, USA. Image (1a) with visible HM; physical (1b) and nutrient (1c) gradient structure, ±1 SD; (1d) DGGE profile of bacterial community composition (near surface water sample at left, three depths per position) 2b 2a 4a Figure 4. (a) Proportion of nearest-neighbor 16S rRNA sequences to clone libraries by habitat type. Rio Salado bacteria are more often related to halotolerant or halophillic organisms (e.g. Rheinheimeria sp., Idiomarina sp.). (b) Proportion of nearest-neighbor 16S rRNA sequences to clone libraries by taxonomic group. Rio Salado bacteria are more often related to members of the Gamma - Proteobacteria, while Upper Onyx River bacteria are more often related to members of the Gemmatimonadetes. ALL libraries are compositionally different from one another (p < 0.01, WebLibshuff) HM Figures 2a-d. Upper Onyx River, Wright Valley, Antarctica. Image (2a) with visible HM; physical (2b) and nutrient (2c) gradient structure, ±1 SD; (2d) DGGE profile of bacterial community composition (near surface water sample at left, three depths per position) 3a 3c 3b 4b HM Figures 3a-d. Lower Onyx River, Wright Valley, Antarctica. Image (3a) with visible HM; physical (3b) and nutrient (3c) gradient structure, ±1 SD; (3d) DGGE profile of bacterial community composition (near surface water sample at left, three depths per position) Patterns of diversity 5 Patterns of nutrient distribution (Table displays alpha, beta and gamma richness metrics from DGGE, with subscripts denoting ANOVA groupings. Figure 5 displays 16S rRNA clone library rarefaction curves representative of each site: all curves are statistically similar) - According to DGGE, the Lower Onyx bacterial community is the most species rich and has the highest heterogeneity across the gradient, and the Rio Salado is the least species rich and most homogeneous. This is suggests that biological processes (faster growth/adaptation/response to disturbance) structure bacterial distribution in the hot desert. - 16S rRNA clone library data show that: (1, Figure 5) all sites are highly diverse. Though no rarefaction curves near a saturation point at the “species” level of sequence similarity, Rio Salado curves begin to diverge toward lower diversity (more sequences are being processed to resolve this pattern). (2, Figure 4) community composition differs between all libraries, and suggests selection for halotolerance at Rio Salado. (Table displays Pearson’s r, * denotes a significant relationship) - All sites show a distance-conductivity and nitrate-conductivity relationship, evapoconcentration of solutes is likely a gradient-forming mechanism in desert stream margins and nitrate availability appears to be controlled by this abiotic process. Ammonium might be best indicator of biological N cycling here. - Nitrate and ammonium are not consistently related, but total DIN is correlated with DGGE richness across all sites (r = 0.898, p = 0.000). Does higher nutrient availability allow higher diversity? How does biotic activity affect nutrient distributions? METHODS DIN is reported as the sediment KCl-extractable fraction. Environmental genomic DNA was extracted using a MoBio PowerSoil extraction kit. DGGEs were run with 3x replication on a 30%-70% gradient with PCR product of 338FGC and 519R 16S bacterial rRNA primers and E. coli standards.Clone libraries are prepared with 8F and 1492R 16S bacterial rRNA primers, composition reported as nearest BLAST hit to sequence, aligned with GreenGenes1, statistics run on distance matrices from ARB using DOTUR2 and WebLibshuff3. Many thanks to: Raytheon Polar Services and Petroleum Helicopters, Inc. for logistical support in Antarctica. The McMurdo and Sevilleta Long-Term Ecological Research programs and personnel. The UNM Hydrogeoecology Group, especially John Craig. Field assistance from D. Bradley Bate, Chelsea Crenshaw, Kenneth Hill and Melissa Northcott. Laboratory assistance from Nathan Daves-Brody, Nick Enquist, Kendra Mitchell, Kris Mossberg and Erin Saulsberry-Abens. Funding provided by the National Science Foundation OPP-#0338267 and PDF grants. *Please direct questions and comments to Lydia H. Zeglin, lzeglin@unm.edu. REFERENCES 1 DeSantis, T. Z., P. Hugenholtz, K. Keller, E. L. Brodie, N. Larsen, Y. M. Piceno, R. Phan, and G. L. Andersen. 2006. NAST: a multiple sequence alignment server for comparative analysis of 16S rRNA genes. Nucleic Acids Res 34: W394-9. 2 Schloss, P. D. and Handelsman, J. 2005. Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness. Appl Environ Microb. 71 (3): 1501-1506. 3 Henriksen, James R. 2004. webLIBSHUFF (http://libshuff.mib.uga.edu) Antarctic Hydrologic Margin Project: http://www.mines.edu/%7Emgooseff/web_antarctica/antarctic_proj.html