Drivers of bacterial β-diversity depend on spatial scale Jennifer B. H. Martinya,1, Jonathan A. Eisenb, Kevin Pennc, Steven D. Allisona,d, and M. Claire Horner-Devinee aDepartment of Ecology and Evolutionary Biology, and dDepartment of Earth System Science, University of California, Irvine, CA 92697; bDepartment of Evolution and Ecology, University of California Davis Genome Center, Davis, CA 95616; cCenter for Marine Biotechnology and Biomedicine, The Scripps Institution of Oceanography, University of California at San Diego, La Jolla, CA 92093; and eSchool of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195 Edited by Edward F. DeLong, Massachusetts Institute of Technology, Cambridge, MA, and approved March 31, 2011 (received for review November 1, 2010) The factors driving β-diversity (variation in community composi- spatial scale (12). Fifty-years ago, Preston (13) noted that the tion) yield insights into the maintenance of biodiversity on the turnover rate (rate of change) of bird species composition across planet. Here we tested whether the mechanisms that underlie space within a continent is lower than that across continents. He bacterial β-diversity vary over centimeters to continental spatial attributed the high turnover rate across continents to evolu- scales by comparing the composition of ammonia-oxidizing bacte- tionary diversification (i.e., speciation) between faunas as a result ria communities in salt marsh sediments. As observed in studies of dispersal limitation and the lower turnover rates of bird spe- β of macroorganisms, the drivers of salt marsh bacterial -diversity cies within continents as a result of environmental variation. depend on spatial scale. In contrast to macroorganism studies, Here we investigate whether the mechanisms underlying β- fi however, we found no evidence of evolutionary diversi cation diversity in bacteria also vary by spatial scale. We chose to focus of ammonia-oxidizing bacteria taxa at the continental scale, de- on the ammonia-oxidizing bacteria (AOB), which along with the spite an overall relationship between geographic distance and ammonia-oxidizing archaea (14), perform the rate-limiting step of community similarity. Our data are consistent with the idea that nitrification and thus play a key role in nitrogen dynamics. We dispersal limitation at local scales can contribute to β-diversity, compared AOB community composition in 106 sediment samples even though the 16S rRNA genes of the relatively common taxa are globally distributed. These results highlight the importance from 12 salt marshes on three continents. A partially nested of considering multiple spatial scales for understanding microbial sampling design achieved a relatively balanced distribution of biogeography. pairwise distance classes over nine orders of magnitude, from 3 cm to 12,500 km (Fig. 1 and Table S1). We limited our sam- microbial composition | distance-decay | Nitrosomonadales | ecological drift pling to a monophyletic group of bacteria, the AOB within the β-Proteobacteria, and one habitat, salt marshes primarily domi- Spartina iodiversity supports the ecosystem processes upon which so- nated by cordgrass ( spp.). This approach constrained Bciety depends (1). Understanding the mechanisms that gen- the pool of total diversity (richness) and kept the environmental erate and maintain biodiversity is thus key to predicting ecosystem and plant variation relatively constant, increasing our ability to fl responses to future environmental changes. The decrease in identify if dispersal limitation in uences AOB composition. i β — community similarity with geographic distance is a universal We then asked two questions: ( ) Does bacterial -diversity fi — biogeographic pattern observed in communities from all speci cally, the slope of the distance-decay curve vary over domains of life (as in refs. 2–4). Pinpointing the underlying local (within marsh), regional (across marshes within a coast), causes of this “distance-decay” pattern continues to be an area of and continental scales? (ii) Do the underlying factors (environ- intense research (5–9), as such studies of β-diversity (variation in mental variation or dispersal limitation) explaining this diversity community composition) yield insights into the maintenance of vary by spatial scale? Because most bacteria are small, abundant, biodiversity. These studies are still relatively rare for micro- and hardy, we predicted that dispersal limitation would occur organisms, however, and thus our understanding of the mecha- primarily across continents, resulting in genetically divergent nisms underlying microbial diversity—most of the tree of life— microbial “provinces” (15). At the same time, we predicted that remains limited. environmental factors would contribute equally to distance- β-Diversity, and therefore distance-decay patterns, could be decay at all scales, resulting in the steepest slope at the continental driven solely by differences in environmental conditions across scale as reported in plant and animal communities (12, 13, 16). space, a hypothesis summed up by microbiologists as, “every- thing is everywhere—the environmental selects” (10). Under this Results and Discussion model, a distance-decay curve is observed because environmen- We characterized AOB community composition by cloning and tal variables tend to be spatially autocorrelated, and organisms Sanger sequencing of 16S rRNA gene regions targeted by two with differing niche preferences are selected from the available primer sets. Here we focus on the results from a subset of those pool of taxa as the environment changes with distance. sequences from the order Nitrosomonadales, generated using β Dispersal limitation can also give rise to -diversity, as it per- primers specific for AOB within the β-Proteobacteria class (17). fl mits historical contingencies to in uence present-day biogeo- The second primer set (18) generated longer sequences from graphic patterns. For example, neutral niche models, in which an organism’s abundance is not influenced by its environmental preferences, predict a distance-decay curve (8, 11). On relatively Author contributions: J.B.H.M. and M.C.H.-D. designed research; J.B.H.M., J.A.E., K.P., and short time scales, stochastic births and deaths contribute to M.C.H.-D. performed research; J.B.H.M., S.D.A., and M.C.H.-D. analyzed data; and J.B.H.M. a heterogeneous distribution of taxa (ecological drift). On longer and M.C.H.-D. wrote the paper. time scales, stochastic genetic processes allow for taxon di- The authors declare no conflict of interest. versification across the landscape (evolutionary drift). If dispersal This article is a PNAS Direct Submission. is limiting, then current environmental or biotic conditions will Freely available online through the PNAS open access option. not fully explain the distance-decay curve, and thus geographic Data deposition: The sequences reported in this paper have been deposited in the Gen- distance will be correlated with community similarity even after Bank database (accession nos. HQ271472–HQ276885 and HQ276886–HQ283075). controlling for other factors (2). 1To whom correspondence should be addressed. E-mail: [email protected]. For macroorganisms, the relative contribution of environ- This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. mental factors or dispersal limitation to β-diversity depends on 1073/pnas.1016308108/-/DCSupplemental. 7850–7854 | PNAS | May 10, 2011 | vol. 108 | no. 19 www.pnas.org/cgi/doi/10.1073/pnas.1016308108 Downloaded by guest on September 27, 2021 Fig. 1. The 13 marshes sampled (see Table S1 for details). Marshes com- pared with one another within regions are circled. (Inset) The arrangement of sampling points within marshes. Six points were sampled along a 100-m transect, and a seventh point was sampled ∼1 km away. Two marshes in the Northeast United States (outlined stars) were sampled more intensively, along four 100-m transects in a grid pattern. a broader range of Proteobacteria, but yielded similar results Fig. 2. Distance-decay curves for the Nitrosomadales communities. The (Fig. S1 and Tables S2 and S3). dashed, blue line denotes the least-squares linear regression across all spatial Across all samples, we identified 4,931 quality Nitrosomadales scales. The solid lines denote separate regressions within each of the three sequences, which grouped into 176 OTUs (operational taxo- spatial scales: within marshes, regional (across marshes within regions circled in nomic units) using an arbitrary 99% sequence similarity cutoff. Fig. 1), and continental (across regions). The slopes of all lines (except the solid fi This cutoff retained a high amount of sequence diversity, but light blue line) are signi cantly less than zero. The slopes of the solid red lines are significantly different from the slope of the all scale (blue dashed) line. minimized the chance of including diversity because of se- quencing or PCR errors. Most (95%) of the sequences appear closely related either to the marine Nitrosospira-like clade, somonadales community similarity. Geographic distance con- known to be abundant in estuarine sediments (e.g., ref. 19) or to tributed the largest partial regression coefficient (b = 0.40, marine bacterium C-17, classified as Nitrosomonas (20) (Fig. S2). P < 0.0001), with sediment moisture, nitrate concentration, plant Pairwise community similarity between the samples was calcu- cover, salinity, and air and water temperature contributing to ECOLOGY