Ancient origins determine global biogeography of hot and cold desert cyanobacteria - PubMed (original) (raw)
Maggie C Y Lau, Gavin J D Smith, Dhanasekaran Vijaykrishna, S Craig Cary, Donnabella C Lacap, Charles K Lee, R Thane Papke, Kimberley A Warren-Rhodes, Fiona K Y Wong, Christopher P McKay, Stephen B Pointing
Affiliations
- PMID: 21266963
- PMCID: PMC3105302
- DOI: 10.1038/ncomms1167
Ancient origins determine global biogeography of hot and cold desert cyanobacteria
Justin Bahl et al. Nat Commun. 2011.
Abstract
Factors governing large-scale spatio-temporal distribution of microorganisms remain unresolved, yet are pivotal to understanding ecosystem value and function. Molecular genetic analyses have focused on the influence of niche and neutral processes in determining spatial patterns without considering the temporal scale. Here, we use temporal phylogenetic analysis calibrated using microfossil data for a globally sampled desert cyanobacterium, Chroococcidiopsis, to investigate spatio-temporal patterns in microbial biogeography and evolution. Multilocus phylogenetic associations were dependent on contemporary climate with no evidence for distance-related patterns. Massively parallel pyrosequencing of environmental samples confirmed that Chroococcidiopsis variants were specific to either hot or cold deserts. Temporally scaled phylogenetic analyses showed no evidence of recent inter-regional gene flow, indicating populations have not shared common ancestry since before the formation of modern continents. These results indicate that global distribution of desert cyanobacteria has not resulted from widespread contemporary dispersal but is an ancient evolutionary legacy. This highlights the importance of considering temporal scales in microbial biogeography.
Conflict of interest statement
The authors declare no competing financial interests.
Figures
Figure 1. Global distribution of Chroococcidiopsis variants.
The map indicates hot (brown boxes) and cold (blue boxes) desert locations from which variants were recovered. Detailed site descriptions and climatic information are given in Supplementary Table S1.
Figure 2. Idealized phylogenies of hypothetical scenarios for global distribution of desert cyanobacteria.
Scenario a, assumes ubiquitous distribution resulting in mixed geographic regions and mixed environments within a phylogeny. Here, root divergence is a relatively recent event. Scenario b, assumes allopatric speciation resulting in distinct geographic regions and mixed environments within a phylogeny (that is, N America and Asia are monophyletic groups). Here, root divergence times correspond with formation of continents. Scenario c, assumes environmental selection corresponding to global climatic change, resulting in a phylogeny with mixed geographic regions and distinct environments. Here, the root divergence times are significantly older than known ages of formation of continents. Asterisks indicate monophyletic groups. Brown boxes and blue boxes indicate hot and cold desert locations, respectively.
Figure 3. Temporal phylogeny for Chroococcidiopsis variants.
Variants were recovered from hot and cold deserts worldwide as shown in Supplementary Table S1. The tree was generated using a Bayesian relaxed-clock phylogenetic approach of the 16S-ITS-23S rDNA regions to estimate divergence dates. The age of the common ancestor for Chroococcidiopsis was estimated with a 95% Bayesian confidence interval, using fossil ancestors for calibration. Blue bars at nodes indicate 95% credible intervals for divergence events. Temporal scale is shown in millions of years.
Figure 4. Relationship between genetic divergence and geographic distance among Chroococcidiopsis variants.
Genetic differentiation was not significantly related to geographic distance (a) on the global scale (_N_=19, _n_=171), or for each of the phylogenetically defined clusters (b) cold (_N_=8, _n_=28), (c) hot 1 (_N_=6, _n_=15) or (d) hot 2 (_N_=5, _n_=10); where N denotes number of samples and n the resultant number of pairwise comparisons. The best-fit linear regression function, the coefficient of determination (R-square) and significance (p) of Mantel test are displayed for individual regression plots. A significance level (alpha) of 0.05 was applied.
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