Water availability shapes edaphic and lithic cyanobacterial communities in the Atacama Desert

In the Atacama Desert, cyanobacteria grow on various substrates such as soils (edaphic) and quartz or granitoid stones (lithic). Both edaphic and lithic cyanobacterial communities have been described but no comparison between both communities of the same locality has yet been undertaken. In the present study, we compared both cyanobacterial communities along a precipitation gradient ranging from the arid National Park Pan de Azúcar (PA), which resembles a large fog oasis in the Atacama Desert extending to the semiarid Santa Gracia Natural Reserve (SG) further south, as well as along a precipitation gradient within PA. Various microscopic techniques, as well as culturing and partial 16S rRNA sequencing, were applied to identify 21 cyanobacterial species; the diversity was found to decline as precipitation levels decreased. Additionally, under increasing xeric stress, lithic community species composition showed higher divergence from the surrounding edaphic community, resulting in indigenous hypolithic and chasmoendolithic cyanobacterial communities. We conclude that rain and fog water, respectively, cause contrasting trends regarding cyanobacterial species richness in the edaphic and lithic microhabitats.     

Cyanobacterial ecology across environmental gradients and spatial scales in China's hot and cold deserts

Lithic photoautotrophic communities function as principal primary producers in the world's driest deserts, yet many aspects of their ecology remain unknown. This is particularly true for Asia, where some of the Earth's oldest and driest deserts occur. Using methods derived from plant landscape ecology, we measured the abundance and spatial distribution of cyanobacterial colonization on quartz stony pavement across environmental gradients of rainfall and temperature in the isolated Taklimakan and Qaidam Basin deserts of western China. Colonization within available habitat ranged from 0.37+/-0.16% to 12.6+/-1.8%, with cold dry desert sites exhibiting the lowest abundance. Variation between sites was most strongly correlated with moisture-related variables and was independent of substrate availability. Cyanobacterial communities were spatially aggregated at multiple scales in patterns distinct from the underlying rock pattern. Site-level differences in cyanobacterial spatial pattern (e.g. mean inter-patch distance) were linked with rainfall, whereas patchiness within sites was correlated with local geology (greater colonization frequency of large rocks) and biology (dispersal during rainfall). We suggest that cyanobacterial patchiness may also in part be self-organized - that is, an outcome of soil water-biological feedbacks. We propose that landscape ecology concepts and models linking desert vegetation, biological feedbacks and ecohydrological processes are applicable to microbial communities.     

How should a clever baboon choose and move rocks?

Chacma baboons (Papio ursinus) intentionally overturn rocks to feed on the invertebrates beneath. However, baboons do not move all the rocks they encounter, with this presumably reflecting cost–benefit (or effort–reward) trade‐offs in their foraging behavior. We ask, how do “clever baboons” choose rock sizes and shapes and move these rocks? Using optimal foraging theory, we predicted that baboons would prefer to move medium‐sized rocks, a trade‐off between moving larger rocks that might require more effort to move, and smaller rocks that likely do not provide enough prey (the reward) to make the effort worthwhile. We also expected baboons to prefer rounded rocks as these will require less energy to move by rolling (rather than being flipped as for flat rocks) and that the effort of rock movement might be offset by moving rocks along the shortest axis. We show that baboons have clear preferences for specific rock sizes (medium‐sized) and shapes (angular and flat when these were medium‐sized), and the way in which rocks are moved (along the shortest axis). Prey occurred infrequently under rocks. The low predictability of prey beneath rocks suggests that such prey, when encountered, is of considerable value to baboons for them to expend the search effort, and also explains the extensive nature of rock movement by baboons in the landscape. Our study provides a novel application of the optimal foraging theory concept and has important implications for understanding and predicting how animals choose to move rocks.     

Spatial patterns of hypolithic cyanobacterial diversity in Northern Australia

Photosynthetic microbial communities under translucent rocks (hypolithic) are found in many arid regions. At the global scale, there has been little intercontinental gene flow, and at a local scale, microbial composition is related to fine‐scale features of the rocks and their environment. Few studies have investigated patterns of hypolithic community composition at intermediate distances. We examined hypolithic cyanobacterial diversity in semi‐arid Australia along a 10‐km transect by sampling six rocks from four adjacent 1 m² quadrats (“distance zero”) and from additional quadrats at 10, 100, 1,000, and 10,000 m to test the hypothesis that diversity would increase with the number of rocks sampled and distance. A total of 3,108 cyanobacterial operational taxonomic units (OTUs) were detected. Most were neither widespread nor abundant. The few that were widespread tended to be abundant. There was no difference in the community composition between the four sites at distance zero, but the samples 10 m away were significantly different, as were those at all other distances compared to distance zero. Many additional OTUs were recorded with increasing distance up to 100 m. These patterns of distribution are consistent with a colonization model involving dispersal from rock to rock. Our results indicate that distance was a significant factor that can be confounded by interrock differences. Most diversity was represented in the first 100 m of the transect, with an additional 1.5% of the total diversity added by the sample at 1 km, but only 0.2% added with the addition of the 10‐km site.