n analysis of 24 coral reef viromes challenges the view that lytic phage are believed to predominate when the density of their hosts increase and shows instead that lysogeny is more important at high host densities; the authors also show that this model is consistent with predator–prey dynamics in a range of other ecosystems, such as animal-associated, sediment and soil systems.

Microbialization refers to the observed shift in ecosystem trophic structure towards higher microbial biomass and energy use. On coral reefs, the proximal causes of microbialization are overfishing and eutrophication, both of which facilitate enhanced growth of fleshy algae, conferring a competitive advantage over calcifying corals and coralline algae. The proposed mechanism for this competitive advantage is the DDAM positive feedback loop (dissolved organic carbon (DOC), disease, algae, microorganism), where DOC released by ungrazed fleshy algae supports copiotrophic, potentially pathogenic bacterial communities, ultimately harming corals and maintaining algal competitive dominance. Using an unprecedented data set of >400 samples from 60 coral reef sites, we show that the central DDAM predictions are consistent across three ocean basins. Reef algal cover is positively correlated with lower concentrations of DOC and higher microbial abundances. On turf and fleshy macroalgal-rich reefs, higher relative abundances of copiotrophic microbial taxa were identified. These microbial communities shift their metabolic potential for carbohydrate degradation from the more energy efficient Embden–Meyerhof–Parnas pathway on coral-dominated reefs to the less efficient Entner–Doudoroff and pentose phosphate pathways on algal-dominated reefs. This ‘yield-to-power’ switch by microorganism directly threatens reefs via increased hypoxia and greater CO 2 release from the microbial respiration of DOC.

Nature 531, 466–470 (2016); doi:10.1038/nature17193 In this Article, the ‘Predator–prey modelling’ section of the Methods shows Lotka–Volterra equations. Although these equations are meant to present a basic Lotka–Volterra model, the term ‘N/K’ in the second equation was inadvertently introduced during proofing, which makes the equations reflect the Piggyback-the-Winner model rather than basic Lotka–Volterra.

Coral reefs are among the most productive and diverse marine ecosystems on the Earth. They are also particularly sensitive to changing energetic requirements by different trophic levels. Microbialization specifically refers to the increase in the energetic metabolic demands of microbes relative to macrobes and is significantly correlated with increasing human influence on coral reefs. In this study, metabolic theory of ecology is used to quantify the relative contributions of two broad bacterioplankton groups, autotrophs and heterotrophs, to energy flux on 27 Pacific coral reef ecosystems experiencing human impact to varying degrees. The effective activation energy required for photosynthesis is lower than the average energy of activation for the biochemical reactions of the Krebs cycle, and changes in the proportional abundance of these two groups can greatly affect rates of energy and materials cycling. We show that reef-water communities with a higher proportional abundance of microbial autotrophs expend more metabolic energy per gram of microbial biomass. Increased energy and materials flux through fast energy channels (i.e. water-column associated microbial autotrophs) may dampen the detrimental effects of increased heterotrophic loads (e.g. coral disease) on coral reef systems experiencing anthropogenic disturbance.

Cryophilic algae thrive in liquid water within snow and ice in alpine and polar regions worldwide. Blooms of these algae lower albedo (reflection of sunlight), thereby altering melting patterns (Kohshima, Seko & Yoshimura, 1993; Lutz et al., 2014; Thomas & Duval, 1995). Here metagenomic DNA analysis and satellite imaging were used to investigate red snow in Franz Josef Land in the Russian Arctic. Franz Josef Land red snow metagenomes confirmed that the communities are composed of the autotroph Chlamydomonas nivalis that is supporting a complex viral and heterotrophic bacterial community. Comparisons with white snow communities from other sites suggest that white snow and ice are initially colonized by fungal-dominated communities and then succeeded by the more complex C. nivalis-heterotroph red snow. Satellite image analysis showed that red snow covers up to 80% of the surface of snow and ice fields in Franz Josef Land and globally. Together these results show that C. nivalis supports a local food web that is on the rise as temperatures warm, with potential widespread impacts on alpine and polar environments worldwide.

National borders play an important role in everyday life. Interest in border studies has increased with recent changes in geographical locations of the border or the fluctuation of the permeability of the border between some countries, such as in the European Union. Whether the nations are trying to increase traffic flow of the border or to implement stricter border control, having appropriate information of the border is crucial for effective policymaking. The objective of this research was to identify areas of high porosity, or high permeability, for pedestrians along the southern national border region in Carinthia, Austria using terrain, land use, and road data along with geocomputational methods. Two unsupervised classification methods, the fuzzy K-means clustering and the Self-Organizing Map, were applied to segment the border into homogeneous zones according to topographic and infrastructural attributes. The fuzzy K-means clustering method was chosen for its ability to allow for a continuous approach to classification. With this method, an object can belong, with different degrees of membership, to multiple classes, which is a more realistic reflection of the natural world than discrete clustering, where each object can only belong to one class. However, the fuzzy K-means clustering method does have disadvantages, i.e. the user must determine the number of classes and the input parameters are required to be in continuous format. The second classification method, the Self-Organizing Map, is a type of artificial neural network and was chosen for its ability to automatically determine the number of classes and handle categorical data. The Self-Organizing Map is unique because it can transform high dimensional data into low dimensional display while preserving the topology and spatial distribution of the input parameters. The results of the two classification methods suggest that the fuzzy K-means classification is more effective than the Self-Organizing Map for this situation. However, more research is needed to determine the fit of these algorithms for particular spatial data classification tasks. The results obtained from this research provide an insight into the permeability of the border region of Carinthia, Slovenia, and Italy to pedestrian traffic and can be potentially useful for decision making processes for tourism development and road transportation management in that region. Furthermore, the approach presented in this article can be applied to other national borders to identify zones permeable to pedestrian traffic.