Mineralizable nitrogen and denitrification enzyme activity drive nitrate concentrations in well-drained stony subsoil under lucerne (Medicago sativa L.)

Nitrogen (N) inputs to agricultural systems contribute substantially to soil nitrate (NO₃¯) concentrations, which increase NO₃¯ leaching and contamination of groundwater. The influence of soil microbes in regulating NO₃¯ concentrations in the topsoil are well studied but it is often assumed that microbial regulation of NO₃¯ concentrations in the subsoil is negligible. The aim of this study was to test this assumption by determining the relationships between microbial properties and NO₃¯ concentrations in both the subsoil and the topsoil. We measured the size of the mineralizable N (Nm) pool, microbial properties (microbial biomass, bacterial richness), nitrifier gene abundance (amoA gene copy number), denitrifier gene abundance (nirK and nirS gene copy number), denitrifier enzyme activity and NO₃¯ concentrations in the topsoil and the subsoil in a well-drained stony soil under an established lucerne crop. We used structural equation modelling (SEM) to identify and compare the linkages of microbial properties with NO₃¯ concentrations at each depth. In the topsoil, we found higher Nm, gene abundance, denitrification enzyme activity, bacterial richness, and microbial biomass than those in the subsoil, but there were no relationships between these variables and NO₃¯ concentrations in the topsoil (the SEM model explained 0.06% of the variability in NO₃¯ concentrations). In contrast, in the subsoil, NO₃¯ concentrations were strongly correlated with bacterial amoA abundance and denitrification enzyme activity, with both variables associated significantly with Nm. We found that bacterial richness was also associated with Nm in the subsoil. Our findings highlight that microbial properties are associated with NO₃¯ concentrations in the subsoil (the SEM model explained 82% the variability in NO₃¯ concentrations) and this suggest that nitrification and denitrification may contribute to regulating NO₃¯ concentrations in the subsoil. Our findings also suggest that denitrification contributes to reducing NO₃¯ concentrations in the subsoil. We conclude that studies addressing drivers of NO₃¯ leaching need to consider the potential for microbially-mediated attenuation (or an increase) in NO₃¯ concentrations throughout the soil profile

Effects of temporal correlations in social multiplex networks

Multi-layered networks represent a major advance in the description of natural complex systems, and their study has shed light on new physical phenomena. Despite its importance, however, the role of the temporal dimension in their structure and function has not been investigated in much detail so far. Here we study the temporal correlations between layers exhibited by real social multiplex networks. At a basic level, the presence of such correlations implies a certain degree of predictability in the contact pattern, as we quantify by an extension of the entropy and mutual information analyses proposed for the single-layer case. At a different level, we demonstrate that temporal correlations are a signature of a ‘multitasking’ behavior of network agents, characterized by a higher level of switching between different social activities than expected in a uncorrelated pattern. Moreover, temporal correlations significantly affect the dynamics of coupled epidemic processes unfolding on the network. Our work opens the way for the systematic study of temporal multiplex networks and we anticipate it will be of interest to researchers in a broad array of fields

Burstiness and tie activation strategies in time-varying social networks

The recent developments in the field of social networks shifted the focus from static to dynamical representations, calling for new methods for their analysis and modelling. Observations in real social systems identified two main mechanisms that play a primary role in networks' evolution and influence ongoing spreading processes: the strategies individuals adopt when selecting between new or old social ties, and the bursty nature of the social activity setting the pace of these choices. We introduce a time-varying network model accounting both for ties selection and burstiness and we analytically study its phase diagram. The interplay of the two effects is non trivial and, interestingly, the effects of burstiness might be suppressed in regimes where individuals exhibit a strong preference towards previously activated ties. The results are tested against numerical simulations and compared with two empirical datasets with very good agreement. Consequently, the framework provides a principled method to classify the temporal features of real networks, and thus yields new insights to elucidate the effects of social dynamics on spreading processes

Soil microbial sensitivity to temperature remains unchanged despite community compositional shifts along geothermal gradients.

Climate warming may be exacerbated if rising temperatures stimulate losses of soil carbon to the atmosphere. The direction and magnitude of this carbon-climate feedback are uncertain, largely due to lack of knowledge of the thermal adaptation of the physiology and composition of soil microbial communities. Here, we applied the macromolecular rate theory (MMRT) to describe the temperature response of the microbial decomposition of soil organic matter (SOM) in a natural long-term warming experiment in a geothermally active area in New Zealand. Our objective was to test whether microbial communities adapt to long-term warming with a shift in their composition and their temperature response that are consistent with evolutionary theory of trade-offs between enzyme structure and function. We characterized the microbial community composition (using metabarcoding) and the temperature response of microbial decomposition of SOM (using MMRT) of soils sampled along transects of increasing distance from a geothermally active zone comprising two biomes (a shrubland and a grassland) and sampled at two depths (0?50 and 50?100 mm), such that ambient soil temperature and soil carbon concentration varied widely and independently. We found that the different environments were hosting microbial communities with distinct compositions, with thermophile and thermotolerant genera increasing in relative abundance with increasing ambient temperature. However, the ambient temperature had no detectable influence on the MMRT parameters or the relative temperature sensitivity of decomposition (Q10). MMRT parameters were, however, strongly correlated with soil carbon concentration and carbon:nitrogen ratio. Our findings suggest that, while long-term warming selects for warm-adapted taxa, substrate quality and quantity exert a stronger influence than temperature in selecting for distinct thermal traits. The results have major implications for our understanding of the role of soil microbial processes in the long-term effects of climate warming on soil carbon dynamics and will help increase confidence in carbon-climate feedback projections

Asymptotic theory of time-varying social networks with heterogeneous activity and tie allocation

The dynamic of social networks is driven by the interplay between diverse mechanisms that still challenge our theoretical and modelling efforts. Amongst them, two are known to play a central role in shaping the networks evolution, namely the heterogeneous propensity of individuals to i) be socially active and ii) establish a new social relationships with their alters. Here, we empirically characterise these two mechanisms in seven real networks describing temporal human interactions in three different settings: scientific collaborations, Twitter mentions, and mobile phone calls. We find that the individuals’ social activity and their strategy in choosing ties where to allocate their social interactions can be quantitatively described and encoded in a simple stochastic network modelling framework. The Master Equation of the model can be solved in the asymptotic limit. The analytical solutions provide an explicit description of both the system dynamic and the dynamical scaling laws characterising crucial aspects about the evolution of the networks. The analytical predictions match with accuracy the empirical observations, thus validating the theoretical approach. Our results provide a rigorous dynamical system framework that can be extended to include other processes shaping social dynamics and to generate data driven predictions for the asymptotic behaviour of social networks

Modern temporal network theory: A colloquium

The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.Comment: Final accepted versio

: s'informer, se protéger, influencer

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