scholarly journals Disentangling temporal associations in marine microbial networks

2021 ◽  
Author(s):  
Ina Maria Deutschmann ◽  
Anders K. Krabberod ◽  
L. Felipe Benites ◽  
Francisco Latorre ◽  
Erwan Delage ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Annual Cycle ◽  
Ecosystem Functioning ◽  
Temporal Dynamics ◽  
Biogeochemical Cycling ◽  
Microbial Interactions ◽  
Temporal Network ◽  
Network Construction ◽  
Microbial Associations

Microbial interactions are fundamental for Earth's ecosystem functioning and biogeochemical cycling. Nevertheless, they are challenging to identify and remain barely known. The omics-based censuses are helpful to predict microbial interactions through the inference of static association networks. However, since microbial interactions are highly dynamic, we have developed a post-network-construction approach to generate a temporal network from a single static network. We applied it to understand the monthly microbial associations' dynamics occurring over ten years in the Blanes Bay Microbial Observatory (Mediterranean Sea). For the decade, we identified persistent, seasonal, and temporary microbial associations. Moreover, we found that the temporal network appears to follow an annual cycle, collapsing and reassembling when transiting between colder and warmer waters. We observed higher repeatability in colder than warmer months. Altogether, our results indicate that marine microbial networks follow recurrent temporal dynamics, which need to be accounted to better understand the dynamics of the ocean microbiome.

scholarly journals Disentangling temporal associations in marine microbial networks

2021 ◽  
Author(s):  
Ina Deutschmann ◽  
Anders Krabberød ◽  
L. Benites ◽  
Francisco Latorre ◽  
Erwan Delage ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Annual Cycle ◽  
Ecosystem Functioning ◽  
Temporal Dynamics ◽  
Biogeochemical Cycling ◽  
Microbial Interactions ◽  
Temporal Network ◽  
Microbial Associations

Abstract Microbial interactions are fundamental for Earth’s ecosystem functioning and biogeochemical cycling. Nevertheless, they are challenging to identify and remain barely known. The omics-based censuses are helpful to predict microbial interactions through the inference of static association networks. However, since microbial interactions are highly dynamic, we have developed an approach to generate a temporal network from a single static network. We applied it to understand the monthly microbial associations’ dynamics occurring over ten years in the Blanes Bay Microbial Observatory (Mediterranean Sea). For the decade, we identified persistent, seasonal, and temporary microbial associations. Moreover, we found that the temporal network appears to follow an annual cycle, collapsing and reassembling when transiting between colder and warmer waters. We observed higher repeatability in colder than warmer months. Altogether, our results indicate that marine microbial networks follow recurrent temporal dynamics, which need to be accounted to better understand the dynamics of the ocean microbiome.

scholarly journals Climate change alters temporal dynamics of alpine soil microbial functioning and biogeochemical cycling via earlier snowmelt

2021 ◽  
Author(s):  
Arthur A. D. Broadbent ◽  
Helen S. K. Snell ◽  
Antonios Michas ◽  
William J. Pritchard ◽  
Lindsay Newbold ◽  
...  
Keyword(s):  
Climate Change ◽  
Temporal Dynamics ◽  
Biogeochemical Cycling ◽  
Soil Microbial ◽  
Alpine Soil

scholarly journals From static to temporal network theory: Applications to functional brain connectivity

2017 ◽  
Vol 1 (2) ◽  
pp. 69-99 ◽  
Author(s):  
William Hedley Thompson ◽  
Per Brantefors ◽  
Peter Fransson
Keyword(s):  
Network Theory ◽  
Temporal Dynamics ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Network Activity ◽  
Temporal Network ◽  
Functional Brain ◽  
Brain Connectome ◽  
The Brain ◽  
Network Neuroscience

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.

Temporal dynamics of demersal chondrichthyan species in the central western Mediterranean Sea: The case study in Sardinia Island

2017 ◽  
Vol 193 ◽  
pp. 81-94 ◽  
Author(s):  
Martina F. Marongiu ◽  
Cristina Porcu ◽  
Andrea Bellodi ◽  
Rita Cannas ◽  
Alessandro Cau ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Temporal Dynamics ◽  
Western Mediterranean ◽  
Western Mediterranean Sea

scholarly journals Fungal-bacterial diversity and microbiome complexity predict ecosystem functioning

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Cameron Wagg ◽  
Klaus Schlaeppi ◽  
Samiran Banerjee ◽  
Eiko E. Kuramae ◽  
Marcel G. A. van der Heijden
Keyword(s):  
Microbial Communities ◽  
Bacterial Communities ◽  
Ecosystem Functioning ◽  
Microbial Interactions ◽  
Soil Microbiome ◽  
Ecosystem Functions ◽  
Microbial Network ◽  
Grassland Ecosystems ◽  
Microbiome Diversity ◽  
Ecological Associations

Abstract The soil microbiome is highly diverse and comprises up to one quarter of Earth’s diversity. Yet, how such a diverse and functionally complex microbiome influences ecosystem functioning remains unclear. Here we manipulated the soil microbiome in experimental grassland ecosystems and observed that microbiome diversity and microbial network complexity positively influenced multiple ecosystem functions related to nutrient cycling (e.g. multifunctionality). Grassland microcosms with poorly developed microbial networks and reduced microbial richness had the lowest multifunctionality due to fewer taxa present that support the same function (redundancy) and lower diversity of taxa that support different functions (reduced  functional uniqueness). Moreover, different microbial taxa explained different ecosystem functions pointing to the significance of functional diversity in microbial communities. These findings indicate the importance of microbial interactions within and among fungal and bacterial communities for enhancing ecosystem performance and demonstrate that the extinction of complex ecological associations belowground can impair ecosystem functioning.

The exergy of a phase shift: Ecosystem functioning loss in seagrass meadows of the Mediterranean Sea

2015 ◽  
Vol 156 ◽  
pp. 186-194 ◽  
Author(s):  
Monica Montefalcone ◽  
Paolo Vassallo ◽  
Giulia Gatti ◽  
Valeriano Parravicini ◽  
Chiara Paoli ◽  
...  
Keyword(s):  
Phase Shift ◽  
Mediterranean Sea ◽  
Ecosystem Functioning ◽  
Seagrass Meadows ◽  
The Mediterranean

scholarly journals Phytoplankton temporal dynamics in the coastal waters of the north-eastern Adriatic Sea (Mediterranean Sea) from 2010 to 2017

2019 ◽  
Vol 34 ◽  
pp. 343-372 ◽  
Author(s):  
Federica Cerino ◽  
Daniela Fornasaro ◽  
Martina Kralj ◽  
Michele Giani ◽  
Marina Cabrini
Keyword(s):  
Mediterranean Sea ◽  
Seasonal Cycle ◽  
Phytoplankton Community ◽  
Spring Bloom ◽  
Temporal Dynamics ◽  
Late Winter ◽  
Research Station ◽  
Phytoplankton Assemblages ◽  
The North ◽  
Long Term Ecological Research

Phytoplankton community structure was analysed from 2010 to 2017 at C1-LTER, the coastal Long-Term Ecological Research station located in the Gulf of Trieste, which is the northernmost part of the Mediterranean Sea. Phytoplankton abundance and relevant oceanographic parameters were measured monthly in order to describe the seasonal cycle and interannual variability of the main phytoplankton taxa (diatoms, dinoflagellates, coccolithophores and flagellates) and to analyse their relationship with environmental conditions. Overall, phytoplankton abundances showed a marked seasonal cycle characterised by a bloom in spring, with the peak in May. During the summer, phytoplankton abundances gradually decreased until September, then slightly increased again in October and reached their minima in winter. In general, the phytoplankton community was dominated by flagellates (generally <10 µm) and diatoms co-occurring in the spring bloom. In this period, diatoms were also represented by nano-sized species, gradually replaced by larger species in summer and autumn. Phytoplankton assemblages differed significantly between seasons (Pseudo-F = 9.59; p < 0.01) and temperature and salinity were the best predictor variables explaining the distribution of the multivariate data cloud. At the interannual scale, a strong decrease of the late-winter bloom was observed in recent years with the spring bloom being the main phytoplankton increase of the year.

scholarly journals Short-term temporal variations of heterotrophic bacterial abundance and production in the open NW Mediterranean Sea

2008 ◽  
Vol 5 (3) ◽  
pp. 1899-1932 ◽  
Author(s):  
G. Mével ◽  
M. Vernet ◽  
J. F. Ghiglione
Keyword(s):  
Mediterranean Sea ◽  
Time Scale ◽  
Bacterial Abundance ◽  
Temporal Dynamics ◽  
Bacterial Biomass ◽  
Tight Coupling ◽  
Production Values ◽  
Nw Mediterranean ◽  
Wind Events ◽  
Nw Mediterranean Sea

Abstract. We present the vertical and temporal dynamics of total vs. particle-attached bacterial abundance and activity over a 5 week period under summer to autumn transition in NW Mediterranean Sea. By comparison to previous investigations in the same area but during different seasons, we found that total bacterial biomass and production values were consistent with the hydrological conditions of the summer-fall transition. At a weekly time scale, total bacterial biomass and production in the euphotic layers was significantly correlated with phytoplanktonic biomass. At an hourly time scale, total bacterial biomass responded very rapidly to chlorophyll-a fluctuations, suggesting a tight coupling between phytoplankton and bacteria for resource partitioning during summer-autumn transition. In contrast, no influence of diel changes on bacterial parameters was detected. Episodic events such as coastal water intrusions had a significant positive effect on total bacterial abundance and production, whereas we could not detect any influence of short wind events whatever the magnitude. Finally, we show that particle-attached bacteria can represent a large proportion (until 49%) of the total bacterial activity in the euphotic layer but display rapid and sporadic changes at hourly time scales. This study underlines the value of large datasets covering different temporal scales to clarify the biogeochemical role of bacteria in the cycling of organic matter in open seawater.

Temporal Dynamics of Streamflow Using Complex Networks

2020 ◽  
Author(s):  
Bellie Sivakumar
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Complex Networks ◽  
Chaos Theory ◽  
Temporal Dynamics ◽  
The United States ◽  
Phase Space Reconstruction ◽  
Clustering Coefficient ◽  
Network Construction ◽  
Original Time

<p>Modeling the dynamics of streamflow continues to be highly challenging. The present study proposes a new approach to study the temporal dynamics of streamflow. The approach couples the concepts of complex networks and chaos theory. Applications of the concepts of complex networks for studying streamflow dynamics have been gaining momentum in recent years. A key step in such applications is the construction of the network – a network is a set of points (nodes) connected by lines (links). The present study uses the concept of phase-space reconstruction, an essential first step in chaos theory-based methods, for network construction to study the temporal dynamics of streamflow. The phase-space reconstruction involves representation of a single-variable time series in a multi-dimensional phase space using delay embedding. The reconstructed phase space is treated as a network, with the reconstructed vectors (rather than the original time series) serving as the nodes and the connections between them serving as the links. With this network construction, the clustering coefficient of the individual nodes and the entire network is calculated to assess the node and network strengths. The approach is employed to a large number of streamflow time series observed in the United States. The results indicate the usefulness and effectiveness of the phase-space reconstruction-based approach for network construction. The implications of the outcomes for identification of the appropriate type and complexity of model as well as for classification of catchments are discussed.</p>

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