scholarly journals Extinction models of robustness for weighted ecological networks

2017 ◽  
Author(s):  
Miranda S. Bane ◽  
Michael J. O. Pocock ◽  
Richard James
Keyword(s):  
Network Structure ◽  
Structural Heterogeneity ◽  
Ecological Networks ◽  
List Type ◽  
Bipartite Networks ◽  
Opposing Effects ◽  
The Impact ◽  
Plant Pollinator ◽  
Binary Networks ◽  
New Framework

AbstractAnalysis of ecological networks is a valuable approach to understanding the vulnerability of systems to environmental change. The tolerance of ecological networks to co-extinctions, resulting from sequences of primary extinctions, is a widely-used tool for modelling network ‘robustness’. Previously, these ‘extinction models’ have been developed for and applied mostly to binary networks and recently used to predict cascades of co-extinctions in plant-pollinator networks. There is a need for robustness models that can make the most of the weighted data available and most importantly there is a need to understand how the structure of a network affects its robustness.Here, we developed a framework of extinction models for bipartite ecological networks (specifically plant-pollinator networks). In previous models co-extinctions occurred when nodes lost all their links, but by relaxing this rule (according to a set threshold) our models can be applied to binary and weighted networks, and can permit structurally correlated extinctions, i.e. the potential for avalanches of extinctions. We tested how the average and the range of robustness values is impacted by network structure and the impact of structurally-correlated extinctions sampling non-uniformly from the distribution of random extinction sequences.We found that the way that structurally-correlated extinctions are modelled impacts the results; our two ecologically-plausible models produce opposing effects which shows the importance of understanding the model. We found that when applying the models to networks with weighted interactions, the effects are amplified and the variation in robustness increases. Variation in robustness is a key feature of these extinction models and is driven by the structural heterogeneity (i.e. the skewness of the degree distribution) of nodes (specifically, plant nodes) in the network.Our new framework of models enables us to calculate robustness with weighted, as well as binary, bipartite networks, and to make direct comparisons between models and between networks. This allows us to differentiate effects of the model and of the data (network structure) which is vital for those making ecological inferences from robustness models. The models can be applied to mutualistic and antagonistic networks, and can be extended to food webs.

scholarly journals Uncovering indirect interactions in bipartite ecological networks

2018 ◽  
Author(s):  
Benno I. Simmons ◽  
Alyssa R. Cirtwill ◽  
Nick J. Baker ◽  
Lynn V. Dicks ◽  
Daniel B. Stouffer ◽  
...  
Keyword(s):  
Ecological Networks ◽  
Indirect Interactions ◽  
Ecological Communities ◽  
Bipartite Networks ◽  
New Paradigm ◽  
Diverse Range ◽  
Constituent Species ◽  
Quantitative Analyses ◽  
Plant Pollinator ◽  
New Framework

AbstractIndirect interactions play an essential role in governing population, community and coevolutionary dynamics across a diverse range of ecological communities. Such communities are widely represented as bipartite networks: graphs depicting interactions between two groups of species, such as plants and pollinators or hosts and parasites. For over thirty years, studies have used indices, such as connectance and species degree, to characterise the structure of these networks and the roles of their constituent species. However, compressing a complex network into a single metric necessarily discards large amounts of information about indirect interactions. Given the large literature demonstrating the importance and ubiquity of indirect effects, many studies of network structure are likely missing a substantial piece of the ecological puzzle. Here we use the emerging concept of bipartite motifs to outline a new framework for bipartite networks that incorporates indirect interactions. While this framework is a significant departure from the current way of thinking about networks, we show that this shift is supported by quantitative analyses of simulated and empirical data. We use simulations to show how consideration of indirect interactions can highlight ecologically important differences missed by the current index paradigm. We extend this finding to empirical plant-pollinator communities, showing how two bee species, with similar direct interactions, differ in how specialised their competitors are. These examples underscore the need for a new paradigm for bipartite ecological networks: one incorporating indirect interactions.

scholarly journals Reconstruction of plant–pollinator networks from observational data

2019 ◽  
Author(s):  
Jean-Gabriel Young ◽  
Fernanda S. Valdovinos ◽  
M. E. J. Newman
Keyword(s):  
Food Webs ◽  
Observational Data ◽  
Network Structure ◽  
Rare Species ◽  
Statistical Technique ◽  
Ecological Networks ◽  
Statistical Analyses ◽  
Published Data ◽  
Statistical Errors ◽  
Plant Pollinator

Empirical measurements of ecological networks such as food webs and mutualistic networks are often rich in structure but also noisy and error-prone, particularly for rare species for which observations are sparse. Focusing on the case of plant–pollinator networks, we here describe a Bayesian statistical technique that allows us to make accurate estimates of network structure and ecological metrics from such noisy observational data. Our method yields not only estimates of these quantities, but also estimates of their statistical errors, paving the way for principled statistical analyses of ecological variables and outcomes. We demonstrate the use of the method with an application to previously published data on plant–pollinator networks in the Seychelles archipelago, calculating estimates of network structure, network nestedness, and other characteristics.

Effect of interictal epileptiform discharges on EEG-based functional connectivity networks

2019 ◽  
Author(s):  
Derek K. Hu ◽  
Daniel W. Shrey ◽  
Beth A. Lopour
Keyword(s):  
Functional Connectivity ◽  
Network Structure ◽  
Infantile Spasms ◽  
List Type ◽  
Interictal Spikes ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Network Strength ◽  
The Impact ◽  
Connectivity Strength

AbstractObjectiveFunctional connectivity networks (FCNs) based on interictal electroencephalography (EEG) can identify pathological brain networks associated with epilepsy. FCNs are altered by interictal epileptiform discharges (IEDs), but it is unknown whether this is due to the morphology of the IED or the underlying pathological activity. Therefore, we characterized the impact of IEDs on the FCN through simulations and EEG analysis.MethodsWe introduced simulated IEDs to sleep EEG recordings of eight healthy controls and analyzed the effect of IED amplitude and rate on the FCN. We then generated FCNs based on epochs with and without IEDs and compared them to the analogous FCNs from eight subjects with infantile spasms (IS), based on 1,340 visually marked IEDs. Differences in network structure and strength were assessed.ResultsIEDs in IS subjects caused increased connectivity strength but no change in network structure. In controls, simulated IEDs with physiological amplitudes and rates did not alter network strength or structure.ConclusionsIncreases in connectivity strength in IS subjects are not artifacts caused by the interictal spike waveform and may be related to the underlying pathophysiology of IS.SignificanceDynamic changes in EEG-based FCNs during IEDs may be valuable for identification of pathological networks associated with epilepsy.HighlightsInfantile spasms subjects exhibit broadly increased connectivity strength during interictal spikesFunctional connectivity network structure is unaltered by interictal spikes in infantile spasmsSimulated spikes in healthy control EEG did not alter network strength or structure

scholarly journals bmotif: a package for motif analyses of bipartite networks

2018 ◽  
Author(s):  
Benno I. Simmons ◽  
Michelle J. M. Sweering ◽  
Maybritt Schillinger ◽  
Lynn V. Dicks ◽  
William J. Sutherland ◽  
...  
Keyword(s):  
Scale Structure ◽  
Individual Species ◽  
Mathematical Framework ◽  
List Type ◽  
Bipartite Networks ◽  
Diverse Range ◽  
Wide Range ◽  
Macro Scale ◽  
Plant Pollinator ◽  
Meso Scale

AbstractBipartite networks are widely-used to represent a diverse range of species interactions, such as pollination, herbivory, parasitism and seed dispersal. The structure of these networks is usually characterised by calculating one or more metrics that capture different aspects of network architecture. While these metrics capture useful properties of networks, they only consider structure at the scale of the whole network (the macro-scale) or individual species (the micro-scale). ‘Meso-scale’ structure between these scales is usually ignored, despite representing ecologically-important interactions. Network motifs are a framework for capturing this meso-scale structure and are gaining in popularity. However, there is no software available in R, the most popular programming language among ecologists, for conducting motif analyses in bipartite networks. Similarly, no mathematical formalisation of bipartite motifs has been developed.Here we introduce bmotif: a package for counting motifs, and species positions within motifs, in bipartite networks. Our code is primarily an R package, but we also provide MATLAB and Python code of the core functionality. The software is based on a mathematical framework where, for the first time, we derive formal expressions for motif frequencies and the frequencies with which species occur in different positions within motifs. This framework means that analyses with bmotif are fast, making motif methods compatible with the permutational approaches often used in network studies, such as null model analyses.We describe the package and demonstrate how it can be used to conduct ecological analyses, using two examples of plant-pollinator networks. We first use motifs to examine the assembly and disassembly of an Arctic plant-pollinator community, and then use them to compare the roles of native and introduced plant species in an unrestored site in Mauritius.bmotif will enable motif analyses of a wide range of bipartite ecological networks, allowing future research to characterise these complex networks without discarding important meso-scale structural detail.

scholarly journals Site-specific phosphorylation of PSD-95 dynamically regulates the post-synaptic density as observed by phase separation

2021 ◽  
Author(s):  
Maria Vistrup-Parry ◽  
Xudong Chen ◽  
Thea L. Johansen ◽  
Sofie Bach ◽  
Sara C. Buch-Larsen ◽  
...  
Keyword(s):  
Phase Separation ◽  
Postsynaptic Density ◽  
Scaffolding Protein ◽  
List Type ◽  
Pdz Domains ◽  
Site Specific ◽  
Glutamatergic Neurons ◽  
Opposing Effects ◽  
Zona Occludens ◽  
The Impact

SUMMARYPostsynaptic density protein 95 is a key scaffolding protein in the postsynaptic density of excitatory glutamatergic neurons, organizing signaling complexes primarily via its three PSD-95/Discs-large/Zona occludens domains. PSD-95 is regulated by phosphorylation, but technical challenges have limited studies of the molecular details. Here, we genetically introduced site-specific phosphorylations in single, tandem and full-length PSD-95 and generated a total of 11 phosphorylated protein variants. We examined how these phosphorylations affected binding to known interaction partners and the impact on phase separation of PSD-95 complexes, and identified two new phosphorylation sites with opposing effects. Phosphorylation of Ser78 inhibited phase separation with the glutamate receptor subunit GluN2B and the auxiliary protein stargazin, whereas phosphorylation of Ser116 induced phase separation with stargazin only. Thus, by genetically introducing phosphoserine site-specifically and exploring the impact on phase separation, we have provided new insights into the regulation of PSD-95 by phosphorylation and the dynamics of the PSD.Graphical Abstract: Phosphorylation of PSD-95.Illustration of the interaction between the PDZ domains of PSD-95 and the NMDA receptor (NMDAR) (PDB: 4PE5) at the plasma membrane. PSD-95 interacts with the AMPA receptor (AMPAR) (PDB: 3KG2) through Stargazin (PDB: 5KBU). Using the software Ranch, a model of FL PSD-95 was generated using known structural domains of PSD-95 connected via a fully-flexible linker (PDZ1-2, PDB: 3GSL; PDZ3, PDB: 5JXB; SH3-GK, PDB: 1KJW).Illustration of the effect of phosphorylation of PSD-95 on the formation of condensates at the PSD. PSD-95 can phase separate with receptors and intracellular proteins, and phosphorylation is can affect this interaction and lead to alterations, both negative and positive, in the formation of condensates.

scholarly journals Reconstruction of plant–pollinator networks from observational data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jean-Gabriel Young ◽  
Fernanda S. Valdovinos ◽  
M. E. J. Newman
Keyword(s):  
Food Webs ◽  
Observational Data ◽  
Network Structure ◽  
Rare Species ◽  
National Park ◽  
Statistical Technique ◽  
Ecological Networks ◽  
Statistical Analyses ◽  
Published Data ◽  
Plant Pollinator

AbstractEmpirical measurements of ecological networks such as food webs and mutualistic networks are often rich in structure but also noisy and error-prone, particularly for rare species for which observations are sparse. Focusing on the case of plant–pollinator networks, we here describe a Bayesian statistical technique that allows us to make accurate estimates of network structure and ecological metrics from such noisy observational data. Our method yields not only estimates of these quantities, but also estimates of their statistical errors, paving the way for principled statistical analyses of ecological variables and outcomes. We demonstrate the use of the method with an application to previously published data on plant–pollinator networks in the Seychelles archipelago and Kosciusko National Park, calculating estimates of network structure, network nestedness, and other characteristics.

scholarly journals Using Satellite Data for CBRN (Chemical, Biological, Radiological, and Nuclear) Threat Detection, Monitoring, and Modelling

2021 ◽  
Author(s):  
Gary Sutlieff ◽  
Lucy Berthoud ◽  
Mark Stinchcombe
Keyword(s):  
Satellite Data ◽  
Meteorological Data ◽  
Atmospheric Composition ◽  
Chemical Detection ◽  
List Type ◽  
Data Types ◽  
Wide Range ◽  
Future Technologies ◽  
The Impact ◽  
Near Future

Abstract CBRN (Chemical, Biological, Radiological, and Nuclear) threats are becoming more prevalent, as more entities gain access to modern weapons and industrial technologies and chemicals. This has produced a need for improvements to modelling, detection, and monitoring of these events. While there are currently no dedicated satellites for CBRN purposes, there are a wide range of possibilities for satellite data to contribute to this field, from atmospheric composition and chemical detection to cloud cover, land mapping, and surface property measurements. This study looks at currently available satellite data, including meteorological data such as wind and cloud profiles, surface properties like temperature and humidity, chemical detection, and sounding. Results of this survey revealed several gaps in the available data, particularly concerning biological and radiological detection. The results also suggest that publicly available satellite data largely does not meet the requirements of spatial resolution, coverage, and latency that CBRN detection requires, outside of providing terrain use and building height data for constructing models. Lastly, the study evaluates upcoming instruments, platforms, and satellite technologies to gauge the impact these developments will have in the near future. Improvements in spatial and temporal resolution as well as latency are already becoming possible, and new instruments will fill in the gaps in detection by imaging a wider range of chemicals and other agents and by collecting new data types. This study shows that with developments coming within the next decade, satellites should begin to provide valuable augmentations to CBRN event detection and monitoring. Article Highlights There is a wide range of existing satellite data in fields that are of interest to CBRN detection and monitoring. The data is mostly of insufficient quality (resolution or latency) for the demanding requirements of CBRN modelling for incident control. Future technologies and platforms will improve resolution and latency, making satellite data more viable in the CBRN management field

scholarly journals Does agricultural intensification cause tipping points in ecosystem services?

2021 ◽  
Author(s):  
Stephen C. L. Watson ◽  
Adrian C. Newton ◽  
Lucy E. Ridding ◽  
Paul M. Evans ◽  
Steven Brand ◽  
...  
Keyword(s):  
Ecosystem Services ◽  
Agricultural Intensification ◽  
Spatial Dynamics ◽  
Policy Option ◽  
List Type ◽  
Tipping Points ◽  
Software Models ◽  
Non Linear ◽  
Study Interval ◽  
The Impact

Abstract Context Agricultural intensification is being widely pursued as a policy option to improve food security and human development. Yet, there is a need to understand the impact of agricultural intensification on the provision of multiple ecosystem services, and to evaluate the possible occurrence of tipping points. Objectives To quantify and assess the long-term spatial dynamics of ecosystem service (ES) provision in a landscape undergoing agricultural intensification at four time points 1930, 1950, 1980 and 2015. Determine if thresholds or tipping points in ES provision may have occurred and if there are any detectable impacts on economic development and employment. Methods We used the InVEST suite of software models together with a time series of historical land cover maps and an Input–Output model to evaluate these dynamics over an 85-year period in the county of Dorset, southern England. Results Results indicated that trends in ES were often non-linear, highlighting the potential for abrupt changes in ES provision to occur in response to slight changes in underlying drivers. Despite the fluctuations in provision of different ES, overall economic activity increased almost linearly during the study interval, in line with the increase in agricultural productivity. Conclusions Such non-linear thresholds in ES will need to be avoided in the future by approaches aiming to deliver sustainable agricultural intensification. A number of positive feedback mechanisms are identified that suggest these thresholds could be considered as tipping points. However, further research into these feedbacks is required to fully determine the occurrence of tipping points in agricultural systems.

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