scholarly journals Predictability of bee community composition after floral removals differs by floral trait group

2017 ◽  
Vol 13 (11) ◽  
pp. 20170515 ◽  
Author(s):  
Katherine R. Urban-Mead
Keyword(s):  
Complex Networks ◽  
Community Composition ◽  
Network Topology ◽  
Floral Trait ◽  
Ecological Traits ◽  
Diverse Communities ◽  
Bee Foraging ◽  
Bee Community ◽  
Composition I ◽  
Trait Group

Plant–bee visitor communities are complex networks. While studies show that deleting nodes alters network topology, predicting these changes in the field remains difficult. Here, a simple trait-based approach is tested for predicting bee community composition following disturbance. I selected six fields with mixed cover of flower species with shallow (open) and deep (tube) nectar access, and removed all flowers or flower heads of species of each trait in different plots paired with controls, then observed bee foraging and composition. I compared the bee community in each manipulated plot with bees on the same flower species in control plots. The bee morphospecies composition in manipulations with only tube flowers remaining was the same as that in the control plots, while the bee morphospecies on only open flowers were dissimilar from those in control plots. However, the proportion of short- and long-tongued bees on focal flowers did not differ between control and manipulated plots for either manipulation. So, bees within some functional groups are more strongly linked to their floral trait partners than others. And, it may be more fruitful to describe expected bee community compositions in terms of relative proportions of relevant ecological traits than species, particularly in species-diverse communities.

A Bird's-Eye View of Pollination: Biotic Interactions as Drivers of Adaptation and Community Change

2019 ◽  
Vol 50 (1) ◽  
pp. 477-502 ◽  
Author(s):  
Anton Pauw
Keyword(s):  
Community Composition ◽  
Bird Species ◽  
Biotic Interactions ◽  
Population Level ◽  
Community Change ◽  
Plant Evolution ◽  
Relative Fitness ◽  
Fitness Advantage ◽  
Bird Evolution ◽  
Composition I

Nectarivorous birds and bird-pollinated plants are linked by a network of interactions. Here I ask how these interactions influence evolution and community composition. I find near complete evidence for the effect of birds on plant evolution. Experiments show the process in action—birds select among floral phenotypes in a population—and comparative studies find the resulting pattern—bird-pollinated species have long-tubed, red flowers with large nectar volumes. Speciation is accomplished in one “magical” step when adaptation for bird pollination brings about divergent morphology and reproductive isolation. In contrast, evidence that plants drive bird evolution is fragmentary. Studies of selection on population-level variation are lacking, but the resulting pattern is clear—nectarivorous birds have evolved a remarkable number of times and often have long bills and brush-tipped or tubular tongues. At the level of the ecological guild, birds select among plant species via an effect on seed set and thus determine plant community composition. Plants simultaneously influence the relative fitness of bird species and thus determine the composition of the bird guild. Interaction partners may give one guild member a constant fitness advantage, resulting in competitive exclusion and community change, or may act as limiting resources that depress the fitness of frequent species, thus stabilizing community composition and allowing the coexistence of diversity within bird and plant guilds.

scholarly journals PROBING COMPLEX NETWORKS FROM MEASURED TIME SERIES

2012 ◽  
Vol 22 (10) ◽  
pp. 1250236 ◽  
Author(s):  
LIANG HUANG ◽  
YING-CHENG LAI ◽  
MARY ANN F. HARRISON
Keyword(s):  
Time Series ◽  
Complex Networks ◽  
Network Topology ◽  
Efficient Method ◽  
Relative Importance ◽  
Computationally Efficient ◽  
Heuristic Argument ◽  
Principal Eigenvector ◽  
Measured Time ◽  
Potential Applications

We propose a method to detect nodes of relative importance, e.g. hubs, in an unknown network based on a set of measured time series. The idea is to construct a matrix characterizing the synchronization probabilities between various pairs of time series and examine the components of the principal eigenvector. We provide a heuristic argument indicating the existence of an approximate one-to-one correspondence between the components and the degrees of the nodes from which measurements are obtained. The striking finding is that such a correspondence appears to be quite robust, which holds regardless of the detailed node dynamics and of the network topology. Our computationally efficient method thus provides a general means to address the important problem of network detection, with potential applications in a number of fields.

Application of Complex Network Theory in Computer Network Topology Optimization Research

2014 ◽  
Vol 989-994 ◽  
pp. 4237-4240
Author(s):  
Zhi Kun Wang
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Complex Network ◽  
Network Topology ◽  
Computer Network ◽  
Transportation Network ◽  
Power Grids ◽  
Research Network ◽  
Complex Network Theory ◽  
New Research

If we apply the system internal elements as nodes, and the relationship between the elements as connection, then the system form a network. If we put emphasis on the structure of the system and analyze the function of the system from the angle of structure, we’ll find that real network topology properties differ from previous research network, and has numerous nodes, which is called complex networks. In the real word, many complex systems can be basically described by the network, while the reality is that complex systems can be called as “complex network”, such as social network, transportation network, power grids and internet etc. In recent years, many articles about the complex networks are released in the international first-class publications such as Nature, PRL, PNAS, which reflects that the complex networks has become a new research focus.

scholarly journals Incremental Density-Based Link Clustering Algorithm for Community Detection in Dynamic Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Fanrong Meng ◽  
Feng Zhang ◽  
Mu Zhu ◽  
Yan Xing ◽  
Zhixiao Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Topology ◽  
Clustering Algorithm ◽  
Dynamic Networks ◽  
Experimental Results ◽  
Extended Version ◽  
High Quality ◽  
Detection Algorithms

Community detection in complex networks has become a research hotspot in recent years. However, most of the existing community detection algorithms are designed for the static networks; namely, the connections between the nodes are invariable. In this paper, we propose an incremental density-based link clustering algorithm for community detection in dynamic networks, iDBLINK. This algorithm is an extended version of DBLINK which is proposed in our previous work. It can update the local link community structure in the current moment through the change of similarity between the edges at the adjacent moments, which includes the creation, growth, merging, deletion, contraction, and division of link communities. Extensive experimental results demonstrate that iDBLINK not only has a great time efficiency, but also maintains a high quality community detection performance when the network topology is changing.

scholarly journals Modeling Complex Networks Based on Deep Reinforcement Learning

2022 ◽  
Vol 9 ◽  
Author(s):  
Wenbo Song ◽  
Wei Sheng ◽  
Dong Li ◽  
Chong Wu ◽  
Jun Ma
Keyword(s):  
Reinforcement Learning ◽  
Complex Networks ◽  
Network Topology ◽  
Network Structure ◽  
Dynamic Change ◽  
Intelligent Agent ◽  
Small World ◽  
Network Nodes ◽  
Scale Free ◽  
Internal Mechanism

The network topology of complex networks evolves dynamically with time. How to model the internal mechanism driving the dynamic change of network structure is the key problem in the field of complex networks. The models represented by WS, NW, BA usually assume that the evolution of network structure is driven by nodes’ passive behaviors based on some restrictive rules. However, in fact, network nodes are intelligent individuals, which actively update their relations based on experience and environment. To overcome this limitation, we attempt to construct a network model based on deep reinforcement learning, named as NMDRL. In the new model, each node in complex networks is regarded as an intelligent agent, which reacts with the agents around it for refreshing its relationships at every moment. Extensive experiments show that our model not only can generate networks owing the properties of scale-free and small-world, but also reveal how community structures emerge and evolve. The proposed NMDRL model is helpful to study propagation, game, and cooperation behaviors in networks.

scholarly journals Exact probabilities for the indeterminacy of complex networks as perceived through press perturbations

2016 ◽  
Author(s):  
David Koslicki ◽  
Mark Novak
Keyword(s):  
Complex Networks ◽  
Network Topology ◽  
Network Dynamics ◽  
Ecological Networks ◽  
Primary Result ◽  
Qualitative Modeling ◽  
Expected Number ◽  
Recent Advances ◽  
Modeling Techniques

AbstractWe consider the goal of predicting how complex networks respond to chronic (press) perturbations when characterizations of their network topology and interaction strengths are associated with uncertainty. Our primary result is the derivation of exact formulas for the expected number and probability of qualitatively incorrect predictions about a system’s responses under uncertainties drawn form arbitrary distributions of error. These formulas obviate the current use of simulations, algorithms, and qualitative modeling techniques. Additional indices provide new tools for identifying which links in a network are most qualitatively and quantitatively sensitive to error, and for determining the volume of errors within which predictions will remain qualitatively determinate (i.e. sign insensitive). Together with recent advances in the empirical characterization of uncertainty in ecological networks, these tools bridge a way towards probabilistic predictions of network dynamics.

scholarly journals Detecting different topologies immanent in scale-free networks with the same degree distribution

2019 ◽  
Vol 116 (14) ◽  
pp. 6701-6706 ◽  
Author(s):  
Dimitrios Tsiotas
Keyword(s):  
Complex Networks ◽  
Power Law ◽  
Network Topology ◽  
Degree Distribution ◽  
Growth Process ◽  
Preferential Attachment ◽  
Self Similarity ◽  
Scale Free ◽  
Scale Free Networks ◽  
Definition Of

The scale-free (SF) property is a major concept in complex networks, and it is based on the definition that an SF network has a degree distribution that follows a power-law (PL) pattern. This paper highlights that not all networks with a PL degree distribution arise through a Barabási−Albert (BA) preferential attachment growth process, a fact that, although evident from the literature, is often overlooked by many researchers. For this purpose, it is demonstrated, with simulations, that established measures of network topology do not suffice to distinguish between BA networks and other (random-like and lattice-like) SF networks with the same degree distribution. Additionally, it is examined whether an existing self-similarity metric proposed for the definition of the SF property is also capable of distinguishing different SF topologies with the same degree distribution. To contribute to this discrimination, this paper introduces a spectral metric, which is shown to be more capable of distinguishing between different SF topologies with the same degree distribution, in comparison with the existing metrics.

scholarly journals Mapping the topology of the air transport network in Turkey

2019 ◽  
Vol 52 (1) ◽  
pp. 6-9
Author(s):  
Umut Erdem ◽  
K. Mert Cubukcu ◽  
Dimitrios Tsiotas
Keyword(s):  
Economic Growth ◽  
Complex Networks ◽  
Network Topology ◽  
Transport Network ◽  
Air Transport ◽  
Uneven Distribution ◽  
Population Concentration ◽  
Network Topologies ◽  
Equal Population

Recent technological and philosophical research has revealed that almost everything around us is heavily dependent on network-based complexities. Airport network topologies are complex networks and their analyses are crucial regarding the fact that the evolution of airport network topology influences the economic growth of regions and countries. An equal population cartogram is derived displaying the distortion of the air transport network of Turkey in accordance with the uneven distribution of passengers. The regions between İstanbul, Ankara, İzmir, and Antalya are shrunk by the force of higher population concentration. The shrinkage across the eastern regions is less than that in the western regions; still, the distortion of the regions is dominated by particular regional hubs.

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