Riverine landscapes: taking landscape ecology into the water

1. Landscape ecology deals with the influence of spatial pattern on ecological processes. It considers the ecological consequences of where things are located in space, where they are relative to other things, and how these relationships and their consequences are contingent on the characteristics of the surrounding landscape mosaic at multiple scales in time and space. Traditionally, landscape ecologists have focused their attention on terrestrial ecosystems, and rivers and streams have been considered either as elements of landscape mosaics or as units that are linked to the terrestrial landscape by flows across boundaries or ecotones. Less often, the heterogeneity that exists within a river or stream has been viewed as a `riverscape' in its own right.
2. Landscape ecology can be unified about six central themes: (1) patches differ in quality (2) patch boundaries affect flows, (3) patch context matters, (4) connectivity is critical, (5) organisms are important, and (6) the importance of scale. Although riverine systems differ from terrestrial systems by virtue of the strong physical force of hydrology and the inherent connectivity provided by water flow, all of these themes apply equally to aquatic and terrestrial ecosystems, and to the linkages between the two.
3. Landscape ecology therefore has important insights to offer to the study of riverine ecosystems, but these systems may also provide excellent opportunities for developing and testing landscape ecological theory. The principles and approaches of landscape ecology should be extended to include freshwater systems; it is time to take the `land' out of landscape ecology.     

Geometric interpretation of gene coexpression network analysis

THE MERGING OF NETWORK THEORY AND MICROARRAY DATA ANALYSIS TECHNIQUES HAS SPAWNED A NEW FIELD: gene coexpression network analysis. While network methods are increasingly used in biology, the network vocabulary of computational biologists tends to be far more limited than that of, say, social network theorists. Here we review and propose several potentially useful network concepts. We take advantage of the relationship between network theory and the field of microarray data analysis to clarify the meaning of and the relationship among network concepts in gene coexpression networks. Network theory offers a wealth of intuitive concepts for describing the pairwise relationships among genes, which are depicted in cluster trees and heat maps. Conversely, microarray data analysis techniques (singular value decomposition, tests of differential expression) can also be used to address difficult problems in network theory. We describe conditions when a close relationship exists between network analysis and microarray data analysis techniques, and provide a rough dictionary for translating between the two fields. Using the angular interpretation of correlations, we provide a geometric interpretation of network theoretic concepts and derive unexpected relationships among them. We use the singular value decomposition of module expression data to characterize approximately factorizable gene coexpression networks, i.e., adjacency matrices that factor into node specific contributions. High and low level views of coexpression networks allow us to study the relationships among modules and among module genes, respectively. We characterize coexpression networks where hub genes are significant with respect to a microarray sample trait and show that the network concept of intramodular connectivity can be interpreted as a fuzzy measure of module membership. We illustrate our results using human, mouse, and yeast microarray gene expression data. The unification of coexpression network methods with traditional data mining methods can inform the application and development of systems biologic methods.     

Estimating Temporal Causal Interaction between Spike Trains with Permutation and Transfer Entropy

Estimating the causal interaction between neurons is very important for better understanding the functional connectivity in neuronal networks. We propose a method called normalized permutation transfer entropy (NPTE) to evaluate the temporal causal interaction between spike trains, which quantifies the fraction of ordinal information in a neuron that has presented in another one. The performance of this method is evaluated with the spike trains generated by an Izhikevich’s neuronal model. Results show that the NPTE method can effectively estimate the causal interaction between two neurons without influence of data length. Considering both the precision of time delay estimated and the robustness of information flow estimated against neuronal firing rate, the NPTE method is superior to other information theoretic method including normalized transfer entropy, symbolic transfer entropy and permutation conditional mutual information. To test the performance of NPTE on analyzing simulated biophysically realistic synapses, an Izhikevich’s cortical network that based on the neuronal model is employed. It is found that the NPTE method is able to characterize mutual interactions and identify spurious causality in a network of three neurons exactly. We conclude that the proposed method can obtain more reliable comparison of interactions between different pairs of neurons and is a promising tool to uncover more details on the neural coding.     

Role of connectivity in immune and neural network models: memory development and aging.

In this paper we analyzed how connectivity (defined as number of connections between network elements) can affect the memory capacity of a network-based model of the Immune System (IS) and of a model of the Nervous System (NS) synaptic plasticity (BCM model). The key point is the concept of competition between the characteristic variables that represent the response of such systems to environmental stimuli: the clonal concentrations for the IS, and the neuron responses for the BCM model. The memory states of both systems are characterized by a high selectivity to specific input patterns, reflecting a similar behaviour of their development rules. This selectivity property of memory states can be controlled by changing the degree of the internal connectivity in each system. We can explain the changes occurring in IS memory states during lifespan as due to a reshaping of its internal connectivity. This assumption is in agreement with experimental observations, reporting an increase of IS memory cells during lifespan. The change of connectivity in the BCM model leads to the introduction of quasilocal variables governing the plasticity of groups of synaptic junctions. This could be interpreted as the result of a refinement of neuron internal mechanisms during development, or it could be seen as a different learning rule deriving from the original BCM theory. We argue that connectivity seems to play an important role in a large class of biological systems controlled by competition mechanisms. Moreover, changes in connectivity may lead to changes in memory properties during development and aging.     

Connecting the dots in ethology: applying network theory to understand neural and animal collectives

A major goal shared by neuroscience and collective behavior is to understand how dynamic interactions between individual elements give rise to behaviors in populations of neurons and animals, respectively. This goal has recently become within reach, thanks to techniques providing access to the connectivity and activity of neuronal ensembles as well as to behaviors among animal collectives. The next challenge using these datasets is to unravel network mechanisms generating population behaviors. This is aided by network theory, a field that studies structure–function relationships in interconnected systems. Here we review studies that have taken a network view on modern datasets to provide unique insights into individual and collective animal behaviors. Specifically, we focus on how analyzing signal propagation, controllability, symmetry, and geometry of networks can tame the complexity of collective system dynamics. These studies illustrate the potential of network theory to accelerate our understanding of behavior across ethological scales.     

Empirical assessment of the relation between ecological connectivity and land complexity based on information-theoretic metrics

Habitat fragmentation and connectivity loss pose significant threats to biodiversity at both local and landscape levels. Strategies to increase ecological connectivity and preserve strong connectivity are important for dealing with the potential threat of habitat degradation. Various metrics have been used to measure (i.e., quantify) landscape composition and configuration in landscape ecology. However, their relationship with ecological connectivity must be understood to interpret landscape patterns comprehensively. In the present study, correlations between ecological connectivity and land complexity are examined based on information-theory metrics. Two primary questions are explored: (1) to what extent are landscape mosaic measures of entropy correlated with ecological connectivity, with landscape gradient-based measures, and with each other? (2) are landscape gradient-based entropy measures correlated with ecological connectivity more than discrete entropy measures? Results show that all information theoretic metrics are statistically significant (p < 0.05) for modelling ecological connectivity. Among categorically-based indices, the relationship between ECI and joint entropy was the most significant, while a generalized additive model indicated that Boltzmann entropy could predict the ecological connectivity index, explaining ∼60% of the variance. Therefore, configurational entropy can be used for improving ecological connectivity models.     

Detection of cellular rhythms and global stability within interlocked feedback systems

In this paper, we show how to detect cellular rhythm and its global stability by extending the techniques from the recently developed theory of monotone systems. We establish theoretical results for globally asymptotic stability with consideration of delay by a discrete map. The relationship between positive, negative elements and delay in a general class of interlocked feedback networks can be understood in a system level. Moreover, the correspondence of attractors between a network and its reduced map is obtained and can be used to detect cellular rhythm, and further control the dynamics of the network. We show that global cellular rhythms can always be obtained, thereby enhancing robustness against perturbations of initial conditions and avoiding chaotic oscillations or complete abolishment of oscillations. In this paper, we focus on analyzing the circadian oscillator in Drosophila as an example to detect the occurrence of cellular rhythm and its global stability.     

Meta-ecosystem dynamics and functioning on finite spatial networks

The addition of spatial structure to ecological concepts and theories has spurred integration between sub-disciplines within ecology, including community and ecosystem ecology. However, the complexity of spatial models limits their implementation to idealized, regular landscapes. We present a model meta-ecosystem with finite and irregular spatial structure consisting of local nutrient–autotrophs–herbivores ecosystems connected through spatial flows of materials and organisms. We study the effect of spatial flows on stability and ecosystem functions, and provide simple metrics of connectivity that can predict these effects. Our results show that high rates of nutrient and herbivore movement can destabilize local ecosystem dynamics, leading to spatially heterogeneous equilibria or oscillations across the meta-ecosystem, with generally increased meta-ecosystem primary and secondary production. However, the onset and the spatial scale of these emergent dynamics depend heavily on the spatial structure of the meta-ecosystem and on the relative movement rate of the autotrophs. We show how this strong dependence on finite spatial structure eludes commonly used metrics of connectivity, but can be predicted by the eigenvalues and eigenvectors of the connectivity matrix that describe the spatial structure and scale. Our study indicates the need to consider finite-size ecosystems in meta-ecosystem theory.     

Multiplex Networks of Cortical and Hippocampal Neurons Revealed at Different Timescales

Recent studies have emphasized the importance of multiplex networks – interdependent networks with shared nodes and different types of connections – in systems primarily outside of neuroscience. Though the multiplex properties of networks are frequently not considered, most networks are actually multiplex networks and the multiplex specific features of networks can greatly affect network behavior (e.g. fault tolerance). Thus, the study of networks of neurons could potentially be greatly enhanced using a multiplex perspective. Given the wide range of temporally dependent rhythms and phenomena present in neural systems, we chose to examine multiplex networks of individual neurons with time scale dependent connections. To study these networks, we used transfer entropy – an information theoretic quantity that can be used to measure linear and nonlinear interactions – to systematically measure the connectivity between individual neurons at different time scales in cortical and hippocampal slice cultures. We recorded the spiking activity of almost 12,000 neurons across 60 tissue samples using a 512-electrode array with 60 micrometer inter-electrode spacing and 50 microsecond temporal resolution. To the best of our knowledge, this preparation and recording method represents a superior combination of number of recorded neurons and temporal and spatial recording resolutions to any currently available in vivo system. We found that highly connected neurons (“hubs”) were localized to certain time scales, which, we hypothesize, increases the fault tolerance of the network. Conversely, a large proportion of non-hub neurons were not localized to certain time scales. In addition, we found that long and short time scale connectivity was uncorrelated. Finally, we found that long time scale networks were significantly less modular and more disassortative than short time scale networks in both tissue types. As far as we are aware, this analysis represents the first systematic study of temporally dependent multiplex networks among individual neurons.     

Effects of Hebbian learning on the dynamics and structure of random networks with inhibitory and excitatory neurons

The aim of the present paper is to study the effects of Hebbian learning in random recurrent neural networks with biological connectivity, i.e. sparse connections and separate populations of excitatory and inhibitory neurons. We furthermore consider that the neuron dynamics may occur at a (shorter) time scale than synaptic plasticity and consider the possibility of learning rules with passive forgetting. We show that the application of such Hebbian learning leads to drastic changes in the network dynamics and structure. In particular, the learning rule contracts the norm of the weight matrix and yields a rapid decay of the dynamics complexity and entropy. In other words, the network is rewired by Hebbian learning into a new synaptic structure that emerges with learning on the basis of the correlations that progressively build up between neurons. We also observe that, within this emerging structure, the strongest synapses organize as a small-world network. The second effect of the decay of the weight matrix spectral radius consists in a rapid contraction of the spectral radius of the Jacobian matrix. This drives the system through the "edge of chaos" where sensitivity to the input pattern is maximal. Taken together, this scenario is remarkably predicted by theoretical arguments derived from dynamical systems and graph theory.     

Dynamic large-scale connectivity of intrinsic cortical oscillations supports adaptive listening in challenging conditions

In multi-talker situations, individuals adapt behaviorally to this listening challenge mostly with ease, but how do brain neural networks shape this adaptation? We here establish a long-sought link between large-scale neural communications in electrophysiology and behavioral success in the control of attention in difficult listening situations. In an age-varying sample of N = 154 individuals, we find that connectivity between intrinsic neural oscillations extracted from source-reconstructed electroencephalography is regulated according to the listener’s goal during a challenging dual-talker task. These dynamics occur as spatially organized modulations in power-envelope correlations of alpha and low-beta neural oscillations during approximately 2-s intervals most critical for listening behavior relative to resting-state baseline. First, left frontoparietal low-beta connectivity (16 to 24 Hz) increased during anticipation and processing of a spatial-attention cue before speech presentation. Second, posterior alpha connectivity (7 to 11 Hz) decreased during comprehension of competing speech, particularly around target-word presentation. Connectivity dynamics of these networks were predictive of individual differences in the speed and accuracy of target-word identification, respectively, but proved unconfounded by changes in neural oscillatory activity strength. Successful adaptation to a listening challenge thus latches onto two distinct yet complementary neural systems: a beta-tuned frontoparietal network enabling the flexible adaptation to attentive listening state and an alpha-tuned posterior network supporting attention to speech.

This study investigates how intrinsic neural oscillations, acting in concert, tune into attentive listening. Using electroencephalography signals collected from people in a dual-talker listening task, the authors find that network connectivity of frontoparietal beta and posterior alpha oscillations is regulated according to the listener’s goal.     

Graph theoretic topology of the Great but small Barrier Reef world

The transport of larvae between coral reefs is critical to the functioning of Australia’s Great Barrier Reef (GBR) because it determines recruitment rates and genetic exchange. One way of modelling the transport of larvae from one reef to another is to use information about currents. However the connectivity relationships of the entire system have not been fully examined. Graph theory provides a framework for the representation and analysis of connections via larval transport. In the past, the geometric arrangement (topology) of biological systems, such as food webs and neural networks, has revealed a common set of characteristics known as the ‘small world’ property. We use graph theory to examine and describe the topology and connectivity of a species living in 321 reefs in the central section of the GBR over 32 years. This section of the GBR can be described by a directional weighted graph, and we discovered that it exhibits scale-free small-world characteristics. The conclusion that the GBR is a small-world network for biological organisms is robust to variation in both the life history of the species modelled and yearly variation in hydrodynamics. The GBR is the first reported mesoscale biological small-world network.     

探索景观异质性的热力学基础和信息论

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Graph theoretical analysis of complex networks in the brain

Since the discovery of small-world and scale-free networks the study of complex systems from a network perspective has taken an enormous flight. In recent years many important properties of complex networks have been delineated. In particular, significant progress has been made in understanding the relationship between the structural properties of networks and the nature of dynamics taking place on these networks. For instance, the 'synchronizability' of complex networks of coupled oscillators can be determined by graph spectral analysis. These developments in the theory of complex networks have inspired new applications in the field of neuroscience. Graph analysis has been used in the study of models of neural networks, anatomical connectivity, and functional connectivity based upon fMRI, EEG and MEG. These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity. This small-world structure is hypothesized to reflect an optimal situation associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration. The topological structure of functional networks is probably restrained by genetic and anatomical factors, but can be modified during tasks. There is also increasing evidence that various types of brain disease such as Alzheimer's disease, schizophrenia, brain tumours and epilepsy may be associated with deviations of the functional network topology from the optimal small-world pattern.     

Photobiosynthesis of isoprene as an example of leaf excretory function in the light of contemporary thermodynamics

Various aspects in photobiosynthesis of isoprene and its release from leaves into the environment are presently well known. The release of isoprene from the cell can be regarded as dissipation of excess energy (entropy). The systemic release of metabolites into the external medium should be considered as a result of cell excretory activity, one of the most important functions of living systems. Energy dissipation terminates the sustained passage of thermodynamic flows and regulates the overall stability of cell stationary condition. These issues are considered in this review from the standpoint of contemporary thermodynamics. It is concluded that the excretory capacity of living cell is based on thermodynamic dissipation of entropy during irreversible processes that provide for stability and sustainable development of the living organism.     

Motor Imagery Learning Modulates Functional Connectivity of Multiple Brain Systems in Resting State

Background

Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning.

Methodology/Principal Findings

We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN.

Conclusions/Significance

These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning.     

The impact of environmental fluctuations on structured discrete time population models: resonance, synchrony and threshold behaviour

External forcing of a discrete time ecological system does not just add variation to existing dynamics but can change the dynamics. We study the mechanisms that can bring this about, focusing on the key concepts of excitation and suppression which emerge when analysing the power spectra of the system in linear approximation. Excitation, through resonance between the system dynamics and the external forcing, is the greater the closer the system is to the boundary of the stability region. This amplification means that the extinction of populations becomes possible sooner than expected and, conversely, invasion can be significantly delayed. Suppression and the consequent redistribution of power within the spectrum proves to be a function both of the connectivity of the network graph of the system and the way that external forcing is applied to the system. It is also established that colour in stochastic forcing can have a major impact, by enhancing resonance and by greater redistribution of power. This can mean a higher risk of extinction through larger fluctuations in population numbers and a higher degree of synchrony between populations. The implications of external forcing for stage-structured species, for populations in competition and for trophic web systems are studied using the tools and concepts developed in the paper.     

Incorporating landscape connectivity into household pond configuration in a hilly agricultural landscape

Manmade ponds are common landscape features in rural areas and also important habitats for maintaining biodiversity. However, they are vulnerable to anthropogenic activities, land-use changes, and habitat degradation; many ponds being filled or (re)created arbitrarily. Little attention has been paid to quantifying the spatial structure of these manmade ponds at a landscape scale, nor to their potential functional benefits in promoting ecological flows and interactions between habitats for whole-ecosystem integrity. In this study, we investigated the patch-based landscape connectivity of household ponds, a particular type of domestic pond prevalent in hilly rural areas of China, by using least-cost path modelling and graph theory based network analysis. A hierarchical network was modelled consisting of 4606 individual ponds, 373 pond patches and 772 potential links within a 1.5-km threshold distance. Network importance analysis revealed that the largest pond patch contributes 24.5 % to network building and that patches with larger areas are generally more important. In contrast, the importance of the simulated links is only 2.3 % at most, indicating that the network has spatial redundancy which can strengthen resilience to uncertain disturbances. Our study moves beyond network simulation and importance assessment by directly relating the connectivity analysis to a real construction context through the incorporation of a spatially explicit land suitability analysis. This approach systematises the analysis of pond landscapes and guides integration with the wider landscape matrix. It provides operational spatial suggestions for holistic landscape planning across local to regional scales.     

Quantifying dynamic stability of genetic memory circuits

Bistability/Multistability has been found in many biological systems including genetic memory circuits. Proper characterization of system stability helps to understand biological functions and has potential applications in fields such as synthetic biology. Existing methods of analyzing bistability are either qualitative or in a static way. Assuming the circuit is in a steady state, the latter can only reveal the susceptibility of the stability to injected DC noises. However, this can be inappropriate and inadequate as dynamics are crucial for many biological networks. In this paper, we quantitatively characterize the dynamic stability of a genetic conditional memory circuit by developing new dynamic noise margin (DNM) concepts and associated algorithms based on system theory. Taking into account the duration of the noisy perturbation, the DNMs are more general cases of their static counterparts. Using our techniques, we analyze the noise immunity of the memory circuit and derive insights on dynamic hold and write operations. Considering cell-to-cell variations, our parametric analysis reveals that the dynamic stability of the memory circuit has significantly varying sensitivities to underlying biochemical reactions attributable to differences in structure, time scales, and nonlinear interactions between reactions. With proper extensions, our techniques are broadly applicable to other multistable biological systems.