Regional and inter-regional effects in evolving climate networks

Peer reviewedPublisher PD

Regional and inter-regional effects in evolving climate networks

Complicated systems composed of many interacting subsystems are frequently studied as complex networks. In the simplest approach, a given real-world system is represented by an undirected graph composed of nodes standing for the subsystems and non-oriented unweighted edges for interactions present among the nodes; the characteristic properties of the graph are subsequently studied and related to the system's behaviour. More detailed graph models may include edge weights, orientations or multiple types of links; potential time-dependency of edges is conveniently captured in so-called evolving networks. Recently, it has been shown that an evolving climate network can be used to disentangle different types of El Niño episodes described in the literature. The time evolution of several graph characteristics has been compared with the intervals of El Niño and La Niña episodes. In this study we identify the sources of the evolving network characteristics by considering a reduced-dimensionality description of the climate system using network nodes given by rotated principal component analysis. The time evolution of structures in local intra-component networks is studied and compared to evolving inter-component connectivity

Compound Specific Trends of Chemical Defences in Ficus Along an Elevational Gradient Reflect a Complex Selective Landscape

Elevational gradients affect the production of plant secondary metabolites through changes in both biotic and abiotic conditions. Previous studies have suggested both elevational increases and decreases in host-plant chemical defences. We analysed the correlation of alkaloids and polyphenols with elevation in a community of nine Ficus species along a continuously forested elevational gradient in Papua New Guinea. We sampled 204 insect species feeding on the leaves of these hosts and correlated their community structure to the focal compounds. Additionally, we explored species richness of folivorous mammals along the gradient. When we accounted for Ficus species identity, we found a general elevational increase in flavonoids and alkaloids. Elevational trends in non-flavonol polyphenols were less pronounced or showed non-linear correlations with elevation. Polyphenols responded more strongly to changes in temperature and humidity than alkaloids. The abundance of insect herbivores decreased with elevation, while the species richness of folivorous mammals showed an elevational increase. Insect community structure was affected mainly by alkaloid concentration and diversity. Although our results show an elevational increase in several groups of metabolites, the drivers behind these trends likely differ. Flavonoids may provide figs with protection against abiotic stressors. In contrast, alkaloids affect insect herbivores and may provide protection against mammalian herbivores and pathogens. Concurrent analysis of multiple compound groups alongside ecological data is an important approach for understanding the selective landscape that shapes plant defences

Functional connectivity in resting-state fMRI: is linear correlation sufficient?

Functional connectivity (FC) analysis is a prominent approach to analyzing fMRI data, especially acquired under the resting state condition. The commonly used linear correlation FC measure bears an implicit assumption of Gaussianity of the dependence structure. If only the marginals, but not all the bivariate distributions are Gaussian, linear correlation consistently underestimates the strength of the dependence. To assess the suitability of linear correlation and the general potential of nonlinear FC measures, we present a framework for testing and estimating the deviation from Gaussianity by means of comparing mutual information in the data and its Gaussianized counterpart. We apply this method to 24 sessions of human resting state fMRI. For each session, matrix of connectivities between 90 anatomical parcel time series is computed using mutual information and compared to results from its multivariate Gaussian surrogate that conserves the correlations but cancels any nonlinearity. While the group-level tests confirmed non-Gaussianity in the FC, the quantitative assessment revealed that the portion of mutual information neglected by linear correlation is relatively minor-on average only about 5% of the mutual information already captured by the linear correlation. The marginality of the non-Gaussianity was confirmed in comparisons using clustering of the parcels-the disagreement between clustering obtained from mutual information and linear correlation was attributable to random error. We conclude that for this type of data, practical relevance of nonlinear methods trying to improve over linear correlation might be limited by the fact that the data are indeed almost Gaussian