FALCON: a software package for analysis of nestedness in bipartite networks

This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.Nestedness is a statistical measure used to interpret bipartite interaction data in several ecological and evolutionary contexts, e.g. biogeography (species-site relationships) and species interactions (plant-pollinator and host-parasite networks). Multiple methods have been used to evaluate nestedness, which differ in how the metrics for nestedness are determined. Furthermore, several different null models have been used to calculate statistical significance of nestedness scores. The profusion of measures and null models, many of which give conflicting results, is problematic for comparison of nestedness across different studies. We developed the FALCON software package to allow easy and efficient comparison of nestedness scores and statistical significances for a given input network, using a selection of the more popular measures and null models from the current literature. FALCON currently includes six measures and five null models for nestedness in binary networks, and two measures and four null models for nestedness in weighted networks. The FALCON software is designed to be efficient and easy to use. FALCON code is offered in three languages (R, MATLAB, Octave) and is designed to be modular and extensible, enabling users to easily expand its functionality by adding further measures and null models. FALCON provides a robust methodology for comparing the strength and significance of nestedness in a given bipartite network using multiple measures and null models. It includes an “adaptive ensemble” method to reduce undersampling of the null distribution when calculating statistical significance. It can work with binary or weighted input networks. FALCON is a response to the proliferation of different nestedness measures and associated null models in the literature. It allows easy and efficient calculation of nestedness scores and statistical significances using different methods, enabling comparison of results from different studies and thereby supporting theoretical study of the causes and implications of nestedness in different biological contexts

Fluctuating ecological networks: a synthesis of maximum entropy approaches for pattern and perturbation detection

Ecological networks such as plant-pollinator systems vary systematically in space and time. This variability includes fluctuations in global network properties such as total number and intensity of interactions in the network, but also in the local properties of individual nodes, such as the number and intensity of species-level interactions. Fluctuations of local properties can significantly affect higher-order network features, e.g. robustness and nestedness. These fluctuations should therefore be controlled for in applications that rely on null models, including pattern detection, perturbation experiments and network reconstruction from limited observations. By contrast, most randomization methods used by ecologists treat node-level local properties as hard constraints that cannot fluctuate. Here we synthesise a set of methods based on the statistical mechanics of networks, which we illustrate with some practical examples. We illustrate how this approach can be used by experimental ecologists to study the statistical significance of network patterns and the rewiring of networks under simulated perturbations. Modelling species heterogeneity, while allowing for local fluctuations around a theoretically grounded notion of structural equilibrium, will offer a new generation of models and experiments to understand the assembly and resilience of ecological networks.Comment: submitte

Complex networks analysis in socioeconomic models

This chapter aims at reviewing complex networks models and methods that were either developed for or applied to socioeconomic issues, and pertinent to the theme of New Economic Geography. After an introduction to the foundations of the field of complex networks, the present summary adds insights on the statistical mechanical approach, and on the most relevant computational aspects for the treatment of these systems. As the most frequently used model for interacting agent-based systems, a brief description of the statistical mechanics of the classical Ising model on regular lattices, together with recent extensions of the same model on small-world Watts-Strogatz and scale-free Albert-Barabasi complex networks is included. Other sections of the chapter are devoted to applications of complex networks to economics, finance, spreading of innovations, and regional trade and developments. The chapter also reviews results involving applications of complex networks to other relevant socioeconomic issues, including results for opinion and citation networks. Finally, some avenues for future research are introduced before summarizing the main conclusions of the chapter.Comment: 39 pages, 185 references, (not final version of) a chapter prepared for Complexity and Geographical Economics - Topics and Tools, P. Commendatore, S.S. Kayam and I. Kubin Eds. (Springer, to be published

Moderation is best: Effects of grazing intensity on plant-flower visitor networks in Mediterranean communities

The structure of pollination networks is an important indicator of ecosystem stability and functioning. Livestock grazing is a frequent land use practice that directly affects the abundance and diversity of flowers and pollinators and, therefore, may indirectly affect the structure of pollination networks. We studied how grazing intensity affected the structure of plant-flower visitor networks along a wide range of grazing intensities by sheep and goats, using data from 11 Mediterranean plant-flower visitor communities from Lesvos Island, Greece. We hypothesized that intermediate grazing might result in higher diversity as predicted by the Intermediate Disturbance Hypothesis, which could in turn confer more stability to the networks. Indeed, we found that networks at intermediate grazing intensities were larger, more generalized, more modular, and contained more diverse and even interactions. Despite general responses at the network level, the number of interactions and selectiveness of particular flower visitor and plant taxa in the networks responded differently to grazing intensity, presumably as a consequence of variation in the abundance of different taxa with grazing. Our results highlight the benefit of maintaining moderate levels of livestock grazing by sheep and goats to preserve the complexity and biodiversity of the rich Mediterranean communities, which have a long history of grazing by these domestic animals.The research has been co-financed by the European Union (European Social Fund—ESF) and Greek National funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program: THALES: Investing in knowledge society through the European Social FundPeer Reviewe

Interaction strength in plant-pollinator networks : Are we using the right measure?

Understanding how ecological networks are assembled is important because network structure reflects ecosystem functioning and stability. Quantitative network analysis incorporates measures of interaction strength as an estimate of the magnitude of the effect of interaction partners on one another. Most plant-pollinator network studies use frequency of interaction between individual pollinators and individual plants (encounter) as a surrogate of interaction strength. However, the number of flowers visited per encounter may strongly vary among pollinator and plant species, and therefore not all encounters are quantitatively equivalent. We sampled plant-pollinator interactions in a Mediterranean scrubland and tested whether using a measure of interaction strength based on the number of flowers visited resulted in changes in species (species strength, interaction species asymmetry, specialization) and network descriptors (nestedness, H2', interaction evenness, plant generality, pollinator generality) compared to the encounter-based measure. Several species (including some of the most abundant ones) showed important changes in species descriptors, notably in specialization. These changes were especially important in plant species with large floral displays, which became less specialized with the visit-based measure of interaction strength. At the network level we found significant changes in all properties analysed. With the encounter-based approach plant generality was much higher than pollinator generality (high specialization asymmetry between trophic levels). However, with the visit-based approach plant generality was greatly reduced so that plants and pollinators had similar levels of generalization. Interaction evenness also decreased strongly with the visit-based approach. We conclude that accounting for the number of flowers visited per encounter provides a more ecologically relevant measure of interaction strength. Our results have important implications for the stability of pollination networks and the evolution of plant-pollinator interactions. The use of a visit-based approach is especially important in studies relating interaction network structure and ecosystem function (pollination and/or exploitation of floral resources)

MBI: an R package for calculating multiple-site beta diversity indices

Beta diversity is one of most important features in community ecology. Indices for pairwise comparison of beta diversity have been extensively developed, but the ones specifically designed for multiple-site comparison of beta diversity are still limited. Currently, by compiling all the available metrics based on the previous literature, plus some new metrics developed in the present report, we made the calculation of these multiplesite beta-diversity statistics become ready for ecologists using R computing environment. An empirical study was present using 290 real-world presence/absence matrices. The results showed that (1) mean pairwise indices could be good surrogates for multiple-site indices in principle, except the mean pairwise richness different index; (2) most of the indices were highly correlated, as indicated by Pearson correlation and significance test. The new R package "MBI" for calculating multiple-site diversity indices could be downloaded from the http://cran.r-project.org/web/packages/MBI/

A general framework for analyzing beta diversity, nestedness and related community-level phenomena based on abundance data

We describe a procedure for evaluating the relative importance of beta diversity, nestedness, and similarity properties of ecological data matrices containing density, cover or biomass scores of species. Our goals are achieved by extension of the simplex approach – originally proposed for presence–absence data – to abundances. Basically, the method involves decomposition of the Marczewski–Steinhaus coefficient of dissimilarity between pairs of sites into two fractions, one derived from differences between total abundance and the other from differences due to abundance replacement. These are contrasted by the similarity function counterpart, known as the Ruzicka coefficient, and are displayed graphically using ternary (or 2D simplex) plots. Interpretation is aided by calculating percentage contributions from these components to the (dis)similarity structure. Measures of replacement and nestedness are new for abundance data; these are considered complementary phenomena reflecting antithetic ecological processes that are analogous to those operating at the presence–absence level. The method is illustrated by artificial data and a range of actual ecological data sets representing different groups of organisms, different scales and different types of data. While the simplex diagrams and associated coefficients are meaningful by themselves, their comparison with presence–absence based results gives additional insight into data structure and background factors

Nestedness across biological scales

This is the final published version. Available from Public Library of Science via the DOI in this record.All data sets are available for download in the repository https:// bitbucket.org/maucantor/unodf_analyses/src.Biological networks pervade nature. They describe systems throughout all levels of biological organization, from molecules regulating metabolism to species interactions that shape ecosystem dynamics. The network thinking revealed recurrent organizational patterns in complex biological systems, such as the formation of semi-independent groups of connected elements (modularity) and non-random distributions of interactions among elements. Other structural patterns, such as nestedness, have been primarily assessed in ecological networks formed by two non-overlapping sets of elements; information on its occurrence on other levels of organization is lacking. Nestedness occurs when interactions of less connected elements form proper subsets of the interactions of more connected elements. Only recently these properties began to be appreciated in one-mode networks (where all elements can interact) which describe a much wider variety of biological phenomena. Here, we compute nestedness in a diverse collection of one-mode networked systems from six different levels of biological organization depicting gene and protein interactions, complex phenotypes, animal societies, metapopulations, food webs and vertebrate metacommunities. Our findings suggest that nestedness emerge independently of interaction type or biological scale and reveal that disparate systems can share nested organization features characterized by inclusive subsets of interacting elements with decreasing connectedness. We primarily explore the implications of a nested structure for each of these studied systems, then theorize on how nested networks are assembled. We hypothesize that nestedness emerges across scales due to processes that, although system-dependent, may share a general compromise between two features: specificity (the number of interactions the elements of the system can have) and affinity (how these elements can be connected to each other). Our findings suggesting occurrence of nestedness throughout biological scales can stimulate the debate on how pervasive nestedness may be in nature, while the theoretical emergent principles can aid further research on commonalities of biological networks.Conselho Nacional de Desenvolvimento Científico e TecnológicoSão Paulo Research FoundationKillam TrustsThe Brazilian Federal Agency for Support and Evaluation of Graduate Education within the Ministry of Education of BrazilFundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarin