Javier Galeano

Short communication: Weighted-Interaction Nestedness Estimator (WINE): A new estimator to calculate over frequency matrices

We propose a new nestedness estimator that takes into account the weight of the interactions, that is, it runs over frequency matrices. A nestedness measurement is calculated through the average distance from each matrix cell containing a link to the cell with the lowest marginal totals, in the packed matrix, using a weighted Manhattan distance. The significance of this nestedness measure is tested against a null model that constraints matrix fill to observed values and retains the distribution of number of events. This is the first methodological approach that allows for the characterization of weighted nestedness. We have developed a graphical user interface (GUI) running in Matlab to compute all these parameters. The software is also available as a script for R-package and in C++ version.

Rethinking the logistic approach for population dynamics of mutualistic interactions

Mutualistic communities have an internal structure that makes them resilient to external perturbations. Late research has focused on their stability and the topology of the relations between the different organisms to explain the reasons of the system robustness. Much less attention has been invested in analyzing the systems dynamics. The main population models in use are modifications of the r-K formulation of logistic equation with additional terms to account for the benefits produced by the interspecific interactions. These models have shortcomings as the so-called r-K formulation diverges under some conditions. In this work, we introduce a model for population dynamics under mutualism that preserves the original logistic formulation. It is mathematically simpler than the widely used type II models, although it shows similar complexity in terms of fixed points and stability of the dynamics. We perform an analytical stability analysis and numerical simulations to study the model behavior in general interaction scenarios including tests of the resilience of its dynamics under external perturbations. Despite its simplicity, our results indicate that the model dynamics shows an important richness that can be used to gain further insights in the dynamics of mutualistic communities.

Robustness of Alpine Pollination Networks: Effects of Network Structure and Consequences for Endemic Plants

Abstract Global threats to plant-pollinator interactions are potentially serious in alpine ecosystems, which combine great diversity with particular fragility. We utilized tools from complex network theory to assess the robustness to species extinction of two Spanish alpine pollination networks. A comparison with ten additional alpine and subalpine pollination (ASP) networks allowed us to give our assessment a broader scope and provide a general view of ASP network robustness. We found a broad range of robustness among ASP networks. The two Spanish pollination networks ranked intermediate to high in robustness. This could be due to two of their structural features, connectance (proportion of potential interactions actually observed) and asymmetry (normalized difference between pollinator and plant richness), which showed a positive relationship with network robustness. A finer-scale focus on the two Spanish networks did not reveal differences between endemic and nonendemic plants in their functional role within the network but indicated that they differed in their robustness to pollinator extinction. Contrasting patterns across networks suggested that endemic robustness depends on community particularities. To improve the utility of robustness assessment as a conservation tool, we should increase our understanding on (1) the order in which network species will get extinct, (2) how species rewire once they have lost their partners, and (3) how much species depend on their mutualistic interaction.

Vulnerability of state-interdependent networks under malware spreading

Computer viruses are evolving by developing spreading mechanisms based on the use of multiple vectors of propagation. The use of the social network as an extra vector of attack to penetrate the security measures in IP networks is improving the effectiveness of malware, and have therefore been used by the most aggressive viruses, like Conficker and Stuxnet. In this work we use interdependent networks to model the propagation of these kind of viruses. In particular, we study the propagation of a SIS model on interdependent networks where the state of each node is layer-independent and the dynamics in each network follows either a contact process or a reactive process, with different propagation rates. We apply this study to the case of existing interdependent networks, namely a Spanish scientific community of Statistical Physics, formed by a social network of scientific collaborations and a physical network of connected computers in each institution. We show that the interplay between layers increases dramatically the infectivity of viruses in the long term and their robustness against immunization.

Effects of topology on robustness in ecological bipartite networks

High robustness of complex ecological systems in the face of species extinction has been hypothesized based on the redundancy in species. We explored how differences in network topology may affect robustness. Ecological bipartite networks used to be small, asymmetric and sparse matrices. We created synthetic networks to study the influence of the properties of network dimensions asymmetry, connectance and type of degree distribution on network robustness. We used two extinction strategies: node extinction and link extinction, and three extinction sequences differing in the order of species removal (least-to-most connected, random, most-to-least connected). We assessed robustness to extinction of simulated networks, which differed in one of the three topological features. Simulated networks indicated that robustness decreases when (a) extinction involved those nodes belonging to the most species-rich guild and (b) networks had lower connectance. We also compared simulated networks with different degree- distribution networks, and they showed important differences in robustness depending on the extinction scenario. In the link extinction strategy, the robustness of synthetic networks was clearly determined by the asymmetry in the network dimensions, while the variation in connectance produced negligible differences.

Individual Patterns of Complexity in Cystic Fibrosis Lung Microbiota, Including Predator Bacteria, over a 1-Year Period

ABSTRACT Cystic fibrosis (CF) lung microbiota composition has recently been redefined by the application of next-generation sequencing (NGS) tools, identifying, among others, previously undescribed anaerobic and uncultivable bacteria. In the present study, we monitored the fluctuations of this ecosystem in 15 CF patients during a 1-year follow-up period, describing for the first time, as far as we know, the presence of predator bacteria in the CF lung microbiome. In addition, a new computational model was developed to ascertain the hypothetical ecological repercussions of a prey-predator interaction in CF lung microbial communities. Fifteen adult CF patients, stratified according to their pulmonary function into mild (n = 5), moderate (n = 9), and severe (n = 1) disease, were recruited at the CF unit of the Ramón y Cajal University Hospital (Madrid, Spain). Each patient contributed three or four induced sputum samples during a 1-year follow-up period. Lung microbiota composition was determined by both cultivation and NGS techniques and was compared with the patients’ clinical variables. Results revealed a particular microbiota composition for each patient that was maintained during the study period, although some fluctuations were detected without any clinical correlation. For the first time, Bdellovibrio and Vampirovibrio predator bacteria were shown in CF lung microbiota and reduced-genome bacterial parasites of the phylum Parcubacteria were also consistently detected. The newly designed computational model allows us to hypothesize that inoculation of predators into the pulmonary microbiome might contribute to the control of chronic colonization by CF pathogens in early colonization stages. IMPORTANCE The application of NGS to sequential samples of CF patients demonstrated the complexity of the organisms present in the lung (156 species) and the constancy of basic individual colonization patterns, although some differences between samples from the same patient were observed, probably related to sampling bias. Bdellovibrio and Vampirovibrio predator bacteria were found for the first time by NGS as part of the CF lung microbiota, although their ecological significance needs to be clarified. The newly designed computational model allows us to hypothesize that inoculation of predators into the lung microbiome can eradicate CF pathogens in early stages of the process. Our data strongly suggest that lower respiratory microbiome fluctuations are not necessarily related to the patient’s clinical status. IMPORTANCE The application of NGS to sequential samples of CF patients demonstrated the complexity of the organisms present in the lung (156 species) and the constancy of basic individual colonization patterns, although some differences between samples from the same patient were observed, probably related to sampling bias. Bdellovibrio and Vampirovibrio predator bacteria were found for the first time by NGS as part of the CF lung microbiota, although their ecological significance needs to be clarified. The newly designed computational model allows us to hypothesize that inoculation of predators into the lung microbiome can eradicate CF pathogens in early stages of the process. Our data strongly suggest that lower respiratory microbiome fluctuations are not necessarily related to the patient’s clinical status.

BIPARTITE NETWORKS PROVIDE NEW INSIGHTS ON INTERNATIONAL TRADE MARKETS

Adam Smith is considered the father of modern economics. His research on the Wealth of Nations (10) is the rst scientic work that theo- rized about the complexity of economic systems and how an invisible hand self-regulates markets and their behavior. In this way, we study international trade markets as complex networks. We analyze their topological properties, structure and temporal dynamics based on actual data. Our main premise states that trade networks are bipartite in nature because importers and ex- porters play a dierent role in the system. We apply a methodology developed for mutualistic ecosystems, nding minor gaps in it. We address such gaps by using well-known techniques from other related scientic work. The evidence supports the fact that our premise is a realistic hypothesis.

New dynamic scaling in increasing systems

We report a new dynamic scaling ansatz for systems whose system size is increasing with time. We apply this new hypothesis in the Eden model in two geometries. In strip geometry, we impose the system to increase with a power law, L ∼ ha. In increasing linear clusters, if a < 1/z, where z is the dynamic exponent, the correlation length reaches the whole system, and we find two regimes: the first, where the interface fluctuations initially grow with an exponent β = 0.3, and the second, where a crossover comes out and fluctuations evolve as haα. If a = 1/z, there is not a crossover and fluctuations keep on growing in a unique regimen with the same exponent β. In particular, in circular geometry, a = 1, we find this kind of regime and in consequence, a unique regime holds.

Ranking of critical species to preserve the functionality of mutualistic networks using the k-core decomposition

Background Network analysis has become a relevant approach to analyze cascading species extinctions resulting from perturbations on mutualistic interactions as a result of environmental change. In this context, it is essential to be able to point out key species, whose stability would prevent cascading extinctions, and the consequent loss of ecosystem function. In this study, we aim to explain how the k-core decomposition sheds light on the understanding the robustness of bipartite mutualistic networks. Methods We defined three k-magnitudes based on the k-core decomposition: k-radius, k-degree, and k-risk. The first one, k-radius, quantifies the distance from a node to the innermost shell of the partner guild, while k-degree provides a measure of centrality in the k-shell based decomposition. k-risk is a way to measure the vulnerability of a network to the loss of a particular species. Using these magnitudes we analyzed 89 mutualistic networks involving plant pollinators or seed dispersers. Two static extinction procedures were implemented in which k-degree and k-risk were compared against other commonly used ranking indexes, as for example MusRank, explained in detail in Material and Methods. Results When extinctions take place in both guilds, k-risk is the best ranking index if the goal is to identify the key species to preserve the giant component. When species are removed only in the primary class and cascading extinctions are measured in the secondary class, the most effective ranking index to identify the key species to preserve the giant component is k-degree. However, MusRank index was more effective when the goal is to identify the key species to preserve the greatest species richness in the second class. Discussion The k-core decomposition offers a new topological view of the structure of mutualistic networks. The new k-radius, k-degree and k-risk magnitudes take advantage of its properties and provide new insight into the structure of mutualistic networks. The k-risk and k-degree ranking indexes are especially effective approaches to identify key species to preserve when conservation practitioners focus on the preservation of ecosystem functionality over species richness.

XV Reunión científica anual de ECOFLOR

Delgado L.A. 2018. Landscape Heterogeneity and tree species diversity in a tropical forest. Development and validation of a methodological proposal. Ecosistemas 27(1): 105-115. Doi.: 10.7818/ECOS.1475 Many landscapes exist as unstable spatial-temporal mosaics where changes in patterns of biodiversity are affected by nature processes and the dynamic interaction between social and ecological factors. It is a consequence of the natural dynamics of socio-economic systems that regulate man-made tropical forests. However, a significant proportion of studies have made generalizations about the relative values of biodiversity, without taking into account the high levels of internal heterogeneity in the biophysical properties and land uses of each site. The purpose of this study is to propose and validate a methodology to delimit the heterogeneity of the landscape based on criteria that integrate the coupling of human-ecological systems such as: space-time dynamics of deforestation and fragmentation; complexity of the landscape structure; current and historical land use and biophysical variability. For this, the use of satellite images, landscape metrics, field work, documentary review and multivariate analysis were combined. The proposed methodology is intended to help guide the empirical delimitation of landscape heterogeneity as a prerequisite for the selection of similar landscapes and forest patches in studies of the diversity of tree species, in order to provide an opportunity to control the possible difficulties caused by variability in the proportion of forests, landscape configuration and successional states, in estimating its effects on forest richness and floristic composition.