Matching–centrality decomposition and the forecasting of new links in networks

Networks play a prominent role in the study of complex systems of interacting entities in biology, sociology, and economics. Despite this diversity, we demonstrate here that a statistical model decomposing networks into matching and centrality components provides a comprehensive and unifying quantification of their architecture. The matching term quantifies the assortative structure in which node makes links with which other node, whereas the centrality term quantifies the number of links that nodes make. We show, for a diverse set of networks, that this decomposition can provide a tight fit to observed networks. Then we provide three applications. First, we show that the model allows very accurate prediction of missing links in partially known networks. Second, when node characteristics are known, we show how the matching–centrality decomposition can be related to this external information. Consequently, it offers us a simple and versatile tool to explore how node characteristics explain network architecture. Finally, we demonstrate the efficiency and flexibility of the model to forecast the links that a novel node would create if it were to join an existing network

The feasibility of equilibria in large ecosystems: A primary but neglected concept in the complexity-stability debate

The consensus that complexity begets stability in ecosystems was challenged in the seventies, a result recently extended to ecologically-inspired networks. The approaches assume the existence of a feasible equilibrium, i.e. with positive abundances. However, this key assumption has not been tested. We provide analytical results complemented by simulations which show that equilibrium feasibility vanishes in species rich systems. This result leaves us in the uncomfortable situation in which the existence of a feasible equilibrium assumed in local stability criteria is far from granted. We extend our analyses by changing interaction structure and intensity, and find that feasibility and stability is warranted irrespective of species richness with weak interactions. Interestingly, we find that the dynamical behaviour of ecologically inspired architectures is very different and richer than that of unstructured systems. Our results suggest that a general understanding of ecosystem dynamics requires focusing on the interplay between interaction strength and network architecture

Including community composition in biodiversity–productivity models

Studies on biodiversity and ecosystem functioning (BEF) have elicited debate over the interpretation of the positive relationship between species richness and plant productivity. Manipulating richness cannot be achieved without affecting composition; it is thus essential to consider the latter in statistical models.We firstly review existing approaches that use species richness as an explanatory variable and propose modifications to improve their performance. We use an original data set to illustrate the analyses. The classical method where composition is coded as a factor with a level for each different species mixture can be improved by defining the levels using clustering. Methods based on ordinations reduce the dimensionality of plant composition and use the new coordinates as fixed effects; they provide a much better fit to our observations.Secondly, we develop a new method where composition is included as a similarity matrix affecting the residual variance–covariance. Similarity in composition between plots is treated in the same way as shared evolutionary history between species in phylogenetic regression. We find that it outperforms the other models.We discuss the different approaches and suggest that our method is particularly suited for observational studies or for manipulative studies where plant diversity is not kept constant by weeding. By treating species composition in an intuitive and sensible way, it offers a valuable and powerful complement to existing models

The relative contributions of species richness and species composition to ecosystem functioning

How species diversity influences ecosystem functioning has been the subject of many experiments and remains a key question for ecology and conservation biology. However, the fact that diversity cannot be manipulated without affecting species composition makes this quest methodologically challenging. Here, we evaluate the relative importance of diversity and of composition on biomass production, by using partial Mantel tests for one variable while controlling for the other. We analyse two datasets, from the Jena (2002–2008) and the Grandcour (2008–2009) Experiments. In both experiments, plots were sown with different numbers of species to unravel mechanisms underlying the relationship between biodiversity and ecosystem functioning (BEF). Contrary to Jena, plots were neither mowed nor weeded in Grandcour, allowing external species to establish. Based on the diversity–ecosystem functioning and competition theories, we tested two predictions: 1) the contribution of composition should increase with time; 2) the contribution of composition should be more important in non-weeded than in controlled systems. We found support for the second hypothesis, but not for the first. On the contrary, the contribution of species richness became markedly more important few years after the start of the Jena Experiment. This result can be interpreted as suggesting that species complementarity, rather than intraspecific competition, is the driving force in this system. Finally, we explored to what extent the estimated relative importance of both factors varied when measured on different spatial scales of the experiment (in this case, increasing the number of plots included in the analyses). We found a strong effect of scale, suggesting that comparisons between studies, and more generally the extrapolation of results from experiments to natural situations, should be made with caution

Increased temperature disrupts the biodiversity–ecosystem functioning relationship

Gaining knowledge of how ecosystems provide essential services to humans is of primary importance, especially with the current threat of climate change. Yet little is known about how increased temperature will impact the biodiversity–ecosystem functioning (BEF) relationship. We tackled this subject theoretically and experimentally. We developed a BEF theory based on mechanistic population dynamic models, which allows the inclusion of the effect of temperature. Using experimentally established relationships between attack rate and temperature, the model predicts that temperature increase will intensify competition, and consequently the BEF relationship will flatten or even become negative. We conducted a laboratory experiment with natural microbial microcosms, and the results were in agreement with the model predictions. The experimental results also revealed that an increase in both temperature average and variation had a more intense effect than an increase in temperature average alone. Our results indicate that under climate change, high diversity may not guarantee high ecosystem functioning

Understanding negative biodiversity–ecosystem functioning relationship in semi-natural wildflower strips

Studies on biodiversity–ecosystem functioning (BEF) in highly controlled experiments often yield results incompatible with observations from natural systems: experimental results often reveal positive relationships between diversity and productivity, while for natural systems, zero or even negative relationships have been reported. The discrepancy may arise due to a limited or closed local species pool in experiments, while natural systems in meta-community contexts experience dynamic processes, i.e., colonization and extinctions. In our study, we analysed plant community properties and above-ground biomass within a semi-natural (i.e., not weeded) experiment in an agricultural landscape. Eleven replicates with four different diversity levels were created from a species pool of 20 wildflower species. We found an overall significant negative relationship between total diversity and productivity. This relationship likely resulted from invasion resistance: in plots sown with low species numbers, we observed colonization by low-performing species; colonization increased species richness but did not contribute substantially to productivity. Interestingly, when analysing the biomass of the sown and the colonizer species separately, we observed in both cases positive BEF relationships, while this relationship was negative for the whole system. A structural equation modelling approach revealed that higher biomass of the sown species was linked to higher species richness, while the positive BEF relationship of the colonizers was indirect and constrained by the sown species biomass. Our results suggest that, in semi-natural conditions common in extensive agroecosystems, the negative BEF relationship results from the interplay between local dominant species and colonization from the regional species pool by subordinate species

Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level

Noise Characterization and Filtering in the MicroBooNE Liquid Argon TPC

The low-noise operation of readout electronics in a liquid argon time projection chamber (LArTPC) is critical to properly extract the distribution of ionization charge deposited on the wire planes of the TPC, especially for the induction planes. This paper describes the characteristics and mitigation of the observed noise in the MicroBooNE detector. The MicroBooNE's single-phase LArTPC comprises two induction planes and one collection sense wire plane with a total of 8256 wires. Current induced on each TPC wire is amplified and shaped by custom low-power, low-noise ASICs immersed in the liquid argon. The digitization of the signal waveform occurs outside the cryostat. Using data from the first year of MicroBooNE operations, several excess noise sources in the TPC were identified and mitigated. The residual equivalent noise charge (ENC) after noise filtering varies with wire length and is found to be below 400 electrons for the longest wires (4.7 m). The response is consistent with the cold electronics design expectations and is found to be stable with time and uniform over the functioning channels. This noise level is significantly lower than previous experiments utilizing warm front-end electronics.Comment: 36 pages, 20 figure

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.Comment: Preprint to be submitted to The European Physical Journal