SVD Entropy Reveals the High Complexity of Ecological Networks

Quantifying the complexity of ecological networks has remained elusive. Primarily, complexity has been defined on the basis of the structural (or behavioural) complexity of the system. These definitions ignore the notion of “physical complexity,” which can measure the amount of information contained in an ecological network, and how difficult it would be to compress. We present relative rank deficiency and SVD entropy as measures of “external” and “internal” complexity, respectively. Using bipartite ecological networks, we find that they all show a very high, almost maximal, physical complexity. Pollination networks, in particular, are more complex when compared to other types of interactions. In addition, we find that SVD entropy relates to other structural measures of complexity (nestedness, connectance, and spectral radius), but does not inform about the resilience of a network when using simulated extinction cascades, which has previously been reported for structural measures of complexity. We argue that SVD entropy provides a fundamentally more “correct” measure of network complexity and should be added to the toolkit of descriptors of ecological networks moving forward

Solid Organ Transplantation During COVID-19 Pandemic: An International Web-based Survey on Resources’ Allocation

Background. Solid organ transplants (SOTs) are life-saving interventions, recently challenged by coronavirus disease 2019 (COVID-19). SOTs require a multistep process, which can be affected by COVID-19 at several phases. Methods. SOT-specialists, COVID-19-specialists, and medical ethicists designed an international survey according to CHERRIES guidelines. Personal opinions about continuing SOTs, safe managing of donors and recipients, as well as equity of resources' allocation were investigated. The survey was sent by e-mail. Multiple approaches were used (corresponding authors from Scopus, websites of scientific societies, COVID-19 webinars). After the descriptive analysis, univariate and multivariate ordinal regression analysis was performed. Results. There were 1819 complete answers from 71 countries. The response rate was 49%. Data were stratified according to region, macrospecialty, and organ of interest. Answers were analyzed using univariate- multivariate ordinal regression analysis and thematic analysis. Overall, 20% of the responders thought SOTs should not stop (continue transplant without restriction); over 70% suggested SOTs should selectively stop, and almost 10% indicated they should completely stop. Furthermore, 82% agreed to shift resources from transplant to COVID-19 temporarily. Briefly, main reason for not stopping was that if the transplant will not proceed, the organ will be wasted. Focusing on SOT from living donors, 61% stated that activity should be restricted only to "urgent"cases. At the multivariate analysis, factors identified in favor of continuing transplant were Italy, ethicist, partially disagreeing on the equity question, a high number of COVID-19- related deaths on the day of the answer, a high IHDI country. Factors predicting to stop SOTs were Europe except-Italy, public university hospital, and strongly agreeing on the equity question. Conclusions. In conclusion, the majority of responders suggested that transplant activity should be continued through the implementation of isolation measures and the adoption of the COVID-19-free pathways. Differences between professional categories are less strong than supposed

Exploiting node metadata to predict interactions in bipartite networks using graph embedding and neural networks

Networks are increasingly used in various fields to represent systems with the aim of understanding the underlying rules governing observed interactions, and hence predict how the system is likely to behave in the future. Recent developments in network science highlight that accounting for node metadata improves both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, to predict interactions in a network within existing statistical and machine learning frameworks, we need to learn objects that rapidly grow in dimension with the number of nodes. Thus, the task becomes computationally and conceptually challenging for networks. Here, we present a new predictive procedure combining a statistical, low-rank graph embedding method with machine learning techniques which reduces substantially the complexity of the learning task and allows us to efficiently predict interactions from node metadata in bipartite networks. To illustrate its application on real-world data, we apply it to a large dataset of tourist visits across a country. We found that our procedure accurately reconstructs existing interactions and predicts new interactions in the network. Overall, both from a network science and data science perspective, our work offers a flexible and generalizable procedure for link prediction

Unmasking structural patterns in incidence matrices: an application to ecological data

Null models have become a crucial tool for understanding structure within incidence matrices across multiple biological contexts. For example, they have been widely used for the study of ecological and biogeographic questions, testing hypotheses regarding patterns of community assembly, species co-occurrence and biodiversity. However, to our knowledge we remain without a general and flexible approach to study the mechanisms explaining such structures. Here, we provide a method for generating ‘correlation-informed’ null models, which combine the classic concept of null models and tools from community ecology, like joint statistical modelling. Generally, this model allows us to assess whether the information encoded within any given correlation matrix is predictive for explaining structural patterns observed within an incidence matrix. To demonstrate its utility, we apply our approach to two different case studies that represent examples of common scenarios encountered in community ecology. First, we use a phylogenetically informed null model to detect a strong evolutionary fingerprint within empirically observed food webs, reflecting key differences in the impact of shared evolutionary history when shaping the interactions of predators or prey. Second, we use multiple informed null models to identify which factors determine structural patterns of species assemblages, focusing in on the study of nestedness and the influence of site size, isolation, species range and species richness. In addition to offering a versatile way to study the mechanisms shaping the structure of any incidence matrix, including those describing ecological communities, our approach can also be adapted further to test even more sophisticated hypotheses

Using network analysis to study and manage human-mediated dispersal of exotic species

Human-mediated dispersal is a major contributor of biological invasions. To reduce impacts induced by the introduction and spread of exotic species, biosecurity interventions are put into place. These interventions often rely on risk-assessment procedures, whereby biosecurity practitioners (which includes researchers, stakeholders such as national park managers, and all other decision makers who determine when and how to protect biodiversity) attempt to preemptively identify and predict which exotic species could potentially become a threat to natural ecosystems. In theory, extensive field and experimental studies would be required to accurately and precisely determine the risks of biological invasion of a species or group of species. However, due to a lack of resources or knowledge, such critical studies are limited. As a result, biosecurity practitioners rarely have a full picture of the extent to which the exotic species has and will spread at the time of decision making. Hence, they instead opt for preventive measures such as identifying and managing potential target exotic species which are likely to be invasive or dispersal pathways through which exotic species are likely to be introduced and spread. As most of the uncertainties pertaining to biosecurity interventions lie in the resolution of data made available to practitioners at the time of decision making, we first present some of the different types of information which are readily available during the risk-assessment procedure. We then highlight how one could exploit these different resolutions of data during the risk-assessment procedure using network analysis to better understand human-mediated dispersal of exotic species. By doing so, our paper puts forward what network analysis has to offer practitioners in the context of biosecurity interventions.ISSN:1387-3547ISSN:1573-146

A review of species role concepts in food webs

Many different concepts have been used to describe species' roles in food webs (i.e., the ways in which species participate in their communities as consumers and resources). As each concept focuses on a different aspect of food-web structure, it can be difficult to relate these concepts to each other and to other aspects of ecology. Here we use the Eltonian niche as an overarching framework, within which we summarize several commonly-used role concepts (degree, trophic level, motif roles, and centrality). We focus mainly on the topological versions of these concepts but, where dynamical versions of a role concept exist, we acknowledge these as well. Our aim is to highlight areas of overlap and ambiguity between different role concepts and to describe how these roles can be used to group species according to different strategies (i.e., equivalence and functional roles). The existence of “gray areas” between role concepts make it essential for authors to carefully consider both which role concept(s) are most appropriate for the analyses they wish to conduct and what aspect of species' niches (if any) they wish to address. The ecological meaning of differences between species' roles can change dramatically depending on which role concept(s) are used

Related plants tend to share pollinators and herbivores, but strength of phylogenetic signal varies among plant families

Related plants are often hypothesized to interact with similar sets of pollinators and herbivores, but this idea has only mixed empirical support. This may be because plant families vary in their tendency to share interaction partners.We quantify overlap of interaction partners for all pairs of plants in 59 pollination and 11 herbivory networks based on the numbers of shared and unshared interaction partners (thereby capturing both proportional and absolute overlap). We test for relationships between phylogenetic distance and partner overlap within each network; whether these relationships varied with the composition of the plant community; and whether well-represented plant families showed different relationships.Across all networks, more closely related plants tended to have greater overlap. The strength of this relationship within a network was unrelated to the composition of the network's plant component, but, when considered separately, different plant families showed different relationships between phylogenetic distance and overlap of interaction partners.The variety of relationships between phylogenetic distance and partner overlap in different plant families probably reflects a comparable variety of ecological and evolutionary processes. Considering factors affecting particular species-rich groups within a community could be the key to understanding the distribution of interactions at the network level

Graph embedding and transfer learning can help predict potential species interaction networks despite data limitations

Abstract Metawebs (networks of potential interactions within a species pool) are a powerful abstraction to understand how large‐scale species interaction networks are structured. Because metawebs are typically expressed at large spatial and taxonomic scales, assembling them is a tedious and costly process; predictive methods can help circumvent the limitations in data deficiencies, by providing a first approximation of metawebs. One way to improve our ability to predict metawebs is to maximize available information by using graph embeddings, as opposed to an exhaustive list of species interactions. Graph embedding is an emerging field in machine learning that holds great potential for ecological problems. Here, we outline how the challenges associated with inferring metawebs line‐up with the advantages of graph embeddings; followed by a discussion as to how the choice of the species pool has consequences on the reconstructed network, specifically as to the role of human‐made (or arbitrarily assigned) boundaries and how these may influence ecological hypotheses

Analysing ecological networks of species interactions

Network approaches to ecological questions have been increasingly used, particularly in recent decades. The abstraction of ecological systems - such as communities - through networks of interactions between their components indeed provides a way to summarize this information with single objects. The methodological framework derived from graph theory also provides numerous approaches and measures to analyze these objects and can offer new perspectives on established ecological theories as well as tools to address new challenges. However, prior to using these methods to test ecological hypotheses, it is necessary that we understand, adapt, and use them in ways that both allow us to deliver their full potential and account for their limitations. Here, we attempt to increase the accessibility of network approaches by providing a review of the tools that have been developed so far, with - what we believe to be - their appropriate uses and potential limitations. This is not an exhaustive review of all methods and metrics, but rather, an overview of tools that are robust, informative, and ecologically sound. After providing a brief presentation of species interaction networks and how to build them in order to summarize ecological information of different types, we then classify methods and metrics by the types of ecological questions that they can be used to answer from global to local scales, including methods for hypothesis testing and future perspectives. Specifically, we show how the organization of species interactions in a community yields different network structures (e.g., more or less dense, modular or nested), how different measures can be used to describe and quantify these emerging structures, and how to compare communities based on these differences in structures. Within networks, we illustrate metrics that can be used to describe and compare the functional and dynamic roles of species based on their position in the network and the organization of their interactions as well as associated new methods to test the significance of these results. Lastly, we describe potential fruitful avenues for new methodological developments to address novel ecological questions9411636sem informaçãosem informaçã

Epidemiological trends and trajectories of MAFLD-associated hepatocellular carcinoma 2002-2033: the ITA.LI.CA database

Background: Metabolic dysfunction-associated fatty liver disease (MAFLD) represents a new inclusive definition of the whole spectrum of liver diseases associated to metabolic disorders. The main objective of this study was to compare patients with MAFLD and non-MAFLD with hepatocellular carcinoma (HCC) included in a nationally representative cohort. Methods: We analysed 6882 consecutive patients with HCC enrolled from 2002 to 2019 by 23 Italian Liver Cancer centres to compare epidemiological and future trends in three subgroups: pure, single aetiology MAFLD (S-MAFLD); mixed aetiology MAFLD (metabolic and others, M-MAFLD); and non-MAFLD HCC. Results: MAFLD was diagnosed in the majority of patients with HCC (68.4%). The proportion of both total MAFLD and S-MAFLD HCC significantly increased over time (from 50.4% and 3.6% in 2002-2003, to 77.3% and 28.9% in 2018-2019, respectively, p<0.001). In Italy S-MAFLD HCC is expected to overcome M-MAFLD HCC in about 6 years. Patients with S-MAFLD HCC were older, more frequently men and less frequently cirrhotic with clinically relevant portal hypertension and a surveillance-related diagnosis. They had more frequently large tumours and extrahepatic metastases. After weighting, and compared with patients with non-MAFLD, S-MAFLD and M-MAFLD HCC showed a significantly lower overall (p=0.026, p=0.004) and HCC-related (p<0.001, for both) risk of death. Patients with S-MAFLD HCC showed a significantly higher risk of non-HCC-related death (p=0.006). Conclusions: The prevalence of MAFLD HCC in Italy is rapidly increasing to cover the majority of patients with HCC. Despite a less favourable cancer stage at diagnosis, patients with MAFLD HCC have a lower risk of HCC-related death, suggesting reduced cancer aggressiveness