Coupling between infectious diseases leads to synchronization of their dynamics.

Interactions between different diseases may change their dynamics. Thus, these interactions represent a source of uncertainty in the modeling of empirical data when the symptoms of both infections are hard to distinguish. We recall previously proposed models of interacting infections, generalizing them to non-symmetric scenarios, showing that both cooperative and competitive interactions lead to synchronization of the maximum fraction of infected individuals in their dynamics. We exemplify this framework with a model coupling the dynamics of COVID-19 and seasonal influenza, simulating cooperation, competition, and asymmetric interactions. We find that the coupling synchronizes both infections, with a stronger influence on the dynamics of influenza.

Host-dependent nitrogen recycling as a mechanism of symbiont control in Aiptasia.

The metabolic symbiosis with photosynthetic algae allows corals to thrive in the oligotrophic environments of tropical seas. Different aspects of this relationship have been investigated using the emerging model organism Aiptasia. However, many fundamental questions, such as the nature of the symbiotic relationship and the interactions of nutrients between the partners remain highly debated. Using a meta-analysis approach, we identified a core set of 731 high-confidence symbiosis-associated genes that revealed host-dependent recycling of waste ammonium and amino acid synthesis as central processes in this relationship. Subsequent validation via metabolomic analyses confirmed that symbiont-derived carbon enables host recycling of ammonium into nonessential amino acids. We propose that this provides a regulatory mechanism to control symbiont growth through a carbon-dependent negative feedback of nitrogen availability to the symbiont. The dependence of this mechanism on symbiont-derived carbon highlights the susceptibility of this symbiosis to changes in carbon translocation, as imposed by environmental stress.

Host-association as major driver of microbiome structure and composition in Red Sea seagrass ecosystems.

The role of the microbiome in sustaining seagrasses has recently been highlighted. However, our understanding of the seagrass microbiome lacks behind that of other organisms. Here, we analyse the endophytic and total bacterial communities of leaves, rhizomes, and roots of six Red Sea seagrass species and their sediments. The structure of seagrass bacterial communities revealed that the 1% most abundant OTUs accounted for 87.9% and 74.8% of the total numbers of reads in sediment and plant tissue samples, respectively. We found taxonomically distinct bacterial communities in vegetated and bare sediments. Yet, our results suggest that lifestyle (i.e. free-living or host-association) is the main driver of bacterial community composition. Seagrass bacterial communities were tissue- and species-specific and differed from those of surrounding sediments. We identified OTUs belonging to genera related to N and S cycles in roots, and members of Actinobacteria, Bacteroidetes, and Firmicutes phyla as particularly enriched in root endosphere. The finding of highly similar OTUs in well-defined sub-clusters by network analysis suggests the co-occurrence of highly connected key members within Red Sea seagrass bacterial communities. These results provide key information towards the understanding of the role of microorganisms in seagrass ecosystem functioning framed under the seagrass holobiont concept.

Design of Deployment Strategies to Monitor the Movement of Animals with Passive Electronic Devices.

Current animal monitoring systems have improved our knowledge of quantitative animal ecology. There are many electronic tracking technologies such as VHF/UHF telemetry, light-level geolocation, ARGOS satellite telemetry and GPS tracking. To reach the desired level of information retrieval requires the planning of adequate equipment effort and coverage, which depends on the properties of the system. We propose an equipment arrangement model consisting of a given number of receiver stations in a two-dimensional space in which the animals move according to a central place movement model. The objective is to characterize how the transmission of tracking data depends on the movement of the animals and the design of the equipment deployment: quantity and location of the receiver stations and their associated reception radius. We also implement the model using real trajectories of southern elephant seals and Australian sea lions publicly available online and tracked during the years 2010-2012. We characterize the data transmission based on different equipment configurations and we obtained analogous results to the theoretical model.

Sequencing effort dictates gene discovery in marine microbial metagenomes.

Massive metagenomic sequencing combined with gene prediction methods were previously used to compile the gene catalogue of the ocean and host-associated microbes. Global expeditions conducted over the past 15 years have sampled the ocean to build a catalogue of genes from pelagic microbes. Here we undertook a large sequencing effort of a perturbed Red Sea plankton community to uncover that the rate of gene discovery increases continuously with sequencing effort, with no indication that the retrieved 2.83 million non-redundant (complete) genes predicted from the experiment represented a nearly complete inventory of the genes present in the sampled community (i.e., no evidence of saturation). The underlying reason is the Pareto-like distribution of the abundance of genes in the plankton community, resulting in a very long tail of millions of genes present at remarkably low abundances, which can only be retrieved through massive sequencing. Microbial metagenomic projects retrieve a variable number of unique genes per Tera base-pair (Tbp), with a median value of 14.7 million unique genes per Tbp sequenced across projects. The increase in the rate of gene discovery in microbial metagenomes with sequencing effort implies that there is ample room for new gene discovery in further ocean and holobiont sequencing studies.

Is the voter model a model for voters?

The voter model has been studied extensively as a paradigmatic opinion dynamics model. However, its ability to model real opinion dynamics has not been addressed. We introduce a noisy voter model (accounting for social influence) with recurrent mobility of agents (as a proxy for social context), where the spatial and population diversity are taken as inputs to the model. We show that the dynamics can be described as a noisy diffusive process that contains the proper anisotropic coupling topology given by population and mobility heterogeneity. The model captures statistical features of U.S. presidential elections as the stationary vote-share fluctuations across counties and the long-range spatial correlations that decay logarithmically with the distance. Furthermore, it recovers the behavior of these properties when the geographical space is coarse grained at different scales-from the county level through congressional districts, and up to states. Finally, we analyze the role of the mobility range and the randomness in decision making, which are consistent with the empirical observations.

Disentangling the Influence of Mutation and Migration in Clonal Seagrasses Using the Genetic Diversity Spectrum for Microsatellites.

The recurrent lack of isolation by distance reported at regional scale in seagrass species was recently suggested to stem from stochastic events of large-scale dispersal. We explored the usefulness of phylogenetic information contained in microsatellite loci to test this hypothesis by using the Genetic Diversity Spectrum (GDS) on databases containing, respectively, 7 and 9 microsatellites genotypes for 1541 sampling units of Posidonia oceanica and 1647 of Cymodocea nodosa The simultaneous increase of microsatellite and geographic distances that emerges reveals a coherent pattern of isolation by distance in contrast to the chaotic pattern previously described using allele frequencies, in particular, for the long-lived P. oceanica These results suggest that the lack of isolation by distance, rather than the resulting from rare events of large-scale dispersal, reflects at least for some species a stronger influence of mutation over migration at the scale of the distribution range. The global distribution of genetic polymorphism may, therefore, result predominantly from ancient events of step-by-step (re)colonization followed by local recruitment and clonal growth, rather than contemporary gene flow. The analysis of GDS appears useful to unravel the evolutionary forces influencing the dynamics and evolution at distinct temporal and spatial scales by accounting for phylogenetic information borne by microsatellites, under an appropriate mutation model. This finding adds nuance to the generalization of the influence of large-scale dispersal on the dynamics of seagrasses.

Temporal networks: slowing down diffusion by long lasting interactions.

Interactions among units in complex systems occur in a specific sequential order, thus affecting the flow of information, the propagation of diseases, and general dynamical processes. We investigate the Laplacian spectrum of temporal networks and compare it with that of the corresponding aggregate network. First, we show that the spectrum of the ensemble average of a temporal network has identical eigenmodes but smaller eigenvalues than the aggregate networks. In large networks without edge condensation, the expected temporal dynamics is a time-rescaled version of the aggregate dynamics. Even for single sequential realizations, diffusive dynamics is slower in temporal networks. These discrepancies are due to the noncommutability of interactions. We illustrate our analytical findings using a simple temporal motif, larger network models, and real temporal networks.

Lyapunov exponents for temporal networks.

By interpreting a temporal network as a trajectory of a latent graph dynamical system, we introduce the concept of dynamical instability of a temporal network and construct a measure to estimate the network maximum Lyapunov exponent (nMLE) of a temporal network trajectory. Extending conventional algorithmic methods from nonlinear time-series analysis to networks, we show how to quantify sensitive dependence on initial conditions and estimate the nMLE directly from a single network trajectory. We validate our method for a range of synthetic generative network models displaying low- and high-dimensional chaos and finally discuss potential applications.

Key epidemiological indicators and spatial autocorrelation patterns across five waves of COVID-19 in Catalonia.

This research studies the evolution of COVID-19 crude incident rates, effective reproduction number R(t) and their relationship with incidence spatial autocorrelation patterns in the 19 months following the disease outbreak in Catalonia (Spain). A cross-sectional ecological panel design based on n = 371 health-care geographical units is used. Five general outbreaks are described, systematically preceded by generalized values of R(t) > 1 in the two previous weeks. No clear regularities concerning possible initial focus appear when comparing waves. As for autocorrelation, we identify a wave's baseline pattern in which global Moran's I increases rapidly in the first weeks of the outbreak to descend later. However, some waves significantly depart from the baseline. In the simulations, both baseline pattern and departures can be reproduced when measures aimed at reducing mobility and virus transmissibility are introduced. Spatial autocorrelation is inherently contingent on the outbreak phase and is also substantially modified by external interventions affecting human behavior.

Wearable sensors for monitoring marine environments and their inhabitants.

Human societies depend on marine ecosystems, but their degradation continues. Toward mitigating this decline, new and more effective ways to precisely measure the status and condition of marine environments are needed alongside existing rebuilding strategies. Here, we provide an overview of how sensors and wearable technology developed for humans could be adapted to improve marine monitoring. We describe barriers that have slowed the transition of this technology from land to sea, update on the developments in sensors to advance ocean observation and advocate for more widespread use of wearables on marine organisms in the wild and in aquaculture. We propose that large-scale use of wearables could facilitate the concept of an 'internet of marine life' that might contribute to a more robust and effective observation system for the oceans and commercial aquaculture operations. These observations may aid in rationalizing strategies toward conservation and restoration of marine communities and habitats.

Rapid evolution of SARS-CoV-2 challenges human defenses.

The race between pathogens and their hosts is a major evolutionary driver, where both reshuffle their genomes to overcome and reorganize the defenses for infection, respectively. Evolutionary theory helps formulate predictions on the future evolutionary dynamics of SARS-CoV-2, which can be monitored through unprecedented real-time tracking of SARS-CoV-2 population genomics at the global scale. Here we quantify the accelerating evolution of SARS-CoV-2 by tracking the SARS-CoV-2 mutation globally, with a focus on the Receptor Binding Domain (RBD) of the spike protein determining infection success. We estimate that the > 820 million people that had been infected by October 5, 2021, produced up to 1021 copies of the virus, with 12 new effective RBD variants appearing, on average, daily. Doubling of the number of RBD variants every 89 days, followed by selection of the most infective variants challenges our defenses and calls for a shift to anticipatory, rather than reactive tactics involving collaborative global sequencing and vaccination.

Inequalities in COVID-19 inequalities research: Who had the capacity to respond?

The COVID-19 pandemic has been testing countries' capacities and scientific preparedness to actively respond and collaborate on a common global threat. It has also heightened awareness of the urgent need to empirically describe and analyze health inequalities to be able to act effectively. In turn, this raises several important questions that need answering: What is known about the rapidly emerging COVID-19 inequalities research field? Which countries and world regions have been able to rapidly produce research on this topic? What research patterns and trends have emerged, and how to these compared to the (pre-COVID-19) global health inequalities research field? Which countries have been scientifically collaborating on this important topic? Where are the scientific knowledge gaps, and indirectly where might research capacities need to be strengthened? In order to answer these queries, we analyzed the global scientific production (2020-2021) on COVID-19 associated inequalities by conducting bibliometric and network analyses using the Scopus database. Specifically, we analyzed the volume of scientific production per country (via author affiliations), its distribution by country income groups and world regions, as well as the inter-country collaborations within this production. Our results indicate that the COVID-19 inequalities research field has been highly collaborative; however, a number of significant inequitable research practices exist. When compared to the (pre-COVID-19) global health inequalities research field, similar inequalities were identified, however, several new dynamics and partnerships have also emerged that warrant further in-depth exploration. To ensure preparedness for future crises, and effective strategies to tackle growing social inequalities in health, investment in global health inequalities research capacities must be a priority for all.

Scaling properties of protein family phylogenies.

BACKGROUND: One of the classical questions in evolutionary biology is how evolutionary processes are coupled at the gene and species level. With this motivation, we compare the topological properties (mainly the depth scaling, as a characterization of balance) of a large set of protein phylogenies with those of a set of species phylogenies. RESULTS: The comparative analysis between protein and species phylogenies shows that both sets of phylogenies share a remarkably similar scaling behavior, suggesting the universality of branching rules and of the evolutionary processes that drive biological diversification from gene to species level. In order to explain such generality, we propose a simple model which allows us to estimate the proportion of evolvability/robustness needed to approximate the scaling behavior observed in the phylogenies, highlighting the relevance of the robustness of a biological system (species or protein) in the scaling properties of the phylogenetic trees. CONCLUSIONS: The invariance of the scaling properties at levels spanning from genes to species suggests that rules that govern the incapability of a biological system to diversify are equally relevant both at the gene and at the species level.

Network analysis identifies weak and strong links in a metapopulation system.

The identification of key populations shaping the structure and connectivity of metapopulation systems is a major challenge in population ecology. The use of molecular markers in the theoretical framework of population genetics has allowed great advances in this field, but the prime question of quantifying the role of each population in the system remains unresolved. Furthermore, the use and interpretation of classical methods are still bounded by the need for a priori information and underlying assumptions that are seldom respected in natural systems. Network theory was applied to map the genetic structure in a metapopulation system by using microsatellite data from populations of a threatened seagrass, Posidonia oceanica, across its whole geographical range. The network approach, free from a priori assumptions and from the usual underlying hypotheses required for the interpretation of classical analyses, allows both the straightforward characterization of hierarchical population structure and the detection of populations acting as hubs critical for relaying gene flow or sustaining the metapopulation system. This development opens perspectives in ecology and evolution in general, particularly in areas such as conservation biology and epidemiology, where targeting specific populations is crucial.

Phase clustering in complex networks of delay-coupled oscillators.

We study the clusterization of phase oscillators coupled with delay in complex networks. For the case of diffusive oscillators, we formulate the equations relating the topology of the network and the phases and frequencies of the oscillators (functional response). We solve them exactly in directed networks for the case of perfect synchronization. We also compare the reliability of the solution of the linear system for non-linear couplings. Taking advantage of the form of the solution, we propose a frequency adaptation rule to achieve perfect synchronization. We also propose a mean-field theory for uncorrelated random networks that proves to be pretty accurate to predict phase synchronization in real topologies, as for example, the Caenorhabditis elegans or the autonomous systems connectivity.

The global network of ports supporting high seas fishing.

Fisheries in waters beyond national jurisdiction ("high seas") are difficult to monitor and manage. Their regulation for sustainability requires critical information on how fishing effort is distributed across fishing and landing areas, including possible border effects at the exclusive economic zone (EEZ) limits. We infer the global network linking harbors supporting fishing vessels to fishing areas in high seas from automatic identification system tracking data in 2014, observing a modular structure, with vessels departing from a given harbor fishing mostly in a single province. The top 16% of these harbors support 84% of fishing effort in high seas, with harbors in low- and middle-income countries ranked among the top supporters. Fishing effort concentrates along narrow strips attached to the boundaries of EEZs with productive fisheries, identifying a free-riding behavior that jeopardizes efforts by nations to sustainably manage their fisheries, perpetuating the tragedy of the commons affecting global fishery resources.

Modelling the Impact of Robotics on Infectious Spread Among Healthcare Workers.

The Coronavirus disease 2019 (Covid-19) pandemic has brought the world to a standstill. Healthcare systems are critical to maintain during pandemics, however, providing service to sick patients has posed a hazard to frontline healthcare workers (HCW) and particularly those caring for elderly patients. Various approaches are investigated to improve safety for HCW and patients. One promising avenue is the use of robots. Here, we model infectious spread based on real spatio-temporal precise personal interactions from a geriatric unit and test different scenarios of robotic integration. We find a significant mitigation of contamination rates when robots specifically replace a moderate fraction of high-risk healthcare workers, who have a high number of contacts with patients and other HCW. While the impact of robotic integration is significant across a range of reproductive number R0, the largest effect is seen when R0 is slightly above its critical value. Our analysis suggests that a moderate-sized robotic integration can represent an effective measure to significantly reduce the spread of pathogens with Covid-19 transmission characteristics in a small hospital unit.

The soundscape of the Anthropocene ocean.

Oceans have become substantially noisier since the Industrial Revolution. Shipping, resource exploration, and infrastructure development have increased the anthrophony (sounds generated by human activities), whereas the biophony (sounds of biological origin) has been reduced by hunting, fishing, and habitat degradation. Climate change is affecting geophony (abiotic, natural sounds). Existing evidence shows that anthrophony affects marine animals at multiple levels, including their behavior, physiology, and, in extreme cases, survival. This should prompt management actions to deploy existing solutions to reduce noise levels in the ocean, thereby allowing marine animals to reestablish their use of ocean sound as a central ecological trait in a healthy ocean.

Risk of Secondary Infection Waves of COVID-19 in an Insular Region: The Case of the Balearic Islands, Spain.

The Spanish government declared the lockdown on March 14th, 2020 to tackle the fast-spreading of COVID-19. As a consequence, the Balearic Islands remained almost fully isolated due to the closing of airports and ports, these isolation measures and the home-based confinement have led to a low prevalence of COVID-19 in this region. We propose a compartmental model for the spread of COVID-19 including five compartments (Susceptible, Exposed, Presymptomatic Infective, Diseased, and Recovered), and the mobility between municipalities. The model parameters are calibrated with the temporal series of confirmed cases provided by the Spanish Ministry of Health. After calibration, the proposed model captures the trend of the official confirmed cases before and after the lockdown. We show that the estimated number of cases depends strongly on the initial dates of the local outbreak onset and the number of imported cases before the lockdown. Our estimations indicate that the population has not reached the level of herd immunization necessary to prevent future outbreaks. While the low prevalence, in comparison to mainland Spain, has prevented the saturation of the health system, this low prevalence translates into low immunization rates, therefore facilitating the propagation of new outbreaks that could lead to secondary waves of COVID-19 in the region. These findings warn about scenarios regarding after-lockdown-policies and the risk of second outbreaks, emphasize the need for widespread testing, and could potentially be extrapolated to other insular and continental regions.