Disentangling nestedness from models of ecological complexity

Dietary metabarcoding reveals the simplification of bird-pest interaction networks across a gradient of agricultural cover

Agriculture is vital for supporting human populations, but its intensification often leads to landscape homogenization and a decline in non-provisioning ecosystem services. Ecological intensification and multifunctional landscapes are suggested as nature-based alternatives to intensive agriculture, using ecological processes like natural pest regulation to maximize food production. Birds are recognized for their role in increasing crop yields by consuming invertebrate pests in several agroecosystems. However, the understanding of how bird species, their traits and agricultural land cover influence the structure of bird-pest interactions remains limited. We sampled bird-pest interactions monthly for 1 year, at four sites within a multifunctional landscape, following a gradient of increasing agricultural land cover. We analysed 2583 droppings of 55 bird species with DNA metabarcoding and detected 225 pest species in 1139 samples of 42 bird species. As expected, bird-pest interactions were highly variable across bird species. Dietary pest richness was lower in the fully agricultural site, while predation frequency remained consistent across the agricultural land cover gradient. Network analysis revealed a reduction in the complexity of bird-pest interactions as agricultural coverage increased. Bird species abundance affected the bird's contribution to the network structure more than any of the bird traits analysed (weight, phenology, invertebrate frequency in diet and foraging strata), with more common birds being more important to network structure. Overall, our results show that increasing agricultural land cover increases the homogenization of bird-pest interactions. This shows the importance of maintaining natural patches within agricultural landscapes for biodiversity conservation and enhanced biocontrol.

The reliability of regional ecological knowledge to build local interaction networks: a test using seed-dispersal networks across land-bridge islands

Building ecological networks is the fundamental basis of depicting how species in communities interact, but sampling complex interaction networks is extremely labour intensive. Recently, indirect ecological information has been applied to build interaction networks. Here we propose to extend the source of indirect ecological information, and applied regional ecological knowledge to build local interaction networks. Using a high-resolution dataset consisting of 22 locally observed networks with 17 572 seed-dispersal events, we test the reliability of indirectly derived local networks based on regional ecological knowledge (REK) across islands. We found that species richness strongly influenced 'local interaction rewiring' (i.e. the proportion of locally observed interactions among regionally interacting species), and all network properties were biased using REK-based networks. Notably, species richness and local interaction rewiring strongly affected estimations of REK-based network structures. However, locally observed and REK-based networks detected the same trends of how network structure correlates to island area and isolation. These results suggest that we should use REK-based networks cautiously for reflecting actual interaction patterns of local networks, but highlight that REK-based networks have great potential for comparative studies across environmental gradients. The use of indirect regional ecological information may thus advance our understanding of biogeographical patterns of species interactions.

Rising temperature drives tipping points in mutualistic networks

The effect of climate warming on species' physiological parameters, including growth rate, mortality rate and handling time, is well established from empirical data. However, with an alarming rise in global temperature more than ever, predicting the interactive influence of these changes on mutualistic communities remains uncertain. Using 139 real plant-pollinator networks sampled across the globe and a modelling approach, we study the impact of species' individual thermal responses on mutualistic communities. We show that at low mutualistic strength plant-pollinator networks are at potential risk of rapid transitions at higher temperatures. Evidently, generalist species play a critical role in guiding tipping points in mutualistic networks. Further, we derive stability criteria for the networks in a range of temperatures using a two-dimensional reduced model. We identify network structures that can ascertain the delay of a community collapse. Until the end of this century, on account of increasing climate warming many real mutualistic networks are likely to be under the threat of sudden collapse, and we frame strategies to mitigate this. Together, our results indicate that knowing individual species' thermal responses and network structure can improve predictions for communities facing rapid transitions.

Plant-frugivore network simplification under habitat fragmentation leaves a small core of interacting generalists

Habitat fragmentation impacts seed dispersal processes that are important in maintaining biodiversity and ecosystem functioning. However, it is still unclear how habitat fragmentation affects frugivorous interactions due to the lack of high-quality data on plant-frugivore networks. Here we recorded 10,117 plant-frugivore interactions from 22 reservoir islands and six nearby mainland sites using the technology of arboreal camera trapping to assess the effects of island area and isolation on the diversity, structure, and stability of plant-frugivore networks. We found that network simplification under habitat fragmentation reduces the number of interactions involving specialized species and large-bodied frugivores. Small islands had more connected, less modular, and more nested networks that consisted mainly of small-bodied birds and abundant plants, as well as showed evidence of interaction release (i.e., dietary expansion of frugivores). Our results reveal the importance of preserving large forest remnants to support plant-frugivore interaction diversity and forest functionality.

Smaller communities, such as those on islands, under ecological network simplification reduce interactions between specialist organisms.

The role of E. maritimum (L.) in the dune pollination network of the Balearic Islands

Eryngium maritimum L. (Apiaceae) is a geophyte that inhabits in the dunes of the Mediterranean and Atlantic. Although it is a highly entomophilous species, there is little literature on its pollinator assemblage. The aim of this study is to analyze the role played by E. maritimum in the dune pollination network of the Balearic Islands, where there is an intense anthropogenic impact in its habitat. For this purpose, two populations located in the North and South of Mallorca were chosen, in which diurnal transects were carried out to observe and capture pollinators on 15 plant species during the anthesis period of E. maritimum. The flowering period of 10 plant species flowering at the same period than E. maritimum was analyzed to identify periods of competition. A total of 71 pollinator species were found, belonging to 30 different families. Eryngium maritimum is a strongly generalist species, with a total of 45 pollinator species. Two new species, Odice blandula and Leucospis gigas, were found for the first time in Mallorca. In terms of pollinators, Teucrium dunense and Helichrysum stoechas are the most similar species to E. maritimum. However, analysis of phenology suggests that these three species have been able to decouple their blooms to avoid competition. The present study shows that E. maritimum plays an important role in the dune pollination network, being its anthesis located at the end of the dune flowering season, when there are no functionally similar species in flower.

Small Run-of-River Dams Affect Taxonomic and Functional β-Diversity, Community Assembly Process of Benthic Diatoms

Being increasingly constructed worldwide, dams are a main driver of flow regime change and biodiversity decline. Although small run-of-river dams have exceeded the number of large dams, their impacts on taxonomic and functional β-diversity as well as community assembly process of aquatic organisms have been largely neglected. Ninety sites within twenty three small run-of-river dams in the Xiangxi River were selected, and the hydrological and physicochemical variables for each site were measured. We analyzed the traits and β-diversity of benthic diatoms, and explored the key driving mechanism of benthic diatom community assembly. Our results indicated that the construction of small run-of-river dams could affect the β-diversity of benthic diatoms and the mechanism of community assembly. Specifically, we found that small run-of-river dams could change the relative contribution of nestedness components to the trait-based β-diversity of benthic diatoms, but generally the taxonomy-based β-diversity was relatively higher than the trait-based β-diversity. Furthermore, the community assembly process of benthic diatoms was also affected. In areas affected directly by small run-of-river dams, dispersal assembly was the key mechanism for community assembly. Compared to unregulated habitats, the dispersal assembly process between the impacted and the unregulated habitats has been enhanced. We advocate that this study can be expanded to other organisms (such as macroinvertebrates, phytoplankton, fish) in future to fully understand impacts of small run-of-river dams on biodiversity from a multi-trophic level aspect. Based on our results, we suggest that maintaining genetic and ecological connectivity based on an effective impact assessment in dry seasons is a potential solution to mitigate the impacts of such dams, as key to adaptive management and sustainability.

Measuring node centrality when local and global measures overlap

Centrality metrics aim to identify the most relevant nodes in a network. In the literature, a broad set of metrics exists, measuring either local or global centrality characteristics. Nevertheless, when networks exhibit a high spectral gap, the usual global centrality measures typically do not add significant information with respect to the degree, i.e., the simplest local metric. To extract different information from this class of networks, we propose the use of the Generalized Economic Complexity index (GENEPY). Despite its original definition within the economic field, the GENEPY can be easily applied and interpreted on a wide range of networks, characterized by high spectral gap, including monopartite and bipartite network systems. Tests on synthetic and real-world networks show that the GENEPY can shed light about the node centrality, carrying information generally poorly correlated with the node number of direct connections (node degree).

Discriminating ecological processes affecting different dimensions of α- and β-diversity in desert plant communities

Understanding the spatial distribution of plant diversity and its drivers are major challenges in biogeography and conservation biology. Integrating multiple facets of biodiversity (e.g., taxonomic, phylogenetic, and functional biodiversity) may advance our understanding on how community assembly processes drive the distribution of biodiversity. In this study, plant communities in 60 sampling plots in desert ecosystems were investigated. The effects of local environment and spatial factors on the species, functional, and phylogenetic α- and β-diversity (including turnover and nestedness components) of desert plant communities were investigated. The results showed that functional and phylogenetic α-diversity were negatively correlated with species richness, and were significantly positively correlated with each other. Environmental filtering mainly influenced species richness and Rao quadratic entropy; phylogenetic α-diversity was mainly influenced by dispersal limitation. Species and phylogenetic β-diversity were mainly consisted of turnover component. The functional β-diversity and its turnover component were mainly influenced by environmental factors, while dispersal limitation dominantly effected species and phylogenetic β-diversity and their turnover component of species and phylogenetic β-diversity. Soil organic carbon and soil pH significantly influenced different dimensions of α-diversity, and soil moisture, salinity, organic carbon, and total nitrogen significantly influenced different dimensions of α- and β-diversity and their components. Overall, it appeared that the relative influence of environmental and spatial factors on taxonomic, functional, and phylogenetic diversity differed at the α and β scales. Quantifying α- and β-diversity at different biodiversity dimensions can help researchers to more accurately assess patterns of diversity and community assembly.

Ecological network complexity scales with area

Larger geographical areas contain more species-an observation raised to a law in ecology. Less explored is whether biodiversity changes are accompanied by a modification of interaction networks. We use data from 32 spatial interaction networks from different ecosystems to analyse how network structure changes with area. We find that basic community structure descriptors (number of species, links and links per species) increase with area following a power law. Yet, the distribution of links per species varies little with area, indicating that the fundamental organization of interactions within networks is conserved. Our null model analyses suggest that the spatial scaling of network structure is determined by factors beyond species richness and the number of links. We demonstrate that biodiversity-area relationships can be extended from species counts to higher levels of network complexity. Therefore, the consequences of anthropogenic habitat destruction may extend from species loss to wider simplification of natural communities.

Leapfrog dynamics in phage‐bacteria coevolution revealed by joint analysis of cross‐infection phenotypes and whole genome sequencing

Viruses and their hosts can undergo coevolutionary arms races where hosts evolve increased resistance and viruses evolve counter-resistance. Given these arms race dynamics (ARD), both players are predicted to evolve along a single trajectory as more recently evolved genotypes replace their predecessors. By coupling phenotypic and genomic analyses of coevolving populations of bacteriophage λ and Escherichia coli, we find conflicting evidence for ARD. Virus-host infection phenotypes fit the ARD model, yet genomic analyses revealed fluctuating selection dynamics. Rather than coevolution unfolding along a single trajectory, cryptic genetic variation emerges and is maintained at low frequency for generations until it eventually supplants dominant lineages. These observations suggest a hybrid 'leapfrog' dynamic, revealing weaknesses in the predictive power of standard coevolutionary models. The findings shed light on the mechanisms that structure coevolving ecological networks and reveal the limits of using phenotypic or genomic data alone to differentiate coevolutionary dynamics.

Stability and selective extinction in complex mutualistic networks

We study species abundance in the empirical plant-pollinator mutualistic networks exhibiting broad degree distributions, with uniform intragroup competition assumed, by the Lotka-Volterra equation. The stability of a fixed point is found to be identified by the signs of its nonzero components and those of its neighboring fixed points. Taking the annealed approximation, we derive the nonzero components to be formulated in terms of degrees and the rescaled interaction strengths, which lead us to find different stable fixed points depending on parameters, and we obtain the phase diagram. The selective extinction phase finds small-degree species extinct and effective interaction reduced, maintaining stability and hindering the onset of instability. The nonzero minimum species abundances from different empirical networks show data collapse when rescaled as predicted theoretically.

Quantifying persistence in the T-cell signaling network using an optically controllable antigen receptor

T cells discriminate between healthy and infected cells with remarkable sensitivity when mounting an immune response, which is hypothesized to depend on T cells combining stimuli from multiple antigen-presenting cell interactions into a more potent response. To quantify the capacity for T cells to accomplish this, we have developed an antigen receptor that is optically tunable within cell conjugates, providing control over the duration, and intensity of intracellular T-cell signaling. We observe limited persistence within the T-cell intracellular network on disruption of receptor input, with signals dissipating entirely in ~15 min, and directly show sustained proximal receptor signaling is required to maintain gene transcription. T cells thus primarily accumulate the outputs of gene expression rather than integrate discrete intracellular signals. Engineering optical control in a clinically relevant chimeric antigen receptor (CAR), we show that this limited signal persistence can be exploited to increase CAR-T cell activation threefold using pulsatile stimulation. Our results are likely to apply more generally to the signaling dynamics of other cellular networks.

An ecological approach to structural flexibility in online communication systems

Human cognitive abilities are limited resources. Today, in the age of cheap information-cheap to produce, to manipulate, to disseminate-this cognitive bottleneck translates into hypercompetition for rewarding outcomes among actors. These incentives push actors to mutualistically interact with specific memes, seeking the virality of their messages. In turn, memes' chances to persist and spread are subject to changes in the communication environment. In spite of all this complexity, here we show that the underlying architecture of empirical actor-meme information ecosystems evolves into recurring emergent patterns. We then propose an ecology-inspired modelling framework, bringing to light the precise mechanisms causing the observed flexible structural reorganisation. The model predicts-and the data confirm-that users' struggle for visibility induces a re-equilibration of the network's mesoscale towards self-similar nested arrangements. Our final microscale insights suggest that flexibility at the structural level is not mirrored at the dynamical one.

The CAR T-Cell Mechanoimmunology at a Glance

Chimeric antigen receptor (CAR) T-cell transfer is a novel paradigm of adoptive T-cell immunotherapy. When coming into contact with a target cancer cell, CAR T-cell forms a nonclassical immunological synapse with the cancer cell and dynamically orchestrates multiple critical forces to commit cytotoxic immune function. Such an immunologic process involves a force transmission in the CAR and a spatiotemporal remodeling of cell cytoskeleton to facilitate CAR activation and CAR T-cell cytotoxic function. Yet, the detailed understanding of such mechanotransduction at the interface between the CAR T-cell and the target cell, as well as its molecular structure and signaling, remains less defined and is just beginning to emerge. This article summarizes the basic mechanisms and principles of CAR T-cell mechanoimmunology, and various lessons that can be comparatively learned from interrogation of mechanotransduction at the immunological synapse in normal cytotoxic T-cell. The recent development and future application of novel bioengineering tools for studying CAR T-cell mechanoimmunology is also discussed. It is believed that this progress report will shed light on the CAR T-cell mechanoimmunology and encourage future researches in revealing the less explored yet important mechanosensing and mechanotransductive mechanisms involved in CAR T-cell immuno-oncology.

Resolving Food-Web Structure

Food webs are a major focus and organizing theme of ecology, but the data used to assemble them are deficient. Early debates over food-web data focused on taxonomic resolution and completeness, lack of which had produced spurious inferences. Recent data are widely believed to be much better and are used extensively in theoretical and meta-analytic research on network ecology. Confidence in these data rests on the assumptions ( a) that empiricists correctly identified consumers and their foods and ( b) that sampling methods were adequate to detect a near-comprehensive fraction of the trophic interactions between species. Abundant evidence indicates that these assumptions are often invalid, suggesting that most topological food-web data may remain unreliable for inferences about network structure and underlying ecological and evolutionary processes. Morphologically cryptic species are ubiquitous across taxa and regions, and many trophic interactions routinely evade detection by conventional methods. Molecular methods have diagnosed the severity of these problems and are a necessary part of the cure.

Measuring nestedness: A comparative study of the performance of different metrics

Nestedness is a property of interaction networks widely observed in natural mutualistic communities, among other systems. A perfectly nested network is characterized by the peculiarity that the interactions of any node form a subset of the interactions of all nodes with higher degree. Despite a widespread interest on this pattern, no general consensus exists on how to measure it. Instead, several nestedness metrics, based on different but not necessarily independent properties of the networks, coexist in the literature, blurring the comparison between ecosystems. In this work, we present a detailed critical study of the behavior of six nestedness metrics and the variants of two of them. In order to evaluate their performance, we compare the obtained values of the nestedness of a large set of real networks among them and against a maximum-entropy and maximum-likelihood null model. We also analyze the dependencies of each metrics on different network parameters, as size, fill, and eccentricity. Our results point out, first, that the metrics do not rank networks universally in terms of their degree of nestedness. Furthermore, several metrics show significant dependencies on the network properties considered. The study of these dependencies allows us to understand some of the observed systematic shifts against the null model. Altogether, this paper intends to provide readers with a critical guide on how to measure nestedness patterns, by explaining the functioning of several metrics and disclosing their qualities and flaws. Besides, we also aim to extend the application of null models based on maximum entropy to the scarcely explored area of ecological networks. Finally, we provide a fully documented repository that allows constructing the null model and calculating the studied nestedness indexes. In addition, it provides the probability matrices to build the null model for a large dataset of more than 200 bipartite networks.

Species richness and redundancy promote persistence of exploited mutualisms in yeast

Mutualisms, or reciprocally beneficial interspecific interactions, constitute the foundation of many ecological communities and agricultural systems. Mutualisms come in different forms, from pairwise interactions to extremely diverse communities, and they are continually challenged with exploitation by nonmutualistic community members (exploiters). Thus, understanding how mutualisms persist remains an essential question in ecology. Theory suggests that high species richness and functional redundancy could promote mutualism persistence in complex mutualistic communities. Using a yeast system (Saccharomyces cerevisiae), we experimentally show that communities with the greatest mutualist richness and functional redundancy are nearly two times more likely to survive exploitation than are simple communities. Persistence increased because diverse communities were better able to mitigate the negative effects of competition with exploiters. Thus, large mutualistic networks may be inherently buffered from exploitation.

Assessment of the Resilience of a Tartary Buckwheat (Fagopyrum tataricum) Cultivation System in Meigu, Southwest China

Recent socioeconomic development, increased transport and new agricultural technology are endangering the survival of traditional agriculture and the Yi people’s traditional knowledge of cultivating Tartary buckwheat. The cultural heritage of Tartary buckwheat cultivation among the Yi communities needs to be investigated and protected before its loss. The main objectives of this study are to document the Tartary buckwheat cultivation system, to analyze the agroecosystem networks that support the current system, and to measure the resilience of the ecological, agricultural and social systems using relevant indicators. The Tartary buckwheat cultivation system in Meigu County uses a rotation system, in which various crops are planted alternatively (e.g., Tartary buckwheat, green manure and potato/corn), utilizing bunch planting and furrow drilling technology. Tartary buckwheat has an important position in the major festival activities among the Yi people’s communities. Network analysis on the current agricultural system, ecosystem and social system indicated that the system was stable. The mean score of ecological, agricultural and social stability were 2.50, 2.85 and 2.53, respectively, indicating moderately stability. In contrast, socio-ecological production landscapes and seascapes (SEPLS) resilience indicators in Meigu performed only moderately, with a score of 2.63. The assessment of the resilience of the Tartary buckwheat cultivation system can provide some guidance for policy makers to strengthen biodiversity conservation, sustainable agricultural production and livelihood development (e.g., land use, responding to extreme environmental stresses and improving education levels).

Predicting collapse of complex ecological systems: quantifying the stability-complexity continuum

Dynamical shifts between the extremes of stability and collapse are hallmarks of ecological systems. These shifts are limited by and change with biodiversity, complexity, and the topology and hierarchy of interactions. Most ecological research has focused on identifying conditions for a system to shift from stability to any degree of instability-species abundances do not return to exact same values after perturbation. Real ecosystems likely have a continuum of shifting between stability and collapse that depends on the specifics of how the interactions are structured, as well as the type and degree of disturbance due to environmental change. Here we map boundaries for the extremes of strict stability and collapse. In between these boundaries, we find an intermediate regime that consists of single-species extinctions, which we call the extinction continuum. We also develop a metric that locates the position of the system within the extinction continuum-thus quantifying proximity to stability or collapse-in terms of ecologically measurable quantities such as growth rates and interaction strengths. Furthermore, we provide analytical and numerical techniques for estimating our new metric. We show that our metric does an excellent job of capturing the system's behaviour in comparison with other existing methods-such as May's stability criteria or critical slowdown. Our metric should thus enable deeper insights about how to classify real systems in terms of their overall dynamics and their limits of stability and collapse.

Asymmetric interactions of seed-predation network contribute to rare-species advantage

Although the asymmetry of species linkage within ecological networks is now well recognized, its effect on communities has scarcely been empirically investigated. Based on theory, we predicted that an asymmetric architecture of antagonistic plant-herbivore networks would emerge at the community level and that this asymmetry would negatively affect community-common plants more than rare ones. We tested this prediction by analyzing the architectural properties of an alpine plant and pre-dispersal seed-predator network and its effect on seed loss rate of plants in the Tibetan Plateau. This network showed an asymmetric architecture, where the common plant species (with a larger aboveground biomass per area) were infested by a higher number of predator species. Moreover, they asymmetrically interacted with specialized herbivores, presumably because of greater seed resource abundance. In turn, the asymmetric interactions led to a higher proportion of seed loss in the common plants at the species level. Our results suggest that asymmetric antagonistic networks may improve species coexistence by contributing to a mechanism of rare-species advantage.

DNA Framework-Based Topological Cell Sorters

Molecular recognition in cell biological process is characterized with specific locks-and-keys interactions between ligands and receptors, which are ubiquitously distributed on cell membrane with topological clustering. Few topologically-engineered ligand systems enable the exploration of the binding strength between ligand-receptor topological organization. Herein, we generate topologically controlled ligands by developing a family of tetrahedral DNA frameworks (TDFs), so the multiple ligands are stoichiometrically and topologically arranged. This topological control of multiple ligands changes the nature of the molecular recognition by inducing the receptor clustering, so the binding strength is significantly improved (ca. 10-fold). The precise engineering of topological complexes formed by the TDFs are readily translated into effective binding control for cell patterning and binding strength control of cells for cell sorting. This work paves the way for the development of versatile design of topological ligands.

Revealing biases in the sampling of ecological interaction networks

The structure of ecological interactions is commonly understood through analyses of interaction networks. However, these analyses may be sensitive to sampling biases with respect to both the interactors (the nodes of the network) and interactions (the links between nodes), because the detectability of species and their interactions is highly heterogeneous. These ecological and statistical issues directly affect ecologists’ abilities to accurately construct ecological networks. However, statistical biases introduced by sampling are difficult to quantify in the absence of full knowledge of the underlying ecological network’s structure. To explore properties of large-scale ecological networks, we developed the software EcoNetGen, which constructs and samples networks with predetermined topologies. These networks may represent a wide variety of communities that vary in size and types of ecological interactions. We sampled these networks with different mathematical sampling designs that correspond to methods used in field observations. The observed networks generated by each sampling process were then analyzed with respect to the number of components, size of components and other network metrics. We show that the sampling effort needed to estimate underlying network properties depends strongly both on the sampling design and on the underlying network topology. In particular, networks with random or scale-free modules require more complete sampling to reveal their structure, compared to networks whose modules are nested or bipartite. Overall, modules with nested structure were the easiest to detect, regardless of the sampling design used. Sampling a network starting with any species that had a high degree (e.g., abundant generalist species) was consistently found to be the most accurate strategy to estimate network structure. Because high-degree species tend to be generalists, abundant in natural communities relative to specialists, and connected to each other, sampling by degree may therefore be common but unintentional in empirical sampling of networks. Conversely, sampling according to module (representing different interaction types or taxa) results in a rather complete view of certain modules, but fails to provide a complete picture of the underlying network. To reduce biases introduced by sampling methods, we recommend that these findings be incorporated into field design considerations for projects aiming to characterize large species interaction networks.

Beta diversity and factors that drive land-snail patterns in Jiangxi Province, People’s Republic of China

Jiangxi Province is a biodiversity hotspot in the People’s Republic of China and has abundant land-snail species (247). Beta diversity is a key concept for understanding the functioning of ecosystems, the conservation of biodiversity, and the management of ecosystems. Here, the pattern of beta diversity for land snails in Jiangxi Province was analyzed. The results showed that the spatial turnover component was the main contributor to beta diversity, indicating that additional conservation efforts must target an increase in the number of protected areas, which should be spread across each one of the areas, to maximize the protection of species diversity. The nestedness component of diversity was always low, but there was a marked difference between microsnails, in which zero values occurred in 41.3% of all cases, and macrosnails, in which zero values occurred in only 2.7% of cases. There was a difference in the pattern of beta diversity between the two. The principal coordinate analysis showed a clear pattern with four groups in Jiangxi Province. In addition, we found significant effects of precipitation and altitude on overall beta diversity. These results will provide important basic information for the conservation of biodiversity in land snails.

Is the addition of higher-order interactions in ecological models increasing the understanding of ecological dynamics?

Recent work has shown that higher-order terms in population dynamics models can increase the stability, promote the diversity, and better explain the dynamics of ecological systems. While it is known that these perceived benefits come from an increasing number of alternative solutions given by the nature of multivariate polynomials, this mathematical advantage has not been formally quantified. Here, we develop a general method to quantify the mathematical advantage of adding higher-order interactions in ecological models based on the number of free-equilibrium points that can emerge in a system (i.e., equilibria that can be feasible or unfeasible as a function of model parameters). We apply this method to calculate the number of free-equilibrium points in Lotka-Volterra dynamics. While it is known that Lotka-Volterra models without higher-order interactions only have one free-equilibrium point regardless of the number of parameters, we find that by adding higher-order terms this number increases exponentially with the dimension of the system. Hence, the number of free-equilibrium points can be used to compare more fairly between ecological models. Our results suggest that while adding higher-order interactions in ecological models may be good for prediction purposes, they cannot provide additional explanatory power of ecological dynamics if model parameters are not ecologically restricted.

Using Network Theory to Understand and Predict Biological Invasions

Understanding and predicting biological invasions is challenging because of the complexity of many interacting players. A holistic approach is needed with the potential to simultaneously consider all relevant effects and effectors. Using networks to describe the relevant anthropogenic and ecological factors, from community-level to global scales, promises advances in understanding aspects of invasion from propagule pressure, through establishment, spread, and ecological impact of invaders. These insights could lead to development of new tools for prevention and management of invasions that are based on species' network characteristics and use of networks to predict the ecological effects of invaders. Here, we review the findings from network ecology that show the most promise for invasion biology and identify pressing needs for future research.

Synthetic Biology: Engineering Mammalian Cells To Control Cell-to-Cell Communication at Will

Cell-to-cell communication plays a key role in the regulation of many natural biological processes. Recent advances in mammalian synthetic biology are making it possible to rationally engineer cell-to-cell communication for therapeutic and other purposes. Here, we review state-of-the-art engineering principles to control cell-to-cell communication, focusing on communication between mammalian cells with diffusible factors (e.g., small molecules or exosomes) or direct cell contact, and on interkingdom communication between mammalian cells and bacteria. Potential applications include construction of artificial tissues able to perform complex computations, sophisticated cell-based cancer therapies, use of mammalian cells as a new class of cargo delivery modality, development of design principles to control pattern formation of cell populations, and treatment of infectious diseases. We also discuss the challenges facing practical applications, and possible enabling technologies to overcome them.

Effects of model choice, network structure, and interaction strengths on knockout extinction models of ecological robustness

Analysis of ecological networks is a valuable approach to understanding the vulnerability of systems to disturbance. The tolerance of ecological networks to coextinctions, resulting from sequences of primary extinctions (here termed "knockout extinction models", in contrast with other dynamic approaches), is a widely used tool for modeling network "robustness". Currently, there is an emphasis to increase biological realism in these models, but less attention has been given to the effect of model choices and network structure on robustness measures. Here, we present a suite of knockout extinction models for bipartite ecological networks (specifically plant-pollinator networks) that can all be analyzed on the same terms, enabling us to test the effects of extinction rules, interaction weights, and network structure on robustness. We include two simple ecologically plausible models of propagating extinctions, one new and one adapted from existing models. All models can be used with weighted or binary interaction data. We found that the choice of extinction rules impacts robustness; our two propagating models produce opposing effects in all tests on observed plant-pollinator networks. Adding weights to the interactions tends to amplify the opposing effects and increase the variation in robustness. Variation in robustness is a key feature of these extinction models and is driven by the structural heterogeneity of nodes (specifically, the skewness of the plant degree distribution) in the network. Our analysis therefore reveals the mechanisms and fundamental network properties that drive observed trends in robustness.

Network resilience of mutualistic ecosystems and environmental changes: an empirical study

It is theorized that a mutualistic ecosystem's resilience against perturbations (e.g. species extinction) is determined by a single macroscopic parameter (network resilience), calculable from the network. Given that such perturbations occur owing to environmental changes (e.g. climate change and human impact), it has been predicted that mutualistic ecosystems that exist despite extensive environmental changes exhibit higher network resilience; however, such a prediction has not been confirmed using real-world data. Thus, in this study, the effects of climate change velocity and human activities on mutualistic network resilience were investigated. A global dataset of plant-animal mutualistic networks was used, and spatial analysis was performed to examine the effects. Moreover, the potential confounding effects of network size, current climate and altitude were statistically controlled. It was demonstrated that mutualistic network resilience was globally influenced by warming velocity and human impact, in addition to current climate. Specifically, pollination network resilience increased in response to human impact, and seed-dispersal network resilience increased with warming velocity. The effect of environmental changes on network resilience for plants was remarkable. The results confirmed the prediction obtained based on the theory and imply that real-world mutualistic networks have a structure that increases ecosystem resilience against environmental changes. These findings will enhance the understanding of ecosystem resilience.

Coexistence of many species in random ecosystems

Rich ecosystems harbour thousands of species interacting in tangled networks encompassing predation, mutualism and competition. Such widespread biodiversity is puzzling, because in ecological models it is exceedingly improbable for large communities to stably coexist. One aspect rarely considered in these models, however, is that coexisting species in natural communities are a selected portion of a much larger pool, which has been pruned by population dynamics. Here we compute the distribution of the number of species that can coexist when we start from a pool of species interacting randomly, and show that even in this case we can observe rich, stable communities. Interestingly, our results show that, once stability conditions are met, network structure has very little influence on the level of biodiversity attained. Our results identify the main drivers responsible for widespread coexistence in natural communities, providing a baseline for determining which structural aspects of empirical communities promote or hinder coexistence.

The joint influence of competition and mutualism on the biodiversity of mutualistic ecosystems

In the past years, there have been many advances –but also many debates– around mutualistic communities, whose structural features appear to facilitate mutually beneficial interactions and increase biodiversity, under some given population dynamics. However, most approaches neglect the structure of inter-species competition by adopting a mean-field perspective that does not deal with competitive interactions properly. Here, we build up a multilayer network that naturally accounts for mutualism and competition and show, through a dynamical population model and numerical simulations, that there is an intricate relation between competition and mutualism. Specifically, the multilayer structure is coupled to a dynamical model in which the intra-guild competitive terms are weighted by the abundance of shared mutualistic relations. We find that mutualism does not have the same consequences on the evolution of specialist and generalist species, and that there is a non-trivial profile of biodiversity in the parameter space of competition and mutualism. Our findings emphasize how the simultaneous consideration of positive and negative interactions derived from the real networks is key to understand the delicate trade-off between topology and biodiversity in ecosystems and call for the need to incorporate more realistic interaction patterns when modeling the structural and dynamical stability of mutualistic systems.

Invasion waves in the biochemical warfare between living organisms

Microorganisms and plants very commonly release toxic secondary chemical compounds (allelochemicals) that inhibit or kill sensitive strains or individuals from their own or other species. In this work we study a model that describes two species interacting through allelopathic suppression and competing for resources. Employing linear stability analysis, the conditions for coexistence or extinction of species in spatially homogeneous systems were determined. We found that the borders between the regimes of bistability, coexistence, and the extinction of the weaker by the stronger competitor, are altered by allelopathic interactions. In addition, traveling wave solutions for one species invasion were obtained considering the spatially explicit nature of the model. Our findings indicate that the minimum speed of the invasion wavefronts depends primarily on the competition coefficients and the parameters characterizing the species' functional responses to their allelochemicals. As a general rule, the species provided with the most effective chemical weapons dominates the population dynamics. Finally, we found a tristability at the coexistence region due to the combination of allelopathy and patchy population distributions in space. So, our model provides a distinct mechanism, independent of social behaviors, that produces such unexpected tristability impossible in classical competition models involving one-to-one individual interactions.

Bipartite graphs in systems biology and medicine: a survey of methods and applications

The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.

Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks

Studies of microbial communities by targeted sequencing of rRNA genes lead to recovering numerous rare low-abundance taxa with unknown biological roles. We propose to study associations of such rare organisms with their environments by a computational framework based on transformation of the data into qualitative variables. Namely, we analyze the sparse table of putative species or OTUs (operational taxonomic units) and samples generated in such studies, also known as an OTU table, by collecting statistics on co-occurrences of the species and on shared species richness across samples. Based on the statistics we built two association networks, of the rare putative species and of the samples respectively, using a known computational technique, Association networks (Anets) developed for analysis of qualitative data. Clusters of samples and clusters of OTUs are then integrated and combined with metadata of the study to produce a map of associated putative species in their environments. We tested and validated the framework on two types of microbiomes, of human body sites and that of the Populus tree root systems. We show that in both studies the associations of OTUs can separate samples according to environmental or physiological characteristics of the studied systems.

Will a small randomly assembled community be feasible and stable?

How likely is it that few species can randomly assemble into a feasible and stable community? Some studies have answered that as long as the community is feasible, it will nearly always be stable. In contrast, other studies have answered that the likelihood is almost null. Here, we show that the origin of this debate has been the underestimation of the association of the parameter space of intrinsic growth rates with the feasibility and stability properties of small randomly-assembled communities. In particular, we demonstrate that not all parameterizations and sampling distributions of intrinsic growth rates lead to the same probabilities of stability and feasibility, which could mistakenly lead to under- or overestimate the stability properties of feasible communities. Additionally, we find that stability imposes a filtering of species abundances "towards" more even distributions in small feasible randomly-assembled communities. This indicates that the stability of feasible communities is inherently linked to the starting distribution of species abundances, a characteristic that many times has been ignored, but should be incorporated in manageable lab and field experiments. Overall, the return to this debate is a central reminder that a more systematic exploration of the feasible parameter space is necessary to derive general conclusions about the stability properties of ecological communities.

Unifying relationships between complexity and stability in mutualistic ecological communities

Conserving ecosystem function and associated services requires deep understanding of the underlying basis of system stability. While the study of ecological dynamics is a mature and diverse field, the lack of a general model that predicts a broad range of theoretical and empirical observations has allowed unresolved contradictions to persist. Here we provide a general model of mutualistic ecological interactions between two groups and show for the first time how the conditions for bi-stability, the nature of critical transitions, and identifiable leading indicators in time-series can be derived from the basic parameters describing the underlying ecological interactions. Strong mutualism and nonlinearity in handling-time are found to be necessary conditions for the occurrence of critical transitions. We use the model to resolve open questions concerning the effects of heterogeneity in inter-species interactions on both resilience and abundance, and discuss these in terms of potential trade-offs in real systems. This framework provides a basis for rich investigations of ecological system dynamics, and may be generalizable across many ecological contexts.

The architecture of mutualistic networks as an evolutionary spandrel

Mutualistic networks have been shown to involve complex patterns of interactions among animal and plant species, including a widespread presence of nestedness. The nested structure of these webs seems to be positively correlated with higher diversity and resilience. Moreover, these webs exhibit marked measurable structural patterns, including broad distributions of connectivity, strongly asymmetrical interactions and hierarchical organization. Hierarchical organization is an especially interesting property, since it is positively correlated with biodiversity and network resilience, thus suggesting potential selection processes favouring the observed web organization. However, here we show that all these structural quantitative patterns-and nestedness in particular-can be properly explained by means of a very simple dynamical model of speciation and divergence with no selection-driven coevolution of traits. The agreement between observed and modelled networks suggests that the patterns displayed by real mutualistic webs might actually represent evolutionary spandrels.