Network ecology: topological constraints on ecosystem dynamics

Environment and traits affect parasite and host species positions but not roles in flea-mammal networks

We studied spatial variation in the effects of environment and network size on species positions and roles in multiple flea-mammal networks from four biogeographic realms. We asked whether species positions (measured as species strength [SS], the degree of interaction specialization [d'], and the eigenvector centrality [C]) or the roles of fleas and their hosts in the interaction networks: (a) are repeatable/conserved within a flea or a host species; (b) vary in dependence on environmental variables and/or network size; and (c) the effects of environment and network size on species positions or roles in the networks depend on species traits. The repeatability analysis of species position indices for 441 flea and 429 host species, occurring in at least two networks, demonstrated that the repeatability of SS, d', and C within a species was significant, although not especially high, suggesting that the indices' values were affected by local factors. The majority of flea and host species in the majority of networks demonstrated a peripheral role. A value of at least one index of species position was significantly affected by environmental variables or network size in 41 and 36, respectively, of the 52 flea and 52 host species that occurred in multiple networks. In both fleas and hosts, the occurrence of the significant effect of environment or network size on at least one index of species position, but not on a species' role in a network, was associated with some species traits.

Sensitivity of habitat network models to changes in maximum dispersal distance

Predicting the presence or absence (occurrence-state) of species in a certain area is highly important for conservation. Occurrence-state can be assessed by network models that take suitable habitat patches as nodes, connected by potential dispersal of species. To determine connections, a connectivity threshold is set at the species' maximum dispersal distance. However, this requires field observations prone to underestimation, so for most animal species there are no trustable maximum dispersal distance estimations. This limits the development of accurate network models to predict species occurrence-state. In this study, we performed a sensitivity analysis of the performance of network models to different settings of maximum dispersal distance. Our approach, applied on six amphibian species in Switzerland, used habitat suitability modelling to define habitat patches, which were linked within a dispersal distance threshold to form habitat networks. We used network topological measures, patch suitability, and patch size to explain species occurrence-state in habitat patches through boosted regression trees. These modelling steps were repeated on each species for different maximum dispersal distances, including a species-specific value from literature. We evaluated mainly the predictive performance and predictor importance among the network models. We found that predictive performance had a positive relation with the distance threshold, and that almost none of the species-specific values from literature yielded the best performance across tested thresholds. With increasing dispersal distance, the importance of the habitat-quality-related variable decreased, whereas that of the topology-related predictors increased. We conclude that the sensitivity of these models to the dispersal distance parameter stems from the very different topologies formed with different movement assumptions. Most reported maximum dispersal distances are underestimated, presumably due to leptokurtic dispersal distribution. Our results imply that caution should be taken when selecting a dispersal distance threshold, considering higher values than those derived from field reports, to account for long-distance dispersers.

Structure and function in artificial, zebrafish and human neural networks

Network science provides a set of tools for the characterization of the structure and functional behavior of complex systems. Yet a major problem is to quantify how the structural domain is related to the dynamical one. In other words, how the diversity of dynamical states of a system can be predicted from the static network structure? Or the reverse problem: starting from a set of signals derived from experimental recordings, how can one discover the network connections or the causal relations behind the observed dynamics? Despite the advances achieved over the last two decades, many challenges remain concerning the study of the structure-dynamics interplay of complex systems. In neuroscience, progress is typically constrained by the low spatio-temporal resolution of experiments and by the lack of a universal inferring framework for empirical systems. To address these issues, applications of network science and artificial intelligence to neural data have been rapidly growing. In this article, we review important recent applications of methods from those fields to the study of the interplay between structure and functional dynamics of human and zebrafish brain. We cover the selection of topological features for the characterization of brain networks, inference of functional connections, dynamical modeling, and close with applications to both the human and zebrafish brain. This review is intended to neuroscientists who want to become acquainted with techniques from network science, as well as to researchers from the latter field who are interested in exploring novel application scenarios in neuroscience.

Comparison of three modelling frameworks for aquatic ecosystems: practical aspects and applicability

Freshwater ecosystems are under multiple stressors and it is crucial to find methods to better describe, manage, and sustain aquatic ecosystems. Ecosystem modelling has become an important tool in integrating trophic relationships into food webs, assessing important nodes using network analysis, and making predictions via simulations. Fortunately, several modelling techniques exist, but the question is which approach is relevant and applicable when? In this study, we compare three modelling frameworks (Ecopath, Loop Analysis in R, STELLA software) using a case study of a small aquatic network (8 nodes). The choice of framework depends on the research question and data availability. We approach this topic from a methodological aspect by describing the data requirements and by comparing the applicability and limitations of each modelling approach. Each modelling framework has its specific focus, but some functionalities and outcomes can be compared. The predictions of Loop Analysis as compared to Ecopath’s Mixed Trophic Impact plot are in good agreement at the top and bottom trophic levels, but the middle trophic levels are less similar. This suggests that further comparisons are needed of networks of varying resolution and size. Generally, when data are limiting, Loop Analysis can provide qualitative predictions, while the other two methods provide quantitative results, yet rely on more data.

Ambient climate determines the directional trend of community stability under warming and grazing

Changes in ecological processes over time in ambient treatments are often larger than the responses to manipulative treatments in climate change experiments. However, the impacts of human-driven environmental changes on the stability of natural grasslands have been typically assessed by comparing differences between manipulative plots and reference plots. Little is known about whether or how ambient climate regulates the effects of manipulative treatments and their underlying mechanisms. We collected two datasets, one a 36-year long-term observational dataset from 1983 to 2018, and the other a 10-year manipulative asymmetric warming and grazing experiment using infrared heaters with moderate grazing from 2006 to 2015 in an alpine meadow on the Tibetan Plateau. The 36-year observational dataset shows that there was a nonlinear response of community stability to ambient temperature with a positive relationship between them due to an increase in ambient temperature in the first 25 years and then a decrease in ambient temperature thereafter. Warming and grazing decreased community stability with experiment duration through an increase in legume cover and a decrease in species asynchrony, which was due to the decreasing background temperature through time during the 10-year experiment period. Moreover, the temperature sensitivity of community stability was higher under the ambient treatment than under the manipulative treatments. Therefore, our results suggested that ambient climate may control the directional trend of community stability while manipulative treatments may determine the temperature sensitivity of the response of community stability to climate relative to the ambient treatment. Our study emphasizes the importance of the context dependency of the response of community stability to human-driven environmental changes.

Changing our view of the Schistosoma granuloma to an ecological standpoint

Schistosomiasis, a neglected parasitic tropical disease that has plagued humans for centuries, remains a major public health burden. A primary challenge to understanding schistosomiasis is deciphering the most remarkable pathological feature of this disease, the granuloma - a highly dynamic and self-organized structure formed by both host and parasite components. Granulomas are considered a remarkable example of how parasites evolved with their hosts to establish complex and intimate associations. However, much remains unclear regarding life within the granuloma, and strategies to restrain its development are still lacking. Here we explore current information on the hepatic Schistosoma mansoni granuloma in the light of Ecology and propose that this intricate structure acts as a real ecosystem. The schistosomal granuloma is formed by cells (biotic component), protein scaffolds, fibres, and chemical compounds (abiotic components) with inputs/outputs of energy and matter, as complex as in classical ecosystems. We review the distinct cell populations ('species') within the granuloma and examine how they integrate with each other and interact with their microenvironment to form a multifaceted cell community in different space-time frames. The colonization of the hepatic tissue to form granulomas is explained from the point of view of an ecological succession whereby a community is able to modify its physical environment, creating conditions and resources for ecosystem construction. Remarkably, the granuloma represents a dynamic evolutionary system that undergoes progressive changes in the 'species' that compose its community over time. In line with ecological concepts, we examine the granuloma not only as a place where a community of cells is settled (spatial niche or habitat) but also as a site in which the functional activities of these combined populations occur in an orchestrated way in response to microenvironmental gradients such as cytokines and egg antigens. Finally, we assert how the levels of organization of cellular components in a granuloma as conventionally defined by Cell Biology can fit perfectly into a hierarchical structure of biological systems as defined by Ecology. By rethinking the granuloma as an integrating and evolving ecosystem, we draw attention to the inner workings of this structure that are central to the understanding of schistosomiasis and could guide its future treatment.

Application of Bipartite Networks to the Study of Water Quality

Water is a basic natural resource for life and the sustainable development of society. Methods to assess water quality in freshwater ecosystems based on environmental quality bioindicators have proven to be low cost, reliable, and can be adapted to ecosystems with well-defined structures. The objective of this paper is to propose an interdisciplinary approach for the assessment of water quality in freshwater ecosystems through bioindicators. From the presence/absence of bioindicator organisms and their sensitivity/tolerance to environmental stress, we constructed a bipartite network, G. In this direction, we propose a new method that combines two research approaches, Graph Theory and Random Matrix Theory (RMT). Through the topological properties of the graph G, we introduce a topological index, called J P ( G ) , to evaluate the water quality, and we study its properties and relationships with known indices, such as Biological Monitoring Working Party ( B M W P ) and Shannon diversity ( H ′ ). Furthermore, we perform a scaling analysis of random bipartite networks with already specialized parameters for our case study. We validate our proposal for its application in the reservoir of Guájaro, Colombia. The results obtained allow us to infer that the proposed techniques are useful for the study of water quality, since they detect significant changes in the ecosystem.

Rewiring and indirect effects underpin modularity reshuffling in a marine food web under environmental shifts

Species are characterized by physiological and behavioral plasticity, which is part of their response to environmental shifts. Nonetheless, the collective response of ecological communities to environmental shifts cannot be predicted from the simple sum of individual species responses, since co-existing species are deeply entangled in interaction networks, such as food webs. For these reasons, the relation between environmental forcing and the structure of food webs is an open problem in ecology. To this respect, one of the main problems in community ecology is defining the role each species plays in shaping community structure, such as by promoting the subdivision of food webs in modules-that is, aggregates composed of species that more frequently interact-which are reported as community stabilizers. In this study, we investigated the relationship between species roles and network modularity under environmental shifts in a highly resolved food web, that is, a "weighted" ecological network reproducing carbon flows among marine planktonic species. Measuring network properties and estimating weighted modularity, we show that species have distinct roles, which differentially affect modularity and mediate structural modifications, such as modules reconfiguration, induced by environmental shifts. Specifically, short-term environmental changes impact the abundance of planktonic primary producers; this affects their consumers' behavior and cascades into the overall rearrangement of trophic links. Food web re-adjustments are both direct, through the rewiring of trophic-interaction networks, and indirect, with the reconfiguration of trophic cascades. Through such "systemic behavior," that is, the way the food web acts as a whole, defined by the interactions among its parts, the planktonic food web undergoes a substantial rewiring while keeping almost the same global flow to upper trophic levels, and energetic hierarchy is maintained despite environmental shifts. This behavior suggests the potentially high resilience of plankton networks, such as food webs, to dramatic environmental changes, such as those provoked by global change.

Predicting species occurrences with habitat network models

Biodiversity conservation requires modeling tools capable of predicting the presence or absence (i.e., occurrence-state) of species in habitat patches. Local habitat characteristics of a patch (lh), the cost of traversing the landscape matrix between patches (weighted connectivity [wc]), and the position of the patch in the habitat network topology (nt) all influence occurrence-state. Existing models are data demanding or consider only local habitat characteristics. We address these shortcomings and present a network-based modeling approach, which aims to predict species occurrence-state in habitat patches using readily available presence-only records.For the tree frog Hyla arborea in the Swiss Plateau, we delineated habitat network nodes from an ensemble habitat suitability model and used different cost surfaces to generate the edges of three networks: one limited only by dispersal distance (Uniform), another incorporating traffic, and a third based on inverse habitat suitability. For each network, we calculated explanatory variables representing the three categories (lh, wc, and nt). The response variable, occurrence-state, was parametrized by a sampling intensity procedure assessing observations of comparable species over a threshold of patch visits. The explanatory variables from the three networks and an additional non-topological model were related to the response variable with boosted regression trees.The habitat network models had a similar fit; they all outperformed the non-topological model. Habitat suitability index (lh) was the most important predictor in all networks, followed by third-order neighborhood (nt). Patch size (lh) was unimportant in all three networks.We found that topological variables of habitat networks are relevant for the prediction of species occurrence-state, a step-forward from models considering only local habitat characteristics. For any habitat patch, occurrence-state is most prominently influenced by its habitat suitability and then by the number of patches in a wide neighborhood. Our approach is generic and can be applied to multiple species in different habitats.

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 questions.

Two dynamic regimes in the human gut microbiome

The gut microbiome is a dynamic system that changes with host development, health, behavior, diet, and microbe-microbe interactions. Prior work on gut microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra). However, we show that most of the variance in microbial time series is non-autoregressive. In addition, we show how community state-clustering is flawed when it comes to characterizing within-host dynamics and that more continuous methods are required. Most organisms exhibited stable, mean-reverting behavior suggestive of fixed carrying capacities and abundant taxa were largely shared across individuals. This mean-reverting behavior allowed us to apply sparse vector autoregression (sVAR)—a multivariate method developed for econometrics—to model the autoregressive component of gut community dynamics. We find a strong phylogenetic signal in the non-autoregressive co-variance from our sVAR model residuals, which suggests niche filtering. We show how changes in diet are also non-autoregressive and that Operational Taxonomic Units strongly correlated with dietary variables have much less of an autoregressive component to their variance, which suggests that diet is a major driver of microbial dynamics. Autoregressive variance appears to be driven by multi-day recovery from frequent facultative anaerobe blooms, which may be driven by fluctuations in luminal redox. Overall, we identify two dynamic regimes within the human gut microbiota: one likely driven by external environmental fluctuations, and the other by internal processes.

Dynamics reveal crucial information about how a system functions. In this study, we develop an approach for disentangling two types of dynamics within the human gut microbiome. We find that autoregressive dynamics involve recovery from large deviations in community structure. These recovery processes appear to involve the blooming of facultative anaerobes and aerotolerant taxa, likely due to transient shifts in redox potential, followed by re-establishment of obligate anaerobes. Non-autoregressive dynamics carry a strong phylogenetic signal, wherein highly related taxa fluctuate coherently. These non-autoregressive dynamics appear to be driven by external, non-autoregressive variables like diet. We find that most of the community variance is driven by day-to-day fluctuations in the environment, with occasional autoregressive dynamics as the system recovers from larger shocks. Despite frequently observed disruptions to the gut ecosystem, there exists a returning force that continually pushes the gut microbiome back towards its steady-state configuration.

Motif analysis in directed ordered networks and applications to food webs

The analysis of small recurrent substructures, so called network motifs, has become a standard tool of complex network science to unveil the design principles underlying the structure of empirical networks. In many natural systems network nodes are associated with an intrinsic property according to which they can be ordered and compared against each other. Here, we expand standard motif analysis to be able to capture the hierarchical structure in such ordered networks. Our new approach is based on the identification of all ordered 3-node substructures and the visualization of their significance profile. We present a technique to calculate the fine grained motif spectrum by resolving the individual members of isomorphism classes (sets of substructures formed by permuting node-order). We apply this technique to computer generated ensembles of ordered networks and to empirical food web data, demonstrating the importance of considering node order for food-web analysis. Our approach may not only be helpful to identify hierarchical patterns in empirical food webs and other natural networks, it may also provide the base for extending motif analysis to other types of multi-layered networks.

Ecological network indicators of ecosystem status and change in the Baltic Sea

Several marine ecosystems under anthropogenic pressure have experienced shifts from one ecological state to another. In the central Baltic Sea, the regime shift of the 1980s has been associated with food-web reorganization and redirection of energy flow pathways. These long-term dynamics from 1974 to 2006 have been simulated here using a food-web model forced by climate and fishing. Ecological network analysis was performed to calculate indices of ecosystem change. The model replicated the regime shift. The analyses of indicators suggested that the system’s resilience was higher prior to 1988 and lower thereafter. The ecosystem topology also changed from a web-like structure to a linearized food-web.

A systems approach to the policy-level risk assessment of exotic animal diseases: network model and application to classical swine fever

Exotic animal diseases (EADs) are characterized by their capacity to spread global distances, causing impacts on animal health and welfare with significant economic consequences. We offer a critique of current import risk analysis approaches employed in the EAD field, focusing on their capacity to assess complex systems at a policy level. To address the shortcomings identified, we propose a novel method providing a systematic analysis of the likelihood of a disease incursion, developed by reference to the multibarrier system employed for the United Kingdom. We apply the network model to a policy-level risk assessment of classical swine fever (CSF), a notifiable animal disease caused by the CSF virus. In doing so, we document and discuss a sequence of analyses that describe system vulnerabilities and reveal the critical control points (CCPs) for intervention, reducing the likelihood of U.K. pig herds being exposed to the CSF virus.

Linking topological structure and dynamics in ecological networks

Interaction networks are basic descriptions of ecological communities and are at the core of community dynamics models. Knowledge of their structure should enable us to understand dynamical properties of ecological communities. However, the relationships between dynamical properties of communities and qualitative descriptors of network structure remain unclear. To improve our understanding of such relationships, we develop a framework based on the concept of strongly connected components, which are key structural components of networks necessary to explain stability properties such as persistence and robustness. We illustrate this framework for the analysis of qualitative empirical food webs and plant-plant interaction networks. Both types of networks exhibit high persistence (on average, 99% and 80% of species, respectively, are expected to persist) and robustness (only 0.2% and 2% of species are expected to disappear following the extinction of a species). Each of the networks is structured as a large group of interconnected species accompanied by much smaller groups that most often consist of a single species. This low-modularity configuration can be explained by a negative modularity-stability relationship. Our results suggest that ecological communities are not typically structured in multispecies compartments and that compartmentalization decreases robustness.

Simulating food web dynamics along a gradient: quantifying human influence

Realistically parameterized and dynamically simulated food-webs are useful tool to explore the importance of the functional diversity of ecosystems, and in particular relations between the dynamics of species and the whole community. We present a stochastic dynamical food web simulation for the Kelian River (Borneo). The food web was constructed for six different locations, arrayed along a gradient of increasing human perturbation (mostly resulting from gold mining activities) along the river. Along the river, the relative importance of grazers, filterers and shredders decreases with increasing disturbance downstream, while predators become more dominant in governing eco-dynamics. Human activity led to increased turbidity and sedimentation which adversely impacts primary productivity. Since the main difference between the study sites was not the composition of the food webs (structure is quite similar) but the strengths of interactions and the abundance of the trophic groups, a dynamical simulation approach seemed to be useful to better explain human influence. In the pristine river (study site 1), when comparing a structural version of our model with the dynamical model we found that structurally central groups such as omnivores and carnivores were not the most important ones dynamically. Instead, primary consumers such as invertebrate grazers and shredders generated a greater dynamical response. Based on the dynamically most important groups, bottom-up control is replaced by the predominant top-down control regime as distance downstream and human disturbance increased. An important finding, potentially explaining the poor structure to dynamics relationship, is that indirect effects are at least as important as direct ones during the simulations. We suggest that our approach and this simulation framework could serve systems-based conservation efforts. Quantitative indicators on the relative importance of trophic groups and the mechanistic modeling of eco-dynamics could greatly contribute to understanding various aspects of functional diversity.

Generalized walks-based centrality measures for complex biological networks

A strategy for zooming in and out the topological environment of a node in a complex network is developed. This approach is applied here to generalize the subgraph centrality of nodes in complex networks. In this case the zooming in strategy is based on the use of some known matrix functions which allow focusing locally on the environment of a node. When a zooming out strategy is applied new matrix functions are introduced, which give a more global picture of the topological surrounds of a node. These indices permit a modulation of the scales at which the environment of a node influences its centrality. We apply them to the study of 10 protein-protein interaction (PPI) networks. We illustrate the similarities and differences between the generalized subgraph centrality indices as well as among them and some classical centrality measures. We show here that the use of centrality indices based on the zooming in strategy identifies a larger number of essential proteins in the yeast PPI network than any of the other centrality measures studied.

Keystone species and food webs

Different species are of different importance in maintaining ecosystem functions in natural communities. Quantitative approaches are needed to identify unusually important or influential, 'keystone' species particularly for conservation purposes. Since the importance of some species may largely be the consequence of their rich interaction structure, one possible quantitative approach to identify the most influential species is to study their position in the network of interspecific interactions. In this paper, I discuss the role of network analysis (and centrality indices in particular) in this process and present a new and simple approach to characterizing the interaction structures of each species in a complex network. Understanding the linkage between structure and dynamics is a condition to test the results of topological studies, I briefly overview our current knowledge on this issue. The study of key nodes in networks has become an increasingly general interest in several disciplines: I will discuss some parallels. Finally, I will argue that conservation biology needs to devote more attention to identify and conserve keystone species and relatively less attention to rarity.

A Proposed Target State for a Floodplain Forest Ecosystem Within an Ecological Network, with Reference to the Ecological Requirements of an Umbrella Bird Species: The Common Kingfisher

The present day cultural landscape of Europe is comprised of an ecological network of corridors and core areas (biocentres). This article proposes the use of umbrella species to define the target state of an ecosystem in a floodplain biocentre of the European Ecological Network. The umbrella species used were chosen to represent typical bird species of forested floodplains. Case studies were developed in the Litovelské Pomoraví Protected Landscape Area, a Bird Area in the Czech Republic.

Ecological network analysis: an application to the evaluation of effects of pesticide use in an agricultural environment

Ecological network analysis is used to evaluate the impact of pesticide use on ecological systems in the context of agricultural farmland environments. The aim is to provide support for the design of effective and minimally damaging pest control strategies. The ecological network analysis can identify species that are important to the integrity of the ecological network. The methodology can be used to monitor the impact of shifts in terms of types of pesticide used on the ecological system. The authors' intention is to use this methodology to provide supporting evidence for the UK Voluntary Initiative programme aimed at convincing farmers voluntarily to make improved choices in the use of a wide range of pesticides.

Scaling relations in food webs

In the last three decades, researchers have tried to identify universal patterns in the structure of food webs. It was recently proposed that the exponent eta characterizing the efficiency of the transport of energy in large and small food webs might have a universal value (eta = 1.13). In this work we establish lower and upper bounds for this exponent in a general spanning tree with a fixed number of trophic species and levels. When the number of species is large, the lower and upper bounds are equal to 1, implying that the result eta = 1.13 is due to finite-size effects and that the value of this exponent depends on the size of the web. We also evaluate analytically and numerically the exponent eta for hierarchical and random networks. In all cases the exponent eta depends on the number of trophic species K, and when K is large we have that eta-->1. Moreover, this result holds for any fixed number M of trophic levels.