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In this paper we investigate ecological flow networks via graph theory in search of the real sequential chains through which energy passes from producers to consumers in complex food webs. We obtain such fundamental pathways by identifying strongly connected components (SCCs), subsystems that groups species that take part in cycling, and performing topological sorting on the acyclic graphs that are obtained. Topological sorting identifies preferential directions for energy to flow from sources to sinks, while recycling remains confined within each SCC. Resolving food web networks for SCC highlights the possibility that compartments can be found in ecosystems, but this does not seem a general rule. The four aquatic food webs described in detail show a rather clear subdivision between benthic and pelagic subcommunities, a result that is discussed in the light of other studies. Should further research confirm these results, new insight into the way ecosystems use energy will be provided, with implications on cycling, reciprocal dependency of variables and indirect effects. [ABSTRACT FROM AUTHOR]

Species' interactions are shaped by their traits. Thus, we expect traits—in particular, trait (dis)similarity—to play a central role in determining whether a particular set of species coexists. Traits are, in turn, the outcome of an eco-evolutionary process summarized by a phylogenetic tree. Therefore, the phylogenetic tree associated with a set of species should carry information about the dynamics and assembly properties of the community. Many studies have highlighted the potentially complex ways in which this phylogenetic information is translated into species' ecological properties. However, much less emphasis has been placed on developing clear, quantitative expectations for community properties under a particular hypothesis. To address this gap, we couple a simple model of trait evolution on a phylogenetic tree with Lotka-Volterra community dynamics. This allows us to derive properties of a community of coexisting species as a function of the number of traits, tree topology, and the size of the species pool. Our analysis highlights how phylogenies, through traits, affect the coexistence of a set of species. Together, these results provide much-needed baseline expectations for the ways in which evolutionary history, summarized by phylogeny, is reflected in the size and structure of ecological communities. [ABSTRACT FROM AUTHOR]

The authors discuss the formula that estimate the h-index of life scientists. They state the construction of history of publication, citation, and funding of neuroscientists, researchers, and evolutionary scientists. They mention the use of linear regression with elastic net regularization. They say that the formula shows that future of scientists is not random and rejection of paper could increase predictability of h-index.

The competition-colonization (CC) trade-off is a well-studied coexistence mechanism for metacommunities. In this setting, it is believed that the coexistence of all species requires their traits to satisfy restrictive conditions limiting their similarity. To investigate whether diverse metacommunities can assemble in a CC trade-off model, we study their assembly from a probabilistic perspective. From a pool of species with parameters (corresponding to traits) sampled at random, we compute the probability that any number of species coexist and characterize the set of species that emerges through assembly. Remarkably, almost exactly half of the species in a large pool typically coexist, with no saturation as the size of the pool grows, and with little dependence on the underlying distribution of traits. Through a mix of analytical results and simulations, we show that this unlimited niche packing emerges as assembly actively moves communities toward overdispersed configurations in niche space. Our findings also apply to a realistic assembly scenario where species invade one at a time from a fixed regional pool. When diversity arises de novo in the metacommunity, richness still grows without bound, but more slowly. Together, our results suggest that the CC trade-off can support the robust emergence of diverse communities, even when coexistence of the full species pool is exceedingly unlikely. [ABSTRACT FROM AUTHOR]

For two competing species, intraspecific competition must exceed interspecific competition for coexistence. To generalize this well-known criterion to multiple competing species, one must take into account both the distribution of interaction strengths and community structure. Here we derive a multispecies generalization of the two-species rule in the context of symmetric Lotka- Volterra competition and obtain explicit stability conditions for random competitive communities. We then explore the influence of community structure on coexistence. Results show that both the most and least stabilized cases have striking global structures, with a nested pattern emerging in both cases. The distribution of intraspecific coefficients leading to the most and least stabilized communities also follows a predictable pattern that can be justified analytically. In addition, we show that the size of the parameter space allowing for feasible communities always increases with the strength of intraspecific effects in a characteristic way that is independent of the interspecific interaction structure. We conclude by discussing possible extensions of our results to nonsymmetric competition. [ABSTRACT FROM AUTHOR]

Abstract: Prescriptions for a more sustainable society are usually piecemeal. They are inspired by single issue criteria, no matter if sustainability is, rather, a whole system trait as it pertains to growth and development, that are overall system attributes. In this paper urban sustainability is discussed in a whole system perspective using the ecosystem approach as a framework. This required that urban systems were described as flow networks and investigated through ecological network analysis. Three cities are discussed as a case study and their network representation concerned water flows that were identified knowing water exchanges between city components (i.e. sectors of human activity). Network analysis yielded system level indices that condense the complexity of the flow structure (representing system''s metabolism) in a few measures that provide information on how systems grow and develop; as such they allow to explore sustainability at the whole system scale. For every system the present network is compared with an alternative scenario envisioned considering policies that foster sustainability. The results show that although all the alternative scenarios would improve sustainability through reducing water consumption, effects at the whole system level may diverge from the expectation. Because system sustainability depends on the balance between organization of flows (order and coherence of flows) and flexibility (redundancy of connections), network reshaping may bring about a reduction in both these fundamental properties, with negative effects on system''s propensity to be sustainable. System level indices are holistic measures that unveil the relation between internal processes and whole system performance. Understanding this relation is crucial because the former are the target of environmental policies but sustainability, the objective of such policies, is an overall system trait. [Copyright &y& Elsevier]

Background: The detrimental effects of a short bout of stress can persist and potentially turn lethal, long after the return to normal conditions. Thermotolerance, which is the capacity of an organism to withstand relatively extreme temperatures, is influenced by the response during stress exposure, as well as the recovery process afterwards. While heat-shock response mechanisms have been studied intensively, predicting thermal tolerance remains a challenge. Results: Here, we use the nematode Caenorhabditis elegans to measure transcriptional resilience to heat stress and predict thermotolerance. Using principal component analysis in combination with genome-wide gene expression profiles collected in three high-resolution time series during control, heat stress, and recovery conditions, we infer a quantitative scale capturing the extent of stress-induced transcriptome dynamics in a single value. This scale provides a basis for evaluating transcriptome resilience, defined here as the ability to depart from stress-expression dynamics during recovery. Independent replication across multiple highly divergent genotypes reveals that the transcriptional resilience parameter measured after a spike in temperature is quantitatively linked to long-term survival after heat stress. Conclusion: Our findings imply that thermotolerance is an intrinsic property that pre-determines long-term outcome of stress and can be predicted by the transcriptional resilience parameter. Inferring the transcriptional resilience parameters of higher organisms could aid in evaluating rehabilitation strategies after stresses such as disease and trauma. [ABSTRACT FROM AUTHOR]

Representing data as networks cuts across all sub-disciplines in ecology and evolutionary biology. Besides providing a compact representation of the interconnections between agents, network analysis allows the identification of especially important nodes, according to various metrics that often rely on the calculation of the shortest paths connecting any two nodes. While the interpretation of a shortest paths is straightforward in binary, unweighted networks, whenever weights are reported, the calculation could yield unexpected results. We analyzed 129 studies of ecological networks published in the last decade that use shortest paths, and discovered a methodological inaccuracy related to the edge weights used to calculate shortest paths (and related centrality measures), particularly in interaction networks. Specifically, 49% of the studies do not report sufficient information on the calculation to allow their replication, and 61% of the studies on weighted networks may contain errors in how shortest paths are calculated. Using toy models and empirical ecological data, we show how to transform the data prior to calculation and illustrate the pitfalls that need to be avoided. We conclude by proposing a five-point check-list to foster best-practices in the calculation and reporting of centrality measures in ecology and evolution studies. [ABSTRACT FROM AUTHOR]

Abstract: Parasites are ubiquitous and have been shown to influence macroscopic measures of ecological network structure, such as connectance and robustness, as well as local structure, such as subgraph frequencies. Nevertheless, they are often under‐represented in ecological studies due to their small size and often complex life cycles. We consider whether or not parasites play structurally unique roles in ecological networks; that is, can we distinguish parasites from other species using network structure alone? We partition the species in a community statistically using the group model, and we test whether or not parasites tend to cluster in their own groups, using a measure of “imbalance.” We find that parasites form highly imbalanced groups, and that concomitant predation, in which a predator consumes a prey and its parasites, but not the number of interactions, improves the group model's ability to distinguish parasites from non‐parasites. This work demonstrates that parasites and non‐parasites interact in networks in statistically distinct ways, and that these differences are partly, but not entirely, due to the existence of concomitant predation. [ABSTRACT FROM AUTHOR]

The most highly cited ecologists and environmental scientists provide both a benchmark and unique opportunity to consider the importance of research funding. Here, we use citation data and self-reported funding levels to assess the relative importance of various factors in shaping productivity and potential impact. The elite were senior Americans, well funded, with large labs. In contrast to Canadian NSERC grant holders (not in the top 1%), citations per paper did not increase with higher levels of funding within the ecological elite. We propose that this is good news for several reasons. It suggests that the publications generated by the top ecologists and environmental scientists are subject to limitations, that higher volume of publications is always important, and that increased funding to ecologists in general can shift our discipline to wider research networks. As expected, collaboration was identified as an important factor for the elite, and hopefully, this serves as a positive incentive to funding agencies since it increases the visibility of their research. [ABSTRACT FROM AUTHOR]

Abstract: Food webs, the networks describing “who eats whom” in an ecosystem, are nearly interval, i.e. there is a way to order the species so that almost all the resources of each consumer are adjacent in the ordering. This feature has important consequences, as it means that the structure of food webs can be described using a single (or few) species'' traits. Moreover, exploiting the quasi-intervality found in empirical webs can help build better models for food web structure. Here we investigate which species trait is a good proxy for ordering the species to produce quasi-interval orderings. We find that body size produces a significant degree of intervality in almost all food webs analyzed, although it does not match the maximum intervality for the networks. There is also a great variability between webs. Other orderings based on trophic levels produce a lower level of intervality. Finally, we extend the concept of intervality from predator-centered (in which resources are in intervals) to prey-centered (in which consumers are in intervals). In this case as well we find that body size yields a significant, but not maximal, level of intervality. These results show that body size is an important, although not perfect, trait that shapes species interactions in food webs. This has important implications for the formulation of simple models used to construct realistic representations of food webs. [Copyright &y& Elsevier]

Link arrangement in food webs is determined by the species’ feeding habits. This work investigates whether food web topology is organized in a gradient of trophic positions from producers to consumers. To this end, we analyzed 26 food webs for which the consumption rate of each species was specified. We computed the trophic positions and the link densities of all species in the food webs. Link density measures how much each species contributes to the distribution of energy in the system. It is expressed as the number of links species establish with other nodes, weighted by their magnitude. We computed these two metrics using various formulations developed in the ecological network analysis framework. Results show a positive correlation between trophic position and link density across all the systems, regardless the specific formulas used to measure the two quantities. We performed the same analysis on the corresponding binary matrices (i.e. removing information about rates). In addition, we investigated the relation between trophic position and link density in: a) simulated binary webs with same connectance as the original ones; b) weighted webs with constant topology but randomized link strengths and c) weighted webs with constant connectance where both topology and link strengths are randomized. The correlation between the two indices attenuates, vanishes or becomes negative in the case of binary food webs and simulated data (weighted and unweighted). According to our analysis, link density in food webs decreases with trophic position so that it is greatly reduced toward the top of the trophic hierarchy. This outcome, that seems to challenge previous conclusions based on null models, strongly depends on link quantification. Including interaction strengths may improve substantially our understanding of food web organization, and possibly contradict results based on the analysis of binary webs. [ABSTRACT FROM AUTHOR]

Species' traits and interactions are products of evolutionary history. Despite the long-standing hypothesis that closely related species possess similar traits, and thus experience stronger competition, measuring the effect of evolutionary history on the ecology of natural communities remains challenging. We propose a novel framework to test whether phylogeny influences patterns of coexistence and abundance of species assemblages. In our approach, phylogenetic trees are used to parameterize species' interactions, which in turn determine the abundance of species in a given assemblage. We use likelihoods to score models parameterized with a given phylogeny, and contrast them with models built using random trees, allowing us to test whether phylogenetic information helps to predict species' abundances. Our statistical framework reveals that interactions are indeed structured by phylogeny in a large set of experimental plant communities. Our results confirm that evolutionary history can help predict, and potentially manage or conserve, the structure and function of complex ecological communities., (© 2024 The Author(s). Ecology Letters published by John Wiley & Sons Ltd.)

AbstractClassic ecological theory explains species coexistence in variable environments. While spatial variation is often treated as an intrinsic feature of a landscape, it may be shaped and even generated by the resident community. All species modify their local environment to some extent, driving changes that can feed back to affect the composition and coexistence of the community, potentially over timescales very different from population dynamics. We introduce a simple nested modeling framework for community dynamics in heterogeneous environments, including the possible evolution of heterogeneity over time due to community-environment feedbacks. We use this model to derive analytical conditions for species coexistence in environments where heterogeneity is either fixed or shaped by feedbacks. Among other results, our approach reveals how dispersal and environmental specialization interact to shape realized patterns of habitat association and demonstrates that environmental feedbacks can tune landscape conditions to allow the stable coexistence of any number of species. Our flexible modeling framework helps explain feedback dynamics that arise in a wide range of ecosystems and offers a generic platform for exploring the interplay between species and landscape diversity.

Across the tree of life, organisms modify their local environment, rendering it more or less hospitable for other species. Despite the ubiquity of these processes, simple models that can be used to develop intuitions about the consequences of widespread habitat modification are lacking. Here, we extend the classic Levins metapopulation model to a setting where each of n species can colonize patches connected by dispersal, and when patches are vacated via local extinction, they retain a "memory" of the previous occupant-modeling habitat modification. While this model can exhibit a wide range of dynamics, we draw several overarching conclusions about the effects of modification and memory. In particular, we find that any number of species may potentially coexist, provided that each is at a disadvantage when colonizing patches vacated by a conspecific. This notion is made precise through a quantitative stability condition, which provides a way to unify and formalize existing conceptual models. We also show that when patch memory facilitates coexistence, it generically induces a positive relationship between diversity and robustness (tolerance of disturbance). Our simple model provides a portable, tractable framework for studying systems where species modify and react to a shared landscape., Competing Interests: The authors declare no competing interest.

The study of experimental communities is fundamental to the development of ecology. Yet, for most ecological systems, the number of experiments required to build, model or analyse the community vastly exceeds what is feasible using current methods. Here, we address this challenge by presenting a statistical approach that uses the results of a limited number of experiments to predict the outcomes (coexistence and species abundances) of all possible assemblages that can be formed from a given pool of species. Using three well-studied experimental systems-encompassing plants, protists, and algae with grazers-we show that this method predicts the results of unobserved experiments with high accuracy, while making no assumptions about the dynamics of the systems. These results demonstrate a fundamentally different way of building and quantifying experimental systems, requiring far fewer experiments than traditional study designs. By developing a scalable method for navigating large systems, this work provides an efficient approach to studying highly diverse experimental communities.

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 stability of complex ecological networks depends both on the interactions between species and the direct effects of the species on themselves. These self-effects are known as 'self-regulation' when an increase in a species' abundance decreases its per-capita growth rate. Sources of self-regulation include intraspecific interference, cannibalism, time-scale separation between consumers and their resources, spatial heterogeneity and nonlinear functional responses coupling predators with their prey. The influence of self-regulation on network stability is understudied and in addition, the empirical estimation of self-effects poses a formidable challenge. Here, we show that empirical food web structures cannot be stabilized unless the majority of species exhibit substantially strong self-regulation. We also derive an analytical formula predicting the effect of self-regulation on network stability with high accuracy and show that even for random networks, as well as networks with a cascade structure, stability requires negative self-effects for a large proportion of species. These results suggest that the aforementioned potential mechanisms of self-regulation are probably more important in contributing to the stability of observed ecological networks than was previously thought.

In biology, last names have been used as proxy for genetic relatedness in pioneering studies of neutral theory and human migrations. More recently, analyzing the last name distribution of Italian academics has raised the suspicion of nepotism, with faculty hiring their relatives for academic posts. Here, we analyze three large datasets containing the last names of all academics in Italy, researchers from France, and those working at top public institutions in the United States. Through simple randomizations, we show that the US academic system is geographically well-mixed, whereas Italian academics tend to work in their native region. By contrasting maiden and married names, we can detect academic couples in France. Finally, we detect the signature of nepotism in the Italian system, with a declining trend. The claim that our tests detect nepotism as opposed to other effects is supported by the fact that we obtain different results for the researchers hired after 2010, when an antinepotism law was in effect., Competing Interests: The authors declare no conflict of interest.

The tremendous diversity of species in ecological communities has motivated a century of research into the mechanisms that maintain biodiversity. However, much of this work examines the coexistence of just pairs of competitors. This approach ignores those mechanisms of coexistence that emerge only in diverse competitive networks. Despite the potential for these mechanisms to create conditions under which the loss of one competitor triggers the loss of others, we lack the knowledge needed to judge their importance for coexistence in nature. Progress requires borrowing insight from the study of multitrophic interaction networks, and coupling empirical data to models of competition.

Managing ecosystems to provide ecosystem services in the face of global change is a pressing challenge for policy and science. Predicting how alternative management actions and changing future conditions will alter services is complicated by interactions among components in ecological and socioeconomic systems. Failure to understand those interactions can lead to detrimental outcomes from management decisions. Network theory that integrates ecological and socioeconomic systems may provide a path to meeting this challenge. While network theory offers promising approaches to examine ecosystem services, few studies have identified how to operationalize networks for managing and assessing diverse ecosystem services. We propose a framework for how to use networks to assess how drivers and management actions will directly and indirectly alter ecosystem services., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Historical ecological datasets from a coastal marine community of crustose coralline algae (CCA) enabled the documentation of ecological changes in this community over 30 years in the Northeast Pacific. Data on competitive interactions obtained from field surveys showed concordance between the 1980s and 2013, yet also revealed a reduction in how strongly species interact. Here, we extend these empirical findings with a cellular automaton model to forecast ecological dynamics. Our model suggests the emergence of a new dominant competitor in a global change scenario, with a reduced role of herbivory pressure, or trophic control, in regulating competition among CCA. Ocean acidification, due to its energetic demands, may now instead play this role in mediating competitive interactions and thereby promote species diversity within this guild., (© 2016 The Author(s).)

Much of the focus in evolutionary biology has been on the adaptive differentiation among organisms. It is equally important to understand the processes that result in similarities of structure among systems. Here, we discuss examples of similarities occurring at different ecological scales, from predator-prey relations (attack rates and handling times) through communities (food-web structures) to ecosystem properties. Selection among systemic configurations or patterns that differ in their intrinsic stability should lead generally to increased representation of relatively stable structures. Such nonadaptive, but selective processes that shape ecological communities offer an enticing mechanism for generating widely observed similarities, and have sparked new interest in stability properties. This nonadaptive systemic selection operates not in opposition to, but in parallel with, adaptive evolution., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Explaining the structure of ecosystems is one of the great challenges of ecology. Simple models for food web structure aim at disentangling the complexity of ecological interaction networks and detect the main forces that are responsible for their shape. Trophic interactions are influenced by species traits, which in turn are largely determined by evolutionary history. Closely related species are more likely to share similar traits, such as body size, feeding mode and habitat preference than distant ones. Here, we present a theoretical framework for analysing whether evolutionary history--represented by taxonomic classification--provides valuable information on food web structure. In doing so, we measure which taxonomic ranks better explain species interactions. Our analysis is based on partitioning of the species into taxonomic units. For each partition, we compute the likelihood that a probabilistic model for food web structure reproduces the data using this information. We find that taxonomic partitions produce significantly higher likelihoods than expected at random. Marginal likelihoods (Bayes factors) are used to perform model selection among taxonomic ranks. We show that food webs are best explained by the coarser taxonomic ranks (kingdom to class). Our methods provide a way to explicitly include evolutionary history in models for food web structure.

Few food web theory hypotheses/predictions can be readily tested using likelihoods of reproducing the data. Simple probabilistic models for food web structure, however, are an exception as their likelihoods were recently derived. Here I test the performance of a more complex model for food web structure that is grounded in the allometric scaling of interactions with body size and the theory of optimal foraging (Allometric Diet Breadth Model-ADBM). This deterministic model has been evaluated by measuring the fraction of trophic relations it correctly predicts. I contrasted this value with that produced by simpler models based on body sizes and found that the quantitative information on allometric scaling and optimal foraging does not significantly increase model fit. Also, I present a method to compute the p-value for the fraction of trophic interactions correctly predicted by the ADBM, or any other model, with respect to three probabilistic models. I find that the ADBM predicts significantly more links than random graphs, but other models can outperform it. Although optimal foraging and allometric scaling may improve our understanding of food webs, the ADBM needs to be modified or replaced to find support in the data., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

Intransitive communities, those in which species' abilities cannot be ranked in a hierarchy, have been the focus of theoretical and empirical research, as intransitivity could help explain the maintenance of biodiversity. Here we show that models for intransitive competition embedding slightly different interaction rules can produce opposite patterns. In particular, we find that interactions in which an individual can be outcompeted by its neighbors, but cannot outcompete its neighbors, produce negative frequency dependence that, in turn, promotes coexistence. Whenever the interaction rule is modified toward symmetry (the individual and the neighbors can outcompete each other) the negative frequency dependence vanishes, producing different coexistence levels. Macroscopically, we find that asymmetric interactions yield highest biodiversity if species compete globally, while symmetric interactions favor highest biodiversity if competition takes place locally.

Nonhierarchical competition between species has been proposed as a potential mechanism for biodiversity maintenance, but theoretical and empirical research has thus far concentrated on systems composed of relatively few species. Here we develop a theory of biodiversity based on a network representation of competition for systems with large numbers of competitors. All species pairs are connected by an arrow from the inferior to the superior. Using game theory, we show how the equilibrium density of all species can be derived from the structure of the network. We show that when species are limited by multiple factors, the coexistence of a large number of species is the most probable outcome and that habitat heterogeneity interacts with network structure to favor diversity.

In science, a relatively small pool of researchers garners a disproportionally large number of citations. Still, very little is known about the social characteristics of highly cited scientists. This is unfortunate as these researchers wield a disproportional impact on their fields, and the study of highly cited scientists can enhance our understanding of the conditions which foster highly cited work, the systematic social inequalities which exist in science, and scientific careers more generally. This study provides information on this understudied subject by examining the social characteristics and opinions of the 0.1% most cited environmental scientists and ecologists. Overall, the social characteristics of these researchers tend to reflect broader patterns of inequality in the global scientific community. However, while the social characteristics of these researchers mirror those of other scientific elites in important ways, they differ in others, revealing findings which are both novel and surprising, perhaps indicating multiple pathways to becoming highly cited.

In ecosystems, a single extinction event can give rise to multiple 'secondary' extinctions. Conservation effort would benefit from tools that help forecast the consequences of species removal. One such tool is the dominator tree, a graph-theoretic algorithm that when applied to food webs unfolds their complex architecture, yielding a simpler topology made of linear pathways that are essential for energy delivery. Each species along these chains is responsible for passing energy to the taxa that follow it and, as such, it is indispensable for their survival. To assess the predictive potential of the dominator tree, we compare its predictions with the effects that followed the collapse of the capelin (Mallotus villosus) in the Barents Sea ecosystem. To this end, we first compiled a food web for this ecosystem, then we built the corresponding dominator tree and, finally, we observed whether model predictions matched the empirical observations. This analysis shows the potential and the drawbacks of the dominator trees as a tool for understanding the causes and consequences of extinctions in food webs.

The robustness of ecosystems to species losses is a central question in ecology, given the current pace of extinctions and the many species threatened by human impacts, including habitat destruction and climate change. Robustness from the perspective of secondary extinctions has been addressed in the context of food webs to consider the complex network of species interactions that underlie responses to perturbations. In-silico removal experiments have examined the structural properties of food webs that enhance or hamper the robustness of ecosystems to species losses, with a focus on the role of hubs, the most connected species. Here we take a different approach and focus on the role of the connections themselves. We show that trophic links can be divided into functional and redundant based on their contribution to robustness. The analysis of empirical webs shows that hubs are not necessarily the most important species as they may hold many redundant links. Furthermore, the fraction of functional connections is high and constant across systems regardless of size and interconnectedness. The main consequence of this scaling pattern is that ecosystem robustness can be considerably reduced by species extinctions even when these do not result in any secondary extinctions. This introduces the possibility of tipping points in the collapse of ecosystems.

A central problem in ecology is determining the processes that shape the complex networks known as food webs formed by species and their feeding relationships. The topology of these networks is a major determinant of ecosystems' dynamics and is ultimately responsible for their responses to human impacts. Several simple models have been proposed for the intricate food webs observed in nature. We show that the three main models proposed so far fail to fully replicate the empirical data, and we develop a likelihood-based approach for the direct comparison of alternative models based on the full structure of the network. Results drive a new model that is able to generate all the empirical data sets and to do so with the highest likelihood.