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Ecologists have been compiling ecological networks for over a century, detailing the interactions between species in a variety of ecosystems. To this end, they have built networks for mutualistic (e.g., pollination, seed dispersal) as well as antagonistic (e.g., herbivory, parasitism) interactions. The type of interaction being represented is believed to be reflected in the structure of the network, which would differ substantially between mutualistic and antagonistic networks. Here, we put this notion to the test by attempting to determine the type of interaction represented in a network based solely on its structure. We find that, although it is easy to separate different kinds of nonecological networks, ecological networks display much structural variation, making it difficult to distinguish between mutualistic and antagonistic interactions. We therefore frame the problem as a challenge for the community of scientists interested in computational biology and machine learning. We discuss the features a good solution to this problem should possess and the obstacles that need to be overcome to achieve this goal., Competing Interests: The authors have declared that no competing interests exist.

Peer review is an important element of scientific communication but deserves quantitative examination. We used data from the handling service manuscript Central for ten mid-tier ecology and evolution journals to test whether number of external reviews completed improved citation rates for all accepted manuscripts. Contrary to a previous study examining this issue using resubmission data as a proxy for reviews, we show that citation rates of manuscripts do not correlate with the number of individuals that provided reviews. Importantly, externally-reviewed papers do not outperform editor-only reviewed published papers in terms of visibility within a 5-year citation window. These findings suggest that in many instances editors can be all that is needed to review papers (or at least conduct the critical first review to assess general suitability) if the purpose of peer review is to primarily filter and that journals can consider reducing the number of referees associated with reviewing ecology and evolution papers.

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.

Food webs, networks of feeding relationships in an ecosystem, provide fundamental insights into mechanisms that determine ecosystem stability and persistence. A standard approach in food-web analysis, and network analysis in general, has been to identify compartments, or modules, defined by many links within compartments and few links between them. This approach can identify large habitat boundaries in the network but may fail to identify other important structures. Empirical analyses of food webs have been further limited by low-resolution data for primary producers. In this paper, we present a Bayesian computational method for identifying group structure using a flexible definition that can describe both functional trophic roles and standard compartments. We apply this method to a newly compiled plant-mammal food web from the Serengeti ecosystem that includes high taxonomic resolution at the plant level, allowing a simultaneous examination of the signature of both habitat and trophic roles in network structure. We find that groups at the plant level reflect habitat structure, coupled at higher trophic levels by groups of herbivores, which are in turn coupled by carnivore groups. Thus the group structure of the Serengeti web represents a mixture of trophic guild structure and spatial pattern, in contrast to the standard compartments typically identified. The network topology supports recent ideas on spatial coupling and energy channels in ecosystems that have been proposed as important for persistence. Furthermore, our Bayesian approach provides a powerful, flexible framework for the study of network structure, and we believe it will prove instrumental in a variety of biological contexts. [ABSTRACT FROM AUTHOR]

Parasitism is the most common consumer strategy among organisms, yet only recently has there been a call for the inclusion of infectious disease agents in food webs. The value of this effort hinges on whether parasites affect food-web properties. Increasing evidence suggests that parasites have the potential to uniquely alter food-web topology in terms of chain length, connectance and robustness. In addition, parasites might affect food-web stability, interaction strength and energy flow. Food-web structure also affects infectious disease dynamics because parasites depend on the ecological networks in which they live. Empirically, incorporating parasites into food webs is straightforward. We may start with existing food webs and add parasites as nodes, or we may try to build food webs around systems for which we already have a good understanding of infectious processes. In the future, perhaps researchers will add parasites while they construct food webs. Less clear is how food-web theory can accommodate parasites. This is a deep and central problem in theoretical biology and applied mathematics. For instance, is representing parasites with complex life cycles as a single node equivalent to representing other species with ontogenetic niche shifts as a single node? Can parasitism fit into fundamental frameworks such as the niche model? Can we integrate infectious disease models into the emerging field of dynamic food-web modelling? Future progress will benefit from interdisciplinary collaborations between ecologists and infectious disease biologists. [ABSTRACT FROM AUTHOR]

Abstract: In the tropho-dynamic analysis of ecosystems the heuristic, discrete concept of trophic level has been replaced by the more realistic, continuous definition of trophic position. In ecological network analysis (ENA) the suite of matrix manipulations called canonical trophic aggregation (CTA) apportions each species’ feeding activity to a series of discrete trophic levels sensu Lindeman. The effective trophic position is computed as the sum of the fractions of trophic activity that each species performs at different trophic levels. In this paper we present an extension of the CTA that combines matrix manipulation and sensitivity analysis. Applying this “extended” CTA to an hypothetical network and to real ecosystems we show how trophic position can be computed taking into account the contribution of external inflows, making it scale-insensitive. Moreover “extended” CTA solves ambiguities related to trophic position in the presence of multiple non-living nodes, considering them as imports. [Copyright &y& Elsevier]

Abstract: Ecological networks are quantitative, graph-based descriptions of ecosystems, consisting of compartments (trophospecies and nutrient pools) that exchange fluxes of nutrients or energy. Previous research pointed out how the model''s design is a crucial task that can heavily influence analyses results, and how merging compartments for the purpose of comparing two or more different ecosystems can significantly alter the indices on which the comparison is based. All these works have been focused on the aggregation of trophospecies, whereas networks may comprise several nutrient compartments that may be lumped as well, either for lack of information or for comparison constraints. We show how the aggregation of these non-living compartments can have a greater influence on network analysis results than trophospecies clustering. This problem should on the one hand encourage modelers to make an effort to test the possible effects of aggregations, and on the other show how the role of non-living compartments could be very important in determining network dynamics. [Copyright &y& Elsevier]

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]

A chief cybernetic feature of natural living systems is the recycling of nutrients, which tends to enhance stability and is one of the principal causes of ecosystem complexity. In 1976, Finn proposed a simple and effective measure (later known as the Finn cycling index [FCI]) to assess the quantitative importance of cycles in ecosystems. This index was successfully applied as a gauge of ecosystem health and maturity in a wide variety of studies.It turns out, however, that FCI is biased as a measure of cycling in ecosystems, because it does not include all flows engaged in recycling. A new, more inclusive version of the index is possible. What is called the comprehensive cycling index (CCI) accounts for all of the fluxes generated by cycling. Computing the new measure requires a large amount of time, however, even with ad-hoc software. To obviate the necessity for such heavy computation, a linear transformation of the FCI into the CCI is proposed. [Copyright &y& Elsevier]