Search

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., Comment: Accepted in American Naturalist, https://doi.org/10.1086/733415

Random matrix theory successfully connects the structure of interactions of large ecological communities to their ability to respond to perturbations. One of the most debated aspects of this approach is the missing role of population abundances. Despite being one of the most studied patterns in ecology, and one of the most empirically accessible quantities, population abundances are always neglected in random matrix approaches and their role in determining stability is still not understood. Here, we tackle this question by explicitly including population abundances in a random matrix framework. We obtain an analytical formula that describes the spectrum of a large community matrix for arbitrary feasible species abundance distributions. The emerging picture is remarkably simple: while population abundances affect the rate to return to equilibrium after a perturbation, the stability of large ecosystems is uniquely determined by the interaction matrix. We confirm this result by showing that the likelihood of having a feasible and unstable solution in the Lotka-Volterra system of equations decreases exponentially with the number of species for stable interaction matrices., Comment: 23 pages, 12 figures

The role of species interactions in controlling the interplay between the stability of an ecosystem and its biodiversity is still not well understood. The ability of ecological communities to recover after a small perturbation of the species abundances (local asymptotic stability) has been well studied, whereas the likelihood of a community to persist when the interactions are altered (structural stability) has received much less attention. Our goal is to understand the effects of diversity, interaction strenghts and ecological network structure on the volume of parameter space leading to feasible equilibria, i.e., ones in which all populations have positive abundances. We develop a geometrical framework to study the range of conditions necessary for feasible coexistence in both mutualistic and consumer-resource systems. Using analytical and numerical methods, we show that feasibility is determined by just a handful of quantities describing the interactions, yielding a nontrivial complexity-feasibility relationship. Analyzing more than 100 empirical networks, we show that the range of coexistence conditions in mutualistic systems can be analytically predicted by means of a null model of random interactions, whereas food webs are characterized by smaller coexistence domains than those expected by chance. Finally, we characterize the geometric shape of the feasibility domain, thereby identifying the direction of perturbations that are more likely to cause extinctions. Interestingly, the structure of mutualistic interactions leads to very heterogeneous responses to perturbations, making those systems more fragile than expected by chance., Comment: 61 pages, 17 figures

All dynamical systems of biological interest--be they food webs, regulation of genes, or contacts between healthy and infectious individuals--have complex network structure. Wigner's semicircular law and Girko's circular law describe the eigenvalues of systems whose structure is a fully connected network. However, these laws fail for systems with complex network structure. Here we show that in these cases the eigenvalues are described by superellipses. We also develop a new method to analytically estimate the dominant eigenvalue of complex networks., Comment: 28 pages, 16 figures, 2 tables

In a recent article, I showed that in several academic disciplines in Italy, professors display a paucity of last names that cannot be explained by unbiased, random, hiring processes. I suggested that this scarcity of last names could be related to the prevalence of nepotistic hires, i.e., professors engaging in illegal practices to have their relatives hired as academics. My findings have recently been questioned through repeat analysis to the United Kingdom university system. Ferlazzo & Sdoia found that several disciplines in this system also display a scarcity of last names, and that a similar scarcity is found when analyzing the first (given) names of Italian professors. Here I show that the scarcity of first names in Italian disciplines is completely explained by uneven male/female representation, while the scarcity of last names in United Kingdom academia is due to discipline-specific immigration. However, these factors cannot explain the scarcity of last names in Italian disciplines. Geographic and demographic considerations -- proposed as a possible explanation of my findings -- appear to have no significant effect: after correcting for these factors, the scarcity of last names remains highly significant in several disciplines, and there is a marked trend from north to south, with a higher likelihood of nepotism in the south and in Sicily. Moreover, I show that in several Italian disciplines positions tend to be inherited as with last names (i.e., from father to son, but not from mother to daughter). Taken together, these results strenghten the case for nepotism, highlighting that statistical tests cannot be applied to a dataset without carefully considering the characteristics of the data and critically interpreting the results., Comment: 17 pages, 1 figure

Forty years ago, Robert May questioned a central belief in ecology by proving that sufficiently large or complex ecological networks have probability of persisting close to zero. To prove this point, he analyzed large networks in which species interact at random. However, in natural systems pairs of species have well-defined interactions (e.g., predator-prey, mutualistic or competitive). Here we extend May's results to these relationships and find remarkable differences between predator-prey interactions, which increase stability, and mutualistic and competitive, which are destabilizing. We provide analytic stability criteria for all cases. These results have broad applicability in ecology. For example, we show that, surprisingly, the probability of stability for predator-prey networks is decreased when we impose realistic food web structure or we introduce a large preponderance of weak interactions. Similarly, stability is negatively impacted by nestedness in bipartite mutualistic networks., Comment: 12 pages 7 figures 1 table

Food webs, networks of feeding relationships among organisms, provide fundamental insights into mechanisms that determine ecosystem stability and persistence. Despite long-standing interest in the compartmental structure of food webs, past network analyses of food webs have been constrained by a standard definition of compartments, or modules, that requires many links within compartments and few links between them. Empirical analyses have been further limited by low-resolution data for primary producers. In this paper, we present a Bayesian computational method for identifying group structure in food webs using a flexible definition of a group that can describe both functional roles and standard compartments. The Serengeti ecosystem provides an opportunity to examine structure in a newly compiled food web that includes species-level resolution among plants, allowing us to address whether groups in the food web correspond to tightly-connected compartments or functional groups, and whether network structure reflects spatial or trophic organization, or a combination of the two. We have compiled the major mammalian and plant components of the Serengeti food web from published literature, and we infer its group structure using our method. We find that network structure corresponds to spatially distinct plant groups 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 patterns, in contrast to the standard compartments typically identified in ecological networks. From data consisting only of nodes and links, the group structure that emerges supports recent ideas on spatial coupling and energy channels in ecosystems that have been proposed as important for persistence., Comment: 28 pages, 6 figures (+ 3 supporting), 2 tables (+ 4 supporting)

Few of food web theory hypotheses/predictions can be readily tested using empirical data. An exception is represented by simple probabilistic models for food web structure, for which the likelihood has been 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 measuring the fraction of trophic relations correctly predicted. I contrast this value with that produced by simpler models based on body sizes and find that the data does not favor the more complex model: the information on allometric scaling and optimal foraging does not significantly increase the fit to the data. Also, I take a different approach and compute the p-value for the fraction of trophic interactions correctly predicted by ADBM with respect to three probabilistic null models. I find that the ADBM is clearly better at predicting links than random graphs, but other models can do even better. Although optimal foraging and allometric scaling could improve our understanding of food webs, the models need to be ameliorated to find support in the data., Comment: 28 pages, 3 figures, 4 tables

The number of scientific publications is constantly rising, increasing the strain on the review process. The number of submissions is actually higher, as each manuscript is often reviewed several times before publication. To face the deluge of submissions, top journals reject a considerable fraction of manuscripts without review, potentially declining manuscripts with merit. The situation is frustrating for authors, reviewers and editors alike. Recently, several editors wrote about the ``tragedy of the reviewer commons', advocating for urgent corrections to the system. Almost every scientist has ideas on how to improve the system, but it is very difficult, if not impossible, to perform experiments to test which measures would be most effective. Surprisingly, relatively few attempts have been made to model peer review. Here I implement a simulation framework in which ideas on peer review can be quantitatively tested. I incorporate authors, reviewers, manuscripts and journals into an agent-based model and a peer review system emerges from their interactions. As a proof-of-concept, I contrast an implementation of the current system, in which authors decide the journal for their submissions, with a system in which journals bid on manuscripts for publication. I show that, all other things being equal, this latter system solves most of the problems currently associated with the peer review process. Manuscripts' evaluation is faster, authors publish more and in better journals, and reviewers' effort is optimally utilized. However, more work is required from editors. This modeling framework can be used to test other solutions for peer review, leading the way for an improvement of how science is disseminated., Comment: 9 pages 2 figures