Biological networks show strong departures from simple models of random graphs. For example, they display broader degree distributions, high modularity, and strong preponderance of certain motifs.
One might be tempted to interpret these features as a signal of a selective force pruning the space of possible networks, resulting in empirical networks possessing certain features.
In one of my all-time favorite papers, Ricard Solé & Sergi Valverde proposed an alternative explanation: these features might be by-products of how the network was assembled. They dubbed this the “network-spandrel” hypothesis, referencing the famous paper by Gould & Lewontin.
In a new paper just published in Ecology Letters, Dan Maynard, Carlos Serván and I show a simple model of ecological assembly where by slightly tweaking the rules of assembly we can obtain dramatically different network structures—a paradigmatic case of network spandrels:
In a new paper published today in PNAS, Jacopo and I try to extract as much information as possible from an ostensibly meager source of data: a list of the names of all the researchers working in Italy, at the Centre National de la Recherche Scientifique in France, and at public universities in the US.
We show that by using simple randomizations, one can highlight several interesting facts about these different academic systems.
The work is a bona fide exercise in style: by introducing subtle variations of the randomization algorithm, we show that in Italy researchers work in the region where they were born and raised, while in the US geography does not influence the distribution of researchers; in France, we can detect academic couples working in the same unit; we demonstrate that academic nepotism in Italy (the focus of a previous paper of mine) is declining; finally, we show that in the US immigration is field-specific.
It is not unusual to read the tirade of a senior scientist complaining that science was better back then, when papers were fewer, and ideas better (a perfect example of this genre is here). Usually, the conclusion is that we should publish less, lest producing lower-quality science.
These considerations are based on a quite precise hypothesis—that a scientist can either produce many papers, or produce fewer good ones. Detecting such a trade-off in actual data is quite difficult, though, as scientists vary dramatically in productivity, as well as field of study.
Matt and I tried a different route, and compared scientists with themselves: does a scientist produce better papers in the years when she’s most productive? For testing our method, we took the members of the National Academy of Sciences, and reconstructed their publication history. (The rationale being that their best papers must be of high quality).
We found that these scientists tend to produce their most recognized work in years when they’re most productive. However, they also tend to produce their least impactful articles during the same productive years. This is consistent with the “random impact” hypothesis: by publishing many papers, scientists sample their distribution of good ideas more thoroughly, leading to higher maxima and lower minima.
You can read the paper here:
One of the main obsessions in the laboratory is to build robust ecological theories for communities composed of many species.
This is especially important in competitive systems—much of our understanding in this area descends from the analysis of the dynamics of two competitors.
In a new review published this week in Nature, Jonathan Levine, Jordi Bascompte, Peter Adler, and yours truly provide a roadmap for extending these considerations to systems with more than two species.
Interestingly, certain mechanisms, such as higher-order interactions and intransitive competition, can only be studied in high-dimensional systems.
You can read the paper here:
In this Review, we suggest that coexistence mechanisms that emerge only in systems with more than two competitors exert a largely unexplored control over the maintenance of diversity in species-rich communities. We also highlight that when studying more than two competitors, ecologists necessarily confront an ecological network. However, it remains largely unknown how the structure of the network influences coexistence. The sparseness of evidence results from the intractability of empirically evaluating competition between many species and the technical difficulties that are inherent in tightly coupling theory to data. Despite these challenges, there are compelling reasons to deepen our understanding of these more complex mechanisms of coexistence. Armed with advances in data-driven modelling and network analyses that have been developed for multitrophic systems, ecologists are well-positioned to determine, for at least some species-rich communities, how much of the coexistence results from mechanisms that emerge only in diverse systems. Few other questions in ecology have such great potential to radically shift how we think about the maintenance and fragility of biodiversity.