Nicvert, L. | Search bioRxiv

bioRxiv2025

Strategic citations for a fairer academic landscape

Scientific publishing is increasingly dominated by for-profit journals, which attract prestige and submissions through high impact factors (IF). While some of these partly reinvest in research and dissemination and can be considered academia-friendly, non-profit journals - those that fully reinvest revenue into the academic community - often struggle for visibility despite promoting more equitable publishing models. Using citation data from over 70,000 publications in ecology and evolution, we show that citation practices are siloed: for-profit journals disproportionately cite other for-profit journals, academia-friendly journals preferentially cite other academia-friendly journals, and non-profit journals likewise favor citations to non-profit sources. This asymmetry structurally reinforces the IF advantage of for-profit journals simply because they are dominant in the publishing system. To address this inequity, we propose a soft-power, low-risk approach of "strategic citation". By deliberately choosing to cite relevant articles from non-profit journals when multiple references would be equally valid, researchers can contribute to increasing those journals visibility and IF. This approach preserves scientific rigor and does not restrict publishing choices, but adds a layer of ethical intentionality to citation practices. Strategic citation offers a practical, actionable lever for researchers to promote a more balanced and ethical publishing system that complements broader structural reforms.

ecology↗

bioRxiv2024

Trait matching without traits: using correspondence analysis to analyze the latent structure of interaction networks

Species interactions in ecological communities are often represented as networks, the structure of which is thought to be linked to species interaction niches (or Eltonian niches). Interaction niches are intimately related to the notion of trait matching, which posits that a species interacts preferentially with partners whose traits are complementary to their own. Multivariate methods are commonly used to quantify species environmental niches (or Grinnellian niches). More recently, some of these methods have also been used to study the interaction niche, but they consider only the niche optimum and require trait data. In this article, we use the correspondence analysis (CA) framework to study interaction networks and investigate trait matching without requiring trait data, using the notion of latent traits. We use reciprocal scaling, a method related to CA, to estimate niche optima and breadths, defined respectively as the mean and standard deviation of the latent traits of species interacting partners. We present the method, test its performance using a simulation model we designed, and analyze a real frugivory network between birds and plants. The simulation study shows that the method is able to recover niche breadths and optima for data generated with parameters typical of ecological networks. The birds-plants network analysis shows strong relationships between species latent traits and niche breadths: a posteriori correlation with measured traits suggests that birds and plants of intermediate size tend to have the broadest niches. Additionally, birds preferentially foraging in the understory have broader niches than birds preferentially foraging in the canopy. CA and reciprocal scaling are described as fruitful exploratory methods to characterize species interaction profiles, provide an ecologically meaningful graphical representation of interaction niches, and explore the effect of latent traits on network structure.

ecology↗