S. Billiard | Searcharxiv

arXiv2021

A paradigm shift, or a paradigm adjustment? The evolution of the Oleaceae mating system as a small-scale Kuhnian case-study

Kuhn (1962) proposed an evolutionary model to explain how scientific knowledge is built, based on the concept of paradigm. Even though Kuhn's model is general, it has been applied only to a few topics in evolutionary biology, especially broad-based paradigms. Our goal here is to analyze a small-scale paradigm change that occurred about the mating system of a Mediterranean shrub: P. angustifolia (Oleaceae) through the lens of Kuhn's model. We first summarize the different steps of the paradigm change and replace them in the more general context of the sex ratio theory. Second, we show how the different steps of the paradigm changes can be interpreted by Kuhnian concepts and tools. Finally, we discuss the actual and future status of the new paradigm.

q-bio.PE↗

arXiv2017

Rejuvenating functional responses with renewal theory

Functional responses are widely used to describe interactions and resources exchange between individuals in ecology. The form given to functional responses dramatically affects the dynamics and stability of populations and communities. Despite their importance, functional responses are generally considered with a phenomenological approach, without clear mechanistic justifications from individual traits and behaviors. Here, we develop a bottom-up stochastic framework grounded in Renewal Theory showing how functional responses emerge from the level of the individuals through the decomposition of interactions into different activities. Our framework has many applications for conceptual, theoretical and empirical purposes. First, we show how the mean and variance of classical functional responses are obtained with explicit ecological assumptions, for instance regarding foraging behaviors. Second, we give examples in specific ecological contexts, such as in nuptial-feeding species or size dependent handling times. Finally, we demonstrate how to analyze data with our framework, especially highlighting that observed variability in the number of interactions can be used to infer parameters and compare functional response models.

q-bio.PE↗