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Related Experiment Video

Updated: Feb 5, 2026

Quantification of Self-renewal in Murine Mammosphere Cultures

Quantification of Self-renewal in Murine Mammosphere Cultures

Published on: November 26, 2019

Rejuvenating functional responses with renewal theory.

Sylvain Billiard¹, Vincent Bansaye², J-R Chazottes³

¹Université de Lille, CNRS, UMR 8198, Evo-Eco-Paleo, 59000 Lille, France sylvain.billiard@univ-lille1.fr.

Journal of the Royal Society, Interface

|September 7, 2018

Summary

This study introduces a new ecological framework to understand functional responses, explaining how individual behaviors create population dynamics. It offers a mechanistic approach for analyzing ecological interactions and stability.

Keywords:

cooperation ecology emerging properties inference predation stochastic model

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Area of Science:

Ecology
Theoretical Ecology
Mathematical Biology

Background:

Functional responses are crucial for understanding ecological interactions and population dynamics.
Current approaches often lack mechanistic grounding in individual traits and behaviors.
The form of functional responses significantly impacts community stability.

Purpose of the Study:

To develop a novel, bottom-up stochastic framework for functional responses based on renewal theory.
To provide mechanistic justifications for functional responses derived from individual-level activities.
To offer a versatile tool for conceptual, theoretical, and empirical ecological research.

Main Methods:

Developed a stochastic framework grounded in renewal theory.
Decomposed ecological interactions into distinct individual activities.
Derived mean and variance of classical functional responses from explicit ecological assumptions.

Main Results:

Demonstrated how functional responses emerge mechanistically from individual behaviors.
Obtained classical functional response forms with explicit foraging behavior assumptions.
Illustrated applications in nuptial-feeding species and size-dependent handling times.

Conclusions:

The new framework provides a mechanistic basis for functional responses.
It allows for the analysis of variability in interaction numbers to infer parameters.
This approach enhances the comparison and understanding of functional response models in ecology.