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Linking Predation Risk, Herbivore Physiological Stress and Microbial Decomposition of Plant Litter

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Integrating Network and Meta-Ecosystem Models for Developing a Zoogeochemical Theory.

Shawn J Leroux¹, Oswald J Schmitz²

¹Department of Biology, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador, Canada.

Ecology Letters

|February 18, 2025

Summary

This summary is machine-generated.

A new framework integrates animal traits, interactions, and movement to predict how wildlife influences Earth's cycles. This zoogeochemistry approach aids conservation and climate solutions.

Keywords:

abiotic animals biogeochemistry biotic community ecosystem elemental cycling food web landscape species interactions

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Published on: October 29, 2016

Area of Science:

Ecology
Biogeochemistry
Theoretical Ecology

Background:

Human activities significantly alter animal populations, interactions, movement, and diversity.
These alterations impact the control animals exert over biogeochemical cycles.
A unified theoretical framework is lacking to predict changes in animal-mediated biogeochemical cycling (zoogeochemistry).

Purpose of the Study:

To present a general theoretical framework integrating animal abundance, interactions, movement, and diversity to predict zoogeochemical changes.
To guide the development of mathematical models linking network theory and meta-ecosystem theory for zoogeochemistry.
To inform conservation efforts by understanding animal functional roles in ecosystem services.

Main Methods:

Developed a framework linking network theory (species interactions, diversity) and meta-ecosystem theory (organism and material movement).
Incorporated biotic and abiotic feedbacks to model animal control over biogeochemical cycling.
Applied the framework to a case study of a primary producer-herbivore network in a boreal forest.

Main Results:

The framework provides a method to coherently integrate diverse ecological factors influencing zoogeochemistry.
Demonstrated application of the framework for predictive modeling in specific ecosystem contexts.
Identified priorities for enhancing model development, data integration, and application of the framework.

Conclusions:

The proposed framework is a significant step towards understanding and predicting animal impacts on biogeochemical cycles.
Enhancing empirical research and model development will better inform conservation and restoration efforts.
This work supports conserving animal populations and their roles in ecosystem services and climate solutions.