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Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

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Process-explicit models reveal the structure and dynamics of biodiversity patterns.

July A Pilowsky^1,2, Robert K Colwell^2,3,4,5, Carsten Rahbek^2,6,7,8

¹The Environment Institute, School of Biological Sciences, University of Adelaide, Adelaide, Australia.

Science Advances

|August 5, 2022

Summary

Process-explicit models, fueled by big data and computing power, now reveal ecological and evolutionary drivers of biodiversity patterns. This approach enhances understanding and management of life's distribution across space and time.

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Area of Science:

Ecology
Evolutionary Biology
Computational Biology
Biodiversity Science

Background:

Increasing data and computational power enable wider use of process-explicit models.
Many biodiversity distribution questions are experimentally intractable due to large spatiotemporal scales.
Process-explicit models offer a powerful alternative for investigating ecological and evolutionary mechanisms.

Purpose of the Study:

To review recent developments in process-explicit modeling for understanding biodiversity patterns.
To highlight how these models advance ecological and evolutionary theory.
To demonstrate the utility of process-explicit models for biodiversity management.

Main Methods:

Encoding proposed ecological and evolutionary mechanisms into computational models.
Simulating biodiversity patterns in virtual environments.
Validating model outputs against real-world data and theoretical expectations.

Main Results:

Process-explicit models effectively reveal mechanisms driving spatiotemporal biodiversity patterns.
This approach improves understanding of generalizable ecological and evolutionary processes.
Recent developments have significantly enhanced knowledge of life's distribution and dynamics.

Conclusions:

Process-explicit models are crucial for advancing basic and applied biodiversity science.
Deeper recognition of shaping processes leads to better biodiversity understanding and management.
This modeling paradigm is essential for addressing complex questions about genetic, species, and ecosystem diversity.