Nine things to keep in mind about mathematical modelling in ecology and evolution.

Area of Science:

• Ecology and Evolutionary Biology
• Mathematical and Physical Sciences

Background:

• Modeling in ecology and evolution is often conducted by physicists/engineers lacking biological expertise.
• A misconception exists that strong mathematical skills alone suffice for effective cross-disciplinary modeling.

Purpose of the Study:

• To address the fallacy that modeling skills are universally transferable across disciplines like physics and ecology.
• To highlight the distinct nature of ecological and evolutionary modeling compared to physics.
• To guide young researchers in ecology, evolution, and physical sciences on effective modeling practices.

Main Methods:

• Conceptual analysis and discussion of the differences between modeling in physics and in ecology/evolution.
• Exploration of the philosophical aspects of modeling, including subjectivity vs. objectivity.
• Emphasis on the importance of understanding the studied system in scientific modeling.

Main Results:

• Ecological and evolutionary modeling fundamentally differs from physics modeling, despite similar mathematical forms.
• Success in ecological/evolutionary modeling hinges on understanding the biological system, not solely mathematical prowess.
• Comfort with models and the modeling process is crucial for empirical researchers in ecology and evolution.

Conclusions:

• Researchers in ecology and evolution need system-specific knowledge for effective modeling.
• Physical scientists modeling biological systems must understand ecological/evolutionary principles and differences from physics.
• Meaningful scientific understanding from modeling requires a deep appreciation of the subject system.