A practical guide to selecting models for exploration, inference, and prediction in ecology

Area of Science:

• Ecology
• Statistics
• Ecological Modeling

Background:

• Statistical model selection is a fundamental yet confusing challenge in scientific research.
• Conflicting recommendations and numerous techniques contribute to the complexity of choosing appropriate statistical models.

Purpose of the Study:

• To clarify confusion in statistical model selection by emphasizing the importance of defining the analysis purpose.
• To identify and differentiate three distinct goals for statistical modeling in ecology: data exploration, inference, and prediction.
• To guide ecologists in selecting appropriate model selection procedures based on their specific modeling goals.

Main Methods:

• Reviewing various statistical model selection approaches.
• Analyzing the strengths and weaknesses of each approach relative to the goals of data exploration, inference, and prediction.
• Illustrating best practices with examples of ecological modeling for butterfly population counts.

Main Results:

• Clear articulation of the analysis purpose simplifies the identification of appropriate model selection procedures.
• Different modeling goals (exploration, inference, prediction) lead to the selection of different models, even with identical datasets.
• Demonstrated how a goal-driven approach to model selection enhances scientific clarity and application.

Conclusions:

• Specifying the purpose of statistical modeling is crucial for effective model selection in ecology.
• Ecological modeling requires critical thinking and validation with independent data, rather than reliance on "statistical recipes."
• Adopting a goal-oriented approach ensures that chosen models align with the intended scientific outcomes.