PERFICT: A Re-imagined foundation for predictive ecology

Area of Science:

• Ecological modeling
• Predictive ecology
• Data science integration

Background:

• Evaluating ecological models is crucial for scientific advancement and policy integration.
• Current approaches to using model predictions are fragmented across disciplines.
• Disparate perspectives hinder the effective use of predictions for model development, validation, and application.

Purpose of the Study:

• To present an updated foundation for Predictive Ecology.
• To introduce a unified framework for ecological modeling based on seven core principles.
• To enhance the use of model predictions in science and management.

Main Methods:

• The study proposes the PERFICT framework: Predictions, Evaluation, Reusability, Free accessibility, Interoperability, Continuous workflows, and Testing.
• This framework provides a structured approach to ecological modeling.
• Principles are designed to improve model development, validation, and application.

Main Results:

• The PERFICT framework offers a coherent approach to evaluating model quality.
• Adoption of these principles can accelerate scientific research.
• The framework facilitates better integration of ecological modeling with data science and policy-making.

Conclusions:

• The PERFICT framework provides a robust foundation for Predictive Ecology.
• Implementing these principles enhances scientific rigor and collaboration.
• This approach improves the translation of ecological insights into actionable management decisions and policies.